Beyond microarrays: Finding key transcription factors controlling signal transduction pathways

BACKGROUND: Massive gene expression changes in different cellular states measured by microarrays, in fact, reflect just an "echo" of real molecular processes in the cells. Transcription factors constitute a class of the regulatory molecules that typically require posttranscriptional modifications or ligand binding in order to exert their function. Therefore, such important functional changes of transcription factors are not directly visible in the microarray experiments. RESULTS: We developed a novel approach to find key transcription factors that may explain concerted expression changes of specific components of the signal transduction network. The approach aims at revealing evidence of positive feedback loops in the signal transduction circuits through activation of pathway-specific transcription factors. We demonstrate that promoters of genes encoding components of many known signal transduction pathways are enriched by binding sites of those transcription factors that are endpoints of the considered pathways. Application of the approach to the microarray gene expression data on TNF-alpha stimulated primary human endothelial cells helped to reveal novel key transcription factors potentially involved in the regulation of the signal transduction pathways of the cells. CONCLUSION: We developed a novel computational approach for revealing key transcription factors by knowledge-based analysis of gene expression data with the help of databases on gene regulatory networks (TRANSFAC(® )and TRANSPATH(®)). The corresponding software and databases are available at

Создание коллекции МСКТ-изображений и клинических данных при острых нарушениях мозгового кровообращения

Background The use of neuroimaging methods is an integral part of the process of assisting patients with acute cerebrovascular events (ACVE), and computed tomography (CT) is the «gold standard» for examining this category of patients. The capabilities of the analysis of CT images may be significantly expanded with modern methods of machine learning including the application of the principles of radiomics. However, since the use of these methods requires large arrays of DICOM (Digital Imaging and Communications in Medicine)-images, their implementation into clinical practice is limited by the lack of representative sample sets. Inaddition, at present, collections (datasets) of CT images of stroke patients, that are suitable for machine learning, are practically not available in the public domain.Aim of study Regarding the aforesaid, the aim of this work was to create a DICOM images dataset of native CT and CT-angiography of patients with different types of stroke. Material and meth ods The collection was based on the medical cases of patients hospitalized in the Regional Vascular Center of the N.V. Sklifosovsky Research Institute for Emergency Medicine. We used a previously developed specialized platform to enter clinical data on the stroke cases, to attach CT DICOMimages to each case, to contour 3D areas of interest, and to tag (label) them. A dictionary was developed for tagging, where elements describe the type of lesion, location, and vascular territory.Results A dataset of clinical cases and images was formed in the course of the work. It included anonymous information about 220 patients, 130 of them with ischemic stroke, 40 with hemorrhagic stroke, and 50 patients without cerebrovascular disorders. Clinical data included information about type of stroke, presence of concomitant diseases and complications, length of hospital stay, methods of treatment, and outcome. The results of 370 studies of native CT and 102 studies of CT-angiography were entered for all patients. The areas of interest corresponding to direct and indirect signs of stroke were contoured and tagged by radiologists on each series of images.Conclusion The resulting collection of images will enable the use of various methods of data analysis and machine learning in solving the most important practical problems including diagnosis of the stroke type, assessment of lesion volume, and prediction of the degree of neurological deficit.Актуальность Применение методов нейровизуализации является неотъемлемой частью процесса оказания помощи больным с острыми нарушениями мозгового кровообращения (ОНМК), при этом золотым стандартом обследования данной категории больных является компьютерная томография (КТ). Значительно расширить возможности анализа КТ-изображений возможно с помощью современных методов машинного обучения, в том числе на основе применения принципов радиомики. Однако, так как использование этих методов требует наличия больших массивов DICOM (Digital Imaging and Communications in Medicine)-изображений, их внедрение в клиническую практику ограничено проблемой набора репрезентативных выборок. Кроме того, в настоящее время в открытом доступе практически не представлены коллекции, содержащие КТ-изображения больных c ОНМК, которые были бы пригодны для машинного обучения.Цель В связи с вышесказанным, целью данной работы являлось создание коллекции DICOM-изображений нативной КТ и КТ-ангиографии у пациентов с различными типами ОНМК.Материал и методы Основой для создания коллекции стали истории болезни пациентов, госпитализированных в региональный сосудистый центр НИИ СП им. Н.В. Склифосовского. Для формирования коллекции использовалась разработанная нами ранее специализированная платформа, позволяющая вводить клинические данные о случаях ОНМК, прикреплять к каждому случаю DICOM-изображения проведенных исследований, а также оконтуривать и тегировать (размечать) 3D-области интереса. Для тегирования был разработан словарь, элементы которого описывают тип патологического образования, локализацию и бассейн кровоснабжения.Результаты В ходе работы была сформирована коллекция клинических случаев и изображений, включающая анонимизированную информацию о 220 пациентах, из них 130 - с ишемическим инсультом, 40 - с геморрагическим инсультом, а также 50 человек без цереброваскулярной патологии. Клинические данные включали сведения о типе ОНМК, наличии сопутствующих заболеваний и осложнений, длительности госпитализации, способе лечения и исходе. Всего для пациентов были введены результаты 370 исследований нативной КТ и 102 исследования КТ-ангиографии. На каждой серии изображений врачом-экспертом были оконтурены и протегированы области интереса, соответствующие прямым и косвенным признакам ОНМК.Вывод Сформированная коллекция изображений позволит в последующем применить различные методы анализа данных и машинного обучения в решении важнейших практических задач, в том числе диагностики типа ОНМК, оценки объема поражения, прогноза степени неврологического дефицита

