1. DNA与程序编码的类比基础：4进制序列的“执行”逻辑
1.1 DNA与二进制程序的结构对应性
DNA，作为生命体的遗传物质，其核心在于由四种核苷酸（腺嘌呤A、鸟嘌呤G、胞嘧啶C、胸腺嘧啶T）组成的线性序列。这种四进制（4-ary）编码方式与计算机科学中的二进制（binary）程序存在着深刻的结构和功能类比。在生物系统中，DNA序列并非随意排列，而是携带了构建和维持生命活动的指令，这些指令通过“基因”的形式组织起来，并最终指导蛋白质的合成。
具体而言，DNA的四种碱基可以被赋予独特的2比特标识符，从而将三联体密码子（即3个碱基）表示为一个6比特的标签。这种二进制编码不仅反映了核苷酸的结构相似性，也体现了所编码氨基酸的理化性质1。这种将DNA序列抽象为比特流的方式，使得我们可以从信息论的角度审视遗传信息。
在执行层面，DNA的“指令”是通过遗传密码子表来解读的。每个密码子由三个连续的核苷酸组成，例如CAG、GGC等，它们编码了特定的氨基酸，是蛋白质合成的基本单位。这类似于计算机程序中指令单元的执行方式，这些指令单元承载着特定的操作码和操作数，驱动着计算机的逻辑运算和数据处理。虽然位数不同（DNA密码子可视为6位二进制，而计算机指令通常为32或64位），但两者都代表了系统能够识别和执行的最小“操作单元”1。
“执行单元”在生物与计算系统中的核心作用在于其作为信息载体和功能实现的桥梁。在DNA中，密码子决定了合成蛋白质的序列，进而影响蛋白质的结构和功能，最终体现为生物体的性状；在计算机中，指令单元则驱动着处理器的逻辑门，实现复杂的计算任务。这种深层的类比为我们理解DNA序列的“反编译”提供了基础，即尝试从已知的序列“机器码”逆向推导出其背后的“高级语言”逻辑。早期的研究也曾提出一种分析矩阵模型，该模型模拟了核苷酸碱基的出现过程，并通过二进制三联体配置来编码，暗示了DNA编码的逻辑性和动态平衡，并认为这种模型可以用于研究蛋白质序列的逻辑操作和基因生物编程2。甚至有研究将DNA序列转换为4个素数（2, 3, 5, 7）的编码，以实现更高效的插入缺失解密和序列比对，表明DNA的编码方式可以被数学化和程序化处理3。
1.2 密码子表与翻译机制的“编译”功能
密码子表是DNA“执行”其遗传信息的核心规则集，它将DNA或mRNA上的三联体核苷酸序列（密码子）“翻译”成特定的氨基酸。这个过程与计算机科学中的编译器功能有着高度的类比性。在计算机领域，编译器将人类可读的高级程序语言（如Python、C++）转换为机器可以直接执行的低级机器码（如二进制指令）。类似地，在生物体内，密码子表扮演着将DNA的“高级指令”（基因序列）“编译”成蛋白质的“机器语言”（氨基酸序列）的角色。
每个密码子，由三个连续的核苷酸组成，对应着一种特定的氨基酸。例如，UGC编码半胱氨酸，AUA编码异亮氨酸。这种从密码子到氨基酸的映射是高度特异但存在简并性的：大多数氨基酸可以由多个不同的密码子编码，这被称为同义密码子。例如，六个不同的密码子（UUA, UUG, CUU, CUC, CUA, CUG）都可以编码亮氨酸。这种简并性并非随机，而是在进化中受到选择压力，可能承载了额外的生物信息，例如影响核小体定位或翻译效率 4。有研究利用这种特性，通过在不改变编码蛋白质的前提下调整同义密码子，实现了同时调节翻译效率和核小体定位 4。
翻译过程，即从mRNA序列合成蛋白质的过程，则进一步体现了“编译”的复杂性与高效性。核糖体作为“翻译机器”，通过识别mRNA上的密码子，并与带有相应氨基酸的转运RNA（tRNA）结合，将氨基酸逐个添加到正在延伸的肽链上 5。tRNA在其中扮演着关键的“适配器”角色，它一端带有反密码子，能够与mRNA上的密码子配对，另一端携带相应的氨基酸 6。
核糖体在翻译过程中还具备复杂的纠错与效率调控机制。例如，翻译起始是一个高度受控的过程，涉及到43S前起始复合物的组装，以及帽结合复合物eIF4F招募到mRNA的5'端 5。核糖体沿着mRNA的5'非翻译区扫描，选择起始密码子，随后60S大亚基结合形成80S起始复合物，开始蛋白质链的延伸 5。在应激条件下，核糖体的某些功能单元（如Rps26和Rpl10）可能会受到氧化损伤，细胞通过伴侣蛋白Tsr2和Sqt1将受损蛋白从核糖体中释放，随后用新的蛋白修复受损的核糖体，从而维持翻译的效率和质量，这类似于程序执行过程中的错误检测与恢复机制 7。
此外，翻译速率并非恒定不变，它受到多种因素的调控，包括新兴蛋白质产生的机械力 8。核糖体在翻译过程中产生的力会影响肽酰转移酶中心的活性，进而影响翻译速率，这反映了生物系统在“编译”过程中对精细调控的需求。这种复杂的调控网络确保了基因表达的准确性和适应性，使得生命体能够根据内外环境的变化灵活调整蛋白质的合成。
2. 密码子使用偏好：DNA“代码”的隐性规则挖掘
2.1 同义密码子的非随机分布与功能关联
尽管遗传密码具有简并性，即多个同义密码子可以编码同一种氨基酸，但这些同义密码子的使用频率在不同物种甚至同一物种内部的不同基因之间却呈现出非随机分布的特性，这种现象被称为密码子使用偏好（Codon Usage Bias, CUB）9。这种偏好并非偶然，而是进化选择的结果，对基因表达效率、蛋白质折叠及功能具有深远影响，揭示了DNA“代码”中隐藏的优化规则。
同义密码子的非随机分布主要体现在以下几个方面：
首先，密码子使用偏好与翻译效率密切相关。研究表明，在基因组中，尤其是高表达基因中，偏好的密码子往往与细胞内丰度较高的tRNA相匹配，从而提高翻译速率和效率91011。例如，在细菌中，高表达基因（Highly Expressed Genes, HEGs）倾向于使用UAA终止密码子，而低表达基因（Lowly Expressed Genes, LEGs）则倾向于使用UGA，这与释放因子（RF1和RF2）的相对丰度有关，RF1解码UAA和UAG，RF2解码UAA和UGA12。最近的研究甚至发现，密码子偏好，特别是核糖体蛋白基因中的密码子偏好，是原位细菌生长速率和转录活性的最强预测因子13。这种对最优密码子的选择性使用能够显著加速蛋白质合成，确保细胞在资源有限或快速响应需求时的正常功能。
其次，密码子使用偏好对蛋白质的共翻译折叠（cotranslational folding）起着关键作用。非随机的密码子分布可以在mRNA上创建翻译速度的“暂停”或“减速带”，这些速度差异允许新生肽链有足够的时间正确折叠，避免错误折叠和聚集101114。例如，在病毒衣壳蛋白的翻译过程中，去优化（deoptimized）的密码子被证实能够影响共翻译折叠，进而影响病毒的功能14。此外，非最优同义密码子不仅会降低翻译延伸速度，还可能通过影响翻译起始来抑制基因表达15。这表明同义密码子并非“沉默”突变，而是基因表达和蛋白质功能调控的重要编码层。
再者，密码子偏好还受到基因组组成、GC含量、基因长度、mRNA折叠以及tRNA丰度等多种因素的影响916。例如，高GC含量的基因组通常会偏好G/C结尾的密码子1617。对小麦及其祖先物种的研究显示，六倍体小麦表现出比其祖先更强的G/C结尾密码子偏好，且这种偏好与GC含量显著相关16。对水稻染色体和核苷酸的分析也发现，密码子适应指数（CAI）与GC含量之间存在关联，且不同染色体区域的密码子偏好存在差异18。这些量化关系揭示了序列中隐含的“优化规则”，反映了不同物种在长期进化过程中对翻译效率和蛋白质功能优化的选择压力。
最后，同义密码子偏好还可用于评估物种的起源、突变模式和进化路径，识别驱动进化的选择力，甚至可以用于基因工程中通过密码子优化来提高基因表达水平9。例如，对人类白蛋白超家族基因的研究显示，这些基因具有较低的GC含量和较高的有效密码子数（ENC），表明其高表达特性和较低的密码子使用偏好，且不受突变选择压力19。通过计算诸如密码子使用相似性指数（COUSIN）等标准化指标，研究人员可以更好地定量和定性分析基因组中的密码子使用偏好，揭示物种特异性20。甚至有研究通过对大肠杆菌进行大规模并行基因表达变异测量，发现具有较高tRNA适应指数（TAI）和密码子适应指数（CAI）的序列具有更高的基因表达方差，进一步证明了密码子使用偏好在微调基因表达中的作用21。这些研究共同描绘了密码子使用偏好作为DNA“代码”中一个复杂且多功能隐性规则的图景。
2.2 密码子偏倚的进化选择压力
密码子使用偏好（CUB）并非仅仅关乎翻译效率，它在很大程度上也反映了生物体在长期进化过程中所面临的选择压力，这些压力塑造了DNA“代码”以优化蛋白质功能、合成效率和代谢成本。这种进化选择驱动的密码子偏倚，可以类比为高级语言中为了性能、资源优化或特定平台兼容性而进行的“代码优化”。
一个典型的例子是线粒体遗传密码的重分配。线粒体作为细胞内的能量工厂，拥有独立的基因组和遗传密码系统，但其密码子表与核基因组的通用密码子表存在差异。例如，在某些酵母（如念珠菌属）的线粒体基因组中，UGA不再是终止密码子，而是编码色氨酸；CUG在某些物种中不再编码亮氨酸，而是编码丝氨酸或丙氨酸 2223。这种密码子重分配被认为是线粒体基因组在长期进化过程中，为了适应其独特的内部环境和功能需求，以及可能存在的较小基因组和tRNA集而发生的一种“优化”过程。这种“优化”有时甚至可以促进蛋白质的特定细胞内定位。例如，对一种钾离子通道蛋白（Kesv）的研究发现，通过改变特定区域的密码子使用偏好，可以将其从线粒体重新定向到内质网，这表明密码子选择可以通过影响翻译过程中的疏水性氨基酸插入和编码效率，从而在局部影响新生蛋白质的折叠和最终的细胞内运输 2425。
氨基酸合成成本是影响密码子偏倚的另一个重要因素。研究发现，许多原核生物，包括大肠杆菌、枯草芽孢杆菌以及衣原体等，在高表达基因中倾向于使用合成成本较低的氨基酸 26。例如，在某些致病性梭状芽孢杆菌中，由于其基因组较小且富含AT，它们倾向于使用合成成本较低的氨基酸，并且表现出严格的密码子使用偏好（在编码20种氨基酸的61种密码子中，仅使用35种），这被认为是降低蛋白质能量成本，以匹配其富含AT的人类宿主细胞环境的一种适应性策略 27。这种偏好可以被视为一种“资源优化”的代码策略，即生物体通过选择编码相同氨基酸但合成耗能更低的密码子，来节约宝贵的代谢能量，尤其是在高表达的蛋白质中，这种节能效应会显著累积。
此外，蛋白质的功能需求也直接影响密码子选择。例如，为了提高蛋白质的稳定性、溶解性或酶活性，特定的氨基酸序列可能需要更高效地合成，或在翻译过程中保持特定的速度模式以促进正确的折叠。通过对密码子进行优化（即调整同义密码子的使用），可以在不改变蛋白质氨基酸序列的前提下，显著提高异源蛋白的表达水平和活性。例如，在酿酒酵母中高水平表达黑曲霉的内切-β-1,4-葡聚糖酶时，通过密码子优化，G+C含量从54%降至44.2%，酶活性显著提高 28。类似地，优化乳酸乳球菌的肠球菌素A基因的密码子在巴斯德毕赤酵母中表达，使其抗李斯特菌活性达到51,200 AU/ml，比未优化前高出数倍 29。其他研究也通过密码子优化成功提升了多种酶（如硝化水合酶、脂肪酶）在异源宿主中的表达量和活性 30313233。这些案例表明，生物系统在进化过程中也会进行类似的“代码重构”和“性能调优”，以确保关键蛋白质的稳定和高效生产。
密码子偏倚的进化选择压力还体现在不同物种的基因组组成、GC含量和基因转换等因素上。例如，非禾本科单子叶植物的基因组研究显示，密码子使用偏好和核苷酸组成主要由GC偏向基因转换（gBGC）驱动，且最优密码子倾向于在第三位点使用G/C 34。蝴蝶和蛾类（鳞翅目）的比较基因组学研究也揭示，基因序列进化受突变偏好、密码子使用和gBGC的复杂相互作用影响，表现出G/C到A/T的替换偏好，但gBGC和纯化选择部分抵消了这种影响 35。在人类线粒体基因组中，不同大陆人群的密码子使用模式也存在差异，突变压力和自然选择共同塑造了这种偏倚，且地理距离是进化的重要因素 36。这些证据共同描绘了密码子偏倚作为一种多维度进化适应策略的图景，它不仅优化了微观的翻译过程，也反映了宏观的物种进化历史和生态适应性。这种复杂性为我们“反编译”DNA的“高级语言”提供了丰富的线索，即识别这些“代码优化”背后的驱动力，从而理解生物体如何通过调整其基因“代码”来适应环境和实现特定功能。
3. DNA序列的逆向解析：从“机器码”到功能模块的解码实践
3.1 基因调控网络的逆向推断技术
DNA序列可以被视为生物体的“机器码”，而基因调控网络（Gene Regulatory Networks, GRNs）则是控制这些“机器码”何时、何地、以何种强度“执行”的“控制流”与“函数调用”集合。对GRNs的逆向推断，正是从DNA序列及其实时表达数据（如同程序的运行状态）出发，反向构建其背后复杂的调控逻辑，这一过程与计算机科学中从二进制指令反编译高级语言的控制流和函数调用有着异曲同工之妙。
在生物信息学领域，单细胞RNA测序（scRNA-seq）技术的飞速发展为GRN的逆向推断提供了前所未有的机遇，因为它能捕捉到细胞异质性和动态过程中的基因表达快照 37。例如，pySCENIC（Python Single-Cell rEgulatory Network Inference and Clustering）就是一种广泛使用的工具，它能够高效地从单细胞转录组数据中重构调控子（regulons），即由一个转录因子（Transcription Factor, TF）及其直接靶基因组成的调控单元 3839。
pySCENIC的工作流程可以被类比为多阶段的“反编译”过程：
- 共表达模块的推断（指令与操作数的初步关联）：首先，pySCENIC通过基于回归的方法（如GRNBoost2）识别转录因子和它们潜在靶基因之间的共表达关系 3839。这类似于反编译器首先识别机器码中哪些部分可能是一起执行的操作序列，形成初步的“代码块”。这种方法基于的假设是，如果一个转录因子调节某个基因，那么它们的表达模式很可能在细胞群体中呈现出协同变化。
- 顺式调控基序发现与间接靶点的剪枝（识别直接“函数调用”）：仅仅共表达并不意味着直接调控，因为许多基因可能只是因为受到同一个上游转录因子的间接影响而共表达。为了区分直接和间接调控，pySCENIC利用cisTarget模块进行顺式调控基序（cis-regulatory motif）富集分析 3839。它会检查潜在的靶基因启动子区域是否富集了特定转录因子的结合位点。如果一个基因与某个转录因子共表达，并且其启动子区域确实包含该转录因子的结合基序，那么这个基因才会被认为是该转录因子的直接靶点。这个步骤类似于反编译器通过分析内存地址、寄存器使用和函数签名来确定哪些指令确实是直接调用某个子程序，而非仅仅是数据传递或间接效应。
- 调控子活性的量化（评估“函数”的实际执行强度）：最后，pySCENIC使用AUCell（Area Under the enrichment Curve）算法量化每个调控子在单个细胞中的活性 3839。它计算调控子中所有靶基因的表达水平在细胞中的富集程度，从而得到一个反映该调控子活跃状态的分数。这可以类比于反编译器在重构出函数调用后，进一步分析程序运行时这些函数被调用的频率和对系统状态的影响程度。
通过这些步骤，pySCENIC能够生成详细的细胞特异性GRNs，揭示不同细胞类型或状态下的核心调控机制 39。例如，在分析肿瘤浸润免疫细胞时，研究人员使用pySCENIC识别了免疫检查点抑制剂（ICI）应答相关的调控子，并发现了一个与ICI应答相关的PPARG调控子 40。在胰腺癌（PDAC）研究中，pySCENIC被用来分析单细胞RNA测序数据，以探索癌细胞和巨噬细胞之间的相互作用及其在疾病进展中的作用 41。其他研究也利用pySCENIC揭示了阿尔茨海默病（AD）患者大脑中小胶质细胞和星形胶质细胞中APOE基因的调控网络，发现CEBP、JUN、FOS和FOXO转录因子家族是关键的APOE表达调节因子 42。
这些研究案例表明，GRN的逆向推断技术，特别是基于scRNA-seq数据的方法，有效地将看似无序的基因表达数据（“机器码的运行输出”）转化为具有生物学意义的调控关系（“高级语言的逻辑结构”）。尽管GRN的重建仍面临诸多挑战，如数据稀疏性、噪声以及网络复杂性，但诸如CARDAMOM等新算法通过利用细胞分化的亚稳态和转录爆发特性，进一步提高了GRN推断的可靠性和计算速度 43。此外，像DigNet这样的扩散生成模型也正被开发，以从scRNA-seq数据中生成细胞特异性GRNs，并能有效处理数据中的噪声和稀疏性 44。这些进展正逐步将我们带向更深层次地理解DNA“机器码”背后的“生物高级语言”。
3.2 表观遗传标记与“注释信息”的解码
在生物信息学的视角下，DNA序列的“反编译”不仅要解读其编码的蛋白质信息，更要理解那些不直接编码氨基酸，却深刻影响基因表达和功能的“元数据”或“注释信息”。表观遗传标记正是这类信息的核心组成部分，它们在不改变DNA一级序列的前提下，通过对DNA或组蛋白进行化学修饰，精密调控基因的开启与关闭，类似于计算机程序中的代码注释——不影响程序的逻辑执行，但对理解程序意图和功能至关重要。
其中，DNA甲基化是最为广泛研究的表观遗传修饰之一。它主要发生在哺乳动物基因组中胞嘧啶的C5位点上添加一个甲基基团，形成5-甲基胞嘧啶（5mC）45。这种修饰通过招募抑制基因表达的蛋白或阻止转录因子结合DNA来调节基因表达45。在发育过程中，DNA甲基化模式是动态变化的，涉及从头甲基化和去甲基化过程，最终形成调节组织特异性基因转录的稳定且独特的DNA甲基化模式4546。例如，在神经系统中，DNA甲基化对于正常的认知功能至关重要，其改变可能导致精神障碍45。
表观遗传标记在疾病诊断中，尤其是在肿瘤的早期筛查中，展现出巨大的“注释”价值，可以类比为程序中的“debug日志”或“诊断信息”。循环肿瘤DNA（ctDNA）甲基化检测是液体活检领域的一个重要进展。癌细胞常常表现出大规模的表观遗传学改变，这些改变具有组织和癌症特异性47。通过分析血液中游离DNA（cell-free DNA, cfDNA）的甲基化图谱，可以在癌症早期阶段实现敏感的肿瘤检测和分类474849。例如，一项研究开发了一种基于免疫沉淀的敏感协议，分析少量cfDNA的甲基化组，能够有效检测和分类多种肿瘤类型47。
这种技术在结直肠癌（CRC）的诊断和预后管理中表现突出。对ctDNA中6个DNA甲基化标记的检测，不仅能评估分子残留病变（MRD），还能对I至III期结直肠癌患者进行风险分层50。研究表明，术后一个月ctDNA阳性的患者复发风险是阴性患者的17.5倍50。ctDNA甲基化状态在辅助化疗后的动态监测，也能有效预测复发，且中位提前时间可达3.3个月，远早于影像学确认50。这使得医生能够根据这些“注释信息”调整治疗策略，实现更精准的干预。
除了肿瘤，DNA甲基化在许多其他疾病中也扮演着关键角色，其模式的改变可作为诊断或预后的标志。例如，假性甲状旁腺功能减退症（PHP）及相关疾病的诊断也可能需要通过分子遗传分析来确认，其中可能涉及与表观遗传相关的基因异常51。抑郁症的风险也受到遗传和环境因素的共同影响，表观遗传机制，特别是DNA甲基化和miRNA，被认为是介导不良生活事件导致抑郁风险增加的关键5253。甚至吸烟这类环境因素，也会在肺、结肠等多种人体组织中引起特异性的DNA甲基化和基因表达变化，这些变化可能介导吸烟对健康的影响54。
表观遗传标记，特别是DNA甲基化，也影响着增强子活性，进而调控基因表达55。这表明DNA序列上的“注释”并非静态，而是动态参与基因调控的复杂网络。合成生物学中的研究也利用患者来源的类器官进行药物筛选，发现了影响结直肠癌的表观遗传脆弱性，例如BET溴区蛋白抑制剂被发现是癌症选择性生长抑制剂56。这进一步强调了表观遗传信息作为理解疾病发生发展“注释”的重要性，以及其在开发新型治疗策略中的潜在应用。
综上所述，表观遗传标记，尤其是DNA甲基化，就像DNA“机器码”旁边的精妙“注释”。它们不直接改变“代码”本身，却提供了丰富的上下文信息，指示“代码”的执行方式和优先级。解码这些“注释”对于理解基因功能、疾病机制以及开发新的诊断和治疗方法具有不可估量的价值，是DNA序列“反编译”过程中从“机器码”层面走向“高级语言”理解不可或缺的一环。
4. 遗传信息的抽象层次：是否存在“生物高级语言”？
4.1 功能模块的跨尺度组织特征
在探索DNA序列的“反编译”过程中，一个核心问题是：遗传信息是否存在类似于计算机高级语言中“函数”、“库”或“模块”这样的抽象层次？越来越多的证据表明，生物系统通过构建功能模块（functional modules）来实现复杂的功能，这些模块在DNA序列层面表现出跨尺度的组织特征，并在进化中展现出保守性和可复用性。
基因共表达模块是理解生物功能模块化组织的一个重要途径。在生物体内，基因并非孤立地发挥作用，而是倾向于协同表达，形成复杂的调控网络。通过加权基因共表达网络分析（Weighted Gene Co-expression Network Analysis, WGCNA）等系统生物学方法，研究人员可以识别出高度共表达的基因集合，这些集合被称为共表达模块，它们往往与特定的生物学过程、细胞类型或疾病状态相关联 575859。例如，在小鼠肝细胞的研究中，WGCNA识别出与肝脏再生相关的特定基因模块，并从中找到了关键的枢纽基因，揭示了复杂的分子机制 57。在苹果果皮的研究中，也通过WGCNA构建了共表达模块，并识别出与花青素积累呈负相关的特定模块，进一步挖掘出调控花青素生物合成的关键基因 58。
这些共表达模块可以类比为高级语言中的“函数库”或“类”，它们封装了完成特定任务所需的基因集合，并通过协调的表达来执行特定的功能。这种模块化组织使得生物系统能够高效地管理基因表达，并在面对环境变化时展现出适应性。模块内部的基因相互作用紧密，而模块之间则相对独立，这有助于缓冲扰动并维持系统的稳定性。然而，也有研究指出，蛋白质-蛋白质相互作用（PPI）网络中观察到的结构模块性，可能并非总是对应于严格的功能单元，并且在不同物种之间（如酵母、果蝇和线虫）的模块缺乏进化保守性，这提示我们需要更深入地理解模块与功能的对应关系 60。
除了基因共表达模块，转座子编码的RNA引导内切酶也为理解DNA序列中的“函数”和“库”提供了另一个引人入胜的视角。IS200/IS605家族的转座子编码的IscB和TnpB蛋白，是最近被发现的RNA引导核酸内切酶，它们被认为是CRISPR-Cas9和Cas12系统（广泛应用于基因编辑的“分子剪刀”）的进化祖先 616263。这些转座子通过编码RNA引导的内切酶，能够特异性地识别并切割DNA，实现自身的“跳跃”或插入到基因组中的新位置。这种RNA引导的精确切割机制，本质上就是一种可重用的“功能模块”——一段DNA序列（转座子）携带了执行特定“操作”（基因组切割和插入）所需的全部“代码”（IscB/TnpB蛋白和引导RNA）。
这些转座子编码的内切酶及其引导RNA构成了紧凑且高效的遗传单元，类似于程序设计中的“模块化工具包”。它们在微生物基因组中广泛存在，其进化多样性巨大，许多都具有在人类细胞中进行基因编辑的潜力 64。TnpB蛋白的结构研究揭示了其作为Cas12核酸酶家族最小功能核心的特性，为开发更小型、高效的基因编辑工具提供了蓝图 63。更有趣的是，这些转座子编码的核酸酶在进化上被“劫持”，从最初促进自身在基因组中“自私”传播的角色，演变为抵御病毒入侵的自适应免疫系统（CRISPR-Cas），这充分体现了这些功能模块的可复用性 65。最近的研究甚至追踪了真核生物RNA引导核酸内切酶（Fanzor1s和Fanzor2s）的进化路径，发现它们起源于一类独特的细菌IS607 TnpB，这进一步证明了这些模块在跨界进化中的保守性和适应性 66。
因此，从基因共表达模块到转座子编码的RNA引导内切酶，我们看到了DNA序列中存在着不同抽象层次的功能单元。这些单元在“高级语言”的意义上，可以被理解为实现特定生物学功能的“函数”或“库”，它们在进化中被选择、优化、保守甚至重用，共同构建了生命体复杂的遗传程序。这种模块化组织为“反编译”DNA序列提供了关键的线索，即识别和解析这些功能模块，从而理解更深层次的生物学逻辑。
4.2 合成生物学中的“逆向工程”验证
合成生物学作为一门新兴学科，其核心目标之一是通过工程化手段设计、构建和重新编程生物系统，以实现特定功能 6768。这门学科在探索DNA序列是否能够“反编译”出其背后的“高级语言”方面，扮演着关键的“逆向工程”和实验验证角色。它通过从头构建生命系统或其关键组分，来检验我们对生物遗传信息编码和执行机制的理解，从而探讨从简单序列到复杂功能的“编译-反编译”可能性。
其中一个引人注目的方向是构建自我复制的合成原细胞（protocell），并观察其在体外环境中的进化过程 6970。如果能在一个极简的人工系统中，观察到DNA序列通过突变积累实现功能优化，就意味着我们部分理解了从“机器码”（DNA序列）到“高级语言”（功能）的转化逻辑，并能够对其进行“逆向工程”和“前向设计”。
丹麦技术大学的研究团队在《自然通讯》上发表了一项开创性研究，成功在脂质体隔室中实现了DNA的达尔文式进化，构建了一个能够自我复制并进化的DNA系统 69。该系统以Phi29噬菌体DNA聚合酶为核心。Phi29 DNA聚合酶是一种高效的DNA复制酶，可用于DNA的扩增 71。在这个合成原细胞模型中，研究人员将编码Phi29 DNA聚合酶及其末端蛋白的线性DNA模板封装在脂质体中，并结合“利用重组元件进行蛋白质合成”（PURE）系统，实现了DNA的复制和蛋白质表达的偶联。
这项研究的“逆向工程”和“反编译”意义体现在：
- 实现DNA的持续自我复制与进化：研究团队设计了两种实验配置，支持间歇性或半连续的进化（是否需要DNA提取、PCR和重新封装）。他们证明，在脂质体中，线性的DNA模板可以持续复制多轮，并表达自我复制所需的蛋白质。在仅仅十轮进化后，该自我复制器就积累了赋予选择优势的突变 69。
- 突变积累与功能优化的分析：通过新一代测序技术对进化的DNA序列进行分析，并对一些富集突变进行“逆向工程”验证，研究人员揭示了这些突变对复制能力非同寻常且依赖于上下文的影响。例如，一些突变可能导致Phi29 DNA聚合酶活性提高，或者降低了重组酶对聚合酶活性的抑制作用 6972。这表明，DNA序列的微小改变（突变）能够通过影响蛋白质功能，进而改变整个系统的“行为”（复制效率），这正是从“机器码”到“高级语言”功能演变的关键一步。
- 理解从简单序列到复杂功能的机制：这个实验系统提供了一个受控的平台，用于研究生命信息如何传播和进化。通过在最小化的无细胞系统中研究进化过程，研究人员能够更好地理解构成复杂生命系统基础的遗传原理 69。例如，早期对最小细胞合成的研究已经提出了一个包含113 kbp和151个基因的最小基因组，旨在构建一个功能性和结构上都能被理解的自我复制生物系统 73。类似地，通过适应性实验室进化（Adaptive Laboratory Evolution, ALE）和反向遗传工程，研究人员也成功地提高了毕赤酵母的自养生长能力，并确定了影响改善表型的单核苷酸多态性（SNPs）7475。这些研究都试图通过“逆向工程”来理解和优化生物功能。
