• David Kreil

• Catherine Lozupone

The commensal gut microbiome plays an essential role in protecting against opportunistic pathogens and maintaining immune homeostasis. Dysbiosis, an imbalance in microbial communities, is linked with disease when this imbalance disturbs microbiota functions essential for maintaining health or introduces processes that promote disease. By performing meta-analyses of many studies that have sequenced the 16S ribosomal RNA gene to characterize gut microbial communities in different disease and health contexts, we have defined very young age and Western versus Developing world/Agrarian cultures to be two major axes of gut microbiome compositional variation that are important for explaining variability across healthy humans. Interestingly, among Western adults, individuals with different diseases or microbiome disturbances have migration along both of these major axes of health-associated gut microbiome variation. For instance, obese Western individuals sometimes have microbiomes that cluster closer to Prevotella-rich/Bacteroides-poor microbiome types in the developing world and this is more common in African versus European Americans. Related to age, gut microbiomes of adults with recurrent Clostridioides difficile infection, Inflammatory Bowel Disease, cancer, and intake of broad-spectrum antibiotics all tend to cluster closer to healthy infant gut microbiomes, characterized by low diversity with increased representation of facultative versus strict anaerobes. The relationship between highly disturbed and infant gut microbiome compositions is likely related to parallel processes that occur in primary versus secondary ecological succession, where absence of a complex community of healthy gut commensals allows for the colonization of opportunistic, early succession adapted organism that undergo an ordered turnover of membership. By coupling co-occurrence patterns and longitudinal analyses of dense time-series data with genomic and metabolic network interrogations to explore underlying drivers of microbial cooperation and competition, we have been generating hypotheses regarding important interactions that occur during succession and testing them in humanized mice.

• Kinga Zielińska

Microbiome-based disease prediction has significant potential as an early, non-invasive marker of multiple health conditions attributable to dysbiosis of the human gut microbiota, thanks in part to decreasing sequencing and analysis costs. Existing tools, or microbiome health indexes, are often based solely on microbiome richness and are heavily dependent on taxonomic classification. More recently, an ecological approach has led to increased understanding of microbiome, which reveals substantial restrictions of such approaches. In this study, we introduce a new health index created as an answer to updated microbiome definitions. The novelty of our approach is a shift from a traditional approach of phylogenetic classification, towards a more holistic consideration of metabolic function including ecological interactions between species in the effort to distinguish between healthy and diseased states. We compare this to not only the taxonomy-based Gut Microbiome Health Index (GMHI) and the high dimensional principal component analysis (hiPCA)method, the most comprehensive indices to date, but also to taxon- and function-based Shannon entropy and demonstrate a significant improvement to these approaches. We validate our index’s performance using a variety of complementary benchmarking approaches on datasets representing a range of gut health conditions and showcase the robustness of its superiority over the GMHI and the hiPCA. Overall, we emphasize the potential of this approach and advocate a shift towards functional approaches in order to better understand and assess microbiome health as well as to provide directions for future index enhancements. Our method, q2-predict-dysbiosis, is freely available as a QIIME 2 plugin (https://github.com/bioinf-mcb/q2-predict-dysbiosis).

• Shaday Guerrero Flores, Shaday Guerrero Flores, Centro de Ciencias Matemáticas UNAM
• Nelly Selem Mojica, Nelly Selem Mojica, Centro de Ciencias Matemáticas UNAM
• Adriana Haydee Contreras Peruyero, Adriana Haydee Contreras Peruyero, Centro de Ciencias Matemáticas UNAM
• Juan Francisco Espinosa Maya, Juan Francisco Espinosa Maya, Centro de Ciencias Matemáticas UNAM
• Rafael Perez Estrada, Rafael Perez Estrada, Centro de Ciencias Matemáticas UNAM
• Mario Jardon, Mario Jardon, Centro de Ciencias Matemáticas UNAM
• Orlando Camargo Escalante, Orlando Camargo Escalante, Centro de Investigación y de Estudios Avanzados (CINVESTAV)
• Miguel Nakamura, Miguel Nakamura, Centro de investigación en Matemáticas CIMAT
• Kotaro Hata, Kotaro Hata, Centro de investigación en Matemáticas CIMAT
• David Alberto Garcia, David Alberto Garcia, Centro de Investigación y de Estudios Avanzados (CINVESTAV)
• Luis Yovanny Bedolla Galvan, Luis Yovanny Bedolla Galvan, Escuela Nacional de Estudios Superiores UNAM
• Goretty Mendoza, Goretty Mendoza

