Comparison of assembly algorithms for improving rate of metatranscriptomic functional annotation

BACKGROUND: Microbiome-wide gene expression profiling through high-throughput RNA sequencing (‘metatranscriptomics’) offers a powerful means to functionally interrogate complex microbial communities. Key to successful exploitation of these datasets is the ability to confidently match relatively short sequence reads to known bacterial transcripts. In the absence of reference genomes, such annotation efforts may be enhanced by assembling reads into longer contiguous sequences (‘contigs’), prior to database search strategies. Since reads from homologous transcripts may derive from several species, represented at different abundance levels, it is not clear how well current assembly pipelines perform for metatranscriptomic datasets. Here we evaluate the performance of four currently employed assemblers including de novo transcriptome assemblers - Trinity and Oases; the metagenomic assembler - Metavelvet; and the recently developed metatranscriptomic assembler IDBA-MT. RESULTS: We evaluated the performance of the assemblers on a previously published dataset of single-end RNA sequence reads derived from the large intestine of an inbred non-obese diabetic mouse model of type 1 diabetes. We found that Trinity performed best as judged by contigs assembled, reads assigned to contigs, and number of reads that could be annotated to a known bacterial transcript. Only 15.5% of RNA sequence reads could be annotated to a known transcript in contrast to 50.3% with Trinity assembly. Paired-end reads generated from the same mouse samples resulted in modest performance gains. A database search estimated that the assemblies are unlikely to erroneously merge multiple unrelated genes sharing a region of similarity (<2% of contigs). A simulated dataset based on ten species confirmed these findings. A more complex simulated dataset based on 72 species found that greater assembly errors were introduced than is expected by sequencing quality. Through the detailed evaluation of assembly performance, the insights provided by this study will help drive the design of future metatranscriptomic analyses. CONCLUSION: Assembly of metatranscriptome datasets greatly improved read annotation. Of the four assemblers evaluated, Trinity provided the best performance. For more complex datasets, reads generated from transcripts sharing considerable sequence similarity can be a source of significant assembly error, suggesting a need to collate reads on the basis of common taxonomic origin prior to assembly

Generation and analysis of a mouse intestinal metatranscriptome through Illumina based RNA-sequencing

With the advent of high through-put sequencing (HTS), the emerging science of metagenomics is transforming our understanding of the relationships of microbial communities with their environments. While metagenomics aims to catalogue the genes present in a sample through assessing which genes are actively expressed, metatranscriptomics can provide a mechanistic understanding of community inter-relationships. To achieve these goals, several challenges need to be addressed from sample preparation to sequence processing, statistical analysis and functional annotation. Here we use an inbred non-obese diabetic (NOD) mouse model in which germ-free animals were colonized with a defined mixture of eight commensal bacteria, to explore methods of RNA extraction and to develop a pipeline for the generation and analysis of metatranscriptomic data. Applying the Illumina HTS platform, we sequenced 12 NOD cecal samples prepared using multiple RNA-extraction protocols. The absence of a complete set of reference genomes necessitated a peptide-based search strategy. Up to 16% of sequence reads could be matched to a known bacterial gene. Phylogenetic analysis of the mapped ORFs revealed a distribution consistent with ribosomal RNA, the majority from Bacteroides or Clostridium species. To place these HTS data within a systems context, we mapped the relative abundance of corresponding Escherichia coli homologs onto metabolic and protein-protein interaction networks. These maps identified bacterial processes with components that were well-represented in the datasets. In summary this study highlights the potential of exploiting the economy of HTS platforms for metatranscriptomics

Human IFN-γ immunity to mycobacteria is governed by both IL-12 and IL-23

Hundreds of patients with autosomal recessive, complete IL-12p40 or IL-12Rß1 deficiency have been diagnosed over the last 20 years. They typically suffer from invasive mycobacteriosis and, occasionally, from mucocutaneous candidiasis. Susceptibility to these infections is thought to be due to impairments of IL- 12–dependent IFN-? immunity and IL-23–dependent IL-17A/IL-17F immunity, respectively. We report here patients with autosomal recessive, complete IL- 12Rß2 or IL-23R deficiency, lacking responses to IL-12 or IL- 23 only, all of whom, unexpectedly, display mycobacteriosis without candidiasis. We show that aß T, ?d T, B, NK, ILC1, and ILC2 cells from healthy donors preferentially produce IFN-? in response to IL-12, whereas NKT cells and MAIT cells preferentially produce IFN-? in response to IL-23. We also show that the development of IFN-?–producing CD4+ T cells, including, in particular, mycobacterium-specific TH1* cells (CD45RA-CCR6+), is dependent on both IL-12 and IL-23. Last, we show that IL12RB1, IL12RB2, and IL23R have similar frequencies of deleterious variants in the general population. The comparative rarity of symptomatic patients with IL-12Rß2 or IL-23R deficiency, relative to IL-12Rß1 deficiency, is, therefore, due to lower clinical penetrance. There are fewer symptomatic IL-23R– and IL-12Rß2–deficient than IL-12Rß1–deficient patients, not because these genetic disorders are rarer, but because the isolated absence of IL-12 or IL-23 is, in part, compensated by the other cytokine for the production of IFN-?, thereby providing some protection against mycobacteria. These experiments of nature show that human IL-12 and IL-23 are both required for optimal IFN-?–dependent immunity to mycobacteria, both individually and much more so cooperatively

