Abhijeet R. Sonawane

Understanding Tissue-Specific Gene Regulation

Summary Although all human tissues carry out common processes, tissues are distinguished by gene expression patterns, implying that distinct regulatory programs control tissue specificity. In this study, we investigate gene expression and regulation across 38 tissues profiled in the Genotype-Tissue Expression project. We find that network edges (transcription factor to target gene connections) have higher tissue specificity than network nodes (genes) and that regulating nodes (transcription factors) are less likely to be expressed in a tissue-specific manner as compared to their targets (genes). Gene set enrichment analysis of network targeting also indicates that the regulation of tissue-specific function is largely independent of transcription factor expression. In addition, tissue-specific genes are not highly targeted in their corresponding tissue network. However, they do assume bottleneck positions due to variability in transcription factor targeting and the influence of non-canonical regulatory interactions. These results suggest that tissue specificity is driven by context-dependent regulatory paths, providing transcriptional control of tissue-specific processes.

Sexual dimorphism in gene expression and regulatory networks across human tissues

Sexual dimorphism manifests in many diseases and may drive sex-specific therapeutic responses. To understand the molecular basis of sexual dimorphism, we conducted a comprehensive assessment of gene expression and regulatory network modeling in 31 tissues using 8716 human transcriptomes from GTEx. We observed sexually dimorphic patterns of gene expression involving as many as 60% of autosomal genes, depending on the tissue. Interestingly, sex hormone receptors do not exhibit sexually dimorphic expression in most tissues; however, differential network targeting by hormone receptors and other transcription factors (TFs) captures their downstream sexually dimorphic gene expression. Furthermore, differential network wiring was found extensively in several tissues, particularly in brain, in which not all regions exhibit strong differential expression. This systems-based analysis provides a new perspective on the drivers of sexual dimorphism, one in which a repertoire of TFs plays important roles in sex-specific rewiring of gene regulatory networks. Highlights Sexual dimorphism manifests in both gene expression and gene regulatory networks Substantial sexual dimorphism in regulatory networks was found in several tissues Many differentially regulated genes are not differentially expressed Sex hormone receptors do not exhibit sexually dimorphic expression in most tissues

Exploring regulation in tissues with eQTL networks

Significance A core tenet in genetics is that genotype influences phenotype. In an individual, the same genome can be expressed in substantially different ways, depending on the tissue. Expression quantitative trait locus (eQTL) analysis, which associates genetic variants at millions of locations across the genome with the expression levels of each gene, can provide insight into genetic regulation of phenotype. In each of 13 tissues we performed an eQTL analysis, represented significant associations as edges in a network, and explored the structure of those networks. We found clusters of eQTL linked to shared functions across tissues and tissue-specific clusters linked to tissue-specific functions, driven by genetic variants with tissue-specific regulatory potential. Our findings provide unique insight into the genotype–phenotype relationship. Characterizing the collective regulatory impact of genetic variants on complex phenotypes is a major challenge in developing a genotype to phenotype map. Using expression quantitative trait locus (eQTL) analyses, we constructed bipartite networks in which edges represent significant associations between genetic variants and gene expression levels and found that the network structure informs regulatory function. We show, in 13 tissues, that these eQTL networks are organized into dense, highly modular communities grouping genes often involved in coherent biological processes. We find communities representing shared processes across tissues, as well as communities associated with tissue-specific processes that coalesce around variants in tissue-specific active chromatin regions. Node centrality is also highly informative, with the global and community hubs differing in regulatory potential and likelihood of being disease associated.

Manipulation of extreme events on scale-free networks.

Extreme events taking place on networks are not uncommon. We show that it is possible to manipulate the extreme event occurrence probabilities and distribution over the nodes of a scale-free network by tuning the nodal capacity. This can be used to reduce the number of extreme event occurrences. However, monotonic nodal capacity enhancements, beyond a point, do not lead to any substantial reduction in the number of extreme events. We point out the practical implication of this result for network design in the context of reducing extreme event occurrences.

Regulatory network changes between cell lines and their tissues of origin

BackgroundCell lines are an indispensable tool in biomedical research and often used as surrogates for tissues. Although there are recognized important cellular and transcriptomic differences between cell lines and tissues, a systematic overview of the differences between the regulatory processes of a cell line and those of its tissue of origin has not been conducted. The RNA-Seq data generated by the GTEx project is the first available data resource in which it is possible to perform a large-scale transcriptional and regulatory network analysis comparing cell lines with their tissues of origin.ResultsWe compared 127 paired Epstein-Barr virus transformed lymphoblastoid cell lines (LCLs) and whole blood samples, and 244 paired primary fibroblast cell lines and skin samples. While gene expression analysis confirms that these cell lines carry the expression signatures of their primary tissues, albeit at reduced levels, network analysis indicates that expression changes are the cumulative result of many previously unreported alterations in transcription factor (TF) regulation. More specifically, cell cycle genes are over-expressed in cell lines compared to primary tissues, and this alteration in expression is a result of less repressive TF targeting. We confirmed these regulatory changes for four TFs, including SMAD5, using independent ChIP-seq data from ENCODE.ConclusionsOur results provide novel insights into the regulatory mechanisms controlling the expression differences between cell lines and tissues. The strong changes in TF regulation that we observe suggest that network changes, in addition to transcriptional levels, should be considered when using cell lines as models for tissues.

