Biology subjects

McCall, M. N.

Publications and source records attributed to McCall, M. N..

4 recordsLinked to original sources

bioRxiv2018

Cardioprotection by the Mitochondrial Unfolded Protein Response is Mediated by ATF5

The mitochondrial unfolded protein response (UPRmt)1 is a cytoprotective signaling pathway triggered by mitochondrial dysfunction. Activation of the UPRmt upregulates nuclear-encoded mitochondrial genes, including those for chaperones, proteases, and antioxidants, as well as glycolysis, to restore proteostasis and cell energetics. Activating transcription factor 5 (ATF5), a protein with both mitochondrial and nuclear targeting sequences, is proposed to mediate mammalian UPRmt signaling. Since proteostasis and bioenergetics are important in the response of organs such as the heart to injury, we hypothesized that pharmacologic UPRmt activation may be cardioprotective against ischemia-reperfusion (IR) injury and that such protection would require ATF5. Using a perfused heart IR injury model in wild-type and global Atf5-/- mice, we found that in-vivo administration of the UPRmt inducers oligomycin or doxycycline 6 h prior to ex-vivo IR injury was cardioprotective. Such protection was absent in hearts from Atf5-/- mice, and no protection was observed with acute ex-vivo cardiac administration of doxycycline. Loss of ATF5 also did not alter baseline IR injury (without UPRmt induction). Cardiac gene expression analysis by RNA-Seq revealed mild induction of numerous genes in an ATF5-dependent manner, which may be important for cardioprotection. Analysis of hearts by qPCR showed that oligomycin at 6 h significantly induced genes encoding ATF5 and several known UPRmt-linked proteins. We conclude that ATF5 is required for cardioprotection induced by drugs that activate the UPRmt.

cell biology↗

bioRxiv2018

xMD-miRNA-seq to generate near in vivo miRNA expression estimates in colon epithelial cells

Accurate, RNA-seq based, microRNA (miRNA) expression estimates from primary cells have recently been described. However, this in vitro data is mainly obtained from cell culture, which is known to alter cell maturity/differentiation status, significantly changing miRNA levels. What is needed is a robust method to obtain in vivo miRNA expression values directly from cells. We introduce expression microdissection miRNA small RNA sequencing (xMD-miRNA-seq), a method to isolate cells directly from formalin fixed paraffin-embedded (FFPE) tissues. xMD-miRNA-seq is a low-cost, high-throughput, immunohistochemistry-based method to capture any cell type of interest. As a proof-of-concept, we isolated colon epithelial cells from two specimens and performed low-input small RNA-seq. We generated up to 600,000 miRNA reads from the samples. Isolated epithelial cells, had abundant epithelial-enriched miRNA expression (miR-192; miR-194; miR-200b; miR-200c; miR-215; miR-375) and overall similar miRNA expression patterns to other epithelial cell populations (colonic enteroids and flow-isolated colon epithelium). xMD-derived epithelial cells were generally not contaminated by other adjacent cells of the colon as noted by t-SNE analysis. xMD-miRNA-seq allows for simple, economical, and efficient identification of cell-specific miRNA expression estimates. Further development will enhance rapid identification of cell-specific miRNA expression estimates in health and disease for nearly any cell type using archival FFPE material.

molecular biology↗

bioRxiv2017

Statistical Approaches to Decreasing the Discrepancy of Non-detects in qPCR Data

Quantitative real-time PCR (qPCR) is one of the most widely used methods to measure gene expression. Despite extensive research in qPCR laboratory protocols, normalization, and statistical analysis, little attention has been given to qPCR non-detects - those reactions failing to produce a minimum amount of signal. While most current software replaces these non-detects with a value representing the limit of detection, recent work suggests that this introduces substantial bias in estimation of both absolute and differential expression. Recently developed single imputation procedures, while better than previously used methods, underestimate residual variance, which can lead to anti-conservative inference. We propose to treat non-detects as non-random missing data, model the missing data mechanism, and use this model to impute missing values or obtain direct estimates of relevant model parameters. To account for the uncertainty inherent in the imputation, we propose a multiple imputation procedure, which provides a set of plausible values for each non-detect. In the proposed modeling framework, there are three sources of uncertainty: parameter estimation, the missing data mechanism, and measurement error. All three sources of variability are incorporated in the multiple imputation and direct estimation algorithms. We demonstrate the applicability of these methods on three real qPCR data sets and perform an extensive simulation study to assess model sensitivity to misspecification of the missing data mechanism, to the number of replicates within the sample, and to the overall size of the data set. The proposed methods result in unbiased estimates of the model parameters; therefore, these approaches may be beneficial when estimating both absolute and differential gene expression. The developed methods are implemented in the R/Bioconductor package nondetects. The statistical methods introduced here reduce discrepancies in gene expression values derived from qPCR experiments, providing more confidence in generating scientific hypotheses and performing downstream analysis.

bioinformatics↗

bioRxiv2017

Towards the human cellular microRNAome

microRNAs are short RNAs that serve as master regulators of gene expression and are essential components of normal development as well as modulators of disease. MicroRNAs generally act cell autonomously and thus their localization to specific cell types is needed to guide our understanding of microRNA activity. Current tissue-level data has caused considerable confusion and comprehensive cell-level data does not yet exist. Here we establish the landscape of human cell-specific microRNA expression. This project evaluated 8 billion small RNA-seq reads from 46 primary cell types, 42 cancer or immortalized cell lines, and 26 tissues. It identified both specific and ubiquitous patterns of expression that strongly correlate with adjacent super-enhancer activity. Analysis of unaligned RNA reads uncovered 207 unknown minor strand (passenger) microRNAs of known microRNA loci and 2,632 novel putative microRNA loci. Although cancer cell lines generally recapitulated the expression patterns of matched primary cells, their isomiR sequence families exhibited increased disorder suggesting Drosha and Dicer-dependent microRNA processing variability. Cell-specific patterns of microRNA expression were used to deconvolute variable cellular composition of adipose tissue samples highlighting one use of this cell-specific microRNA expression data. Characterization of cellular microRNA expression across a wide variety of cell types provides a new understanding of this critical regulatory RNA species.

genomics↗