Machine learning classification by fitting amplicon sequences to existing OTUs.

The ability to use 16S rRNA gene sequence data to train machine learning classification models offers the opportunity to diagnose patients based on the composition of their microbiome. In some applications, the taxonomic resolution that provides the best models may require the use of de novo operational taxonomic units (OTUs) whose composition changes when new data are added. We previously developed a new reference-based approach, OptiFit, that fits new sequence data to existing de novo OTUs without changing the composition of the original OTUs. While OptiFit produces OTUs that are as high quality as de novo OTUs, it is unclear whether this method for fitting new sequence data into existing OTUs will impact the performance of classification models relative to models trained and tested only using de novo OTUs. We used OptiFit to cluster sequences into existing OTUs and evaluated model performance in classifying a dataset containing samples from patients with and without colonic screen relevant neoplasia (SRN). We compared the performance of this model to standard methods including de novo and database-reference-based clustering. We found that using OptiFit performed as well or better in classifying SRNs. OptiFit can streamline the process of classifying new samples by avoiding the need to retrain models using reclustered sequences. IMPORTANCE There is great potential for using microbiome data to aid in diagnosis. A challenge with de novo operational taxonomic unit (OTU)-based classification models is that 16S rRNA gene sequences are often assigned to OTUs based on similarity to other sequences in the dataset. If data are generated from new patients, the old and new sequences must be reclustered to OTUs and the classification model retrained. Yet there is a desire to have a single, validated model that can be widely deployed. To overcome this obstacle, we applied the OptiFit clustering algorithm to fit new sequence data to existing OTUs allowing for reuse of the model. A random forest model implemented using OptiFit performed as well as the traditional reassign and retrain approach. This result shows that it is possible to train and apply machine learning models based on OTU relative abundance data that do not require retraining or the use of a reference database.

Developing and deploying an integrated workshop curriculum teaching computational skills for reproducible research.

Inspired by well-established material and pedagogy provided by The Carpentries (Wilson, 2016), we developed a two-day workshop curriculum that teaches introductory R programming for managing, analyzing, plotting and reporting data using packages from the tidyverse (Wickham et al., 2019), the Unix shell, version control with git, and GitHub. While the official Software Carpentry curriculum is comprehensive, we found that it contains too much content for a two-day workshop. We also felt that the independent nature of the lessons left learners confused about how to integrate the newly acquired programming skills in their own work. Thus, we developed a new curriculum that aims to teach novices how to implement reproducible research principles in their own data analysis. The curriculum integrates live coding lessons with individual-level and group-based practice exercises, and also serves as a succinct resource that learners can reference both during and after the workshop. Moreover, it lowers the entry barrier for new instructors as they do not have to develop their own teaching materials or sift through extensive content. We developed this curriculum during a two-day sprint, successfully used it to host a two-day virtual workshop with almost 40 participants, and updated the material based on instructor and learner feedback. We hope that our new curriculum will prove useful to future instructors interested in teaching workshops with similar learning objectives.

A Goldilocks Principle for the Gut Microbiome: Taxonomic Resolution Matters for Microbiome-Based Classification of Colorectal Cancer.

Colorectal cancer is a common and deadly disease in the United States accounting for over 50,000 deaths in 2020. This progressive disease is highly preventable with early detection and treatment, but many people do not comply with the recommended screening guidelines. The gut microbiome has emerged as a promising target for noninvasive detection of colorectal cancer. Most microbiome-based classification efforts utilize taxonomic abundance data from operational taxonomic units (OTUs) or amplicon sequence variants (ASVs) with the goal of increasing taxonomic resolution. However, it is unknown which taxonomic resolution is optimal for microbiome-based classification of colorectal cancer. To address this question, we used a reproducible machine learning framework to quantify classification performance of models based on data annotated to phylum, class, order, family, genus, OTU, and ASV levels. We found that model performance increased with increasing taxonomic resolution, up to the family level where performance was equal (P > 0.05) among family (mean area under the receiver operating characteristic curve [AUROC], 0.689), genus (mean AUROC, 0.690), and OTU (mean AUROC, 0.693) levels before decreasing at the ASV level (P < 0.05; mean AUROC, 0.676). These results demonstrate a trade-off between taxonomic resolution and prediction performance, where coarse taxonomic resolution (e.g., phylum) is not distinct enough, but fine resolution (e.g., ASV) is too individualized to accurately classify samples. Similar to the story of Goldilocks and the three bears (L. B. Cauley, Goldilocks and the Three Bears, 1981), mid-range resolution (i.e., family, genus, and OTU) is "just right" for optimal prediction of colorectal cancer from microbiome data. IMPORTANCE Despite being highly preventable, colorectal cancer remains a leading cause of cancer-related death in the United States. Low-cost, noninvasive detection methods could greatly improve our ability to identify and treat early stages of disease. The microbiome has shown promise as a resource for detection of colorectal cancer. Research on the gut microbiome tends to focus on improving our ability to profile species and strain level taxonomic resolution. However, we found that finer resolution impedes the ability to predict colorectal cancer based on the gut microbiome. These results highlight the need for consideration of the appropriate taxonomic resolution for microbiome analyses and that finer resolution is not always more informative.

Supplementation with Sea Vegetables Palmaria mollis and Undaria pinnatifida Exerts Metabolic Benefits in Diet-Induced Obesity in Mice.