合成生物学中的“逆向工程”不仅仅是重建已知系统，更是通过构建新的系统来验证和加深对生命基本原理的理解。例如，通过设计正交遗传系统，科学家们探索了与天然DNA正交的异核酸（XNA）聚合物作为遗传信息存储的可能性，这不仅具有实际应用价值，也为理解核酸骨架的结构和功能提供了新的见解 76。在另一个层面，对原细胞中连锁和基因组扩张的进化研究，揭示了染色体如何从非连锁基因中组装，并降低了复制器的分类负荷和基因组内冲突，即使染色体相对于非连锁基因存在复制劣势，也能固定下来 77。
这些实验进展为理解生物体的复杂功能如何从简单的DNA序列中“编译”出来，以及我们如何能够“反编译”这些序列以揭示其深层逻辑，提供了坚实的实验依据。通过对突变积累、功能优化以及系统适应性的观察和分析，合成生物学正在逐步揭示“生物高级语言”的构建块和语法规则。
5.1 非编码区与调控元件的功能模糊性
尽管在DNA序列的“反编译”方面取得了显著进展，但我们对是否存在“生物高级语言”的理解仍面临诸多挑战，尤其是在非编码区与调控元件的功能模糊性方面。这些基因组区域不直接编码蛋白质，但在基因表达调控中扮演着至关重要的角色。然而，其序列特征与功能关联的复杂性，以及它们在三维染色质结构中的动态作用，使得将其精确地“解码”为高级语言的组成部分变得异常困难。
非编码RNA（non-coding RNAs, ncRNAs）是基因组非编码区中的一大类分子，它们不翻译成蛋白质，却在从表观遗传调控到染色质三维结构维持等多种细胞过程中发挥关键作用 78798081。根据长度，ncRNAs可分为小分子ncRNAs（如siRNA、miRNA）和长链非编码RNA（long non-coding RNAs, lncRNAs）。lncRNAs的长度超过200个核苷酸，它们的功能多样且复杂，包括：
- 作为信号分子：lncRNAs可以感知细胞内外的信号，并介导基因表达的响应 81。
- 作为诱饵或海绵：它们可以结合microRNA、蛋白质或DNA，从而调节它们的活性 81。
- 作为引导序列：lncRNAs可以引导染色质修饰复合物（如Polycomb Repressive Complex 2, PRC2）到特定的基因组位点，调控基因的沉默或激活 788283。例如，lncRNA HOTAIR能够介导PRC2在HOXD基因座上的结合，从而导致该区域的基因沉默 78。
- 作为支架：lncRNAs可以作为多种蛋白质复合体的组装平台，协调基因调控过程 81。
- 参与染色质结构形成：一些lncRNAs参与染色质三维结构的构建和维持，例如Xist lncRNA在X染色体失活中起着核心作用，通过在整个X染色体上顺式扩散，促进形成遗传性沉默的异染色质区域 83。
然而，尽管功能多样，lncRNAs的功能特异性往往较差，其作用机制也难以通过简单的序列分析来预测。例如，许多lncRNAs表现出较低的序列保守性，这使得通过同源性预测功能变得困难 8485。此外，lncRNAs的功能通常依赖于其复杂的二级和三级结构，以及与多种蛋白质和核酸的相互作用，这些都是单纯的线性序列信息难以捕捉的 81。有研究表明，肿瘤中的lncRNA突变可以增强细胞适应性，但这种功能改变的机制也极为复杂 86。
除了非编码RNA，增强子和沉默子等顺式调控元件也构成了“生物高级语言”中的复杂语法。增强子是能够显著提高远端基因转录水平的DNA序列，而沉默子则具有抑制基因表达的作用 8788。这些元件通常包含多个转录因子结合位点（transcription factor binding sites, TFBSs），转录因子通过识别这些TFBSs来调控基因表达 8889。然而，它们的运作机制远非简单的“开/关”开关：
- 远距离作用：增强子可以位于其靶基因的上游、下游甚至内含子区域，距离可达数万甚至数十万碱基对 90。它们如何实现这种远距离的特异性调控，通常涉及染色质的三维折叠，通过形成染色质环将远端的增强子与启动子拉近 9192。
- 多效性与背景依赖性：同一个增强子在不同的细胞类型或发育阶段可能具有不同的活性，甚至调控不同的靶基因 90。其活性还受到其周围DNA序列环境（例如转座子元件的插入）以及表观遗传修饰（如DNA甲基化）的影响 9394。
- 序列特征的复杂性：增强子的活性不仅仅取决于其内部的TFBSs，还取决于这些TFBSs的排列方式、间距以及与侧翼序列的相互作用，形成复杂的“语法规则” 8788。例如，DeepSTARR模型可以预测增强子活性，并识别出相关的转录因子基序和更高阶的语法规则，包括同一TF基序的功能非等效实例，这些实例由基序侧翼序列和基序间距决定 88。尽管如此，设计具有特定活性的人工增强子仍然是一个挑战。
- CHD4的编辑作用：染色质重塑因子（如CHD4）通过调节染色质可及性，对转录因子结合位点的选择具有编辑作用，防止不必要的基因表达 89。这进一步增加了理解调控元件功能调控的复杂性。
总而言之，非编码区和调控元件的功能模糊性构成了“反编译”DNA序列为“生物高级语言”的巨大瓶颈。它们的多样性、非线性相互作用、对染色质三维结构的依赖以及缺乏简单的序列-功能对应关系，都使得我们难以建立一套普适的语法规则来描述其复杂的调控逻辑。未来的研究需要更深入地结合多组学数据（基因组学、转录组学、表观基因组学、蛋白质组学）以及先进的计算模型，才能逐步揭示这些“暗物质”基因组元件的真正“高级语言”含义。
5.2 多组学整合与智能模型的应用前景
在DNA序列“反编译”的征途中，尽管非编码区和调控元件的功能模糊性带来了巨大的挑战，但多组学整合（Multi-omics Integration）和智能模型（Intelligent Models），特别是AI驱动的基因组学模型，正展现出前所未有的潜力，有望加速“反编译”工具的开发，推动从序列到功能的精准解析。这可以类比为计算机领域中，通过整合多源信息（代码、运行日志、系统配置等）并利用先进的AI工具（如代码分析器、编译器优化器），来更全面地理解和优化复杂软件系统。
1. AI驱动的基因组学模型在序列模式识别中的潜力
近年来，深度学习（Deep Learning）在基因组学领域取得了显著进展，成为许多基因组建模任务的首选方法，包括预测遗传变异对基因调控机制（如DNA可及性和剪接）的影响 95。这些模型能够有效地利用大规模数据集，捕获数据中的依赖关系并提出新的生物学假设。
DNA基础模型（DNA Foundation Models）：受自然语言处理领域大型语言模型（LLMs）成功的启发，研究人员正在开发DNA语言模型，将DNA序列视为一种“语言” 96。这些模型通过在海量基因组数据上进行预训练，学习DNA序列的内在语法和语义。例如，像DNABERT这样的DNA语言模型，通过将重叠的k-mers作为token进行训练，旨在学习token的序列识别和更大的序列上下文 96。尽管在学习序列上下文方面仍面临挑战 96，但这些模型通过强大的模式识别能力，有望识别出传统方法难以发现的调控元件、基因功能域，甚至预测序列变异带来的功能影响。它们可以帮助识别DNA“代码”中的“关键词”、“语法结构”，从而为“反编译”提供基础。
增强子预测与调控代码学习：深度卷积神经网络（CNNs）已被成功应用于学习可及基因组的调控代码。例如，Basset工具利用CNNs从DNAse-seq数据中学习DNA序列的功能活性，其预测精度优于现有方法 97。Basset能够预测遗传变异对染色质可及性的影响，对于理解疾病相关的非编码变异机制至关重要 97。这表明AI模型能够从DNA序列中自动提取复杂的特征，并将其与功能联系起来，类似于从机器码中学习汇编指令的语义。
基因功能预测：随着测序技术的进步，海量的基因组数据被生成，但基因功能注释仍然是一个挑战。机器学习算法能够整合大量异构数据并检测出通过基于规则的方法难以发现的模式，因此在植物基因功能预测等方面得到越来越多的应用 98。AI模型能够通过学习序列特征、结构域、互作网络等信息，自动预测基因的分子功能、生物学过程和细胞组分 99。
2. 多组学数据融合加速“反编译”工具的开发
生物系统是高度复杂的，单一组学数据（如基因组、转录组或表观基因组）往往只能提供部分信息。将多组学数据进行整合，能够提供更全面、多维度的视角，揭示基因调控的深层机制，从而加速“反编译”工具的开发。这就像将程序的源代码、编译日志、运行时内存快照、CPU指令流等所有相关信息整合起来，以全面理解其运行机制。
基因组、转录组与表观基因组的整合：
- 基因调控网络的重建：整合转录组数据（基因表达）、表观基因组数据（如DNA甲基化、组蛋白修饰、染色质可及性）以及基因组序列信息（如转录因子结合位点），能够更准确地逆向推断基因调控网络 100。贝叶斯数据融合方法可以将ChIP-seq数据（转录因子结合）和基因表达谱数据整合，构建全基因组范围的转录网络 100。这有助于揭示转录因子如何在复杂的表观遗传景观中“解读”DNA“代码”并激活或抑制基因表达。
- 疾病机制的解析：多组学整合在理解疾病的发生发展中具有重要作用。例如，在急性髓系白血病（AML）中，通过多组学方法揭示了GFI1-36N种系变异影响DNA修复，并使AML细胞对DNA损伤治疗敏感的机制 101。在结直肠癌的研究中，单细胞多组学测序揭示了肿瘤内异质性以及表观遗传和转录组动态变化，为理解癌症的遗传谱系提供了新见解 102。
- 非编码区功能的理解：非编码区的功能往往与染色质结构、表观遗传状态和核酸相互作用紧密相关。整合lncRNA表达谱（转录组）、DNA甲基化数据（表观基因组）和Hi-C数据（染色质三维结构）等，可以更全面地理解非编码RNA如何参与染色质重塑和基因调控。
单细胞多组学技术：单细胞分辨率的多组学技术能够捕捉细胞间的异质性，提供前所未有的细节水平，极大地推动了“反编译”研究。例如，线粒体单细胞ATAC-seq技术能够同时检测线粒体基因型和染色质可及性，用于高通量识别线粒体基因组变异和细胞状态，并推断细胞克隆关系 103。这为在单个细胞层面理解DNA“代码”的执行和调控提供了基础。
临床应用前景：全基因组测序（WGS）与多组学数据的整合分析，正在推动精准医疗的发展，包括罕见病诊断、肿瘤基因组学、药物基因组学等领域 104。通过整合临床数据和多组学信息，AI模型可以识别出与疾病表型相关的遗传和表观遗传“代码”，为疾病的早期诊断、风险评估和个性化治疗提供依据。例如，对儿童脑肿瘤的超低输入无细胞DNA测序结合低覆盖度全基因组测序（lcWGS），可用于肿瘤检测和分子特征分析，尤其是在脑脊液中显示出对疾病监测的价值 105。
总之，多组学整合提供了DNA“代码”的多维视图，而智能模型则提供了从这些复杂数据中学习、识别模式和进行预测的能力。两者结合，正在克服传统方法在处理DNA序列功能模糊性方面的局限，有望将我们对DNA“机器码”的理解提升到一个“生物高级语言”的抽象层次，实现从序列到功能的精准“反编译”。
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参考文献
1A binary representation of the genetic code.PubMed
Louis R Nemzer
Biosystems. 2017 May;155:10-19. doi: 10.1016/j.biosystems.2017.03.001. Epub 2017 Mar 12.
This article introduces a novel binary representation of the canonical genetic code based on both the structural similarities of the nucleotides, as well as the physicochemical properties of the encoded amino acids. Each of the four mRNA bases is assigned a unique 2-bit identifier, so that the 64 triplet codons are each indexed by a 6-bit label. The ordering of the bits reflects the hierarchical organization manifested by the DNA replication/repair and tRNA translation systems. In this system, transition and transversion mutations are naturally expressed as binary operations, and the severities of the different point mutations can be analyzed. Using a principal component analysis, it is shown that the physicochemical properties of amino acids related to protein folding also correlate with certain bit positions of their respective labels. Thus, the likelihood for a point mutation to be conservative, and less likely to cause a change in protein functionality, can be estimated.
2A constructionist model predicting the emergence, complementarity and classification of the nucleotide bases.PubMed
M Mussat, M E Bégin, J P Bureau
Med Hypotheses. 1998 Dec;51(6):511-23. doi: 10.1016/s0306-9877(98)90075-5.
We are proposing an analytical matrix that models a logical process simulating the emergence of the bases of vital nucleotides. The construction and properties of the matricial model are outlined. The Graph 1 matrix specifies a unique distribution pattern of eight terms coded in binary triplet configurations and obeys specific dynamic laws. The whole set of binary triplet configurations is in dynamic equilibrium. For that reason, it is possible to carry out an analysis by entering by any one of the eight terms on the condition that the operating mode obeys all the internal laws of orientation and symmetries inherent in the matrix. The four chemical elements at the origin of life are distributed following their atomic structures in the order hydrogen (H), carbon (C), nitrogen (N) and oxygen (O) and organized according to the model. The dynamic properties of the model necessitate the running of three successive circular periodic studies per analysis in order to show the emergence of the four bases--adenine, guanine, cytosine and thymine--precisely in that order. The fifth base of the nucleotides--uracil--shows up twice but always in an intermediate position, thus in transition, as it is the case for messenger ribonucleic acid (mRNA). We show also that the model provides for a logical explanation of the law of complementarity of the bases and their chemical classification. It is proposed that subsequent developments of the dynamic laws of this matrix may lead to the study of the logical operations for the formation of protein sequences and of their analysis and to genetic bioprogramming in general. Thus, a strictly logical and dynamic approach to molecular genetics is possible.
3PrimeIndel: four-prime-number genetic code for indel decryption and sequence read alignment.PubMed
Ching-Wan Lam
Clin Chim Acta. 2014 Sep 25;436:1-4. doi: 10.1016/j.cca.2014.04.006. Epub 2014 Apr 24.
BACKGROUND: To decrypt a doubly heterozygous sequence (DHS) in order to define the indel mutation for mutation reporting, an algorithm recursively searching the overlapped nucleotide using an offset of nucleotide positions can decrypt the indel without using a reference sequence. However, as genetic code is letter-based, special computer programs are required to run the decryption algorithm. METHODS: The previous text-based algorithm was converted to a number-based algorithm by expressing DNA sequence from a 4-letter genetic code to a 4-prime-number genetic code, i.e., converting A, C, G, T to 2, 3, 5, and 7. This algorithm based on prime-number genetic code is called PrimeIndel and is executable by spreadsheet. Using prime number coded DNA sequence, the overlapped nucleotide between any 2 positions of the DHS is represented by the greatest common divisor (GCD) of the multiplication product of 2 prime numbers. This algorithm can also be used for aligning multiple overlapping sequence reads by in-silico DHS formation. The indel size of the in-silico formed DHS indicates the positions in the paired sequences for correct alignment. RESULTS: DHSs were successfully decrypted by the prime number-based algorithm and sequence reads were aligned correctly. CONCLUSIONS: DNA sequence expressed in prime numbers can be used for the decryption of DHS and the alignment of sequence reads using a well-known mathematical function GCD of a spreadsheet program. PrimeIndel is a useful tool for mutation reporting in clinical laboratories. The software is downloadable from http://www.patho.hku.hk/staff/list/cwlam.htm.
4Multiplexing mechanical and translational cues on genes.PubMed
Martijn Zuiddam, Bahareh Shakiba, Helmut Schiessel
Biophys J. 2022 Nov 15;121(22):4311-4324. doi: 10.1016/j.bpj.2022.10.011. Epub 2022 Oct 13.
The genetic code gives precise instructions on how to translate codons into amino acids. Due to the degeneracy of the genetic code-18 out of 20 amino acids are encoded for by more than one codon-more information can be stored in a basepair sequence. Indeed, various types of additional information have been discussed in the literature, e.g., the positioning of nucleosomes along eukaryotic genomes and the modulation of the translating efficiency in ribosomes to influence cotranslational protein folding. The purpose of this study is to show that it is indeed possible to carry more than one additional layer of information on top of a gene. In particular, we show how much translation efficiency and nucleosome positioning can be adjusted simultaneously without changing the encoded protein. We achieve this by mapping genes on weighted graphs that contain all synonymous genes, and then finding shortest paths through these graphs. This enables us, for example, to readjust the disrupted translational efficiency profile after a gene has been introduced from one organism (e.g., human) into another (e.g., yeast) without greatly changing the nucleosome landscape intrinsically encoded by the DNA molecule.
5The molecular basis of translation initiation and its regulation in eukaryotes.PubMed
Jailson Brito Querido, Irene Díaz-López, V Ramakrishnan
Nat Rev Mol Cell Biol. 2024 Mar;25(3):168-186. doi: 10.1038/s41580-023-00624-9. Epub 2023 Dec 5.
