Project title: Microbiome power-calculation tool for biologists: towards rigorous, reproducible microbiome study-design

Rationale: Measured differences between sample groups can result from any number of experimental artifacts not reflective of actual biology, including differing definitions of what a clinical population signifies within different studies, how samples are prepared, and analytical decisions (e.g., bioinformatic and statistical tool-selection, parameter-settings ^{2– 4} ). Statistical power calculations are a key part of quality study-design, informing the sample-size required to have sufficient statistical power to detect differences between experimental groups. The size of this difference between groups—the effect size—should also be taken into account during experimental planning; smaller effects are more sensitive to being obscured by experimental noise. Sufficiently powered studies are critical for robust biological conclusions, and funding agencies increasingly require power and sample-size analyses to consider applications for support.

R-based software packages enabling power analyses modeling relationships between sample-size and detectable effect-size using PERMANOVA-based methods have been developed to estimate required samples for microbiome experimental design ⁵ , given input data from pilot studies. These handy tools are not generally accessible to biologists with limited computational experience and/or a more cursory grasp of statistics. We sought to build on these methods to create a more intuitive calculator/guide for biologists, who often need only a quick point-and-click reference for experimental planning.

Goal: To provide an intuitive power- and effect-size calculator-tool for biologists with limited computational experience.

MicroPower Plus ⁸ is most useful as a statistical reference-guide for biologists to make quick calculations to aid in experimental design of microbiome studies. We built a user-interface around the human gut microbiome reference dataset that allows the user to visualize the relationship between sample size, effect size and statistical power as a proof of concept using R Shiny ¹⁰ . Resulting effect size is reported as a bar graph, with reference to effect sizes reported in the literature for comparisons. We created an additional tool that allows the user to input their own data, calculate the effect size from their experiment and report it as a bar graph. Future iterations of this tool will include interactive visualizations for the pre-computed reference data from other body-sites.

The provided tutorial walks the user through an example power calculation ( Figure 3) and effect size calculation ( Figure 4) using the pre-computed human gut microbiome datasets.

Project title: Environmental Chemicals: Impact on Human Microbiomes

Rationale: Environmental exposures to chemicals have been a public health concern due to the ubiquitous nature of its effects on human health and the environment. Industries and manufacturing sectors contribute to chemical exposures by releasing these chemicals into the environment. Chemicals commonly found in commercial products, such as heavy metals and chlorinated hydrocarbon solvents, can persist in the environment for extended periods, increasing the latency of exposure ¹³ .

A lack of information led to relatively few rules for handling and disposing of chemicals in the first part of the 20 ^th century, which resulted in the random release of these hazardous chemicals and toxins into the environment. Knowledge of toxic waste dumps and their associated human health and environmental health consequences received national attention in the late 1970’s ¹⁴ . In response to public outcry, Congress created “Superfund” in the 1980’s to fund toxic waste clean-up at industrial sites ^{14, 15} . Superfund sites require long-term remediation efforts, and sites are evaluated for eligibility on a point-based system requiring a preliminary assessment and site-inspection (known as the Hazard Ranking System, or HRS) ¹⁶ . Reporting from the public or an agency is also considered in assessing a site for the qualification. Superfund sites are prioritized by HRS score onto the National Priority List (NPL) ¹⁶ . Currently there are 1335 NPL sites around the U.S., each having specific chemical contaminations.

Human exposure to toxic chemicals has been shown to elicit different effects depending on the host’s immune response, with long-term exposures associating with a range of serious maladies varying from cancers acting on various bodily tissues to neurological effects ¹⁷ . The gut microbiome is hypothesized to have a unique role in enhancing and maintaining host health through the microbiome-gut-brain axis and can impact endocrine, immunological and nutrient signals ¹⁸ . Microbiome dysbiosis can occur with exposure to toxic environmental contaminants via ingestion or inhalation and can lead to several chronic conditions. Due to its diverse functions in the body, the gut microbiome acts as an indicator for health, and there is a growing body of literature exploring the interactions of environmental contaminants with the host microbiome ^{13, 17, 18} .

