Biology subjects

Ramachandran, S.

Publications and source records attributed to Ramachandran, S..

89 records · Page 5Linked to original sources

bioRxiv2021

Mapping Transcription Factor-Nucleosome Dynamics from Plasma cfDNA

Cell-free DNA (cfDNA) contains a composite map of the epigenomes of its cells-of-origin. Tissue-specific transcription factor (TF) binding inferred from cfDNA could enable us to track disease states in humans in a minimally invasive manner. Here, by enriching for short cfDNA fragments, we directly map TF footprints at single binding sites from plasma. We show that the enrichment of TF footprints in plasma reflects the binding strength of the TF in cfDNA tissue-of-origin. Based on this principle, we were able to identify the subset of genome-wide binding sites for selected TFs that leave TF-specific footprints in plasma. These footprints enabled us to not only identify the tissue-of-origin of cfDNA but also map the chromatin structure around the factor-bound sites in their cells-of-origin. To ask if we can use these plasma TF footprints to map cancer states, we first defined pure tumor TF signatures in plasma in vivo using estrogen receptor-positive (ER+) breast cancer xenografts. We found that the tumor-specific cfDNA protections of ER- could distinguish WT, ER-amplified, and ER-mutated xenografts. Further, tumor-specific cfDNA protections of ER- and FOXA1 reflect TF-specific accessibility across human breast tumors, demonstrating our ability to capture tumor TF binding in plasma. We then scored TF binding in human plasma samples and identified specific binding sites whose plasma TF protections can identify the presence of cancer and specifically breast cancer. Thus, plasma TF footprints enable minimally invasive mapping of the regulatory landscape of cancer in humans.

genomics↗

bioRxiv2021

Single calcium channel nanodomains drive presynaptic calcium entry at individual lamprey reticulospinal presynaptic terminals

Synchronous neurotransmission is central to efficient information transfer in neural circuits, requiring precise coupling between action potentials, Ca2+ entry and neurotransmitter release. However, determinations of Ca2+ requirements for release, which may originate from entry through single voltage-gated Ca2+ channels, remain largely unexplored in simple active zone synapses common in the nervous system. Understanding these requirements is key to addressing Ca2+ channel and synaptic dysfunction underlying numerous neurological and neuropsychiatric disorders. Here, we present single channel analysis of evoked active zone Ca2+ entry, using cell-attached patch clamp and lattice light sheet microscopy over active zones at single central lamprey reticulospinal presynaptic terminals. Our findings show a small pool (mean of 23) of Ca2+ channels at each terminal, comprising subtypes N-type (CaV2.2), P/Q-type (CaV2.1) and R-type (CaV2.3), available to gate neurotransmitter release. Significantly, of this pool only 1-6 (mean of 4) channels open upon depolarization. High temporal fidelity lattice light sheet imaging reveals AP-evoked Ca2+ transients exhibiting quantal amplitude variations between action potentials and stochastic variation of precise locations of Ca2+ entry within the active zone. Further, Ca2+ channel numbers at each active zone correlate to the number of presynaptic primed synaptic vesicles. Together, our findings indicate 1:1 association of Ca2+ channels with primed vesicles, suggesting Ca2+ entry via as few as one channel may trigger neurotransmitter release.

neuroscience↗

bioRxiv2020

Pathway Analysis within Multiple Human Ancestries Reveals Novel Signals for Epistasis in Complex Traits

Genome-wide association (GWA) studies have identified thousands of significant genetic associations in humans across a number of complex traits. However, the majority of these studies focus on linear additive relationships between genotypic and phenotypic variation. Epistasis, or non-additive genetic interactions, has been identified as a major driver of both complex trait architecture and evolution in multiple model organisms; yet, this same phenomenon is not considered to be a significant factor underlying human complex traits. There are two possible reasons for this assumption. First, most large GWA studies are conducted solely with European cohorts; therefore, our understanding of broad-sense heritability for many complex traits is limited to just one ancestry group. Second, current epistasis mapping methods commonly identify significant genetic interactions by exhaustively searching across all possible pairs of SNPs. In these frameworks, estimated epistatic effects size are often small and power can be low due to the multiple testing burden. Here, we present a case study that uses a novel region-based mapping approach to analyze sets of variants for the presence of epistatic effects across six diverse subgroups within the UK Biobank. We refer to this method as the "MArginal ePIstasis Test for Regions" or MAPIT-R. Even with limited sample sizes, we find a total of 245 pathways within the KEGG and REACTOME databases that are significantly enriched for epistatic effects in height and body mass index (BMI), with 67% of these pathways being detected within individuals of African ancestry. As a secondary analysis, we introduce a novel region-based "leave-one-out" approach to localize pathway-level epistatic signals to specific interacting genes in BMI. Overall, our results indicate that non-European ancestry populations may be better suited for the discovery of non-additive genetic variation in human complex traits -- further underscoring the need for publicly available, biobank-sized datasets of diverse groups of individuals.

