Biology subjects

Parker, S.

Publications and source records attributed to Parker, S..

17 recordsLinked to original sources

bioRxiv2026

Single-nucleus multi-omics reveals reversible molecular signatures in skeletal muscle of patients with heart failure with preserved ejection fraction

Heart failure with preserved ejection fraction (HFpEF) is a complex systemic syndrome characterized by severe exercise intolerance. In addition to cardiac dysfunction, abnormalities in peripheral skeletal muscle are key contributors to reduced exercise capacity in HFpEF. Multiple studies show that exercise training and caloric restriction improve exercise capacity and quality of life in HFpEF patients. To map the HFpEF-associated molecular signatures in skeletal muscle and the effect of lifestyle intervention, we generated single-nucleus (sn) multiome (joint snRNA-seq and snATAC-seq) profiles of 140 biopsies. Our study included 95 individuals: 25 healthy controls and 70 HFpEF patients, of whom 45 had paired biopsies collected before and after a supervised diet and exercise intervention. From 132,568 nuclei, we resolved 15 skeletal muscle cell types and identified HFpEF-specific remodeling, including type 1 fiber and pericyte depletion and type 2x fiber enrichment, reflecting the cellular basis of exercise intolerance. Transcriptional changes in HFpEF muscle indicated dysregulation of metabolic pathways, stress-responsive programs, cytoskeletal integrity, and calcium signaling. Transcription factor (TF) motifs enriched in differentially accessible chromatin corresponded to concordantly differentially expressed TF-genes, revealing coordinated regulatory programs underlying these changes. Importantly, HFpEF-related changes in both chromatin accessibility and transcription were partially reversible following diet and exercise. Integrative multiomic velocity analyses revealed increased coordinated repression in both chromatin accessibility and transcription of calcium handling genes in HFpEF. HFpEF-linked chromatin was enriched for HF and cardiometabolic GWAS signals, and cross-modal analyses further nominated candidate causal variants and effector genes linking genetic risk to peripheral muscle dysfunction in HFpEF. Together, these findings highlight the molecular mechanisms underlying skeletal muscle remodeling in HFpEF and identify pathways responsive to lifestyle interventions.

genomics↗

bioRxiv2026

Erosion of regenerative regulation: age-associated shifts in the skeletal muscle fiber epigenome and transcriptome

Skeletal muscle aging is characterized by the deterioration of muscle function, which can lead to negative quality-of-life outcomes including frailty and sarcopenia. While understanding the mechanisms of this process is increasingly important as the global population ages, previous molecular studies of skeletal muscle aging have been limited by statistical power and cell type resolution. In this study, we analyzed single-nucleus gene expression and chromatin accessibility data from 287 human skeletal muscle samples from individuals aged 20-79 years to explore sex- and cell type- specific aging effects. Across 467,126 nuclei from 13 cell types, we identify 384 age-associated genes and 4,061 age-associated chromatin regions. These age-associated molecular features are enriched for functional pathways, including metabolic processes, cell-to-cell communication, and senescence Kyoto Encyclopedia of Genes and Genomes KEGG terms. Age-associated closing chromatin was more common across fiber types and sexes than opening chromatin, and was enriched in active enhancer regions while depleted for active transcription start sites. We observe enrichment for specific transcription factor motifs in closing chromatin, including those of glucocorticoid and androgen receptors, both of which play a key role in the maintenance of healthy skeletal muscle. Together, these findings identify an age-associated regulatory shift, largely invisible in matched transcriptomic data, characterized by closing chromatin which reduces accessibility to hormone receptor binding sites and enhancer regions in the muscle fiber epigenome.

bioinformatics↗

bioRxiv2026

Genome writing and Targeted Delivery of the NKX6-3/ANK1 gene cluster and its Type 2 Diabetes GWAS Variants to Human iPSCs

