Biology subjects

Abegaz, F.

Publications and source records attributed to Abegaz, F..

6 recordsLinked to original sources

bioRxiv2023

A Strategy for Differential Abundance Analysis of Sparse Microbiome Data with Group-wise Structured Zeros

Comparing the abundance of microbial communities between different groups or obtained under different experimental conditions using count sequence data is a challenging task due to various issues such as inflated zero counts, overdispersion, and non-normality. Several methods and procedures based on counts, their transformation and compositionality have been proposed in the literature to detect differentially abundant species in datasets containing hundreds to thousands of microbial species. Despite efforts to address the the large numbers of zeros present in microbiome datasets, even after careful data preprocessing, the performance of existing methods is impaired by the presence of inflated zero counts and group-wise structured zeros (i.e., all zero counts in a group). We propose and validate using extensive simulations an approach combining two differential abundance testing methods, namely DESeq2-ZINBWaVE and DESeq2, to address the issues of zero-inflation and group-wise structured zeros, respectively. This combined approach was subsequently successfully applied to two plant microbiome datasets that revealed a number of taxa as interesting candidates for further experimental validation.

bioinformatics↗

bioRxiv2023

Age and diet modulate the insulin-sensitizing effects of exercise: a tracer-based oral glucose tolerance test

Diet modulates the development of insulin resistance during aging. This includes tissue-specific alterations in insulin signaling and mitochondrial function, which ultimately affect glucose homeostasis. Exercise stimulates glucose clearance, mitochondrial lipid oxidation and enhances insulin sensitivity. It is not well known how exercise interacts with age and diet in the development of insulin resistance. To investigate this, oral glucose tolerance tests (OGTT) with a tracer were conducted in mice ranging from 4 to 21 months of age, fed a low- (LFD) or high-fat diet (HFD), with or without life-long voluntary access to a running wheel (RW). We developed a computational model to derive glucose fluxes, which were commensurate with independent values from steady-state tracer infusions. Both insulin sensitivity indices derived for peripheral tissues and liver (IS-P and IS-L, respectively) were steeply decreased by aging and a HFD. This preceded the age-dependent decline in the mitochondrial capacity to oxidize lipids. In LFD young animals, RW access enhanced the IS-P concomitantly with the muscle {beta}-oxidation capacity. Surprisingly, RW access completely prevented the age-dependent IS-L decrease, but only in LFD animals. This study indicates, therefore, that endurance exercise can improve the age-dependent decline in organ-specific IS mostly in the context of a healthy diet.

physiology↗

bioRxiv2021

Age-related susceptibility to insulin resistance is due to a combination of CPT1B decline and lipid overload

BACKGROUNDAdvanced age increases the susceptibility to diet-induced insulin resistance (IR). A key driver of this phenomenon is lipid accumulation in the skeletal muscle. It is debated, however, whether this is due to dietary lipid overload or decline of mitochondrial function. To address the interplay of diet and age in the flexibility of muscle lipid and glucose handling, we put young and aged mice on a low- or high-fat diet (HFD). RESULTSAs expected, aged mice were more susceptible to IR when given a HFD than young mice. The HFD induced intramuscular lipid accumulation specifically in aged mice, including C18:0-containing ceramides and diacylglycerols. This was reflected by the mitochondrial {beta}-oxidation capacity, which was upregulated by the HFD in young, but not in old mice. Conspicuously, most {beta}-oxidation proteins were upregulated by the HFD in both groups, but carnitine palmitoyltransferase 1B (CPT1B) declined in aged animals. Computational modelling traced the flux control mostly to CPT1B, suggesting a CPT1B-driven loss of flexibility to the HFD with age. Finally, in old animals glycolytic protein levels were reduced and less flexible to the diet. CONCLUSIONWe conclude that intramuscular lipid accumulation and decreased insulin sensitivity are not due to age-related mitochondrial dysfunction or nutritional overload alone, but rather to their interaction. Moreover, we identify CPT1B as a potential target to counteract age-dependent intramuscular lipid accumulation and thereby IR.