The regulation of gene expression is fundamental for life. Whereas the role of transcriptional regulation of gene expression has been studied for several decades, it has been clear over the past two decades that post-transcriptional regulation of gene expression, of which translation regulation is a major part, can be equally important. Translation can be divided into four main stages: initiation, elongation, termination and ribosome recycling. Translation is controlled mainly during its initiation, a process which culminates in a ribosome positioned with an initiator tRNA over the start codon and, thus, ready to begin elongation of the protein chain. mRNA translation has emerged as a powerful tool for the development of innovative therapies, yet the detailed mechanisms underlying the complex process of initiation remain unclear. Recent studies in yeast and mammals have started to shed light on some previously unclear aspects of this process. In this Review, we discuss the current state of knowledge on eukaryotic translation initiation and its regulation in health and disease. Specifically, we focus on recent advances in understanding the processes involved in assembling the 43S pre-initiation complex and its recruitment by the cap-binding complex eukaryotic translation initiation factor 4F (eIF4F) at the 5' end of mRNA. In addition, we discuss recent insights into ribosome scanning along the 5' untranslated region of mRNA and selection of the start codon, which culminates in joining of the 60S large subunit and formation of the 80S initiation complex.
6tRNA therapeutics for genetic diseases.PubMed
Jeff Coller, Zoya Ignatova
Nat Rev Drug Discov. 2024 Feb;23(2):108-125. doi: 10.1038/s41573-023-00829-9. Epub 2023 Dec 4.
Transfer RNAs (tRNAs) have a crucial role in protein synthesis, and in recent years, their therapeutic potential for the treatment of genetic diseases - primarily those associated with a mutation altering mRNA translation - has gained significant attention. Engineering tRNAs to readthrough nonsense mutation-associated premature termination of mRNA translation can restore protein synthesis and function. In addition, supplementation of natural tRNAs can counteract effects of missense mutations in proteins crucial for tRNA biogenesis and function in translation. This Review will present advances in the development of tRNA therapeutics with high activity and safety in vivo and discuss different formulation approaches for single or chronic treatment modalities. The field of tRNA therapeutics is still in its early stages, and a series of challenges related to tRNA efficacy and stability in vivo, delivery systems with tissue-specific tropism, and safe and efficient manufacturing need to be addressed.
7Chaperone-directed ribosome repair after oxidative damage.PubMed
Yoon-Mo Yang, Youngeun Jung, Daniel Abegg, et al.
Mol Cell. 2023 May 4;83(9):1527-1537.e5. doi: 10.1016/j.molcel.2023.03.030. Epub 2023 Apr 21.
Because of the central role ribosomes play for protein translation and ribosome-mediated mRNA and protein quality control (RQC), the ribosome pool is surveyed and dysfunctional ribosomes degraded both during assembly, as well as the functional cycle. Oxidative stress downregulates translation and damages mRNAs and ribosomal proteins (RPs). Although damaged mRNAs are detected and degraded via RQC, how cells mitigate damage to RPs is not known. Here, we show that cysteines in Rps26 and Rpl10 are readily oxidized, rendering the proteins non-functional. Oxidized Rps26 and Rpl10 are released from ribosomes by their chaperones, Tsr2 and Sqt1, and the damaged ribosomes are subsequently repaired with newly made proteins. Ablation of this pathway impairs growth, which is exacerbated under oxidative stress. These findings reveal an unanticipated mechanism for chaperone-mediated ribosome repair, augment our understanding of ribosome quality control, and explain previous observations of protein exchange in ribosomes from dendrites, with broad implications for aging and health.
8Mechanochemistry in Translation.PubMed
Sarah E Leininger, Karthik Narayan, Carol Deutsch, et al.
Biochemistry. 2019 Nov 26;58(47):4657-4666. doi: 10.1021/acs.biochem.9b00260. Epub 2019 Jun 11.
As the influence of translation rates on protein folding and function has come to light, the mechanisms by which translation speed is modulated have become an important issue. One mechanism entails the generation of force by the nascent protein. Cotranslational processes, such as nascent protein folding, the emergence of unfolded nascent chain segments from the ribosome's exit tunnel, and insertion of the nascent chain into or translocation of the nascent chain through membranes, can generate forces that are transmitted back to the peptidyl transferase center and affect translation rates. In this Perspective, we examine the processes that generate these forces, the mechanisms of transmission along the ribosomal exit tunnel to the peptidyl transferase center, and the effects of force on the ribosome's catalytic cycle. We also discuss the physical models that have been developed to predict and explain force generation for individual processes and speculate about other processes that may generate forces that have yet to be tested.
9Codon usage bias.PubMed
Sujatha Thankeswaran Parvathy, Varatharajalu Udayasuriyan, Vijaipal Bhadana
Mol Biol Rep. 2022 Jan;49(1):539-565. doi: 10.1007/s11033-021-06749-4. Epub 2021 Nov 25.
Codon usage bias is the preferential or non-random use of synonymous codons, a ubiquitous phenomenon observed in bacteria, plants and animals. Different species have consistent and characteristic codon biases. Codon bias varies not only with species, family or group within kingdom, but also between the genes within an organism. Codon usage bias has evolved through mutation, natural selection, and genetic drift in various organisms. Genome composition, GC content, expression level and length of genes, position and context of codons in the genes, recombination rates, mRNA folding, and tRNA abundance and interactions are some factors influencing codon bias. The factors shaping codon bias may also be involved in evolution of the universal genetic code. Codon-usage bias is critical factor determining gene expression and cellular function by influencing diverse processes such as RNA processing, protein translation and protein folding. Codon usage bias reflects the origin, mutation patterns and evolution of the species or genes. Investigations of codon bias patterns in genomes can reveal phylogenetic relationships between organisms, horizontal gene transfers, molecular evolution of genes and identify selective forces that drive their evolution. Most important application of codon bias analysis is in the design of transgenes, to increase gene expression levels through codon optimization, for development of transgenic crops. The review gives an overview of deviations of genetic code, factors influencing codon usage or bias, codon usage bias of nuclear and organellar genes, computational methods to determine codon usage and the significance as well as applications of codon usage analysis in biological research, with emphasis on plants.
10Synonymous but Not Silent: The Codon Usage Code for Gene Expression and Protein Folding.PubMed
Yi Liu, Qian Yang, Fangzhou Zhao
Annu Rev Biochem. 2021 Jun 20;90:375-401. doi: 10.1146/annurev-biochem-071320-112701. Epub 2021 Jan 13.
Codon usage bias, the preference for certain synonymous codons, is found in all genomes. Although synonymous mutations were previously thought to be silent, a large body of evidence has demonstrated that codon usage can play major roles in determining gene expression levels and protein structures. Codon usage influences translation elongation speed and regulates translation efficiency and accuracy. Adaptation of codon usage to tRNA expression determines the proteome landscape. In addition, codon usage biases result in nonuniform ribosome decoding rates on mRNAs, which in turn influence the cotranslational protein folding process that is critical for protein function in diverse biological processes. Conserved genome-wide correlations have also been found between codon usage and protein structures. Furthermore, codon usage is a major determinant of mRNA levels through translation-dependent effects on mRNA decay and translation-independent effects on transcriptional and posttranscriptional processes. Here, we discuss the multifaceted roles and mechanisms of codon usage in different gene regulatory processes.
11The ribosome in action: Tuning of translational efficiency and protein folding.PubMed
Marina V Rodnina
Protein Sci. 2016 Aug;25(8):1390-406. doi: 10.1002/pro.2950. Epub 2016 Jun 8.
The cellular proteome is shaped by the combined activities of the gene expression and quality control machineries. While transcription plays an undoubtedly important role, in recent years also translation emerged as a key step that defines the composition and quality of the proteome and the functional activity of proteins in the cell. Among the different post-transcriptional control mechanisms, translation initiation and elongation provide multiple checkpoints that can affect translational efficiency. A multitude of specific signals in mRNAs can determine the frequency of translation initiation, choice of the open reading frame, global and local elongation velocities, and the folding of the emerging protein. In addition to specific signatures in the mRNAs, also variations in the global pools of translation components, including ribosomes, tRNAs, mRNAs, and translation factors can alter translational efficiencies. The cellular outcomes of phenomena such as mRNA codon bias are sometimes difficult to understand due to the staggering complexity of covariates that affect codon usage, translation, and protein folding. Here we summarize the experimental evidence on how the ribosome-together with the other components of the translational machinery-can alter translational efficiencies of mRNA at the initiation and elongation stages and how translation velocity affects protein folding. We seek to explain these findings in the context of mechanistic work on the ribosome. The results argue in favour of a new understanding of translation control as a hub that links mRNA homeostasis to production and quality control of proteins in the cell.
12Coevolution between Stop Codon Usage and Release Factors in Bacterial Species.PubMed
Yulong Wei, Juan Wang, Xuhua Xia
Mol Biol Evol. 2016 Sep;33(9):2357-67. doi: 10.1093/molbev/msw107. Epub 2016 Jun 13.
Three stop codons in bacteria represent different translation termination signals, and their usage is expected to depend on their differences in translation termination efficiency, mutation bias, and relative abundance of release factors (RF1 decoding UAA and UAG, and RF2 decoding UAA and UGA). In 14 bacterial species (covering Proteobacteria, Firmicutes, Cyanobacteria, Actinobacteria and Spirochetes) with cellular RF1 and RF2 quantified, UAA is consistently over-represented in highly expressed genes (HEGs) relative to lowly expressed genes (LEGs), whereas UGA usage is the opposite even in species where RF2 is far more abundant than RF1. UGA usage relative to UAG increases significantly with PRF2 [=RF2/(RF1 + RF2)] as expected from adaptation between stop codons and their decoders. PRF2 is > 0.5 over a wide range of AT content (measured by PAT3 as the proportion of AT at third codon sites), but decreases rapidly toward zero at the high range of PAT3 This explains why bacterial lineages with high PAT3 often have UGA reassigned because of low RF2. There is no indication that UAG is a minor stop codon in bacteria as claimed in a recent publication. The claim is invalid because of the failure to apply the two key criteria in identifying a minor codon: (1) it is least preferred by HEGs (or most preferred by LEGs) and (2) it corresponds to the least abundant decoder. Our results suggest a more plausible explanation for why UAA usage increases, and UGA usage decreases, with PAT3, but UAG usage remains low over the entire PAT3 range.
13Codon bias, nucleotide selection, and genome size predict in situ bacterial growth rate and transcription in rewetted soil.PubMed
Peter F Chuckran, Katerina Estera-Molina, Alexa M Nicolas, et al.
Proc Natl Acad Sci U S A. 2025 Jan 21;122(3):e2413032122. doi: 10.1073/pnas.2413032122. Epub 2025 Jan 13.
In soils, the first rain after a prolonged dry period represents a major pulse event impacting soil microbial community function, yet we lack a full understanding of the genomic traits associated with the microbial response to rewetting. Genomic traits such as codon usage bias and genome size have been linked to bacterial growth in soils-however, often through measurements in culture. Here, we used metagenome-assembled genomes (MAGs) with O-water stable isotope probing and metatranscriptomics to track genomic traits associated with growth and transcription of soil microorganisms over one week following rewetting of a grassland soil. We found that codon bias in ribosomal protein genes was the strongest predictor of growth rate. We also found higher growth rates in bacteria with smaller genomes, suggesting that reduced genome size enables a faster response to pulses in soil bacteria. Faster transcriptional upregulation of ribosomal protein genes was associated with high codon bias and increased nucleotide skew. We found that several of these relationships existed within phyla, indicating that these associations between genomic traits and activity could be generalized characteristics of soil bacteria. Finally, we used publicly available metagenomes to assess the distribution of codon bias across a pH gradient and found that microbial communities in higher pH soils-which are often more water limited and pulse driven-have higher codon usage bias in their ribosomal protein genes. Together, these results provide evidence that genomic characteristics affect soil microbial activity during rewetting and pose a potential fitness advantage for soil bacteria where water and nutrient availability are episodic.
14The Codon Usage Code for Cotranslational Folding of Viral Capsids.PubMed
Rosa M Pintó, Albert Bosch
Genome Biol Evol. 2021 Sep 1;13(9). doi: 10.1093/gbe/evab089.
Codon bias is common to all organisms and is the result of mutation, drift, and selection. Selection for the efficiency and accuracy of translation is well recognized as a factor shaping the codon usage. In contrast, fewer studies report the control of the rate of translation as an additional selective pressure influencing the codon usage of an organism. Experimental molecular evolution using RNA virus populations is a powerful tool for the identification of mechanisms underlying the codon bias. Indeed, the role of deoptimized codons on the cotranslational folding has been proven in the capsids of two fecal-orally transmitted picornaviruses, poliovirus, and the hepatitis A virus, emphasizing the role of the frequency of codons in determining the phenotype. However, most studies on virus codon usage rely only on computational analyses, and experimental studies should be encouraged to clearly define the role of selection on codon evolution.
15Synonymous codon usage regulates translation initiation.PubMed
Chloe L Barrington, Gabriel Galindo, Amanda L Koch, et al.
Cell Rep. 2023 Dec 26;42(12):113413. doi: 10.1016/j.celrep.2023.113413. Epub 2023 Dec 12.
Nonoptimal synonymous codons repress gene expression, but the underlying mechanisms are poorly understood. We and others have previously shown that nonoptimal codons slow translation elongation speeds and thereby trigger messenger RNA (mRNA) degradation. Nevertheless, transcript levels are often insufficient to explain protein levels, suggesting additional mechanisms by which codon usage regulates gene expression. Using reporters in human and Drosophila cells, we find that transcript levels account for less than half of the variation in protein abundance due to codon usage. This discrepancy is explained by translational differences whereby nonoptimal codons repress translation initiation. Nonoptimal transcripts are also less bound by the translation initiation factors eIF4E and eIF4G1, providing a mechanistic explanation for their reduced initiation rates. Importantly, translational repression can occur without mRNA decay and deadenylation, and it does not depend on the known nonoptimality sensor, CNOT3. Our results reveal a potent mechanism of regulation by codon usage where nonoptimal codons repress further rounds of translation.
16Comparative Analysis of Genomic and Transcriptome Sequences Reveals Divergent Patterns of Codon Bias in Wheat and Its Ancestor Species.PubMed
Chenkang Yang, Qi Zhao, Ying Wang, et al.
Front Genet. 2021 Aug 20;12:732432. doi: 10.3389/fgene.2021.732432. eCollection 2021.
The synonymous codons usage shows a characteristic pattern of preference in each organism. This codon usage bias is thought to have evolved for efficient protein synthesis. Synonymous codon usage was studied in genes of the hexaploid wheat (AABBDD) and its progenitor species, (AA), (DD), and (AABB). exhibited stronger usage bias for G/C-ending codons than did the three progenitor species, and this bias was especially higher compared to and . High GC content is a primary factor influencing codon usage in . Neutrality analysis showed a significant positive correlation (<0.001) between GC12 and GC3 in the four species with regression line slopes near zero (0.16-0.20), suggesting that the effect of mutation on codon usage was only 16-20%. The GC3s values of genes were associated with gene length and distribution density within chromosomes. tRNA abundance data indicated that codon preference corresponded to the relative abundance of isoaccepting tRNAs in the four species. Both mutation and selection have affected synonymous codon usage in hexaploid wheat and its progenitor species. GO enrichment showed that GC biased genes were commonly enriched in physiological processes such as photosynthesis and response to acid chemical. In some certain gene families with important functions, the codon usage of small parts of genes has changed during the evolution process of .
17Characterization of the complete chloroplast genome of Brassica oleracea var. italica and phylogenetic relationships in Brassicaceae.PubMed
Zhenchao Zhang, Meiqi Tao, Xi Shan, et al.
PLoS One. 2022 Feb 24;17(2):e0263310. doi: 10.1371/journal.pone.0263310. eCollection 2022.
Broccoli (Brassica oleracea var. italica) is an important B. oleracea cultivar, with high economic and agronomic value. However, comparative genome analyses are still needed to clarify variation among cultivars and phylogenetic relationships within the family Brassicaceae. Herein, the complete chloroplast (cp) genome of broccoli was generated by Illumina sequencing platform to provide basic information for genetic studies and to establish phylogenetic relationships within Brassicaceae. The whole genome was 153,364 bp, including two inverted repeat (IR) regions of 26,197 bp each, separated by a small single copy (SSC) region of 17,834 bp and a large single copy (LSC) region of 83,136 bp. The total GC content of the entire chloroplast genome accounts for 36%, while the GC content in each region of SSC,LSC, and IR accounts for 29.1%, 34.15% and 42.35%, respectively. The genome harbored 133 genes, including 88 protein-coding genes, 37 tRNAs, and 8 rRNAs, with 17 duplicates in IRs. The most abundant amino acid was leucine and the least abundant was cysteine. Codon usage analyses revealed a bias for A/T-ending codons. A total of 35 repeat sequences and 92 simple sequence repeats were detected, and the SC-IR boundary regions were variable between the seven cp genomes. A phylogenetic analysis suggested that broccoli is closely related to Brassica oleracea var. italica MH388764.1, Brassica oleracea var. italica MH388765.1, and Brassica oleracea NC_0441167.1. Our results are expected to be useful for further species identification, population genetics analyses, and biological research on broccoli.
18Analysis of chromosomes and nucleotides in rice to predict gene expression through codon usage pattern.PubMed
Meshal M Almutairi
Saudi J Biol Sci. 2021 Aug;28(8):4569-4574. doi: 10.1016/j.sjbs.2021.04.059. Epub 2021 Apr 30.
Amino acids are essential measurements for the potential growth stage because of connecting to protein structures and functions. The objective of this paper was to analyze chromosomes feature at plastid region of rice represented by nucleotide, synonymous codon, and amino acid usage to predict gene expression through codon usage pattern. The results showed that the values of the codon adaption index ranged from 0.733 in chromosome 9 to 0.631 in chromosome 8 with full length of these two chromosomes were 3738 and 1635 respectively. The higher value of guanine and cytosine content was 60% in chromosomes 9 while the lower values was 37% in chromosomes 11. Eight chromosomes (ch1, ch2, ch3, ch5, ch7, ch8, ch10, and ch12) were greater value of modified relative codon bias than threshold (threshold: 0.66) especially in cysteine for ch1, ch2, ch5, ch10, and ch12. While other remaining chromosomes were less than the threshold. Relative synonymous codon usage found that the over-represented of amino acids were asparagine, aspartate, cysteine, glutamate, and phenylalanine across all 12 chromosomes. These results would establish a platform for more and further projects concerning rice breeding and genetics and codon optimization in the amino acids for developing varieties. These results also will help breeders to select desirable genes through the genome for improve target traits.
19A comparative analysis of synonymous codon usage bias pattern in human albumin superfamily.PubMed
Hoda Mirsafian, Adiratna Mat Ripen, Aarti Singh, et al.
ScientificWorldJournal. 2014 Feb 20;2014:639682. doi: 10.1155/2014/639682. eCollection 2014.
Synonymous codon usage bias is an inevitable phenomenon in organismic taxa across the three domains of life. Though the frequency of codon usage is not equal across species and within genome in the same species, the phenomenon is non random and is tissue-specific. Several factors such as GC content, nucleotide distribution, protein hydropathy, protein secondary structure, and translational selection are reported to contribute to codon usage preference. The synonymous codon usage patterns can be helpful in revealing the expression pattern of genes as well as the evolutionary relationship between the sequences. In this study, synonymous codon usage bias patterns were determined for the evolutionarily close proteins of albumin superfamily, namely, albumin, α-fetoprotein, afamin, and vitamin D-binding protein. Our study demonstrated that the genes of the four albumin superfamily members have low GC content and high values of effective number of codons (ENC) suggesting high expressivity of these genes and less bias in codon usage preferences. This study also provided evidence that the albumin superfamily members are not subjected to mutational selection pressure.
20COUSIN (COdon Usage Similarity INdex): A Normalized Measure of Codon Usage Preferences.PubMed
Jérôme Bourret, Samuel Alizon, Ignacio G Bravo
Genome Biol Evol. 2019 Dec 1;11(12):3523-3528. doi: 10.1093/gbe/evz262.
Codon Usage Preferences (CUPrefs) describe the unequal usage of synonymous codons at the gene, chromosome, or genome levels. Numerous indices have been developed to evaluate CUPrefs, either in absolute terms or with respect to a reference. We introduce the normalized index COUSIN (for COdon Usage Similarity INdex), that compares the CUPrefs of a query against those of a reference and normalizes the output over a Null Hypothesis of random codon usage. The added value of COUSIN is to be easily interpreted, both quantitatively and qualitatively. An eponymous software written in Python3 is available for local or online use (http://cousin.ird.fr). This software allows for an easy and complete analysis of CUPrefs via COUSIN, includes seven other indices, and provides additional features such as statistical analyses, clustering, and CUPrefs optimization for gene expression. We illustrate the flexibility of COUSIN and highlight its advantages by analyzing the complete coding sequences of eight divergent genomes. Strikingly, COUSIN captures a bimodal distribution in the CUPrefs of human and chicken genes hitherto unreported with such precision. COUSIN opens new perspectives to uncover CUPrefs specificities in genomes in a practical, informative, and user-friendly way.
21Massively parallel gene expression variation measurement of a synonymous codon library.PubMed
Alexander Schmitz, Fuzhong Zhang
BMC Genomics. 2021 Mar 2;22(1):149. doi: 10.1186/s12864-021-07462-z.
BACKGROUND: Cell-to-cell variation in gene expression strongly affects population behavior and is key to multiple biological processes. While codon usage is known to affect ensemble gene expression, how codon usage influences variation in gene expression between single cells is not well understood. RESULTS: Here, we used a Sort-seq based massively parallel strategy to quantify gene expression variation from a green fluorescent protein (GFP) library containing synonymous codons in Escherichia coli. We found that sequences containing codons with higher tRNA Adaptation Index (TAI) scores, and higher codon adaptation index (CAI) scores, have higher GFP variance. This trend is not observed for codons with high Normalized Translation Efficiency Index (nTE) scores nor from the free energy of folding of the mRNA secondary structure. GFP noise, or squared coefficient of variance (CV), scales with mean protein abundance for low-abundant proteins but does not change at high mean protein abundance. CONCLUSIONS: Our results suggest that the main source of noise for high-abundance proteins is likely not originating at translation elongation. Additionally, the drastic change in mean protein abundance with small changes in protein noise seen from our library implies that codon optimization can be performed without concerning gene expression noise for biotechnology applications.
22Codon usage bias analysis of mitochondrial protein-coding genes in 12 species of .PubMed
Fen Wang, Nan Zhang, Chunling Zhao, et al.
J Genet. 2023;102.
The incidence of diseases that are caused by fungal infection is gradually increasing, together with antibiotic abuse and the number of patients with hypoimmunity. The many challenges in clinical anti-fungi treatment include serious adverse effects and drug resistance. The mitochondria of fungi have been found to be closely associated with pathopoiesia and drug resistance. Hence, we investigated patterns in mitochondrial genes codon usage bias to provide new information to guide anti-fungal research. According to the nucleotide composition results, most mitochondrial genes of the analysed Candida tended to use A/T bases rather than G/C bases. The relative synonymous codon usage values demonstrated that UUA, AGU, CCU, GCU, UGA, AGA and GGU were the common preferential codons of mitochondrial genes in 12 species. Codon adaptation index (CAI) analysis indicated that the of Candida parapsilosis had the highest value, and the of had the lowest value. The CAI clearly correlated with the codon bias index, except in and , and was significantly positively correlated with the average GC content. Together, our results suggested that the codon usage pattern is affected by multiple factors, among which GC content is critical. Nucleotide composition, selection pressure and mutation pressure influence codon bias in mitochondrial genes, with dominant status to mutation pressure. Codon usage bias analyses of mitochondrial genes may provide new insight into its evolution.
