Biology subjects

Barra, J.

Publications and source records attributed to Barra, J..

3 recordsLinked to original sources

bioRxiv2023

Nuclear cytoglobin associates with HMGB2 and regulates DNA damage and genome-wide transcriptional output in the vasculature

Identifying novel regulators of vascular smooth muscle cell function is necessary to further understand cardiovascular diseases. We previously identified cytoglobin, a hemoglobin homolog, with myogenic and cytoprotective roles in the vasculature. The specific mechanism of action of cytoglobin is unclear but does not seem to be related to oxygen transport or storage like hemoglobin. Herein, transcriptomic profiling of injured carotid arteries in cytoglobin global knockout mice revealed that cytoglobin deletion accelerated the loss of contractile genes and increased DNA damage. Overall, we show that cytoglobin is actively translocated into the nucleus of vascular smooth muscle cells through a redox signal driven by NOX4. We demonstrate that nuclear cytoglobin heterodimerizes with the non-histone chromatin structural protein HMGB2. Our results are consistent with a previously unknown function by which a non-erythrocytic hemoglobin inhibits DNA damage and regulates gene programs in the vasculature by modulating the genome-wide binding of HMGB2.

cell biology↗

bioRxiv2022

DMT1 bridges endosomes and mitochondria to modulate mitochondrial iron translocation

Transient "kiss-and-run" endosome-mitochondria interactions can mediate mitochondrial iron translocation (MIT) but the associated mechanisms are still elusive. We show that Divalent Metal Transporter 1 (DMT1) modulates MIT via endosome-mitochondria interactions in invasive MDA-MB-231, but not in non-invasive T47D breast cancer cells. CRISPR/Cas9-based DMT1 knockout (KO) stable cells were used to demonstrate that DMT1 regulates MIT, endosomal speed, and labile iron pool (LIP) levels only in MDA-MB-231. DMT1 silencing increases PINK1/Parkin mitophagy markers, the autophagy marker LC3B, as well as mitochondrial ferritin in MDA-MB-231, but not in T47D. Strikingly, re-expression of DMT1 in MDA-MB-231 DMT KO cells rescues all protein levels evaluated. DMT1 silencing decreases Tom20 colocalization with PMPCB, a DMT1 interactor that regulates mitophagy hyperactivation. In MDA-MB-231 both mitochondrial metabolism and invasion were impaired by DMT1 silencing and rescued by DMT1 re-expression. DMT1 acts as a bridge between endosomes and mitochondria to support higher MIT/lower LIP levels, which are necessary for sustaining mitochondrial bioenergetics and invasive cancer cell migration. SummaryCellular iron metabolism is tightly regulated, and cancer cells rely on mitochondrial iron for malignancy. Here, we report that the divalent metal transporter DMT1 serves as a bridge between endosomes and mitochondria regulating mitochondrial iron translocation in breast cancer cells.

cell biology↗

bioRxiv2022

ERROR MODELLED GENE EXPRESSION ANALYSIS (EMOGEA) PROVIDES A SUPERIOR OVERVIEW OF TIME COURSE RNA-SEQ MEASUREMENTS AND LOW COUNT GENE EXPRESSION

Serial RNA-seq studies of bulk samples are widespread and provide an opportunity for improved understanding of gene regulation during e.g., development or response to an incremental dose of a pharmacotherapeutic. In addition, the widely popular single cell RNA-seq (scRNA-seq) data implicitly exhibit serial characteristics because measured gene expression values recapitulate cellular transitions. Unfortunately serial RNA-seq data continue to be analyzed by methods that ignore this ordinal structure and yield results that are difficult to interpret. Here, we present Error Modelled Gene Expression Analysis (EMOGEA), a principled framework for analyzing RNA-seq data that incorporates measurement uncertainty in the analysis, while introducing a special formulation for modelling data that are acquired as a function of time or other continuous variable. By incorporating uncertainties in the analysis, EMOGEA is specifically suited for RNA-seq studies in which low-count transcripts with small fold-changes lead to significant biological effects. Such transcripts include signaling mRNAs and non-coding RNAs (ncRNA) that are known to exhibit low levels of expression. Through this approach, missing values are handled by associating with them disproportionately large uncertainties which makes it particularly useful for single cell RNA-seq data. We demonstrate the utility of this framework by extracting a cascade of gene expression waves from a well-designed RNA-seq study of zebrafish embryogenesis and, a scRNA-seq study of mouse pre-implantation and provide unique biological insights into the regulation of genes in each wave. For non-ordinal measurements, we show that EMOGEA has a much higher rate of true positive calls and a vanishingly small rate for false negative discoveries compared to common approaches. Finally, we provide an R package (https://github.com/itikadi/EMOGEA) that is self-contained and easy to use. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=147 SRC="FIGDIR/small/481000v1_figG1.gif" ALT="Figure 1"> View larger version (47K): org.highwire.dtl.DTLVardef@18f3f99org.highwire.dtl.DTLVardef@1989635org.highwire.dtl.DTLVardef@ad964borg.highwire.dtl.DTLVardef@6650be_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical Abstract:C_FLOATNO Graphical representation of EMOGEA indicating the incorporation of measurement errors in modeling RNA-seq data to generate superior results in exploratory analysis, differential gene expression analyses and, scRNA-seq and Time Course analyses. C_FIG

bioinformatics↗