Biology subjects

Pierce, B.

Publications and source records attributed to Pierce, B..

16 recordsLinked to original sources

bioRxiv2026

Activation pathway of the human voltage-gated proton channel

Many protein functions are coupled to proton transfer, yet the underlying mechanisms remain extremely difficult to resolve by experiment or conventional molecular dynamics (MD) simulations. The human voltage-gated proton channel HV1 (hHV1) plays key roles in human physiology, but how voltage and pH together drive its activation remains unresolved. Experimental structures of hHV1 are limited to the X-ray structure of a mouse chimera and NMR models of a truncated hHV1, both representing an intermediate resting state. Here we determine the activation pathway of hHV1, together with structural models of its resting and activated states, using an approach that integrates all-atom continuous constant-pH MD (CpHMD), weighted-ensemble (WE) path sampling, and AlphaFold modeling. Starting from an AlphaFold model selected for consistency with resting-state experimental data, over 100 s of simulations reveal a mechanism in which transmembrane voltage, assisted by proton titration, drives an outward displacement of the transmembrane segment S4 accompanied by a one-click reorganization of countercharge pairings. These events are enabled by a delicately balanced electrostatic landscape and pH-modulated breathing motions in the resting state that prime the channel for protonation-state switches upon voltage sensing, a principle that may underlie other proton-coupled voltage-gating processes. The simulations also uncover lipid-binding sites at which POPG selectively stabilizes the S4-up conformation, whereas POPE can stabilize either state. These predictions are validated against extensive electrophysiology, mutagenesis data, and new smFRET experiments, which support the one-click S4 displacement and the lipid effects. WE-CpHMD, implemented in the AMBER package, is applicable to other proton-coupled processes that remain elusive.

biophysics↗

bioRxiv2026

Incorporation of native HCV E1E2 into a nanoparticle vaccine platform

Development of an effective HCV vaccine requires the induction of both broadly neutralizing antibodies (bnAbs) and a robust cellular response. One issue that has arisen is that HCV subunit vaccines have limited immunogenicity, thus requiring multivalent formats in order to elicit a robust anti-HCV immune response. Toward that end, nanoparticle vaccines possess the ability to facilitate a controlled multivalent presentation and trafficking to lymph nodes, where they can interact with both arms of the immune system. Here, we used a soluble, secreted form of E1E2 (sE1E2) to assemble native E1E2 into a nanoparticle platform using a post-purification coupling assembly system. Nanoparticles were assembled by purifying sE1E2 containing a C-terminal SpyTag and an mi3-SpyCatcher fusion separately and covalently coupling the components via incubation. Free sE1E2-SpyTag was removed from nanoparticle preparations via gel filtration. The sE1E2-mi3 nanoparticles are fully competent to bind conformation-dependent bnAbs, indicating retention of a native assembly in the nanoparticle format. Electron microscopy analysis showed a clear incorporation of sE1E2 on the surface of the nanoparticle. Immunogenicity of sE1E2-mi3 nanoparticles was examined relative to sE1E2 alone and membrane-bound E1E2 (mbE1E2) following inoculation of groups of CD1 mice. Assessment of the immunogenicity of the sE1E2-mi3 nanoparticles showed that the nanoparticle assembly has a similar immunogenicity profile to that of mbE1E2 after only a prime and one boost, and overall superior to sE1E2. This proof-of-principle study sets the stage for further exploration of nanoparticles and other multivalent platforms for the development of E1E2-based vaccines. ImportanceHepatitis C virus infects approximately 50 million people, and at present no effective HCV vaccine exists. Due to the high sequence variability of HCV and the resulting difficulty in developing a vaccine that elicits a broadly neutralizing response, multiple efforts are underway to enhance the immunogenicity of HCV vaccine candidates. In this study, we incorporated native soluble, secreted E1E2 (sE1E2) into a 60-mer nanoparticle via the SpyTag-SpyCatcher system and covalent isopeptide bond attachment using the purified components. These nanoparticles are antigenically intact and elicit a neutralizing antibody response at an earlier time point in the immunization regimen than the corresponding subunit vaccine. These studies show that a well-characterized sE1E2 platform compatible with multiple genotypes can be coupled to nanoparticles for use as a vaccine candidate.