genomics↗

bioRxiv2020

Cooperative Binding of Transcription Factors is a Hallmark of Active Enhancers

Enhancers harbor binding motifs that recruit transcription factors (TFs) for gene activation. While cooperative binding of TFs at enhancers is known to be critical for transcriptional activation of a handful of developmental enhancers, the extent TF cooperativity genome-wide is unknown. Here, we couple high-resolution nuclease footprinting with single-molecule methylation profiling to characterize TF cooperativity at active enhancers in the Drosophila genome. Enrichment of short MNase-protected DNA segments indicates that the majority of enhancers harbor two or more TF binding sites, and we uncover protected fragments that correspond to co-bound sites in thousands of enhancers. We integrate MNase-seq, methylation accessibility profiling, and CUT&RUN chromatin profiling as a comprehensive strategy to characterize co-binding of the Trithorax-like (TRL) DNA binding protein and multiple other TFs and identify states where an enhancer is bound by no TF, by either single factor, by multiple factors, or where binding sites are occluded by nucleosomes. From the analysis of co-binding, we find that cooperativity dominates TF binding in vivo at a majority of active enhancers. TF cooperativity can occur without apparent protein-protein interactions and provides a mechanism to effectively clear nucleosomes and promote enhancer function.

molecular biology↗

bioRxiv2020

Multi-scale Genomic Inference using Biologically Annotated Neural Networks

In this article, we present Biologically Annotated Neural Networks (BANNs), a nonlinear probabilistic framework for association mapping in genome-wide association (GWA) studies. BANNs are feedforward models with partially connected architectures that are based on biological annotations. This setup yields a fully interpretable neural network where the input layer encodes SNP-level effects, and the hidden layer models the aggregated effects among SNP-sets. We treat the weights and connections of the network as random variables with prior distributions that reflect how genetic effects manifest at different genomic scales. The BANNs software uses variational inference to provide posterior summaries which allow researchers to simultaneously perform (i) mapping with SNPs and (ii) enrichment analyses with SNP-sets on complex traits. Through simulations, we show that our method improves upon state-of-the-art association mapping and enrichment approaches across a wide range of genetic architectures. We then further illustrate the benefits of BANNs by analyzing real GWA data assayed in approximately 2,000 heterogenous stock of mice from the Wellcome Trust Centre for Human Genetics and approximately 7,000 individuals from the Framingham Heart Study. Lastly, using a random subset of individuals of European ancestry from the UK Biobank, we show that BANNs is able to replicate known associations in high and low-density lipoprotein cholesterol content. Author SummaryA common goal in genome-wide association (GWA) studies is to characterize the relationship between genotypic and phenotypic variation. Linear models are widely used tools in GWA analyses, in part, because they provide significance measures which detail how individual single nucleotide polymorphisms (SNPs) are statistically associated with a trait or disease of interest. However, traditional linear regression largely ignores non-additive genetic variation, and the univariate SNP-level mapping approach has been shown to be underpowered and challenging to interpret for certain trait architectures. While nonlinear methods such as neural networks are well known to account for complex data structures, these same algorithms have also been criticized as "black box" since they do not naturally carry out statistical hypothesis testing like classic linear models. This limitation has prevented nonlinear regression approaches from being used for association mapping tasks in GWA applications. Here, we present Biologically Annotated Neural Networks (BANNs): a flexible class of feedforward models with partially connected architectures that are based on biological annotations. The BANN framework uses approximate Bayesian inference to provide interpretable probabilistic summaries which can be used for simultaneous (i) mapping with SNPs and (ii) enrichment analyses with SNP-sets (e.g., genes or signaling pathways). We illustrate the benefits of our method over state-of-the-art approaches using extensive simulations. We also demonstrate the ability of BANNs to recover novel and previously discovered genomic associations using quantitative traits from the Wellcome Trust Centre for Human Genetics, the Framingham Heart Study, and the UK Biobank.