Environmental contaminants present in Superfund sites around the U.S. can significantly affect the health of the population in the surrounding areas. To illustrate this effect, we created a tool for visualizing the impact of environmental toxicants on the gut microbiome.

Goals: 1) To illustrate the trends of environmental chemical exposures from U.S. Superfund sites over time. 2) to create a tool for visualizing the impact of exposure to environmental chemicals on the gut microbiome around the U.S.

Geographic distribution of select Superfund-site contaminants and abundance of Bacteriodetes OTUs are shown in Figure 5. We next explored a potential relationship between abundance of this bacterial phylum and individual contaminants, and further possible predictive efficacy of contaminants for certain OTUs, using proof-of-concept modeling. We restricted samples to those within 5 km of a Superfund site for these analyses. We constructed a random forest using each contaminant as a binary predictor-variable. We found a strong relationship between several contaminants and microbial composition. The two most predictive contaminants were polycyclic aromatic hydrocarbons and poly-chlorinated biphenyls (PAH, 94% and PCB, 81%, respectively). The contaminant with the lowest accuracy was lead (60%).

It is worth noting that PAH are known to bio-amplify as they go through food-webs. Other health outcomes linked to PAH exposure are various forms of cancer, as well as developmental impacts. PCB have been banned in the manufacturing process since 1979, yet they do not readily break down and remain a hazard over long periods of time. Because of these properties, they are commonly listed as Superfund contaminants of high concern. In conclusion, we found that for several contaminants the microbial composition varied significantly among individuals living near Superfund sites with high or low levels of PAH and PCB, respectively.

Project title: Creating a web app to study human gut microbiome variation across geographic regions of the world

Project Rationales, Descriptions and Goals

Rationale: The human gut microbiome is one of the most densely populated sites by bacteria in the human body. It performs numerous functions, and its dysbiosis has been associated with several diseases. A major goal of microbiome researchers has been to understand the diversity of the gut microbiome across human populations. Although several studies have been undertaken for this purpose, these studies are limited in scope and comparative ability. Therefore, the rationale of the present work was to create a web tool which will be equipped with reference databases, populations and necessary scripts for the users to upload, analyze and visualize their own microbiome data at the server, with additional options to compare with the reference populations. Results can subsequently be downloaded by the user. Finally, all the reference population data is to be made available for download, along with necessary scripts to enable the user to run the program on their local computers, without the need to upload their raw data. Such a tool will be extremely useful to any interdisciplinary researchers who may have microbiome-related research questions but are not experienced in writing code, handling large microbiome datasets or who do not have access to advanced computational facilities. The codes, instructions and guidelines are available through a GitHub repository. The flowchart summarizing the approach is provided in Figure 6.

Goals: 1) To download raw microbiome data (V4 region of 16S rRNA gene) from various world populations and generate amplicon sequence variant (ASV) table for comparison purposes. 2) Construct simple, but informative plots such as heatmaps and principle component analysis (PCA) plots to visualize relationships/patterns in the data through the proposed web app. 3) Provide all raw sequencing data, bash scripts and R scripts to run all steps of the analyses, as well as appropriate documentation and guidelines for an easy and error-free run of the pipeline on the user’s local computer.

Our work was aimed at creating a web app to study the geographical patterns of the human microbiome and selecting features which could be useful to distinguish the populations. Using publicly available resources, we were able to include different geographical populations and select features to identify differences across groups. The resources for our study are deposited in our GitHub repository (see Software availability). Limitations of this study include that factors such as age, gender and other participant phenotypes which could be contributing to geographical patterns were not included in these analyses. However, we were able to create a web-app prototype for identifying features from the composition of the human gut microbiome related to geographical population. In the future, this work can be extended to include other variable regions of the 16S rRNA gene, as well as including other body sites such as the oral cavity, skin, etc. Similarly, batch-effect correction-tools need to be incorporated for unbiased comparison across different studies.

