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Briscoe, L.

Publications and source records attributed to Briscoe, L..

4 recordsLinked to original sources

Uniform bacterial genetic diversity along the guts of mice inoculated with human stool

Environmental gradients throughout the digestive tract shape spatial variation in the composition and abundance of bacterial species along the gut. However, much less is known about how genetic diversity within bacterial species is distributed along the gut. Understanding this distribution is important because bacterial genetic variants confer traits that influence both microbiome function and host physiology, including local inflammation and nutrient metabolism. Thus, to understand how the microbiome functions at a mechanistic level, it is essential to understand how genetic diversity is organized along the gut. In this study, we profiled genetic diversity of approximately 30 common gut commensal bacteria in five regions along the gut lumen in germ-free mice colonized with the same healthy human stool sample. Although species composition varied considerably along the gut, genetic diversity within species was substantially more uniform. Driving this uniformity were similar strain frequencies along the gut, implying that multiple, genetically divergent strains of the same species can coexist within a host without spatially segregating. Additionally, the approximately 60 unique evolutionary adaptations arising within mice tended to sweep throughout the gut, showing little gut region specificity. We then analyzed metagenomic samples collected along the guts of conventional mice and healthy humans and found similar dynamics with their natural microbiomes, suggesting that uniform bacterial genetic diversity may be common to multiple host species. Together, our findings demonstrate that uniform spatial distribution of genetic diversity along the gastrointestinal tract is a robust feature of mammalian gut ecosystems.

microbiology↗

Microbiome source tracking using single nucleotide variants

Elucidating the sources of a microbiome can provide insight into the ecological dynamics responsible for the formation of these communities. "Source tracking" approaches to date leverage species abundance information, however, single nucleotide variants (SNVs) may be more informative because of their high specificity to certain sources. To overcome the computational burden of utilizing all SNVs for a given sample, we introduce a novel method to identify signature SNVs for source tracking. We show that signature SNVs used as input into a previously designed source tracking algorithm, FEAST, can more accurately estimate contributions than species and provide novel insights, demonstrated in three case studies.

bioinformatics↗

Correcting for Background Noise Improves Phenotype Prediction from Human Gut Microbiome Data

The ability to predict human phenotypes accurately from metagenomic data is crucial for developing biomarkers and therapeutics for diseases. However, metagenomic data is commonly affected by technical or biological variables, unrelated to the phenotype of interest, such as sequencing protocol or host sex, which can greatly reduce or, when correlated to the phenotype of interest, inflate prediction accuracy. We perform a comparative analysis of the ability of different data transformations and existing supervised and unsupervised methods to correct microbiome data for background noise. We find that supervised methods are limited because they cannot account for unmeasured sources of variation. In addition, we observe that unsupervised approaches are often superior in addressing these issues, but existing methods developed for other omic data types, e.g., gene expression and methylation, are restricted by parametric assumptions unsuitable for microbiome data, which is typically compositional, highly skewed, and sparse. We show that application of the centered log-ratio transformation prior to correction with unsupervised approaches improves prediction accuracy for many phenotypes while simultaneously reducing variance due to unwanted sources of variation. As new and larger metagenomic datasets become increasingly available, background noise correction will become essential for generating reproducible microbiome analyses.

bioinformatics↗

Impact of soil salinity on the cowpea nodule-microbiome and the isolation of halotolerant PGPR strains to promote plant growth under salinity stress

Four soil samples (SS-1--SS-4) isolated from semi-arid soils in Punjab, Pakistan were used as inocula for cowpea (Vigna unguiculata L.) grown under salinity stress to analyze the composition of bacteria in the rhizosphere and within nodules through cultivation-dependent and cultivation-independent methods. Two cowpea varieties, 603 and the salt-tolerant CB 46, were each inoculated with four different native soil samples, and data showed that plants inoculated with soil samples SS-2 and SS-4 grew better than plants inoculated with soil samples SS-1 and SS-3. Bacteria were isolated from both soils and nodules, and 34 of the 51 original isolates tested positive for PGPR traits in plate assays with many exhibiting multiple plant growth-promoting properties. A number of isolates were positive for all PGPR traits tested. For the microbiome studies, environmental DNA (eDNA) was isolated from SS-1 and SS-4, which represented the extremes of the Pakistan soils to which the plants responded, and by 16S rRNA gene sequencing analysis were found to consist mainly of Actinobacteria, Firmicutes, and Proteobacteria. However, sequencing analysis of eDNA isolated from cowpea nodules established by the trap plants grown in the four Pakistan soils indicated that the nodule microbiome consisted almost exclusively of Proteobacterial sequences, particularly Bradyrhizobium. Yet, many other bacteria including Rhizobium, Mesorhizobium, Pseudomonas, as well as Paenibacillus, Bacillus as well as non-proteobacterial genera were isolated from the nodules of soil-inoculated cowpea plants. This discrepancy between the bacteria isolated from cowpea nodules (Proteobacteria and non-Proteobacteria) versus those detected in the nodule microbiome (Proteobacteria) needs further study.

plant biology↗