Biology subjects

Katz, L. S.

Publications and source records attributed to Katz, L. S..

6 recordsLinked to original sources

bioRxiv2024

Molecular glues of the regulatory ChREBP/14-3-3 complex protect beta cells from glucolipotoxicity.

The Carbohydrate Response Element Binding Protein (ChREBP) is a glucose-responsive transcription factor (TF) with two major splice isoforms ( and {beta}). In chronic hyperglycemia and glucolipotoxicity, ChREBP-mediated ChREBP{beta} expression surges, leading to insulin-secreting {beta}-cell dedifferentiation and death. 14-3-3 binding to ChREBP results in cytoplasmic retention and suppression of transcriptional activity. Thus, small molecule-mediated stabilization of this protein-protein interaction (PPI) may be of therapeutic value. Here, we show that structure-based optimizations of a molecular glue compound led to potent ChREBP/14-3-3 PPI stabilizers with cellular activity. In primary human {beta}-cells, the most active compound retained ChREBP in the cytoplasm, and efficiently protected {beta}-cells from glucolipotoxicity while maintaining {beta}-cell identity. This study may thus not only provide the basis for the development of a unique class of compounds for the treatment of Type 2 Diabetes but also showcases an alternative molecular glue approach for achieving small molecule control of notoriously difficult to target TFs. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=106 SRC="FIGDIR/small/580675v2_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@18a17c8org.highwire.dtl.DTLVardef@11f6c0forg.highwire.dtl.DTLVardef@1ef4e63org.highwire.dtl.DTLVardef@34799_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2023

Fasten with Pipes

Fasten is a versatile bioinformatics tool suite developed to address gaps in the manipulation of sequence data files in the fastq format. Existing tools often lack support for interleaved fastq files and Unix-style pipe file control. Fasten provides a solution by allowing bioinformaticians to efficiently work with paired-end sequence data, facilitating data transformation, and offering competitive speeds. This suite, implemented in Rust, offers a comprehensive manual, continuous integration, and seamless integration into the command line with Unix pipes. Fasten is poised to become an invaluable resource for routine command-line bioinformatics tasks, enhancing the accessibility and flexibility of data processing in the field.

bioinformatics↗

bioRxiv2021

Nrf2 Regulates β-cell Mass by Suppressing Cell Death and Promoting Proliferation

Finding therapies that can protect and expand functional {beta}-cell mass is a major goal of diabetes research. Here we generated {beta}-cell-specific conditional knockout and gain-of-function mouse models and used human islet transplant experiments to examine how manipulating Nrf2 levels affects {beta}-cell survival, proliferation and mass. Depletion of Nrf2 in {beta}-cells resulted in decreased glucose-stimulated {beta}-cell proliferation ex vivo and decreased adaptive {beta}-cell proliferation and {beta}-cell mass expansion after a high fat diet in vivo. Nrf2 protects {beta}-cells from apoptosis after a high fat diet. Nrf2 loss-of-function decreases Pdx1 abundance and insulin content. Activating Nrf2 in a {beta}-cell-specific manner increases {beta}-cell proliferation and {beta}-cell mass. Human islets transplanted under the kidney capsule of immunocompromised mice and treated systemically with CDDO-Me, an Nrf2 activator, display increased {beta}-cell proliferation. Thus, Nrf2 regulates {beta}-cell mass and is an exciting therapeutic target for expanding {beta}-cell mass in diabetes.

physiology↗

bioRxiv2019

HB-EGF Signaling is Required for Glucose-Induced Pancreatic β-Cell Proliferation in Rats