biochemistry↗

bioRxiv2019

Epistasis Detection using Model-Based Multifactor Dimensionality Reduction in Structured Populations

In genome-wide association studies, the extent and impact of confounding due population structure have been well recognized. Inadequate handling of such confounding is likely to lead to spurious associations, hampering replication and the identification of causal variants. Several strategies have been developed for protecting associations against confounding, the most popular one is based on Principal Component Analysis. In contrast, the extent and impact of confounding due to population structure in gene-gene interaction association epistasis studies are much less investigated and understood. In particular, the role of non-linear genetic population substructure in epistasis detection is largely under-investigated, especially outside a regression framework. In order to identify causal variants in synergy, to improve interpretability and replicability of epistasis results, we introduce three strategies based on model-based multifactor dimensionality reduction (MB-MDR) approach for structured populations. We demonstrate through extensive simulation studies the effect of various degrees of genetic population structure and relatedness on epistasis detection and propose appropriate remedial measures based on linear and non-linear sample genetic similarity. Authors SummaryOne of the biggest challenges in human genetics is to understand the genetic basis of complex diseases such as cancer, diabetes, heart disease, depression, asthma, inflammatory bowel disease and hypertension, for instance via identifying genes, gene-gene and gene-environment interactions in association studies. Over the years, a more prominent role has been given to gene-gene interaction (epistasis) detection, in view of precision medicine and the hunt for novel drug targets and biomarkers. However, the increasing number of consortium-based epistasis studies that are marked by heterogeneous sample collections due to population structure or shared genetic ancestry are likely to be prone to spurious association and low power detection of associated or causal genes. In this work we introduced various strategies in epistasis studies with correction for confounding due to population structure. Based on extensive simulation studies we demonstrated the effect of genetic population structure on epistasis detection and investigated remedial measures to confounding by linear and nonlinear sample genetic similarity.

bioinformatics↗

bioRxiv2017

A methodology for unsupervised clustering using iterative pruning to capture fine-scale structure

SNP-based information is used in several existing clustering methods to detect shared genetic ancestry or to identify population substructure. Here, we present a methodology for unsupervised clustering using iterative pruning to capture fine-scale structure called IPCAPS. Our method supports ordinal data which can be applied directly to SNP data to identify fine-scale population structure. We compare our method to existing tools for detecting fine-scale structure via simulations. The simulated data do not take into account haplotype information, therefore all markers are independent. Although haplotypes may be more informative than SNPs, especially in fine-scale detection analyses, the haplotype inference process often remains too computationally intensive. Therefore, our strategy has been to restrict attention to SNPs and to investigate the scale of the structure we are able to detect with them. We show that the experimental results in simulated data can be highly accurate and an improvement to existing tools. We are convinced that our method has a potential to detect fine-scale structure.

bioinformatics↗

bioRxiv2017

IPCAPS: an R package for iterative pruning to capture population structure

BackgroundResolving population genetic structure is challenging, especially when dealing with closely related or geographically confined populations. Although Principal Component Analysis (PCA)-based methods and genomic variation with single nucleotide polymorphisms (SNPs) are widely used to describe shared genetic ancestry, improvements can be made especially when fine-scale population structure is the target.\n\nResultsThis work presents an R package called IPCAPS, which uses SNP information for resolving possibly fine-scale population structure. The IPCAPS routines are built on the iterative pruning Principal Component Analysis (ipPCA) framework that systematically assigns individuals to genetically similar subgroups. In each iteration, our tool is able to detect and eliminate outliers, hereby avoiding severe misclassification errors.\n\nConclusionsIPCAPS supports different measurement scales for variables used to identify substructure. Hence, panels of gene expression and methylation data can be accommodated as well. The tool can also be applied in patient sub-phenotyping contexts. IPCAPS is developed in R and is freely available from bio3.giga.ulg.ac.be/ipcaps

bioinformatics↗