Recipient-independent, high-accuracy FMT-response prediction and optimization in mice and humans

Current landscape of fecal microbiota transplantation in treating depression

Depression, projected to be the predominant contributor to the global disease burden, is a complex condition with diverse symptoms including mood disturbances and cognitive impairments. Traditional treatments such as medication and psychotherapy often fall short, prompting the pursuit of alternative interventions. Recent research has highlighted the significant role of gut microbiota in mental health, influencing emotional and neural regulation. Fecal microbiota transplantation (FMT), the infusion of fecal matter from a healthy donor into the gut of a patient, emerges as a promising strategy to ameliorate depressive symptoms by restoring gut microbial balance. The microbial-gut-brain (MGB) axis represents a critical pathway through which to potentially rectify dysbiosis and modulate neuropsychiatric outcomes. Preclinical studies reveal that FMT can enhance neurochemicals and reduce inflammatory markers, thereby alleviating depressive behaviors. Moreover, FMT has shown promise in clinical settings, improving gastrointestinal symptoms and overall quality of life in patients with depression. The review highlights the role of the gut-brain axis in depression and the need for further research to validate the long-term safety and efficacy of FMT, identify specific therapeutic microbial strains, and develop targeted microbial modulation strategies. Advancing our understanding of FMT could revolutionize depression treatment, shifting the paradigm toward microbiome-targeting therapies.

Transfer of modified gut viromes improves symptoms associated with metabolic syndrome in obese male mice

Metabolic syndrome encompasses amongst other conditions like obesity and type-2 diabetes and is associated with gut microbiome (GM) dysbiosis. Fecal microbiota transplantation (FMT) has been explored to treat metabolic syndrome by restoring the GM; however, concerns on accidentally transferring pathogenic microbes remain. As a safer alternative, fecal virome transplantation (FVT, sterile-filtrated feces) has the advantage over FMT in that mainly bacteriophages are transferred. FVT from lean male donors have shown promise in alleviating the metabolic effects of high-fat diet in a preclinical mouse study. However, FVT still carries the risk of eukaryotic viral infections. To address this, recently developed methods are applied for removing or inactivating eukaryotic viruses in the viral component of FVT. Modified FVTs are compared with unmodified FVT and saline in a diet-induced obesity model on male C57BL/6 N mice. Contrasted with obese control, mice administered a modified FVT (nearly depleted for eukaryotic viruses) exhibits enhanced blood glucose clearance but not weight loss. The unmodified FVT improves liver pathology and reduces the proportions of immune cells in the adipose tissue with a non-uniform response. GM analysis suggests that bacteriophage-mediated GM modulation influences outcomes. Optimizing these approaches could lead to the development of safe bacteriophage-based therapies targeting metabolic syndrome through GM restoration.

mi-Mic: a novel multi-layer statistical test for microbiota-disease associations

mi-Mic, a novel approach for microbiome differential abundance analysis, tackles the key challenges of such statistical tests: a large number of tests, sparsity, varying abundance scales, and taxonomic relationships. mi-Mic first converts microbial counts to a cladogram of means. It then applies a priori tests on the upper levels of the cladogram to detect overall relationships. Finally, it performs a Mann-Whitney test on paths that are consistently significant along the cladogram or on the leaves. mi-Mic has much higher true to false positives ratios than existing tests, as measured by a new real-to-shuffle positive score.

Dissecting mechanisms of fecal microbiota transplantation efficacy in disease

Fecal microbiota transplantation (FMT) has emerged as an alternative or adjunct experimental therapy for microbiome-associated diseases following its success in the treatment of recurrent Clostridioides difficile infections (rCDIs). However, the mechanisms of action involved remain relatively unknown. The term 'dysbiosis' has been used to describe microbial imbalances in relation to disease, but this traditional definition fails to consider the complex cross-feeding networks that define the stability of the microbiome. Emerging research transitions toward the targeted restoration of microbial functional networks in treating different diseases. In this review, we explore potential mechanisms responsible for the efficacy of FMT and future therapeutic applications, while revisiting definitions of 'dysbiosis' in favor of functional network restoration in rCDI, inflammatory bowel diseases (IBDs), metabolic diseases, and cancer.

Ordering taxa in image convolution networks improves microbiome-based machine learning accuracy

The human gut microbiome is associated with a large number of disease etiologies. As such, it is a natural candidate for machine-learning-based biomarker development for multiple diseases and conditions. The microbiome is often analyzed using 16S rRNA gene sequencing or shotgun metagenomics. However, several properties of microbial sequence-based studies hinder machine learning (ML), including non-uniform representation, a small number of samples compared with the dimension of each sample, and sparsity of the data, with the majority of taxa present in a small subset of samples. We show here using a graph representation that the cladogram structure is as informative as the taxa frequency. We then suggest a novel method to combine information from different taxa and improve data representation for ML using microbial taxonomy. iMic (image microbiome) translates the microbiome to images through an iterative ordering scheme, and applies convolutional neural networks to the resulting image. We show that iMic has a higher precision in static microbiome gene sequence-based ML than state-of-the-art methods. iMic also facilitates the interpretation of the classifiers through an explainable artificial intelligence (AI) algorithm to iMic to detect taxa relevant to each condition. iMic is then extended to dynamic microbiome samples by translating them to movies.