BackgroundSufficient knowledge of molecular and genetic interactions, which comprise the entire basis of the functioning of living systems, is one of the necessary requirements for successfully answering almost any research question in the field of biology and medicine. To date, more than 24 million scientific papers can be found in PubMed, with many of them containing descriptions of a wide range of biological processes. The analysis of such tremendous amounts of data requires the use of automated text-mining approaches. Although a handful of tools have recently been developed to meet this need, none of them provide error-free extraction of highly detailed information.ResultsThe ANDSystem package was developed for the reconstruction and analysis of molecular genetic networks based on an automated text-mining technique. It provides a detailed description of the various types of interactions between genes, proteins, microRNA's, metabolites, cellular components, pathways and diseases, taking into account the specificity of cell lines and organisms. Although the accuracy of ANDSystem is comparable to other well known text-mining tools, such as Pathway Studio and STRING, it outperforms them in having the ability to identify an increased number of interaction types.ConclusionThe use of ANDSystem, in combination with Pathway Studio and STRING, can improve the quality of the automated reconstruction of molecular and genetic networks. ANDSystem should provide a useful tool for researchers working in a number of different fields, including biology, biotechnology, pharmacology and medicine.

BackgroundConsideration of tissue-specific gene expression in reconstruction and analysis of molecular genetic networks is necessary for a proper description of the processes occurring in a specified tissue. Currently, there are a number of computer systems that allow the user to reconstruct molecular-genetic networks using the data automatically extracted from the texts of scientific publications. Examples of such systems are STRING, Pathway Commons, MetaCore and Ingenuity. The MetaCore and Ingenuity systems permit taking into account tissue-specific gene expression during the reconstruction of gene networks. Previously, we developed the ANDSystem tool, which also provides an automated extraction of knowledge from scientific texts and allows the reconstruction of gene networks. The main difference between our system and other tools is in the different types of interactions between objects, which makes the ANDSystem complementary to existing well-known systems. However, previous versions of the ANDSystem did not contain any information on tissue-specific expression.ResultsA new version of the ANDSystem has been developed. It offers the reconstruction of associative gene networks while taking into account the tissue-specific gene expression. The ANDSystem knowledge base features information on tissue-specific expression for 272 tissues. The system allows the reconstruction of combined gene networks, as well as performing the filtering of genes from such networks using the information on their tissue-specific expression. As an example of the application of such filtering, the gene network of the extrinsic apoptotic signaling pathway was analyzed. It was shown that considering different tissues can lead to changes in gene network structure, including changes in such indicators as betweenness centrality of vertices, clustering coefficient, network centralization, network density, etc.ConclusionsThe consideration of tissue specificity can play an important role in the analysis of gene networks, in particular solving the problem of finding the most significant central genes. Thus, the new version of ANDSystem can be employed for a wide range of tasks related to biomedical studies of individual tissues. It is available at http://www-bionet.sscc.ru/and/cell/.Electronic supplementary materialThe online version of this article (10.1186/s12859-018-2567-6) contains supplementary material, which is available to authorized users.

Background: Hypertension and bronchial asthma are a major issue for people's health. As of 2014, approximately one billion adults, or~22% of the world population, have had hypertension. As of 2011, 235-330 million people globally have been affected by asthma and approximately 250,000-345,000 people have died each year from the disease. The development of the effective treatment therapies against these diseases is complicated by their comorbidity features. This is often a major problem in diagnosis and their treatment. Hence, in this study the bioinformatical methodology for the analysis of the comorbidity of these two diseases have been developed. As such, the search for candidate genes related to the comorbid conditions of asthma and hypertension can help in elucidating the molecular mechanisms underlying the comorbid condition of these two diseases, and can also be useful for genotyping and identifying new drug targets. Results: Using ANDSystem, the reconstruction and analysis of gene networks associated with asthma and hypertension was carried out. The gene network of asthma included 755 genes/proteins and 62,603 interactions, while the gene network of hypertension -713 genes/proteins and 45,479 interactions. Two hundred and five genes/proteins and 9638 interactions were shared between asthma and hypertension. An approach for ranking genes implicated in the comorbid condition of two diseases was proposed. The approach is based on nine criteria for ranking genes by their importance, including standard methods of gene prioritization (Endeavor, ToppGene) as well as original criteria that take into account the characteristics of an associative gene network and the presence of known polymorphisms in the analysed genes. According to the proposed approach, the genes IL10, TLR4, and CAT had the highest priority in the development of comorbidity of these two diseases. Additionally, it was revealed that the list of top genes is enriched with apoptotic genes and genes involved in biological processes related to the functioning of central nervous system.