genetics↗

bioRxiv2020

Natural variation in the regulation of neurodevelopmental genes modifies flight performance in Drosophila

The winged insects of the order Diptera are colloquially named for their most recognizable phenotype: flight. These insects rely on flight for a number of important life history traits, like dispersal, foraging, and courtship. Despite the importance of flight, relatively little is known about the genetic architecture of variation for flight performance. Accordingly, we sought to uncover the genetic modifiers of flight using a measure of flies reaction and response to an abrupt drop in a vertical flight column. We conducted an association study using 197 of the Drosophila Genetic Reference Panel (DGRP) lines, and identified a combination of additive and marginal variants, epistatic interactions, whole genes, and enrichment across interaction networks. We functionally validated 13 of these candidate genes (Adgf-A/Adgf-A2/CG32181, bru1, CadN, CG11073, CG15236, CG9766, CREG, Dscam4, form3, fry, Lasp/CG9692, Pde6, Snoo) contribution to flight, two of which (fry and Snoo) also validate a whole gene analysis we introduce for the DGRP: PEGASUS_flies. Overall, our results suggest modifiers of muscle and wing morphology, and peripheral and central nervous system assembly and function are all important for flight performance. Additionally, we identified ppk23, an Acid Sensing Ion Channel (ASIC) homolog, as an important hub for epistatic interactions. These results represent a snapshot of the genetic modifiers affecting drop-response flight performance in Drosophila, with implications for other insects. It also draws connections between genetic modifiers of performance and BMP signaling and ASICs as targets for treating neurodegeneration and neurodysfunction. Author summaryInsect flight is a widely recognizable phenotype of winged insects, hence the name: flies. While fruit flies, or Drosophila melanogaster, are a genetically tractable model, flight performance is a highly integrative phenotype, making it challenging to comprehensively identify the genetic modifiers that contribute to its genetic architecture. Accordingly, we screened 197 Drosophila Genetic Reference Panel lines for their ability to react and respond to an abrupt drop. Using several computational tools, we successfully identified several additive, marginal, and epistatic variants, as well as whole genes and altered sub-networks of gene-gene and protein-protein interaction networks, demonstrating the benefits of using multiple methodologies to elucidate the genetic architecture of complex traits more generally. Many of these significant genes and variants mapped to regions of the genome that affect development of sensory and motor neurons, wing and muscle development, and regulation of transcription factors. We also introduce PEGASUS_flies, a Drosophila-adapted version of the PEGASUS platform first used in human studies, to infer gene-level significance of association based on the distribution of individual variant P-values. Our results contribute to the debate over the relative importance of individual, additive factors and epistatic, or higher order, interactions, in the mapping of genotype to phenotype.

genetics↗

bioRxiv2020

Hypoxia-induced SETX links replication stress with the unfolded protein response

The levels of hypoxia associated with resistance to radiotherapy significantly impact cancer patient prognosis. These levels of hypoxia initiate a unique transcriptional response with the rapid activation of numerous transcription factors in a background of global repression of transcription. Here, we show that the biological response to radiobiological hypoxia includes the induction of the DNA/RNA helicase SETX. In the absence of hypoxia-induced SETX, R-loop levels increase, DNA damage accumulates, and DNA replication rates decrease. SETX plays a key role in protecting cells from DNA damage induced during transcription in hypoxia. Importantly, we show that the mechanism of SETX induction is reliant on the PERK/ATF4 arm of the unfolded protein response. These data not only highlight the unique cellular response to radiobiological hypoxia, which includes both a replication stress dependent DNA damage response and an unfolded protein response but uncover a novel link between these two distinct pathways.

cancer biology↗

bioRxiv2020

A conserved neuropeptide system links head and body motor circuits to enable adaptive behavior

