Keynote talk
Learning from unstable microbiomes
By Prof. Deepa Agashe, National Centre for Biological Sciences
Highlights
Prioritizing gut microbial SNPs linked to immunotherapy outcomes in NSCLC patients by integrative bioinformatics analysis
By Muhammad Faheem Raziq, National University of Sciences and Technology
Background
The human gut microbiome has emerged as a potential modulator of treatment efficacy for different cancers, including non-small cell lung cancer (NSCLC) patients undergoing immune checkpoint inhibitor (ICI) therapy. In this study, we investigated the association of gut microbial variations with response against ICIs by analyzing the gut metagenomes of NSCLC patients.
Methods
Strain identification from the publicly available metagenomes of 87 NSCLC patients, treated with nivolumab and collected at three different timepoints (T0, T1, and T2), was performed using StrainPhlAn3. Variant calling and annotations were performed using Snippy and associations between microbial genes and genomic variations with treatment responses were evaluated using MaAsLin2. Supervised machine learning models were developed to prioritize single nucleotide polymorphisms (SNPs) predictive of treatment response. Structural bioinformatics approaches were employed using MUpro, I-Mutant 2.0, CASTp and PyMOL to access the functional impact of prioritized SNPs on protein stability and active site interactions.
Results
Our findings revealed the presence of strains for several microbial species (e.g., Lachnospira eligens) exclusively in Responders (R) or Non-responders (NR) (e.g., Parabacteroides distasonis). Variant calling and annotations for the identified strains from R and NR patients highlighted variations in genes (e.g., ftsA, lpdA, and nadB) that were significantly associated with the NR status of patients. Among the developed models, Logistic Regression performed best (accuracy > 90% and AUC ROC > 95%) in prioritizing SNPs in genes that could distinguish R and NR at T0. These SNPs included Ala168Val (lpdA) in Phocaeicola dorei and Tyr233His (lpdA), Leu330Ser (lpdA), and His233Arg (obgE) in Parabacteroides distasonis. Lastly, structural analyses of these prioritized variants in objE and lpdA revealed their involvement in the substrate binding site and an overall reduction in protein stability. This suggests that these variations might likely disrupt substrate interactions and compromise protein stability, thereby impairing normal protein functionality.
Conclusion
The integration of metagenomics, machine learning, and structural bioinformatics provides a robust framework for understanding the association between gut microbial variations and treatment response, paving the way for personalized therapies for NSCLC in the future. These findings emphasize the potential clinical implications of microbiome-based biomarkers in guiding patient-specific treatment strategies and improving immunotherapy outcomes.
Sulfated dietary fiber protects gut microbiota from antibiotics
By Fuqing Wu, The University of Texas Health Science Center at Houston
Antibiotics, while essential for combating pathogens, also disrupt commensal bacteria, leading to gut microbiota imbalance and associated diseases. However, strategies to mitigate such collateral damage remain largely underexplored. In this study, we found that fucoidan, a marine polysaccharide derived from brown seaweed, provides broad-spectrum growth protection against multiple classes of antibiotics for human gut microbial isolates in vitro and for fecal communities ex vivo. This protective effect is dependent on the structural integrity, molecular weight, and sulfur content of the polysaccharide. Transcriptomic analysis showed that while fucoidan had minimal impact on baseline gene expression, it counteracted about 60% of the genes induced by kanamycin, suggesting a potential inhibition of kanamycin. Mass spectrometry results further showed that this inhibition may be due to the non-specific binding of fucoidan to kanamycin in solution. Finally, animal model experiments revealed that fucoidan facilitated the recovery of gut microbes following antibiotic treatment in vivo. These findings suggest fucoidan could serve as a potential intervention to help protect gut microbiota during antibiotic therapy. Further studies are needed to evaluate its clinical potential and ensure it does not compromise antimicrobial efficacy.
