Biology subjects

Van Eyk, J. E.

Publications and source records attributed to Van Eyk, J. E..

27 records · Page 2Linked to original sources

bioRxiv2021

Protective role of the Atg8 1 homologue Gabarapl1 in regulating cardiomyocyte glycophagy in diabetic heart disease

Diabetic heart disease is highly prevalent and characterized by diastolic dysfunction. The mechanisms of diabetic heart disease are poorly understood and no targeted therapies are available. Here we show that the diabetic myocardium (type 1 and type 2) is characterized by marked glycogen elevation and ectopic cellular localization - a paradoxical metabolic pathology given suppressed cardiomyocyte glucose uptake in diabetes. We demonstrate involvement of a glycogen-selective autophagy pathway ( glycophagy) defect in mediating this pathology. Genetically manipulated deficiency of Gabarapl1, an Atg8 autophagy homologue, induces cardiac glycogen accumulation and diastolic dysfunction. Stbd1, the Gabarapl1 cognate autophagosome partner is identified as a unique component of the early glycoproteome response to hyperglycemia in cardiac, but not skeletal muscle. Cardiac-targeted in vivo Gabarapl1 gene delivery normalizes glycogen levels, diastolic function and cardiomyocyte mechanics. These findings reveal that cardiac glycophagy is a key metabolic homeostatic process perturbed in diabetes that can be remediated by Gabarapl1 intervention.

molecular biology↗

bioRxiv2021

DDN2.0: R and Python packages for differential dependency network analysis of biological systems

Data-driven differential dependency network analysis identifies in a complex and often unknown overall molecular circuitry a network of differentially connected molecular entities (pairwise selective coupling or uncoupling depending on the specific phenotypes or experimental conditions) (Herrington, et al. 2018; Zhang, et al., 2009; Zhang and Wang, 2010; Zhang, et al., 2016). Such differential dependency networks are typically used to assist in the inference of potential key pathways. Based on our previously developed Differential Dependency Network (DDN) method, we report here the fully implemented R and Python software tool packages for public use. The DDN2.0 algorithm uses a fused Lasso model and block-wise coordinate descent to estimate both the common and differential edges of dependency networks. The identified DDN can help to provide plausible interpretation of data, gain new insight of disease biology, and generate novel hypotheses for further validation and investigations. To address the imbalanced sample group problem, we propose a sample-size normalized formulation to correct systematic bias. To address high computational complexity, we propose four strategies to accelerate DDN2.0 learning. The experimental results show that new DDN2.0+ learning speed with combined four accelerating strategies is hundreds of times faster than that of DDN2.0 algorithm on medium-sized data (Fu, 2019). To detect intra-omics and inter-omics network rewiring, we propose multiDDN using a multi-layer signaling model to integrate multi-omics data. The simulation study shows that the multiDDN method can achieve higher accuracy of detecting network rewiring (Fu, 2019).

bioinformatics↗

bioRxiv2021

COT: an efficient Python tool for detecting marker genes among many subtypes

We develop an accurate and efficient method to detect marker genes among many subtypes using subtype-enriched expression profiles. We implement a Cosine based One-sample Test (COT) Python software that is easy to use and applicable to multi-omics data. We demonstrate the performance and utility of COT on gene expression and proteomics data acquired from tissue or cell subtypes. Formulated as a one-sample test with Cosine similarity test statistic in scatter space, the detected de novo marker genes will allow biologists to perform a more comprehensive and unbiased molecular characterization, deconvolution and classification of complex tissue or cell subtypes.

