Biology subjects

Hannenhalli, S.

Publications and source records attributed to Hannenhalli, S..

32 records · Page 2Linked to original sources

bioRxiv2021

De novo human brain enhancers created by single nucleotide mutations

Advanced human cognition is attributed to increased neocortex size and complexity, but the underlying gene regulatory mechanisms are unknown. Using deep learning model of embryonic neocortical enhancers, and human and macaque embryonic neocortex H3K27ac data, we identified ~4000 enhancers gained de novo in the human, largely attributable to single-nucleotide essential mutations. The genes near de novo gained enhancers exhibit increased expression in human embryonic neocortex relative to macaque, are involved in critical neural developmental processes, and are expressed specifically in the progenitor cells and interneurons. The gained enhancers, especially the essential mutations, are associated with central nervous system disorders/traits. Integrative computational analyses suggest that the essential mutations establish enhancer activities through affecting binding of key transcription factors of embryonic neocortex. Overall, our results suggest that non-coding mutations may have led to de novo enhancer gains in the embryonic human neocortex, that orchestrate the expression of genes involved in critical developmental processes associated with human cognition.

genomics↗

bioRxiv2021

Cross-species identification of cancer-resistance associated genes uncovers their relevance to human cancer risk

Cancer is an evolutionarily conserved disease that occurs in a wide variety of species. We applied a comparative genomics approach to systematically characterize the genes whose conservation levels significantly correlates positively (PC) or negatively (NC) with a broad spectrum of cancer-resistance estimates, computed across almost 200 vertebrate species. PC genes are enriched in pathways relevant to tumor suppression including cell cycle, DNA repair, and immune response, while NC genes are enriched with a host of metabolic pathways. The conservation levels of the PC and NC genes in a species serve to build the first genomics-based predictor of its cancer resistance score. We find that PC genes are less tolerant to loss of function (LoF) mutations, are enriched in cancer driver genes and are associated with germline mutations that increase human cancer risk. Furthermore, their expression levels are associated with lifetime cancer risk across human tissues. Finally, their knockout in mice results in increased cancer incidence. In sum, we find that many genes associated with cancer resistance across species are implicated in human cancers, pointing to several additional candidate genes that may have a functional role in human cancer.

cancer biology↗

bioRxiv2021

Prioritizing and characterizing functionally relevant genes across human tissues

Knowledge of genes that are critical to a tissues function remains difficult to ascertain and presents a major bottleneck toward a mechanistic understanding of genotype-phenotype links. Here, we present the first machine learning model - FUGUE - combining transcriptional and network features, to predict tissue-relevant genes across 30 human tissues. FUGUE achieves an average cross-validation auROC of 0.86 and auPRC of 0.50 (expected 0.09). In three independent datasets, FUGUE accurately distinguishes tissue or cell type-specific genes, significantly outperforming the conventional metric based on tissue-specific expression alone. Comparison of tissue-relevant transcription factors across tissue recapitulate their developmental relationships. Interestingly, the tissue-relevant genes cluster on the genome within topologically associated domains and furthermore, are highly enriched for differentially expressed genes in the corresponding cancer type. We provide the prioritized gene lists in 30 human tissues and an open-source software to prioritize genes in a novel context given multi-sample transcriptomic data.

systems biology↗

bioRxiv2021

A transcriptionally distinct subpopulation of healthy acinar cells exhibit features of pancreatic progenitors and PDAC

Pancreatic ductal adenocarcinoma (PDAC) tumors can originate either from acinar or ductal cells in the adult pancreas. We re-analyze multiple pancreas and PDAC single-cell RNA-seq datasets and find a subset of non-malignant acinar cells, which we refer to as acinar edge (AE) cells, whose transcriptomes highly diverge from a typical acinar cell in each dataset. Genes up-regulated among AE cells are enriched for transcriptomic signatures of pancreatic progenitors, acinar dedifferentiation, and several oncogenic programs. AE-upregulated genes are up-regulated in human PDAC tumors, and consistently, their promoters are hypo-methylated. High expression of these genes is associated with poor patient survival. The fraction of AE-like cells increases with age in healthy pancreatic tissue, which is not explained by clonal mutations, thus pointing to a non-genetic source of variation. We also find edge-like states in lung and liver tissues, suggesting that sub-populations of healthy cells across tissues can exist in pre-malignant states.