Genome-wide association studies (GWAS) identified over 600 loci containing single-nucleotide polymorphisms (SNPs) associated with type 2 diabetes (T2D), most of which reside in non-coding regions. Among the set of T2D SNPs, linking causal genome variants to disease risk experimentally has remained a challenge; however, advances in synthetic mammalian genome writing techniques now enable the delivery of multiple haplotypes to human induced pluripotent stem cells (hiPSCs) to create a series of isogenic cell lines that can be differentiated and phenotyped in vitro. Here, to begin efforts in dissecting a T2D GWAS locus, we engineered an NKX6-3/ANK1 gene cluster knockout hiPSC line and introduced a landing pad facilitating the delivery of synthetic haplotype payloads. We built four haplotypes, including several that are not observed in nature, containing risk SNPs spanning the NKX6-3/ANK1 gene cluster using a method called "variant Switching Auxotrophic markers for Integration" (vSwAP-In), and integrated them precisely into hiPSCs. NKX6-3/ANK1 deletion blocked pancreatic progenitor and skeletal muscle differentiation, suggesting that NKX6-3 and ANK1 are required for early pancreatic and skeletal muscle development, and perhaps related to the existence of two nonoverlapping sets of SNPs in linkage disequilibrium that associate with the expression of the two adjacent genes. When NKX6-3/ANK1 T2D "Risk" haplotypes were reintroduced, skeletal muscle and pancreatic progenitor differentiation capabilities were restored. ANK1 expression was elevated in the ANK1 Risk and All-Risk haplotypes compared to the NKX6-3 Risk and Non-Risk haplotypes, establishing a functional experimental platform to examine risk SNP clusters in their native contexts. Overall, this work establishes a platform for the dissection of GWAS loci using synthetic haplotype genomics in hiPSCs. Significance StatementGenome-wide association studies have been used to identify disease-associated SNPs; however, most SNPs lie in non-coding regions, making functional experimentation difficult to perform. Using vSwAP-In, a yeast-based DNA variant-building method, and mSwAP-In, a mammalian genome engineering approach, we establish a platform for functional GWAS dissection in hiPSCs. This platform allows us to build DNA harboring virtually any combination of disease-risk SNPs, allowing for functional characterization of SNPs without the limitations of linkage disequilibrium. We demonstrate this approach using a Type 2 diabetes GWAS gene cluster, NKX6-3/ANK1.

genetics↗

bioRxiv2025

Acute session of three endurance exercise intensities alters subcutaneous adipose tissue transcriptome in regular exercisers

The primary aim of this study was to compare the acute effects of three exercise intensities on abdominal subcutaneous adipose tissue (aSAT) transcriptome in regular exercisers. A total of 45 adults who exercise regularly were assigned to perform a single session of either low-intensity continuous (LOW; 60min at 30% VO2max; n=15), moderate-intensity continuous (MOD; 45min at 65% VO2max; n=15), or high-intensity interval exercise (HIGH; 10x1min at 90% VO2max interspersed with 1min active recovery; n=15). aSAT biopsy samples were collected before and 1.5hours after the exercise session for bulk RNA sequencing and targeted protein immunoassays. HIGH upregulated genes involved in cytokine secretion, insulin signaling, and proteolysis while MOD and LOW upregulated genes regulating ECM remodeling, ribosome biogenesis, and oxidative phosphorylation pathways. Exercise-induced changes in aSAT angiogenic, MAPK cascade, and clock genes, ERK protein phosphorylation, and circulating cytokines were similar after all three exercise treatments. Network analysis identified exercise-responsive gene clusters linked to cardiometabolic health traits. Cell-type analysis highlighted a heterogeneous response of aSAT cell types to exercise, with distinct patterns observed across exercise intensities. Collectively, our data characterizes early responses in aSAT after a single session of exercise. Because adaptations to exercise training stem from an accrual of responses after each session of exercise, these early responses to exercise are likely important contributors to the long-term structural and functional changes that occur in adipose tissue in response to exercise training.

