Transcriptional profiling of coaggregation interactions between Streptococcus gordonii and Veillonella parvula by Dual RNA-Seq

Interspecies coaggregation promotes transcriptional changes in oral bacteria, affecting bacterial pathogenicity. Streptococcus gordonii (S. gordonii) and Fusobacterium nucleatum (F. nucleatum) are common oral inhabitants. The present study investigated the transcriptional profiling of S. gordonii and F. nucleatum subsp. polymorphum in response to the dual-species coaggregation using RNA-seq. Macrophages were infected with both species to explore the influence of bacterial coaggregation on both species’ abilities to survive within macrophages and induce inflammatory responses. Results indicated that, after the 30-min dual-species coaggregation, 116 genes were significantly up-regulated, and 151 genes were significantly down-regulated in S. gordonii; 97 genes were significantly down-regulated, and 114 genes were significantly up-regulated in F. nucleatum subsp. polymorphum. Multiple S. gordonii genes were involved in the biosynthesis and export of cell-wall proteins and carbohydrate metabolism. F. nucleatum subsp. polymorphum genes were mostly associated with translation and protein export. The coaggregation led to decreased expression levels of genes associated with lipopolysaccharide and peptidoglycan biosynthesis. Coaggregation between S. gordonii and F. nucleatum subsp. polymorphum significantly promoted both species’ intracellular survival within macrophages and attenuated the production of pro-inflammatory cytokines IL-6 and IL-1β. Physical interactions between these two species promoted a symbiotic lifestyle and repressed macrophage’s killing and pro-inflammatory responses.

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Hybridization-based capture of pathogen mRNA enables paired host-pathogen transcriptional analysis

Scientific Reports ◽

10.1038/s41598-019-55633-6 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 3

Author(s):

Viktoria Betin ◽

Cristina Penaranda ◽

Nirmalya Bandyopadhyay ◽

Rui Yang ◽

Angela Abitua ◽

...

Keyword(s):

Transcriptional Profiling ◽

Transcript Abundance ◽

Abundant Species ◽

Single Step ◽

Sequencing Depth ◽

Transcriptional Analysis ◽

Rna Seq ◽

Low Input ◽

Temporal Gene Expression ◽

Host Pathogen

AbstractDual transcriptional profiling of host and bacteria during infection is challenging due to the low abundance of bacterial mRNA. We report Pathogen Hybrid Capture (PatH-Cap), a method to enrich for bacterial mRNA and deplete bacterial rRNA simultaneously from dual RNA-seq libraries using transcriptome-specific probes. By addressing both the differential RNA content of the host relative to the infecting bacterium and the overwhelming abundance of uninformative structural RNAs (rRNA, tRNA) of both species in a single step, this approach enables analysis of very low-input RNA samples. By sequencing libraries before (pre-PatH-Cap) and after (post-PatH-Cap) enrichment, we achieve dual transcriptional profiling of host and bacteria, respectively, from the same sample. Importantly, enrichment preserves relative transcript abundance and increases the number of unique bacterial transcripts per gene in post-PatH-Cap libraries compared to pre-PatH-Cap libraries at the same sequencing depth, thereby decreasing the sequencing depth required to fully capture the transcriptional profile of the infecting bacteria. We demonstrate that PatH-Cap enables the study of low-input samples including single eukaryotic cells infected by 1–3 Pseudomonas aeruginosa bacteria and paired host-pathogen temporal gene expression analysis of Mycobacterium tuberculosis infecting macrophages. PatH-Cap can be applied to the study of a range of pathogens and microbial species, and more generally, to lowly-abundant species in mixed populations.

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Feasibility of Metatranscriptome Analysis from Infant Gut Microbiota: Adaptation to Solid Foods Results in Increased Activity of Firmicutes at Six Months

International Journal of Microbiology ◽

10.1155/2017/9547063 ◽

2017 ◽

Vol 2017 ◽

pp. 1-9 ◽

Cited By ~ 5

Author(s):

Floor Hugenholtz ◽

Jarmo Ritari ◽

Lotta Nylund ◽

Mark Davids ◽

Reetta Satokari ◽

...

Keyword(s):

Gut Microbiota ◽

Transcriptional Profiling ◽

Lactobacillus Rhamnosus ◽

Rna Seq ◽

Faecal Samples ◽

Carbohydrate Fermentation ◽

Stool Samples ◽

The Impact ◽

Increased Activity ◽

Insight Into

Newborns are rapidly colonized by microbes and their intestinal tracts contain highly dynamic and rapidly developing microbial communities in the first months of life. In this study, we describe the feasibility of isolating mRNA from rapidly processed faecal samples and applying deep RNA-Seq analysis to provide insight into the active contributors of the microbial community in early life. Specific attention is given to the impact of removing rRNA from the mRNA on the phylogenetic and transcriptional profiling and its analysis depth. A breastfed baby was followed in the first six months of life during adaptation to solid food, dairy products, and formula. It was found that, in the weaning period, the total transcriptional activity of Actinobacteria, mainly represented by Bifidobacterium, decreased while that of Firmicutes increased over time. Moreover, Firmicutes and Actinobacteria, including the canonical Bifidobacteria as well as Collinsella, were found to be important contributors to carbohydrate fermentation and vitamin biosynthesis in the infant intestine. Finally, the expression of Lactobacillus rhamnosus-like genes was detected, likely following transfer from the mother who consumed L. rhamnosus GG. The study indicates that metatranscriptome analysis of the infant gut microbiota is feasible on infant stool samples and can be used to provide insight into the core activities of the developing community.

