Biology subjects

Azeloglu, E. U.

Publications and source records attributed to Azeloglu, E. U..

8 recordsLinked to original sources

bioRxiv2022

Biochemical network motifs can transduce and process oscillatory information

Biological networks that are formed through amalgamation of signaling pathways include recurrent configurations called network motifs. These statistically over-represented subgraphs are often formed through interconnected enzyme-substrate relationships that are known to result in highly dynamic downstream behavior, including oscillatory output. Such signals are abundant in biology: heartbeats, circadian rhythms, and cell cycles all exhibit characteristic frequencies. Though there has been great emphasis on how oscillations can be generated through network dynamics, little is known about oscillatory information processing or transduction capacity of network motifs. We employ ordinary differential equations-based dynamical modeling to understand how different network topologies impact oscillatory signal propagation through a multi-enzyme network. We model enzyme-substrate interactions of 20 commonly observed motifs using Michaelis-Menten kinetics. We then perform deterministic Monte Carlo simulations using a range of biologically relevant enzymatic parameters and input frequencies. From these simulations, we quantify signal propagation characteristics using cluster analysis, categorize different motif responses based on output characteristics, and identify potential mechanisms for oscillatory signal processing using parameter sensitivity analysis. We see that the input-output responses depend on network topology and enzyme kinetic parameters. Enzymatic motifs show median oscillatory suppression of 30-135 decibels, with three-node coherent feedforward loops showing the lowest propensity for oscillatory signal suppression. Motifs that contained negative feedback or four-node coherent feedforward loops had the biggest potential to act as AC-to-DC converters, translating oscillatory input signals into transient impulses or sustained continuous outputs, respectively. We conclude that enzyme networks can process and decode information within oscillatory inputs in a frequency- and network-dependent manner.

systems biology↗

bioRxiv2020

A Library Of Induced Pluripotent Stem Cells From Clinically Well-Characterized, Diverse Healthy Human Individuals

A library of well-characterized human induced pluripotent stem cell (hiPSC) lines from clinically healthy human subjects could serve as a useful resource of normal controls for in vitro human development, disease modeling, genotype-phenotype association studies, and drug response evaluation. We report generation and extensive characterization of a gender-balanced, racially/ethnically diverse library of hiPSC lines from 40 clinically healthy human individuals who range in age from 22-61. The hiPSCs match the karyotype and short tandem repeat identity of their parental fibroblasts, and have a transcription profile characteristic of pluripotent stem cells. We provide whole genome sequencing data for one hiPSC clone from each individual, genomic ancestry determination, and analysis of Mendelian disease genes and risks. We document similar transcriptomic profiles, single-cell RNA-seq derived cell clusters and physiology of cardiomyocytes differentiated from multiple independent hiPSC lines. This extensive characterization makes this hiPSC library a valuable resource for many studies on human biology.

cell biology↗

bioRxiv2020

Towards Building a Smart Kidney Atlas: Network-based integration of multimodal transcriptomic, proteomic, metabolomic and imaging data in the Kidney Precision Medicine Project

AO_SCPLOWBSTRACTC_SCPLOWKidney Precision Medicine Project (KPMP) is building a spatially-specified human tissue atlas at the single-cell resolution with molecular details of the kidney in health and disease. Here, we describe the construction of an integrated reference tissue map of cells, pathways and genes using unaffected regions of nephrectomy tissues and undiseased human biopsies from 55 subjects. We use single-cell and -nucleus transcriptomics, subsegmental laser microdissection bulk transcriptomics and proteomics, near-single-cell proteomics, 3-D nondestructive and CODEX imaging, and spatial metabolomics data to hierarchically identify genes, pathways and cells. Integrated data from these different technologies coherently describe cell types/subtypes within different nephron segments and interstitium. These spatial profiles identify cell-level functional organization of the kidney tissue as indicative of their physiological functions and map different cell subtypes to genes, proteins, metabolites and pathways. Comparison of transcellular sodium reabsorption along the nephron to levels of mRNAs encoding the different sodium transporter genes indicate that mRNA levels are largely congruent with physiological activity.This reference atlas provides an initial framework for molecular classification of kidney disease when multiple molecular mechanisms underlie convergent clinical phenotypes.

bioinformatics↗

bioRxiv2020

Protein structure-based gene expression signatures

Gene expression signatures (GES) connect phenotypes to mRNA expression patterns, providing a powerful approach to define cellular identity, function, and the effects of perturbations. However, the use of GES has suffered from vague assessment criteria and limited reproducibility. The structure of proteins defines the functional capability of genes, and hence, we hypothesized that enrichment of structural features could be a generalizable representation of gene sets. We derive structural gene expression signatures (sGES) using features from various levels of protein structure (e.g. domain, fold) encoded by the transcribed genes in GES, to describe cellular phenotypes. Comprehensive analyses of data from the Genotype-Tissue Expression Project (GTEx), ARCHS4, and mRNA expression of drug effects on cardiomyocytes show that structural GES (sGES) are useful for identifying robust signatures of biological phenomena. sGES also enables the characterization of signatures across experimental platforms, facilitates the interoperability of expression datasets, and can describe drug action on cells.

bioinformatics↗

bioRxiv2020

Proteomic cellular signatures of kinase inhibitor-induced cardiotoxicity: Mount Sinai DToxS LINCS Center Dataset

