Biology subjects

Ryaboshapkina, M.

Publications and source records attributed to Ryaboshapkina, M..

5 recordsLinked to original sources

bioRxiv2024

IAPP Marks Mono-hormonal Stem-cell Derived β Cells that Maintain Stable Insulin Production in vitro and in vivo

Islet transplantation for treatment of diabetes is limited by availability of donor islets and requirements for immunosuppression. Stem cell-derived islets might circumvent these issues. SC-islets effectively control glucose metabolism post transplantation, but do not yet achieve full function in vitro with current published differentiation protocols. We aimed to identify markers of mature subpopulations of SC-{beta} cells by studying transcriptional changes associated with in vivo maturation of SC-{beta} cells using RNA-seq and co-expression network analysis. The {beta} cell-specific hormone islet amyloid polypeptide (IAPP) emerged as the top candidate to be such a marker. IAPP+ cells had more mature {beta} cell gene expression and higher cellular insulin content than IAPP- cells in vitro. IAPP+ INS+ cells were more stable in long-term culture than IAPP- INS+ cells and retained insulin expression after transplantation into mice. Finally, we conducted a small molecule screen to identify compounds that enhance IAPP expression. Aconitine up-regulated IAPP and could help to optimize differentiation protocols. HighlightsO_LIIAPP expression in vitro marks a mono-hormonal subpopulation of SC-{beta} cells excluding endocrine hormones other than insulin C_LIO_LIOnly INS+ IAPP+ cells maintain stable INS expression in vitro up to 100 days after differentiation C_LIO_LIThe small molecule aconitine accelerates IAPP expression in SC-{beta} cells in vitro C_LI

developmental biology↗

bioRxiv2023

Sample size calculation for a NanoString GeoMx spatial transcriptomics experiment to study predictors of fibrosis progression in non-alcoholic fatty liver disease

Sample size calculation for spatial transcriptomics is a novel and understudied research topic. Prior publications focused on powering spatial transcriptomics studies to detect specific cell populations or spatially variable expression patterns on tissue slides. However, power calculations for translational or clinical studies often relate to the difference between patient groups, and this is poorly described in the literature. Here, we present a stepwise process for sample size calculation to identify predictors of fibrosis progression in non-alcoholic fatty liver disease as a case study. We illustrate how to infer study hypothesis from prior bulk RNA-sequencing data, gather input requirements and perform a simulation study to estimate required sample size to evaluate gene expression differences between patients with stable fibrosis and fibrosis progressors with NanoString GeoMx Whole Transcriptome Atlas assay. Key pointsSpatial transcriptomics is predominantly used in unpowered exploratory research. We demonstrate that spatial transcriptomics studies with NanoString GeoMx Whole Transcriptome Atlas can be powered to address differences between patient groups and illustrate the sample size calculation using fibrosis progression in non-alcoholic fatty liver disease as an example. We note that established software implementing negative binomial mixed effect models lme4 and GLMMadaptive yield more similar fold change estimates to simulated data than the recently published software GeoDiff.

bioinformatics↗

bioRxiv2021

PCSK9 affects expression of key surface proteins in human pancreatic beta cells through intra- and extracellular regulatory circuits

Aims/hypothesisProprotein convertase subtilisin/kexin 9 (PCSK9) is involved in the degradation of LDLR. However, PCSK9 can target other proteins in a cell-type specific manner. While PCSK9 has been detected in pancreatic islets, its expression in insulin-producing pancreatic beta cells is debated. Herein, we studied PCSK9 expression, regulation and function in the human pancreatic beta cell line EndoC-{beta}H1. MethodsWe assessed PCSK9 expression in mouse and human pancreatic islets, and in the pancreatic beta cell line EndoC-{beta}H1. We also studied PCSK9 regulation by cholesterol, lipoproteins, Mevastatin, and by SREBPs transcription factors. To evaluate PCSK9 function in pancreatic beta cells, we performed PCSK9 gain-and loss-of-function experiments in EndoC-{beta}H1 using siPCSK9 or recombinant PCSK9 treatments, respectively. ResultsWe demonstrate that PCSK9 is expressed and secreted by pancreatic beta cells. In EndoC-{beta}H1 cells, PCSK9 expression is regulated by cholesterol and by SREBPs transcription factors. Importantly, PCSK9 knockdown results in multiple transcriptome, proteome and secretome deregulations and impaired insulin secretion. By gain- and loss-of-function experiments, we observed that PCSK9 regulates the expression levels of LDLR and VLDLR through an extracellular mechanism while CD36, PD-L1 and HLA-ABC are regulated through an intracellular mechanism. Conclusions/interpretationCollectively, these results highlight PCSK9 as an important regulator of CD36, PD-L1 and HLA-ABC cell surface expression in pancreatic beta cells. Data availabilityRNA-seq data have been deposited to GEO database with accession number GSE182016. Mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the following identifiers: PXD027921, PXD027911 and PXD027913.

