Our molecular division, the Laboratory of functional genomics, is a unit of the Research Centre for Medical Genetics. Here are some of its ongoing projects.
Functional interpretation of clinically uncategorised genetic variations in patients with inherited disorders is our major priority. in a huge number of cases phenotypical interpretation of nucleotide variations is a challenge only functional analysis can tackle. Since a variant's impact depends on various gene disruption mechanisms and the protein properties affected, in our research we use a wide spectrum of molecular techniques: RNA analysis, immunofluorescence, reporter assays and enzymatic activity measurements. We carry out our assays on in vitro systems and a patient's primary cell cultures which we then deposit in the BioBank at the Research Centre for Medical Genetics to assure they could be accessed for further investigations should the patient develop additional symptoms or novel insights into gene function become available. Among many other results our laboratory has discovered novel genes which were not previously linked to human disease and deleterious deep intronic nucleotide variants. This work provides a link between fundamental human biology and clinical genetics to broaden our understanding of etiopathological mechanisms of human disease as well as human gene function.
Natural antisense transcripts (NATs) represent RNAs containing sequences that are complementary to other endogenous RNAs, including pre-mRNAs. There are two forms of NATs: cis-NATs arise from reverse complimentary DNA strands at the same genomic loci while trans-NATS are transcribed from remote loci (trans-NATs). Recently it has been shown that NATs are implicated in many aspects of eukaryotic gene expression including genomic imprinting, RNA interference, translational regulation, alternative splicing, X-inactivation and RNA editing. Moreover, there is growing evidence to suggest that antisense transcription might have a key role in a wide range of human diseases. Although over 20% of human genes might form cis-NATs pairs, the extent to which they are involved in antisense regulation is currently unknown. The connection between the level of antisense transcription throughout the genome and the progression of malignant tumours is especially important. To study this relationship we are investigating sense-antisense pairs that significantly differ in their expression levels in normal and tumour human cells. By comparing the antisense RNA-dependent gene expression regulation in normal and tumour cells we seek to shed light on one of this crucial transcriptional regulation mechanism. We extend our studies to cover not only NATs in tumorigenesis, but the roles other non-coding RNAs play in gene expression regulation in humans.
While investigating the roots of B-cell chronic lymphocytic leukaemia, we have discovered a novel potential tumour suppressor gene KCNRG. It appears the protein contains a single conserved T1 potassium channel tetramerisation domain imperative for its interaction with voltage-gated potassium channel subunits. KCNRG has been shown to down-regulate potassium currents in vitro, suppress proliferation and activate apoptosis in cancer cell lines. Subsequent in silico screening of whole-genome human sequences has revealed a large group of T1 domain proteins called the KCTD family. Our laboratory is focused on the interactome of KCTD proteins and their potential functions.
We also help clinical geneticists with diagnosing different rare hereditary disorders by developing novel means of molecular diagnostics. In particular, we've been focues on Clouston's hidrotic ectodermal dysplasia, facioscapulohumeral muscular dystrophy Landouzy-Dejerine, cystic fibrosis, hereditary hypotrichosis along with other disorders.
Our computational division is a research unit of the Moscow Institute of Physics and technology. The division was separated from the molecular division in 2015 when we reinforced our commitment to computational biology. Here you can find the ongoing project.
Nowadays computational biology is becoming increasingly data-driven. On the one hand, this opens new possibilities for statistical modelling of complicated biological systems. On the other, extracting meaningful abstractions from a data flood is no small feat. In our laboratory we work on the very frontier of statistical inference in various fields of biomedical science. We research, develop and apply new deep-learning and Bayesian methods to analyse amino acid substitutions, text mining and biological networks to achieve great generalisability. Our models, such as BadMut (Korvigo et al., 2017, pre-print: https://doi.org/10.1101/126532) demonstrate state-of-the-art performance at predicting causative mutations in proteins.
miRNAs are a family of small RNA molecules that regulate a wide range of biological processes through post-transcriptional regulation of gene expression. Despite years of research, miRNA targeting is not completely understood. Recent high-throughput projects CLASH (Helwak et al., 2013) and CLEAR-CLIP (Moore et al., 2015) provide sequences for captured miRNAs ligated to their endogenous mRNA targets in HEK293 cell line and in human hepatoma cells respectively. We apply bioinformatics methods for furthering our understanding of miRNA-mRNA interactions in human cells. To accomplish this, we investigate association between miRNAs and mRNA expression levels.
There are more than 100 different RNA modifications. Recent technical advances have revealed mRNAs with N6-methyladenosine, 5-methylcytosine and pseudouridine modifications. RNA modification plays an role in RNA stability, translation efficiency and genetic recoding (Gilbert, 2016). There are available whole-transcriptomic data about several types of RNA modifications, but the frequency of pathogenic mutations at modification sites has not been studied yet. We seek to alleviate this.