Tissue-aware RNA-Seq processing and normalization for heterogeneous and sparse data

BackgroundAlthough ultrahigh-throughput RNA-Sequencing has become the dominant technology for genome-wide transcriptional profiling, the vast majority of RNA-Seq studies typically profile only tens of samples, and most analytical pipelines are optimized for these smaller studies. However, projects are generating ever-larger data sets comprising RNA-Seq data from hundreds or thousands of samples, often collected at multiple centers and from diverse tissues. These complex data sets present significant analytical challenges due to batch and tissue effects, but provide the opportunity to revisit the assumptions and methods that we use to preprocess, normalize, and filter RNA-Seq data – critical first steps for any subsequent analysis.ResultsWe find that analysis of large RNA-Seq data sets requires both careful quality control and the need to account for sparsity due to the heterogeneity intrinsic in multi-group studies. We developed Yet Another RNA Normalization software pipeline (YARN), that includes quality control and preprocessing, gene filtering, and normalization steps designed to facilitate downstream analysis of large, heterogeneous RNA-Seq data sets and we demonstrate its use with data from the Genotype-Tissue Expression (GTEx) project.ConclusionsAn R package instantiating YARN is available at http://bioconductor.org/packages/yarn.

A network-based approach to eQTL interpretation and SNP functional characterization

Expression quantitative trait locus (eQTL) analysis associates genotype with gene expression, but most eQTL studies only include cis-acting variants and generally examine a single tissue. We used data from 13 tissues obtained by the Genotype-Tissue Expression (GTEx) project v6.0 and, in each tissue, identified both cis- and trans-eQTLs. For each tissue, we represented significant associations between single nucleotide polymorphisms (SNPs) and genes as edges in a bipartite network. These networks are organized into dense, highly modular communities often representing coherent biological processes. Global network hubs are enriched in distal gene regulatory regions such as enhancers, but are devoid of disease-associated SNPs from genome wide association studies. In contrast, local, community-specific network hubs (core SNPs) are preferentially located in regulatory regions such as promoters and enhancers and highly enriched for trait and disease associations. These results provide help explain how many weak-effect SNPs might together influence cellular function and phenotype.

Dynamic phase transition from localized to spatiotemporal chaos in coupled circle map with feedback.

We investigate coupled circle maps in the presence of feedback and explore various dynamical phases observed in this system of coupled high dimensional maps. We observe an interesting transition from localized chaos to spatiotemporal chaos. We study this transition as a dynamic phase transition. We observe that persistence acts as an excellent quantifier to describe this transition. Taking the location of the fixed point of circle map (which does not change with feedback) as a reference point, we compute a number of sites which have been greater than (less than) the fixed point until time t. Though local dynamics is high dimensional in this case, this definition of persistence which tracks a single variable is an excellent quantifier for this transition. In most cases, we also obtain a well defined persistence exponent at the critical point and observe conventional scaling as seen in second order phase transitions. This indicates that persistence could work as a good order parameter for transitions from fully or partially arrested phase. We also give an explanation of gaps in eigenvalue spectrum of the Jacobian of localized state.

USING NETWORK SCIENCE TOOLS TO IDENTIFY NOVEL DIET PATTERNS IN PRODROMAL DEMENTIA: THE THREE-CITY STUDY

Forms with the original layout, structure, and format intact. This allowed data collection and immediate storage using portable devices through a wireless internet connection. Formative research based on mock interviews consisted on qualitative data collection on the acceptability, practicality, and feasibility of data collection in three diverse settings and cultures (India, Switzerland, and USA), within volunteers (N1⁄445) with highly diverse demographic characteristics. Results:Data collection was efficient, faster compared to previous 1066 studies, reliable in all settings, and allowed for the automatic, real-time transmission and storage of data. The use of smartphones or tablets was well-received by all subjects, and the interviewers appreciated the convenience provided by the mobility and usability of the portable devices, the ease of use of electronic versions of the questionnaires (which included embedded instructions and hints), and found data entry swift, accurate, stable and secure. Conclusions:This qualitative pilot study demonstrated the good acceptability, efficiency, and accuracy of data collection using an alpha electronic version of the 1066 Short Schedule Dementia Diagnostic Routine in three culturally and geographically diverse low-middle and high-income settings. A cross-cultural validation study is warranted before this innovative tool is used in large scale surveys into dementia occurrence.

Transcriptional landscape of cell lines and their tissues of origin

Cell lines are an indispensable tool in biomedical research and often used as surrogates for tissues. An important question is how well a cell line’s transcriptional and regulatory processes reflect those of its tissue of origin. We analyzed RNA-Seq data from GTEx for 127 paired Epstein-Barr virus transformed lymphoblastoid cell lines and whole blood samples; and 244 paired fibroblast cell lines and skin biopsies. A combination of gene expression and network analyses shows that while cell lines carry the expression signatures of their primary tissues, albeit at reduced levels, they also exhibit changes in their patterns of transcription factor regulation. Cell cycle genes are over-expressed in cell lines compared to primary tissue, and they have a reduction of repressive transcription factor targeting. Our results provide insight into the expression and regulatory alterations observed in cell lines and suggest that these changes should be considered when using cell lines as models. Highlights Cell lines differ from their source tissues in gene expression and regulation Distinct cell lines share altered patterns of cell cycle regulation Cell cycle genes are less strongly targeted by repressive TFs in cell lines Cell lines share expression with their source tissue, but at reduced levels