Co-occurrence of anaerobic bacteria in colorectal carcinomas.

BACKGROUND: Numerous cancers have been linked to microorganisms. Given that colorectal cancer is a leading cause of cancer deaths and the colon is continuously exposed to a high diversity of microbes, the relationship between gut mucosal microbiome and colorectal cancer needs to be explored. Metagenomic studies have shown an association between Fusobacterium species and colorectal carcinoma. Here, we have extended these studies with deeper sequencing of a much larger number (n = 130) of colorectal carcinoma and matched normal control tissues. We analyzed these data using co-occurrence networks in order to identify microbe-microbe and host-microbe associations specific to tumors. RESULTS: We confirmed tumor over-representation of Fusobacterium species and observed significant co-occurrence within individual tumors of Fusobacterium, Leptotrichia and Campylobacter species. This polymicrobial signature was associated with over-expression of numerous host genes, including the gene encoding the pro-inflammatory chemokine Interleukin-8. The tumor-associated bacteria we have identified are all Gram-negative anaerobes, recognized previously as constituents of the oral microbiome, which are capable of causing infection. We isolated a novel strain of Campylobacter showae from a colorectal tumor specimen. This strain is substantially diverged from a previously sequenced oral Campylobacter showae isolate, carries potential virulence genes, and aggregates with a previously isolated tumor strain of Fusobacterium nucleatum. CONCLUSIONS: A polymicrobial signature of Gram-negative anaerobic bacteria is associated with colorectal carcinoma tissue.

Derivation of HLA types from shotgun sequence datasets.

The human leukocyte antigen (HLA) is key to many aspects of human physiology and medicine. All current sequence-based HLA typing methodologies are targeted approaches requiring the amplification of specific HLA gene segments. Whole genome, exome and transcriptome shotgun sequencing can generate prodigious data but due to the complexity of HLA loci these data have not been immediately informative regarding HLA genotype. We describe HLAminer, a computational method for identifying HLA alleles directly from shotgun sequence datasets (http://www.bcgsc.ca/platform/bioinfo/software/hlaminer). This approach circumvents the additional time and cost of generating HLA-specific data and capitalizes on the increasing accessibility and affordability of massively parallel sequencing.