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Transcriptional Profiling of Psoriasis Using RNA-seq Reveals Previously Unidentified Differentially Expressed Genes

Journal of Investigative Dermatology ◽

10.1038/jid.2011.267 ◽

2012 ◽

Vol 132 (1) ◽

pp. 246-249 ◽

Cited By ~ 57

Author(s):

Ali Jabbari ◽

Mayte Suárez-Fariñas ◽

Scott Dewell ◽

James G. Krueger

Keyword(s):

Differentially Expressed Genes ◽

Transcriptional Profiling ◽

Differentially Expressed ◽

Rna Seq

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Transcriptomic profiling of human effector and regulatory T cell subsets identifies predictive population signatures

10.1101/2020.05.13.093567 ◽

2020 ◽

Cited By ~ 1

Author(s):

Barbara Höllbacher ◽

Thomas Duhen ◽

Samantha Motley ◽

Maria M. Klicznik ◽

Iris K. Gratz ◽

...

Keyword(s):

Treg Cell ◽

Peripheral Blood ◽

Chemokine Receptors ◽

Transcriptional Profiling ◽

Treg Cells ◽

T Cell Subsets ◽

Cell Populations ◽

Rna Seq ◽

Th Cells ◽

Th Cell

AbstractAfter activation, CD4+ T helper (Th) cells differentiate into functionally specialized populations that coordinate distinct immune responses and protect against different types of pathogens. In humans, these effector and memory Th cell subsets can be readily identified in peripheral blood based on their differential expression of chemokine receptors that govern their homeostatic and inflammatory trafficking. Foxp3+ regulatory T (Treg) cells can also be divided into subsets that phenotypically mirror each of these effector populations, and share expression of key transcription factors and effector cytokines. In this study, we performed comprehensive transcriptional profiling of 11 phenotypically distinct Th and Treg cell subsets sorted from peripheral blood of healthy individuals. Despite their shared phenotypes, we found that mirror Th and Treg subsets were transcriptionally dissimilar, and that Treg cell populations showed limited transcriptional diversity compared to Th cells. We identified core transcriptional signatures shared across all Th and Treg cell populations, and unique signatures that define each of the Th or Treg populations. Finally, we applied these signatures to bulk Th and Treg RNA-seq data and found enrichment of specific Th and Treg cell populations in different human tissues. These results further define the molecular basis for the functional specialization and differentiation of Th and Treg cell populations, and provide a new resource for examining Th and Treg specialization in RNA-seq data.

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Comprehensive single cell transcriptional profiling of a multicellular organism by combinatorial indexing

10.1101/104844 ◽

2017 ◽

Cited By ~ 8

Author(s):

Junyue Cao ◽

Jonathan S. Packer ◽

Vijay Ramani ◽

Darren A. Cusanovich ◽

Chau Huynh ◽

...

Keyword(s):

Single Cell ◽

Expression Profiles ◽

Single Cells ◽

Transcriptional Profiling ◽

Cost Effective ◽

Cell Types ◽

Cell Capture ◽

Rna Seq ◽

Major Step ◽

C Elegans

AbstractConventional methods for profiling the molecular content of biological samples fail to resolve heterogeneity that is present at the level of single cells. In the past few years, single cell RNA sequencing has emerged as a powerful strategy for overcoming this challenge. However, its adoption has been limited by a paucity of methods that are at once simple to implement and cost effective to scale massively. Here, we describe a combinatorial indexing strategy to profile the transcriptomes of large numbers of single cells or single nuclei without requiring the physical isolation of each cell (Single cell Combinatorial Indexing RNA-seq or sci-RNA-seq). We show that sci-RNA-seq can be used to efficiently profile the transcriptomes of tens-of-thousands of single cells per experiment, and demonstrate that we can stratify cell types from these data. Key advantages of sci-RNA-seq over contemporary alternatives such as droplet-based single cell RNA-seq include sublinear cost scaling, a reliance on widely available reagents and equipment, the ability to concurrently process many samples within a single workflow, compatibility with methanol fixation of cells, cell capture based on DNA content rather than cell size, and the flexibility to profile either cells or nuclei. As a demonstration of sci-RNA-seq, we profile the transcriptomes of 42,035 single cells from C. elegans at the L2 stage, effectively 50-fold “shotgun cellular coverage” of the somatic cell composition of this organism at this stage. We identify 27 distinct cell types, including rare cell types such as the two distal tip cells of the developing gonad, estimate consensus expression profiles and define cell-type specific and selective genes. Given that C. elegans is the only organism with a fully mapped cellular lineage, these data represent a rich resource for future methods aimed at defining cell types and states. They will advance our understanding of developmental biology, and constitute a major step towards a comprehensive, single-cell molecular atlas of a whole animal.

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