Biology subjects

Venkatraman, V.

Publications and source records attributed to Venkatraman, V..

11 recordsLinked to original sources

bioRxiv2024

The Area Law of Molecular Entropy: Moving Beyond Harmonic Approximation.

Inspired by black hole thermodynamics, the area law that entropy is proportional to horizon area has been proposed in quantum entanglement entropy and has largely maintained its validity. This article shows that the area law is also valid for the thermodynamic entropy of molecules. We showed that the gas-phase entropy of molecules obeys the area law with our proposed correction for the different curvatures of the molecular surface. The coefficient for the ultraviolet cutoff for the molecular entropy, calculated from our curated experimental data, is tantalizingly close to the value [Formula] proposed by Hawking [Hawking, 1976]. The ability to estimate gas-phase entropy by the area law also allows us to calculate molecular entropy faster and more accurately than currently popular methods of estimating molecular entropy with harmonic oscillator approximation. The speed and accuracy of our method will open up new possibilities for the explicit inclusion of entropy in computational biology methods, such as virtual screening applications.

biophysics↗

bioRxiv2022

Molecular fingerprints are not useful in large-scale search for similarly active compounds

Computational approaches for small-molecule drug discovery now regularly scale to consideration of libraries containing billions of candidate small molecules. One promising approach to increased speed in evaluating billion-molecule libraries is to develop succinct representations of each molecule that enable rapid identification of molecules with similar properties. Molecular fingerprints are thought to provide a mechanism for producing such representations. Here, we explore the utility of commonly-used fingerprints in the context of predicting similar molecular activity. We show that fingerprint similarity provides little discriminative power between active and inactive molecules for a target protein based on a known active - while they may sometimes provide some enrichment for active molecules in a drug screen, a screened dataset will still be dominated by inactive molecules. We also demonstrate that high-similarity actives appear to share a scaffold with the query active, meaning that they could more easily be identified by structural enumeration. Furthermore, even when limited to only active molecules, fingerprint similarity values do not correlate with compound potency. In sum, these results highlight the need for a new wave of molecular representations that will improve the capacity to detect biologically active molecules based on similarity to other such molecules.

pharmacology and toxicology↗

bioRxiv2021

ImaGene: A web-based software platform for tumor radiogenomic evaluation and reporting

The field of radiomics has undergone several advancements in approaches to uncovering hidden quantitative features from tumor imaging data for use in guiding clinical decision-making for cancer patients. Radiographic imaging techniques provide insight into the imaging features of tumor regions of interest (ROIs), while immunohistochemistry and sequencing techniques performed on biopsy samples yield omics data. Potential associations between tumor genotype and phenotype can be identified from imaging and omics data via traditional correlation analysis, as well as through artificial intelligence (AI) models. However, at present the radiogenomics community lacks a unified software platform for which to conduct such analyses in a reproducible manner. To address this gap, we propose ImaGene, a web-based platform that takes tumor omics and imaging data sets as input, performs correlation analysis between them, and constructs AI models (optionally using only those features found to exhibit statistically significant correlation with some element of the opposing dataset). ImaGene has several modifiable configuration parameters, providing users complete control over their analysis. For each run, ImaGene produces a comprehensive report displaying a number of intuitive model diagnostics. To demonstrate the utility of ImaGene, exploratory studies surrounding Invasive Breast Carcinoma (IBC) and Head and Neck Squamous Cell Carcinoma (HNSCC) on datasets acquired from public databases are conducted. Potential associations are identified between several imaging features and 6 genes: CRABP1, SMTNL2, FABP1, HAS1, FAM163A and DSG1 for IBC, and 4 genes: CEACAM6, NANOG, ACSM2B, and UPK2 for HNSCC. In summary, the software provides researchers with a transparent tool for which to begin radiogenomic analysis and explore possible further directions in their research. We anticipate that ImaGene will become the standard platform for tumor analyses in the field of radiogenomics due to its ease of use, flexibility, and reproducibility, and that it can serve as an enabling centrepoint for an emerging radiogenomic knowledge base.

bioinformatics↗

bioRxiv2021

Mechanistic dissection of global proteomic changes in rats with heart failure and preserved ejection fraction