physiology↗

bioRxiv2024

Building a literature knowledge base towards transparent biomedical AI

As artificial intelligence (AI) continues to advance and scale up in biomedical research, concerns about AIs trustworthiness and transparency have grown. There is a critical need to systematically bring accurate and relevant biomedical knowledge into AI applications for transparency and provenance. Knowledge graphs have emerged as a powerful tool that integrates heterogeneous knowledge by explicitly describing biomedical knowledge as entities and relationships between entities. However, PubMed, the largest and most comprehensive repository of biomedical knowledge, exists primarily as unstructured text and is under utilized for advanced machine learning tasks. To address the challenge, we developed LiteralGraph, a computational framework to extract biomedical terms and relationships from PubMed literature into a unified knowledge graph. Using this framework, we established the Genomic Literature Knowledge Base (GLKB), which consolidates 14,634,427 biomedical relationships between 3,276,336 biomedical terms from over 33 million PubMed abstracts and nine well-established biomedical repositories. The database is coupled with RESTful APIs and a user-friendly web interface that makes it accessible to researchers for various usages. We demonstrated the broad utility of GLKB towards transparent AI in three distinct application scenarios. In the LLM grounding scenario, we developed a Retrieval Augmented Generation (RAG) agent to reduce LLM hallucination in biomedical question answering. In the hypothesis generation scenario, we elucidated the potential functions of RFX6 in type 2 diabetes (T2D) using the vast evidence from PubMed articles. In the machine learning scenario, we utilized GLKB to provide semantic knowledge in predictive tasks and scientific fact-checking.

bioinformatics↗

bioRxiv2024

Extracellular Vesicles heterogeneity through the lens of multiomics

Extracellular vesicles (EVs) are heterogenous in size, biogenesis, cargo and function. Beside small EVs, aggressive tumor cells release a population of particularly large EVs, namely large oncosomes (LO). This study provides the first resource of label-free quantitative proteomics of LO and small EVs obtained from distinct cancer cell types (prostate, breast, and glioma). This dataset was integrated with a SWATH Proteomic assay on LO, rigorously isolated from the plasma of patients with metastatic prostate cancer (PC). Proteins enriched in LO, which were identified also at the RNA level, and found in plasma LO significantly correlated with PC progression. Single EV RNA-Seq of the PC cell-derived LO confirmed some of the main findings from the bulk RNA-Seq, providing first evidence that single cell technologies can be successfully applied to EVs. This multiomics resource of cancer EVs can be leveraged for developing a multi-analyte approach for liquid biopsy.

cancer biology↗

bioRxiv2023

17 β-estradiol impedes aortic root dilation and rupture in male Marfan mice

Marfan syndrome causes a hereditary form of thoracic aortic aneurysms with dilation of the aortic root. Human and animal models suggest a worse phenotype for males compared to females with respect to aneurysm size and risk of dissection. In this study we examine the effects of 17 {beta}-estradiol on aortic dilation and rupture in a Marfan mouse model. Marfan male mice were administered 17 {beta}-estradiol and the growth in aortic root size along with the risk of aortic rupture or dissection with the addition of angiotensin II was measured. Transcriptomic profiling was used to identify enriched pathways from 17 {beta}-estradiol treatment. Aortic smooth muscle cells were then treated with cytokines in order to validate the mechanism of 17 {beta}-estradiol protection. We show that 17 {beta}-estradiol decreased the size and rate of aortic root dilation and improved survival from rupture and dissection after treatment with angiotensin II. The Marfan transcriptome was enriched in inflammatory genes and the addition of 17 {beta}-estradiol modulated a set of genes that function through TNF mediated NF-{kappa}B signaling. These included many proteins known to play a role in the phenotypic shift of aortic smooth muscle cells from a contractile to a more inflammatory-like state such as Vcam-1, Mcp-1, Lgals3, Il-6, Il-1b, and C3. In addition, 17 {beta}-estradiol suppressed the induction of these TNF induced genes in aortic smooth muscle cells in vitro and this effect appears to be NF-{kappa}B dependent. In conclusion, 17 {beta}-estradiol protects against the dilation and rupture of aortic roots in Marfan male mice through the inhibition of TNF -NF-{kappa}B signaling and thus prevents the phenotypic switch of aortic smooth muscle cells from a contractile to an inflammatory state.

genetics↗

bioRxiv2023

Machine Learning Identifies Plasma Proteomic Signatures of Descending Thoracic Aortic Disease

