Biology subjects

Lang, J. M.

Publications and source records attributed to Lang, J. M..

4 recordsLinked to original sources

bioRxiv2020

Machine learning reveals time-varying microbial predictors with complex effects on glucose regulation

The incidence of type 2 diabetes (T2D) has been increasing globally and a growing body of evidence links type 2 diabetes with altered microbiota composition. Type 2 diabetes is preceded by a long pre-diabetic state characterized by changes in various metabolic parameters. We tested whether the gut microbiome could have predictive potential for T2D development during the healthy and pre-diabetic disease stages. We used prospective data of 608 well-phenotyped Finnish men collected from the population-based Metabolic Syndrome In Men (METSIM) study to build machine learning models for predicting continuous glucose and insulin measures in a shorter (1.5 year) and longer (4.5 year) period. Our results show that the inclusion of gut microbiome improves prediction accuracy for modelling T2D associated parameters such as glycosylated hemoglobin and insulin measures. We identified novel microbial biomarkers and described their effects on the predictions using interpretable machine learning techniques, which revealed complex linear and non-linear associations. Additionally, the modelling strategy carried out allowed us to compare the stability of model performances and biomarker selection, also revealing differences in short-term and long-term predictions. The identified microbiome biomarkers provide a predictive measure for various metabolic traits related to T2D, thus providing an additional parameter for personal risk assessment. Our work also highlights the need for robust modelling strategies and the value of interpretable machine learning. ImportanceRecent studies have shown a clear link between gut microbiota and type 2 diabetes. However, current results are based on cross-sectional studies that aim to determine the microbial dysbiosis when the disease is already prevalent. In order to consider microbiome as a factor in disease risk assessment, prospective studies are needed. Our study is the first study that assesses the gut microbiome as a predictive measure for several type 2 diabetes associated parameters in a longitudinal study setting. Our results revealed a number of novel microbial biomarkers that can improve the prediction accuracy for continuous insulin measures and glycosylated hemoglobin levels. These results make the prospect of using microbiome in personalized medicine promising.

microbiology↗

bioRxiv2020

Repeated gain and loss of a single gene modulates the evolution of vascular pathogen lifestyles

Vascular pathogens travel long distances through host veins leading to life-threatening, systemic infections. In contrast, non-vascular pathogens remain restricted to infection sites, triggering localized symptom development. The contrasting features of vascular and non-vascular diseases suggest distinct etiologies, but the basis for each remains unclear. Here, we show that the hydrolase CbsA acts as a phenotypic switch between vascular and non-vascular plant pathogenesis. cbsA was enriched in genomes of vascular phytopathogenic bacteria in the Xanthomonadaceae family and absent in most non-vascular species. CbsA expression allowed non-vascular Xanthomonas to cause vascular blight while cbsA mutagenesis resulted in reduction of vascular or enhanced non-vascular symptom development. Phylogenetic hypothesis testing further revealed that cbsA was lost in multiple non-vascular lineages and more recently gained by some vascular subgroups, suggesting that vascular pathogenesis is ancestral. Our results overall demonstrate how the gain and loss of single loci can facilitate the evolution of complex ecological traits.

microbiology↗

bioRxiv2019

Genomic acquisitions in emerging populations of Xanthomonas vasicola pv. vasculorum infecting corn in the U.S. and Argentina

Xanthomonas vasicola pv. vasculorum (Xvv) is an emerging bacterial plant pathogen that causes bacterial leaf streak on corn. First described in South Africa in 1949, reports of this bacteria have greatly increased in the past years in South America and in the U.S., where it is now present in most of the corn producing states. Phenotypic characterization showed that the emerging U.S. and South American Xvv populations may have increased virulence in corn compared to older strains. To understand the genetic mechanisms behind the increased virulence in this group, we used comparative genomics to identify gene acquisitions in Xvv genomes from the U.S. and Argentina. We sequenced 41 genomes of Xvv and the related sorghum-infecting X. vasicola pv. holcicola (Xvh). A comparison of all available X. vasicola genomes showed the phylogenetic relationships in the group and identified clusters of genes associated with the emerging Xvv populations. The newly acquired gene clusters showed evidence of horizontal transfer to Xvv and included candidate virulence factors. One cluster, in particular, corresponded to a prophage transferred from Xvh to all Xvv from Argentina and the U.S. The prophage contains putative secreted proteins, which represent candidates for virulence determinants in these populations and await further molecular characterization.

microbiology↗

bioRxiv2016

Global-scale structure of the eelgrass microbiome

Plant-associated microorganisms are essential for their hosts' survival and performance. Yet, most plant microbiome studies to date have focused on terrestrial plant species sampled across relatively small spatial scales. Here we report results of a global-scale analysis of microbial communities associated with leaf and root surfaces of the marine eelgrass Zostera marina throughout its range in the Northern Hemisphere. By contrasting host microbiomes with those of their surrounding seawater and sediment communities, we uncovered the structure, composition and variability of microbial communities associated with Z. marina. We also investigated hypotheses about the mechanisms driving assembly of the eelgrass microbiome using a whole-genomic metabolic modeling approach. Our results reveal aboveground leaf communities displaying high variability and spatial turnover, that strongly mirror their adjacent coastal seawater microbiomes. In contrast, roots showed relatively low spatial turnover and were compositionally distinct from surrounding sediment communities -- a result largely driven by the enrichment of predicted sulfur-oxidizing bacterial taxa on root surfaces. Metabolic modeling of enriched taxa was consistent with an assembly process whereby similarity in resource use drives taxonomic co-occurrence patterns on belowground, but not aboveground, host tissues. Our work provides evidence for a core Z. marina root microbiome with putative functional roles and highlights potentially disparate processes influencing microbiome assembly on different plant compartments.

microbiology↗