Imputed gene associations identify replicable trans-acting genes enriched in transcription pathways and complex traits.

Regulation of gene expression is an important mechanism through which genetic variation can affect complex traits. A substantial portion of gene expression variation can be explained by both local (cis) and distal (trans) genetic variation. Much progress has been made in uncovering cis-acting expression quantitative trait loci (cis-eQTL), but trans-eQTL have been more difficult to identify and replicate. Here we take advantage of our ability to predict the cis component of gene expression coupled with gene mapping methods such as PrediXcan to identify high confidence candidate trans-acting genes and their targets. That is, we correlate the cis component of gene expression with observed expression of genes in different chromosomes. Leveraging the shared cis-acting regulation across tissues, we combine the evidence of association across all available Genotype-Tissue Expression Project tissues and find 2,356 trans-acting/target gene pairs with high mappability scores. Reassuringly, trans-acting genes are enriched in transcription and nucleic acid binding pathways and target genes are enriched in known transcription factor binding sites. Interestingly, trans-acting genes are more significantly associated with selected complex traits and diseases than target or background genes, consistent with percolating trans effects. Our scripts and summary statistics are publicly available for future studies of trans-acting gene regulation.

Machine Learning Techniques for Predicting Drug-Related Side Effects: A Scoping Review.

BACKGROUND: Drug safety relies on advanced methods for timely and accurate prediction of side effects. To tackle this requirement, this scoping review examines machine-learning approaches for predicting drug-related side effects with a particular focus on chemical, biological, and phenotypical features. METHODS: This was a scoping review in which a comprehensive search was conducted in various databases from 1 January 2013 to 31 December 2023. RESULTS: The results showed the widespread use of Random Forest, k-nearest neighbor, and support vector machine algorithms. Ensemble methods, particularly random forest, emphasized the significance of integrating chemical and biological features in predicting drug-related side effects. CONCLUSIONS: This review article emphasized the significance of considering a variety of features, datasets, and machine learning algorithms for predicting drug-related side effects. Ensemble methods and Random Forest showed the best performance and combining chemical and biological features improved prediction. The results suggested that machine learning techniques have some potential to improve drug development and trials. Future work should focus on specific feature types, selection techniques, and graph-based methods for even better prediction.

Identifying strengths and weaknesses of methods for computational network inference from single-cell RNA-seq data.

Single-cell RNA-sequencing (scRNA-seq) offers unparalleled insight into the transcriptional programs of different cellular states by measuring the transcriptome of thousands of individual cells. An emerging problem in the analysis of scRNA-seq is the inference of transcriptional gene regulatory networks and a number of methods with different learning frameworks have been developed to address this problem. Here, we present an expanded benchmarking study of eleven recent network inference methods on seven published scRNA-seq datasets in human, mouse, and yeast considering different types of gold standard networks and evaluation metrics. We evaluate methods based on their computing requirements as well as on their ability to recover the network structure. We find that, while most methods have a modest recovery of experimentally derived interactions based on global metrics such as Area Under the Precision Recall curve, methods are able to capture targets of regulators that are relevant to the system under study. Among the top performing methods that use only expression were SCENIC, PIDC, MERLIN or Correlation. Addition of prior biological knowledge and the estimation of transcription factor activities resulted in the best overall performance with the Inferelator and MERLIN methods that use prior knowledge outperforming methods that use expression alone. We found that imputation for network inference did not improve network inference accuracy and could be detrimental. Comparisons of inferred networks for comparable bulk conditions showed that the networks inferred from scRNA-seq datasets are often better or at par with the networks inferred from bulk datasets. Our analysis should be beneficial in selecting methods for network inference. At the same time, this highlights the need for improved methods and better gold standards for regulatory network inference from scRNAseq datasets.

Inferring Regulatory Programs Governing Region Specificity of Neuroepithelial Stem Cells during Early Hindbrain and Spinal Cord Development.

Neuroepithelial stem cells (NSC) from different anatomical regions of the embryonic neural tube's rostrocaudal axis can differentiate into diverse central nervous system tissues, but the transcriptional regulatory networks governing these processes are incompletely understood. Here, we measure region-specific NSC gene expression along the rostrocaudal axis in a human pluripotent stem cell model of early central nervous system development over a 72-h time course, spanning the hindbrain to cervical spinal cord. We introduce Escarole, a probabilistic clustering algorithm for non-stationary time series, and combine it with prior-based regulatory network inference to identify genes that are regulated dynamically and predict their upstream regulators. We identify known regulators of patterning and neural development, including the HOX genes, and predict a direct regulatory connection between the transcription factor POU3F2 and target gene STMN2. We demonstrate that POU3F2 is required for expression of STMN2, suggesting that this regulatory connection is important for region specificity of NSCs.

Network-based approaches for analysis of complex biological systems.

Cells function and respond to changes in their environment by the coordinated activity of their molecular components, including mRNAs, proteins and metabolites. At the heart of proper cellular function are molecular networks connecting these components to process extra-cellular environmental signals and drive dynamic, context-specific cellular responses. Network-based computational approaches aim to systematically integrate measurements from high-throughput experiments to gain a global understanding of cellular function under changing environmental conditions. We provide an overview of recent methodological developments toward solving two major computational problems within this field in the past two years (2013-2015): network reconstruction and network-based interpretation. Looking forward, we envision development of methods that can predict phenotypes with high accuracy as well as provide biologically plausible mechanistic hypotheses.

A multi-task graph-clustering approach for chromosome conformation capture data sets identifies conserved modules of chromosomal interactions.

Chromosome conformation capture methods are being increasingly used to study three-dimensional genome architecture in multiple cell types and species. An important challenge is to examine changes in three-dimensional architecture across cell types and species. We present Arboretum-Hi-C, a multi-task spectral clustering method, to identify common and context-specific aspects of genome architecture. Compared to standard clustering, Arboretum-Hi-C produced more biologically consistent patterns of conservation. Most clusters are conserved and enriched for either high- or low-activity genomic signals. Most genomic regions diverge between clusters with similar chromatin state except for a few that are associated with lamina-associated domains and open chromatin.

SIRT3 mediates multi-tissue coupling for metabolic fuel switching.

SIRT3 is a member of the Sirtuin family of NAD(+)-dependent deacylases and plays a critical role in metabolic regulation. Organism-wide SIRT3 loss manifests in metabolic alterations; however, the coordinating role of SIRT3 among metabolically distinct tissues is unknown. Using multi-tissue quantitative proteomics comparing fasted wild-type mice to mice lacking SIRT3, innovative bioinformatic analysis, and biochemical validation, we provide a comprehensive view of mitochondrial acetylation and SIRT3 function. We find SIRT3 regulates the acetyl-proteome in core mitochondrial processes common to brain, heart, kidney, liver, and skeletal muscle, but differentially regulates metabolic pathways in fuel-producing and fuel-utilizing tissues. We propose an additional maintenance function for SIRT3 in liver and kidney where SIRT3 expression is elevated to reduce the acetate load on mitochondrial proteins. We provide evidence that SIRT3 impacts ketone body utilization in the brain and reveal a pivotal role for SIRT3 in the coordination between tissues required for metabolic homeostasis.