The number of microbiome-related studies has notably increased the availability of data on human microbiome composition and function. These studies provide the essential material to deeply explore host-microbiome associations and their relation to the development and progression of various complex diseases. Improved data-analytical tools are needed to exploit all information from these biological datasets, taking into account the peculiarities of microbiome data, i.e., compositional, heterogeneous and sparse nature of these datasets. The possibility of predicting host-phenotypes based on taxonomy-informed feature selection to establish an association between microbiome and predict disease states is beneficial for personalized medicine. In this regard, machine learning (ML) provides new insights into the development of models that can be used to predict outputs, such as classification and prediction in microbiology, infer host phenotypes to predict diseases and use microbial communities to stratify patients by their characterization of state-specific microbial signatures. Here we review the state-of-the-art ML methods and respective software applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on the application of ML in microbiome studies related to association and clinical use for diagnostics, prognostics, and therapeutics. Although the data presented here is more related to the bacterial community, many algorithms could be applied in general, regardless of the feature type. This literature and software review covering this broad topic is aligned with the scoping review methodology. The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach.

/pdf/applications-of-machine-learning-in-human-microbiome-studies-4i82asw5rc.pdf

Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment.

Metagenomics and artificial intelligence in the context of human health.

Background Food waste management is a key issue to global food security and friendly environmental governance. Worldwide, one-third of food produced for human consumption is lost or wasted along the food supply chain, primary production and food processing representing the most significant loses. Therefore, the need to achieve zero waste production schemes is becoming a priority to meet Sustainable Development Goals. Increasing evidence points towards vegetable food waste as a rich source of a wide array of carbohydrate structures and fibres providing the opportunity to identify and develop alternative approaches to valorize agro-food waste. Scope and approach This review describes the valorization of vegetable waste and by-products via production of (novel) substrates targeted to gut microbiota modulation, emphasizing the importance of raw materials and structural-functional properties of carbohydrates. Furthermore, we propose a novel framework for the rational selection of vegetable sources with potential prebiotic activity, based on machine learning and other computational tools applied to available literature and public database information. Key findings and conclusions Integration of the body of knowledge within the field of vegetable food waste valorization, from different perspectives, allows a rational selection of carbohydrate-based substrates with promising prebiotic activities. By exploring the interactions among dietary fibre and gut microbial ecosystems using computational tools fed with structural, functional and genomic data, we can identify substrates with potential to selectively stimulate gut commensals, in agreement with experimental evidence. Our approach establishes a new framework that can be extended to a wide range of commensal microbes and carbohydrate structures.

Vegetable waste and by-products to feed a healthy gut microbiota: Current evidence, machine learning and computational tools to design novel microbiome-targeted foods

Diabetes is a disease that is closely linked to genetics and epigenetics, yet mechanisms for clarifying the onset and/or progression of the disease have sometimes not been fully managed. In recent years and due to the large number of recent studies, it is known that changes in the balance of the microbiota can cause a high battery of diseases, including diabetes. Machine Learning (ML) techniques are able to identify complex, non-linear patterns of expression and relationships within the data set to extract intrinsic knowledge without any biological assumptions about the data. At the same time, mass sequencing techniques allow us to obtain the metagenomic profile of an individual, whether it is a body part, organ or tissue, and thus identify the composition of a given microbe. The great increase in the development of both technologies in their respective fields of study leads to the logical union of both to try to identify the bases of a complex disease such as diabetes. To this end, a Random Forest model has been developed at different taxonomic levels, obtaining results above 0.80 in AUC for families and above 0.98 at species level, following a strict experimental design to ensure that results are compared under equal conditions. It is identified how, in infants, the species Bacteroides uniformis, Bacteroides dorei and Bacteroides thetaiotaomicron are reduced in the microbiota of those with T1D, while, the populations of Prevotella copri increase slightly and that of Bacteroides vulgatus is much higher. Finally, thanks to the more specific metagenomic signature at species level, a model has been generated to predict those seroconverted patients not previously diagnosed with diabetes but who have expressed at least two of the autoantibodies analysed.

Machine Learning analysis of the human infant gut microbiome identifies influential species in type 1 diabetes

Abstract Purpose Gut microbiota dysbiosis, a core pathophysiology of irritable bowel syndrome (IBS), is closely related to immunological and metabolic functions. Gut microbiota-based therapeutics have been recently explored in several studies. Bifico is a probiotic cocktail widely used in gastrointestinal disorders which relate to the imbalance of gut microbiota. However, the efficacy and potential mechanisms of Bifico treatment in IBS remains incompletely understood. Methods Adopting a wrap restraint stress (WRS) -induced IBS mice model. Protective effect of Bifico in IBS mice was examined through abdominal withdrawal reflex (AWR) scores. 16S rDNA, 1 H nuclear magnetic resonance ( 1 H-NMR) and western blot assays were performed to analyze alterations of gut microbiota, microbiome metabolites and inflammatory cytokines, respectively. Results Bifico could decrease intestinal visceral hypersensitivity. Although gut microbiota diversity did not increase, composition of gut microbiota was changed after treatment of Bifico, which were characterized by an increase of Proteobacteria phylum and Actinobacteria phylum, Muribaculum genus, Bifidobacterium genus and a decrease of Parabacteroides genus, Sutterella genus and Lactobacillus genus. Moreover, Bifico elevated the concentration of short-chain fatty acids (SCFAs) and reduced protein levels of interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α). From further Spearman’s correlation analysis, Bifidobacterium genus were positively correlated with SCFAs including propionate, butyrate, valerate and negatively correlated with IL-6 and TNF-α. Conclusion Bifico could alleviate symptoms of IBS mice through regulation of the gut microbiota, elevating production of SCFAs and reducing the colonic inflammatory response. 

https://link.springer.com/content/pdf/10.1007/s00394-022-02958-0.pdf

Bifico relieves irritable bowel syndrome by regulating gut microbiota dysbiosis and inflammatory cytokines

Irritable bowel syndrome (IBS) is diagnosed by subjective clinical symptoms. We aimed to establish an objective IBS prediction model based on gut microbiome analyses employing machine learning. We collected fecal samples and clinical data from 85 adult patients who met the Rome III criteria for IBS, as well as from 26 healthy controls. The fecal gut microbiome profiles were analyzed by 16S ribosomal RNA sequencing, and the determination of short-chain fatty acids was performed by gas chromatography-mass spectrometry. The IBS prediction model based on gut microbiome data after machine learning was validated for its consistency for clinical diagnosis. The fecal microbiome alpha-diversity indices were significantly smaller in the IBS group than in the healthy controls. The amount of propionic acid and the difference between butyric acid and valerate were significantly higher in the IBS group than in the healthy controls (p 80%; specificity >90%). Gut microbiome analysis using machine learning is useful for identifying patients with IBS.

/pdf/usefulness-of-machine-learning-based-gut-microbiome-analysis-4h2mwylo3s.pdf

Usefulness of Machine Learning-Based Gut Microbiome Analysis for Identifying Patients with Irritable Bowels Syndrome.

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Usefulness of Machine Learning-Based Gut Microbiome Analysis for Identifying Patients with Irritable Bowels Syndrome.