IntroductionHeart failure with preserved ejection fraction (HFpEF) is characterized by diastolic dysfunction, pulmonary congestion and exercise intolerance. Previous preclinical studies show that treatment with cardiosphere-derived cells (CDCs) improves diastolic function, attenuates arrhythmias and prolongs survival in a rat HFpEF model. Here we characterize the myocardial proteome and diastolic function in HFpEF, with and without CDC therapy. As an initial strategy for identifying pathways worthy of further mechanistic dissection, we correlated CDC-responsive proteomic changes with functional improvements. Methods and ResultsDahl salt-sensitive rats fed high-salt diet, with verified diastolic dysfunction, were randomly assigned to intracoronary CDCs or placebo. Dahl rats fed a low salt diet served as controls. Phenotyping was by echocardiography (E/A ratio) and invasive hemodynamic monitoring (time constant of relaxation Tau, and left ventricular end-diastolic pressure [LVEDP]). CDC treatment improved diastolic function as indicated by a normalized E/A ratio, a 33.3% reduction in Tau, and a 47% reduction of LVEDP. Mass spectrometry of left ventricular tissues (n=6/group) revealed changes in transcription and translation pathways in this rat HFpEF model and was also recapitulated in human HFpEF. These pathways were enhanced following CDC treatment in the animal model (205 proteins and 32 phosphorylated residues accounting for 37% and 19% of all changes, respectively). Among all CDC-sensitive pathways, 65% can be linked to at least 1 of 7 upstream regulators, among which several are of potential relevance for regulating protein expression. To probe newly-synthesized proteins AHA labeling was carried out in isolated rat cardiomyocytes obtained from HFpEF groups, with and without CDC therapy. Five of the initial upstream regulators (HNF4A, MTOR, MYC, TGF{beta}1, and TP53) were linked to proteins expressed exclusively (or increased) with CDC treatment. All 32 phosphorylated residues of proteins involved in transcription/translation altered specifically by CDC treatment had predicted kinases (Protein kinase C (PKC) being the most dominant) and known to be regulated by MYC, TGF{beta}1 and/or TP53. Western blot analysis of those 5 upstream regulators showed that TGF{beta}1, TP53, and Myc were significantly decreased in LV from CDC treated animals, whereas MTOR and HNF4A showed a significant increase compared to HFpEF alone. The cellular quantities of several upstream regulator correlated with indices of diastolic function (E/A ratio, Tau and/or LVEDP). Since CDCs act via the secretion of exosomes laden with signaling cargo, it is relevant that all 7 upstream regulators could, in principle, be regulated by proteins or miRNA that are present in CDC-derived exosomes. ConclusionWe identified key cellular regulators of transcription and translation that underlie the therapeutic effects of CDCs in HFpEF, whose levels correlate quantitatively with measures of diastolic function. Among the multifarious proteomic changes associated with rat model of HFpEF which were also observed in human HFpEF samples, we propose that these regulators, and downstream effector kinases, be prioritized for further dedicated mechanistic dissection.

molecular biology↗

bioRxiv2021

Fast and Accurate Estimation of Gas-Phase Entropy from the Molecular Surface Curvature

Estimating entropy is crucial for understanding and modifying biological systems, such as protein-ligand binding. Current computational methods to estimate entropy require extensive, or at times prohibitively extensive, computational resources. This article presents SHAPE (SHape-based Accurate Predictor of Entropy), a new method that estimates the gas-phase entropy of small molecules purely from their surface geometry. The gas-phase entropy of small molecules can be computed in {approx}0.01 CPU hours with run time complexity of [Formula], where Na is the number of atoms. The accuracy of SHAPE is within 1 - 2% of computationally expensive quantum mechanical or molecular mechanical calculations. We further show that the inclusion of gas-phase entropy, estimated using SHAPE, improves the rank-order correlation between binding affinity and binding score from 0.18 to 0.40. The speed and accuracy of SHAPE make it well-suited for inclusion in molecular docking or QSAR (quantitative structure-activity relationships) methods. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=112 SRC="FIGDIR/small/445640v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@1cc10d1org.highwire.dtl.DTLVardef@6448d6org.highwire.dtl.DTLVardef@1e9e308org.highwire.dtl.DTLVardef@385eb2_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2021

Standardized workflow for precise mid- and high-throughput proteomics of blood biofluids