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A study of the molecular genetics mechanisms of host-pathogen interactions is of paramount importance in developing drugs against viral diseases. Currently, the literature contains a huge amount of information that describes interactions between HCV and human proteins. In addition, there are many factual databases that contain experimentally verified data on HCV-host interactions. The sources of such data are the original data along with the data manually extracted from the literature. However, the manual analysis of scientific publications is time consuming and, because of this, databases created with such an approach often do not have complete information. One of the most promising methods to provide actualisation and completeness of information is text mining. Here, with the use of a previously developed method by the authors using ANDSystem, an automated extraction of information on the interactions between HCV and human proteins was conducted. As a data source for the text mining approach, PubMed abstracts and full text articles were used. Additionally, external factual databases were analyzed. On the basis of this analysis, a special version of ANDSystem, extended with the HCV interactome, was created. The HCV interactome contains information about the interactions between 969 human and 11 HCV proteins. Among the 969 proteins, 153 'new' proteins were found not previously referred to in any external databases of protein-protein interactions for HCV-host interactions. Thus, the extended ANDSystem possesses a more comprehensive detailing of HCV-host interactions versus other existing databases. It was interesting that HCV proteins more preferably interact with human proteins that were already involved in a large number of protein-protein interactions as well as those associated with many diseases. Among human proteins of the HCV interactome, there were a large number of proteins regulated by microRNAs. It turned out that the results obtained for protein-protein interactions and microRNA-regulation did not depend on how well the proteins were studied, while protein-disease interactions appeared to be dependent on the level of study. In particular, the mean number of diseases linked to well-studied proteins (proteins were considered well-studied if they were mentioned in 50 or more PubMed publications) from the HCV interactome was 20.8, significantly exceeding the mean number of associations with diseases (10.1) for the total set of well-studied human proteins present in ANDSystem. For proteins not highly poorly-studied investigated, proteins from the HCV interactome (each protein was referred to in less than 50 publications) distribution of the number of diseases associated with them had no statistically significant differences from the distribution of the number of diseases associated with poorly-studied proteins based on the total set of human proteins stored in ANDSystem. With this, the average number of associations with diseases for the HCV interactome and the total set of human proteins were 0.3 and 0.2, ...

BackgroundGeothermal areas are of great interest for the study of microbial communities. The results of such investigations can be used in a variety of fields (ecology, microbiology, medicine) to answer fundamental questions, as well as those with practical benefits. Uzon caldera is located in the Uzon-Geyser depression that is situated in the centre of the Karym-Semyachin region of the East Kamchatka graben-synclinorium. The microbial communities of Zavarzin spring are well studied; however, its benthic microbial mat has not been previously described.ResultsPyrosequencing of the V3 region of the 16S rRNA gene was used to study the benthic microbial community of the Zavarzin thermal spring (Uzon Caldera, Kamchatka). The community is dominated by bacteria (>95% of all sequences), including thermophilic, chemoorganotrophic Caldiserica (33.0%) and Dictyoglomi (24.8%). The benthic community and the previously examined planktonic community of Zavarzin spring have qualitatively similar, but quantitatively different, compositions.ConclusionsIn this study, we performed a metagenomic analysis of the benthic microbial mat of Zavarzin spring. We compared this benthic community to microbial communities found in the water and of an integral probe consisting of water and bottom sediments. Various phylogenetic groups of microorganisms, including potentially new ones, represent the full-fledged trophic system of Zavarzin. A thorough geochemical study of the spring was performed.