23Comparison of codon usage and tRNAs in mitochondrial genomes of Candida species.PubMed
Takaaki Kamatani, Tetsuya Yamamoto
Biosystems. 2007 Sep-Oct;90(2):362-70. doi: 10.1016/j.biosystems.2006.09.039. Epub 2006 Oct 5.
To gain insight into the nature of the mitochondrial genomes (mtDNA) of different Candida species, the synonymous codon usage bias of mitochondrial protein coding genes and the tRNAs in C. albicans, C. parapsilosis, C. stellata, C. glabrata and the closely related yeast Saccharomyces cerevisiae were analyzed. Common features of the mtDNA in Candida species are a strong A+T pressure on protein coding genes, and insufficient mitochondrial tRNA species are encoded to perform protein synthesis. The wobble site of the anticodon is always U for the NNR (NNA and NNG) codon families, which are dominated by A-ending codons, and always G for the NNY (NNC and NNU) codon families, which is dominated by U-ending codons, and always U for the NNN (NNA, NNU, NNC and NNG) codon families, which are dominated by A-ending codons and U-ending codons. Patterns of synonymous codon usage of Candida species can be classified into three groups: (1) optimal codon-anticodon usage, Glu, Lys, Leu (translated by anti-codon UAA), Gln, Arg (translated by anti-codon UCU) and Trp are containing NNR codons. NNA, whose corresponding tRNA is encoded in the mtDNA, is used preferentially. (2) Non-optimal codon-anticodon usage, Cys, Asp, Phe, His, Asn, Ser (translated by anti-codon GCU) and Tyr are containing NNY codons. The NNU codon, whose corresponding tRNA is not encoded in the mtDNA, is used preferentially. (3) Combined codon-anticodon usage, Ala, Gly, Leu (translated by anti-codon UAG), Pro, Ser (translated by anti-codon UGA), Thr and Val are containing NNN codons. NNA (tRNA encoded in the mtDNA) and NNU (tRNA not encoded in the mtDNA) are used preferentially. In conclusion, we propose that in Candida species, codons containing A or U at third position are used preferentially, regardless of whether corresponding tRNAs are encoded in the mtDNA. These results might be useful in understanding the common features of the mtDNA in Candida species and patterns of synonymous codon usage.
24Codon Bias Can Determine Sorting of a Potassium Channel Protein.PubMed
Anja J Engel, Marina Kithil, Markus Langhans, et al.
Cells. 2021 May 7;10(5):1128. doi: 10.3390/cells10051128.
Due to the redundancy of the genetic code most amino acids are encoded by multiple synonymous codons. It has been proposed that a biased frequency of synonymous codons can affect the function of proteins by modulating distinct steps in transcription, translation and folding. Here, we use two similar prototype K channels as model systems to examine whether codon choice has an impact on protein sorting. By monitoring transient expression of GFP-tagged channels in mammalian cells, we find that one of the two channels is sorted in a codon and cell cycle-dependent manner either to mitochondria or the secretory pathway. The data establish that a gene with either rare or frequent codons serves, together with a cell-state-dependent decoding mechanism, as a secondary code for sorting intracellular membrane proteins.
25Combination of hydrophobicity and codon usage bias determines sorting of model K channel protein to either mitochondria or endoplasmic reticulum.PubMed
Anja J Engel, Steffen Paech, Markus Langhans, et al.
Traffic. 2023 Nov;24(11):533-545. doi: 10.1111/tra.12915. Epub 2023 Aug 14.
When the K channel-like protein Kesv from Ectocarpus siliculosus virus 1 is heterologously expressed in mammalian cells, it is sorted to the mitochondria. This targeting can be redirected to the endoplasmic reticulum (ER) by altering the codon usage in distinct regions of the gene or by inserting a triplet of hydrophobic amino acids (AAs) into the protein's C-terminal transmembrane domain (ct-TMD). Systematic variations in the flavor of the inserted AAs and/or its codon usage show that a positive charge in the inserted AA triplet alone serves as strong signal for mitochondria sorting. In cases of neutral AA triplets, mitochondria sorting are favored by a combination of hydrophilic AAs and rarely used codons; sorting to the ER exhibits the inverse dependency. This propensity for ER sorting is particularly high when a common codon follows a rarer one in the AA triplet; mitochondria sorting in contrast is supported by codon uniformity. Since parameters like positive charge, hydrophobic AAs, and common codons are known to facilitate elongation of nascent proteins in the ribosome the data suggest a mechanism in which local changes in elongation velocity and co-translational folding in the ct-TMD influence intracellular protein sorting.
26Amino acid cost and codon-usage biases in 6 prokaryotic genomes: a whole-genome analysis.PubMed
Esley M Heizer, Douglas W Raiford, Michael L Raymer, et al.
Mol Biol Evol. 2006 Sep;23(9):1670-80. doi: 10.1093/molbev/msl029. Epub 2006 Jun 5.
For most prokaryotic organisms, amino acid biosynthesis represents a significant portion of their overall energy budget. The difference in the cost of synthesis between amino acids can be striking, differing by as much as 7-fold. Two prokaryotic organisms, Escherichia coli and Bacillus subtilis, have been shown to preferentially utilize less costly amino acids in highly expressed genes, indicating that parsimony in amino acid selection may confer a selective advantage for prokaryotes. This study confirms those findings and extends them to 4 additional prokaryotic organisms: Chlamydia trachomatis, Chlamydophila pneumoniae AR39, Synechocystis sp. PCC 6803, and Thermus thermophilus HB27. Adherence to codon-usage biases for each of these 6 organisms is inversely correlated with a coding region's average amino acid biosynthetic cost in a fashion that is independent of chemoheterotrophic, photoautotrophic, or thermophilic lifestyle. The obligate parasites C. trachomatis and C. pneumoniae AR39 are incapable of synthesizing many of the 20 common amino acids. Removing auxotrophic amino acids from consideration in these organisms does not alter the overall trend of preferential use of energetically inexpensive amino acids in highly expressed genes.
27Codon usage behavior distinguishes pathogenic Clostridium species from the non-pathogenic species.PubMed
Anuj Sharma, Shelly Gupta, Karan Paul
Gene. 2023 Jul 15;873:147394. doi: 10.1016/j.gene.2023.147394. Epub 2023 May 1.
Genus Clostridium is of the largest genus in class Clostridia. It is comprised of spore-forming, anaerobic, gram-positive organisms. The members of this genus include human pathogens to free-living nitrogen fixing bacteria. In the present study, we have performed a comparison of the choice of preferred codons, codon usage patterns, dinucleotide and amino acid usage pattern of 76 species of Genus Clostridium. We found the pathogenic clostridium species to have smaller AT-rich genomes as compared to opportunistic and non-pathogenic clostridium species. The choice of preferred and optimal codons was also influenced by genomic GC/AT content of the respective clostridium species. The pathogenic clostridium species displayed a strict bias in the codon usage, employing 35 of the 61 codons encoding for 20 amino acids. Comparison of amino acid usage revealed an increased usage of amino acids with lower biosynthetic cost by pathogenic clostridium species as compared to opportunistic and non-pathogenic clostridium species. Smaller genome, strict codon usage bias and amino acid usage lead to lower protein energetic cost for the clostridial pathogens. Overall, we found the pathogenic members of genus Clostridium to prefer small, AT-rich codons to reduce biosynthetic costs and match the cellular environment of its AT-rich human host.
28High-level expression of an Aspergillus niger endo-beta-1,4-glucanase in Pichia pastoris through gene codon optimization and synthesis.PubMed
Zhao Shumiao, Jun Huang, Changyi Zhang, et al.
J Microbiol Biotechnol. 2010 Mar;20(3):467-73.
To improve the expression efficiency of recombinant endo-beta-1,4-glucanase in P. pastoris, the endo-beta-1,4-glucanase (egI) gene from Aspergillus niger was synthesized using optimized codons. Fourteen pairs of oligonucleotides with 15 bp overlap were designed and the full-length syn-egI gene was generated by two-step PCR-based DNA synthesis. In the synthesized endo-beta-1,4-glucanase gene syn-egI, 193 nucleotides were changed, and the G+C content was decreased from 54% to 44.2%. The syn-egI gene was inserted into pPIC9K and transformed into P. pastoris GS115 by electroporation. The enzyme activity of recombinant P. pastoris stain 2-7# reached 20.3 U/ml with 1% barley beta-glucan and 3.3 U/ml with 1% carboxymethylcellulose (CMC) as substrate in shake flasks versus 1270.3 U/ml and 220.7 U/ml for the same substrates in 50 l fermentors. The molecular mass of the recombinant protein was approximately 40 kDa as determined by SDS-PAGE analysis, the optimal temperature for recombinant enzyme activity was 70 degrees C and the optimal pH was 5.0 when CMC was used as the substrate.
29Biotechnical paving of recombinant enterocin A as the candidate of anti-Listeria agent.PubMed
Xiaoyuan Hu, Ruoyu Mao, Yong Zhang, et al.
BMC Microbiol. 2014 Aug 28;14:220. doi: 10.1186/s12866-014-0220-8.
BACKGROUND: Enterocin A is a classic IIa bacteriocin isolated firstly from Enterococcus faecium CTC492 with selective antimicrobial activity against Listeria strains. However, the application of enterocin A as an anti-Listeria agent has been limited due to its very low native yield. The present work describes high production of enterocin A through codon optimization strategy and its character study. RESULTS: The gene sequence of enterocin A was optimized based on preferential codon usage in Pichia pastoris to increase its expression efficiency. The highest anti-Listeria activity reached 51,200 AU/ml from 180 mg/l of total protein after 24 h of induction in a 5-L fermenter. Recombinant enterocin A (rEntA), purified by gel filtration chromatography, showed very strong activity against Listeria ivanovii ATCC 19119 with a low MIC of 20 ng/ml. In addition, the rEntA killed over 99% of tested L. ivanovii ATCC19119 within 4 h when exposed to 4 × MIC (80 ng/ml). Moreover, it showed high stability under a wide pH range (2-10) and maintained full activity after 1 h of treatment at 80°C within a pH range of 2-8. Its antimicrobial activity was enhanced at 25 and 50 mM NaCl, while 100-400 mM NaCl had little effect on the bactericidal ability of rEntA. CONCLUSION: The EntA was successfully expressed in P. pastoris, and this feasible system could pave the pre-industrial technological path of rEntA as a competent candidate as an anti-Listeria agent. Furthermore, it showed high stability under wide ranges of conditions, which could be potential as the new candidate of anti-Listeria agent.
30Exploration and heterologous expression of laccase genes and pesticide degradation ability of laccases from Cerrena unicolor GC.u01.PubMed
Jie Chu, Xiaoxiao Zhang, Ruihong Sun, et al.
World J Microbiol Biotechnol. 2025 Jun 4;41(6):188. doi: 10.1007/s11274-025-04435-2.
Laccases are valuable industrial enzymes with applications across various fields. While heterologous expression in Pichia pastoris is a common strategy, current approaches face limitations in yield, stability, and catalytic efficiency against recalcitrant agrochemicals. In this study, we sequenced and annotated the first high-quality genome of Cerrena unicolor strain GC.u01 (30.95 Mb, 8,089 genes), revealing a unique laccase gene family comprising nine members. Structural analysis revealed novel catalytic motifs in Lac2, which was successfully expressed in P. pastoris GS115 through codon optimization, yielding a novel recombinant enzyme (70 kDa) with exceptional pH stability (retaining > 80% activity at pH 3.0-8.0 for 24 h) and thermotolerance (> 60% activity at 40 °C), surpassing most reported fungal laccases. Notably, Lac2 demonstrated unprecedented degradation efficiency for azoxystrobin (96.2) and phoxim (30.7%)-the first report of a Cerrena unicolor laccase degrading these pesticides-achieving significantly higher rates than previously described laccases under similar conditions. This study integrates genome mining, enzyme engineering, and functional validation to establish a new paradigm for developing robust biocatalysts against recalcitrant agrochemicals. These unique characteristics of Lac2 suggest the potential of this enzyme in biotechnological and industrial applications.
31Overexpression and characterization of two types of nitrile hydratases from Rhodococcus rhodochrous J1.PubMed
Yao Lan, Xiaohuan Zhang, Zhongmei Liu, et al.
PLoS One. 2017 Jun 23;12(6):e0179833. doi: 10.1371/journal.pone.0179833. eCollection 2017.
Nitrile hydratase (NHase) from Rhodococcus rhodochrous J1 is widely used for industrial production of acrylamide and nicotinamide. However, the two types of NHases (L-NHase and H-NHase) from R. rhodochrous J1 were only slightly expressed in E. coli by routine methods, which limits the comprehensive and systematic characterization of the enzyme properties. We successfully expressed the two types of recombinant NHases in E. coli by codon-optimization, engineering of Ribosome Binding Site (RBS) and spacer sequences. The specific activity of the purified L-NHase and H-NHase were 400 U/mg and 234 U/mg, respectively. The molecular mass of L-NHase and H-NHase was identified to be 94 kDa and 504 kDa, respectively, indicating that the quaternary structure of the two types of NHases was the same as those in R. rhodochrous J1. H-NHase exhibited higher substrate and product tolerance than L-NHase. Moreover, higher activity and shorter culture time were achieved in recombinant E. coli, and the whole cell catalyst of recombinant E. coli harboring H-NHase has equivalent efficiency in tolerance to the high-concentration product relative to that in R. rhodochrous J1. These results indicate that biotransformation of nitrile by R. rhodochrous J1 represents a potential alternative to NHase-producing E. coli.
32Stepwise engineering of a Pichia pastoris D-amino acid oxidase whole cell catalyst.PubMed
Sandra Abad, Jozef Nahalka, Gabriele Bergler, et al.
Microb Cell Fact. 2010 Apr 26;9:24. doi: 10.1186/1475-2859-9-24.
BACKGROUND: Trigonopsis variabilis D-amino acid oxidase (TvDAO) is a well characterized enzyme used for cephalosporin C conversion on industrial scale. However, the demands on the enzyme with respect to activity, operational stability and costs also vary with the field of application. Processes that use the soluble enzyme suffer from fast inactivation of TvDAO while immobilized oxidase preparations raise issues related to expensive carriers and catalyst efficiency. Therefore, oxidase preparations that are more robust and active than those currently available would enable a much broader range of economically viable applications of this enzyme in fine chemical syntheses. A multi-step engineering approach was chosen here to develop a robust and highly active Pichia pastoris TvDAO whole-cell biocatalyst. RESULTS: As compared to the native T. variabilis host, a more than seven-fold enhancement of the intracellular level of oxidase activity was achieved in P. pastoris through expression optimization by codon redesign as well as efficient subcellular targeting of the enzyme to peroxisomes. Multi copy integration further doubled expression and the specific activity of the whole cell catalyst. From a multicopy production strain, about 1.3 x 103 U/g wet cell weight (wcw) were derived by standard induction conditions feeding pure methanol. A fed-batch cultivation protocol using a mixture of methanol and glycerol in the induction phase attenuated the apparent toxicity of the recombinant oxidase to yield final biomass concentrations in the bioreactor of >or= 200 g/L compared to only 117 g/L using the standard methanol feed. Permeabilization of P. pastoris using 10% isopropanol yielded a whole-cell enzyme preparation that showed 49% of the total available intracellular oxidase activity and was notably stabilized (by three times compared to a widely used TvDAO expressing Escherichia coli strain) under conditions of D-methionine conversion using vigorous aeration. CONCLUSIONS: Stepwise optimization using a multi-level engineering approach has delivered a new P. pastoris whole cell TvDAO biocatalyst showing substantially enhanced specific activity and stability under operational conditions as compared to previously reported preparations of the enzyme. The production of the oxidase through fed-batch bioreactor culture and subsequent cell permeabilization is high-yielding and efficient. Therefore this P. pastoris catalyst has been evaluated for industrial purposes.
33Highly efficient expression of Rasamsonia emersonii lipase in Pichia pastoris: characterization and gastrointestinal simulated digestion in vitro.PubMed
Buqing Wang, Yasen Wang, Xiaoman Zhou, et al.
J Sci Food Agric. 2024 Jul;104(9):5603-5613. doi: 10.1002/jsfa.13390. Epub 2024 Mar 4.
BACKGROUND: Acidic lipases with high catalytic activities under acidic conditions have important application values in the food, feed and pharmaceutical industries. However, the availability of acidic lipases is still the main obstacle to their industrial applications. Although a novel acidic lipase Rasamsonia emersonii (LIPR) was heterologously expressed in Escherichia coli, the expression level was unsatisfactory. RESULTS: To achieve the high-efficiency expression and secretion of LIPR in Pichia pastoris GS115, the combinatorial optimization strategy was adopted including gene codon preference, signal peptide, molecular chaperone co-expression and disruption of vacuolar sorting receptor VPS10. The activity of the combinatorial optimization engineered strain in a shake flask reached 1480 U mL, which was 8.13 times greater than the P. pastoris GS115 parental strain. After high-density fermentation in a 5-L bioreactor, the highest enzyme activity reached as high as 11 820 U mL. LIPR showed the highest activity at 40 °C and pH 4.0 in the presence of Ca ion. LIPR exhibited strong tolerance to methanol, indicating its potential application in biodiesel biosynthesis. Moreover, the gastrointestinal digestion simulation results demonstrated that LIPR was tolerant to pepsin and trypsin, but its activity was inhibited by sodium taurodeoxycholate. CONCLUSION: This study provided an effective approach for the high expression of acidic lipase LIPR. LIPR was more appropriate for lipid digestion in the stomach than in intestine according to the gastrointestinal digestion simulation results. © 2024 Society of Chemical Industry.
34Codon usage and codon pair patterns in non-grass monocot genomes.PubMed
Purabi Mazumdar, RofinaYasmin Binti Othman, Katharina Mebus, et al.
Ann Bot. 2017 Nov 28;120(6):893-909. doi: 10.1093/aob/mcx112.
BACKGROUND AND AIMS: Studies on codon usage in monocots have focused on grasses, and observed patterns of this taxon were generalized to all monocot species. Here, non-grass monocot species were analysed to investigate the differences between grass and non-grass monocots. METHODS: First, studies of codon usage in monocots were reviewed. The current information was then extended regarding codon usage, as well as codon-pair context bias, using four completely sequenced non-grass monocot genomes (Musa acuminata, Musa balbisiana, Phoenix dactylifera and Spirodela polyrhiza) for which comparable transcriptome datasets are available. Measurements were taken regarding relative synonymous codon usage, effective number of codons, derived optimal codon and GC content and then the relationships investigated to infer the underlying evolutionary forces. KEY RESULTS: The research identified optimal codons, rare codons and preferred codon-pair context in the non-grass monocot species studied. In contrast to the bimodal distribution of GC3 (GC content in third codon position) in grasses, non-grass monocots showed a unimodal distribution. Disproportionate use of G and C (and of A and T) in two- and four-codon amino acids detected in the analysis rules out the mutational bias hypothesis as an explanation of genomic variation in GC content. There was found to be a positive relationship between CAI (codon adaptation index; predicts the level of expression of a gene) and GC3. In addition, a strong correlation was observed between coding and genomic GC content and negative correlation of GC3 with gene length, indicating a strong impact of GC-biased gene conversion (gBGC) in shaping codon usage and nucleotide composition in non-grass monocots. CONCLUSION: Optimal codons in these non-grass monocots show a preference for G/C in the third codon position. These results support the concept that codon usage and nucleotide composition in non-grass monocots are mainly driven by gBGC.
35Base Composition, Codon Usage, and Patterns of Gene Sequence Evolution in Butterflies.PubMed
Karin Näsvall, Jesper Boman, Venkat Talla, et al.
Genome Biol Evol. 2023 Aug 1;15(8). doi: 10.1093/gbe/evad150.
Coding sequence evolution is influenced by both natural selection and neutral evolutionary forces. In many species, the effects of mutation bias, codon usage, and GC-biased gene conversion (gBGC) on gene sequence evolution have not been detailed. Quantification of how these forces shape substitution patterns is therefore necessary to understand the strength and direction of natural selection. Here, we used comparative genomics to investigate the association between base composition and codon usage bias on gene sequence evolution in butterflies and moths (Lepidoptera), including an in-depth analysis of underlying patterns and processes in one species, Leptidea sinapis. The data revealed significant G/C to A/T substitution bias at third codon position with some variation in the strength among different butterfly lineages. However, the substitution bias was lower than expected from previously estimated mutation rate ratios, partly due to the influence of gBGC. We found that A/T-ending codons were overrepresented in most species, but there was a positive association between the magnitude of codon usage bias and GC-content in third codon positions. In addition, the tRNA-gene population in L. sinapis showed higher GC-content at third codon positions compared to coding sequences in general and less overrepresentation of A/T-ending codons. There was an inverse relationship between synonymous substitutions and codon usage bias indicating selection on synonymous sites. We conclude that the evolutionary rate in Lepidoptera is affected by a complex interaction between underlying G/C -> A/T mutation bias and partly counteracting fixation biases, predominantly conferred by overall purifying selection, gBGC, and selection on codon usage.
36Codon usage and expression level of human mitochondrial 13 protein coding genes across six continents.PubMed
Supriyo Chakraborty, Arif Uddin, Tarikul Huda Mazumder, et al.
Mitochondrion. 2018 Sep;42:64-76. doi: 10.1016/j.mito.2017.11.006. Epub 2017 Dec 2.
The study of codon usage coupled with phylogenetic analysis is an important tool to understand the genetic and evolutionary relationship of a gene. The 13 protein coding genes of human mitochondria are involved in electron transport chain for the generation of energy currency (ATP). However, no work has yet been reported on the codon usage of the mitochondrial protein coding genes across six continents. To understand the patterns of codon usage in mitochondrial genes across six different continents, we used bioinformatic analyses to analyze the protein coding genes. The codon usage bias was low as revealed from high ENC value. Correlation between codon usage and GC3 suggested that all the codons ending with G/C were positively correlated with GC3 but vice versa for A/T ending codons with the exception of ND4L and ND5 genes. Neutrality plot revealed that for the genes ATP6, COI, COIII, CYB, ND4 and ND4L, natural selection might have played a major role while mutation pressure might have played a dominant role in the codon usage bias of ATP8, COII, ND1, ND2, ND3, ND5 and ND6 genes. Phylogenetic analysis indicated that evolutionary relationships in each of 13 protein coding genes of human mitochondria were different across six continents and further suggested that geographical distance was an important factor for the origin and evolution of 13 protein coding genes of human mitochondria.
37A review on gene regulatory network reconstruction algorithms based on single cell RNA sequencing.PubMed
Hyeonkyu Kim, Hwisoo Choi, Daewon Lee, et al.
Genes Genomics. 2024 Jan;46(1):1-11. doi: 10.1007/s13258-023-01473-8. Epub 2023 Nov 30.