BackgroundDescending thoracic aortic aneurysms and dissections can go undetected until severe and catastrophic, and few clinical indices exist to screen for aneurysms or predict risk of dissection. MethodsThis study generated a plasma proteomic dataset from 75 patients with descending type B dissection (Type B) and 62 patients with descending thoracic aortic aneurysm (DTAA). Standard statistical approaches were compared to supervised machine learning (ML) algorithms to distinguish Type B from DTAA cases. Quantitatively similar proteins were clustered based on linkage distance from hierarchical clustering and ML models were trained with uncorrelated protein lists across various linkage distances with hyperparameter optimization using 5-fold cross validation. Permutation importance (PI) was used for ranking the most important predictor proteins of ML classification between disease states and the proteins among the top 10 PI protein groups were submitted for pathway analysis. ResultsOf the 1,549 peptides and 198 proteins used in this study, no peptides and only one protein, hemopexin (HPX), were significantly different at an adjusted p-value <0.01 between Type B and DTAA cases. The highest performing model on the training set (Support Vector Classifier) and its corresponding linkage distance (0.5) were used for evaluation of the test set, yielding a precision-recall area under the curve of 0.7 to classify between Type B from DTAA cases. The five proteins with the highest PI scores were immunoglobulin heavy variable 6-1 (IGHV6-1), lecithin-cholesterol acyltransferase (LCAT), coagulation factor 12 (F12), HPX, and immunoglobulin heavy variable 4-4 (IGHV4-4). All proteins from the top 10 most important correlated groups generated the following significantly enriched pathways in the plasma of Type B versus DTAA patients: complement activation, humoral immune response, and blood coagulation. ConclusionsWe conclude that ML may be useful in differentiating the plasma proteome of highly similar disease states that would otherwise not be distinguishable using statistics, and, in such cases, ML may enable prioritizing important proteins for model prediction.

systems biology↗

bioRxiv2022

Deep Brain Magnetothermal Silencing of Dopaminergic Neurons via Endogenous TREK1 Channels Abolishes Place Preference in Mice

Remote neuromodulation techniques have revolutionized our understanding of brain circuits and their role in behavior. The reversible silencing of specific neuronal populations has emerged as a powerful tool to investigate the necessity and sufficiency of these populations in behavioral responses. Here, we apply magnetothermal silencing using endogenous TREK-1 channels to selectively suppress dopaminergic reward in the ventral tegmental area (VTA) to prove that activation of this specific reward circuit is required for place preference in mice. Magnetothermal silencing entails the application of alternating magnetic fields that penetrate tissue, leading to the heating of superparamagnetic nanoparticles at the target cells, without causing any attenuation or adverse effects. The resultant slight, rapid, and reversible elevation in temperature effectively suppresses neuronal firing, without necessitating genetic modification of the neurons. We demonstrate that two-pore potassium channels, TREK-1, are responsible for this thermal neuronal silencing. Using fiber-based optogenetics we measured both the heating and neuronal silencing in the VTA brain of the animals. We show that in a place preference assay, magnetothermal neuronal silencing of the dopaminergic neurons in the VTA is sufficient to abolish the place preference. Notably, TREK1 knock-out mice exhibit immunity to magnetothermal silencing, behaving as if the magnetic field was not applied. These results underscore the critical role of dopaminergic neuronal activity in the VTA for the establishment of place preference and highlight the dependency on functional TREK1 channels in this magnetothermal silencing approach. HighlightsO_LITREK1 is a highly efficient, thermally activated neuronal silencer C_LIO_LIFirst magnetothermal neuronal silencing in behaving mice C_LIO_LIFiber photometry quantification of local heating and silencing of target neurons in the ventral tegmental area C_LIO_LIMagnetothermal suppression of dopaminergic reward response in the ventral tegmental area is sufficient to abolish place preference C_LI

neuroscience↗

bioRxiv2022

Analysis of synthetic genetic interactions of non-coding RNAs in Saccharomyces cerevisiae

