Biology subjects

Nearing, J. T.

Publications and source records attributed to Nearing, J. T..

4 recordsLinked to original sources

bioRxiv2022

Investigating the oral microbiome in retrospective and prospective cases of prostate, colon, and breast cancer.

The human microbiome has been proposed as a useful biomarker for several different human diseases including various cancers. To answer this question, we examined salivary samples from two Canadian population cohorts, the Atlantic Partnership for Tomorrows Health project (PATH) and Albertas Tomorrow Project (ATP). Sample selection was then divided into both a retrospective and prospective case control design examining individuals with prostate, breast, or colon cancer. In total 89 retrospective and 260 prospective cancer cases were matched to non-cancer controls and saliva samples were sequenced using 16S rRNA gene sequencing to compare bacterial diversity, and taxonomic composition. We found no significant differences in alpha or beta diversity across any of the three cancer types and two study designs. Although retrospective colon cancer samples did show evidence on visual clustering in weighted beta diversity metrics. Differential abundance analysis of individual taxon showed several taxa that were associated with previous cancer diagnosis in all three groupings within the retrospective study design. However, only one genus (Ruminococcaceae UCG-014) in breast cancer and one ASV (Fusobacterium periodonticum) in colon cancer was identified by more than one differential abundance (DA) tool. In prospective cases of disease three ASVs were associated with colon cancer, one ASV with breast cancer, and one ASV with prostate cancer. None overlapped between the two different study cohorts. Attempting to identify microbial signals using Random Forest classification showed relatively low levels of signal in both prospective and retrospective cases of breast and prostate cancer (AUC range: 0.394-0.665). Contrastingly, colon cancer did show signal in our retrospective analysis (AUC: 0.745) and in one of two prospective cohorts (AUC: 0.717). Overall, our results indicate that it is unlikely that reliable oral microbial biomarkers of disease exist in the context of both breast and prostate cancer. However, they do suggest that further research into the relationship between the oral microbiome and colon cancer could be fruitful. Particularly in the context of early disease progression and risk of cancer development.

microbiology↗

bioRxiv2022

Implications of taxonomic bias for microbial differential-abundance analysis

Differential-abundance (DA) analyses enable microbiome researchers to assess how microbial species vary in relative or absolute abundance with specific host or environmental conditions, such as health status or pH. These analyses typically use sequencing-based community measurements that are taxonomically biased to measure some species more efficiently than others. Understanding the effects that taxonomic bias has on the results of a DA analysis is essential for achieving reliable and translatable findings; yet currently, these effects are unknown. Here, we characterized these effects for DA analyses of both relative and absolute abundances, using a combination of mathematical theory and data analysis of real and simulated case studies. We found that, for analyses based on species proportions, taxonomic bias can cause significant errors in DA results if the average measurement efficiency of the community is associated with the condition of interest. These errors can be avoided by using more robust DA methods (based on species ratios) or quantified and corrected using appropriate controls. Wide adoption of our recommendations can improve the reproducibility, interpretability, and translatability of microbiome DA studies. This manuscript was rendered from commit 7412a36 of https://github.com/mikemc/differential-abundance-theory. Supporting data analyses can be found in the accompanying computational research notebook. Please post comments or questions on GitHub. The manuscript is licensed under a CC BY 4.0 License. See the GitHub Releases or Zenodo record for earlier versions.

bioinformatics↗

bioRxiv2021

Microbiome differential abundance methods produce disturbingly different results across 38 datasets

Identifying differentially abundant microbes is a common goal of microbiome studies. Multiple methods have been applied for this purpose, which are largely used interchangeably in the literature. Although it has been observed that these tools can produce different results, there have been very few large-scale comparisons to describe the scale and significance of these differences. In addition, it is challenging for microbiome researchers to know which differential abundance tools are appropriate for their study and how these tools compare to one another. Here, we have investigated these questions by analyzing 38 16S rRNA gene datasets with two sample groups for differential abundance testing. We tested for differences in amplicon sequence variants and operational taxonomic units (referred to as ASVs for simplicity) between these groups with 14 commonly used differential abundance tools. Our findings confirmed that these tools identified drastically different numbers and sets of significant ASVs, however, for many tools the number of features identified correlated with aspects of the tested study data, such as sample size, sequencing depth, and effect size of community differences. We also found that the ASVs identified by each method were dependent on whether the abundance tables were prevalence-filtered before testing. ALDEx2 and ANCOM produced the most consistent results across studies and agreed best with the intersect of results from different approaches. In contrast, several methods, such as LEfSe, limma voom, and edgeR, produced inconsistent results and in some cases were unable to control the false discovery rate. In addition to these observations, we were unable to find supporting evidence for a recent recommendation that limma voom, corncob, and DESeq2 are more reliable overall compared with other methods. Although ALDEx2 and ANCOM are two promising conservative methods, we argue that those researchers requiring more sensitive methods should use a consensus approach based on multiple differential abundance methods to help ensure robust biological interpretations.

bioinformatics↗

bioRxiv2020

Assessing the variation within the oral microbiome of healthy adults

Over 1000 different species of microbes have been found to live within the human oral cavity where they play important roles in maintaining both oral and systemic health. Several studies have identified the core members of this microbial community, however, the factors that determine oral microbiome composition are not well understood. In this study we exam the salivary oral microbiome of 1049 Atlantic Canadians using 16S rRNA gene sequencing in order to determine which dietary, lifestyle, and anthropometric features play a role in shaping microbial community composition. Features that were identified as being significantly associated with overall composition were then additionally examined for genera and amplicon sequence variants that were associated with these features. Several associations were replicated in an additional secondary validation dataset. Overall, we found that several anthropometric measurements including waist hip ratio, height, and fat free mass, as well as age and sex, were associated with oral microbiome composition in both our exploratory and validation cohorts. We were unable to validate dietary impacts on the oral microbiome but did find evidence to suggest potential contributions from factors such as the number of vegetable and refined grain servings an individual consumes. Interestingly, each one of these factors on their own were associated with only minor shifts in the oral microbiome suggesting that future biomarker identification for several diseases associated with the oral microbiome may be undertaken without the worry of confounding factors obscuring biological signal. ImportanceThe human oral cavity is inhabited by a diverse community of microbes known as the human oral microbiome. These microbes play a role in maintaining both oral and systemic health and as such have been proposed to be useful biomarkers of disease. However, to identify these biomarkers, we first need to determine the composition and variation of the healthy oral microbiome. Within this report we investigate the oral microbiome of 1049 healthy individuals to determine which genera and amplicon sequence variants are commonly found between individual oral microbiomes. We then further investigate how lifestyle, anthropometric, and dietary choices impact overall microbiome composition. Interestingly, the results from this investigation showed that while many features were significantly associated with oral microbiome composition no single biological factor explained a variation larger than 2%. These results indicate that future work on biomarker detection may be encourage by the lack of strong confounding factors.

microbiology↗