The molecular mechanisms of {beta}-cell compensation to metabolic stress are poorly understood. We previously observed that nutrient-induced {beta}-cell proliferation in rats is dependent on Epidermal Growth Factor Receptor (EGFR) signaling. The aim of this study was to determine the role of the EGFR ligand Heparin-Binding EGF-like Growth Factor (HB-EGF) in the {beta}-cell proliferative response to glucose, a {beta}-cell mitogen and key regulator of {beta}-cell mass in response to increased insulin demand. We show that exposure of isolated rat and human islets to HB-EGF stimulates {beta}-cell proliferation. In rat islets, inhibition of EGFR or HB-EGF blocks the proliferative response not only to HB-EGF but also to glucose. Furthermore, knockdown of HB-EGF in rat islets blocks {beta}-cell proliferation in response to glucose ex vivo and in vivo in transplanted glucose-infused rats. Mechanistically, we demonstrate that HB-EGF mRNA levels are increased in {beta} cells in response to glucose in a Carbohydrate Response Element Binding Protein (ChREBP)-dependent manner. In addition, chromatin-immunoprecipitation studies identified ChREBP binding sites in proximity to the HB-EGF gene. Finally, inhibition of Src family kinases, known to be involved in HB-EGF processing, abrogated glucose-induced {beta}-cell proliferation. Our findings identify a novel glucose/HB-EGF/EGFR axis implicated in {beta}-cell compensation to increased metabolic demand.

cell biology↗

bioRxiv2017

Comparison Of Multi-locus Sequence Typing Software For Next Generation Sequencing Data

Multi-locus sequence typing (MLST) is a widely used method for categorising bacteria. Increasingly MLST is being performed using next generation sequencing data by reference labs and for clinical diagnostics. Many software applications have been developed to calculate sequence types from NGS data; however, there has been no comprehensive review to date on these methods. We have compared six of these applications against real and simulated data and present results on: 1. the accuracy of each method against traditional typing methods, 2. the performance on real outbreak datasets, 3. in the impact of contamination and varying depth of coverage, and 4. the computational resource requirements.\n\nDATA SUMMARYO_LISimulated reads for datasets testing coverage and mixed samples have been deposited in Figshare; DOI: https://doi.org/10.6084/m9.figshare.4602301.vl\nC_LIO_LIOutbreak databases are available from Github; url - https://github.com/WGS-standards-and-analysis/datasets\nC_LIO_LIDocker containers used to run each of the applications are available from Github; url - https://tinyurl.com/z7ks2ft\nC_LIO_LIAccession numbers for the data used in this paper are available in the Supplementary material.\nC_LI\n\nWe confirm all supporting data, code and protocols have been provided within the article or through supplementary data files. {ballotx}\n\nIMPACT STATEMENTSequence typing is rapidly transitioning from traditional sequencing methods to using whole genome sequencing. A number of in silico prediction methods have been developed on an ad hoc basis and aim to replicate Multi-locus sequence typing (MLST). This is the first study to comprehensively evaluate multiple MLST software applications on real validated datasets and on common simulated difficult cases. It will give researchers a clearer understanding of the accuracy, limitations and computational performance of the methods they use, and will assist future researchers to choose the most appropriate method for their experimental goals.

bioinformatics↗

bioRxiv2016

SNVPhyl: A Single Nucleotide Variant Phylogenomics pipeline for microbial genomic epidemiology

MotivationThe recent widespread application of whole-genome sequencing (WGS) for microbial disease investigations has spurred the development of new bioinformatics tools, including a notable proliferation of phylogenomics pipelines designed for infectious disease surveillance and outbreak investigation. Transitioning the use of WGS data out of the research lab and into the front lines of surveillance and outbreak response requires user-friendly, reproducible, and scalable pipelines that have been well validated.\n\nResultsSNVPhyl (Single Nucleotide Variant Phylogenomics) is a bioinformatics pipeline for identifying high-quality SNVs and constructing a whole genome phylogeny from a collection of WGS reads and a reference genome. Individual pipeline components are integrated into the Galaxy bioinformatics framework, enabling data analysis in a user-friendly, reproducible, and scalable environment. We show that SNVPhyl can detect SNVs with high sensitivity and specificity and identify and remove regions of high SNV density (indicative of recombination). SNVPhyl is able to correctly distinguish outbreak from non-outbreak isolates across a range of variant-calling settings, sequencing-coverage thresholds, or in the presence of contamination.\n\nAvailabilitySNVPhyl is available as a Galaxy workflow, Docker and virtual machine images, and a Unix-based command-line application. SNVPhyl is released under the Apache 2.0 license and available at http://snvphyl.readthedocs.io/ or at https://github.com/phac-nml/snvphyl-galaxy.

bioinformatics↗