Bioinformatics benefits from Siberia: on the anniversary of Nikolay Aleksandrovich Kolchanov, Academician of the Russian Academy of Sciences (RAS)

January 9, 2022 marks the 75th anniversary of Nikolai Aleksandrovich Kolchanov, Doctor of Biological Sciences, Professor, Academician of the Russian Academy of Sciences, Scientific Leader of the Federal Research Center the Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, First Vice-President of the Vavilov Society of Geneticists and Breeders. Acad. N.A. Kolchanov is a prominent specialist in the field of bioinformatics and systems computational biology, under whose guidance the largest domestic scientific school in this area has formed and received global development. He is the author and co-author of about 700 publications in domestic and foreign press, holder of 18 copyright certificates and 8 patents. For almost 20 years, Acad. Kolchanov is the Head and Professor of the Department of Information Biology with the Faculty of Natural Sciences of Novosibirsk State University. He supervised the work of 12 doctoral and 2 senior doctorate students. His students, who work in leading domestic and foreign scientific centers, are the pride of Russian science and make a significant contribution to the world level of development of modern bioinformatics at the global level

Machine Learning Models for Deciphering Regulatory Mechanisms and Morphological Variations in Cancer

The exponential growth of multi-omics biological datasets is resulting in an emerging paradigm shift in fundamental biological research. In recent years, imaging and transcriptomics datasets are increasingly incorporated into biological studies, pushing biology further into the domain of data-intensive-sciences. New approaches and tools from statistics, computer science, and data engineering are profoundly influencing biological research. Harnessing this ever-growing deluge of multi-omics biological data requires the development of novel and creative computational approaches. In parallel, fundamental research in data sciences and Artificial Intelligence (AI) has advanced tremendously, allowing the scientific community to generate a massive amount of knowledge from data. Advances in Deep Learning (DL), in particular, are transforming many branches of engineering, science, and technology. Several of these methodologies have already been adapted for harnessing biological datasets; however, there is still a need to further adapt and tailor these techniques to new and emerging technologies. In this dissertation, we present computational algorithms and tools that we have developed to study gene-regulation and cellular morphology in cancer. The models and platforms that we have developed are general and widely applicable to several problems relating to dysregulation of gene expression in diseases. Our pipelines and software packages are disseminated in public repositories for larger scientific community use. This dissertation is organized in three main projects. In the first project, we present Causal Inference Engine (CIE), an integrated platform for the identification and interpretation of active regulators of transcriptional response. The platform offers visualization tools and pathway enrichment analysis to map predicted regulators to Reactome pathways. We provide a parallelized R-package for fast and flexible directional enrichment analysis to run the inference on custom regulatory networks. Next, we designed and developed MODEX, a fully automated text-mining system to extract and annotate causal regulatory interaction between Transcription Factors (TFs) and genes from the biomedical literature. MODEX uses putative TF-gene interactions derived from high-throughput ChIP-Seq or other experiments and seeks to collect evidence and meta-data in the biomedical literature to validate and annotate the interactions. MODEX is a complementary platform to CIE that provides auxiliary information on CIE inferred interactions by mining the literature. In the second project, we present a Convolutional Neural Network (CNN) classifier to perform a pan-cancer analysis of tumor morphology, and predict mutations in key genes. The main challenges were to determine morphological features underlying a genetic status and assess whether these features were common in other cancer types. We trained an Inception-v3 based model to predict TP53 mutation in five cancer types with the highest rate of TP53 mutations. We also performed a cross-classification analysis to assess shared morphological features across multiple cancer types. Further, we applied a similar methodology to classify HER2 status in breast cancer and predict response to treatment in HER2 positive samples. For this study, our training slides were manually annotated by expert pathologists to highlight Regions of Interest (ROIs) associated with HER2+/- tumor microenvironment. Our results indicated that there are strong morphological features associated with each tumor type. Moreover, our predictions highly agree with manual annotations in the test set, indicating the feasibility of our approach in devising an image-based diagnostic tool for HER2 status and treatment response prediction. We have validated our model using samples from an independent cohort, which demonstrates the generalizability of our approach. Finally, in the third project, we present an approach to use spatial transcriptomics data to predict spatially-resolved active gene regulatory mechanisms in tissues. Using spatial transcriptomics, we identified tissue regions with differentially expressed genes and applied our CIE methodology to predict active TFs that can potentially regulate the marker genes in the region. This project bridged the gap between inference of active regulators using molecular data and morphological studies using images. The results demonstrate a significant local pattern in TF activity across the tissue, indicating differential spatial-regulation in tissues. The results suggest that the integrative analysis of spatial transcriptomics data with CIE can capture discriminant features and identify localized TF-target links in the tissue

