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The relevance of specific microbial colonisation to colorectal cancer (CRC) disease pathogenesis is increasingly recognised, but our understanding of possible underlying molecular mechanisms that may link colonisation to disease in vivo remains limited. Here, we investigate the relationships between the most commonly studied CRC-associated bacteria (Enterotoxigenic Bacteroides fragilis, pks+ Escherichia coli, Fusobacterium spp., afaC+ E. coli, Enterococcus faecalis & Enteropathogenic E. coli) and altered transcriptomic and methylation profiles of CRC patients, in order to gain insight into the potential contribution of these bacteria in the aetiopathogenesis of CRC. We show that colonisation by E. faecalis and high levels of Fusobacterium is associated with a specific transcriptomic subtype of CRC that is characterised by CpG island methylation, microsatellite instability and a significant increase in inflammatory and DNA damage pathways. Analysis of the significant, bacterially-associated changes in host gene expression, both at the level of individual genes as well as pathways, revealed a transcriptional remodeling that provides a plausible mechanistic link between specific bacterial colonisation and colorectal cancer disease development and progression in this subtype; these included upregulation of REG3A, REG1A and REG1P in the case of high-level colonization by Fusobacterium, and CXCL10 and BMI1 in the case of colonisation by E. faecalis. The enrichment of both E. faecalis and Fusobacterium in this CRC subtype suggests that polymicrobial colonisation of the colonic epithelium may well be an important aspect of colonic tumourigenesis. [ABSTRACT FROM AUTHOR]

Objective: Protein microarrays provide a high-throughput platform to measure protein interactions and associated functions, and can aid in the discovery of cancer biomarkers. The resulting protein microarray data can however be subject to systematic bias and noise, thus requiring a robust data processing, normalization and analysis pipeline to ensure high quality and robust results. To date, a comprehensive data processing pipeline is yet to be developed. Furthermore, a lack of analysis consistency is evident amongst different research groups, thereby impeding collaborative data consolidation and comparison. Thus, we sought to develop an accessible data processing tool using methods that are generalizable to the protein microarray field and which can be adapted to individual array layouts with minimal software engineering expertise. Results: We developed an improved version of a previously developed pipeline of protein microarray data processing and implemented it as an open source software tool, with particular focus on widening its use and applicability. The Protein Microarray Analyser software presented here includes the following tools: (1) neighbourhood background correction, (2) net intensity correction, (3) user-defined noise threshold, (4) user-defined CV threshold amongst replicates and (5) assay controls, (6) composite ‘pin-to-pin’ normalization amongst sub-arrays, and (7) ‘array-to-array’ normalization amongst whole arrays. [ABSTRACT FROM AUTHOR]