Project title: A web-based machine learning pipeline for disease prediction using microbial data

Project Rationales, Descriptions and Goals

Rationale: High-throughput sequencing technologies have resulted in the generation of an increasing amount of microbial data, such as microbiome data. Using these data, machine learning methods are powerful in identifying functionally active microbes and predicting disease status. Even though machine learning algorithms are popular approaches to investigate microbiome, to adopt these methods effectively usually requires specialized training. In addition, model selection and hyper-parameter tuning can be time-consuming even for experienced practitioners. Thus, our project focused on the efficiency of AI in solving big-data problems and facilitating humans to perform other cognition-demanding tasks by developing a GUI-based pipeline for training machine learning algorithms on taxonomic microbiome data. Our pipeline expands access of computational tools to researchers in non-computational disciplines to improve cross-disciplinary study. As a proof of concept, we successfully utilized our pipeline to train a predictive algorithm for obesity rates based upon orthogonal taxonomic units which may be applied toward generating health-related features from clinical, historical, or forensic samples. Our code utilizes three methods: K-nearest neighbors (KNN), support vector machine (SVM), and adaptive boosting (AdaBoost) to achieve respective accuracies near eighty-four, ninety-one, and eighty-six percent. Both KNN and SVM utilized a 10-fold cross-validations to prevent overtraining. Under this method, training was achieved near instantaneously on a 16 GB MacBook to demonstrate feasibility. Outputs are processed into interactive graphical visualizations to improve ease-of-use. Although previous projects have utilized these computational techniques toward processing microbiome data, our pipeline removes barriers to use for researchers without coding backgrounds while streamlining efficiency for all users.

Studies have revealed significant diversity in the gut microbiome composition related to various phenotypes. Obesity has been associated with changes in the microbiota at phylum-level, reduction in bacterial diversity, and different representations of bacterial genes. For example, studies of lean and obese mice suggest a strong relationship between gut microbiome and obesity. Phylogenetic marker genes uncovered by 16S rRNA gene amplicon sequencing have revolutionized the field of microbial ecology. This PCR-based method has the advantage of identifying difficult to culture bacterial organisms. Various bioinformatic pipelines can then group these sequences into clusters called OTUs. OTUs are based on their sequence similarity to each other rather than a reference taxonomic dataset which may be biased towards existing taxonomic classification ²⁷ .

Goals: We were interested in finding out if there is an association between gut microbiome OTUs and obesity. Additionally, we wanted to be able to use this data to distinguish between lean, overweight, and obese phenotypes in humans. We were able to successfully develop a machine-learning based pipeline that shows the association between gut microbiome OTUs and obesity with high accuracy. Furthermore, this pipeline can predict whether sample OTU data comes from a lean, overweight, or obese human phenotypes. Our work is significant because a heavy coding background is not required for use of high-accuracy machine learning tools.

We show that machine learning can be used to differentiate disease from the normal states using OTU information. We used pre-processed data from a twin study with 281 samples and 5462 OTUs ²⁹ . For exploratory analysis we performed PCA ( Figure 10 and Figure 11; analyses and plots generated using our Shiny app) as shown in Figure 10 and Figure 11. This analysis and plots are generated using the Shiny app. We performed feature selection to select the highly significant features, shown in Table 3. Abundance of significant OTUs is shown in Figure 12. By using a set of predefined hyperparameters for each model, we achieved 10-fold cross validated accuracy of 0.936 using a linear support vector machine ( Figure 13). Additionally, 10 OTUs we identified as important to obesity-status are provided in Table 3. While we do not have assigned significant functional annotations for them in the current development, future studies could use them as candidate functional groups to aid experimental design for validating clinical and public health microbiome findings.

