Alternative splicing (AS) can significantly impact the transcriptome and proteome of a eukaryotic cell. Here, using transcriptome and proteome profiling data, we analyzed AS in two life forms of the model moss Physcomitrella patens, namely protonemata and gametophores, as well as in protoplasts. We identified 12 043 genes subject to alternative splicing and analyzed the extent to which AS contributes to proteome diversity. We could distinguish a few examples that unambiguously indicated the presence of two or more splice isoforms from the same locus at the proteomic level. Our results indicate that alternative isoforms have a small effect on proteome diversity. We also revealed that mRNAs and pre-mRNAs have thousands of complementary binding sites for long non-coding RNAs (lncRNAs) that may lead to potential interactions in transcriptome. This finding points to an additional level of gene expression and AS regulation by non-coding transcripts in Physcomitrella patens. Among the differentially expressed and spliced genes we found serine/arginine-rich (SR) genes, which are known to regulate AS in cells. We found that treatment with abscisic (ABA) and methyl jasmonic acids (MeJA) led to an isoform-specific response and suggested that ABA in gametophores and MeJA in protoplasts regulate AS and the transcription of SR genes.

Antibiotic therapy can lead to the disruption of gut microbiota community with possible negative outcomes for human health. One of the diseases for which the treatment scheme commonly included antibiotic intake is Helicobacter pylori infection. The changes in taxonomic and functional composition of microbiota in patients can be assessed using “shotgun” metagenomic sequencing. Ten stool samples were collected from 4 patients with Helicobacter pylori infection before and directly after the H. pylori eradication course. Additionally, for two of the subjects, the samples were collected 1 month after the end of the treatment. The samples were subject to “shotgun” (whole-genome) metagenomic sequencing using Illumina HiSeq platform. The reads are deposited in the ENA (project ID: PRJEB18265).

Motivation: The resistance of bacterial pathogens to antibiotics is one of the most important issues of modern health care. The human microbiota can accumulate resistance determinants and transfer them to pathogenic microbiota by means of horizontal gene transfer. Thus, it is important to develop methods of prediction and monitoring of antibiotics resistance in human populations. Results: We present the agent-based VERA model, which allows simulation of the spread of pathogens, including the possible horizontal transfer of resistance determinants from a commensal microbiota community. The model considers the opportunity of residents to stay in the town or in a medical institution, have incorrect self-treatment, treatment with several antibiotics types and transfer and accumulation of resistance determinants from commensal microorganism to a pathogen. In this model, we have also created an assessment of optimum observation frequency of infection spread among the population. Investigating model behavior, we show a number of non-linear dependencies, including the exponential nature of the dependence of the total number of those infected on the average resistance of a pathogen. As the model infection, we chose infection with Shigella spp., though it could be applied to a wide range of other pathogens. Availability and implementation: Source code and binaries VERA and VERA.viewer are freely available for download at github.com/lpenguin/microbiota-resistome. The code is written in Java, JavaScript and R for Linux platform. Supplementary information: Supplementary data are available at Bioinformatics online.

Introduction. The key step in agent-based modeling of epidemics, which allows researchers to take into account individual characteristics of people, is the creation of an artificial population. The main difficulty of this procedure is finding a balance between the detail of the population description and the computational efficiency of the calculations. The aim and objectives of the review: Critically analyze and summarize the current evidence on how to create artificial populations; evaluate the limitations and advantages of available approaches in solving various problems in epidemiology. Materials and methods. An analysis of literature sources devoted to agent-based modeling has been performed. The analysis is focused on algorithms for creating an artificial population with a given level of detail for modeling human respiratory infections. Results. The approaches to the creation of artificial populations are generalized. The main principles of realization of interaction between agents are revealed: by means of networks of contacts between agents and on the basis of taking into account the movement of agents between locations. The first approach is the most computationally efficient and simple; the second approach allows to better take into account the change in the behavior of agents during the development of the epidemic process. Conclusion. Agent-based modeling is an optimal tool for selecting the best scenario for epidemic control and investigating the role of individual characteristics of people in the development of epidemics. When creating an artificial population, it is important to include in the model factors that can be targeted for control. A significant limitation is the lack of factual data on population structure, but this can be overcome by using indirect data.

In the field of modeling social systems, the use of agent-based models (AM) has become a powerful tool for understanding processes occurring in society, in particular, for studying the spread of infections among the population. The agent model is based on a synthetic population consisting of agents, their interactions and sometimes the territory in which the interaction occurs. The choice of an appropriate method for creating such artificial populations and the selection of parameters becomes a key issue affecting the realism of the model and its ability to reflect real-world scenarios. This article delves into the interplay between agent-based modeling, synthetic population construction, and parameter selection, offering our insight into the complex process of modeling infectious disease dynamics. The review concludes with an exploration of extending the Covasim model [1] to experiments in computational epidemiology. Covasim, a dynamic transmission modeling tool, is known for its ability to adapt to different scenarios and offers predictive information critical for managing epidemiological situations. The Covasim model could include improvements to better simulate real-world epidemic conditions, including a revised transmission strategy to account for accumulation through daily contacts. In addition, we propose to add a module for intercity travel and a visualization package that will significantly expand the functionality of Covasim. These improvements make Covasim a more versatile and efficient tool for modeling and analyzing epidemic processes.

The resistance of bacterial pathogens to antibiotics is one of the most important issues of the modern health care. The human microbiota can accumulate resistance determinants and transfer them to pathogenic microflora by means of horizontal gene transfer (HGT). Thus, at the moment it is important to develop methods of prediction and monitoring of antibiotics resistance in human population. We represent the agent-based VERA model, which allows to simulate the spread of pathogen with account of possible horizontal transfer of resistance determinants from commensal microbiota community. The model considers the opportunity of residents to stay in the town or in medical institution, have wrong self-treatment, reception of several antibiotics types, transfer and accumulation of resistance determinants from a microbiota to a pathogen. In this model we have also created assessment of optimum intensity of observation of infection spread among the population. Investigating model behavior we show a number of nonlinear dependencies, including the exponential nature of dependence of total of the diseased on average resistance of a pathogen. As the model infection we considered infection with Shigella spp, though it could be applied to a wide range of other pathogens. Availability and implementation: Source code and binaries VERA and VERA.viewer are freely available for download at github.com/lpenguin/microbiota-resistome. The code is written in Java, JavaScript and R for Linux platform.