Biology subjects

Hicks, J. E.

Publications and source records attributed to Hicks, J. E..

3 recordsLinked to original sources

bioRxiv2022

HIGHLY PHAGOCYTIC LIPID-ASSOCIATED MACROPHAGES (LAMs) ARE INCREASED IN COLONIC LAMINA PROPRIA IN OBESITY

Little is known about the effects of high fat diet (HFD)-induced obesity on resident colonic lamina propria (LP) macrophages (LPMs) function and metabolism. Here, we report that obesity and diabetes resulted in increased macrophage infiltration in the colon. These macrophages exhibited the residency phenotype CX3CR1hiMHCIIhi, and were CD4-TIM4-. During HFD, resident colonic LPM exhibited a lipid metabolism gene expression signature that overlapped that used to define lipid associated macrophages (LAMs). Via single cell RNA sequencing, we identified a sub-cluster of macrophages, increased in HDF, that were responsible for the LAM signature. Compared to other macrophages in the colon, these cells were characterized by elevated glycolysis, phagocytosis and efferocytosis signatures. CX3CR1hiMHCIIhi colonic resident LPMs had fewer lipid droplets (LD) and decreased triacylglycerol (TAG) content compared to equivalent cells in lean mice, and exhibited increased phagocytic capacity, suggesting that HFD induces adaptive responses in LPMs to limit bacterial translocation.

immunology↗

bioRxiv2017

Identifying the phenotypic effect of rare variants by including between-pedigree coancestry in variance components linkage analysis

The contribution of rare variants to disease burden has become an important focus in genetic epidemiology. These effects are difficult to detect in population-based datasets, and as a result, interest in family-based study designs has resurfaced. Linkage analysis tools will need to be updated to accommodate the scale of data generated by modern genotyping and sequencing technologies.\n\nIn conventional linkage analysis individuals in different pedigrees are assumed to be independent of each other. However, cryptic relatedness is often present in populations and haplotypes that harbor rare variants may be shared between pedigrees as well as within them.\n\nWith millions of polymorphisms, Identity-by-descent (IBD) states across the genome can now be inferred without use of pedigree information. This is done by identifying long runs of identical-by-state genotypes which are unlikely to arise without IBD. Previously, IBD had to be estimated in pedigrees from recombination events in a sparse set of markers.\n\nWe present a method for variance-components linkage that can incorporate large number of markers and allows for between-pedigree relatedness. We replace the IBD matrix generated from pedigree-based analysis with one generated from a genotype-based method. All pedigrees in a dataset are considered jointly, allowing between-pedigree IBD to be included in the model.\n\nIn simulated data, we show that power is increased in the scenario when there is a haplotype shared IBD between members of different pedigrees. If there is no between-pedigree IBD, the analysis reduces to conventional variance-components analysis. By determining IBD states by long runs of dense IBS genotypes, linkage signals can be determined from their physical position, allowing more precise localization.

genetics↗

bioRxiv2017

Pydigree: a python library for manipulation and forward-time simulation and of genetic datasets

The development of software for working with data from population genetics or genetic epidemiology often requires substantial time spent implementing common procedures. Pydigree is a cross-platform Python 3 library that contains efficient, user friendly implementations for many of these common functions, and support for input from common file formats. Developers can combine the functions and data structures to rapidly implement programs handling genetic data. Pydigree presents a useful environment for development of applications for genetic data or rapid prototyping before reimplementation in a higher-performance language.\n\nPydigree is freely available under an open source license. Stable sources can be found in the Python Package Index at https://pypi.python.org/pypi/pydigree/, and development sources can be downloaded at https://github.com/jameshicks/pydigree/

bioinformatics↗