microbiology↗

bioRxiv2026

Benchmarking Deep Learning Predictions of Mutation-Induced Fold Switching

Many proteins are known to adopt multiple distinct folded states which are often associated with key functional behavior. A predictive understanding of the properties of such fold-switching or metamorphic proteins can provide insights into protein dynamics and energetics, and enable the design of complex protein functions and molecular machines. Recently developed deep learning modeling tools, including AlphaFold, have led to dramatic increases in accuracy for prediction of protein structures from sequence, but their performance for prediction of point mutant effects or fold switching is unclear. Here we present a systematic NMR-characterized dataset of mutants of the GA/GB model fold-switching system and use it to evaluate whether current structure prediction and design methods can predict mutation-induced changes in fold state. We measured fold-state populations for variants at three key positions that differentially stabilize the 3, 4{beta}+, mixed, or unfolded states, generating a quantitative experimental benchmark for mutation-level fold switching. Using this benchmark to assess and compare a panel of deep learning and physics-based modeling and design algorithms, we found that this benchmark revealed variable and position-dependent performance across methods, with certain AlphaFold2-based algorithms were able to predict mutant effects at individual sites, indicating some understanding of physical effects of residue substitutions. Additional comparisons of predictions with experimentally measured stability changes further highlighted position-dependent success and general challenges for predictive algorithms. Together, this study provides a new benchmark for mutation-induced fold switching and reveals the current capabilities and limitations of deep learning models for predicting mutation-dependent protein conformational states.

biophysics↗

bioRxiv2026

Benchmarking AlphaFold and related deep learning approaches for modeling antibody and TCR antigen recognition

Determining the structural basis of antigen recognition by antibodies and T cell receptors (TCRs) provides critical insights into effective immune targeting and can inform design of biotherapeutics and vaccines. Accurate computational modeling of antibodies and TCRs in complex with their targets poses a major challenge for predictive methods, including AlphaFold, which is generally accurate for modeling protein complexes but has shown limited success for immune recognition. In this study we assessed the performance of AlphaFold2, AlphaFold3, increased sampling protocols, and related deep learning methods for modeling antibody-protein, antibody-peptide, and TCR-peptide-major histocompatibility complex (pMHC) recognition. We show that increased sampling and AlphaFold3 generally improve performance relative to default sampling and AlphaFold2, however predictive accuracy and improvement levels varied considerably among interface classes, with antibody-peptide complexes representing a challenge despite their small antigen size. Comparing per-case success across methods showed some complementarity, indicating opportunities for increased success through model pooling approaches, for instance increasing antibody-peptide near-native success from 41% to 59%. Analysis of AlphaFold confidence scores and modeling of a noncanonical complex provided further insights into predictive performance. These results highlight considerations for predictive antibody and TCR complex modeling efforts, while revealing key distinctions among protocols, scoring, and immune complex classes.

bioinformatics↗

bioRxiv2026

eQTM (expression quantitative trait methylation) Atlas: a comprehensive resource of over 11 million DNA methylation-gene expression associations through across 11 tissues and 4 diseases

MotivationEpigenome-wide association studies (EWAS) have identified numerous DNA methylation (DNAm) CpG sites associated with complex traits and diseases, but interpretation of those CpG sites remains challenging because in EWAS, CpGs are mostly linked to nearby genes based only on genomic proximity. Expression quantitative trait methylation (eQTM) analyses connect DNAm CpGs with statistically associated gene expression levels. However, a comprehensive, searchable resource integrating eQTMs across diverse tissues and disease contexts has been lacking. ResultsWe developed the eQTM Atlas, a web-based resource that manually curates more than 11 million DNAm-gene expression associations from eight cohorts, covering 11 tissue types, four broad disease contexts, 173,886 unique CpG probes and 20,231 unique genes. The Atlas supports gene- or CpG-searches by tissue or disease type and finding associated CpG or genes, visualization of cis- and trans-eQTMs through genome browser, heatmap interfaces across various tissues, and cohort-level data downloads. By integrating eQTM results with EWAS resources, the eQTM Atlas enables users to connect disease- or trait-associated CpGs to statistically associated genes rather than relying solely on proximity-based gene annotation, supporting functional interpretation of EWAS findings and generation of disease-specific regulatory hypotheses. Availability and implementationThe eQTM Atlas is freely available at https://shiny.crc.pitt.edu/eqtm_browser/. The web interface is implemented in R Shiny and hosted through the University of Pittsburgh Center for Research Computing (CRC). Source code is available at https://github.com/ads303/eQTM-Atlas.

genetics↗

bioRxiv2026

Gain and Loss of Acquired Doxycycline Resistance in Lactiplantibacillus plantarum: An Adaptive Laboratory Evolution Study