Neuromodulators promote adaptive behaviors that are often complex and involve concerted activity changes across circuits that are often not physically connected. It is not well understood how neuromodulatory systems accomplish these tasks. Here we show that the C. elegans NLP-12 neuropeptide system shapes responses to food availability by modulating the activity of head and body wall motor neurons through alternate G-protein coupled receptor (GPCR) targets, CKR-1 and CKR-2. We show ckr-2 deletion reduces body bend depth during movement under basal conditions. We demonstrate CKR-1 is a functional NLP-12 receptor and define its expression in the nervous system. In contrast to basal locomotion, biased CKR-1 GPCR stimulation of head motor neurons promotes turning during local searching. Deletion of ckr-1 reduces head neuron activity and diminishes turning while specific ckr-1 overexpression or head neuron activation promote turning. Thus, our studies suggest locomotor responses to changing food availability are regulated through conditional NLP-12 stimulation of head or body wall motor circuits. Impact statementInvestigation of neuromodulatory control of ethologically conserved area-restricted food search behavior shows that NLP-12 stimulation of the head motor circuit promotes food searching through the previously uncharacterized CKR-1 GPCR.

neuroscience↗

bioRxiv2020

Structure of the Visual Signaling Complex between Transducin and Phosphodiesterase 6

Heterotrimeric G proteins communicate signals from activated G protein-coupled receptors to downstream effector proteins. In the phototransduction pathway responsible for vertebrate vision, the G protein-effector complex is comprised of the GTP-bound transducin subunit (GT{middle dot}GTP) and the cyclic GMP (cGMP) phosphodiesterase 6 (PDE6), which stimulates cGMP hydrolysis to transmit signals to the optic nerve. Here we report a cryo-electron microscopy (cryoEM) structure of PDE6 complexed to GTP-bound GT. The structure reveals two GT{middle dot}GTP subunits engaging the PDE6 hetero-tetramer at both the PDE6 catalytic core and the PDE{gamma} subunits, driving extensive rearrangements to relieve all inhibitory constraints on enzyme catalysis. Analysis of the conformational ensemble in the cryoEM data highlights the dynamic nature of the contacts between the two GT{middle dot}GTP subunits and PDE6 that support an alternating-site catalytic mechanism.

biophysics↗

bioRxiv2020

Modeling the post-translational modifications and its effects in the NF-κB pathway

The transcriptional activities of NFKB1 and NFKB2 complexes are regulated by several post-translational modifications at different sites of these complexes. The post-translational modifications reported in the literature include phosphorylation, methylation, acetylation, sulphydration, nitrosylation, ubiquitination and sumoylation. We present a pathway network model with 172 proteins, 313 molecular species and 476 reactions including degradation of IkB (NFKBIA), proteolytic processing, and posttranslational modifications on p65 (RELA), RELB, p50 (NFKB1), and p52 (NFKB2) proteins. Comparing with experimental data on the over expression or knockouts of specific genes afforded qualitative agreement between model predictions and the experimental results. NFKB1:RELA complex is activated by active IKBKB, PIN1, MAP3K14 but repressed by PPARG and PDLIM2. MAP3K14 and UBE2I enhance the NFKB2:RELB activity, whereas NLRP12 is its repressor. The constitutive activation of NFKB1 complex through positive regulation of group of cytokines (IL1, IL1B and IL6) and IKK complex (alpha, beta and gamma) could be annulled by activation of PPARG, PIAS3 and P50-homodimer together instead of individual activations. Thus, the presented network pathway model has predictive utility in inferring NF-{kappa}B activity.

systems biology↗

bioRxiv2019

Bayesian Estimation of Population Size Changes by Sampling Tajima’s Trees

The large state space of gene genealogies is a major hurdle for inference methods based on Kingmans coalescent. Here, we present a new Bayesian approach for inferring past population sizes which relies on a lower resolution coalescent process we refer to as \"Tajimas coalescent\". Tajimas coalescent has a drastically smaller state space, and hence it is a computationally more efficient model, than the standard Kingman coalescent. We provide a new algorithm for efficient and exact likelihood calculations for data without recombination, which exploits a directed acyclic graph and a correspondingly tailored Markov Chain Monte Carlo method. We compare the performance of our Bayesian Estimation of population size changes by Sampling Tajimas Trees (BESTT) with a popular implementation of coalescent-based inference in BEAST using simulated data and human data. We empirically demonstrate that BESTT can accurately infer effective population sizes, and it further provides an efficient alternative to the Kingmans coalescent. The algorithms described here are implemented in the R package phylodyn, which is available for download at https://github.com/JuliaPalacios/phylodyn.

