Biology subjects

Smirnova, A. V.

Publications and source records attributed to Smirnova, A. V..

4 recordsLinked to original sources

bioRxiv2025

Kinetic measurements of the fluorescent protein synthesis facilitate determination of the miRNA activity.

Oncogenesis is inevitably associated with microRNA expression deregulation. Thus, development of both miRNA targeting substances or small RNA for ant-cancer therapy have been reported. Specifically, repression of the miR-16-1-3p and miR-16-2-3p activities play pivotal roles in osteosarcoma and many other cancers. The majority of miRNA sensors use protein degradation to measure miRNA activities. Here we report miRNA sensors that use fluorescent protein synthesis rather than degradation to measure miRNA activity. Specifically, miR-16-1-3p and miR-16-2-3p sensors consist of the bidirectional tet-On system driving the expression of the Katusha2S protein that is regulated by the RNA interference and GFP as a reference. These sensors specifically detect mir16-1-3p and mir16-2-3p small RNA mimics in the osteosarcoma cell line after doxycycline induction. Kinetic measurements of the reporter responses to the miRNA mimics revealed that pre-induced sensors reach significant differences from the control faster, within 2h than the sensors that were induced after mimic transfection. Thus, kinetic measurements of the fluorescent protein synthesis during doxycycline induction of the tet-On system are feasible for determination of the small RNA activity.

molecular biology↗

bioRxiv2025

The Small Non-coding RNA miR-16-1-3p Hampers Cancer Stem Cell Self-renewal and Invasiveness, Boosting Chemosensitivity by Adjusting TGF-β1 Signaling via MDM2/p53 Axis in Human Osteosarcoma.

The TGF-{beta} signaling pathway has both tumor-suppressing and metastasis-promoting effects in cancer. However, the molecular determinants governing this switch remain unclear. Here, we explored the miR-16-1-3p/MDM2/p53 axis as a critical conductor of the TGF-{beta}-Smad pathway in osteosarcoma. Although miR-16-1-3p overexpression by itself markedly reduces proliferative and clonogenic potential of U2OS cells, when paired with TGF-{beta} treatment, it significantly increases arrest cells in G1 phase and nearly extinguishing the growth capability of these cells. MiR-16-1-3p overexpression inhibited TGF-induced actin remodeling and EMT featuring, significantly decreasing vimentin levels. TGF-{beta} enhances both 2D and 3D migration, but miR-16-1-3p overexpression, alone or with TGF-{beta}, strongly counteracts its pro-migratory effects. MiR-16-1-3p restored p53 stability by targeting MDM2, redirecting TGF-{beta}-Smad signaling toward p21 activation and proliferation inhibition while attenuating its EMT-promoting capacity. Administration of TGF-{beta} together with miR-16-1-3p dramatically increases the sensitivity of wild-type U2OS cells to cisplatin, exceeding that of TGF-{beta} therapy alone by more than an order of magnitude. Administering TGF-{beta} and miR-16-1-3p together significantly reduces the tumor nodule volume and Ki67 expression, while effectively eradicates metastases in the chicken chorioallantoic membrane (CAM) in vivo model. For the first time, our research demonstrates that miR-16-1-3p shifts TGF-{beta}1 signaling from a facilitator of metastasis to a promoter of anti-growth effects through MDM2 inhibition and p53 stabilization, effectively reducing the self-renewal and invasiveness of cancer stem cells in human osteosarcoma model. This process preserves TGF-{beta}s tumor-suppressive role while limiting its associated cancer risks.

cancer biology↗

bioRxiv2024

Systematic search for new HLA alleles in 4195 human 30x WGS samples

HLA (Human Leukocyte Antigens) is a highly polymorphic locus in the human genome which also has a high clinical significance. New alleles of HLA genes are constantly being discovered but mostly through the efforts of laboratories which primarily focus on HLA typing and are using field-specific experimental and data processing techniques, like enrichment of HLA region in high-throughput sequencing data. Nevertheless, a vast amount of whole genome sequencing (WGS) data was accumulated over the past years and continues to expand rapidly. Therefore it is an appealing possibility to identify new HLA alleles and refine the information on known alleles from already available WGS data. Currently there are many tools designed for HLA typing, e.g. assigning known alleles, from non HLA enriched WGS data, but none of them specifically tailored towards identification and immediate thorough description of new HLA alleles. Here we are presenting a pipeline HLAchecker, which is specifically designed to identify potentially new HLA alleles based on discrepancies between predicted HLA types, made by any other dedicated tool, and underlying raw 30x WGS data. HLAchecker reports structured in a way which simplifies further validation of potentially new HLA alleles and streamlines submission of alleles to appropriate databases. We validated this tool on 4195 30x WGS samples typed by HLA-HD, discovered 17 potentially new HLA alleles with substitutions in exonic regions and validated five randomly chosen alleles by Sanger sequencing.

bioinformatics↗

bioRxiv2024

Sanger validation of WGS variants - when to?

With the development of Next-Generation Sequencing (NGS) technologies it became possible to simultaneously analyze millions of variants. Despite the quality improvement it is generally still required to confirm the variants before reporting. However, in recent years the dominant idea is that one could define the quality thresholds for "high quality" variants which do not require orthogonal validation. Despite that, no works to date report the concordance between variants from whole genome sequencing and their gold-standard Sanger validation. In this study we analyzed the concordance for 1756 WGS variants in order to establish the appropriate thresholds for high-quality variants filtering. Resulting thresholds allowed us to drastically reduce the number of variants which require validation, to 5,6% and 1.2% of the initial set for caller-agnostic thresholds and caller-dependent QUAL threshold respectively.

genomics↗