genomics↗

bioRxiv2020

Predicting tissue-specific gene expression from whole blood transcriptome

Complex diseases are systemic, largely mediated via transcriptional dysregulation in multiple tissues. Thus, knowledge of tissue-specific transcriptome in an individual can provide important information about an individuals health. Unfortunately, with a few exceptions such as blood, skin, and muscle, an individuals tissue-specific transcriptome is not accessible through non-invasive means. However, due to shared genetics and regulatory programs between tissues, the transcriptome in blood may be predictive of those in other tissues, at least to some extent. Here, based on GTEx data, we address this question in a rigorous, systematic manner, for the first time. We find that an individuals whole blood gene expression and splicing profile can predict tissue-specific expression levels in a significant manner (beyond demographic variables) for many genes. On average, across 32 tissues, the expression of about 60% of the genes is predictable from blood expression in a significant manner, with a maximum of 81% of the genes for the musculoskeletal tissue. Remarkably, the tissue-specific expression inferred from the blood transcriptome is almost as good as the actual measured tissue expression in predicting disease state for six different complex disorders, including Hypertension and Type 2 diabetes, substantially surpassing predictors built directly from the blood transcriptome. The code for our pipeline for tissue-specific gene expression prediction - TEEBoT, is provided, enabling others to study its potential translational value in other indications.

genetics↗

bioRxiv2020

A transcription-centric model of SNP-Age interaction

Complex age-associated phenotypes are caused, in part, by an interaction between an individuals genotype and age. The mechanisms governing such interactions are however not entirely understood. Here, we provide a novel transcriptional mechanism-based framework - SNiPage, to investigate such interactions, whereby a transcription factor (TF) whose expression changes with age (age-associated TF), binds to a polymorphic regulatory element in an allele-dependent fashion, rendering the target genes expression dependent on both, the age and the genotype. Applying SNiPage to GTEx, we detected ~637 interacting TF-SNP-Gene triplets on average across 25 tissues, where the TF binds to a regulatory SNP in the genes promoter or putative enhancer and potentially regulates its expression in an age- and allele-dependent fashion. The detected SNPs are enriched for epigenomic marks indicative of regulatory activity, exhibit allele-specific chromatin accessibility, and spatial proximity to their putative gene targets. Furthermore, the TF-SNP interaction-dependent target genes have established links to aging and to age-associated diseases. In six hypertension-implicated tissues, detected interactions significantly inform hypertension state of an individual. Lastly, the age-interacting SNPs exhibit a greater proximity to the reported phenotype/diseases-associated SNPs than eSNPs identified in an interaction-independent fashion. Overall, we present a novel mechanism-based model, and a novel framework SNiPage, to identify functionally relevant SNP-age interactions in transcriptional control and illustrate their potential utility in understanding complex age-associated phenotypes.

genetics↗

bioRxiv2020

PathExt: a general framework for path-based mining of omics-integrated biological networks

MotivationLarge scale transcriptomic data are routinely used to prioritize genes underlying specific phenotypes. Current approaches largely focus on differentially expressed genes (DEGs), despite the recognition that phenotypes emerge via a network of interactions between genes and proteins, many of which may not be differentially expressed. Furthermore, many practical applications lack sufficient samples or an appropriate control to robustly identify statistically significant DEGs. ResultsWe provide a computational tool - PathExt, which, in contrast to differential genes, identifies differentially active paths when a control is available, and most active paths otherwise, in an omics-integrated biological network. The sub-network comprising such paths, referred to as the Top-Net, captures the most relevant genes and processes underlying the specific biological context. The TopNet forms a well-connected graph, reflecting the tight orchestration in biological systems. Two key advantages of PathExt are (i) it can extract characteristic genes and pathways even when only a single sample is available, and (ii) it can be used to study a system even in the absence of an appropriate control. We demonstrate the utility of PathExt via two diverse sets of case studies, to characterize (a) Mycobacterium tuberculosis (M.tb) response upon exposure to 18 antibacterial drugs where only one transcriptomic sample is available for each exposure; and (b) tissue-relevant genes and processes using transcriptomic data from GTEx (Genotype-Tissue Expression) for 39 human tissues. Overall, PathExt is a general tool for prioritizing context-relevant genes in any omics-integrated biological network for any condition(s) of interest, even with a single sample or in the absence of appropriate controls. AvailabilityThe source code for PathExt is available at https://github.com/NarmadaSambaturu/PathExt. Contactnchandra@iisc.ac.in, sridhar.hannenhalli@nih.gov

