Biology subjects

Tintle, N.

Publications and source records attributed to Tintle, N..

4 recordsLinked to original sources

bioRxiv2021

Using summary statistics to evaluate the genetic architecture of multiplicative combinations of initially analyzed phenotypes with a flexible choice of covariates

While the promise of electronic medical record and biobank data is large, major questions remain about patient privacy, computational hurdles, and data access. One promising area of recent development is pre-computing non-individually identifiable summary statistics to be made publicly available for exploration and downstream analysis. In this manuscript we demonstrate how to utilize pre-computed linear association statistics between individual genetic variants and phenotypes to infer genetic relationships between products of phenotypes (e.g., ratios; logical combinations of binary phenotypes using and and or) with customized covariate choices. We propose a method to approximate covariate adjusted linear models for products and logical combinations of phenotypes using only pre-computed summary statistics. We evaluate our methods accuracy through several simulation studies and an application modeling various fatty acid ratios using data from the Framingham Heart Study. These studies show consistent ability to recapitulate analysis results performed on individual level data including maintenance of the Type I error rate, power, and effect size estimates. An implementation of this proposed method is available in the publicly available R package pcsstools.

genetics↗

bioRxiv2019

Free fatty acid receptor 4 is a nutrient sensor that resolves inflammation to maintain cardiac homeostasis

BackgroundNon-{-}resolving activation of immune responses is central to the pathogenesis of heart failure (HF). Free fatty acid receptor 4 (Ffar4) is a G-protein coupled receptor (GPR) for medium-and long-chain fatty acids (FA) that regulates metabolism and attenuates inflammation in diabetes and obesity. Here, we tested the hypothesis that Ffar4 functions as a cardioprotective nutrient sensor that resolves inflammation to maintain cardiac homeostasis.\n\nMethodsMice with systemic deletion of Ffar4 (Ffar4KO) were subjected to pressure overload by transverse aortic constriction (TAC). Transcriptome analysis of cardiac myocytes was performed three days post-TAC. Additionally, Ffar4-mediated effects on inflammatory oxylipin production in cardiac myocytes and oxylipin composition in plasma lipoproteins were evaluated.\n\nResultsIn Ffar4KO mice, TAC induced more severe remodeling, identifying an entirely novel cardioprotective role for Ffar4 in the heart. Transcriptome analysis 3-days post-TAC indicated a failure to induce cell death and inflammatory genes in Ffar4KO cardiac myocytes, as well as a specific failure to induce cytoplasmic phospholipase A2 (cPLA2) signaling genes. In cardiac myocytes, Ffar4 signaling through cPLA2-cytochrome p450 {omega}/{omega}-1 hydroxylase induced production of the EPA-derived anti-inflammatory oxylipin 18-hydroxyeicosapentaenoic acid (18-HEPE). Systemically, loss of Ffar4 altered oxylipin content in circulating plasma lipoproteins consistent with a loss of anti-inflammatory oxylipins at baseline, and inability to produce both pro-inflammatory and pro-resolving oxylipins following TAC. Finally, we confirmed that Ffar4 is expressed in human heart and down-regulated in HF.\n\nConclusionsOur results identify a novel function for Ffar4 in the heart as a FA nutrient sensor that resolves inflammation to maintain cardiac homeostasis.

physiology↗

bioRxiv2018

Transcriptome assembly and annotation of johnsongrass (Sorghum halepense) rhizomes identifies candidate rhizome specific genes

Rhizomes facilitate the wintering and vegetative propagation of many perennial grasses. Sorghum halepense (johnsongrass) is an aggressive perennial grass that relies on a robust rhizome system to persist through winters and reproduce asexually from its rootstock nodes. This study aimed to sequence and assemble expressed transcripts within the johnsongrass rhizome. A de novo transcriptome assembly was generated from a single johnsongrass rhizome meristem tissue sample. A total of 141,176 probable protein-coding sequences from the assembly were identified and assigned gene ontology terms using Blast2GO. The johnsongrass assembly was compared to Sorghum bicolor, a related non-rhizomatous species, along with an assembly of similar rhizome tissue from the perennial grain crop Thinopyrum intermedium. The presence/absence analysis yielded a set of 259 johnsongrass contigs that are likely associated with rhizome development.

genomics↗

bioRxiv2017

IMPROVEMENTS TO BAYESIAN GENE ACTIVITY STATE ESTIMATION FROM GENOME-WIDE TRANSCRIPTOMICS DATA

An important question in many biological applications, is to estimate or classify gene activity states (active or inactive) based on genome-wide transcriptomics data. Recently, we proposed a Bayesian method, titled MultiMM, which showed superior results compared to existing methods. In short, MultiMM performed better than existing methods on both simulated and real gene expression data, confirming well-known biological results and yielding better agreement with fluxomics data. Despite these promising results, MultiMM has numerous limitations. First, MultiMM leverages co-regulatory models to improve activity state estimates, but information about co-regulation is incorporated in a manner that assumes that networks are known with certainty. Second, MultiMM assumes that genes that change states in the dataset can be distinguished with certainty from those that remain in one state. Third, the model can be sensitive to extreme measures (outliers) of gene expression. In this manuscript, we propose a modified Bayesian approach, which addresses these three limitations by improving outlier handling and by explicitly modeling network and other uncertainty yielding improved gene activity state estimates when compared to MultiMM.

genomics↗