genetics↗

bioRxiv2019

Epsilon-Genic Effects Bridge the Gap Between Polygenic and Omnigenic Complex Traits

Traditional univariate genome-wide association studies generate false positives and negatives due to difficulties distinguishing associated variants from variants with spurious nonzero effects that do not directly influence the trait. Recent efforts have been directed at identifying genes or signaling pathways enriched for mutations in quantitative traits or case-control studies, but these can be computationally costly and hampered by strict model assumptions. Here, we present gene-{varepsilon}, a new approach for identifying statistical associations between sets of variants and quantitative traits. Our key insight is that enrichment studies on the gene-level are improved when we reformulate the genome-wide SNP-level null hypothesis to identify spurious small-to-intermediate SNP effects and classify them as non-causal. gene-{varepsilon} efficiently identifies enriched genes under a variety of simulated genetic architectures, achieving greater than a 90% true positive rate at 1% false positive rate for polygenic traits. Lastly, we apply gene-{varepsilon} to summary statistics derived from six quantitative traits using European-ancestry individuals in the UK Biobank, and identify enriched genes that are in biologically relevant pathways. Author SummaryEnrichment tests augment the standard univariate genome-wide association (GWA) framework by identifying groups of biologically interacting mutations that are enriched for associations with a trait of interest, beyond what is expected by chance. These analyses model local linkage disequilibrium (LD), allow many different mutations to be disease-causing across patients, and generate biologically interpretable hypotheses for disease mechanisms. However, existing enrichment analyses are hampered by high computational costs, and rely on GWA summary statistics despite the high false positive rate of the standard univariate GWA framework. Here, we present the gene-level association framework gene-{varepsilon} (pronounced "genie"), an empirical Bayesian approach for identifying statistical associations between sets of mutations and quantitative traits. The central innovation of gene-{varepsilon} is reformulating the GWA null model to distinguish between (i) mutations that are statistically associated with the disease but are unlikely to directly influence it, and (ii) mutations that are most strongly associated with a disease of interest. We find that, with our reformulated SNP-level null hypothesis, our gene-level enrichment model outperforms existing enrichment methods in simulation studies and scales well for application to emerging biobank datasets. We apply gene-{varepsilon} to six quantitative traits in the UK Biobank and recover novel and functionally validated gene-level associations.

genetics↗

bioRxiv2019

Germline features associated with immune infiltration in solid tumors

Given the clinical success of immune checkpoint blockade (ICB) across a diverse set of solid tumors, and the emerging role for different immune infiltrates in contributing to response to ICB, a comprehensive assessment of the properties that dictate immune infiltrations may reveal new biological insights and inform the development of new effective therapies. Multiple studies have examined somatic and functional immune properties associated with different tumor infiltrates; however, germline features that associate with specific immune infiltrates in cancers have been incompletely characterized. Here, we analyzed over 7 million autosomal germline variants in the TCGA cohort (5788 European-ancestry samples across 30 cancer types) and tested for pan-cancer association with established immune-related phenotypes that describe the tumor immune microenvironment. We identified: one SNP associated with the fraction of follicular helper T cells in bulk tumor; 77 unique candidate genes, some of which are involved in cytokine-mediated signaling (e.g. CNTF and TRIM34) and cancer pathogenesis (e.g. ATR and AKAP9); and subnetworks with genes that are part of DNA repair (RAD51 and XPC) and transcription elongation (CCNT2) pathways. We found a positive association between polygenic risk for rheumatoid arthritis and absolute fraction of infiltrating CD8 T cells. Overall, we identified multiple germline genetic features associated with specific tumor-immune phenotypes across cancer, and developed a framework for probing inherited features that contribute to variation in immune infiltration.

genetics↗

bioRxiv2019

Hierarchical clustering of gene-level association statistics reveals shared and differential genetic architecture among traits in the UK Biobank