systems biology↗

bioRxiv2019

Draft Genome Assembly and Annotation of Red Raspberry Rubus Idaeus

The red raspberry, Rubus idaeus, is widely distributed in all temperate regions of Europe, Asia, and North America and is a major commercial fruit valued for its taste, high antioxidant and vitamin content. However, Rubus breeding is a long and slow process hampered by limited genomic and molecular resources. Genomic resources such as a complete genome sequencing and transcriptome will be of exceptional value to improve research and breeding of this high value crop. Using a hybrid sequence assembly approach including data from both long and short sequence reads, we present the first assembly of the Rubus idaeus genome (Joan J. variety). The de novo assembled genome consists of 2,145 scaffolds with a genome completeness of 95.3% and an N50 score of 638 KB. Leveraging a linkage map, we anchored 80.1% of the genome onto seven chromosomes. Using over 1 billion paired-end RNAseq reads, we annotated 35,566 protein coding genes with a transcriptome completeness score of 97.2%. The Rubus idaeus genome provides an important new resource for researchers and breeders.

genomics↗

bioRxiv2019

Single-cell profiling of tumor-reactive CD4+ T-cells reveals unexpected transcriptomic diversity

Most current tumor immunotherapy strategies leverage cytotoxic CD8+ T cells. Despite evidence for clinical potential of CD4+ tumor-infiltrating lymphocytes (TILs), their functional diversity has limited our ability to harness their activity. To address this issue, we have used single-cell mRNA sequencing to analyze the response of CD4+ T cells specific for a defined recombinant tumor antigen, both in the tumor microenvironment and draining lymph nodes (dLN). Designing new computational approaches to characterize subpopulations, we identify TIL transcriptomic patterns strikingly distinct from those elicited by responses to infection, and dominated by diversity among T-bet-expressing T helper type 1 (Th1)-like cells. In contrast, the dLN response includes follicular helper (Tfh)-like cells but lacks Th1 cells. We identify a type I interferon-driven signature in Th1-like TILs, and show that it is found in human liver cancer and melanoma, in which it is negatively associated with response to checkpoint therapy. Our study unveils unsuspected differences between tumor and virus CD4+ T cell responses, and provides a proof-of-concept methodology to characterize tumor specific CD4+ T cell effector programs. Targeting these programs should help improve immunotherapy strategies. One Sentence SummarySingle-cell RNA sequencing reveals novel and highly diverse transcriptomic patterns characteristic of CD4+ T cell responses to tumors.

immunology↗

bioRxiv2019

Ligand dependent gene regulation by transient ER clustered enhancers

Unliganded nuclear receptors have been implicated in ligand-dependent gene regulation. However, the underlying mechanisms are not fully understood. Here we demonstrate that unliganded ER binds to specific sites in the genome thereby pre-marking them as future functional enhancers. Upon ligand exposure, ER binds to several EREs relatively proximal to the pre-marked, or persistent, ER-bound sites. Interestingly, the persistent sites interact extensively, via chromatin looping, with the proximal transiently bound sites forming ER clustered enhancers in 3D. CRISPR-based deletion of TFF1 persistent site disrupts the formation of its clustered enhancer resulting in the loss of E2-dependent induced expression of TFF1 and its neighboring genes within the same cluster. The clustered enhancers overlap with nuclear ER puncta that coalesce in a ligand-dependent manner. Furthermore, formation of clustered enhancers, as well as puncta, coincide with the active phase of signaling and their later disappearance results in the loss of gene expression even though persistent sites remain bound by ER. Our results establish the role of persistent unliganded ER binding in priming enhancer clusters in 3D that drive transient, but robust, gene expression in a ligand-dependent fashion.