This study aimed to enhance our understanding of metagenomic datasets by applying and innovating bioinformatics tools for the identification and functional characterization of microbial genes and pathways. We utilized tools such as Prokka, Prodigal, EggNog, mi-faser, Metacyc, and DiTing to annotate gene functions and metabolic pathways, generating a detailed functional landscape of the microbial communities. We identified key functional roles in various health conditions through Pearson-Spearman correlation networks but found a notable absence of keystone functions in several categories. On the other hand, we explored microbial health indicators by replicating indices like GMHI and hiPCA and attempting novel integrations with metabolic pathway data. The adapted GMHI and hiPCA indices could distinguish between health states in microbial communities. Moving forward, we aim to refine these indices using expanded datasets, focusing on both taxonomic and functional data. In conclusion, our study enhances the predictive capabilities of metagenomic analyses for assessing microbial community health, paving the way for future developments in microbial ecology and biomedicine.

• Patrick Smyth, Patrick Smyth, National Microbiology Laboratory
• Liam Elson, Liam Elson, National Microbiology Laboratory
• Julie Chih-Yu Chen, Julie Chih-Yu Chen, National Microbiology Laboratory

This study utilizes stool samples from the Human Microbiome Project 2 and American Gut Project cohorts, along with COVID-19 patient data, to develop a superior health index using machine learning techniques.

We employed LightGBM's Dropouts meet Multiple Additive Regression Trees (DART) algorithm, which excels in handling high-dimensional data, for predicting health states based on combined taxonomic and functional profiles. Data preprocessing involved filtering features with a minimum prevalence threshold, as well as aggregating taxonomic pathways.

Two cross-validation strategies, nested stratified 5-fold and Leave-One-Project-Out (LOPO), were implemented to ensure robust model evaluation. Performance metrics such as AUC, F1 Score, and Balanced Accuracy were used to assess model effectiveness. Feature importance analysis identified key taxa and pathways relevant to gut health.

The Gradient Boost Health Index from gut Microbiome data (GBHIM) was introduced, showing improved performance over existing indices like the Gut Microbiome Health Index (GMHI). The inclusion of GMHI as a feature occasionally enhanced model performance. The model demonstrated strong performance across various validation folds and projects, highlighting its potential for accurate health state predictions.

For COVID-19 samples, the model effectively distinguished between healthy and non-healthy states, clustering more closely with non-healthy samples in Principal Coordinates Analysis. This study underscores the importance of leveraging comprehensive microbial data and advanced machine learning techniques for improved health state predictions in microbiome research

• Karwowska Zuzanna, Karwowska Zuzanna, Malopolska Centre of Biotechnology
• Paweł Szczerbiak, Paweł Szczerbiak, Sano Centre for Computational Medicine
• Tomasz Kościółek, Tomasz Kościółek, Sano Centre for Computational Medicine

Despite the majority of microbiome studies being cross-sectional, it is widely acknowledged that the microbiome is a dynamic ecosystem.
Here, we analyse how the gut microbiome changes over time as a community, how different bacterial species behave over time, and whether there are clusters of bacteria that exhibit similar fluctuations?
We show that a healthy human gut microbiome is stationary, seasonal, and non-random. Moreover, we demonstrate that it is self-explanatory to some extent, and its behavior can be predicted.
The analysis of individual bacterial species uncovered the existence of three distinct longitudinal regimes in the healthy human gut microbiome. These regimes consist of 1) stationary and highly prevalent bacteria that exhibit resistance to environmental changes; 2) volatile bacteria that exhibit dynamic reactions to external stimuli, causing their presence to fluctuate over time; and 3) white noise. Clustering analysis revealed the presence of taxonomically diverse bacterial groups that exhibit similar fluctuations over time.
In conclusion, our study highlights the importance of longitudinal data and provides new insights into the dynamics of the healthy human gut microbiome. We offer clear guidelines for clinicians and statisticians who conduct longitudinal studies and develop models to predict the behavior of the gut microbiome over time.