BACKGROUND: Understanding gene regulatory networks (GRNs) is essential for unraveling the molecular mechanisms governing cellular behavior. With the advent of high-throughput transcriptome measurement technology, researchers have aimed to reverse engineer the biological systems, extracting gene regulatory rules from their outputs, which represented by gene expression data. Bulk RNA sequencing, a widely used method for measuring gene expression, has been employed for GRN reconstruction. However, it falls short in capturing dynamic changes in gene expression at the level of individual cells since it averages gene expression across mixed cell populations. OBJECTIVE: In this review, we provide an overview of 15 GRN reconstruction tools and discuss their respective strengths and limitations, particularly in the context of single cell RNA sequencing (scRNA-seq). METHODS: Recent advancements in scRNA-seq break new ground of GRN reconstruction. They offer snapshots of the individual cell transcriptomes and capturing dynamic changes. We emphasize how these technological breakthroughs have enhanced GRN reconstruction. CONCLUSION: GRN reconstructors can be classified based on their requirement for cellular trajectory, which represents a dynamical cellular process including differentiation, aging, or disease progression. Benchmarking studies support the superiority of GRN reconstructors that do not require trajectory analysis in identifying regulator-target relationships. However, methods equipped with trajectory analysis demonstrate better performance in identifying key regulatory factors. In conclusion, researchers should select a suitable GRN reconstructor based on their specific research objectives.
38A scalable SCENIC workflow for single-cell gene regulatory network analysis.PubMed
Bram Van de Sande, Christopher Flerin, Kristofer Davie, et al.
Nat Protoc. 2020 Jul;15(7):2247-2276. doi: 10.1038/s41596-020-0336-2. Epub 2020 Jun 19.
This protocol explains how to perform a fast SCENIC analysis alongside standard best practices steps on single-cell RNA-sequencing data using software containers and Nextflow pipelines. SCENIC reconstructs regulons (i.e., transcription factors and their target genes) assesses the activity of these discovered regulons in individual cells and uses these cellular activity patterns to find meaningful clusters of cells. Here we present an improved version of SCENIC with several advances. SCENIC has been refactored and reimplemented in Python (pySCENIC), resulting in a tenfold increase in speed, and has been packaged into containers for ease of use. It is now also possible to use epigenomic track databases, as well as motifs, to refine regulons. In this protocol, we explain the different steps of SCENIC: the workflow starts from the count matrix depicting the gene abundances for all cells and consists of three stages. First, coexpression modules are inferred using a regression per-target approach (GRNBoost2). Next, the indirect targets are pruned from these modules using cis-regulatory motif discovery (cisTarget). Lastly, the activity of these regulons is quantified via an enrichment score for the regulon's target genes (AUCell). Nonlinear projection methods can be used to display visual groupings of cells based on the cellular activity patterns of these regulons. The results can be exported as a loom file and visualized in the SCope web application. This protocol is illustrated on two use cases: a peripheral blood mononuclear cell data set and a panel of single-cell RNA-sequencing cancer experiments. For a data set of 10,000 genes and 50,000 cells, the pipeline runs in <2 h.
39Inference of Gene Regulatory Network from Single-Cell Transcriptomic Data Using pySCENIC.PubMed
Nilesh Kumar, Bharat Mishra, Mohammad Athar, et al.
Methods Mol Biol. 2021;2328:171-182. doi: 10.1007/978-1-0716-1534-8_10.
With the advent of recent next-generation sequencing (NGS) technologies in genomics, transcriptomics, and epigenomics, profiling single-cell sequencing became possible. The single-cell RNA sequencing (scRNA-seq) is widely used to characterize diverse cell populations and ascertain cell type-specific regulatory mechanisms. The gene regulatory network (GRN) mainly consists of genes and their regulators-transcription factors (TF). Here, we describe the lightning-fast Python implementation of the SCENIC (Single-Cell reEgulatory Network Inference and Clustering) pipeline called pySCENIC. Using single-cell RNA-seq data, it maps TFs onto gene regulatory networks and integrates various cell types to infer cell-specific GRNs. There are two fast and efficient GRN inference algorithms, GRNBoost2 and GENIE3, optionally available with pySCENIC. The pipeline has three steps: (1) identification of potential TF targets based on co-expression; (2) TF-motif enrichment analysis to identify the direct targets (regulons); and (3) scoring the activity of regulons (or other gene sets) on single cell types.
40Integrated analysis of single-cell and bulk RNA sequencing data reveals a myeloid cell-related regulon predicting neoadjuvant immunotherapy response across cancers.PubMed
Hong Liu, Xiaoxian Sima, Bijing Xiao, et al.
J Transl Med. 2024 May 21;22(1):486. doi: 10.1186/s12967-024-05123-9.
BACKGROUND: Immunotherapy has brought about a paradigm shift in the treatment of cancer. However, the majority of patients exhibit resistance or become refractory to immunotherapy, and the underlying mechanisms remain to be explored. METHODS: Sing-cell RNA sequencing (scRNA‑seq) datasets derived from 1 pretreatment and 1 posttreatment achieving pathological complete response (pCR) patient with lung adenocarcinoma (LUAD) who received neoadjuvant immunotherapy were collected, and pySCENIC was used to find the gene regulatory network (GRN) between cell types and immune checkpoint inhibitor (ICI) response. A regulon predicting ICI response was identified and validated using large‑scale pan-cancer data, including a colorectal cancer scRNA‑seq dataset, a breast cancer scRNA‑seq dataset, The Cancer Genome Atlas (TCGA) pan-cancer cohort, and 5 ICI transcriptomic cohorts. Symphony reference mapping was performed to construct the myeloid cell map. RESULTS: Thirteen major cluster cell types were identified by comparing pretreatment and posttreatment patients, and the fraction of myeloid cells was higher in the posttreatment group (19.0% vs. 11.8%). A PPARG regulon (containing 23 target genes) was associated with ICI response, and its function was validated by a colorectal cancer scRNA‑seq dataset, a breast cancer scRNA‑seq dataset, TCGA pan-cancer cohort, and 5 ICI transcriptomic cohorts. Additionally, a myeloid cell map was developed, and cluster I, II, and III myeloid cells with high expression of PPARG were identified. Moreover, we constructed a website called PPARG ( https://pparg.online/PPARG/ or http://43.134.20.130:3838/PPARG/ ), which provides a powerful discovery tool and resource value for researchers. CONCLUSIONS: The PPARG regulon is a predictor of ICI response. The myeloid cell map enables the identification of PPARG subclusters in public scRNA-seq datasets and provides a powerful discovery tool and resource value.
41Integration of single-cell RNA and bulk RNA sequencing revealed malignant ductal cell heterogeneity and prognosis signatures in pancreatic cancer.PubMed
Haiyang Du, Gao Si, Jiqing Si, et al.
Front Immunol. 2025 Jul 1;16:1579184. doi: 10.3389/fimmu.2025.1579184. eCollection 2025.
INTRODUCTION: Pancreatic cancer is a highly malignant tumor of the digestive system with a dismal prognosis. Despite advances in diagnosis and treatment, overall survival remains extremely low. Early diagnostic markers and an improved understanding of tumor-microenvironment interactions are essential for developing more effective therapies. METHODS: We analyzed 74 single-cell RNA sequencing (scRNA-seq) samples, performing unsupervised clustering and marker-gene expression profiling to define major cell types. Large-scale chromosomal copy-number variation (CNV) analysis distinguished malignant from non-malignant ductal cells. Non-negative matrix factorization (NMF) identified stage-associated gene modules, which were integrated with TCGA bulk-RNA data and machine-learning feature selection to pinpoint candidate prognostic genes. Two independent cohorts were used for validation. Regulatory network inference (pySCENIC) and ligand-receptor interaction analysis (CellPhoneDB) explored cross-talk between malignant cells and macrophages. Finally, in vitro knockdown of CTSV assessed its functional role in pancreatic cancer (PAC) cell proliferation and migration. RESULTS: Three prognosis-related genes-ANLN, NT5E, and CTSV-were selected based on their strong association with clinical stage and validated in external datasets. High expression of these genes correlated with poorer overall survival and an increased infiltration of M0 macrophages. CellPhoneDB predicted significant interactions between high-expression malignant ductal cells and M0 macrophages via CXCL14-CXCR4 and IL1RAP-PTPRF axes, with SPI1 identified as an upstream regulator of IL1RAP. In vitro CTSV knockdown significantly inhibited PAC cell proliferation and migration. DISCUSSION: Our integrative single-cell and bulk-RNA workflow identifies ANLN, NT5E, and CTSV as novel prognostic biomarkers in pancreatic cancer and highlights a pro-tumorigenic interaction between malignant ductal cells and macrophages. Targeting CTSV or disrupting CXCL14-CXCR4 and IL1RAP-PTPRF signaling may offer new therapeutic avenues for PAC.
42Comparative gene regulatory networks modulating expression in microglia and astrocytes.PubMed
Logan Brase, Yanbo Yu, Eric McDade, et al.
medRxiv. 2024 Apr 22:2024.04.19.24306098. doi: 10.1101/2024.04.19.24306098.
BACKGROUND: Single-cell technologies have unveiled various transcriptional states in different brain cell types. Transcription factors (TFs) regulate the expression of related gene sets, thereby controlling these diverse expression states. Apolipoprotein E (), a pivotal risk-modifying gene in Alzheimer's disease (AD), is expressed in specific glial transcriptional states associated with AD. However, it is still unknown whether the upstream regulatory programs that modulate its expression are shared across brain cell types or specific to microglia and astrocytes. METHODS: We used pySCENIC to construct state-specific gene regulatory networks (GRNs) for resting and activated cell states within microglia and astrocytes based on single-nucleus RNA sequencing data from AD patients' cortices from the Knight ADRC-DIAN cohort. We then identified replicating TF using data from the ROSMAP cohort. We identified sets of genes co-regulated with by clustering the GRN target genes and identifying genes differentially expressed after the virtual knockout of TFs regulating . We performed enrichment analyses on these gene sets and evaluated their overlap with genes found in AD GWAS loci. RESULTS: We identified an average of 96 replicating regulators for each microglial and astrocyte cell state. Our analysis identified the CEBP, JUN, FOS, and FOXO TF families as key regulators of microglial APOE expression. The steroid/thyroid hormone receptor families, including the THR TF family, consistently regulated APOE across astrocyte states, while CEBP and JUN TF families were also involved in resting astrocytes. AD GWAS-associated genes (, , , , , , , , , and HLA genes) are co-regulated with APOE. We also uncovered that APOE-regulating TFs were linked to circadian rhythm (, , , , , , and ). CONCLUSIONS: Our findings reveal a novel perspective on the transcriptional regulation of in the human brain. We found a comprehensive and cell-type-specific regulatory landscape for , revealing distinct and shared regulatory mechanisms across microglia and astrocytes, underscoring the complexity of regulation. -co-regulated genes might also affect AD risk. Furthermore, our study uncovers a potential link between circadian rhythm disruption and regulation, shedding new light on the pathogenesis of AD.
43Reverse engineering of a mechanistic model of gene expression using metastability and temporal dynamics.PubMed
Elias Ventre
In Silico Biol. 2021;14(3-4):89-113. doi: 10.3233/ISB-210226.
Differentiation can be modeled at the single cell level as a stochastic process resulting from the dynamical functioning of an underlying Gene Regulatory Network (GRN), driving stem or progenitor cells to one or many differentiated cell types. Metastability seems inherent to differentiation process as a consequence of the limited number of cell types. Moreover, mRNA is known to be generally produced by bursts, which can give rise to highly variable non-Gaussian behavior, making the estimation of a GRN from transcriptional profiles challenging. In this article, we present CARDAMOM (Cell type Analysis from scRna-seq Data achieved from a Mixture MOdel), a new algorithm for inferring a GRN from timestamped scRNA-seq data, which crucially exploits these notions of metastability and transcriptional bursting. We show that such inference can be seen as the successive resolution of as many regression problem as timepoints, after a preliminary clustering of the whole set of cells with regards to their associated bursts frequency. We demonstrate the ability of CARDAMOM to infer a reliable GRN from in silico expression datasets, with good computational speed. To the best of our knowledge, this is the first description of a method which uses the concept of metastability for performing GRN inference.
44Diffusion-based generation of gene regulatory networks from scRNA-seq data with DigNet.PubMed
Chuanyuan Wang, Zhi-Ping Liu
Genome Res. 2025 Feb 14;35(2):340-354. doi: 10.1101/gr.279551.124.
A gene regulatory network (GRN) intricately encodes the interconnectedness of identities and functionalities of genes within cells, ultimately shaping cellular specificity. Despite decades of endeavors, reverse engineering of GRNs from gene expression profiling data remains a profound challenge, particularly when it comes to reconstructing cell-specific GRNs that are tailored to precise cellular and genetic contexts. Here, we propose a discrete diffusion generation model, called DigNet, capable of generating corresponding GRNs from high-throughput single-cell RNA sequencing (scRNA-seq) data. DigNet embeds the network generation process into a multistep recovery procedure with Markov properties. Each intermediate step has a specific model to recover a portion of the gene regulatory architectures. It thus can ensure compatibility between global network structures and regulatory modules through the unique multistep diffusion procedure. Furthermore, through iMetacell integration and non-Euclidean discrete space modeling, DigNet is robust to the presence of noise in scRNA-seq data and the sparsity of GRNs. Benchmark evaluation results against more than a dozen state-of-the-art network inference methods demonstrate that DigNet achieves superior performance across various single-cell GRN reconstruction experiments. Furthermore, DigNet provides unique insights into the immune response in breast cancer, derived from differential gene regulation identified in T cells. As an open-source software, DigNet offers a powerful and effective tool for generating cell-specific GRNs from scRNA-seq data.
45DNA methylation and its basic function.PubMed
Lisa D Moore, Thuc Le, Guoping Fan
Neuropsychopharmacology. 2013 Jan;38(1):23-38. doi: 10.1038/npp.2012.112. Epub 2012 Jul 11.
In the mammalian genome, DNA methylation is an epigenetic mechanism involving the transfer of a methyl group onto the C5 position of the cytosine to form 5-methylcytosine. DNA methylation regulates gene expression by recruiting proteins involved in gene repression or by inhibiting the binding of transcription factor(s) to DNA. During development, the pattern of DNA methylation in the genome changes as a result of a dynamic process involving both de novo DNA methylation and demethylation. As a consequence, differentiated cells develop a stable and unique DNA methylation pattern that regulates tissue-specific gene transcription. In this chapter, we will review the process of DNA methylation and demethylation in the nervous system. We will describe the DNA (de)methylation machinery and its association with other epigenetic mechanisms such as histone modifications and noncoding RNAs. Intriguingly, postmitotic neurons still express DNA methyltransferases and components involved in DNA demethylation. Moreover, neuronal activity can modulate their pattern of DNA methylation in response to physiological and environmental stimuli. The precise regulation of DNA methylation is essential for normal cognitive function. Indeed, when DNA methylation is altered as a result of developmental mutations or environmental risk factors, such as drug exposure and neural injury, mental impairment is a common side effect. The investigation into DNA methylation continues to show a rich and complex picture about epigenetic gene regulation in the central nervous system and provides possible therapeutic targets for the treatment of neuropsychiatric disorders.
47Sensitive tumour detection and classification using plasma cell-free DNA methylomes.PubMed
Shu Yi Shen, Rajat Singhania, Gordon Fehringer, et al.
Nature. 2018 Nov;563(7732):579-583. doi: 10.1038/s41586-018-0703-0. Epub 2018 Nov 14.
The use of liquid biopsies for cancer detection and management is rapidly gaining prominence. Current methods for the detection of circulating tumour DNA involve sequencing somatic mutations using cell-free DNA, but the sensitivity of these methods may be low among patients with early-stage cancer given the limited number of recurrent mutations. By contrast, large-scale epigenetic alterations-which are tissue- and cancer-type specific-are not similarly constrained and therefore potentially have greater ability to detect and classify cancers in patients with early-stage disease. Here we develop a sensitive, immunoprecipitation-based protocol to analyse the methylome of small quantities of circulating cell-free DNA, and demonstrate the ability to detect large-scale DNA methylation changes that are enriched for tumour-specific patterns. We also demonstrate robust performance in cancer detection and classification across an extensive collection of plasma samples from several tumour types. This work sets the stage to establish biomarkers for the minimally invasive detection, interception and classification of early-stage cancers based on plasma cell-free DNA methylation patterns.
48Liquid Biopsy of Methylation Biomarkers in Cell-Free DNA.PubMed
Huiyan Luo, Wei Wei, Ziyi Ye, et al.
Trends Mol Med. 2021 May;27(5):482-500. doi: 10.1016/j.molmed.2020.12.011. Epub 2021 Jan 23.
Liquid biopsies, in particular, analysis of cell-free DNA (cfDNA), have emerged as a promising noninvasive diagnostic approach in oncology. Abnormal distribution of DNA methylation is one of the hallmarks of many cancers and methylation changes occur early during carcinogenesis. Systemic analysis of cfDNA methylation profiles is being developed for cancer early detection, monitoring for minimal residual disease (MRD), predicting treatment response and prognosis, and tracing the tissue origin. This review highlights the advantages and disadvantages of ctDNA profiling for noninvasive diagnosis of early-stage cancers and explores recent advances in the clinical application of ctDNA methylation assays. We also summarize the technologies for ctDNA methylation analysis and provide a brief overview of the bioinformatic approaches for analyzing DNA methylation sequencing data.
49Circulating tumor DNA as an early cancer detection tool.PubMed
Andrea Campos-Carrillo, Jeffrey N Weitzel, Prativa Sahoo, et al.
Pharmacol Ther. 2020 Mar;207:107458. doi: 10.1016/j.pharmthera.2019.107458. Epub 2019 Dec 18.
Circulating tumor DNA holds substantial promise as an early detection biomarker, particularly for cancers that do not have currently accepted screening methodologies, such as ovarian, pancreatic, and gastric cancers. Many features intrinsic to ctDNA analysis may be leveraged to enhance its use as an early cancer detection biomarker: including ctDNA fragment lengths, DNA copy number variations, and associated patient phenotypic information. Furthermore, ctDNA testing may be synergistically used with other multi-omic biomarkers to enhance early detection. For instance, assays may incorporate early detection proteins (i.e., CA-125), epigenetic markers, circulating tumor RNA, nucleosomes, exosomes, and associated immune markers. Many companies are currently competing to develop a marketable early cancer detection test that leverages ctDNA. Although some hurdles (like early stage disease assay accuracy, high implementation costs, confounding from clonal hematopoiesis, and lack of clinical utility studies) need to be addressed before integration into healthcare, ctDNA assays hold substantial potential as an early cancer screening test.
50Early Detection of Molecular Residual Disease and Risk Stratification for Stage I to III Colorectal Cancer via Circulating Tumor DNA Methylation.PubMed
Shaobo Mo, Li Ye, Dongyang Wang, et al.
JAMA Oncol. 2023 Jun 1;9(6):770-778. doi: 10.1001/jamaoncol.2023.0425.
IMPORTANCE: Detection of molecular residual disease and risk stratification as early as possible may improve the treatment of patients with cancer. Efficient pragmatic tests are therefore required. OBJECTIVE: To measure circulating tumor DNA (ctDNA) with 6 DNA methylation markers in blood samples and to evaluate the association of the presence of ctDNA with colorectal cancer (CRC) recurrence throughout the disease course. DESIGN, SETTING, AND PARTICIPANTS: In this multicenter prospective longitudinal cohort study performed from December 12, 2019, to February 28, 2022, 350 patients with stage I to III CRC were recruited from 2 hospitals for collection of blood samples before and after surgery, during and after adjuvant chemotherapy, and every 3 months for up to 2 years. A multiplex, ctDNA methylation, quantitative polymerase chain reaction assay was used to detect ctDNA in plasma samples. RESULTS: A total of 299 patients with stage I to III CRC were evaluated. Of 296 patients with preoperative samples, 232 (78.4%) tested positive for any of the 6 ctDNA methylation markers. A total of 186 patients (62.2%) were male, and the mean (SD) age was 60.1 (10.3) years. At postoperative month 1, ctDNA-positive patients were 17.5 times more likely to relapse than were ctDNA-negative patients (hazard ratio [HR], 17.5; 95% CI, 8.9-34.4; P < .001). The integration of ctDNA and carcinoembryonic antigen tests showed risk stratification for recurrence with an HR of 19.0 (95% CI, 8.9-40.7; P < .001). Furthermore, ctDNA status at postoperative month 1 was strongly associated with prognosis in patients treated with adjuvant chemotherapy of different durations and intensities. After adjuvant chemotherapy, ctDNA-positive patients had a significantly shorter recurrence-free survival than did the ctDNA-negative patients (HR, 13.8; 95% CI, 5.9-32.1; P < .001). Longitudinal ctDNA analysis after the postdefinitive treatment showed a discriminating effect in that ctDNA-positive patients had poorer recurrence-free survival than did the ctDNA-negative patients (HR, 20.6; 95% CI, 9.5-44.9; P < .001). The discriminating effect was enhanced (HR, 68.8; 95% CI, 18.4-257.7; P < .001) when ctDNA status was maintained longitudinally. Postdefinitive treatment analysis detected CRC recurrence earlier than radiologically confirmed recurrence, with a median lead time of 3.3 months (IQR, 0.5-6.5 months). CONCLUSIONS AND RELEVANCE: The findings of this cohort study suggest that longitudinal assessment of ctDNA methylation may enable the early detection of recurrence, potentially optimizing risk stratification and postoperative treatment of patients with CRC.
51Diagnosis and management of pseudohypoparathyroidism and related disorders: first international Consensus Statement.PubMed
Giovanna Mantovani, Murat Bastepe, David Monk, et al.
Nat Rev Endocrinol. 2018 Aug;14(8):476-500. doi: 10.1038/s41574-018-0042-0.
This Consensus Statement covers recommendations for the diagnosis and management of patients with pseudohypoparathyroidism (PHP) and related disorders, which comprise metabolic disorders characterized by physical findings that variably include short bones, short stature, a stocky build, early-onset obesity and ectopic ossifications, as well as endocrine defects that often include resistance to parathyroid hormone (PTH) and TSH. The presentation and severity of PHP and its related disorders vary between affected individuals with considerable clinical and molecular overlap between the different types. A specific diagnosis is often delayed owing to lack of recognition of the syndrome and associated features. The participants in this Consensus Statement agreed that the diagnosis of PHP should be based on major criteria, including resistance to PTH, ectopic ossifications, brachydactyly and early-onset obesity. The clinical and laboratory diagnosis should be confirmed by a molecular genetic analysis. Patients should be screened at diagnosis and during follow-up for specific features, such as PTH resistance, TSH resistance, growth hormone deficiency, hypogonadism, skeletal deformities, oral health, weight gain, glucose intolerance or type 2 diabetes mellitus, and hypertension, as well as subcutaneous and/or deeper ectopic ossifications and neurocognitive impairment. Overall, a coordinated and multidisciplinary approach from infancy through adulthood, including a transition programme, should help us to improve the care of patients affected by these disorders.
52Epigenetics and depression .PubMed
Signe Penner-Goeke, Elisabeth B Binder
Dialogues Clin Neurosci. 2019 Dec;21(4):397-405. doi: 10.31887/DCNS.2019.21.4/ebinder.
