Chia, Nicholas

Metagenomic shotgun sequencing for the identification of pathogens is being increasingly utilized as a diagnostic method. Interpretation of large and complicated data sets is a significant challenge, for which multiple commercial tools have been developed. Three commercial metagenomic shotgun sequencing tools, CosmosID, One Codex, and IDbyDNA, were compared to determine whether they result in similar interpretations of the same sequencing data.

Personalized models of the gut microbiome are valuable for disease prevention and treatment. For this, one requires a mathematical model that predicts microbial community composition and the emergent behaviour of microbial communities. We seek a modelling strategy that can capture emergent behaviour when built from sets of universal individual interactions. Our investigation reveals that species–metabolite interaction (SMI) modelling is better able to capture emergent behaviour in community composition dynamics than direct species–species modelling. Using publicly available data, we examine the ability of species–species models and species–metabolite models to predict trio growth experiments from the outcomes of pair growth experiments. We compare quadratic species–species interaction models and quadratic SMI models and conclude that only species–metabolite models have the necessary complexity to explain a wide variety of interdependent growth outcomes. We also show that general species–species interaction models cannot match the patterns observed in community growth dynamics, whereas species–metabolite models can. We conclude that species–metabolite modelling will be important in the development of accurate, clinically useful models of microbial communities.

INTRODUCTION: Primary sclerosing cholangitis (PSC) is a chronic, fibroinflammatory, cholestatic liver disease of unknown etiopathogenesis which is recognized as a major risk factor for cholangiocarcinoma (CCA). We aimed to investigate the roles of the biliary and intestinal microflora in the inflammatory process of PSC and to determine if the microflora is associated with progression to CCA. METHODS: We studied the bile and stool microbiomes in samples obtained from patients with PSC (31 stool and 35 bile samples), sporadic CCA (11 stool and 26 bile samples), and PSC coexisting with CCA (16 stool and bile 17 samples). Control bile was obtained from patients with cholelithiasis or choledocholithiasis (n = 17). DNA extraction was followed by amplification and high-throughput next generation sequencing (MiSeq) of the 16S rDNA (V3-V5) region to identify the microbiota present. We performed: 1. analysis of the stool microbiome between PSC and CCA patients; 2. analysis of the biliary microbiome in PSC and CCA patients; 3. comparison of the biliary microbiome collected before and after biliary stent placement. RESULTS: Although bile samples from study patients had significantly lower sequencing depths than stool, their sequencing depths were much higher than bile from negative controls. The microbiomes of stool and bile samples from the same patients were significantly correlated ( P = 0.022). CCA bile samples had significant difference of alpha diversity from control bile samples with less Streptococcaceae and Desulfovibrionaceae (FDR < 10%) than PSC whereas no significant change of stool microbiome was identified between CCA and PSC. Fusobacterium and Gemella were correlated to PSC duration. An increased abundance of Fusobacteria after stent placement was revealed. Fusobacteria, which is inflammation-promoting, was also increased with the number of stents in the bile duct. CONCLUSION: Our study suggests that the bile microbiome is potentially a more distinctive biomarker than the stool microbiome for PSC and CCA development. There is increased inhabitance of inflammation-promoting bacteria in the bile of CCA patients and after biliary stent placement. Increased PSC duration also increases the microbial burden in the bile, which may also promote CCA development. We further detected specific bile microbes statistically associated with CCA or stent presence in the bile ducts, which raises the possibility of their role in the CCA progression or stent obstruction. Additional analyses with more samples are needed.

Objective Obesity is a risk factor for colorectal cancer (CRC), accounting for more than 14% of CRC incidence. Microbial dysbiosis and chronic inflammation are common characteristics in both obesity and CRC. Human and murine studies, together, demonstrate the significant impact of the microbiome in governing energy metabolism and CRC development; yet, little is understood about the contribution of the microbiome to development of obesity-associated CRC as compared to individuals who are not obese. Design In this study, we conducted a meta-analysis using five publicly available stool and tissue-based 16S rRNA and whole genome sequencing (WGS) data sets of CRC microbiome studies. High-resolution analysis was employed for 16S rRNA data, which allowed us to achieve species-level information to compare with WGS. Results Characterisation of the confounders between studies, 16S rRNA variable region and sequencing method did not reveal any significant effect on alpha diversity in CRC prediction. Both 16S rRNA and WGS were equally variable in their ability to predict CRC. Results from diversity analysis confirmed lower diversity in obese individuals without CRC; however, no universal differences were found in diversity between obese and non-obese individuals with CRC. When examining taxonomic differences, the probability of being classified as CRC did not change significantly in obese individuals for all taxa tested. However, random forest classification was able to distinguish CRC and non-CRC stool when body mass index was added to the model. Conclusion Overall, microbial dysbiosis was not a significant factor in explaining the higher risk of colon cancer among individuals with obesity.

We previously demonstrated that shotgun metagenomic sequencing can detect bacteria in sonicate fluid, providing a diagnosis of prosthetic joint infection (PJI). A limitation of the approach that we used is that data analysis was time-consuming and specialized bioinformatics expertise was required, both of which are barriers to routine clinical use.

Increased amino acids in the dysbiotic gut influences susceptibility to Clostridioides difficile infection in mice and humans.

Abstract Background: A number of cohort studies have collected Scope mouthwash samples by mail, which are being used for microbiota measurements. We evaluated the stability of Scope mouthwash samples at ambient temperature and determined the comparability of Scope mouthwash with saliva collection using the OMNIgene ORAL Kit. Methods: Fifty-three healthy volunteers from Mayo Clinic and 50 cohort members from Bangladesh provided oral samples. One aliquot of the OMNIgene ORAL and Scope mouthwash were frozen immediately and one aliquot of the Scope mouthwash remained at ambient temperature for 4 days and was then frozen. DNA was extracted and the V4 region of the 16S rRNA gene was PCR amplified and sequenced using the HiSeq. Intraclass correlation coefficients (ICC) were calculated. Results: The overall stability of the Scope mouthwash samples was relatively high for alpha and beta diversity. For example, the meta-analyzed ICC for the Shannon index was 0.86 (95% confidence interval, 0.76–0.96). Similarly, the ICCs for the relative abundance of the top 25 genera were generally high. The comparability of the two sample types was relatively low when measured using ICCs, but were increased by using a Spearman correlation coefficient (SCC) to compare the rank order of individuals. Conclusions: Overall, the Scope mouthwash samples appear to be stable at ambient temperature, which suggests that oral rinse samples received by the mail can be used for microbial analyses. However, Scope mouthwash samples were distinct compared with OMNIgene ORAL samples. Impact: Studies should try to compare oral microbial metrics within one sample collection type.

Metagenomic shotgun sequencing has the potential to transform how serious infections are diagnosed by offering universal, culture-free pathogen detection. This may be especially advantageous for microbial diagnosis of prosthetic joint infection (PJI) by synovial fluid analysis since synovial fluid cultures are not universally positive and since synovial fluid is easily obtained preoperatively.