Non-coding RNAs (ncRNAs) with unknown function have been found to be responsible for fitness changes in yeast. With the help of synthetic genetic array (SGA) methodology, genetic interaction screenings between protein-coding genes have contributed to the understanding of complex genetic pathways in eukaryotes. By extending this approach to the ncRNAs, we created more than 15,000 ncRNA double mutants and scored their epistasis in different environments. More than 90% of ncRNA interactions did not correlate with the epistasis of their neighbouring protein-coding genes, showing that the ncRNA and protein interaction networks are largely independent. In the presence of different stressors, epistatic changes were observed with an overall increase in negative interactions. Such network plasticity supports the notion that these ncRNAs may have environmentally dependent functions and can contribute to phenotypic adaptation. The U3 paralogs, SNR17A and SNR17B, share most interactions in rich media, as expected, however, in stressful conditions, there was an increase of paralog-unique interactions, suggesting that SNR17A and SNR17B may have diverged cellular roles under different environmental pressures and may have neo-functionalised after genome duplication. Further investigation of the interaction networks between SNR17 and protein-coding genes revealed the emergence of paralog-specific interactions under stress conditions. Gene Ontology enrichment analysis suggested a novel potential role of SNR17B in chromatin structural remodelling and DNA topology under osmotic and oxidative stress. Overall, this genome-wide study uncovers the level of epistasis involving ncRNA mutants aiding functional assignment and expanding our fundamental knowledge of these genetic elements.

genetics↗

bioRxiv2022

SARS-CoV-2 drives NLRP3 inflammasome activation in human microglia through spike-ACE2 receptor interaction

Coronavirus disease-2019 (COVID-19) is primarily a respiratory disease, however, an increasing number of reports indicate that SARS-CoV-2 infection can also cause severe neurological manifestations, including precipitating cases of probable Parkinsons disease. As microglial NLRP3 inflammasome activation is a major driver of neurodegeneration, here we interrogated whether SARS-CoV-2 can promote microglial NLRP3 inflammasome activation utilising a model of human monocyte-derived microglia. We identified that SARS-CoV-2 isolates can bind and enter microglia, triggering inflammasome activation in the absence of viral replication. Mechanistically, microglial NLRP3 could be both primed and activated with SARS-CoV-2 spike glycoprotein in a NF-{kappa}B and ACE2-dependent manner. Notably, virus- and spike protein-mediated inflammasome activation in microglia was significantly enhanced in the presence of -synuclein fibrils, which was entirely ablated by NLRP3-inhibition. These results support a possible mechanism of microglia activation by SARS-CoV-2, which could explain the increased vulnerability to developing neurological symptoms akin to Parkinsons disease in certain COVID-19 infected individuals, and a potential therapeutic avenue for intervention. SIGNIFICANCE STATEMENTSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) principally affects the lungs, however there is evidence that the virus can also reach the brain and lead to chronic neurological symptoms. In this study, we examined the interaction SARS-CoV-2 with brain immune cells, by using an ex-vivo model of human monocyte-derived microglia. We identified robust activation of the innate immune sensor complex, NLRP3 inflammasome, in cells exposed to SARS-CoV-2. This was dependent on spike protein-ACE2 receptor interaction and was potentiated in the presence of -synuclein. We therefore identify a possible mechanism for SARS-CoV-2 and increased vulnerability to developing neurological dysfunction. These findings support a potential therapeutic avenue for treatment of SARS-CoV-2 driven neurological manifestations, through use of NLRP3 inflammasome or ACE2 inhibitors.

neuroscience↗

bioRxiv2021

The M1 aminopeptidase NPEPPS is a novel regulator of cisplatin sensitivity

There is an unmet need to improve efficacy of platinum-based cancer chemotherapy. Using multi-omic assessment of cisplatin-responsive and -resistant human bladder cancer cell lines and whole-genome CRISPR screens, we identified Puromycin-Sensitive Aminopeptidase, NPEPPS, as a novel driver of cisplatin resistance. NPEPPS depletion sensitizes resistant bladder cancer cells to cisplatin in vitro and in vivo. Conversely, overexpression of NPEPPS in sensitive cells increased cisplatin resistance. We show that NPEPPS affects treatment response by regulating intracellular cisplatin concentrations. Patient-derived organoids (PDOs) generated from bladder cancer samples before and after cisplatin-based treatment, and from patients who did not receive cisplatin, were evaluated for sensitivity to cisplatin and they were found to be concordant with clinical response. In PDOs, shRNA depletion or pharmacologic inhibition of NPEPPS led to increased cisplatin sensitivity, while NPEPPS overexpression had the opposite effect. Our data present NPEPPS as a novel and druggable driver of cisplatin resistance by regulating intracellular cisplatin concentrations, along with providing the preclinical data to support clinical trials combining NPEPPS inhibition with cisplatin.