Genome-wide association (GWA) studies have generally focused on a single phenotype of interest. Emerging biobanks that pair genotype data from thousands of individuals with phenotype data using medical records or surveys enable testing for genetic associations in each phenotype assayed. However, methods for characterizing shared genetic architecture among multiple traits are lagging behind. Here, we present a new method, Ward clustering to identify Internal Node branch length outliers using Gene Scores (WINGS), for characterizing shared and divergent genetic architecture among multiple phenotypes. The objective of WINGS (freely available at https://github.com/ramachandran-lab/PEGASUS-WINGS) is to identify groups of phenotypes, or \"clusters\", that share a core set of genes enriched for mutations in cases. We show in simulations that WINGS can reliably detect phenotype clusters across a range of percent shared architecture and number of phenotypes included. We then use the gene-level association test PEGASUS with WINGS to characterize shared genetic architecture among 87 case-control and seven quantitative phenotypes in 349,468 unrelated European-ancestry individuals from the UK Biobank. We identify 10 significant phenotype clusters that contain two to eight phenotypes. One significant cluster of seven immunological phenotypes is driven by seven genes; these genes have each been associated with two or more of those same phenotypes in past publications. WINGS offers a precise and efficient new application of Ward hierarchical clustering to generate hypotheses regarding shared genetic architecture among phenotypes in the biobank era.

genomics↗

bioRxiv2019

Characterization of prevalence and health consequences of uniparental disomy in four million individuals from the general population

Meiotic nondisjunction and resulting aneuploidy can lead to severe health consequences in humans. Aneuploidy rescue can restore euploidy but may result in uniparental disomy (UPD), the inheritance of both homologs of a chromosome from one parent with no representative copy from the other. Current understanding of UPD is limited to ~3,300 cases for which UPD was associated with clinical presentation due to imprinting disorders or recessive diseases. Thus, the prevalence of UPD and its phenotypic consequences in the general population are unknown. We searched for instances of UPD in over four million consented research participants from the personal genetics company 23andMe, Inc., and 431,094 UK Biobank participants. Using computationally detected DNA segments identical-by-descent (IBD) and runs of homozygosity (ROH), we identified 675 instances of UPD across both databases. Here we present the first characterization of UPD prevalence in the general population, a machine-learning framework to detect UPD using ROH, and a novel association between autism and UPD of chromosome 22.

genomics↗

bioRxiv2018

Structural insights into substrate recognition by the SOCS2 E3 ubiquitin ligase

The suppressor of cytokine signaling 2 (SOCS2) acts as substrate recognition subunit of a Cullin5 E3 ubiquitin ligase complex. SOCS2 binds to phosphotyrosine-modified epitopes as degrons for ubiquitination and proteasomal degradation, yet the molecular basis of substrate recognition has remained elusive. We solved cocrystal structures of SOCS2-ElonginB-ElonginC in complex with phosphorylated peptides from substrates growth hormone receptor (GHR-pY595) and erythropoietin receptor (EpoR-pY426) at 1.98 A{square} and 2.69 A{square}, respectively. Both peptides bind in an extended conformation recapitulating the canonical SH2 domain-pY pose, yet capture different conformations of the EF loop via specific hydrophobic interactions. The flexible BG loop, for the first time fully defined in the electron density, does not contact the substrate degrons directly. Cancer-associated SNPs located around the pY pocket weaken substrate-binding affinity in biophysical assays. Our findings reveal insights into substrate recognition and specificity by SOCS2, and provide a blueprint for small molecule ligand design.

biochemistry↗

bioRxiv2017

Localization of adaptive variants in human genomes using averaged one-dependence estimation

Statistical methods for identifying adaptive mutations from population-genetic data face several obstacles: assessing the significance of genomic outliers, integrating correlated measures of selection into one analytic framework, and distinguishing adaptive variants from hitchhiking neutral variants. Here, we introduce SWIF(r), a probabilistic method that detects selective sweeps by learning the distributions of multiple selection statistics under different evolutionary scenarios and calculating the posterior probability of a sweep at each genomic site. SWIF(r) is trained using simulations from a user-specified demographic model and explicitly models the joint distributions of selection statistics, thereby increasing its power to both identify regions undergoing sweeps and localize adaptive mutations. Using array and exome data from 45 {ddagger}Khomani San hunter-gatherers of southern Africa, we identify an enrichment of adaptive signals in genes associated with metabolism and obesity. SWIF(r) provides a transparent probabilistic framework for localizing beneficial mutations that is extensible to a variety of evolutionary scenarios.

evolutionary biology↗