The risk for major depression is both genetically and environmentally determined. It has been proposed that epigenetic mechanisms could mediate the lasting increases in depression risk following exposure to adverse life events and provide a mechanistic framework within which genetic and environmental factors can be integrated. Epigenetics refers to processes affecting gene expression and translation that do not involve changes in the DNA sequence and include DNA methylation (DNAm) and microRNAs (miRNAs) as well as histone modifications. Here we review evidence for a role of epigenetics in the pathogenesis of depression from studies investigating DNAm, miRNAs, and histone modifications using different tissues and various experimental designs. From these studies, a model emerges where underlying genetic and environmental risk factors, and interactions between the two, could drive aberrant epigenetic mechanisms targeting stress response pathways, neuronal plasticity, and other behaviorally relevant pathways that have been implicated in major depression. .
53The miRNome of Depression.PubMed
Dariusz Żurawek, Gustavo Turecki
Int J Mol Sci. 2021 Oct 20;22(21):11312. doi: 10.3390/ijms222111312.
Depression is an effect of complex interactions between genetic, epigenetic and environmental factors. It is well established that stress responses are associated with multiple modest and often dynamic molecular changes in the homeostatic balance, rather than with a single genetic factor that has a strong phenotypic penetration. As depression is a multifaceted phenotype, it is important to study biochemical pathways that can regulate the overall allostasis of the brain. One such biological system that has the potential to fine-tune a multitude of diverse molecular processes is RNA interference (RNAi). RNAi is an epigenetic process showing a very low level of evolutionary diversity, and relies on the posttranscriptional regulation of gene expression using, in the case of mammals, primarily short (17-23 nucleotides) noncoding RNA transcripts called microRNAs (miRNA). In this review, our objective was to examine, summarize and discuss recent advances in the field of biomedical and clinical research on the role of miRNA-mediated regulation of gene expression in the development of depression. We focused on studies investigating post-mortem brain tissue of individuals with depression, as well as research aiming to elucidate the biomarker potential of miRNAs in depression and antidepressant response.
54The association of cigarette smoking with DNA methylation and gene expression in human tissue samples.PubMed
James L Li, Niyati Jain, Lizeth I Tamayo, et al.
Am J Hum Genet. 2024 Apr 4;111(4):636-653. doi: 10.1016/j.ajhg.2024.02.012. Epub 2024 Mar 14.
Cigarette smoking adversely affects many aspects of human health, and epigenetic responses to smoking may reflect mechanisms that mediate or defend against these effects. Prior studies of smoking and DNA methylation (DNAm), typically measured in leukocytes, have identified numerous smoking-associated regions (e.g., AHRR). To identify smoking-associated DNAm features in typically inaccessible tissues, we generated array-based DNAm data for 916 tissue samples from the GTEx (Genotype-Tissue Expression) project representing 9 tissue types (lung, colon, ovary, prostate, blood, breast, testis, kidney, and muscle). We identified 6,350 smoking-associated CpGs in lung tissue (n = 212) and 2,735 in colon tissue (n = 210), most not reported previously. For all 7 other tissue types (sample sizes 38-153), no clear associations were observed (false discovery rate 0.05), but some tissues showed enrichment for smoking-associated CpGs reported previously. For 1,646 loci (in lung) and 22 (in colon), smoking was associated with both DNAm and local gene expression. For loci detected in both lung and colon (e.g., AHRR, CYP1B1, CYP1A1), top CpGs often differed between tissues, but similar clusters of hyper- or hypomethylated CpGs were observed, with hypomethylation at regulatory elements corresponding to increased expression. For lung tissue, 17 hallmark gene sets were enriched for smoking-associated CpGs, including xenobiotic- and cancer-related gene sets. At least four smoking-associated regions in lung were impacted by lung methylation quantitative trait loci (QTLs) that co-localize with genome-wide association study (GWAS) signals for lung function (FEV1/FVC), suggesting epigenetic alterations can mediate the effects of smoking on lung health. Our multi-tissue approach has identified smoking-associated regions in disease-relevant tissues, including effects that are shared across tissue types.
55Enhancer DNA methylation: implications for gene regulation.PubMed
Allegra Angeloni, Ozren Bogdanovic
Essays Biochem. 2019 Dec 20;63(6):707-715. doi: 10.1042/EBC20190030.
DNA methylation involves the addition of a methyl group to the fifth carbon of the pyrimidine cytosine ring (5-methylcytosine, 5mC). 5mC is widespread in vertebrate genomes where it is predominantly found within CpG dinucleotides. In mammals, 5mC participates in long-term silencing processes such as X-chromosome inactivation, genomic imprinting, somatic silencing of germline genes, and silencing of repetitive DNA elements. The evidence for 5mC as a dynamic gene-regulatory mechanism is mostly limited to specific examples, and is far from being completely understood. Recent work from diverse model systems suggests that 5mC might not always act as a dominant repressive mechanism and that hypermethylated promoters and enhancers can be permissive to transcription in vivo and in vitro. In this review, we discuss the links between 5mC and enhancer activity, and evaluate the role of this biochemical mechanism in various biological contexts.
56Organoid screening reveals epigenetic vulnerabilities in human colorectal cancer.PubMed
Kohta Toshimitsu, Ai Takano, Masayuki Fujii, et al.
Nat Chem Biol. 2022 Jun;18(6):605-614. doi: 10.1038/s41589-022-00984-x. Epub 2022 Mar 10.
Precision oncology presumes an accurate prediction of drug response on the basis of the molecular profile of tumors. However, the extent to which patient-derived tumor organoids recapitulate the response of in vivo tumors to a given drug remains obscure. To gain insights into the pharmacobiology of human colorectal cancer (CRC), we here created a robust drug screening platform for patient-derived colorectal organoids. Application of suspension culture increased organoid scalability, and a refinement of the culture condition enabled incorporation of normal and precursor organoids to high-throughput drug screening. Drug screening identified bromodomain and extra-terminal (BET) bromodomain protein inhibitor as a cancer-selective growth suppressor that targets genes aberrantly activated in CRC. A multi-omics analysis identified an association between checkpoint with forkhead and ring finger domaines (CHFR) silencing and paclitaxel sensitivity, which was further validated by gene engineering of organoids and in xenografts. Our findings highlight the utility of multiparametric validation in enhancing the biological and clinical fidelity of a drug screening system.
57Rat hepatocytes weighted gene co-expression network analysis identifies specific modules and hub genes related to liver regeneration after partial hepatectomy.PubMed
Yun Zhou, Jiucheng Xu, Yunqing Liu, et al.
PLoS One. 2014 Apr 17;9(4):e94868. doi: 10.1371/journal.pone.0094868. eCollection 2014.
The recovery of liver mass is mainly mediated by proliferation of hepatocytes after 2/3 partial hepatectomy (PH) in rats. Studying the gene expression profiles of hepatocytes after 2/3 PH will be helpful to investigate the molecular mechanisms of liver regeneration (LR). We report here the first application of weighted gene co-expression network analysis (WGCNA) to analyze the biological implications of gene expression changes associated with LR. WGCNA identifies 12 specific gene modules and some hub genes from hepatocytes genome-scale microarray data in rat LR. The results suggest that upregulated MCM5 may promote hepatocytes proliferation during LR; BCL3 may play an important role by activating or inhibiting NF-kB pathway; MAPK9 may play a permissible role in DNA replication by p38 MAPK inactivation in hepatocytes proliferation stage. Thus, WGCNA can provide novel insight into understanding the molecular mechanisms of LR.
58Identification of gene co-expression networks and key genes regulating flavonoid accumulation in apple (Malus × domestica) fruit skin.PubMed
Tiyu Ding, Ruiping Zhang, Hengtao Zhang, et al.
Plant Sci. 2021 Mar;304:110747. doi: 10.1016/j.plantsci.2020.110747. Epub 2020 Nov 4.
Anthocyanin provides a red color for apple and health benefit for human. To better understand the molecular mechanisms of regulating apple color formation, we analyzed 27 transcriptomes of fruit skin from three cultivars 'Huashuo' (red-skinned), 'Hongcuibao' (red-skinned), and 'Golden Delicious' (yellow-skinned) at 0, 2, and 6 days after bag removal. Using pairwise comparisons and weighted gene co-expression network analyses (WGCNA), we constructed 17 co-expression modules. Among them, a specific module was negatively correlated to anthocyanin accumulation. The genes in the module are enriched in flavonoid biosynthesis pathways. These pathway genes were used to construct gene co-expression network of anthocyanin accumulation. Finally, a R2R3-MYB repressor designated MdMYB28 was identified as a key hub gene in the anthocyanin metabolism network. During the anthocyanin accumulation of apple fruit skin reaching a peak, MdMYB28 expression level was negatively correlated with the anthocyanin content. MdMYB28 was shown to directly bind to the promoter of MdMYB10 in yeast one-hybrid analyses. Over-expression of MdMYB28 decreased the anthocyanin biosynthesis in tobacco flower petals, suggesting that MdMYB28 acts as a negatively regulator of anthocyanin biosynthesis.
59Identification of Prognostic Genes for Colon Cancer through Gene Co-expression Network Analysis.PubMed
Dan-Wen Wang, Zhang-Shuo Yang, Jian Xu, et al.
Curr Med Sci. 2021 Oct;41(5):1012-1022. doi: 10.1007/s11596-021-2386-2. Epub 2021 Sep 20.
OBJECTIVE: The present study was aimed to identify novel key genes, prognostic biomarkers and molecular pathways implicated in tumorigenesis of colon cancer. METHODS: The microarray data GSE41328 containing 10 colon cancer samples and 10 adjacent normal tissues was analyzed to identify 4763 differentially expressed genes. Meanwhile, another microarray data GSE17536 was performed for weighted gene co-expression network analysis (WGCNA). RESULTS: In present study, 12 co-expressed gene modules associated with tumor progression were identified for further studies. The red module showed the highest association with pathological stage by Pearson's correlation analysis. Functional enrichment analysis revealed that genes in red module focused on cell division, cell proliferation, cell cycle and metabolic related pathway. Then, a total of 26 key hub genes were identified, and GEPIA database was subsequently selected for validation. Holliday junction-recognizing protein (HJURP) and cell division cycle 25 homolog C (CDC25C) were identified as effective prognosis biomarkers, which were all detrimental to prognosis. Gene set enrichment analyses (GSEA) found the two hub genes were enriched in "oocyte meiosis", "oocyte maturation that are progesterone-mediated", "p53 signaling pathway", and "cell cycle". Furthermore, the immunohistochemistry and western blotting showed that HJURP was highly expressed in colon cancer tissue. CONCLUSION: HJURP was identified as a key gene associated with colon cancer progression and prognosis by WGCNA, which might influence the prognosis by regulating cell cycle pathways.
60In search of the biological significance of modular structures in protein networks.PubMed
Zhi Wang, Jianzhi Zhang
PLoS Comput Biol. 2007 Jun;3(6):e107. doi: 10.1371/journal.pcbi.0030107. Epub 2007 Apr 30.
Many complex networks such as computer and social networks exhibit modular structures, where links between nodes are much denser within modules than between modules. It is widely believed that cellular networks are also modular, reflecting the relative independence and coherence of different functional units in a cell. While many authors have claimed that observations from the yeast protein-protein interaction (PPI) network support the above hypothesis, the observed structural modularity may be an artifact because the current PPI data include interactions inferred from protein complexes through approaches that create modules (e.g., assigning pairwise interactions among all proteins in a complex). Here we analyze the yeast PPI network including protein complexes (PIC network) and excluding complexes (PEC network). We find that both PIC and PEC networks show a significantly greater structural modularity than that of randomly rewired networks. Nonetheless, there is little evidence that the structural modules correspond to functional units, particularly in the PEC network. More disturbingly, there is no evolutionary conservation among yeast, fly, and nematode modules at either the whole-module or protein-pair level. Neither is there a correlation between the evolutionary or phylogenetic conservation of a protein and the extent of its participation in various modules. Using computer simulation, we demonstrate that a higher-than-expected modularity can arise during network growth through a simple model of gene duplication, without natural selection for modularity. Taken together, our results suggest the intriguing possibility that the structural modules in the PPI network originated as an evolutionary byproduct without biological significance.
61The widespread IS200/IS605 transposon family encodes diverse programmable RNA-guided endonucleases.PubMed
Han Altae-Tran, Soumya Kannan, F Esra Demircioglu, et al.
Science. 2021 Oct;374(6563):57-65. doi: 10.1126/science.abj6856. Epub 2021 Sep 9.
IscB proteins are putative nucleases encoded in a distinct family of IS200/IS605 transposons and are likely ancestors of the RNA-guided endonuclease Cas9, but the functions of IscB and its interactions with any RNA remain uncharacterized. Using evolutionary analysis, RNA sequencing, and biochemical experiments, we reconstructed the evolution of CRISPR-Cas9 systems from IS200/IS605 transposons. We found that IscB uses a single noncoding RNA for RNA-guided cleavage of double-stranded DNA and can be harnessed for genome editing in human cells. We also demonstrate the RNA-guided nuclease activity of TnpB, another IS200/IS605 transposon-encoded protein and the likely ancestor of Cas12 endonucleases. This work reveals a widespread class of transposon-encoded RNA-guided nucleases, which we name OMEGA (obligate mobile element–guided activity), with strong potential for developing as biotechnologies.
62Cryo-EM structure of the transposon-associated TnpB enzyme.PubMed
Ryoya Nakagawa, Hisato Hirano, Satoshi N Omura, et al.
Nature. 2023 Apr;616(7956):390-397. doi: 10.1038/s41586-023-05933-9. Epub 2023 Apr 5.
The class 2 type V CRISPR effector Cas12 is thought to have evolved from the IS200/IS605 superfamily of transposon-associated TnpB proteins. Recent studies have identified TnpB proteins as miniature RNA-guided DNA endonucleases. TnpB associates with a single, long RNA (ωRNA) and cleaves double-stranded DNA targets complementary to the ωRNA guide. However, the RNA-guided DNA cleavage mechanism of TnpB and its evolutionary relationship with Cas12 enzymes remain unknown. Here we report the cryo-electron microscopy (cryo-EM) structure of Deinococcus radiodurans ISDra2 TnpB in complex with its cognate ωRNA and target DNA. In the structure, the ωRNA adopts an unexpected architecture and forms a pseudoknot, which is conserved among all guide RNAs of Cas12 enzymes. Furthermore, the structure, along with our functional analysis, reveals how the compact TnpB recognizes the ωRNA and cleaves target DNA complementary to the guide. A structural comparison of TnpB with Cas12 enzymes suggests that CRISPR-Cas12 effectors acquired an ability to recognize the protospacer-adjacent motif-distal end of the guide RNA-target DNA heteroduplex, by either asymmetric dimer formation or diverse REC2 insertions, enabling engagement in CRISPR-Cas adaptive immunity. Collectively, our findings provide mechanistic insights into TnpB function and advance our understanding of the evolution from transposon-encoded TnpB proteins to CRISPR-Cas12 effectors.
63TnpB structure reveals minimal functional core of Cas12 nuclease family.PubMed
Giedrius Sasnauskas, Giedre Tamulaitiene, Gytis Druteika, et al.
Nature. 2023 Apr;616(7956):384-389. doi: 10.1038/s41586-023-05826-x. Epub 2023 Apr 5.
The widespread TnpB proteins of IS200/IS605 transposon family have recently emerged as the smallest RNA-guided nucleases capable of targeted genome editing in eukaryotic cells. Bioinformatic analysis identified TnpB proteins as the likely predecessors of Cas12 nucleases, which along with Cas9 are widely used for targeted genome manipulation. Whereas Cas12 family nucleases are well characterized both biochemically and structurally, the molecular mechanism of TnpB remains unknown. Here we present the cryogenic-electron microscopy structures of the Deinococcus radiodurans TnpB-reRNA (right-end transposon element-derived RNA) complex in DNA-bound and -free forms. The structures reveal the basic architecture of TnpB nuclease and the molecular mechanism for DNA target recognition and cleavage that is supported by biochemical experiments. Collectively, these results demonstrate that TnpB represents the minimal structural and functional core of the Cas12 protein family and provide a framework for developing TnpB-based genome editing tools.
64Evolutionary mining and functional characterization of TnpB nucleases identify efficient miniature genome editors.PubMed
Guanghai Xiang, Yuanqing Li, Jing Sun, et al.
Nat Biotechnol. 2024 May;42(5):745-757. doi: 10.1038/s41587-023-01857-x. Epub 2023 Jun 29.
As the evolutionary ancestor of Cas12 nuclease, the transposon (IS200/IS605)-encoded TnpB proteins act as compact RNA-guided DNA endonucleases. To explore their evolutionary diversity and potential as genome editors, we screened TnpBs from 64 annotated IS605 members and identified 25 active in Escherichia coli, of which three are active in human cells. Further characterization of these 25 TnpBs enables prediction of the transposon-associated motif (TAM) and the right-end element RNA (reRNA) directly from genomic sequences. We established a framework for annotating TnpB systems in prokaryotic genomes and applied it to identify 14 additional candidates. Among these, ISAam1 (369 amino acids (aa)) and ISYmu1 (382 aa) TnpBs demonstrated robust editing activity across dozens of genomic loci in human cells. Both RNA-guided genome editors demonstrated similar editing efficiency as SaCas9 (1,053 aa) while being substantially smaller. The enormous diversity of TnpBs holds potential for the discovery of additional valuable genome editors.
65Transposon-encoded nucleases use guide RNAs to promote their selfish spread.PubMed
Chance Meers, Hoang C Le, Sanjana R Pesari, et al.
Nature. 2023 Oct;622(7984):863-871. doi: 10.1038/s41586-023-06597-1. Epub 2023 Sep 27.
Insertion sequences are compact and pervasive transposable elements found in bacteria, which encode only the genes necessary for their mobilization and maintenance. IS200- and IS605-family transposons undergo 'peel-and-paste' transposition catalysed by a TnpA transposase, but they also encode diverse, TnpB- and IscB-family proteins that are evolutionarily related to the CRISPR-associated effectors Cas12 and Cas9, respectively. Recent studies have demonstrated that TnpB and IscB function as RNA-guided DNA endonucleases, but the broader biological role of this activity has remained enigmatic. Here we show that TnpB and IscB are essential to prevent permanent transposon loss as a consequence of the TnpA transposition mechanism. We selected a family of related insertion sequences from Geobacillus stearothermophilus that encode several TnpB and IscB orthologues, and showed that a single TnpA transposase was broadly active for transposon mobilization. The donor joints formed upon religation of transposon-flanking sequences were efficiently targeted for cleavage by RNA-guided TnpB and IscB nucleases, and co-expression of TnpB and TnpA led to substantially greater transposon retention relative to conditions in which TnpA was expressed alone. Notably, TnpA and TnpB also stimulated recombination frequencies, surpassing rates observed with TnpB alone. Collectively, this study reveals that RNA-guided DNA cleavage arose as a primal biochemical activity to bias the selfish inheritance and spread of transposable elements, which was later co-opted during the evolution of CRISPR-Cas adaptive immunity for antiviral defence.
66Eukaryotic RNA-guided endonucleases evolved from a unique clade of bacterial enzymes.PubMed
Peter H Yoon, Petr Skopintsev, Honglue Shi, et al.
Nucleic Acids Res. 2023 Dec 11;51(22):12414-12427. doi: 10.1093/nar/gkad1053.
RNA-guided endonucleases form the crux of diverse biological processes and technologies, including adaptive immunity, transposition, and genome editing. Some of these enzymes are components of insertion sequences (IS) in the IS200/IS605 and IS607 transposon families. Both IS families encode a TnpA transposase and a TnpB nuclease, an RNA-guided enzyme ancestral to CRISPR-Cas12s. In eukaryotes, TnpB homologs occur as two distinct types, Fanzor1s and Fanzor2s. We analyzed the evolutionary relationships between prokaryotic TnpBs and eukaryotic Fanzors, which revealed that both Fanzor1s and Fanzor2s stem from a single lineage of IS607 TnpBs with unusual active site arrangement. The widespread nature of Fanzors implies that the properties of this particular lineage of IS607 TnpBs were particularly suited to adaptation in eukaryotes. Biochemical analysis of an IS607 TnpB and Fanzor1s revealed common strategies employed by TnpBs and Fanzors to co-evolve with their cognate transposases. Collectively, our results provide a new model of sequential evolution from IS607 TnpBs to Fanzor2s, and Fanzor2s to Fanzor1s that details how genes of prokaryotic origin evolve to give rise to new protein families in eukaryotes.
67Synthetic biology.PubMed
A Michael Sismour, Steven A Benner
Expert Opin Biol Ther. 2005 Nov;5(11):1409-14. doi: 10.1517/14712598.5.11.1409.
Chemistry is a broadly powerful discipline in contemporary science because it has the ability to create new forms of the matter that it studies. By doing so, chemistry can test models that connect molecular structure to behaviour without having to rely on what nature has provided. This creation, known as 'synthesis', began to be applied to living systems in the 1980s as recombinant DNA technologies allowed biologists to deliberately change the molecular structure of the microbes that they studied, and automated chemical synthesis of DNA became widely available to support these activities. The impact of the information that has emerged has made biologists aware of a truism that has long been known in chemistry: synthesis drives discovery and understanding in ways that analysis cannot. Synthetic biology is now setting an ambitious goal: to recreate in artificial systems the emergent properties found in natural biology. By doing so, it is advancing our understanding of the molecular basis of genetics in ways that analysis alone cannot. More practically, it has yielded artificial genetic systems that improve the healthcare of some 400,000 Americans annually. Synthetic biology is now set to take the next step, to create artificial Darwinian systems by direct construction. Supported by the National Science Foundation as part of its Chemical Bonding program, this work cannot help but generate clarity in our understanding of how biological systems work.
69Darwinian Evolution of Self-Replicating DNA in a Synthetic Protocell.PubMed
Zhanar Abil, Ana María Restrepo Sierra, Andreea R Stan, et al.
Nat Commun. 2024 Oct 22;15(1):9091. doi: 10.1038/s41467-024-53226-0.
Replication, heredity, and evolution are characteristic of Life. We and others have postulated that the reconstruction of a synthetic living system in the laboratory will be contingent on the development of a genetic self-replicator capable of undergoing Darwinian evolution. Although DNA-based life dominates, the in vitro reconstitution of an evolving DNA self-replicator has remained challenging. We hereby emulate in liposome compartments the principles according to which life propagates information and evolves. Using two different experimental configurations supporting intermittent or semi-continuous evolution (i.e., with or without DNA extraction, PCR, and re-encapsulation), we demonstrate sustainable replication of a linear DNA template - encoding the DNA polymerase and terminal protein from the Phi29 bacteriophage - expressed in the 'protein synthesis using recombinant elements' (PURE) system. The self-replicator can survive across multiple rounds of replication-coupled transcription-translation reactions in liposomes and, within only ten evolution rounds, accumulates mutations conferring a selection advantage. Combined data from next-generation sequencing with reverse engineering of some of the enriched mutations reveal nontrivial and context-dependent effects of the introduced mutations. The present results are foundational to build up genetic complexity in an evolving synthetic cell, as well as to study evolutionary processes in a minimal cell-free system.
70Templated Self-Replication in Biomimetic Systems.PubMed
Kristian Le Vay, Laura Isabel Weise, Kai Libicher, et al.