bioinformatics↗

bioRxiv2021

Sample-wise unsupervised deconvolution of complex tissues

MotivationComplex biological tissues are often a heterogeneous mixture of several molecularly distinct cell or tissue subtypes. Both subtype compositions and expressions in individual samples can vary across different biological states or conditions. Computational deconvolution aims to dissect patterns of bulk gene expression data into subtype compositions and subtype-specific expressions. Typically, existing deconvolution methods can only estimate averaged subtype-specific expressions in a population, while detecting differential expressions or co-expression networks in particular subtypes requires unique subtype expression estimates in individual samples. Different from population-level deconvolution, however, individual-level deconvolution is mathematically an underdetermined problem because there are more variables than observations. ResultsWe report a sample-wise Convex Analysis of Mixtures (swCAM) method that can estimate subtype proportions and subtype-specific expressions in individual samples from bulk tissue transcriptomes. We extend our previous CAM framework to include a new term accounting for between-sample variations and formulate swCAM as a nuclear-norm and{ell} 2,1-norm regularized matrix factorization problem. We determine hyperparameter values using a cross-validation scheme with random entry exclusion and obtain a swCAM solution using an efficient alternating direction method of multipliers. The swCAM is implemented in open-source R scripts. Experimental results on realistic simulation data show that swCAM can accurately estimate subtype-specific expressions in individual samples and successfully extract co-expression networks in particular subtypes that are otherwise unobtainable using bulk expression data. Application of swCAM to bulk-tissue data of 320 samples from bipolar disorder patients and controls identified changes in cell proportions, expression and coexpression modules in patient neurons. Mitochondria related genes showed significant changes suggesting an important role of energy dysregulation in bipolar disorder. Availability and implementationThe R Scripts of swCAM is freely available at https://github.com/Lululuella/swCAM. A users guide and a vignette are provided. Contactyuewang@vt.edu Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2020

Oncometabolism Drives Autophagy Activation in Skeletal Muscle

About 20-30% of cancer-associated deaths are due to complications from cachexia which is characterized by skeletal muscle atrophy. Metabolic reprogramming in cancer cells causes body-wide metabolic and proteomic remodeling, which remain poorly understood. Here, we present evidence that the oncometabolite D-2-hydroxylgutarate (D2-HG) impairs NAD+ redox homeostasis in skeletal myotubes, causing atrophy via deacetylation of LC3-II by the nuclear deacetylase Sirt1. Overexpression of p300 or silencing of Sirt1 abrogate its interaction with LC3, and subsequently reduced levels of LC3 lipidation. Using RNA-sequencing and mass spectrometry-based metabolomics and proteomics, we demonstrate that prolonged treatment with the oncometabolite D2-HG in mice promotes cachexia in vivo and increases the abundance of proteins and metabolites, which are involved in energy substrate metabolism, chromatin acetylation and autophagy regulation. We further show that D2-HG promotes a sex-dependent adaptation in skeletal muscle using network modeling and machine learning algorithms. Our multi-omics approach exposes new metabolic vulnerabilities in response to D2-HG in skeletal muscle and provides a conceptual framework for identifying therapeutic targets in cachexia.

systems biology↗

bioRxiv2020

S-nitrosoglutathione reductase deficiency causes aberrant placental S-nitrosylation and preeclampsia.

Preeclampsia (PE), a leading cause of maternal and fetal mortality and morbidity, is characterized by an increase in S-nitrosylated (SNO) proteins and reactive oxygen species (ROS), suggesting a pathophysiologic role for dysregulation in nitrosylation and nitrosative stress. Here we show that mice lacking S-nitrosoglutathione reductase (GSNOR-/-), a denitrosylase regulating protein S-nitrosylation, exhibit a PE phenotype, including hypertension, proteinuria, renal pathology, cardiac concentric hypertrophy, decreased placental vascularization, and fetal growth retardation. ROS, nitric oxide (NO) and peroxynitrite levels are elevated. Importantly, mass spectrometry reveals elevated placental SNO-amino acid residues in GSNOR-/- mice. Ascorbate reverses the phenotype except for fetal weight, reduces the difference in the S-nitrosoproteome, and identifies a unique set of SNO-proteins in GSNOR-/- mice. Therefore, deficiency of GSNOR creates dysregulation of placental S-nitrosylation and preeclampsia in mice, which can be rescued by ascorbate. These findings offer valuable insights and therapeutic implications for PE. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=144 SRC="FIGDIR/small/183012v3_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@1baa15corg.highwire.dtl.DTLVardef@1b79fe7org.highwire.dtl.DTLVardef@62f899org.highwire.dtl.DTLVardef@1af0b29_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical Abstract:C_FLOATNO Dysregulation in nitrosylation contributes to nitroso-redox imbalance and nitrosative stress contributing to clinical features of PE including hypertension, proteinuria, concentric hypertrophy in the heart, decrease placental vascularization and fetal growth restriction. Antioxidant treatment rescued the PE-like phenotype in the mother. C_FIG