Probiotics interact in a mutualistic way with humans. Exposure of probiotics to sublethal concentrations of commonly prescribed antibiotics can cause resistance to arise. Over 1000 generations, we performed an adaptive laboratory evolution (ALE) experiment to determine if the fitness of the probiotic Lactobacillus plantarum is altered by exposure to doxycycline (DOX) at 1/10 MIC. Compared to the original culture, L. plantarum exposed to sublethal DOX acquired modest resistance ([~]4-fold) over time. When the selection pressure was removed, resistance was lost rapidly, in [~]50 generations. This suggests that resistance, once acquired, is not fixed. The mechanism by which resistance is acquired and subsequently lost was investigated by whole genome sequencing (WGS). Analysis of the single nucleotide variants (SNVs) identified in the WGS of the generation 1000 DOX-treated cultures reveal 16 distinct variants across 15 genes. Two of these variants are in the rpsJ gene, which encodes ribosomal protein s10, a component of the 30S ribosomal subunit, and result in non-synonymous mutations (H56Y and S94N). This gene has been previously reported to harbor mutations associated with tetracycline class resistance, including DOX and tigecycline. WGS of archived cells from generations 350 and 750 reveals that one of these rpsJ variants (H56Y) arises early in the experiment. Additional rpsJ variants at position 57 (K57M, K57I) could be identified within intermediate generations. Most rpsJ SNVs identified from the WGS could be verified by colony PCR and Sanger sequencing. Importantly, no rpsJ variants are observed in the original culture sequences or in same-generation controls. ImportanceProbiotics are beneficial bacteria often found in fermented foods and beverages. When consumed, they can colonize the gut and provide several advantages. Antibiotics, even at levels too low to kill bacteria, can exert pressure that leads to antibiotic resistance. Though well-studied in harmful bacteria, it is less understood what effects this may have on probiotics. Using experimental evolution, we grew a popular probiotic, Lactiplantibacillus plantarum, in the presence of a low concentration of doxycycline, one of the most highly prescribed antibiotics in the US. Over the course of 5 months, roughly a thousand generations, the probiotic became [~]4-fold more resistant; once we removed the antibiotic and continued the experiment, this resistance disappeared rapidly. DNA sequencing identified variants in the rpsJ gene, which encodes a protein important for translation. As doxycycline interferes with this process, the rpsJ variants may underlie the emergence of modest resistance we observe.

microbiology↗

bioRxiv2025

Dynamic S-acylation of GSDMA regulates pyroptosis

GSDMA, the primary member of the gasdermin family found in the skin, is critical for pathogen-induced pyroptosis during infection. Recent studies revealed that another gasdermin, GSDMD, undergoes palmitoylation during pyroptosis. However, whether and how the other gasdermin members undergo lipid modification remains poorly understood. Here, we demonstrate that GSDMA is S-acylated at the conserved cysteine residues in its N-terminal domain. We show that S-acylation of GSDMA promotes pyroptosis by facilitating its membrane anchoring and protein oligomerization, a mechanism distinct from the palmitoylation of GSDMD at the N-terminal C191 residue. Additionally, we present evidence that recombinant proteins of GSDMA and GSDMD can undergo S- acylation in vitro via direct interaction with palmitoyl-CoA, suggesting they potentially possess auto-acylation capacity. Furthermore, we identify ABHD17A as one of the deacylating enzymes that regulate the dynamic fatty acylation cycle of GSDMA. Overall, our studies reveal new molecular mechanisms underlying GSDMA function through S- acylation and underscore its important role in regulating pyroptosis mediated by GSDMA. HighlightsO_LIGSDMA is S-acylated at N-terminal conserved cysteine residues. C_LIO_LIGSDMA S-acylation facilitates membrane anchoring and protein oligomerization. C_LIO_LIGSDMA and GSDMD recombinant proteins can undergo S-acylation in vitro C_LIO_LIThe ABHD17A enzyme is involved in GSDMA deacylation. C_LI

biochemistry↗

bioRxiv2025

Computational Prioritization of T Cell Epitopes to Overcome HLA Restriction and Antigenic Diversity in Plasmodium falciparum