Project title: Tracking ancient global epidemics

Project Rationales, Descriptions and Goals

Rationale: As the collection of human microbiome data grows, developing user-friendly tools to search proteomics databases has become critical. Bridging the gap between computer science and biological science expertise will facilitate microbiome analysis for both explanatory and predictive purposes, making significant additions to general knowledge in this field. Such effective and convenient methods of sifting through vast datasets would be well-suited to the investigation of not only modern-day microbiome samples, but also preserved historical microbial and proteomic data retrieved from ancient populations at archaeological sites worldwide. Proteomic determination of the microbes of deceased individuals would provide another dimension to forensic analysis by uncovering the pathogens that might have been responsible for their death. The significance of this determination goes beyond simply detecting the presence of bacterial peptides, also extending to tracking the migration and virulence of diseases over time in human populations.

Exploring ancient or paleolithic host-microbiome interactions is an emerging approach to explore widespread microbial infectious diseases, and even pandemics, by identifying pathogen-expressed proteins in human dental calculus. This approach is supplemented by data from metabolomic analyses, anthropological and paleopathological data from the skeletal material, archaeological contexts, and archival data. Examining protein content of dental calculus has typically given insight into diet and oral health of communities of past generations ^{34– 36} .

Since dental calculus is formed as a result of bacterial plaque accumulation around the gingiva, dental calculus consists primarily of bacteria. Thus, dental calculus lends itself well to oral microbiome analysis. For example, it was found in a medieval sample that 85–95% of the calculus was composed of bacterial proteins ³⁶ . This indicates a novel method of examining the constituents of the oral microbiome and its variation across cultures, geographies, and various historical periods.

The availability of a unique set of data from the first quarantine in the world will enable substantial focus on infectious diseases and the modeling of ancient epidemics ( Figure 14). All of the approximately 1500 individuals for this project died of an infectious disease, we know this from archival records. The addition of body responses to the environment and diseases (metabolites), as well as dietary data (stable isotopes to detect malnutrition), will be trialed, providing the best chance to recognize the pathogen responsible and its overall effects. In genetics and medicine, the combination of code, workflow, logic and available data will provide over 300 years of data on epidemics (especially bubonic plague) including the first influenza pandemic, dated 1580, and outbreaks of typhus and measles. It will be possible to reach ca. 600 years of data at one location using historical and medical records. The plague and other similar illnesses provoking fever are replaced by smallpox, measles and flu in later times, as medicine provides therapies, mobility increases and diet changes with many plants cultivated in different continents from where they originated. Our TRACK prototypes will enable investigations related to pathogen evolution, microbiome adaptations and human immunity responses changes.

Goals: To achieve the transdisciplinary goals inherent to the nature of this paleo-omics project, a central database able to contain different data types is required. Towards this objective, we created and implemented a paleo-omics workflow consisting of: 1) a search engine to query the multi-data database, 2) a retrieving pipeline for paleo proteins, and 3) a query gateway for microbiome-human host interactions ( Figure 15).

While mass spectrometry (MS), shotgun sequencing, and 16S rRNA sequencing data can be employed in paleo-omics, we focused on an MS-based meta-proteomics approach for proof-of-concept demonstration of our prototype within the time constraints of the Codeathon, which we applied to data derived from human dental calculus protein-samples taken from archeological sites.

To test our prototype ⁴¹ , we searched for pathogen sequences against the two archaeological samples in the database, one from Denmark 1100-1450 AD ³⁶ and one from the United Kingdom 1770-1855 AD ³⁴ . The medieval Danish samples were used with a complete set of dental pathology characterization and individual data. Consistent with the reported results ³⁶ , there are oral disease pathology and bacteria normally found in the oral microbiome that can be recovered ( Figure 16). For instance, the species Porphyromonas gingivalis is frequently present in individuals with orthodontic diseases, while Streptococcus sanguinis is present in both medieval and contemporary individuals with satisfactory oral health.

