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Disentangling metabolic functions of bacteria in the honey bee gut
It is presently unclear how much individual community members contribute to the overall metabolic output of a gut microbiota. To address this question, we used the honey bee, which harbors a relatively simple and remarkably conserved gut microbiota with striking parallels to the mammalian system and importance for bee health. Using untargeted metabolomics, we profiled metabolic changes in gnotobiotic bees that were colonized with the complete microbiota reconstituted from cultured strains. We then determined the contribution of individual community members in mono-colonized bees and recapitulated our findings using in vitro cultures. Our results show that the honey bee gut microbiota utilizes a wide range of pollen-derived substrates, including flavonoids and outer pollen wall components, suggesting a key role for degradation of recalcitrant secondary plant metabolites and pollen digestion. In turn, multiple species were responsible for the accumulation of organic acids and aromatic compound degradation intermediates. Moreover, a specific gut symbiont, Bifidobacterium asteroides, stimulated the production of host hormones known to impact bee development. While we found evidence for cross-feeding interactions, approximately 80% of the identified metabolic changes were also observed in mono-colonized bees, with Lactobacilli being responsible for the largest share of the metabolic output. These results show that, despite prolonged evolutionary associations, honey bee gut bacteria can independently establish and metabolize a wide range of compounds in the gut. Our study reveals diverse bacterial functions that are likely to contribute to bee health and provide fundamental insights into how metabolic activities are partitioned within gut communities.
Journal Article
From Word Embeddings to Pre-Trained Language Models: A State-of-the-Art Walkthrough
With the recent advances in deep learning, different approaches to improving pre-trained language models (PLMs) have been proposed. PLMs have advanced state-of-the-art (SOTA) performance on various natural language processing (NLP) tasks such as machine translation, text classification, question answering, text summarization, information retrieval, recommendation systems, named entity recognition, etc. In this paper, we provide a comprehensive review of prior embedding models as well as current breakthroughs in the field of PLMs. Then, we analyse and contrast the various models and provide an analysis of the way they have been built (number of parameters, compression techniques, etc.). Finally, we discuss the major issues and future directions for each of the main points.
Journal Article
The promise of the gut microbiome as part of individualized treatment strategies
Variability in disease presentation, progression and treatment response has been a central challenge in medicine. Although variability in host factors and genetics are important, it has become evident that the gut microbiome, with its vast genetic and metabolic diversity, must be considered in moving towards individualized treatment. In this Review, we discuss six broad disease groups: infectious disease, cancer, metabolic disease, cardiovascular disease, autoimmune or inflammatory disease, and allergic and atopic diseases. We highlight current knowledge on the gut microbiome in disease pathogenesis and prognosis, efficacy, and treatment-related adverse events and its promise for stratifying existing treatments and as a source of novel therapies. The Review is not meant to be comprehensive for each disease state but rather highlights the potential implications of the microbiome as a tool to individualize treatment strategies in clinical practice. Although early, the outlook is optimistic but challenges need to be overcome before clinical implementation, including improved understanding of underlying mechanisms, longitudinal studies with multiple data layers reflecting gut microbiome and host response, standardized approaches to testing and reporting, and validation in larger cohorts. Given progress in the microbiome field with concurrent basic and clinical studies, the microbiome will likely become an integral part of clinical care within the next decade.The gut microbiota is increasingly recognized as an important factor in disease development, progression and treatment response. This Review highlights the promise of strategies that target the gut microbiome in the treatment of disease, including cancer and infectious and metabolic diseases.
Journal Article
Task-free MRI predicts individual differences in brain activity during task performance
When asked to perform the same task, different individuals exhibit markedly different patterns of brain activity. This variability is often attributed to volatile factors, such as task strategy or compliance. We propose that individual differences in brain responses are, to a large degree, inherent to the brain and can be predicted from task-independent measurements collected at rest. Using a large set of task conditions, spanning several behavioral domains, we train a simple model that relates task-independent measurements to task activity and evaluate the model by predicting task activation maps for unseen subjects using magnetic resonance imaging. Our model can accurately predict individual differences in brain activity and highlights a coupling between brain connectivity and function that can be captured at the level of individual subjects.
Journal Article
Metabolic cooperation and spatiotemporal niche partitioning in a kefir microbial community
Microbial communities often undergo intricate compositional changes yet also maintain stable coexistence of diverse species. The mechanisms underlying long-term coexistence remain unclear as system-wide studies have been largely limited to engineered communities, ex situ adapted cultures or synthetic assemblies. Here, we show how kefir, a natural milk-fermenting community of prokaryotes (predominantly lactic and acetic acid bacteria) and yeasts (family Saccharomycetaceae), realizes stable coexistence through spatiotemporal orchestration of species and metabolite dynamics. During milk fermentation, kefir grains (a polysaccharide matrix synthesized by kefir microorganisms) grow in mass but remain unchanged in composition. In contrast, the milk is colonized in a sequential manner in which early members open the niche for the followers by making available metabolites such as amino acids and lactate. Through metabolomics, transcriptomics and large-scale mapping of inter-species interactions, we show how microorganisms poorly suited for milk survive in—and even dominate—the community, through metabolic cooperation and uneven partitioning between grain and milk. Overall, our findings reveal how inter-species interactions partitioned in space and time lead to stable coexistence.
Using kefir as a natural model microbial ecosystem, the authors apply metabolomics, transcriptomics and large-scale mapping of inter-species interactions to study the drivers of stable coexistence of species in space and time.
Journal Article