BackgroundAccurate discovery assay workflows are critical for identifying authentic circulating protein biomarkers in diverse blood matrices. Maximizing the commonalities in the proteomic workflows between different biofluids simplifies the approach and increases the likelihood for reproducibility. We developed a workflow that allows flexibility for high and mid-throughput analysis for three blood-based proteomes: naive plasma, plasma depleted of the 14 most abundant proteins, and dried blood. MethodsOptimal conditions for sample preparation and DIA-MS analysis were established in plasma then automated and adapted for depleted plasma and whole blood. The MS workflow was modified to facilitate sensitive high-throughput or deep profile analysis with mid-throughput analysis. Analytical performance was evaluated from 5 complete workflows repeated over 3 days as well as a linearity analysis of a 5-6-point dilution curve. ResultUsing our high-throughput workflow, 74%, 93%, 87% of peptides displayed an inter-day CV<30% in plasma, depleted plasma and whole blood. While the mid-throughput workflow had 67%, 90%, 78% of peptides in plasma, depleted plasma and whole blood meeting the CV<30% standard. Lower limits of detection and quantitation were determined for proteins and peptides observed in each biofluid and workflow. Combining the analysis of both high-throughput plasma fractions exceeded the number of reliably identified proteins for individual biofluids in the mid-throughput workflows. ConclusionThe workflow established here allowed for reliable detection of proteins covering a broad dynamic range. We envisage that implementation of this standard workflow on a large scale will facilitate the translation of candidate markers into clinical use.

biochemistry↗

bioRxiv2020

An integrated multi-omic analysis of iPSC-derived motor neurons from C9ORF72 ALS patients

Neurodegenerative diseases present a challenge for systems biology, due to the lack of reliable animal models and the difficulties in obtaining samples from patients at early stages of disease, when interventions might be most effective. Studying induced pluripotent stem cell (iPSC)-derived neurons could overcome these challenges and dramatically accelerate and broaden therapeutic strategies. Here we undertook a network-based multi-omic characterization of iPSC-derived motor neurons from ALS patients carrying genetically dominant hexanucleotide expansions in C9orf72 to gain a deeper understanding of the relationship between DNA, RNA, epigenetics and protein in the same pool of tissue. ALS motor neurons showed the expected C9orf72-related alterations to specific nucleoporins and production of dipeptide repeats. RNA-seq, ATAC-seq and data-independent acquisition mass-spectrometry (DIA-MS) proteomics were then performed on the same motor neuron cultures. Using integrative computational methods that combined all of the omics, we discovered a number of novel dysregulated pathways including biological adhesion and extracellular matrix organization and disruption in other expected pathways such as RNA splicing and nuclear transport. We tested the relevance of these pathways in vivo in a C9orf72 Drosophila model, analyzing the data to determine which pathways were causing disease phenotypes and which were compensatory. We also confirmed that some pathways are altered in late-stage neurodegeneration by analyzing human postmortem C9 cervical spine data. To validate that these key pathways were integral to the C9 signature, we prepared a separate set of C9orf72 and control motor neuron cultures using a different differentiation protocol and applied the same methods. As expected, there were major overall differences between the differentiation protocols, especially at the level of in individual omics data. However, a number of the core dysregulated pathways remained significant using the integrated multiomic analysis. This new method of analyzing patient specific neural cultures allows the generation of disease-related hypotheses with a small number of patient lines which can be tested in larger cohorts of patients.

neuroscience↗

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↗

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↗

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↗

bioRxiv2017

Reason’s Enemy Is Not Emotion: Engagement of Cognitive Control Networks Explains Biases in Gain/Loss Framing

In the classic gain/loss framing effect, describing a gamble as a potential gain or loss biases people to make risk-averse or risk-seeking decisions, respectively. The canonical explanation for this effect is that frames differentially modulate emotional processes - which in turn leads to irrational choice behavior. Here, we evaluate the source of framing biases by integrating functional magnetic resonance imaging (fMRI) data from 143 human participants performing a gain/loss framing task with meta-analytic data from over 8000 neuroimaging studies. We found that activation during choices consistent with the framing effect were most correlated with activation associated with the resting or default brain, while activation during choices inconsistent with the framing effect most correlated with the task-engaged brain. Our findings argue against the common interpretation of gain/loss framing as a competition between emotion and control. Instead, our study indicates that this effect results from differential cognitive engagement across decision frames.\n\nSignificance StatementThe biases frequently exhibited by human decision-makers have often been attributed to the presence of emotion. Using a large fMRI sample and analysis of whole-brain networks defined with the meta-analytic tool Neurosynth, we find that neural activity during frame-biased decisions are more significantly associated with default behaviors (and the absence of executive control) than with emotion. These findings point to a role for neuroscience in shaping longstanding psychological theories in decision science.

neuroscience↗