Branton William G, Zhang Na, Cohen Eric A, Brew Bruce J, Gill M John, Gelman Benjamin B, Kong Linglong, Power Christopher
Departments of Medicine.
Department of Mathematics & Statistics.
AIDS. 2025 Apr 1;39(5):496-507. doi: 10.1097/QAD.0000000000004116. Epub 2025 Jan 15.
To discover microRNA (miRNA)-RNA transcript interactions dysregulated in brains from persons with HIV-associated neurocognitive disorder (HAND), we investigated RNA expression using machine learning tools.
Brain-derived host RNA transcript and miRNA expression was examined from persons with or without HAND using bioinformatics platforms.
By combining next generation sequencing, droplet digital (dd)PCR quantitation of HIV-1 genomes, with bioinformatics and statistical tools, we investigated differential RNA expression in frontal cortex from persons without HIV [HIV(-)], with HIV without brain disease [HIV(+)], with HAND, or HAND with encephalitis (HIVE).
Expression levels for 147 transcripts and 43 miRNAs showed a minimum four-fold difference between clinical groups with a predominance of antiviral (type I interferon) signaling-related, neural cell maintenance-related, and neurodevelopmental disorder-related genes that was validated by gene ontology and molecular pathway inferences. Scale of signal-to-noise ratio (SSNR) and biweight midcorrelation (bicor) analyses identified 14 miRNAs and 45 RNA transcripts, which were highly correlated and differentially expressed ( P ≤ 0.05). Machine learning applications compared regression models predicated on HIV-1 DNA, or RNA viral quantities that disclosed miR-4683 and miR-154-5p were dominant variables associated with differential expression of host RNAs. These miRNAs were also associated with antiviral-related, cell maintenance-related, and neurodevelopmental disorder-related genes.
Antiviral as well as neurodevelopmental disorder-related pathways in brain were associated with HAND, based on correlated RNA transcripts and miRNAs. Integrated molecular methods with machine learning offer insights into disease mechanisms, underpinning brain-related biotypes among persons with HIV that could direct clinical care.
为了发现人类免疫缺陷病毒相关神经认知障碍（HAND）患者大脑中失调的微小RNA（miRNA）-RNA转录物相互作用，我们使用机器学习工具研究了RNA表达。
使用生物信息学平台检查有或无HAND患者的脑源性宿主RNA转录物和miRNA表达。
通过将下一代测序、HIV-1基因组的液滴数字（dd）PCR定量与生物信息学和统计工具相结合，我们研究了无HIV [HIV（-）]、有HIV但无脑部疾病[HIV（+）]、有HAND或有脑炎的HAND（HIVE）患者额叶皮质中的差异RNA表达。
147个转录物和43个miRNA的表达水平在临床组之间显示出至少四倍的差异，主要是抗病毒（I型干扰素）信号相关、神经细胞维持相关和神经发育障碍相关基因，这通过基因本体论和分子途径推断得到验证。信噪比（SSNR）和双权中值相关（bicor）分析确定了14个miRNA和45个RNA转录物，它们高度相关且差异表达（P≤0.05）。机器学习应用比较了基于HIV-1 DNA或RNA病毒量的回归模型，结果显示miR-4683和miR-154-5p是与宿主RNA差异表达相关的主要变量。这些miRNA也与抗病毒相关、细胞维持相关和神经发育障碍相关基因有关。
基于相关的RNA转录物和miRNA，大脑中的抗病毒以及神经发育障碍相关途径与HAND有关。机器学习的综合分子方法为疾病机制提供了见解，巩固了HIV患者中与大脑相关的生物类型，可为临床护理提供指导。
