Dataset Information

Tunable Expression Tools Enable Single-Cell Strain Distinction in the Gut Microbiome.

ABSTRACT: Applying synthetic biology to engineer gut-resident microbes provides new avenues to investigate microbe-host interactions, perform diagnostics, and deliver therapeutics. Here, we describe a platform for engineering Bacteroides, the most abundant genus in the Western microbiota, which includes a process for high-throughput strain modification. We have identified a novel phage promoter and translational tuning strategy and achieved an unprecedented level of expression that enables imaging of fluorescent-protein-expressing Bacteroides stably colonizing the mouse gut. A detailed characterization of the phage promoter has provided a set of constitutive promoters that span over four logs of strength without detectable fitness burden within the gut over 14 days. These promoters function predictably over a 1,000,000-fold expression range in phylogenetically diverse Bacteroides species. With these promoters, unique fluorescent signatures were encoded to allow differentiation of six species within the gut. Fluorescent protein-based differentiation of isogenic strains revealed that priority of gut colonization determines colonic crypt occupancy.

SUBMITTER: Whitaker WR

PROVIDER: S-EPMC5576361 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

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Tunable Expression Tools Enable Single-Cell Strain Distinction in the Gut Microbiome.

Whitaker Weston R WR Shepherd Elizabeth Stanley ES Sonnenburg Justin L JL

Cell 20170401 3

Applying synthetic biology to engineer gut-resident microbes provides new avenues to investigate microbe-host interactions, perform diagnostics, and deliver therapeutics. Here, we describe a platform for engineering Bacteroides, the most abundant genus in the Western microbiota, which includes a process for high-throughput strain modification. We have identified a novel phage promoter and translational tuning strategy and achieved an unprecedented level of expression that enables imaging of fluo ...[more]

PMID: 28431251

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Project description:Background - Prepregnancy overweight and obesity promote deleterious health impacts on both mothers during pregnancy and the offspring. Significant changes in the maternal peripheral blood mononuclear cells (PBMCs) gene expression due to obesity are well-known. However, during pregnancy the impact of overweight on immune cell gene expression and its association with maternal and infant outcomes is not well explored. Methods – Blood samples were collected from healthy normal weight (NW, BMI 18.5-24.9) or overweight (OW, BMI 25-29.9) 2nd parity pregnant women at 12, 24 and 36 weeks of pregnancy. PBMCs were isolated from the blood and subjected to mRNA sequencing. Maternal and infant microbiota were analyzed by 16S rRNA gene sequencing. Integrative multi-omics data analysis was performed to evaluate the association of gene expression with maternal diet, gut microbiota, milk composition, and infant gut microbiota. Results - Gene expression analysis revealed that 453 genes were differentially expressed in the OW women compared to NW women at 12 weeks of pregnancy, out of which 354 were upregulated and 99 were downregulated. Several up-regulated genes in the OW group were enriched in inflammatory, chemokine-mediated signaling and regulation of interleukin-8 production-related pathways. At 36 weeks of pregnancy healthy eating index score was positively associated with several genes that include, DTD1, ELOC, GALNT8, ITGA6-AS1, KRT17P2, NPW, POT1-AS1 and RPL26. In addition, at 36 weeks of pregnancy, genes involved in adipocyte functions, such as NG2 and SMTNL1, were negatively correlated to human milk 2’FL and total fucosylated oligosaccharides content collected at 1 month postnatally. Furthermore, infant Akkermansia was positively associated with maternal PBMC anti-inflammatory genes that include CPS1 and RAB7B, at 12 and 36 weeks of pregnancy. Conclusions – These findings suggest that prepregnancy overweight impacts the immune cell gene expression profile, particularly at 12 weeks of pregnancy. Further, deciphering the complex association of PBMC’s gene expression levels with maternal gut microbiome and milk composition and infant gut microbiome may aid in developing strategies to mitigate obesity-mediated effects.

Project description:BACKGROUND:The analysis of single-cell RNA sequencing (scRNAseq) data plays an important role in understanding the intrinsic and extrinsic cellular processes in biological and biomedical research. One significant effort in this area is the detection of differentially expressed (DE) genes. scRNAseq data, however, are highly heterogeneous and have a large number of zero counts, which introduces challenges in detecting DE genes. Addressing these challenges requires employing new approaches beyond the conventional ones, which are based on a nonzero difference in average expression. Several methods have been developed for differential gene expression analysis of scRNAseq data. To provide guidance on choosing an appropriate tool or developing a new one, it is necessary to evaluate and compare the performance of differential gene expression analysis methods for scRNAseq data. RESULTS:In this study, we conducted a comprehensive evaluation of the performance of eleven differential gene expression analysis software tools, which are designed for scRNAseq data or can be applied to them. We used simulated and real data to evaluate the accuracy and precision of detection. Using simulated data, we investigated the effect of sample size on the detection accuracy of the tools. Using real data, we examined the agreement among the tools in identifying DE genes, the run time of the tools, and the biological relevance of the detected DE genes. CONCLUSIONS:In general, agreement among the tools in calling DE genes is not high. There is a trade-off between true-positive rates and the precision of calling DE genes. Methods with higher true positive rates tend to show low precision due to their introducing false positives, whereas methods with high precision show low true positive rates due to identifying few DE genes. We observed that current methods designed for scRNAseq data do not tend to show better performance compared to methods designed for bulk RNAseq data. Data multimodality and abundance of zero read counts are the main characteristics of scRNAseq data, which play important roles in the performance of differential gene expression analysis methods and need to be considered in terms of the development of new methods.

Dataset Information

Tunable Expression Tools Enable Single-Cell Strain Distinction in the Gut Microbiome.

Publications

Tunable Expression Tools Enable Single-Cell Strain Distinction in the Gut Microbiome.

Similar Datasets

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