Biology subjects

Moniz, K.

Publications and source records attributed to Moniz, K..

3 recordsLinked to original sources

bioRxiv2025

Industrialization drives convergent microbial and physiological shifts in the human metaorganism

Understanding how host lifestyle and industrialization shape the human gut microbiome and intestinal physiology requires multimodal analyses across diverse global host contexts. Here, we generate multivariate data from the Global Microbiome Conservancy cohort, including gut microbiome, IgA-sequencing, host genotyping, diet, lifestyle and fecal biomarker profiles, to investigate host-microbiome interactions across gradients of industrialization and geography. We show that industrialization is associated with homogenized microbial compositions, reduced microbial diversity, and lower community stability, independent of host confounders. We further show that industrialization is linked to elevated markers of gut stress, increased IgA secretion, and altered patterns of IgA-bacteria interactions. Finally, we show that microbiome-based disease predictors trained on industrialized populations lose accuracy in less industrialized cohorts, highlighting limited cross-population transferability. Together, our results suggest profound restructuring of host-microbiome interactions due to industrialized lifestyles, and emphasize the need for inclusive, globally representative data to improve translational microbiome applications across diverse human populations.

microbiology↗

bioRxiv2020

Industrialization is associated with elevated rates of horizontal gene transfer in the human microbiome

Horizontal Gene Transfer, the process by which bacteria acquire new genes and functions from non-parental sources, is common in the human microbiome 1,2. If the timescale of HGT is rapid compared to the timescale of human colonization, then it could have the effect of personalizing bacterial genomes by providing incoming strains with the genes necessary to adapt to the diet or lifestyle of a new host. The extent to which HGT occurs on the timescale of human colonization, however, remains unclear. Here, we analyzed 6,188 newly isolated and sequenced gut bacteria from 34 individuals in 9 human populations, and show that HGT is more common among bacteria isolated from the same human host, indicating that the timescale of transfer is short compared to the timescale of human colonization. Comparing across 9 human populations reveals that high rates of transfer may be a recent development in human history linked to industrialization and urbanization. In addition, we find that the genes involved in transfer reflect the lifestyle of the human hosts, with elevated transfer of carbohydrate metabolism genes in hunter gatherer populations, and transfer of antibiotic resistance genes among pastoralists who live in close contact with livestock. These results suggest that host-associated bacterial genomes are not static within individuals, but continuously acquire new functionality based on host diet and lifestyle.

microbiology↗

bioRxiv2018

baRcodeR with PyTrackDat: Open-source labelling and tracking of biological samples for repeatable science

Repeatable experiments with accurate data collection and reproducible analyses are fundamental to the scientific method but may be difficult to achieve in practice. Several flexible, open-source tools developed for the R and Python coding environments aid the reproducibility of data wrangling and analysis in scientific research. In contrast, analogous tools are generally lacking for earlier stages, such as systematic labelling and processing of field samples with hierarchical structure (e.g. time points of individuals from multiple lines or populations) or curating heterogenous data collected by different researchers over several years. Such tools are critical for modern research given trends toward globally distributed collaborators using higher-throughput technologies. As a step toward improving repeatability of methods for the collection of biological samples, and curation of biological data, we introduce the R package baRcodeR and the PyTrackDat pipeline in Python. The baRcodeR package provides tools for generating biologically informative, hierarchical labels with digitally encoded 2D barcodes that can be printed and scanned using low-cost commercial hardware. The PyTrackDat pipeline integrates with baRcodeR output to build a web interface for sample management and tracking along with data collection and curation. We briefly describe the application of principles from baRcodeR and PyTrackDat in three large research projects, which demonstrate their value to (i) help document sampling methods, (ii) facilitate collaboration and (iii) reduce opportunities for human errors and omissions that could otherwise propagate through downstream data analysis to compromise biological inference.

bioinformatics↗