• Jesse Shapiro

As variation in microbial community structure is implicated in an increasing number of human diseases and environmental changes, there is strong potential for microbiome-based diagnostics and therapeutics. I will discuss three brief case studies, highlighting how (1) diagnosis is easier than forecasting of future disease or environmental perturbations, (2) predicting simpler disease outcomes (e.g. infection or not) is easier than more complex outcomes (e.g. disease severity) that depend on a larger number of host- and microbe-determined factors, and (3) certain disease outcomes are more predictable based on genetic diversity within a key pathogen species than based on microbiome community composition. If these principles prove to be general, we can move toward more realistic expectations for microbiome-driven predictions and use the best combination of data and methods for the task at hand.

• Carson Stillman, Carson Stillman, University of Florida
• Jonathan E. Bravo, Jonathan E. Bravo, University of Florida
• Christina Boucher, Christina Boucher, University of Florida
• Sara Rampazzi, Sara Rampazzi, University of Florida

Portable genome sequencing technology is revolutionizing genomic research by providing a faster, flexible method of sequencing DNA and RNA. The unprecedented shift from bulky stand-alone benchtop equipment confined in a laboratory setting to small portable devices which can be carried anywhere outside the laboratory network and connected to untrusted computers to perform sequencing raises new security and privacy threats not considered before. Current research primarily addresses the privacy of DNA/RNA data in online databases and the security of stand-alone sequencing devices such as Illumina. However, it overlooks the security risks arising from compromises of computers directly connected to sequencers. While sensitive data, such as the human genome, has become easier to sequence, the networks connecting to these smaller devices and the hardware running basecalling can no longer implicitly be trusted.
Here, we present new security and privacy threats of portable sequencing technology and recommendations to aid in ensuring sequencing data is kept private and secure. First, to prevent unauthorized access to sequencing devices, IP addresses should not be considered a sufficient authentication mechanism. Second, integrity checks are necessary for all data passed from the sequencer to external computers to avoid data manipulation. Finally, encryption should be considered as data is passed from the sequencer to such external computers to prevent eavesdropping on data as it is sent and stored. As devices and technology rapidly change, it becomes paramount to reevaluate security requirements alongside them or risk leaving some of our most sensitive data exposed.

• Miguel Prieto, Miguel Prieto, Simon Fraser University
• William Hsiao, William Hsiao, Simon Fraser University
• Clement Tsui, Clement Tsui, National Centre for Infectious Diseases

Giardia intestinalis parasites are common causes of sporadic gastroenteritis outbreaks in high-income countries. In contrast, giardiasis is endemic in low-income settings with poor sanitation, where it may cause failure to thrive and chronic malnutrition. Whole genome analyses of this microbe are rare because culturing this parasite is a laborious process with a limited success rate. Hence, subtyping for epidemiological tracking and surveillance relies on a three-loci marker scheme based on PCR amplification of stool samples. Here, we developed a nextflow pipeline that creates a core genome multilocus sequence typing (cgMLST) scheme for Giardia intestinalis using chewBBACA. This workflow takes as input all available whole genome sequencing samples of Giardia intestinalis assemblages A and B (the most commonly associated with human infections) in public biorepositories (n=128, after excluding samples producing poor-quality assemblies). The accuracy and reproducibility of the schema calling process were verified using k-fold cross-validation (70/30 splits) with a 95% prevalence cut-off for every locus in the training samples. Finally, the selected sequences with inaccurate alignments against reference genomes by BLAST were removed from the final schema definition. Our gene-by-gene schema is scalable to specific epidemiological settings (adding locally circulating Giardia spp. genomes), and the pipeline is extendable to other pathogens of public health interest. This schema, applied to culture-free stool metagenomics, can be used by interested public health agencies to investigate outbreaks and conduct genomic surveillance of human giardiasis.