Developing a highly effective malaria vaccine remains challenging due to Plasmodium falciparums antigenic diversity and human leukocyte antigen (HLA) polymorphisms, which complicate vaccine antigen selection and limit immune protection. The first recommended malaria vaccine, RTS,S, provides only partial, allele-specific protection with waning immunity over time, and the more recently developed R21 vaccine will likely encounter the same hurdles. To address these challenges, we developed a computational tool that integrates P. falciparum sequence diversity, predicted T cell epitope-HLA binding affinities, and HLA allele frequencies from sub-Saharan Africa to identify conserved, immunogenic epitopes with broad population coverage. We analyzed 42 P. falciparum proteins, previously identified as vaccine candidate antigens, and generated consensus sequences using data from 18 African countries, and then incorporated HLA allele frequencies from 24 sub-Saharan populations. CD8+ and CD4+ T cell epitopes were predicted using NetMHCpan-4.1 and NetMHCIIpan-4.1. Our novel tool, T cell Epitope Nomination (TEpiNom), used greedy optimization to filter and select epitopes based on epitope sequence conservation (>95%), binding affinity (median rank <10%), and broad HLA coverage, minimizing redundancy to reduce immune escape risk. Our tool identified 2,265 MHC I and 1,992 MHC II conserved epitopes spanning pre-erythrocytic, erythrocytic, and sexual stage proteins. Key MHC I epitopes from pre-erythrocytic antigens HSP70-2, SLARP/SAP1, p36, FabZ, LISP1, LSA1, UIS3, p24_2, PL, and FabG achieved near 100% HLA-A, HLA-B, and HLA-C coverage, and MHC II epitopes from pre-erythrocytic, erythrocytic, or sexual antigens provided 98.5%-100% coverage for a given parasite life stage. This strategy advances malaria vaccine design by integrating epitope promiscuity and multistage antigen selection to support broad, durable protection and identify promising multi-epitope malaria vaccine candidates for subsequent experimental validation. Our computational framework is adaptable for vaccine development against other genetically diverse and immunologically evasive pathogens.

bioinformatics↗

bioRxiv2025

Evaluation of Alphafold modeling for elucidation of nanobody-peptide epitope interactions

Models of Ab-antigen complexes can be used to understand interaction mechanisms and for improving affinity. This study evaluates the use of the protein structure prediction algorithm AlphaFold (AF) for exploration of interactions between peptide epitope tags and the smallest functional antibody fragments, nanobodies (Nbs). Although past studies of AF for modeling antibody-target (antigen) interactions suggested modest algorithm performance, those were primarily focused on Ab-protein interactions, while the performance and utility of AF for Nb-peptide interactions, which are generally less complex due to smaller antigens, smaller binding domains, and fewer chains, is less clear. In this study we evaluated the performance of AF for predicting the structures of Nbs bound to experimentally validated, linear, short peptide epitopes (Nb-tag pairs). We expanded the pool of experimental data available for comparison through crystallization and structural determination of a previously reported Nb-tag complex (Nb127). Models of Nb-tag pair structures generated from AF were variable with respect to consistency with experimental data, with good performance in just over half (4 out of 6) of cases. Even among Nb-tag pairs successfully modeled in isolation, efforts to translate modeling to more complex contexts failed, suggesting an underappreciated role of the size and complexity of inputs in AF modeling success. Finally, the model of a Nb-tag pair with minimal previous characterization was used to guide the design of a peptide-electrophile conjugate that undergoes covalent crosslinking with Nb upon binding. These findings highlight the utility of minimized antibody and antigen structures to maximize insights from AF modeling.

biochemistry↗

bioRxiv2025

17β-estradiol status alters NMDAR function and antipsychotic-like activity in female rats

Low 17{beta}-estradiol (E2) in females of reproductive age, and marked E2 decline with menopause, contributes to heightened symptom severity in schizophrenia (i.e. cognitive dysfunction) and diminished response to antipsychotic medications. However, the underlying mechanisms are unknown. N-methyl-D-aspartate receptor (NMDAR) hypofunction contributes to the pathophysiology of schizophrenia, yet impact of E2 depletion on NMDAR function is not well characterized. Quantitative electroencephalography (qEEG), specifically gamma power, is a well-established functional readout of cortical activity that is elevated in patients with schizophrenia and is sensitive to alterations in NMDAR function. Using qEEG and touchscreen cognitive assessments, present studies investigated the effects of E2 on NMDAR function by administering MK-801 (NMDAR antagonist) to ovariectomized rats with or without E2 implants (Ovx+E and Ovx, respectively). Ovx rats were more sensitive to MK-801-induced elevations in gamma power and attentional impairments compared to Ovx+E rats. Further investigation revealed these effects were mediated by reduced synaptic GluN2A expression. Consistent with clinical reports, olanzapine (second-generation antipsychotic) was less effective in mitigating MK-801-induced elevations in gamma power in Ovx rats. Lastly, we examined antipsychotic-like activity of a Group II metabotropic glutamate receptor (mGlu2/3) positive allosteric modulator (PAM), SBI-0646535, as a novel therapeutic in E2-deprived conditions. SBI-0646535 reversed MK-801-induced elevations in gamma power equally regardless of E2 status. Collectively, these studies established a relationship between E2 deprivation and NMDAR function that is in part GluN2A-dependent, supporting the notion that E2 deprivation increases susceptibility to NMDAR hypofunction. This highlights the need to examine age/hormone-specific factors when considering antipsychotic response and designing novel pharmacotherapies.