BackgroundPre-eclampsia is the most common complication occurring during pregnancy. In the majority of cases, it is concurrent with other pathologies in a comorbid manner (frequent co-occurrences in patients), such as diabetes mellitus, gestational diabetes and obesity. Providing bronchial asthma, pulmonary tuberculosis, certain neurodegenerative diseases and cancers as examples, we have shown previously that pairs of inversely comorbid pathologies (rare co-occurrences in patients) are more closely related to each other at the molecular genetic level compared with randomly generated pairs of diseases. Data in the literature concerning the causes of pre-eclampsia are abundant. However, the key mechanisms triggering this disease that are initiated by other pathological processes are thus far unknown. The aim of this work was to analyse the characteristic features of genetic networks that describe interactions between comorbid diseases, using pre-eclampsia as a case in point.ResultsThe use of ANDSystem, Pathway Studio and STRING computer tools based on text-mining and database-mining approaches allowed us to reconstruct associative networks, representing molecular genetic interactions between genes, associated concurrently with comorbid disease pairs, including pre-eclampsia, diabetes mellitus, gestational diabetes and obesity. It was found that these associative networks statistically differed in the number of genes and interactions between them from those built for randomly chosen pairs of diseases. The associative network connecting all four diseases was composed of 16 genes (PLAT, ADIPOQ, ADRB3, LEPR, HP, TGFB1, TNFA, INS, CRP, CSRP1, IGFBP1, MBL2, ACE, ESR1, SHBG, ADA). Such an analysis allowed us to reveal differential gene risk factors for these diseases, and to propose certain, most probable, theoretical mechanisms of pre-eclampsia development in pregnant women. The mechanisms may include the following pathways: [TGFB1 or TNFA]-[IL1B]-[pre-eclampsia]; [TNFA or INS]-[NOS3]-[pre-eclampsia]; [INS]-[HSPA4 or CLU]-[pre-eclampsia]; [ACE]-[MTHFR]-[pre-eclampsia].ConclusionsFor pre-eclampsia, diabetes mellitus, gestational diabetes and obesity, we showed that the size and connectivity of the associative molecular genetic networks, which describe interactions between comorbid diseases, statistically exceeded the size and connectivity of those built for randomly chosen pairs of diseases. Recently, we have shown a similar result for inversely comorbid diseases. This suggests that comorbid and inversely comorbid diseases have common features concerning structural organization of associative molecular genetic networks.

BackgroundMicrobial mats are a good model system for ecological and evolutionary analysis of microbial communities. There are more than 20 alkaline hot springs on the banks of the Barguzin river inflows. Water temperature reaches 75 °C and pH is usually 8.0–9.0. The formation of microbial mats is observed in all hot springs. Microbial communities of hot springs of the Baikal rift zone are poorly studied. Garga is the biggest hot spring in this area.ResultsIn this study, we investigated bacterial and archaeal diversity of the Garga hot spring (Baikal rift zone, Russia) using 16S rRNA metagenomic sequencing. We studied two types of microbial communities: (i) small white biofilms on rocks in the points with the highest temperature (75 °C) and (ii) continuous thick phototrophic microbial mats observed at temperatures below 70 °C. Archaea (mainly Crenarchaeota; 19.8% of the total sequences) were detected only in the small biofilms. The high abundance of Archaea in the sample from hot springs of the Baikal rift zone supplemented our knowledge of the distribution of Archaea. Most archaeal sequences had low similarity to known Archaea. In the microbial mats, primary products were formed by cyanobacteria of the genus Leptolyngbya. Heterotrophic microorganisms were mostly represented by Actinobacteria and Proteobacteria in all studied samples of the microbial mats. Planctomycetes, Chloroflexi, and Chlorobi were abundant in the middle layer of the microbial mats, while heterotrophic microorganisms represented mostly by Firmicutes (Clostridia, strict anaerobes) dominated in the bottom part. Besides prokaryotes, we detect some species of Algae with help of detection their chloroplasts 16 s rRNA.ConclusionsHigh abundance of Archaea in samples from hot springs of the Baikal rift zone supplemented our knowledge of the distribution of Archaea. Most archaeal sequences had low similarity to known Archaea. Metagenomic analysis of microbial communities of the microbial mat of Garga hot spring showed that the three studied points sampled at 70 °C, 55 °C, and 45 °C had similar species composition. Cyanobacteria of the genus Leptolyngbya dominated in the upper layer of the microbial mat. Chloroflexi and Chlorobi were less abundant and were mostly observed in the middle part of the microbial mat. We detected domains of heterotrophic organisms in high abundance (Proteobacteria, Firmicutes, Verrucomicrobia, Planctomicetes, Bacteroidetes, Actinobacteria, Thermi), according to metabolic properties of known relatives, which can form complete cycles of carbon, sulphur, and nitrogen in the microbial mat. The studied microbial mats evolved in early stages of biosphere formation. They can live autonomously, providing full cycles of substances and preventing live activity products poisoning.