cancer biology↗

bioRxiv2020

A Single Dose of Self-Transcribing and Replicating RNA Based SARS-CoV-2 Vaccine Produces Protective Adaptive Immunity In Mice

A self-transcribing and replicating RNA (STARR) based vaccine (LUNAR(R)-COV19) has been developed to prevent SARS-CoV-2 infection. The vaccine encodes an alphavirus-based replicon and the SARS-CoV-2 full length spike glycoprotein. Translation of the replicon produces a replicase complex that amplifies and prolong SARS-CoV-2 spike glycoprotein expression. A single prime vaccination in mice led to robust antibody responses, with neutralizing antibody titers increasing up to day 60. Activation of cell mediated immunity produced a strong viral antigen specific CD8+ T lymphocyte response. Assaying for intracellular cytokine staining for IFN-{gamma} and IL-4 positive CD4+ T helper lymphocytes as well as anti-spike glycoprotein IgG2a/IgG1 ratios supported a strong Th1 dominant immune response. Finally, single LUNAR-COV19 vaccination at both 2 g and 10 g doses completely protected human ACE2 transgenic mice from both mortality and even measurable infection following wild-type SARS-CoV-2 challenge. Our findings collectively suggest the potential of Lunar-COV19 as a single dose vaccine.

immunology↗

bioRxiv2020

Functional and transcriptional profiling of non-coding RNAs in yeast reveal context-dependent phenotypes and widespread in trans effects on the protein regulatory network

Non-coding RNAs (ncRNAs), including the more recently identified Stable Unannotated Transcripts (SUTs) and Cryptic Unstable Transcripts (CUTs), are increasingly being shown to play pivotal roles in the transcriptional and post-transcriptional regulation of genes in eukaryotes. Here, we carried out a large-scale screening of ncRNAs in Saccharomyces cerevisiae, and provide evidence for SUT and CUT function. Phenotypic data on 372 ncRNA deletion strains in 23 different growth conditions were collected, identifying ncRNAs responsible for significant cellular fitness changes. Transcriptome profiles were assembled for 18 haploid ncRNA deletion mutants and 2 essential ncRNA heterozygous deletants. Guided by the resulting RNA-seq data we analysed the genome-wide dysregulation of protein coding genes and non-coding transcripts. Novel functional ncRNAs, SUT125, SUT126, SUT035 and SUT532 that act in trans by modulating transcription factors were identified. Furthermore, we described the impact of SUTs and CUTs in modulating coding gene expression in response of different environmental conditions, regulating important biological process such as respiration (SUT125, SUT126, SUT035, SUT432), steroid biosynthesis (CUT494, SUT530, SUT468) or rRNA processing (SUT075 and snR30). Overall, this data captures and integrates the regulatory and phenotypic network of ncRNAs and protein coding genes, providing genome-wide evidence of the impact of ncRNAs on cellular homeostasis. Author SummaryThe yeast genome contains 25% of non-coding RNA molecules (ncRNAs), which do not translate into proteins but are involved in regulation of gene expression. ncRNAs can affect nearby genes by physically interfering with their transcription (cis mode of action), or they interact with DNA, proteins or others RNAs to regulate the expression of distant genes (trans mode of action). Examples of cis-acting ncRNAs have been broadly described, however genome-wide studies to identify functional trans-acting ncRNAs involved in global gene regulation are still lacking. Here, we used the ncRNA yeast deletion collection to score their impact on cellular function in different environmental conditions. A group of 20 ncRNAs mutants with broad fitness diversity were selected to investigate their effect on the protein and ncRNA expression network. We showed a high correlation between altered phenotypes and global transcriptional changes, in an environmental dependent manner. We confirmed the widespread trans acting expressional regulation of ncRNAs in the genome and their role in affecting transcription factors. These findings support the notion of the involvement on ncRNAs in fine tuning the cellular expression via regulations of TFs, as an advantageous RNA-mediated mechanism that can be fast and cost-effective for the cells.