animal behavior and cognition↗

bioRxiv2023

Fitness seascapes promote genetic heterogeneity through spatiotemporally distinct mutant selection windows

Mutant selection windows (MSWs), the range of drug concentrations that select for drug-resistant mutants, have long been used as a model for predicting drug resistance and designing optimal dosing strategies in infectious disease. The canonical MSW model offers comparisons between two subtypes at a time: drug-sensitive and drug-resistant. In contrast, the fitness landscape model with N alleles, which maps genotype to fitness, allows comparisons between N genotypes simultaneously, but does not encode continuous drug response data. In clinical settings, there may be a wide range of drug concentrations selecting for a variety of genotypes. Therefore, there is a need for a more robust model of the pathogen response to therapy to predict resistance and design new therapeutic approaches. Fitness seascapes, which model genotype-by-environment interactions, permit multiple MSW comparisons simultaneously by encoding genotype-specific dose-response data. By comparing dose-response curves, one can visualize the range of drug concentrations where one genotype is selected over another. In this work, we show how N-allele fitness seascapes allow for N *2N-1 unique MSW comparisons. In spatial drug diffusion models, we demonstrate how fitness seascapes reveal spatially heterogeneous MSWs, extending the MSW model to more accurately reflect the selection fo drug resistant genotypes. Furthermore, we find that the spatial structure of MSWs shapes the evolution of drug resistance in an agent-based model. Our work highlights the importance and utility of considering dose-dependent fitness seascapes in evolutionary medicine. Author SummaryDrug resistance in infectious disease and cancer is a major driver of mortality. While undergoing treatment, the population of cells in a tumor or infection may evolve the ability to grow despite the use of previously effective drugs. Researchers hypothesize that the spatial organization of these disease populations may contribute to drug resistance. In this work, we analyze how spatial gradients of drug concentration impact the evolution of drug resistance. We consider a decades-old model called the mutant selection window (MSW), which describes the drug concentration range that selects for drug-resistant cells. We show how extending this model with continuous dose-response data, which describes how different types of cells respond to drug, improves the ability of MSWs to predict evolution. This work helps us understand how the spatial organization of cells, such as the organization of blood vessels within a tumor, may promote drug resistance. In the future, we may use these methods to optimize drug dosing to prevent resistance or leverage known vulnerabilities of drug-resistant cells.

evolutionary biology↗

bioRxiv2021

An Extended Coatomer Binding Motif in the SARS-CoV-2 Spike Protein

{beta}-Coronaviruses such as SARS-CoV-2 hijack coatomer protein-I (COPI) for spike protein retrograde trafficking to the progeny assembly site in endoplasmic reticulum-Golgi intermediate compartment (ERGIC). However, limited residue-level details are available into how the spike interacts with COPI. Here we identify an extended COPI binding motif in the spike that encompasses the canonical K-x-H dibasic sequence. This motif demonstrates selectivity for COPI subunit. Guided by an in silico analysis of dibasic motifs in the human proteome, we employ mutagenesis and binding assays to show that the spike motif terminal residues are critical modulators of complex dissociation, which is essential for spike release in ERGIC. COPI residues critical for spike motif binding are elucidated by mutagenesis and crystallography and found to be conserved in the zoonotic reservoirs, bats, pangolins, camels, and in humans. Collectively, our investigation on the spike motif identifies key COPI binding determinants with implications for retrograde trafficking.

biochemistry↗

bioRxiv2020

Oral administration of a single anti-CfaE nanobody provides broadly cross-protective immunity against major pathogenic Enterotoxigenic Escherichia coli strains