Genome-environment Interactions in Type 1 Diabetes

This project aims to integrate knowledge of genetic susceptibility, immune cell function, and environmental modifiers in determining risk for type 1 diabetes (T1D) in the non-obese diabetic (NOD) mouse model. Similar to human T1D, disease risk in the NOD mouse is polygenic and has been mapped to multiple Idd loci. We have fine-mapped the Idd4.1 locus and identified Nlrp1b as its candidate gene. We report an alternatively spliced isoform in the diabetes-resistant Nrlp1b allele, resulting in a truncated NLRP1b protein that is unable to activate release of IL-1β. In another aspect of this project, we have characterized the critical contribution to T1D pathogenesis by γδ T cells. We report that CD27- γδT cells infiltrate islets of pre-diabetic NOD mice. Adoptive transfer of T1D to lymphocyte-deficient NOD.SCID recipients was potentiated when CD27- γδ T cells were transferred, compared to transfer of αβ T cells alone. Antibody-mediated blockade of IL-17 prevented T1D transfer in this setting. Moreover, introgression of genetic Tcrd deficiency onto the NOD background provided robust T1D protection. Finally, we report novel relationships between the gut microbiome, host sex hormones and metabolism, and T1D pathogenesis in the NOD mouse. Using germ-free, specific pathogen free, and microbiome-transplanted NOD mice, we show that colonization by commensal microbes elevated serum testosterone and protected NOD males from T1D. Transfer of gut microbiota from adult males to immature females altered the recipients’ microbiota, resulting in elevated testosterone and metabolomic changes, reduced islet inflammation and autoantibody production, and endowed robust T1D protection. Collectively, the data presented in this thesis describe a novel genetic lesion involved in T1D risk and its immunological consequences, demonstrate a potent role for IL-17-producing γδ T cells in NOD mouse model, and uncover a novel relationship between the gut microbiome, host hormonal and metabolic phenotypes, and autoimmunity risk.Ph

Comparison of assembly algorithms for improving rate of metatranscriptomic functional annotation

Abstract Background Microbiome-wide gene expression profiling through high-throughput RNA sequencing (‘metatranscriptomics’) offers a powerful means to functionally interrogate complex microbial communities. Key to successful exploitation of these datasets is the ability to confidently match relatively short sequence reads to known bacterial transcripts. In the absence of reference genomes, such annotation efforts may be enhanced by assembling reads into longer contiguous sequences (‘contigs’), prior to database search strategies. Since reads from homologous transcripts may derive from several species, represented at different abundance levels, it is not clear how well current assembly pipelines perform for metatranscriptomic datasets. Here we evaluate the performance of four currently employed assemblers including de novo transcriptome assemblers - Trinity and Oases; the metagenomic assembler - Metavelvet; and the recently developed metatranscriptomic assembler IDBA-MT. Results We evaluated the performance of the assemblers on a previously published dataset of single-end RNA sequence reads derived from the large intestine of an inbred non-obese diabetic mouse model of type 1 diabetes. We found that Trinity performed best as judged by contigs assembled, reads assigned to contigs, and number of reads that could be annotated to a known bacterial transcript. Only 15.5% of RNA sequence reads could be annotated to a known transcript in contrast to 50.3% with Trinity assembly. Paired-end reads generated from the same mouse samples resulted in modest performance gains. A database search estimated that the assemblies are unlikely to erroneously merge multiple unrelated genes sharing a region of similarity (<2% of contigs). A simulated dataset based on ten species confirmed these findings. A more complex simulated dataset based on 72 species found that greater assembly errors were introduced than is expected by sequencing quality. Through the detailed evaluation of assembly performance, the insights provided by this study will help drive the design of future metatranscriptomic analyses. Conclusion Assembly of metatranscriptome datasets greatly improved read annotation. Of the four assemblers evaluated, Trinity provided the best performance. For more complex datasets, reads generated from transcripts sharing considerable sequence similarity can be a source of significant assembly error, suggesting a need to collate reads on the basis of common taxonomic origin prior to assembly

gamma delta T Cells Are Essential Effectors of Type 1 Diabetes in the Nonobese Diabetic Mouse Model

γδ T cells, a lineage of innate-like lymphocytes, are distinguished from conventional αβ T cells in their antigen-recognition, cell activation requirements and effector functions. γδ T cells have been implicated in the pathology of several human autoimmune and inflammatory diseases and their corresponding mouse models, but their specific roles in these diseases have not been elucidated. We report that γδTCR(+) cells including both the CD27(−)CD44(hi) and CD27(+)CD44(lo) subsets infiltrate islets of pre-diabetic non-obese diabetic (NOD) mice. Moreover, NOD CD27(−)CD44(hi) and CD27(+)CD44(lo) γδ T cells were pre-programmed to secrete IL-17, or IFN-γ upon activation. Adoptive transfer of T1D to T and B lymphocyte-deficient NOD recipients was greatly potentiated when γδ T cells, and specifically the CD27(−) γδ T cell subset, were included compared to transfer of αβ T cells alone. Antibody-mediated blockade of IL-17 prevented T1D transfer in this setting. Moreover, introgression of genetic Tcrd deficiency onto the NOD background provided robust T1D protection, supporting a non-redundant, pathogenic role of γδ T cells in this model. The potent contributions of CD27(−) γδ T cells and IL-17 to islet inflammation and diabetes reported here suggest that these mechanisms may also underlie human T1D