Adv Biosyst. 2019 Jun;3(6):e1800313. doi: 10.1002/adbi.201800313. Epub 2019 Apr 1.
A key characteristic of living systems is the storage and replication of information, and as such the development of self-replicating systems capable of heredity is of great importance to the fields of synthetic biology and origin of life research. In this review, the design and implementation of self-replicating systems in the context of bottom-up synthetic biology is discussed, with a particular focus on nucleic acid-based replication including nonenzymatic systems, ribozyme-based systems, and complex in vitro translation coupled RNA and DNA replication. The current state and remaining challenges of the respective fields are discussed, and the potential of individual replicators for synthetic biology applications such as the creation of artificial life capable of Darwinian evolution is also summarized.
71DNA Nanoparticles for Improved Protein Synthesis In Vitro.PubMed
Robertas Galinis, Greta Stonyte, Vaidotas Kiseliovas, et al.
Angew Chem Int Ed Engl. 2016 Feb 24;55(9):3120-3. doi: 10.1002/anie.201511809. Epub 2016 Jan 28.
The amplification and digital quantification of single DNA molecules are important in biomedicine and diagnostics. Beyond quantifying DNA molecules in a sample, the ability to express proteins from the amplified DNA would open even broader applications in synthetic biology, directed evolution, and proteomics. Herein, a microfluidic approach is reported for the production of condensed DNA nanoparticles that can serve as efficient templates for in vitro protein synthesis. Using phi29 DNA polymerase and a multiple displacement amplification reaction, single DNA molecules were converted into DNA nanoparticles containing up to about 10(4) clonal gene copies of the starting template. DNA nanoparticle formation was triggered by accumulation of inorganic pyrophosphate (produced during DNA synthesis) and magnesium ions from the buffer. Transcription-translation reactions performed in vitro showed that individual DNA nanoparticles can serve as efficient templates for protein synthesis in vitro.
72Continuous Cell-Free Replication and Evolution of Artificial Genomic DNA in a Compartmentalized Gene Expression System.PubMed
Hiroki Okauchi, Norikazu Ichihashi
ACS Synth Biol. 2021 Dec 17;10(12):3507-3517. doi: 10.1021/acssynbio.1c00430. Epub 2021 Nov 15.
In all living organisms, genomic DNA continuously replicates by the proteins encoded in itself and undergoes evolution through many generations of replication. This continuous replication coupled with gene expression and the resultant evolution are fundamental functions of living things, but they have not previously been reconstituted in cell-free systems. In this study, we combined an artificial DNA replication scheme with a reconstituted gene expression system and microcompartmentalization to realize these functions. Circular DNA replicated through rolling-circle replication followed by homologous recombination catalyzed by the proteins, phi29 DNA polymerase, and Cre recombinase expressed from the DNA. We encapsulated the system in microscale water-in-oil droplets and performed serial dilution cycles. Isolated circular DNAs at Round 30 accumulated several common mutations, and the isolated DNA clones exhibited higher replication abilities than the original DNA due to its improved ability as a replication template, increased polymerase activity, and a reduced inhibitory effect of polymerization by the recombinase. The artificial genomic DNA, which continuously replicates using self-encoded proteins and autonomously improves its sequence, provides a useful starting point for the development of more complex artificial cells.
73Towards synthesis of a minimal cell.PubMed
Anthony C Forster, George M Church
Mol Syst Biol. 2006;2:45. doi: 10.1038/msb4100090. Epub 2006 Aug 22.
Construction of a chemical system capable of replication and evolution, fed only by small molecule nutrients, is now conceivable. This could be achieved by stepwise integration of decades of work on the reconstitution of DNA, RNA and protein syntheses from pure components. Such a minimal cell project would initially define the components sufficient for each subsystem, allow detailed kinetic analyses and lead to improved in vitro methods for synthesis of biopolymers, therapeutics and biosensors. Completion would yield a functionally and structurally understood self-replicating biosystem. Safety concerns for synthetic life will be alleviated by extreme dependence on elaborate laboratory reagents and conditions for viability. Our proposed minimal genome is 113 kbp long and contains 151 genes. We detail building blocks already in place and major hurdles to overcome for completion.
74Adaptive laboratory evolution and reverse engineering enhances autotrophic growth in Pichia pastoris.PubMed
Thomas Gassler, Michael Baumschabl, Jakob Sallaberger, et al.
Metab Eng. 2022 Jan;69:112-121. doi: 10.1016/j.ymben.2021.11.007. Epub 2021 Nov 17.
Synthetic biology offers several routes for CO conversion into biomass or bio-chemicals, helping to avoid unsustainable use of organic feedstocks, which negatively contribute to climate change. The use of well-known industrial organisms, such as the methylotrophic yeast Pichia pastoris (Komagataella phaffii), for the establishment of novel C1-based bioproduction platforms could wean biotechnology from feedstocks with alternative use in food production. Recently, the central carbon metabolism of P. pastoris was re-wired following a rational engineering approach, allowing the resulting strains to grow autotrophically with a μ of 0.008 h, which was further improved to 0.018 h by adaptive laboratory evolution. Using reverse genetic engineering of single-nucleotide (SNPs) polymorphisms occurring in the genes encoding for phosphoribulokinase and nicotinic acid mononucleotide adenylyltransferase after evolution, we verified their influence on the improved autotrophic phenotypes. The reverse engineered SNPs lead to lower enzyme activities in putative branching point reactions and in reactions involved in energy balancing. Beyond this, we show how further evolution facilitates peroxisomal import and increases growth under autotrophic conditions. The engineered P. pastoris strains are a basis for the development of a platform technology, which uses CO for production of value-added products, such as cellular biomass, technical enzymes and chemicals and which further avoids consumption of organic feedstocks with alternative use in food production. Further, the identification and verification of three pivotal steps may facilitate the integration of heterologous CBB cycles or similar pathways into heterotrophic organisms.
75Synthetically-primed adaptation of Pseudomonas putida to a non-native substrate D-xylose.PubMed
Pavel Dvořák, Barbora Burýšková, Barbora Popelářová, et al.
Nat Commun. 2024 Mar 26;15(1):2666. doi: 10.1038/s41467-024-46812-9.
To broaden the substrate scope of microbial cell factories towards renewable substrates, rational genetic interventions are often combined with adaptive laboratory evolution (ALE). However, comprehensive studies enabling a holistic understanding of adaptation processes primed by rational metabolic engineering remain scarce. The industrial workhorse Pseudomonas putida was engineered to utilize the non-native sugar D-xylose, but its assimilation into the bacterial biochemical network via the exogenous xylose isomerase pathway remained unresolved. Here, we elucidate the xylose metabolism and establish a foundation for further engineering followed by ALE. First, native glycolysis is derepressed by deleting the local transcriptional regulator gene hexR. We then enhance the pentose phosphate pathway by implanting exogenous transketolase and transaldolase into two lag-shortened strains and allow ALE to finetune the rewired metabolism. Subsequent multilevel analysis and reverse engineering provide detailed insights into the parallel paths of bacterial adaptation to the non-native carbon source, highlighting the enhanced expression of transaldolase and xylose isomerase along with derepressed glycolysis as key events during the process.
76Orthogonal Genetic Systems.PubMed
John C Chaput, Piet Herdewijn, Marcel Hollenstein
Chembiochem. 2020 May 15;21(10):1408-1411. doi: 10.1002/cbic.201900725. Epub 2020 Feb 6.
Xenobiology is an emerging area of synthetic biology that aims to safeguard genetically engineered cells by storing synthetic biology information in xeno-nucleic acid polymers (XNAs). Critical to the success of this effort is the need to establish cellular systems that can maintain an XNA chromosome in actively dividing cells. This viewpoint discusses the structural parameters of the nucleic acid backbone that should be considered when designing an orthogonal genetic system that can replicate without interference from the endogenous genome. In addition to practical value, these studies have the potential to provide new fundamental insight into the structure and function properties of unnatural nucleic acid polymers.
77Evolution of linkage and genome expansion in protocells: The origin of chromosomes.PubMed
András Szilágyi, Viktor Péter Kovács, Eörs Szathmáry, et al.
PLoS Genet. 2020 Oct 29;16(10):e1009155. doi: 10.1371/journal.pgen.1009155. eCollection 2020 Oct.
Chromosomes are likely to have assembled from unlinked genes in early evolution. Genetic linkage reduces the assortment load and intragenomic conflict in reproducing protocell models to the extent that chromosomes can go to fixation even if chromosomes suffer from a replicative disadvantage, relative to unlinked genes, proportional to their length. Here we numerically show that chromosomes spread within protocells even if recurrent deleterious mutations affecting replicating genes (as ribozymes) are considered. Dosage effect selects for optimal genomic composition within protocells that carries over to the genic composition of emerging chromosomes. Lacking an accurate segregation mechanism, protocells continue to benefit from the stochastic corrector principle (group selection of early replicators), but now at the chromosome level. A remarkable feature of this process is the appearance of multigene families (in optimal genic proportions) on chromosomes. An added benefit of chromosome formation is an increase in the selectively maintainable genome size (number of different genes), primarily due to the marked reduction of the assortment load. The establishment of chromosomes is under strong positive selection in protocells harboring unlinked genes. The error threshold of replication is raised to higher genome size by linkage due to the fact that deleterious mutations affecting protocells metabolism (hence fitness) show antagonistic (diminishing return) epistasis. This result strengthens the established benefit conferred by chromosomes on protocells allowing for the fixation of highly specific and efficient enzymes.
78Functional demarcation of active and silent chromatin domains in human HOX loci by noncoding RNAs.PubMed
John L Rinn, Michael Kertesz, Jordon K Wang, et al.
Cell. 2007 Jun 29;129(7):1311-23. doi: 10.1016/j.cell.2007.05.022.
Noncoding RNAs (ncRNA) participate in epigenetic regulation but are poorly understood. Here we characterize the transcriptional landscape of the four human HOX loci at five base pair resolution in 11 anatomic sites and identify 231 HOX ncRNAs that extend known transcribed regions by more than 30 kilobases. HOX ncRNAs are spatially expressed along developmental axes and possess unique sequence motifs, and their expression demarcates broad chromosomal domains of differential histone methylation and RNA polymerase accessibility. We identified a 2.2 kilobase ncRNA residing in the HOXC locus, termed HOTAIR, which represses transcription in trans across 40 kilobases of the HOXD locus. HOTAIR interacts with Polycomb Repressive Complex 2 (PRC2) and is required for PRC2 occupancy and histone H3 lysine-27 trimethylation of HOXD locus. Thus, transcription of ncRNA may demarcate chromosomal domains of gene silencing at a distance; these results have broad implications for gene regulation in development and disease states.
79Long non-coding RNAs reveal new regulatory mechanisms controlling gene expression.PubMed
Martin Crespi
C R Biol. 2023 Feb 16;345(4):15-39. doi: 10.5802/crbiol.106.
A plethora of non-coding RNAs have been found in eukaryotes, notably with the advent of modern sequencing technologies to analyze the transcriptome. Apart from the well-known housekeeping RNA genes (such as the ribosomal RNA or the transfer RNA), many thousands of transcripts detected are not evidently linked to a protein-coding gene. These, so called non-coding RNAs, may code for crucial regulators of gene expression, the small si/miRNAs, for small peptides (translated under specific conditions) or may act as long RNA molecules (antisense, intronic or intergenic long non-coding RNAs or lncRNAs). The lncRNAs interact with members of multiple machineries involved in gene regulation. In this review, we discussed about how plant lncRNAs permitted to discover new regulatory mechanisms acting in epigenetic control, chromatin 3D structure and alternative splicing. These novel regulations diversified the expression patterns and protein variants of target protein-coding genes and are an important element of the response of plants to environmental stresses and their adaptation to changing conditions.
80Integrated lncRNA function upon genomic and epigenomic regulation.PubMed
Allison B Herman, Dimitrios Tsitsipatis, Myriam Gorospe
Mol Cell. 2022 Jun 16;82(12):2252-2266. doi: 10.1016/j.molcel.2022.05.027.
Although some long noncoding (lnc)RNAs are known since the 1950s, the past 25 years have uncovered myriad lncRNAs with diverse sequences, structures, and functions. The advent of high-throughput and sensitive technologies has further uncovered the vast heterogeneity of lncRNA-interacting molecules and patterns of expressed lncRNAs. We propose a unifying functional theme for the expansive family of lncRNAs. At an elementary level, the genomic program of gene expression is elicited via canonical transcription and post-transcriptional mRNA assembly, turnover, and translation. Building upon this regulation, an epigenomic program refines the basic genomic control by modifying chromatin architecture as well as DNA and RNA chemistry. Superimposed over the genomic and epigenomic programs, lncRNAs create an additional regulatory dimension: by interacting with the proteins and nucleic acids that regulate gene expression in the nucleus and cytoplasm, lncRNAs help establish robust, nimble, and specific transcriptional and post-transcriptional control. We describe our present understanding of lncRNA-coordinated control of protein programs and cell fate and discuss challenges and opportunities as we embark on the next 25 years of lncRNA discovery.
81[Long non-coding RNAs--"tuning fork" in regulation of cell processes].PubMed
V V Gordiiuk
Ukr Biochem J. 2014 Mar-Apr;86(2):5-15.
Most of RNA transcripts, instead of serving templates for protein synthesis, perform different functions such as control of embryogenesis, differentiation, imprinting, X-chromosome inactivation, immune response, and stress reactions. A large portion of these RNAs are denoted as long non-coding RNAs (lncRNAs) which function as signaling molecules, navigating systems and platforms for ribonucleic complexes assembly. They also participate in the organization of specific cellular domains. Moreover, they are able to bind regulatory proteins and micro-RNAs, and serve as precursors for small RNAs. It was shown that lncRNAs participate in transcription regulation; they are also involved in alternative splicing, RNA editing, traffic, translation and degradation of RNA. The three-dimensional structure of lncRNAs plays a crucial role in processes of chromatin remodeling and transcription regulation. In this review we discuss various aspects of lncRNAs functioning.
82Long non-coding RNAs and chromatin modifiers: their place in the epigenetic code.PubMed
Francesco P Marchese, Maite Huarte
Epigenetics. 2014 Jan;9(1):21-6. doi: 10.4161/epi.27472. Epub 2013 Dec 13.
The emergence of long non-coding RNAs (lncRNAs) has shaken up our conception of gene expression regulation, as lncRNAs take prominent positions as components of cellular networks. Several cellular processes involve lncRNAs, and a significant number of them have been shown to function in cooperation with chromatin modifying enzymes to promote epigenetic activation or silencing of gene expression. Different model mechanisms have been proposed to explain how lncRNAs achieve regulation of gene expression by interacting with the epigenetic machinery. Here we describe these models in light of the current knowledge of lncRNAs, such as Xist and HOTAIR, and discuss recent literature on the role of the three-dimensional structure of the genome in the mechanism of action of lncRNAs and chromatin modifiers.
83The "lnc" between 3D chromatin structure and X chromosome inactivation.PubMed
Amy Pandya-Jones, Kathrin Plath
Semin Cell Dev Biol. 2016 Aug;56:35-47. doi: 10.1016/j.semcdb.2016.04.002. Epub 2016 Apr 6.
The long non-coding RNA Xist directs a remarkable instance of developmentally regulated, epigenetic change known as X Chromosome Inactivation (XCI). By spreading in cis across the X chromosome from which it is expressed, Xist RNA facilitates the creation of a heritably silent, heterochromatic nuclear territory that displays a three-dimensional structure distinct from that of the active X chromosome. How Xist RNA attaches to and propagates across a chromosome and its influence over the three-dimensional (3D) structure of the inactive X are aspects of XCI that have remained largely unclear. Here, we discuss studies that have made significant contributions towards answering these open questions.
84Computational prediction and experimental validation identify functionally conserved lncRNAs from zebrafish to human.PubMed
Wenze Huang, Tuanlin Xiong, Yuting Zhao, et al.
Nat Genet. 2024 Jan;56(1):124-135. doi: 10.1038/s41588-023-01620-7. Epub 2024 Jan 9.
Functional studies of long noncoding RNAs (lncRNAs) have been hindered by the lack of methods to assess their evolution. Here we present lncRNA Homology Explorer (lncHOME), a computational pipeline that identifies a unique class of long noncoding RNAs (lncRNAs) with conserved genomic locations and patterns of RNA-binding protein (RBP) binding sites (coPARSE-lncRNAs). Remarkably, several hundred human coPARSE-lncRNAs can be evolutionarily traced to zebrafish. Using CRISPR-Cas12a knockout and rescue assays, we found that knocking out many human coPARSE-lncRNAs led to cell proliferation defects, which were subsequently rescued by predicted zebrafish homologs. Knocking down coPARSE-lncRNAs in zebrafish embryos caused severe developmental delays that were rescued by human homologs. Furthermore, we verified that human, mouse and zebrafish coPARSE-lncRNA homologs tend to bind similar RBPs with their conserved functions relying on specific RBP-binding sites. Overall, our study demonstrates a comprehensive approach for studying the functional conservation of lncRNAs and implicates numerous lncRNAs in regulating vertebrate physiology.
85The long non-coding RNA landscape of Candida yeast pathogens.PubMed
Hrant Hovhannisyan, Toni Gabaldón
Nat Commun. 2021 Dec 16;12(1):7317. doi: 10.1038/s41467-021-27635-4.
Long non-coding RNAs (lncRNAs) constitute a poorly studied class of transcripts with emerging roles in key cellular processes. Despite efforts to characterize lncRNAs across a wide range of species, these molecules remain largely unexplored in most eukaryotic microbes, including yeast pathogens of the Candida clade. Here, we analyze thousands of publicly available sequencing datasets to infer and characterize the lncRNA repertoires of five major Candida pathogens: Candida albicans, Candida tropicalis, Candida parapsilosis, Candida auris and Candida glabrata. Our results indicate that genomes of these species encode hundreds of lncRNAs that show levels of evolutionary constraint intermediate between those of intergenic genomic regions and protein-coding genes. Despite their low sequence conservation across the studied species, some lncRNAs are syntenic and are enriched in shared sequence motifs. We find co-expression of lncRNAs with certain protein-coding transcripts, hinting at potential functional associations. Finally, we identify lncRNAs that are differentially expressed during infection of human epithelial cells for four of the studied species. Our comprehensive bioinformatic analyses of Candida lncRNAs pave the way for future functional characterization of these transcripts.
86Tumour mutations in long noncoding RNAs enhance cell fitness.PubMed
Roberta Esposito, Andrés Lanzós, Tina Uroda, et al.
Nat Commun. 2023 Jun 8;14(1):3342. doi: 10.1038/s41467-023-39160-7.
Long noncoding RNAs (lncRNAs) are linked to cancer via pathogenic changes in their expression levels. Yet, it remains unclear whether lncRNAs can also impact tumour cell fitness via function-altering somatic "driver" mutations. To search for such driver-lncRNAs, we here perform a genome-wide analysis of fitness-altering single nucleotide variants (SNVs) across a cohort of 2583 primary and 3527 metastatic tumours. The resulting 54 mutated and positively-selected lncRNAs are significantly enriched for previously-reported cancer genes and a range of clinical and genomic features. A number of these lncRNAs promote tumour cell proliferation when overexpressed in in vitro models. Our results also highlight a dense SNV hotspot in the widely-studied NEAT1 oncogene. To directly evaluate the functional significance of NEAT1 SNVs, we use in cellulo mutagenesis to introduce tumour-like mutations in the gene and observe a significant and reproducible increase in cell fitness, both in vitro and in a mouse model. Mechanistic studies reveal that SNVs remodel the NEAT1 ribonucleoprotein and boost subnuclear paraspeckles. In summary, this work demonstrates the utility of driver analysis for mapping cancer-promoting lncRNAs, and provides experimental evidence that somatic mutations can act through lncRNAs to enhance pathological cancer cell fitness.
87A novel interpretable deep learning-based computational framework designed synthetic enhancers with broad cross-species activity.PubMed
Zhaohong Li, Yuanyuan Zhang, Bo Peng, et al.
Nucleic Acids Res. 2024 Nov 27;52(21):13447-13468. doi: 10.1093/nar/gkae912.
Enhancers play a critical role in dynamically regulating spatial-temporal gene expression and establishing cell identity, underscoring the significance of designing them with specific properties for applications in biosynthetic engineering and gene therapy. Despite numerous high-throughput methods facilitating genome-wide enhancer identification, deciphering the sequence determinants of their activity remains challenging. Here, we present the DREAM (DNA cis-Regulatory Elements with controllable Activity design platforM) framework, a novel deep learning-based approach for synthetic enhancer design. Proficient in uncovering subtle and intricate patterns within extensive enhancer screening data, DREAM achieves cutting-edge sequence-based enhancer activity prediction and highlights critical sequence features implicating strong enhancer activity. Leveraging DREAM, we have engineered enhancers that surpass the potency of the strongest enhancer within the Drosophila genome by approximately 3.6-fold. Remarkably, these synthetic enhancers exhibited conserved functionality across species that have diverged more than billion years, indicating that DREAM was able to learn highly conserved enhancer regulatory grammar. Additionally, we designed silencers and cell line-specific enhancers using DREAM, demonstrating its versatility. Overall, our study not only introduces an interpretable approach for enhancer design but also lays out a general framework applicable to the design of other types of cis-regulatory elements.
88DeepSTARR predicts enhancer activity from DNA sequence and enables the de novo design of synthetic enhancers.PubMed
Bernardo P de Almeida, Franziska Reiter, Michaela Pagani, et al.
Nat Genet. 2022 May;54(5):613-624. doi: 10.1038/s41588-022-01048-5. Epub 2022 May 12.
Enhancer sequences control gene expression and comprise binding sites (motifs) for different transcription factors (TFs). Despite extensive genetic and computational studies, the relationship between DNA sequence and regulatory activity is poorly understood, and de novo enhancer design has been challenging. Here, we built a deep-learning model, DeepSTARR, to quantitatively predict the activities of thousands of developmental and housekeeping enhancers directly from DNA sequence in Drosophila melanogaster S2 cells. The model learned relevant TF motifs and higher-order syntax rules, including functionally nonequivalent instances of the same TF motif that are determined by motif-flanking sequence and intermotif distances. We validated these rules experimentally and demonstrated that they can be generalized to humans by testing more than 40,000 wildtype and mutant Drosophila and human enhancers. Finally, we designed and functionally validated synthetic enhancers with desired activities de novo.
89Genomic transcription factor binding site selection is edited by the chromatin remodeling factor CHD4.PubMed
Mika Saotome, Deepak B Poduval, Sara A Grimm, et al.
Nucleic Acids Res. 2024 Apr 24;52(7):3607-3622. doi: 10.1093/nar/gkae025.
