Noguera-Julian, M. | Search bioRxiv

bioRxiv2018

Host genetic associations with the gut microbiota in HIV-1-infected subjects: a pilot exploratory study

The impact of host genetics on gut microbial dynamics is debated. No study to date has investigated the possible role of host genetics in shaping the gut microbiota in HIV-1 infected subjects. With the aim of generating preliminary data to inform future host genetic studies, we performed an exploratory host exome analysis of 147 subjects either infected or at risk of becoming infected with HIV-1 from the MetaHIV cohort in Barcelona. Using a DNA microarray chip, we sought to identify host genetic variants associated to three specific microbial features with a potentially inheritable component, and which were previously found to be associated with gut dysbiosis in HIV infection, i.e.: gut enterotype, presence of methanogenic archaea and microbial gene richness. After correction for multiple comparisons, we did not observe any statistically significant association between the hosts genetic landscape and the explored gut microbiome traits. These findings will help design future, adequately-powered studies to assess the influence of host genetics in the microbiome of HIV-1-infected subjects.

genomics↗

bioRxiv2017

Balances: a new perspective for microbiome analysis

High-throughput sequencing technologies have revolutionized microbiome research by allowing the relative quantification of microbiome composition and function in different environments. One of the main goals in microbiome analysis is the identification of microbial species that are differentially abundant among groups of samples, or whose abundance is associated with a variable of interest. Most available methods for microbiome abundance testing perform univariate tests for each microbial species or taxa separately, ignoring the compositional nature of microbiome data.\n\nWe propose an alternative approach for microbiome abundance testing that consists on the identification of two groups of taxa whose relative abundance, or balance, is associated with the response variable of interest. This approach is appealing, since it has direct translation to the biological concept of ecological balance between species in an ecosystem. In this work, we present selbal, a greedy stepwise algorithm for balance selection. We illustrate the algorithm with 16s abundance data from an HIV-microbiome study and a Crohn-microbiome study.\n\nImportanceA more meaningful approach for microbiome abundance testing is presented. Instead of testing each taxon separately we propose to explore abundance balances among groups of taxa. This approach acknowledges the compositional nature of microbiome data.

bioinformatics↗