TCS Research, Tata Consultancy Services Limited, Pune, Maharashtra, India.
Appl Environ Microbiol. 2022 Aug 9;88(15):e0059622. doi: 10.1128/aem.00596-22. Epub 2022 Jul 13.
The human microbiota, which comprises an ensemble of taxonomically and functionally diverse but often mutually cooperating microorganisms, benefits its host by shaping the host immunity, energy harvesting, and digestion of complex carbohydrates as well as production of essential nutrients. Dysbiosis in the human microbiota, especially the gut microbiota, has been reported to be linked to several diseases and metabolic disorders. Recent studies have further indicated that tracking these dysbiotic variations could potentially be exploited as biomarkers of disease states. However, the human microbiota is not geography agnostic, and hence a taxonomy-based (microbiome) biomarker for disease diagnostics has certain limitations. In comparison, (microbiome) function-based biomarkers are expected to have a wider applicability. Given that (i) the host physiology undergoes certain changes in the course of a disease and (ii) host-associated microbial communities need to adapt to this changing microenvironment of their host, we hypothesized that signatures emanating from the abundance of bacterial proteins associated with the signal transduction system (herein referred to as sensory proteins [SPs]) might be able to distinguish between healthy and diseased states. To test this hypothesis, publicly available metagenomic data sets corresponding to three diverse health conditions, namely, colorectal cancer, type 2 diabetes mellitus, and schizophrenia, were analyzed. Results demonstrated that SP signatures (derived from host-associated metagenomic samples) indeed differentiated among healthy individual and patients suffering from diseases of various severities. Our finding was suggestive of the prospect of using SP signatures as early biomarkers for diagnosing the onset and progression of multiple diseases and metabolic disorders. The composition of the human microbiota, a collection of host-associated microbes, has been shown to differ among healthy and diseased individuals. Recent studies have investigated whether tracking these variations could be exploited for disease diagnostics. It has been noted that compared to microbial taxonomies, the ensemble of functional proteins encoded by microbial genes are less likely to be affected by changes in ethnicity and dietary preferences. These functions are expected to help the microbe adapt to changing environmental conditions. Thus, healthy individuals might harbor a different set of genes than diseased individuals. To test this hypothesis, we analyzed metagenomes from healthy and diseased individuals for signatures of a particular group of proteins called sensory proteins (SP), which enable the bacteria to sense and react to changes in their microenvironment. Results demonstrated that SP signatures indeed differentiate among healthy individuals and those suffering from diseases of various severities.
人体微生物群由一组在分类学和功能上多样化但通常相互合作的微生物组成,通过塑造宿主的免疫系统、能量收集和复杂碳水化合物的消化以及必需营养素的产生,使宿主受益。人体微生物群(尤其是肠道微生物群)的失调已被报道与多种疾病和代谢紊乱有关。最近的研究进一步表明,跟踪这些失调变化可能可以作为疾病状态的生物标志物。然而,人体微生物群并不是与地理位置无关的,因此基于分类学的(微生物组)疾病诊断生物标志物存在一定的局限性。相比之下,(微生物组)基于功能的生物标志物预计具有更广泛的适用性。鉴于 (i) 宿主生理学在疾病过程中会发生某些变化,以及 (ii) 与宿主相关的微生物群落需要适应宿主不断变化的微环境,我们假设与信号转导系统相关的细菌蛋白丰度产生的特征(在此称为感觉蛋白 [SP])可能能够区分健康和患病状态。为了验证这一假设,分析了三个不同健康状况(结直肠癌、2 型糖尿病和精神分裂症)的公开可用宏基因组数据集。结果表明,SP 特征(源自与宿主相关的宏基因组样本)确实可以区分健康个体和患有不同严重程度疾病的患者。我们的发现表明,使用 SP 特征作为诊断多种疾病和代谢紊乱发作和进展的早期生物标志物具有前景。
人体微生物群的组成,即一组与宿主相关的微生物,在健康个体和患病个体之间存在差异。最近的研究调查了是否可以利用这些变化来进行疾病诊断。据指出,与微生物分类学相比,由微生物基因编码的功能蛋白的整体不太可能受到种族和饮食偏好变化的影响。这些功能有望帮助微生物适应不断变化的环境条件。因此,健康个体可能拥有与患病个体不同的基因集。为了验证这一假设,我们分析了来自健康个体和患病个体的宏基因组,以寻找一组称为感觉蛋白 (SP) 的特定蛋白质的特征,这些蛋白质使细菌能够感知和对其微环境的变化做出反应。结果表明,SP 特征确实可以区分健康个体和患有不同严重程度疾病的个体。