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

Fecal microbiota transplantation: application scenarios, efficacy prediction, and factors impacting donor-recipient interplay

Fecal microbiota transplantation (FMT) represents a therapeutic approach that directly regulates the gut microbiota of recipients, normalizes its composition and reaping therapeutic rewards. Currently, in addition to its general application in treating Clostridium difficile (C. difficile) infection (CDI), FMT treatment has also been extended to the fields of other gastrointestinal diseases, infections, gut-liver or gut-brain axis disorders, metabolic diseases and cancer, etc. Prior to FMT, rigorous donor screening is essential to reduce the occurrence of adverse events. In addition, it is imperative to evaluate whether the recipient can safely and effectively undergo FMT treatment. However, the efficacy of FMT is influenced by the complex interactions between the gut microbiota of donor and recipient, the degree of donor microbiota engraftment is not necessarily positively related with the success rate of FMT. Furthermore, an increasing number of novel factors affecting FMT outcomes are being identified in recent clinical trials and animal experiments, broadening our understanding of FMT treatment. This article provides a comprehensive review of the application scenarios of FMT, the factors influencing the safety and efficacy of FMT from the aspects of both the donors and the recipients, and summarizes how these emerging novel regulatory factors can be combined to predict the clinical outcomes of patients undergoing FMT.

Microbiota modulation for infectious complications following allogeneic hematopoietic stem cell transplantation in pediatric hematological malignancies

The intervention of microbiota modulation in the treatment of infection complications after allogeneic hematopoietic stem cell transplantation in pediatric patients with hematological malignancies has shown potential benefits. Through the use of probiotics, prebiotics, synbiotics, and fecal microbiota transplantation (FMT), these interventions modulate the gut microbiota and enhance immune function to prevent and treat infections. They have been shown to reduce the incidence of diarrhea and intestinal infections, mitigate the issue of antibiotic resistance, and promote the recovery of gut microbiota. Future research is needed to further assess the safety and efficacy of these interventions and to establish standardized treatment protocols.

Phenylethanol Glycoside from Cistanche tubulosa Attenuates BSA-Induced Liver Fibrosis in Rats by Modulating the Gut Microbiota-Liver Axis

This study aimed to investigate the effect of phenylethanol glycoside from Cistanche tubulosa (CPhGs) on the prevention of bovine serum albumin (BSA)-induced hepatic fibrosis in rats. Investigation of the mechanisms of the anti-hepatic fibrosis effect was focused on CPhGs' influence on the "gut-liver" regulation, including the gut microbiota, intestinal barrier, systemic lipopolysaccharide (LPS) concentration, and LPS-related signaling pathway. The results show that CPhGs restored the diversity of gut microbiota, increased the relative abundance of Bacteroidetes, and decreased the relative abundance of Firmicutes and Proteobacteria in the fibrotic rats. In addition, CPhGs promoted the enrichment of probiotics such as Blautia, Oscillospira, Ruminococcus, Odoribacter, Bacteroides, and Parabacteroides in intestines of these rats. Furthermore, CPhGs reduced histopathological injury in the intestine and restored the tight junctions of the intestine by increasing the expression of ZO-1, occludin, and E-cadherin. CPhGs efficiently reduced serum LPS and liver lipopolysaccharide-binding protein (LBP) levels and inhibited the LPS-TLR4/MyD88/NF-κB pathway, which is related to protein expression in the liver. Correlation analysis confirmed that these beneficial bacteria were negatively associated with pathological damage, while LPS and harmful bacteria were positively associated with liver injury. Our fecal microbiota transplantation (FMT) experiment confirmed that gut microbiota is an important part of disease progression and that CPhGs is useful for the prevention and treatment of hepatic fibrosis. Our data demonstrate that the anti-hepatic fibrosis mechanism of CPhGs was mediated by regulation of the "gut-liver" axis. These results can stimulate consideration for its use in clinical practices.

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.

More than just a number: the gut microbiota and brain function across the extremes of life

Understanding the interrelationship between the gut microbiota and host physiology, although still in its relative infancy, has taken important steps forward over the past decade. In the context of brain disorders including those characterized by neurodevelopmental and neurodegenerative changes there have been important advances. However, initially research involved correlational analyses, had limited translational scope, and lacked functional assessments. Thus, largescale longitudinal clinical investigations that assess causation and underlying mechanisms via in depth analysis methods are needed. In neurodegeneration research, strong causal evidence now links the gut microbiome to Alzheimer's (AD), and Parkinson's Disease (PD), as supported by human-to-animal transplantation studies. Longitudinal interventions are being conducted in AD, PD, amyotrophic lateral sclerosis, Huntington's disease, and multiple sclerosis. Neurodevelopmental research has also seen a boon in microbiome-related clinical research including in autism, Attention-deficit/hyperactivity disorder, and schizophrenia, which is confirming prior animal model work regarding the key time-windows in the gut microbiome important for infant cognition. While recent research advances represent important progress, fundamental knowledge gaps and obstacles remain. Knowing how and why the gut microbiome changes at the extremes of life will develop our mechanistic understanding and help build the evidence base as we strive toward counteracting microbial missteps with precision therapeutic interventions.

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.