Separation of Donor and Recipient Microbial Diversity Allows Determination of Taxonomic and Functional Features of Gut Microbiota Restructuring following Fecal Transplantation

Toward an integrative framework for microbial community coalescence

Community coalescence is defined as the mixing of intact ecological communities. From river confluences to fecal microbiota transplantation, community coalescence constitutes a common ecological occurrence affecting natural and engineered microbial systems. In this opinion article, we propose an integrative framework for microbial community coalescence to guide advances in our understanding of this important - yet underexplored - ecological phenomenon. We start by aligning community coalescence with the unified framework of biological invasion and enumerate commonalities and idiosyncrasies between these two analogous processes. Then, we discuss how organismal interactions and cohesive establishment affect coalescence outcomes with direct implications for community functioning. Last, we propose the use of ecological null modeling to study the interplay of ecological processes structuring community reassembly following coalescence.

Consistent Stool Metagenomic Biomarkers Associated with the Response To Melanoma Immunotherapy

The human gut microbiome plays an important role in both health and disease. Recent studies have demonstrated a strong influence of the gut microbiome composition on the efficacy of cancer immunotherapy. However, available studies have not yet succeeded in finding reliable and consistent metagenomic markers that are associated with the response to immunotherapy. Therefore, the reanalysis of the published data may improve our understanding of the association between the composition of the gut microbiome and the treatment response. In this study, we focused on melanoma-related metagenomic data, which are more abundant than are data from other tumor types. We analyzed the metagenomes of 680 stool samples from 7 studies that were published earlier. The taxonomic and functional biomarkers were selected after comparing the metagenomes of patients showing different treatment responses. The list of selected biomarkers was also validated on additional metagenomic data sets that were dedicated to the influence of fecal microbiota transplantation on the response to melanoma immunotherapy. According to our analysis, the resulting cross-study taxonomic biomarkers included three bacterial species: Faecalibacterium prausnitzii, Bifidobacterium adolescentis, and Eubacterium rectale. 101 groups of genes were identified to be functional biomarkers, including those potentially involved in the production of immune-stimulating molecules and metabolites. Moreover, we ranked the microbial species by the number of genes encoding functionally relevant biomarkers that they contained. Thus, we put together a list of potentially the most beneficial bacteria for immunotherapy success. F. prausnitzii, E. rectale, and three species of bifidobacteria stood out as the most beneficial species, even though some useful functions were also present in other bacterial species. IMPORTANCE In this study, we put together a list of potentially the most beneficial bacteria that were associated with a responsiveness to melanoma immunotherapy. Another important result of this study is the list of functional biomarkers of responsiveness to immunotherapy, which are dispersed among different bacterial species. This result possibly explains the existing irregularities between studies regarding the bacterial species that are beneficial to melanoma immunotherapy. Overall, these findings can be utilized to issue recommendations for gut microbiome correction in cancer immunotherapy, and the resulting list of biomarkers might serve as a good stepping stone for the development of a diagnostic test that is aimed at predicting patients' responses to melanoma immunotherapy.

Integrated Analyses of Microbiomics and Metabolomics Explore the Effect of Gut Microbiota Transplantation on Diabetes-Associated Cognitive Decline in Zucker Diabetic Fatty Rats

Diabetes-associated cognitive decline (DACD), one of the complications of type 2 diabetes (T2DM), correlates significantly with the disorder in glycolipid metabolism, insulin/leptin resistance, and accumulation of β-amyloid (Aβ). Although gut microbiota transplantation (GMT), a novel non-invasive physiotherapy strategy, has been a promising intervention to alleviate the symptoms of T2DM, its protective effect on progressive cognitive decline remains elusive. Here, we transplanted the gut microbiota of healthy or cognitive decline donor rats into ZDF or LZ rats, and integrated microbiomics and metabolomics to evaluate the directional effect of the gut microbiota on the recipient rats. The basal metabolism phenotype changed in ZDF rats instead of in LZ rats. One possible mechanism is that the microbiota and metabolites alter the structure of the intestinal tract, stimulate the brain insulin and leptin signaling pathways, and regulate the deposition of Aβ in the brain. It is worth noting that 10 species of genera, such as Parabacteroides, Blautia, and Lactobacillus, can regulate 20 kinds of metabolites, such as propanoic acid, acetic acid, and citramalic acid, and having a significant improvement on the cognitive behavior of ZDF rats. In addition, the correlation analysis indicated the gut microbiota and metabolites are highly associated with host phenotypes affected by GMT. In summary, our study indicates that altering the microbiota-gut-brain axis by reshaping the composition of gut microbiota is a viable strategy that has great potential for improving cognitive function and combatting DACD.

KOMB: K-core based de novo characterization of copy number variation in microbiomes

Characterizing metagenomes via kmer-based, database-dependent taxonomic classification has yielded key insights into underlying microbiome dynamics. However, novel approaches are needed to track community dynamics and genomic flux within metagenomes, particularly in response to perturbations. We describe KOMB, a novel method for tracking genome level dynamics within microbiomes. KOMB utilizes K-core decomposition to identify Structural variations (SVs), specifically, population-level Copy Number Variation (CNV) within microbiomes. K-core decomposition partitions the graph into shells containing nodes of induced degree at least K, yielding reduced computational complexity compared to prior approaches. Through validation on a synthetic community, we show that KOMB recovers and profiles repetitive genomic regions in the sample. KOMB is shown to identify functionally-important regions in Human Microbiome Project datasets, and was used to analyze longitudinal data and identify keystone taxa in Fecal Microbiota Transplantation (FMT) samples. In summary, KOMB represents a novel graph-based, taxonomy-oblivious, and reference-free approach for tracking CNV within microbiomes. KOMB is open source and available for download at https://gitlab.com/treangenlab/komb.