Talks
Accessible Microbiota Drug Development: First Head-to-Head Trial of Defined LBP vs. FMT
By Lukas Bethlehem, Icahn School of Medicine at Mount Sinai
Fecal microbiota transplantation (FMT) is an effective therapy for recurrent Clostridioides difficile infection (rCDI) but has undefined composition and poor scalability. In vitro manufactured live biotherapeutic products (LBP) enable both scalability and defined strain composition but with higher manufacturing complexity, resulting in few LBP trials. We developed an accessible platform to produce human-grade LBPs. We provide regulatory documentation and manufacturing protocols to facilitate translating microbiome advances to human trials. With this platform, we conduct the first direct comparison of the same bacterial strains administered after in vitro manufacturing (LBP) compared to donor sourced (FMT) across two doses. In a phase 1b randomized trial (n=18, NCT05911997), an endoscopic dose of the 15-strain consortium MTC01 was safe with rCDI prevention eight weeks after dosing in seven out of nine LBP patients, similar to eight out of nine FMT patients. Notably, MTC01 strain engraftment was superior to FMT at higher doses.
Healthy microbiome—moving towards functional interpretation
By Kinga Zielińska, Jagiellonian University
"Background
Microbiome-based disease prediction has significant potential as an early, noninvasive marker of multiple health conditions linked to dysbiosis of the human gut microbiota, thanks in part to decreasing sequencing and analysis costs. Microbiome health indices and other computational tools currently proposed in the field often are based on a microbiome’s species richness and are completely reliant on taxonomic classification. A resurgent interest in a metabolism-centric, ecological approach has led to an increased understanding of microbiome metabolic and phenotypic complexity, revealing substantial restrictions of taxonomy-reliant approaches.
Findings
In this study, we introduce a new metagenomic health index developed as an answer to recent developments in microbiome definitions, in an effort to distinguish between healthy and unhealthy microbiomes, here in focus, inflammatory bowel disease (IBD). The novelty of our approach is a shift from a traditional Linnean phylogenetic classification toward a more holistic consideration of the metabolic functional potential underlining ecological interactions between species. Based on well-explored data cohorts, we compare our method and its performance with the most comprehensive indices to date, the taxonomy-based Gut Microbiome Health Index (GMHI), and the high-dimensional principal component analysis (hiPCA) methods, as well as to the standard taxon- and function-based Shannon entropy scoring. After demonstrating better performance on the initially targeted IBD cohorts, in comparison with other methods, we retrain our index on an additional 27 datasets obtained from different clinical conditions and validate our index's ability to distinguish between healthy and disease states using a variety of complementary benchmarking approaches. Finally, we demonstrate its superiority over the GMHI and the hiPCA on a longitudinal COVID-19 cohort and highlight the distinct robustness of our method to sequencing depth.
Conclusions
Overall, we emphasize the potential of this metagenomic approach and advocate a shift toward functional approaches to better understand and assess microbiome health as well as provide directions for future index enhancements. Our method, q2-predict-dysbiosis (Q2PD), is freely available (https://github.com/Kizielins/q2-predict-dysbiosis)."
Microbiome Geographic Population Structure (mGPS): A Powerful Tool for Microbial Forensics
By Eran Elhaik, Lund University
Trace evidence analysis is a specialized forensic science discipline that examines and interprets material transfers between objects, individuals, and environments using sources like hair, fibers, soil, and pollen. Identifying the recent geographic whereabouts of organisms has been challenging in ecology, microbiology, and forensics due to difficulties in uniquely associating biological material with specific sites. While human DNA can predict origins, it has limited forensic value as it remains constant and does not indicate recent movements. Only dynamic spatio-temporal types of information shared and exchanged between organisms and the environment can be used by tracing applications. However, no microbial biogeographical applications are available, and localizing microbes remains an unresolved challenge. We developed the Microbiome Geographic Population Structure (mGPS), the first machine-learning-based tool that leverages microbial relative sequence abundances to pinpoint the fine-scale source locations of microorganisms. mGPS was rigorously tested on microbiomes from the built (MetaSUB project), soil (Soil Atlas project), and marine (Tara Ocean) environments sequenced using different approaches. Applied to 40 global cities, mGPS predicted the sampling sites for 92% of the samples. At the resolution of a single city, mGPS predicted up to 82% of sampling sites, which often were only a few hundred meters apart. Applied to soil and marine microbiomes, mGPS predicted the sampling sites for 86% and 74% of the samples, respectively. We demonstrated that mGPS differentiated local from non-local microorganisms and used it to trace the global spread of antimicrobial resistance genes. mGPS’s ability to localize samples to their waterbody, country, city, and transit stations has forensics, medical, and epidemiological applications.