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

Identification of differentiating metabolic pathways between infant gut microbiome populations reveals depletion of function-level adaptation to human milk in the finnish population

ABSTRACT 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

Heat Stress Affects Pi-related Genes Expression and Inorganic Phosphate Deposition/Accumulation in Barley

Phosphorus (P) in plants is taken from soil as an inorganic phosphate (Pi) and is one of the most important macroelements in growth and development. Plants actively react to Pi starvation by the induced expression of Pi transporters, MIR399, MIR827, and miR399 molecular sponge – IPS1 genes and by the decreased expression of the ubiquitin-conjugating enzyme E2 (PHOSPHATE2 – PHO2) and Pi sensing and transport SPX-MFS genes. The PHO2 protein is involved in the degradation of Pi transporters PHT1;1 (from soil to roots) and PHO1 (from roots to shoots). The decreased expression of PHO2 leads to Pi accumulation in shoots. In contrast, the pho1 mutant shows a decreased level of Pi concentration in shoots. Finally, Pi starvation leads to decreased Pi concentration in all plant tissues. Little is known about plant Pi homeostasis in other abiotic stress conditions. We found that, during the first hour of heat stress, Pi accumulated in barley shoots but not in the roots, and transcriptomic data analysis as well as RT-qPCR led us to propose an explanation for this phenomenon. Pi transport inhibition from soil to roots is balanced by lower Pi efflux from roots to shoots directed by the PHO1 transporter. In shoots, the PHO2 mRNA level is decreased, leading to an increased Pi level. We concluded that Pi homeostasis in barley during heat stress is maintained by dynamic changes in Pi-related genes expression

MetalionRNA: computational predictor of metal-binding sites in RNA structures

Motivation: Metal ions are essential for the folding of RNA molecules into stable tertiary structures and are often involved in the catalytic activity of ribozymes. However, the positions of metal ions in RNA 3D structures are difficult to determine experimentally. This motivated us to develop a computational predictor of metal ion sites for RNA structures

Development of new biological databases of nucleic acids metabolism.

Wydział BiologiiGłównym celem projektu było stworzenie elementów ujednoliconego systemu bazodanowego opisującego cały metabolizm kwasów nukleinowych. Taki system pozwoli na szersze spojrzenie na biologię systemów tych związków, dogłębną analizę szlaków, czy systematyzację wiedzy. Opracowanie tego typu podejścia będzie stanowiło krok milowy w dziedzinie biologii systemów kwasów nukleinowych, pozwoli lepiej zrozumieć wiele ważnych procesów biologicznych i da silny impuls do dalszych analiz doświadczalnych. W ramach niniejszego projektu powstały dwie bazy: REPAIRtoire – baza szlaków naprawy DNA i RNApathwaysDB – baza szlaków dojrzewania i degradacji RNA. Pierwszą bazą jest REPAIRtoire. Choć temat naprawy DNA jest poruszany przez wiele grup badawczych, a informacje znaleźć można w wielu źródłach, do tej pory nie powstało żadne na ten temat. REPAIRtoire to pierwsza taka baza, której celem istnienia jest nie tylko zgromadzenie informacji o systemach naprawy DNA, ale także usystematyzowanie wiedzy o powiązaniach ludzkich chorób z mutacjami występującymi w genach odpowiedzialnych za stabilność DNA oraz dostarczenie danych o źródłach i rodzajach czynników uszkadzających DNA. Drugą bazą jest RNApathwaysDB. Celem tego projektu było stworzenie źródła wiedzy o szlakach dojrzewania i degradacji RNA. Baza ta gromadzi informacje dotyczące reakcji np.: wycinania intronów, dojrzewania i degradacji tRNA, mRNA, rRNA; a także dane o cząsteczkach biorących w nich udział. Opisane bazy w przyszłości zostaną połączone w jeden spójny system, w którym będą nawzajem korzystać ze swoich danych, co spowoduje ich integrację i rozszerzenie funkcjonalności.The main aim of the project was to create elements of a unified database system of nucleic acid metabolism. Such a system will generate a general view of the metabolism of nucleic acids, which will enable eg detailed analysis of particular parts of metabolism. The resulting resource should make a network analysis approach to the biology of nucleic acids not only feasible, but also generate a body of information that is more than the mere sum of its parts. There are two databases developed during the project: REPAIRtoire – a database of DNA repair pathways and RNApathwaysDB – a database of RNA maturation and decay. The first database is REPAIRtoire. Although the topic of DNA repair is covered by many computational resources thus far there has been no specialized database dedicated to DNA repair. REPAIRtoire is the first database of DNA repair pathways, which purpose is to gather together information about all DNA repair systems and proteins and to facilitate the access to knowledge about correlation of human diseases with mutation in genes responsible for DNA stability as well as information about the agents causing DNA damage. The second database is RNApathwaysDB. The goal of this project was to create a resource for RNA maturation anf degradation. The database gathers together set of information on all RNA reaction pathways eg splicing, RNA processing or degradation. The databases are to be merged together in a future to create one unified system

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