Human gut microbiota community structures in urban and rural populations in Russia

The microbial community of the human gut has a crucial role in sustaining host homeostasis. High-throughput DNA sequencing has delineated the structural and functional configurations of gut metagenomes in world populations. The microbiota of the Russian population is of particular interest to researchers, because Russia encompasses a uniquely wide range of environmental conditions and ethnogeographical cohorts. Here we conduct a shotgun metagenomic analysis of gut microbiota samples from 96 healthy Russian adult subjects, which reveals novel microbial community structures. The communities from several rural regions display similarities within each region and are dominated by the bacterial taxa associated with the healthy gut. Functional analysis shows that the metabolic pathways exhibiting differential abundance in the novel types are primarily associated with the trade-off between the Bacteroidetes and Firmicutes phyla. The specific signatures of the Russian gut microbiota are likely linked to the host diet, cultural habits and socioeconomic status. © 2013 Macmillan Publishers Limited. All rights reserved

Study of the resistome of human microbial communities using a targeted panel of antibiotic resistance genes in COVID-19 patients

Aim. To study overall drug resistance genes (resistome) in the human gut microbiome and the changes in these genes during COVID-19 in-hospital therapy. Materials and methods. A single-center retrospective cohort study was conducted. Only cases with laboratory-confirmed SARS-CoV-2 RNA using polymerase chain reaction in oro-/nasopharyngeal swab samples were subject to analysis. The patients with a documented history of or current comorbidities of the hepatobiliary system, malignant neoplasms of any localization, systemic and autoimmune diseases, as well as pregnant women were excluded. Feces were collected from all study subjects for subsequent metagenomic sequencing. The final cohort was divided into two groups depending on the disease severity: mild (group 1) and severe (group 2). Within group 2, five subgroups were formed, depending on the use of antibacterial drugs (ABD): group 2A (receiving ABD), group 2AC (receiving ABD before hospitalization), group 2AD (receiving ABD during hospitalization), group 2AE (receiving ABD during and before hospitalization), group 2B (not receiving ABD). Results. The median number of antibiotic resistance (ABR) genes (cumulative at all time points) was significantly higher in the group of patients treated with ABD: 81.0 (95% CI 73.8–84.5) vs. 51.0 (95% CI 31.1–68.4). In the group of patients treated with ABD (2A), the average number of multidrug resistance genes (efflux systems) was significantly higher than in controls (group 2B): 47.0 (95% CI 46.0–51.2) vs. 21.5 (95% CI 7.0–43.9). Patients with severe coronavirus infection tended to have a higher median number of ABR genes but without statistical significance. Patients in the severe COVID-19 group who did not receive ABD before and during hospitalization also had more resistance genes than the patients in the comparison group. Conclusion. This study demonstrated that fewer ABR genes were identified in the group with a milder disease than in the group with a more severe disease associated with more ABR genes, with the following five being the most common: SULI, MSRC, ACRE, EFMA, SAT