The Drug Toxicity Signature Generation Center (DToxS) at the Icahn School of Medicine at Mount Sinai is one of the centers of the NIH Library of Integrated Network-Based Cellular Signatures (LINCS) program. A key aim of DToxS is to generate both proteomic and transcriptomic signatures that can predict adverse effects, especially cardiotoxicity, of kinase inhibitors approved by the Food and Drug Administration. Towards this goal, high throughput shot-gun proteomics experiments (317 cell line/drug combinations + 64 control lysates) have been conducted at the Center for Advanced Proteomics Research at Rutgers University - New Jersey Medical School. Using computational network analyses, these proteomic data can be integrated with transcriptomic signatures generated in tandem to identify cellular signatures of cardiotoxicity that may predict kinase inhibitor-induced toxicity and possible mitigation. Both raw and processed proteomics data have been carefully screened for quality and made publicly available via the PRIDE database. As such, this broad protein kinase inhibitor-stimulated cardiomyocyte proteomic data and signature set is valuable for the prediction of drug toxicities. Links to: Metadata Tables O_TBL View this table: org.highwire.dtl.DTLVardef@1cda9f8org.highwire.dtl.DTLVardef@1520e05org.highwire.dtl.DTLVardef@16a35borg.highwire.dtl.DTLVardef@3ee6e7org.highwire.dtl.DTLVardef@1a98664_HPS_FORMAT_FIGEXP M_TBL C_TBL

pharmacology and toxicology↗

bioRxiv2019

Plexin-B2 is a key regulator of cell mechanics during multicellular organization

During multicellular organization, individual cells need to constantly respond to environmental cues and adjust contractile and adhesive forces in order to maintain tissue integrity. The signaling pathways linking biochemical cues and tissue mechanics are unclear. Here, we show that Plexin-B2 regulates mechanochemical integration during multicellular organization. In human embryonic stem cells (hESCs), Plexin-B2 controls cell shape and tissue geometry in both 2D epithelial colony and 3D spheroid aggregates by regulating actomyosin contractility and junctional/cell-matrix adhesive properties. Atomic force microscopy (AFM) directly demonstrates that Plexin-B2 modulates cell stiffness in hESC colonies, which in turn impacts cell proliferation and cell fate specification through {beta}-catenin signaling and YAP mechanosensing. YAP also functions as a mechanoregulator downstream of Plexin-B2, thus forming a mechanochemical integrative loop. In human neuroprogenitor cells (hNPCs), Plexin-B2 similarly controls cell stiffness and tensile forces, as revealed by AFM and FRET tension sensor studies. Strikingly, Plexin-B2-deficient hNPCs display accelerated neuronal differentiation. From an organogenesis perspective, Plexin-B2 maintains cytoarchitectural integrity of neuroepithelium, as modeled in cerebral organoids. On a signaling level, Plexin-B2 engages extracellular as well as intracellular Ras-GAP and RBD domains for mechanoregulation through Rap and Rac GTPases. Our data unveil a fundamental function of Plexin-B2 for mechanochemical integration during multicellular organization, and shed light on the principle of force-mediated regulation of stem cell biology and tissue morphogenesis.

developmental biology↗

bioRxiv2017

Validating Antibodies for Quantitative Western Blot Measurements with Microwestern Array

Western blotting is often considered a semi-quantitative or even qualitative assay for assessing changes in protein or protein post-translational modification levels. Fluorescence-based measurement enables acquisition of quantitative data in principal, but requires determining the linear range of detection for each antibody--a labor-intensive task. Here, we describe the use of a high-throughput western blotting technique called microwestern array to more rapidly evaluate suitable conditions for quantitative western blotting with particular antibodies. We can evaluate up to 192 antibody/dilution/replicate combinations on a single standard size gel with a seven-point, two-fold lysate dilution series (~100-fold range). Pilot experiments demonstrate a surprisingly high proportion of investigated antibodies (17/22) are suitable for quantitative use, and that lack of validity might often be a consequence of lysate composition rather than antibody quality. Linear range for all validated antibodies is at least 8-fold, and in some cases nearly two orders of magnitude. That range could be greater as the presented tests did not find a limit for many antibodies. We find that phospho-specific and total antibodies do not have discernable trend differences in linear range or limit of detection, but total antibodies generally required higher working concentrations, suggesting phospho-specific antibodies may be generally higher affinity. Importantly, we demonstrate that results from microwestern analyses scale to normal \"macro\" western for a subset of antibodies. These data indicate that with initial validation, many antibodies can be readily used quantitatively in a reproducible manner. Antibody validation data and standard operating procedures are available online (www.birtwistlelab.com/protocols and www.dtoxs.org).

biochemistry↗

bioRxiv2017

A Comparison of mRNA Sequencing with Random Primed and 3’-Directed Libraries

Deep mRNA sequencing (mRNAseq) is the state-of-the-art for whole transcriptome measurements. A key step is creating a library of cDNA sequencing fragments from RNA. This is generally done by random priming, creating multiple sequencing fragments along the length of each transcript. A 3 end-focused library approach cannot detect differential splicing, but has potentially higher throughput at lower cost (~10-fold lower), along with the ability to improve quantification by using transcript molecule counting with unique molecular identifiers (UMI) to correct for PCR bias. Here, we compare implementation of such a 3-digital gene expression (3-DGE) approach with \"conventional\" random primed mRNAseq, which has not yet been done. We find that while conventional mRNAseq detects ~15% more genes, the resulting lists of differentially expressed genes and therefore biological conclusions and gene signatures are highly concordant between the two techniques. We also find good quantitative agreement on the level of individual genes between the two techniques in terms of both read counts and fold change between two conditions. We conclude that for high-throughput applications, the potential cost savings associated with the 3-DGE approach are a very reasonable tradeoff for modest reduction in sensitivity and inability to observe alternative splicing, and should enable much larger scale studies focused on not only differential expression analysis, but also quantitative transcriptome profiling. The computational scripts and programs, along with experimental standard operating procedures used in our pipeline presented here, are freely available on our website (www.dtoxs.org).

genomics↗