Machine learning on the road to unlocking microbiota's potential for boosting immune checkpoint therapy.

The intestinal microbiota is a complex and diverse ecological community that fulfills multiple functions and substantially impacts human health. Despite its plasticity, unfavorable conditions can cause perturbations leading to so-called dysbiosis, which have been connected to multiple diseases. Unfortunately, understanding the mechanisms underlying the crosstalk between those microorganisms and their host is proving to be difficult. Traditionally used bioinformatic tools have difficulties to fully exploit big data generated for this purpose by modern high throughput screens. Machine Learning (ML) may be a potential means of solving such problems, but it requires diligent application to allow for drawing valid conclusions. This is especially crucial as gaining insight into the mechanistic basis of microbial impact on human health is highly anticipated in numerous fields of study. This includes oncology, where growing amounts of studies implicate the gut ecosystems in both cancerogenesis and antineoplastic treatment outcomes. Based on these reports and first signs of clinical benefits related to microbiota modulation in human trials, hopes are rising for the development of microbiome-derived diagnostics and therapeutics. In this mini-review, we're inspecting analytical approaches used to uncover the role of gut microbiome in immune checkpoint therapy (ICT) with the use of shotgun metagenomic sequencing (SMS) data.

Deep embeddings to comprehend and visualize microbiome protein space.

Understanding the function of microbial proteins is essential to reveal the clinical potential of the microbiome. The application of high-throughput sequencing technologies allows for fast and increasingly cheaper acquisition of data from microbial communities. However, many of the inferred protein sequences are novel and not catalogued, hence the possibility of predicting their function through conventional homology-based approaches is limited, which indicates the need for further research on alignment-free methods. Here, we leverage a deep-learning-based representation of proteins to assess its utility in alignment-free analysis of microbial proteins. We trained a language model on the Unified Human Gastrointestinal Protein catalogue and validated the resulting protein representation on the bacterial part of the SwissProt database. Finally, we present a use case on proteins involved in SCFA metabolism. Results indicate that the deep learning model manages to accurately represent features related to protein structure and function, allowing for alignment-free protein analyses. Technologies that contextualize metagenomic data are a promising direction to deeply understand the microbiome.

Identification of Differentiating Metabolic Pathways between Infant Gut Microbiome Populations Reveals Depletion of Function-Level Adaptation to Human Milk in the Finnish Population.

A variety of autoimmune and allergy events are becoming increasingly common, especially in Western countries. Some pieces of research link such conditions with the composition of microbiota during infancy. In this period, the predominant form of nutrition for gut microbiota is oligosaccharides from human milk (HMO). A number of gut-colonizing strains, such as Bifidobacterium and Bacteroides, are able to utilize HMO, but only some Bifidobacterium strains have evolved to digest the specific composition of human oligosaccharides. Differences in the proportions of the two genera that are able to utilize HMO have already been associated with the frequency of allergies and autoimmune diseases in the Finnish and the Russian populations. Our results show that differences in terms of the taxonomic annotation do not explain the reason for the differences in the Bifidobacterium/Bacteroides ratio between the Finnish and the Russian populations. In this paper, we present the results of function-level analysis. Unlike the typical workflow for gene abundance analysis, BiomeScout technology explains the differences in the Bifidobacterium/Bacteroides ratio. Our research shows the differences in the abundances of the two enzymes that are crucial for the utilization of short type 1 oligosaccharides.IMPORTANCE Knowing the limitations of taxonomy-based research, there is an emerging need for the development of higher-resolution techniques. The significance of this research is demonstrated by the novel method used for the analysis of function-level metagenomes. BiomeScout-the presented technology-utilizes proprietary algorithms for the detection of differences between functionalities present in metagenomic samples.