Enterotoxigenic Escherichia coli (ETEC) is estimated to cause approximately 380,000 deaths annually during sporadic or epidemic outbreaks worldwide. There is currently no vaccine licensed to prevent ETEC. Development of prophylaxis against ETEC is challenging due to the vast heterogeneity of the ETEC strains. The discovery of nanobodies has emerged as a successful new biologics in treating mucosal infectious disease as nanobodies can recognize conserved epitopes on hypervariable pathogens. In this study, we performed large screens using immunized llamas and a naive nanobody yeast display library against adhesins of colonization factors. Cross-protective nanobodies were selected with in vitro activities inhibiting mannose-resistant hemagglutination (MRHA) against all eleven major pathogenic ETEC strains. Oral administration of nanobodies led to significant reduction of bacterial colonization in animals challenged with multiple ETEC strains. Structural analysis revealed novel conserved epitopes as critical structural features for pan-ETEC vaccine design. Two of the lead nanobodies, 2R215 and 1D7, were further engineered as trimer or fused with human IgA Fc-fragments as fusionbodies. Oral administration of the trimers or fusionbodies protected mice from infection at a much lower dose compared to the monomeric format. Importantly, fusionbodies prevented infection as a pre-treatment when administrated 2 hours before ETEC challenge to the animals. Together, our study provides the first proof of concept that oral administration of a single nanobody could confer broad protection against major pathogenic ETEC strains. Technological advances in large-scale manufacturing of biological proteins in plants and microorganisms will make nanobody-based immunotherapy a potent and cost-effective prophylaxis or treatment for ETEC.

microbiology↗

bioRxiv2020

Structure-based design of hepatitis C virus E2 glycoprotein improves serum binding and cross-neutralization

An effective vaccine for hepatitis C virus (HCV) is a major unmet need, and it requires an antigen that elicits immune responses to key conserved epitopes. Based on structures of antibodies targeting HCV envelope glycoprotein E2, we designed immunogens to modulate the structure and dynamics of E2 and favor induction of bNAbs in the context of a vaccine. These designs include a point mutation in a key conserved antigenic site to stabilize its conformation, as well as redesigns of an immunogenic region to add a new N-glycosylation site and mask it from antibody binding. Designs were experimentally characterized for binding to a panel of human monoclonal antibodies (HMAbs) and the coreceptor CD81 to confirm preservation of epitope structure and preferred antigenicity profile. Selected E2 designs were tested for immunogenicity in mice, with and without hypervariable region 1, which is an immunogenic region associated with viral escape. One of these designs showed improvement in polyclonal immune serum binding to HCV pseudoparticles and neutralization of isolates associated with antibody resistance. These results indicate that antigen optimization through structure-based design of the envelope glycoproteins is a promising route to an effective vaccine for HCV.

microbiology↗

bioRxiv2019

Primo: integration of multiple GWAS and omics QTL summary statistics for elucidation of molecular mechanisms of trait-associated SNPs and detection of pleiotropy in complex traits

To provide a comprehensive mechanistic interpretation of how known trait-associated SNPs affect complex traits, we propose a method - Primo - for integrative analysis of GWAS summary statistics with multiple sets of omics QTL summary statistics from different cellular conditions or studies. Primo examines SNPs association patterns to complex and omics traits. In gene regions harboring known susceptibility loci, Primo performs conditional association analysis to account for linkage disequilibrium. Primo allows for unknown study heterogeneity and sample correlations. We show two applications using Primo to examine the molecular mechanisms of known susceptibility loci and to detect and interpret pleiotropic effects.

genomics↗

bioRxiv2016

Co-occurring eQTLs and mQTLs: detecting shared causal variants and shared biological mechanisms

Inherited genetic variation impacts local gene expression and DNA methylation in humans. Expression and methylation quantitative trait loci (cis-eQTLs and cis-mQTLs) often occur at the same genomic location, suggesting a common causal variant and shared mechanism. Using DNA and RNA from peripheral blood of Bangladeshi individuals, we use \"co-localization\" methods to identify 3,695 eQTL-mQTL pairs that are likely to share a causal variant. Using partial correlation analysis and mediation analysis, we identify >500 pairs with evidence of a causal relationships between expression and methylation (i.e., shared mechanism) with many additional pairs that we are underpowered to detect. These co-localized pairs are enriched for SNPs showing opposite effects on expression and methylation, although a many affect multiple CpGs in opposite directions. Evidence of shared SNP-age interaction also supports shared mechanisms for two eQTL-mQTL pairs. This work demonstrates the pervasiveness of co-regulated expression and methylation traits in the human genome. This approach can be applied to other types of molecular QTLs to enhance our understanding of regulatory mechanisms.

genomics↗