BACKGROUND: Sea vegetables are rich sources of nutrients as well as bioactive components that are linked to metabolic health improvement. Algal polysaccharides improve satiety and modulate gut microbiota while proteins, peptides, and phenolic fractions exert anti-inflammatory, antioxidant, and antidiabetic effects. OBJECTIVE: We tested the hypothesis that dietary supplementation with either Pacific dulse (Palmaria mollis, red algae) or wakame (Undaria pinnatifida, brown algae) could remediate metabolic complications in high-fat diet-induced obesity. METHODS: Individually caged C57BL/6J mice (n = 8) were fed ad libitum with either a low-fat diet (LFD), 10% kcal fat; high-fat diet (HFD), 60% kcal fat; HFD + 5% (wt:wt) dulse (HFD + D); or HFD + 5% (wt:wt) wakame (HFD + W) for 8 weeks. Food intake and weight gain were monitored weekly. Glucose tolerance, hepatic lipids, fecal lipids, and plasma markers were evaluated, and the gut microbiome composition was assessed. RESULTS: Despite the tendency of higher food and caloric intake than the HFD (P = 0.04) group, the HFD + D group mice did not exhibit higher body weight, indicating lower food and caloric efficiency (P < 0.001). Sea vegetable supplementation reduced plasma monocyte chemotactic protein (MCP-1) (P < 0.001) and increased fecal lipid excretion (P < 0.001). Gut microbiome analysis showed that the HFD + D group had higher alpha-diversity than the HFD or LFD group, whereas beta-diversity analyses indicated that sea vegetable-supplemented HFD-fed mice (HFD + D and HFD + W groups) developed microbiome compositions more similar to those of the LFD-fed mice than those of the HFD-fed mice. CONCLUSION: Sea vegetable supplementation showed protective effects against obesity-associated metabolic complications in C57BL/6J male mice by increasing lipid excretion, reducing systemic inflammatory marker, and mitigating gut microbiome alteration. While the obese phenotype development was not prevented, metabolic issues related to lipid absorption, inflammation, and gut microbial balance were improved, showing therapeutic promise and warranting eventual mechanistic elucidations.

Microbiome Multi-Omics Network Analysis: Statistical Considerations, Limitations, and Opportunities.

The advent of large-scale microbiome studies affords newfound analytical opportunities to understand how these communities of microbes operate and relate to their environment. However, the analytical methodology needed to model microbiome data and integrate them with other data constructs remains nascent. This emergent analytical toolset frequently ports over techniques developed in other multi-omics investigations, especially the growing array of statistical and computational techniques for integrating and representing data through networks. While network analysis has emerged as a powerful approach to modeling microbiome data, oftentimes by integrating these data with other types of omics data to discern their functional linkages, it is not always evident if the statistical details of the approach being applied are consistent with the assumptions of microbiome data or how they impact data interpretation. In this review, we overview some of the most important network methods for integrative analysis, with an emphasis on methods that have been applied or have great potential to be applied to the analysis of multi-omics integration of microbiome data. We compare advantages and disadvantages of various statistical tools, assess their applicability to microbiome data, and discuss their biological interpretability. We also highlight on-going statistical challenges and opportunities for integrative network analysis of microbiome data.

A Metagenomic Meta-analysis Reveals Functional Signatures of Health and Disease in the Human Gut Microbiome.

While recent research indicates that human health is affected by the gut microbiome, the functional mechanisms that underlie host-microbiome interactions remain poorly resolved. Metagenomic clinical studies can address this problem by revealing specific microbial functions that stratify healthy and diseased individuals. To improve our understanding of the relationship between the gut microbiome and health, we conducted the first integrative functional analysis of nearly 2,000 publicly available fecal metagenomic samples obtained from eight clinical studies. We identified characteristics of the gut microbiome that associate generally with disease, including functional alpha-diversity, beta-diversity, and beta-dispersion. Using regression modeling, we identified specific microbial functions that robustly stratify diseased individuals from healthy controls. Many of these functions overlapped multiple diseases, suggesting a general role in host health, while others were specific to a single disease and may indicate disease-specific etiologies. Our results clarify potential microbiome-mediated mechanisms of disease and reveal features of the microbiome that may be useful for the development of microbiome-based diagnostics. IMPORTANCE The composition of the gut microbiome associates with a wide range of human diseases, but the mechanisms underpinning these associations are not well understood. To shift toward a mechanistic understanding, we integrated distinct metagenomic data sets to identify functions encoded in the gut microbiome that associate with multiple diseases, which may be important to human health. Additionally, we identified functions that associate with specific diseases, which may elucidate disease-specific etiologies. We demonstrated that the functions encoded in the microbiome can be used to classify disease status, but the inclusion of additional patient covariates may be necessary to obtain sufficient accuracy. Ultimately, this analysis advances our understanding of the gut microbiome functions that constitute a healthy microbiome and identifies potential targets for microbiome-based diagnostics and therapeutics.

Identification of two novel functional p53 responsive elements in the herpes simplex virus-1 genome.

Analysis of the herpes simplex virus-1 (HSV-1) genome reveals two candidate p53 responsive elements (p53RE), located in proximity to the replication origins oriL and oriS, referred to as p53RE-L and p53RE-S, respectively. The sequences of p53RE-L and p53RE-S conform to the p53 consensus site and are present in HSV-1 strains KOS, 17, and F. p53 binds to both elements in vitro and in virus-infected cells. Both p53RE-L and p53RE-S are capable of conferring p53-dependent transcriptional activation onto a heterologous reporter gene. Importantly, expression of the essential immediate early viral transactivator ICP4 and the essential DNA replication protein ICP8, that are adjacent to p53RE-S and p53RE-L, are repressed in a p53-dependent manner. Taken together, this study identifies two novel functional p53RE in the HSV-1 genome and suggests a complex mechanism of viral gene regulation by p53 which may determine progression of the lytic viral replication cycle or the establishment of latency.