Genômica do X-frágil: elementos de regulação do Gene FMR1

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-Graduação em Engenharia Química, Florianópolis, 2008.A Síndrome do X Frágil (SXF) é a forma de retardo mental herdado mais comum encontrada, afetando um entre 4000 homens e uma entre 8000 mulheres. Ela é causada devido a alterações do nível de transcrição e tradução do gene FMR1, que nos humanos e em vários outros mamíferos se encontra na região Xq27.3 do cromossomo X humano. Este gene codifica a proteína FMRP, cuja falta de expressão está associada ao retardo mental e a outros fenótipos característicos da SXF. A função da proteína FMRP no organismo ainda não é totalmente conhecida, porém acredita-se que ela esteja intimamente ligada ao transporte de mRNA do núcleo para o citoplasma dos neurônios e na sua condução até os ribossomos, onde faz parte dos complexos de ribonucleoproteínas (RNP) e controla a tradução de determinados mRNA. Diante deste contexto, este trabalho tem o objetivo de contribuir para a compreensão dos elementos reguladores da expressão do gene FMR1 e também incrementar os estudos sobre as funções desempenhadas pela proteína FMRP. Uma análise da região à montante do códon de início do gene FMR1, utilizando técnicas de genômica comparativa, gel shift e espectrometria de massa mostrou potenciais sítios de interação para proteínas de transcrição. Foram identificadas várias proteínas que interagiram com os segmentos de DNA conservados estudados in vitro, com especial significância para as proteínas Pur-a, Pur-b e as ribonucleoproteínas heterogêneas A2/B1 (hnRNP A2/hnRNP B1). O papel da Pur-a e Pur-b como elemento na regulação da transcrição do gene FMR1 nunca foi demonstrado, sendo esse o primeiro trabalho que propõe esta relação. No estudo sobre a função da proteína FMRP utilizou-se análise de fluxos metabólicos para calcular os fluxos intracelulares dos complexos envolvidos na regulação da tradução de mRNA nos dendritos neuronais, onde a FMRP é uma proteína chave. O uso de modelos estequiométricos das reações ou interações e a aplicação de balanços de massa permitiram a estimativa de alguns fluxos importantes no papel biológico da FMRP em nível celular. Esses resultados poderão auxiliar no diagnóstico e tratamento da Síndrome do X-Frágil e doenças relacionadas.Fragile X Syndrome (FXS) is the most common form of inherited mental retardation with the incidence of 1:4000 in males and 1:8000 in females. The syndrome occurs as an effect of the lack of expression of the fragile X mental retardation protein (FMRP) codified by the FMR1 gene located in the q27.3 region of the X chromosome. Absence of the FMRP results in mental retardation and other characteristics of the Fragile X phenotypes. Some FMRP functions in the organism are still unknown; however, structural evidences of the protein suggest that it is involved in nuclear export, cytoplasmic transport, and/or translation control of target mRNA. In this work, we combined in silico comparative genomics tools with proteomic analysis to investigate regulatory elements in the upstream region of the FMR1 gene transcription start site. We identified proteins that interact with studied DNA conserved sequences, particularly Pur-a, Pur-b and heterogeneous nuclear ribonucleoproteins A2/B1 (hnRNP A2/hnRNP B1). To our acknowledge this is the first work that correlate those proteins to the FMR1 gene transcription. We used Metabolic Flux Analysis (MFA) to estimate turnovers of molecules and complexes involved in mRNA transport to dendrites and its translation at synapses. Stoichiometric models of biochemical reactions or interactions and mass balances may be useful in diagnosis and future treatment of the Fragile X Syndrome and related diseases