genomics↗

bioRxiv2018

YAMDA: thousandfold speedup of EM-based motif discovery using deep learning libraries and GPU

MotivationMotif discovery in large biopolymer sequence datasets can be computationally demanding, presenting significant challenges for discovery in omics research. MEME, arguably one of the most popular motif discovery software, takes quadratic time with respect to dataset size, leading to excessively long runtimes for large datasets. Therefore, there is a demand for fast programs that can generate results of the same quality as MEME.\n\nResultsHere we describe YAMDA, a highly scalable motif discovery software package. It is built on Pytorch, a tensor computation deep learning library with strong GPU acceleration that is highly optimized for tensor operations that are also useful for motifs. YAMDA takes linear time to find motifs as accurately as MEME, completing in seconds or minutes, which translates to speedups over a thousandfold.\n\nAvailabilityYAMDA is freely available on Github (https://github.com/daquang/YAMDA)\n\nContactdaquang@umich.edu

bioinformatics↗

bioRxiv2017

Proteomic Architecture of Human Coronary and Aortic Atherosclerosis

The inability to detect premature atherosclerosis significantly hinders implementation of personalized therapy to prevent coronary heart disease. A comprehensive understanding of arterial protein networks and how they change in early atherosclerosis could identify new biomarkers for disease detection and improved therapeutic targets. Here we describe the human arterial proteome and the proteomic features strongly associated with early atherosclerosis based on mass-spectrometry analysis of coronary artery and aortic specimens from 100 autopsied young adults (200 arterial specimens). Convex analysis of mixtures, differential dependent network modeling and bioinformatic analyses defined the composition, network re-wiring and likely regulatory features of the protein networks associated with early atherosclerosis. Among other things the results reveal major differences in mitochondrial protein mass between the coronary artery and distal aorta in both normal and atherosclerotic samples - highlighting the importance of anatomic specificity and dynamic network structures in in the study of arterial proteomics. The publicly available data resource and the description of the analysis pipeline establish a new foundation for understanding the proteomic architecture of atherosclerosis and provide a template for similar investigations of other chronic diseases characterized by multi-cellular tissue phenotypes.\n\nHighlightsO_LILC MS/MS analysis performed on 200 human aortic or coronary artery samples\nC_LIO_LINumerous proteins, networks, and regulatory pathways associated with early atherosclerosis\nC_LIO_LIMitochondrial proteins mass and selected metabolic regulatory pathways vary dramatically by disease status and anatomic location\nC_LIO_LIPublically available data resource and analytic pipeline are provided or described in detail\nC_LI

molecular biology↗

bioRxiv2016

Cis-Compound Mutations are Prevalent in Triple Negative Breast Cancer and Can Drive Tumor Progression

About 16% of breast cancers fall into a clinically aggressive category designated triple negative (TNBC) due to a lack of ERBB2, estrogen receptor and progesterone receptor expression1-3. The mutational spectrum of TNBC has been characterized as part of The Cancer Genome Atlas (TCGA)4; however, snapshots of primary tumors cannot reveal the mechanisms by which TNBCs progress and spread. To address this limitation we initiated the Intensive Trial of OMics in Cancer (ITOMIC)-001, in which patients with metastatic TNBC undergo multiple biopsies over space and time5. Whole exome sequencing (WES) of 67 samples from 11 patients identified 426 genes containing multiple distinct single nucleotide variants (SNVs) within the same sample, instances we term Multiple SNVs affecting the Same Gene and Sample (MSSGS). We find that >90% of MSSGS result from cis-compound mutations (in which both SNVs affect the same allele), that MSSGS comprised of SNVs affecting adjacent nucleotides arise from single mutational events, and that most other MSSGS result from the sequential acquisition of SNVs. Some MSSGS drive cancer progression, as exemplified by a TNBC driven by FGFR2(S252W;Y375C). MSSGS are more prevalent in TNBC than other breast cancer subtypes and occur at higher-than-expected frequencies across TNBC samples within TCGA. MSSGS may denote genes that play as yet unrecognized roles in cancer progression.

clinical trials↗