molecular biology↗

bioRxiv2018

Migration rather than proliferation transcriptomic signatures are strongly associated with breast cancer patient survival

The efficacy of prospective cancer treatments is routinely estimated by in vitro cell-line proliferation screens. However, it is unclear whether tumor aggressiveness and patient survival are influenced more by the proliferative or the migratory properties of cancer cells. To address this question, we experimentally measured proliferation and migration phenotypes across more than 40 breast cancer cell-lines. Based on the latter, we built and validated individual predictors of breast cancer proliferation and migration levels from the cells transcriptomics. We then apply these predictors to estimate the proliferation and migration levels of more than 1000 TCGA breast cancer tumors. Reassuringly, both estimates increase with tumors aggressiveness, as qualified by its stage, grade, and subtype. However, predicted tumor migration levels are significantly more strongly associated with patient survival than the proliferation levels. We confirm these finding by conducting siRNA knock-down experiments on the highly migratory MDA-MB-231 cell lines and deriving gene knock-down based proliferation and migration signatures. We show that cytoskeletal drugs might be more beneficial in patients with high predicted migration levels. Taken together, these results testify to the importance of migration levels in determining patient survival.

bioinformatics↗

bioRxiv2018

Genome-wide prediction of synthetic rescue mediators of resistance to targeted and immunotherapy

Most patients with advanced cancer eventually acquire resistance to targeted therapies, spurring extensive efforts to identify molecular events mediating therapy resistance. Many of these events involve synthetic rescue (SR) interactions, where the reduction in cancer cell viability caused by targeted gene inactivation is rescued by an adaptive alteration of another gene (the rescuer). Here we perform a genome-wide prediction of SR rescuer genes by analyzing tumor transcriptomics and survival data of 10,000 TCGA cancer patients. Predicted SR interactions are validated in new experimental screens. We show that SR interactions can successfully predict cancer patients response and emerging resistance. Inhibiting predicted rescuer genes sensitizes resistant cancer cells to therapies synergistically, providing initial leads for developing combinatorial approaches to overcome resistance proactively. Finally, we show that the SR analysis of melanoma patients successfully identifies known mediators of resistance to immunotherapy and predicts novel rescuers.

cancer biology↗

bioRxiv2018

Beyond synthetic lethality: multiple gene interaction types play a key functional role in cancer

The phenotypic effect of perturbing a genes activity depends on the activity level of other genes, reflecting the notion that phenotypes are emergent properties of a network of functionally interacting genes. In the context of cancer, contemporary investigations have primarily focused on just one type of functional genetic interaction (GI) - synthetic lethality (SL). However, there may be additional types of GIs whose systematic identification would enrich the molecular and functional characterization of cancer. Here, we describe a novel data-driven approach called EnGIne, that applied to TCGA data identifies 71,946 GIs spanning 12 distinct types, only a small minority of which are SLs. The detected GIs explain cancer driver genes tissue-specificity and differences in patients response to drugs, and stratify breast cancer tumors into refined subtypes. These results expand the scope of cancer GIs and lay a conceptual and computational basis for future studies of additional types of GIs and their translational applications. The GI network is accessible online via a web portal [https://amagen.shinyapps.io/cancerapp/].

cancer biology↗

bioRxiv2017

Analysis And Correction Of Compositional Bias In Sparse Sequencing Count Data

Count data derived from high-throughput DNA sequencing is frequently used in quantitative molecular assays. Due to properties inherent to the sequencing process, unnormalized count data is compositional, measuring relative and not absolute abundances of the assayed features. This compositional bias confounds inference of absolute abundances. We demonstrate that existing techniques for estimating compositional bias fail with sparse metagenomic 16S count data and propose an empirical Bayes normalization approach to overcome this problem. In addition, we clarify the assumptions underlying frequently used scaling normalization methods in light of compositional bias, including scaling methods that were not designed directly to address it.

genomics↗