physiology↗

bioRxiv2020

A Simultaneous Methylome and Proteome Quantitative Workflow for Complex Samples

Proteoforms containing post-translational modifications (PTMs) represent a degree of functional diversity only harnessed through analytically precise simultaneous quantification of multiple PTMs. Here we present a method to accurately differentiate an unmodified peptide from its PTM-containing counterpart through data-independent acquisition-mass spectrometry, leveraging small precursor mass windows to physically separate modified peptidoforms from each other during MS2 acquisition. We utilize a lysine and arginine PTM-enriched peptide assay library and site localization algorithm to simultaneously localize and quantify seven PTMs including mono-, di-, and tri-methylation, acetylation, and succinylation in addition to total protein quantification in a single MS run without the need to enrich experimental samples. To evaluate biological relevance, this method was applied to liver lysate from differentially methylated non-alcoholic steatohepatitis (NASH) mouse models. We report altered methylation and acetylation together with total protein changes drive the novel hypothesis of a regulatory function of PTMs in protein synthesis and mRNA stability in NASH. Graphical Abstract O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

systems biology↗

bioRxiv2019

Human Proteome Project Mass Spectrometry Data Interpretation Guidelines 3.0

The Human Proteome Organizations (HUPO) Human Proteome Project (HPP) developed Mass Spectrometry (MS) Data Interpretation Guidelines that have been applied since 2016. These guidelines have helped ensure that the emerging draft of the complete human proteome is highly accurate and with low numbers of false-positive protein identifications. Here, we describe an update to these guidelines based on consensus-reaching discussions with the wider HPP community over the past year. The revised 3.0 guidelines address several major and minor identified gaps. We have added guidelines for emerging data independent acquisition (DIA) MS workflows and for use of the new Universal Spectrum Identifier (USI) system being developed by the HUPO Proteomics Standards Initiative (PSI). In addition, we discuss updates to the standard HPP pipeline for collecting MS evidence for all proteins in the HPP, including refinements to minimum evidence. We present a new plan for incorporating MassIVE-KB into the HPP pipeline for the next (HPP 2020) cycle in order to obtain more comprehensive coverage of public MS data sets. The main checklist has been reorganized under headings and subitems and related guidelines have been grouped. In sum, Version 2.1 of the HPP MS Data Interpretation Guidelines has served well and this timely update to version 3.0 will aid the HPP as it approaches its goal of collecting and curating MS evidence of translation and expression for all predicted [~]20,000 human proteins encoded by the human genome.\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=134 SRC=\"FIGDIR/small/733576v2_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (18K):\norg.highwire.dtl.DTLVardef@8765f2org.highwire.dtl.DTLVardef@136235aorg.highwire.dtl.DTLVardef@14aef76org.highwire.dtl.DTLVardef@af33d2_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2018

Identifying high-priority proteins across the human diseasome using semantic similarity

Knowledge of \"popular proteins\" has been a focus of multiple Human Proteome Organization (HUPO) initiatives and can guide the development of proteomics assays targeting important disease pathways. We report here an updated method to identify prioritized protein lists from the research literature, and apply it to catalog lists of important proteins across multiple cell types, sub-anatomical regions, and disease phenotypes of interest. We provide a systematic collection of popular proteins across 10,129 human diseases as defined by the Disease Ontology, 10,642 disease phenotypes defined by Human Phenotype Ontology, and 2,370 cellular pathways defined by Pathway Ontology. This strategy allows instant retrieval of popular proteins across the human \"diseasome\", and further allows reverse queries from protein to disease, enabling functional analysis of experimental protein lists using bibliometric annotations.

bioinformatics↗