cell biology↗

bioRxiv2021

Characterization of the secretome, transcriptome and proteome of human β cell line EndoC-βH1

Early diabetes research is hampered by limited availability, variable quality and instability of human pancreatic islets in culture. Little is known about the human {beta} cell secretome, and recent studies question translatability of rodent {beta} cell secretory profiles. Here, we verify representativeness of EndoC-{beta}H1, one of the most widely used human {beta} cell lines, as a translational human {beta} cell model based on omics and characterize the EndoC-{beta}H1 secretome. We profiled EndoC-{beta}H1 cells using RNA-seq, Data Independent Acquisition (DIA) and Tandem Mass Tag proteomics of cell lysate. Omics profiles of EndoC-{beta}H1 cells were compared to human {beta} cells and insulinomas. Secretome composition was assessed by DIA proteomics. Agreement between EndoC-{beta}H1 cells and primary adult human {beta} cells was ~90% for global omics profiles as well as for {beta} cell markers, transcription factors and enzymes. Discrepancies in expression were due to elevated proliferation rate of EndoC-{beta}H1 cells compared to adult {beta} cells. Consistently, similarity was slightly higher with benign non-metastatic insulinomas. EndoC-{beta}H1 secreted 671 proteins in untreated baseline state and 3,278 proteins when stressed with non-targeting control siRNA, including known {beta} cell hormones INS, IAPP, and IGF2. Further, EndoC-{beta}H1 secreted proteins known to generate bioactive peptides such as granins and enzymes required for production of bioactive peptides. Unexpectedly, exosomes appeared to be a major mode of secretion in EndoC-{beta}H1 cells. We believe that secretion of exosomes and bioactive peptides warrant further investigation with specialized proteomics workflows in future studies. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=106 SRC="FIGDIR/small/459582v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@1976bc0org.highwire.dtl.DTLVardef@233c4eorg.highwire.dtl.DTLVardef@14c3350org.highwire.dtl.DTLVardef@1bcf0d5_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIWe validate EndoC-{beta}H1 as a translational human {beta} cell model using omics. C_LIO_LIWe present the first unbiased proteomics composition of human {beta} cell line secretome. C_LIO_LIThe secretome of human {beta} cells is more extensive than previously thought. C_LIO_LIUntreated cells secreted 671 proteins and stressed cells secreted 3,278 proteins. C_LIO_LISecretion of exosomes and bioactive peptides constitute directions of future research. C_LI

bioinformatics↗

bioRxiv2018

Tissue-specific genes as an underutilized resource in drug discovery

Tissue-specific genes are believed to be good drug targets due to improved safety. Here we show that this intuitive notion is not reflected in phase 1 and 2 clinical trials, despite the historic success of tissue-specific targets and their 2.3-fold overrepresentation among targets of marketed non-oncology drugs. We compare properties of tissue-specific genes and drug targets. We show that tissue-specificity of the target may also be related to efficacy of the drug. The relationship may be indirect (enrichment in Mendelian disease genes) or direct (elevated ability to spread perturbations in human protein-protein interactome for tissue-specifically produced enzymes and secreted proteins). Reduced evolutionary conservation of tissue-specific genes may represent a bottleneck for drug projects, prompting development of novel models with smaller evolutionary gap to humans. We highlight numerous open opportunities to use tissue-specific genes in drug research and hope that the current study will facilitate discovery efforts.

bioinformatics↗