• Jingxiang Gao, Jingxiang Gao, Undergraduate Student in Computer Science
• George Rivera, George Rivera, Global Society for Philippine Nurse Researchers
• Madhu Sen, Madhu Sen, VIT Vellore
• Matthew Shtrahman, Matthew Shtrahman, UCSD School of Medicine
• Madhavi Ganapathiraju, Madhavi Ganapathiraju, Carnegie Mellon University

An inverted repeat (IR) in DNA is a sequence of nucleotides that is followed by its complementary bases but in reverse order (e.g., CACGGAttgTCCGTG). IRs cause fragile sites endangering genetic stability. In viruses, IRs enable host cell entry, genomic evolution in zoonotic viruses, and more. Despite their importance, IRs have not been studied comprehensively viral genomes at a large scale. We developed a tool into the Biological Language Modeling Toolkit which computes augmented suffix-arrays to efficiently identify IRs, and studied 13,023 viral genomes and catalogued their IRs. We found over 19 million IRs longer than 20 bases (1,300 IRs per virus), including 134 that are longer than 2 kilobases. Among the viruses with large IRs, we identified over 50 large IRs in herpes viruses, and over 10 IRs in pox viruses. There is a prevalence of large ‘terminal’ inverted repeats in bacteriophages. We discovered large IRs in common disease-causing viruses, such as the African swine fever virus (lethal to domestic pigs), paramecium bursaria chlorella virus (important for termination of algae blooms, found to be able to infect humans and decrease the motor skills and reaction speed), Yaba-like disease virus (important in the cancer gene therapy), and human herpes virus. We found 54 viruses with high IR density, including disease-causing viruses like pox and herpes, and lymphocystis disease virus. These results in investigating the prevalence and distribution of inverted repeats in viral genomes suggests potential for discovery of mechanism of action of some of the understudied viruses.

• Christina Bauer, Christina Bauer, Medical University of Vienna
• Christopher Kremslehner, Christopher Kremslehner, Medical University of Vienna
• Florian Gruber, Florian Gruber, Medical University of Vienna

Advancements in spatial transcriptomicshave advanced our understanding of cellular organization and function within skin and other tissues. However, existing techniques often encounter limitations in resolution and coverage, hindering comprehensive analysis. To address this gap, we propose a novel approach to enhance the resolution of spatial transcriptomic data and integrate it into multimodal imaging workflows.
Our project aims to leverage advanced image processing software to generate an approximated cell-level transcriptome from spatial transcriptomic data from juvenile and aged skin. By correlating gene expression profiles with immunofluorescence staining and age-related metabolic activity assays, we seek to gain novel insights into the intricate interplay between gene expression and cellular phenotypes. This would facilitate a more nuanced and analysis and allow to locally correlate complex phenotypes of cellular aging.
Furthermore, we establish a robust analysis pipeline tailored for evaluating skin, streamlining future workloads for similar studies. This pipeline aims to address the complexity associated with spatial transcriptomic data analysis, ensuring accessibility to individuals within the lab, including those without programming expertise.
Through the integration of spatial transcriptomics data into existing analytic imaging workflows, our project seeks to overcome existing limitations and pave the way for comprehensive analyses of cellular dynamics within tissue microenvironments. Our evaluation workflow includes initial assessment and comparative data analysis, utilizing quantitative metrics and established benchmarks to objectively evaluate the performance and accuracy of our approach.
Overall, our project holds significant promise in advancing our understanding of skin aging and offers valuable insights into tissue organization and cellular interactions.

• Joaquin Dopazo