This approach can also be used to discover other bacteria linked to health and possibly reveal other correlations between microbiome bacteria and health status as well as recent evolutionary changes. In archaeology, the current focus is on revealing specific pathogens and there is no established reference material to investigate the past microbiome or its effects on health. Even in recent studies, any conclusions on medieval or older individuals is based on direct comparison with the contemporary microbiome. By using archaeological methods (chronological seriation) together with software developed from our code, it will be possible to investigate any correlation between microbiome and health searching individuals dating to older periods. Such work could provide a reference standard for archaeologists, and evolutionary data to health professionals. For example, using the existing data, we found the opportunistic respiratory pathogen Haemophilus parainfluenzae ^{43, 44} present less frequently in this set of medieval samples (Wilcoxen test, p < 0.05), raising interesting questions about human society transition and infectious diseases. This group appears in Neolithic agrarian human oral microbiomes (7440 BCE) ⁴⁵ , but is at low levels in human groups practicing hunting and gathering (2000 BCE, modern day South Africa). Questions of interest to both health professionals and archaeologists that could be answered by employing our code may be when this pathogen became more frequent and why.

Understanding the origins and evolution of pathogens is very important to prepare for future pandemics. The only successful work attempted on combining archaeology with genetics and health studies to investigate past pathogens, the reconstruction of the 1918 flu pathogen ⁴⁶ proved to be both technically challenging and costly even though fewer than a hundred years had passed since the pandemic because that work tried to reproduce an active virus now extinct. It was also very useful to demonstrate that the strong virulence reported in historical sources, but unconfirmed in medicine, was real. Since 1919, only COVID-19 has demonstrated a similar virulence, proving that data from historical record can be critical in addressing new types of known viruses and pathogens, which can regain traits unseen for a century or more within that category of pathogens (respiratory viruses with flu-like symptoms in this case). That work has shown also how the choice of suitable burial grounds is essential for such work. Our work uses new -omics analyses that are providing new sources of data and could prove equally valuable, revealing the history of recent pathogens, characteristics that may have been present only occasionally, and their successes and failures. Future pathogens might reuse and re-combine successful traits (symptoms, virulence) from past epidemics and therefore our preparedness depends on knowing what to expect, on learning from the past.

The results of our work are therefore limited to making possible future interdisciplinary research and open up a path to answer new questions. Sequencing proteomic and metabolomic data from pre-modern individuals is still rare and there is no existing database, besides data from a few academic papers, that our software code could search. Yet, making possible new studies through a working proof-of-concept will accelerate the production of databases for ancient individuals. Existing archaeological studies have borne out of early full sequencing of genomes and have been severely limited by such approaches. The benefits deriving from new -omics analyses combined with our approach can provide valuable information on older pathogens. Future work may focus on epidemics initially, but with a potential also for revealing and understanding more subtle and complex relationships between human microbiome and health.

Project title: Capturing ecological and host drivers of microbiomes

Project Rationales, Descriptions and Goals

Rationale: One primary goal of host-microbiome studies is to capture and understand ecological and host drivers of microbial diversity. Research on host-microbiome associations across host species has been facilitated by the increasing accessibility of high-throughput sequencing techniques and the availability of integrated microbiome datasets, such as the Earth Microbiome Project dataset ⁴⁷ . These have yielded useful insights on how host-microbiome associations are impacted by host diet ⁴⁸ , host taxonomy or phylogeny ⁴⁹ , host immune system ⁵⁰ , and environmental factors ⁵¹ . However, host species traits vary immensely across species and such diversity has been under sampled in microbiome studies. As a result, the effects of other host factors, including body mass and life history, in relation to previously characterized host and environmental effects, on host-microbiome associations have been understudied.