Biologically precise enhancer licensing by lineage-determining transcription factors enables activation of transcripts appropriate to biological demand and prevents deleterious gene activation. This essential process is challenged by the millions of matches to most transcription factor binding motifs present in many eukaryotic genomes, leading to questions about how transcription factors achieve the exquisite specificity required. The importance of chromatin remodeling factors to enhancer activation is highlighted by their frequent mutation in developmental disorders and in cancer. Here, we determine the roles of CHD4 in enhancer licensing and maintenance in breast cancer cells and during cellular reprogramming. In unchallenged basal breast cancer cells, CHD4 modulates chromatin accessibility. Its depletion leads to redistribution of transcription factors to previously unoccupied sites. During cellular reprogramming induced by the pioneer factor GATA3, CHD4 activity is necessary to prevent inappropriate chromatin opening. Mechanistically, CHD4 promotes nucleosome positioning over GATA3 binding motifs to compete with transcription factor-DNA interaction. We propose that CHD4 acts as a chromatin proof-reading enzyme that prevents unnecessary gene expression by editing chromatin binding activities of transcription factors.
90Decoding the non-coding genome: elucidating genetic risk outside the coding genome.PubMed
C L Barr, V L Misener
Genes Brain Behav. 2016 Jan;15(1):187-204. doi: 10.1111/gbb.12269. Epub 2016 Jan 4.
Current evidence emerging from genome-wide association studies indicates that the genetic underpinnings of complex traits are likely attributable to genetic variation that changes gene expression, rather than (or in combination with) variation that changes protein-coding sequences. This is particularly compelling with respect to psychiatric disorders, as genetic changes in regulatory regions may result in differential transcriptional responses to developmental cues and environmental/psychosocial stressors. Until recently, however, the link between transcriptional regulation and psychiatric genetic risk has been understudied. Multiple obstacles have contributed to the paucity of research in this area, including challenges in identifying the positions of remote (distal from the promoter) regulatory elements (e.g. enhancers) and their target genes and the underrepresentation of neural cell types and brain tissues in epigenome projects - the availability of high-quality brain tissues for epigenetic and transcriptome profiling, particularly for the adolescent and developing brain, has been limited. Further challenges have arisen in the prediction and testing of the functional impact of DNA variation with respect to multiple aspects of transcriptional control, including regulatory-element interaction (e.g. between enhancers and promoters), transcription factor binding and DNA methylation. Further, the brain has uncommon DNA-methylation marks with unique genomic distributions not found in other tissues - current evidence suggests the involvement of non-CG methylation and 5-hydroxymethylation in neurodevelopmental processes but much remains unknown. We review here knowledge gaps as well as both technological and resource obstacles that will need to be overcome in order to elucidate the involvement of brain-relevant gene-regulatory variants in genetic risk for psychiatric disorders.
91RNA exosome-regulated long non-coding RNA transcription controls super-enhancer activity.PubMed
Evangelos Pefanis, Jiguang Wang, Gerson Rothschild, et al.
Cell. 2015 May 7;161(4):774-89. doi: 10.1016/j.cell.2015.04.034.
We have ablated the cellular RNA degradation machinery in differentiated B cells and pluripotent embryonic stem cells (ESCs) by conditional mutagenesis of core (Exosc3) and nuclear RNase (Exosc10) components of RNA exosome and identified a vast number of long non-coding RNAs (lncRNAs) and enhancer RNAs (eRNAs) with emergent functionality. Unexpectedly, eRNA-expressing regions accumulate R-loop structures upon RNA exosome ablation, thus demonstrating the role of RNA exosome in resolving deleterious DNA/RNA hybrids arising from active enhancers. We have uncovered a distal divergent eRNA-expressing element (lncRNA-CSR) engaged in long-range DNA interactions and regulating IgH 3' regulatory region super-enhancer function. CRISPR-Cas9-mediated ablation of lncRNA-CSR transcription decreases its chromosomal looping-mediated association with the IgH 3' regulatory region super-enhancer and leads to decreased class switch recombination efficiency. We propose that the RNA exosome protects divergently transcribed lncRNA expressing enhancers by resolving deleterious transcription-coupled secondary DNA structures, while also regulating long-range super-enhancer chromosomal interactions important for cellular function.
92The three-dimensional structure of the EBV genome plays a crucial role in regulating viral gene expression in EBVaGC.PubMed
Davide Maestri, Giorgia Napoletani, Andrew Kossenkov, et al.
Nucleic Acids Res. 2023 Dec 11;51(22):12092-12110. doi: 10.1093/nar/gkad936.
Epstein-Barr virus (EBV) establishes lifelong asymptomatic infection by replication of its chromatinized episomes with the host genome. EBV exhibits different latency-associated transcriptional repertoires, each with distinct three-dimensional structures. CTCF, Cohesin and PARP1 are involved in maintaining viral latency and establishing episome architecture. Epstein-Barr virus-associated gastric cancer (EBVaGC) represents 1.3-30.9% of all gastric cancers globally. EBV-positive gastric cancers exhibit an intermediate viral transcription profile known as 'Latency II', expressing specific viral genes and noncoding RNAs. In this study, we investigated the impact of PARP1 inhibition on CTCF/Cohesin binding in Type II latency. We observed destabilization of the binding of both factors, leading to a disrupted three-dimensional architecture of the episomes and an altered viral gene expression. Despite sharing the same CTCF binding profile, Type I, II and III latencies exhibit different 3D structures that correlate with variations in viral gene expression. Additionally, our analysis of H3K27ac-enriched interactions revealed differences between Type II latency episomes and a link to cellular transformation through docking of the EBV genome at specific sites of the Human genome, thus promoting oncogene expression. Overall, this work provides insights into the role of PARP1 in maintaining active latency and novel mechanisms of EBV-induced cellular transformation.
93Decoding the Epigenome of Breast Cancer.PubMed
Elisa Cortellesi, Isabella Savini, Matteo Veneziano, et al.
Int J Mol Sci. 2025 Mar 13;26(6):2605. doi: 10.3390/ijms26062605.
Breast cancer (BC) is the most prevalent malignancy among women, characterized by extensive heterogeneity stemming from molecular and genetic alterations. This review explores the intricate epigenetic landscape of BC, highlighting the significant role of epigenetic modifications-particularly DNA methylation, histone modifications, and the influence of non-coding RNAs-in the initiation, progression, and prognosis of the disease. Epigenetic alterations drive crucial processes, including gene expression regulation, cell differentiation, and tumor microenvironment interactions, contributing to tumorigenesis and metastatic potential. Notably, aberrations in DNA methylation patterns, including global hypomethylation and hypermethylation of CpG islands, have been associated with distinct BC subtypes, with implications for early detection and risk assessment. Furthermore, histone modifications, such as acetylation and methylation, affect cancer cell plasticity and aggressiveness by profoundly influencing chromatin dynamics and gene transcription. Finally, non-coding RNAs contribute by modulating epigenetic machinery and gene expression. Despite advances in our knowledge, clinical application of epigenetic therapies in BC is still challenging, often yielding limited efficacy when used alone. However, combining epi-drugs with established treatments shows promise for enhancing therapeutic outcomes. This review underscores the importance of integrating epigenetic insights into personalized BC treatment strategies, emphasizing the potential of epigenetic biomarkers for improving diagnosis, prognosis, and therapeutic response in affected patients.
94Decoding Non-coding Variants: Recent Approaches to Studying Their Role in Gene Regulation and Human Diseases.PubMed
Edwin G Peña-Martínez, José A Rodríguez-Martínez
Front Biosci (Schol Ed). 2024 Mar 1;16(1):4. doi: 10.31083/j.fbs1601004.
Genome-wide association studies (GWAS) have mapped over 90% of disease- and quantitative-trait-associated variants within the non-coding genome. Non-coding regulatory DNA (e.g., promoters and enhancers) and RNA (e.g., 5' and 3' UTRs and splice sites) are essential in regulating temporal and tissue-specific gene expressions. Non-coding variants can potentially impact the phenotype of an organism by altering the molecular recognition of the -regulatory elements, leading to gene dysregulation. However, determining causality between non-coding variants, gene regulation, and human disease has remained challenging. Experimental and computational methods have been developed to understand the molecular mechanism involved in non-coding variant interference at the transcriptional and post-transcriptional levels. This review discusses recent approaches to evaluating disease-associated single-nucleotide variants (SNVs) and determines their impact on transcription factor (TF) binding, gene expression, chromatin conformation, post-transcriptional regulation, and translation.
95Deep learning: new computational modelling techniques for genomics.PubMed
Gökcen Eraslan, Žiga Avsec, Julien Gagneur, et al.
Nat Rev Genet. 2019 Jul;20(7):389-403. doi: 10.1038/s41576-019-0122-6.
As a data-driven science, genomics largely utilizes machine learning to capture dependencies in data and derive novel biological hypotheses. However, the ability to extract new insights from the exponentially increasing volume of genomics data requires more expressive machine learning models. By effectively leveraging large data sets, deep learning has transformed fields such as computer vision and natural language processing. Now, it is becoming the method of choice for many genomics modelling tasks, including predicting the impact of genetic variation on gene regulatory mechanisms such as DNA accessibility and splicing.
96Distinguishing word identity and sequence context in DNA language models.PubMed
Melissa Sanabria, Jonas Hirsch, Anna R Poetsch
BMC Bioinformatics. 2024 Sep 13;25(1):301. doi: 10.1186/s12859-024-05869-5.
Transformer-based large language models (LLMs) are very suited for biological sequence data, because of analogies to natural language. Complex relationships can be learned, because a concept of "words" can be generated through tokenization. Training the models with masked token prediction, they learn both token sequence identity and larger sequence context. We developed methodology to interrogate model learning, which is both relevant for the interpretability of the model and to evaluate its potential for specific tasks. We used DNABERT, a DNA language model trained on the human genome with overlapping k-mers as tokens. To gain insight into the model's learning, we interrogated how the model performs predictions, extracted token embeddings, and defined a fine-tuning benchmarking task to predict the next tokens of different sizes without overlaps. This task evaluates foundation models without interrogating specific genome biology, it does not depend on tokenization strategies, vocabulary size, the dictionary, or the number of training parameters. Lastly, there is no leakage of information from token identity into the prediction task, which makes it particularly useful to evaluate the learning of sequence context. We discovered that the model with overlapping k-mers struggles to learn larger sequence context. Instead, the learned embeddings largely represent token sequence. Still, good performance is achieved for genome-biology-inspired fine-tuning tasks. Models with overlapping tokens may be used for tasks where a larger sequence context is of less relevance, but the token sequence directly represents the desired learning features. This emphasizes the need to interrogate knowledge representation in biological LLMs.
97Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks.PubMed
David R Kelley, Jasper Snoek, John L Rinn
Genome Res. 2016 Jul;26(7):990-9. doi: 10.1101/gr.200535.115. Epub 2016 May 3.
The complex language of eukaryotic gene expression remains incompletely understood. Despite the importance suggested by many noncoding variants statistically associated with human disease, nearly all such variants have unknown mechanisms. Here, we address this challenge using an approach based on a recent machine learning advance-deep convolutional neural networks (CNNs). We introduce the open source package Basset to apply CNNs to learn the functional activity of DNA sequences from genomics data. We trained Basset on a compendium of accessible genomic sites mapped in 164 cell types by DNase-seq, and demonstrate greater predictive accuracy than previous methods. Basset predictions for the change in accessibility between variant alleles were far greater for Genome-wide association study (GWAS) SNPs that are likely to be causal relative to nearby SNPs in linkage disequilibrium with them. With Basset, a researcher can perform a single sequencing assay in their cell type of interest and simultaneously learn that cell's chromatin accessibility code and annotate every mutation in the genome with its influence on present accessibility and latent potential for accessibility. Thus, Basset offers a powerful computational approach to annotate and interpret the noncoding genome.
98Machine learning: A powerful tool for gene function prediction in plants.PubMed
Elizabeth H Mahood, Lars H Kruse, Gaurav D Moghe
Appl Plant Sci. 2020 Jul 28;8(7):e11376. doi: 10.1002/aps3.11376. eCollection 2020 Jul.
Recent advances in sequencing and informatic technologies have led to a deluge of publicly available genomic data. While it is now relatively easy to sequence, assemble, and identify genic regions in diploid plant genomes, functional annotation of these genes is still a challenge. Over the past decade, there has been a steady increase in studies utilizing machine learning algorithms for various aspects of functional prediction, because these algorithms are able to integrate large amounts of heterogeneous data and detect patterns inconspicuous through rule-based approaches. The goal of this review is to introduce experimental plant biologists to machine learning, by describing how it is currently being used in gene function prediction to gain novel biological insights. In this review, we discuss specific applications of machine learning in identifying structural features in sequenced genomes, predicting interactions between different cellular components, and predicting gene function and organismal phenotypes. Finally, we also propose strategies for stimulating functional discovery using machine learning-based approaches in plants.
99Automatic Gene Function Prediction in the 2020's.PubMed
Stavros Makrodimitris, Roeland C H J van Ham, Marcel J T Reinders
Genes (Basel). 2020 Oct 27;11(11):1264. doi: 10.3390/genes11111264.
The current rate at which new DNA and protein sequences are being generated is too fast to experimentally discover the functions of those sequences, emphasizing the need for accurate Automatic Function Prediction (AFP) methods. AFP has been an active and growing research field for decades and has made considerable progress in that time. However, it is certainly not solved. In this paper, we describe challenges that the AFP field still has to overcome in the future to increase its applicability. The challenges we consider are how to: (1) include condition-specific functional annotation, (2) predict functions for non-model species, (3) include new informative data sources, (4) deal with the biases of Gene Ontology (GO) annotations, and (5) maximally exploit the GO to obtain performance gains. We also provide recommendations for addressing those challenges, by adapting (1) the way we represent proteins and genes, (2) the way we represent gene functions, and (3) the algorithms that perform the prediction from gene to function. Together, we show that AFP is still a vibrant research area that can benefit from continuing advances in machine learning with which AFP in the 2020s can again take a large step forward reinforcing the power of computational biology.
100A Bayesian data fusion based approach for learning genome-wide transcriptional regulatory networks.PubMed
Elisabetta Sauta, Andrea Demartini, Francesca Vitali, et al.
BMC Bioinformatics. 2020 May 29;21(1):219. doi: 10.1186/s12859-020-3510-1.
BACKGROUND: Reverse engineering of transcriptional regulatory networks (TRN) from genomics data has always represented a computational challenge in System Biology. The major issue is modeling the complex crosstalk among transcription factors (TFs) and their target genes, with a method able to handle both the high number of interacting variables and the noise in the available heterogeneous experimental sources of information. RESULTS: In this work, we propose a data fusion approach that exploits the integration of complementary omics-data as prior knowledge within a Bayesian framework, in order to learn and model large-scale transcriptional networks. We develop a hybrid structure-learning algorithm able to jointly combine TFs ChIP-Sequencing data and gene expression compendia to reconstruct TRNs in a genome-wide perspective. Applying our method to high-throughput data, we verified its ability to deal with the complexity of a genomic TRN, providing a snapshot of the synergistic TFs regulatory activity. Given the noisy nature of data-driven prior knowledge, which potentially contains incorrect information, we also tested the method's robustness to false priors on a benchmark dataset, comparing the proposed approach to other regulatory network reconstruction algorithms. We demonstrated the effectiveness of our framework by evaluating structural commonalities of our learned genomic network with other existing networks inferred by different DNA binding information-based methods. CONCLUSIONS: This Bayesian omics-data fusion based methodology allows to gain a genome-wide picture of the transcriptional interplay, helping to unravel key hierarchical transcriptional interactions, which could be subsequently investigated, and it represents a promising learning approach suitable for multi-layered genomic data integration, given its robustness to noisy sources and its tailored framework for handling high dimensional data.
101Germ line variant GFI1-36N affects DNA repair and sensitizes AML cells to DNA damage and repair therapy.PubMed
Daria Frank, Pradeep Kumar Patnana, Jan Vorwerk, et al.
Blood. 2023 Dec 21;142(25):2175-2191. doi: 10.1182/blood.2022015752.
Growth factor independence 1 (GFI1) is a DNA-binding transcription factor and a key regulator of hematopoiesis. GFI1-36N is a germ line variant, causing a change of serine (S) to asparagine (N) at position 36. We previously reported that the GFI1-36N allele has a prevalence of 10% to 15% among patients with acute myeloid leukemia (AML) and 5% to 7% among healthy Caucasians and promotes the development of this disease. Using a multiomics approach, we show here that GFI1-36N expression is associated with increased frequencies of chromosomal aberrations, mutational burden, and mutational signatures in both murine and human AML and impedes homologous recombination (HR)-directed DNA repair in leukemic cells. GFI1-36N exhibits impaired binding to N-Myc downstream-regulated gene 1 (Ndrg1) regulatory elements, causing decreased NDRG1 levels, which leads to a reduction of O6-methylguanine-DNA-methyltransferase (MGMT) expression levels, as illustrated by both transcriptome and proteome analyses. Targeting MGMT via temozolomide, a DNA alkylating drug, and HR via olaparib, a poly-ADP ribose polymerase 1 inhibitor, caused synthetic lethality in human and murine AML samples expressing GFI1-36N, whereas the effects were insignificant in nonmalignant GFI1-36S or GFI1-36N cells. In addition, mice that received transplantation with GFI1-36N leukemic cells treated with a combination of temozolomide and olaparib had significantly longer AML-free survival than mice that received transplantation with GFI1-36S leukemic cells. This suggests that reduced MGMT expression leaves GFI1-36N leukemic cells particularly vulnerable to DNA damage initiating chemotherapeutics. Our data provide critical insights into novel options to treat patients with AML carrying the GFI1-36N variant.
102Single-cell multiomics sequencing and analyses of human colorectal cancer.PubMed
Shuhui Bian, Yu Hou, Xin Zhou, et al.
Science. 2018 Nov 30;362(6418):1060-1063. doi: 10.1126/science.aao3791.
Although genomic instability, epigenetic abnormality, and gene expression dysregulation are hallmarks of colorectal cancer, these features have not been simultaneously analyzed at single-cell resolution. Using optimized single-cell multiomics sequencing together with multiregional sampling of the primary tumor and lymphatic and distant metastases, we developed insights beyond intratumoral heterogeneity. Genome-wide DNA methylation levels were relatively consistent within a single genetic sublineage. The genome-wide DNA demethylation patterns of cancer cells were consistent in all 10 patients whose DNA we sequenced. The cancer cells' DNA demethylation degrees clearly correlated with the densities of the heterochromatin-associated histone modification H3K9me3 of normal tissue and those of repetitive element long interspersed nuclear element 1. Our work demonstrates the feasibility of reconstructing genetic lineages and tracing their epigenomic and transcriptomic dynamics with single-cell multiomics sequencing.
103Mitochondrial single-cell ATAC-seq for high-throughput multi-omic detection of mitochondrial genotypes and chromatin accessibility.PubMed
Caleb A Lareau, Vincent Liu, Christoph Muus, et al.
Nat Protoc. 2023 May;18(5):1416-1440. doi: 10.1038/s41596-022-00795-3. Epub 2023 Feb 15.
Natural sequence variation within mitochondrial DNA (mtDNA) contributes to human phenotypes and may serve as natural genetic markers in human cells for clonal and lineage tracing. We recently developed a single-cell multi-omic approach, called 'mitochondrial single-cell assay for transposase-accessible chromatin with sequencing' (mtscATAC-seq), enabling concomitant high-throughput mtDNA genotyping and accessible chromatin profiling. Specifically, our technique allows the mitochondrial genome-wide inference of mtDNA variant heteroplasmy along with information on cell state and accessible chromatin variation in individual cells. Leveraging somatic mtDNA mutations, our method further enables inference of clonal relationships among native ex vivo-derived human cells not amenable to genetic engineering-based clonal tracing approaches. Here, we provide a step-by-step protocol for the use of mtscATAC-seq, including various cell-processing and flow cytometry workflows, by using primary hematopoietic cells, subsequent single-cell genomic library preparation and sequencing that collectively take ~3-4 days to complete. We discuss experimental and computational data quality control metrics and considerations for the extension to other mammalian tissues. Overall, mtscATAC-seq provides a broadly applicable platform to map clonal relationships between cells in human tissues, investigate fundamental aspects of mitochondrial genetics and enable additional modes of multi-omic discovery.
104Implementing Whole Genome Sequencing (WGS) in Clinical Practice: Advantages, Challenges, and Future Perspectives.PubMed
Petar Brlek, Luka Bulić, Matea Bračić, et al.
Cells. 2024 Mar 13;13(6):504. doi: 10.3390/cells13060504.
The integration of whole genome sequencing (WGS) into all aspects of modern medicine represents the next step in the evolution of healthcare. Using this technology, scientists and physicians can observe the entire human genome comprehensively, generating a plethora of new sequencing data. Modern computational analysis entails advanced algorithms for variant detection, as well as complex models for classification. Data science and machine learning play a crucial role in the processing and interpretation of results, using enormous databases and statistics to discover new and support current genotype-phenotype correlations. In clinical practice, this technology has greatly enabled the development of personalized medicine, approaching each patient individually and in accordance with their genetic and biochemical profile. The most propulsive areas include rare disease genomics, oncogenomics, pharmacogenomics, neonatal screening, and infectious disease genomics. Another crucial application of WGS lies in the field of multi-omics, working towards the complete integration of human biomolecular data. Further technological development of sequencing technologies has led to the birth of third and fourth-generation sequencing, which include long-read sequencing, single-cell genomics, and nanopore sequencing. These technologies, alongside their continued implementation into medical research and practice, show great promise for the future of the field of medicine.
105Ultra-low-input cell-free DNA sequencing for tumor detection and characterization in a real-world pediatric brain tumor cohort.PubMed
Tom T Fischer, Kendra K Maaß, Pitithat Puranachot, et al.
Acta Neuropathol Commun. 2025 Jun 28;13(1):134. doi: 10.1186/s40478-025-02024-w.
Molecular profiling of pediatric central nervous system (CNS) tumors has important clinical utility for guiding diagnostic and therapeutic strategies. Cell-free DNA (cfDNA) from liquid biopsies has been used for minimally invasive tumor profiling and longitudinal disease assessment in adult oncology and pediatric hematology. However, in pediatric neuro-oncology, low cfDNA yields pose a major barrier to translating these assays from bench to bedside. Here, we implemented a low-coverage whole genome sequencing (lcWGS) assay for picogram-level cfDNA inputs and applied it to liquid biopsies from a sizeable, population-based, cross-entity pediatric CNS tumor cohort (n = 56 patients). Applying this protocol, cfDNA whole genome profiles were successfully acquired from all liquid biopsy samples (n = 61/61 serum, n = 56/56 CSF, 100%). Based on copy number variations (CNVs), circulating-tumor DNA (ctDNA) was detected in 2/61 serum (3%) and in 25/56 CSF (45%) samples across various brain tumor entities. The integration of cfDNA results with clinical data demonstrated the utility of CSF lcWGS as a biomarker assay at diagnosis to distinguish cancerous from non-cancerous pineal region lesions (n = 6 patients). Additionally, serial CSF assessment in n = 9 patients (n = 29 CSF samples) enabled minimally invasive disease monitoring, with the added value of molecular profile availability in n = 4/6 (67%) patients at relapse. Proof-of-concept data show the feasibility of serial CSF lcWGS to reveal tumor evolution, tumor heterogeneity and potential therapeutic vulnerabilities in a case of medulloblastoma and germ cell tumor. Our study underscores the clinical utility of a robust lcWGS-based liquid biopsy assay optimized for low-input samples. We identify use-cases for implementing liquid biopsies in the clinical management of pediatric CNS tumor patients and provide a strong rationale for integration into future trials.