Goal: In this project, we aim to investigate the effects of various host traits, including diet, host taxonomy, body mass, and longevity, in relation to environmental factors, on the intestine, fecal, foregut, and stomach microbiomes of Metazoan (animal) species. We first mined available microbiome and metadata datasets, then applied unsupervised learning directly on rarefied OTU abundance data to uncover clusters of microbial community similarity among animals.

We analyzed 726 samples spanning 199 terrestrial and freshwater Metazoan species within seven classes ( Figure 17). Our unsupervised learning approach generated three sample clusters ( Figure 18). The largest and most diverse cluster (cluster 1) comprised ~92% of all samples (n=667) from 21 Metazoan orders. These included lepidoptera (butterflies and moths; n=165), primates (n=85), anura (n=79), chiroptera (bats; n=44), carnivora (n=41), passeriformes (perching birds; n=37), hymenoptera (n=27), artiodactyla (n=26), diprotodontia (n=24), rodentia (n=23), lagomorpha (n=19), columbiformes (n=18), cypriniformes (n=18), squamata (n=17), anseriformes (n=9), gasterosteiformes (n=9), coleoptera (n=7), pilosa (n=7), cingulata (n=6), casuariiformes (n=5), and hemiptera (n=1). Cluster 2 comprised 34 samples from bats (n=16), butterflies and moths (n=10), perching birds (n=6), the dung beetle Teuchestes fossor (n=1), and the giant anteater Myrmecophaga tridactyla (n=1). Cluster 3 was the smallest (n=25) and exclusively comprised butterfly and moth samples belonging to seven species. These included Maculinea alcon (n=9), Durbania amakosa (n=5), Spalgis epeus (n=5), Lycaena clarki (n=2), Surendra vivarna (n=2), Anthene usamba (n=1), and Rapla iarbus (n=1).

ANOVA analysis indicated that clusters had the most significant mean differences in microbial alpha diversity, Simpson diversity, Shannon diversity, Faith’s phylogenetic diversity, and Chao 1 diversity ( Table 4). Digestive habitat type, host taxonomy/phylogeny, immune complexity, and life stage, were also significantly different between clusters, along with DNA extraction methods and environmental variables. Notably, body mass and maximum longevity were also significantly different between clusters.

Cluster-specific correlation analyses showed that alpha diversity in clusters 1 and 2 was consistently positively correlated with host taxonomy, immune complexity, diet, maximum longevity and latitude. Body mass, vegetation index, terrain complexity, mean temperature of the driest quarter and precipitation of the warmest and coldest quarters showed positive correlations with alpha diversity in cluster 1, but not cluster 2. Latitude and country were positively correlated with alpha diversity in cluster 2, but not cluster 1. Alpha diversity in cluster 3, which comprised butterflies and moths, was positively correlated with environmental variables (terrain complexity, mean diurnal temperature range, precipitation seasonality, elevation) and host factors (digestive habitat type and diet).

The results support our premise that host traits, including but not limited to body mass and maximum longevity, are under sampled in microbial diversity studies. Understudied host traits could also shape animal internal microbiomes together with well-characterized host traits and environmental variables. Based on our results, we propose comprehensive sampling of host traits in future microbiome studies, which may yield new and unexpected patterns of microbial community organization serving as a baseline for deeper investigations.

Interdisciplinary collaborations have proven to be very productive as shown by our six working prototypes addressing broad microbiome related challenges, ranging from power calculations, AI classifiers, GIS integration and large data set visualizations. Although working across fields has been a challenging task, we found that parsing a complex question into distinct parts can help different domain-experts to work together and accomplish tasks none of the individuals could accomplish in isolation. The codeathon workflow is thus a useful research model for many urgent societal problems that suffer from knowledge-transfer and communication issues. We have made all data and code publicly available for further exploration of these tools. Importantly, we are developing impactful projects to further pursue intersectional research spurred by this event, including microbiome-related machine learning, and data mining across archaeological time and geography.

All data underlying the results are available as part of the article and no additional source data are required.