Maria Poptsova

Pseudohypertriglyceridemia (pseudo-HTG) is a rare laboratory phenomenon in which serum triglyceride levels are falsely elevated due to analytical interference from endogenous glycerol, most often caused by glycerol kinase deficiency (GKD), an X-linked disorder caused by pathogenic variants in the GK gene. We report a 50-year-old man referred for persistent hypertriglyceridemia (4.98–7.42 mmol/L) over two years despite dietary modification and statin–fibrate therapy. He had no history of pancreatitis, alcohol intake, diabetes, renal or thyroid dysfunction. Physical examination showed no xanthomas or corneal arcus; body mass index was 26.9 kg/m². Serum samples were repeatedly visually clear, and lipoprotein electrophoresis revealed an unexpectedly normal lipid distribution. Given the discordance between triglyceride concentrations and the clinical and electrophoretic findings, pseudo-HTG due to GKD was suspected. Glycerol-specific biochemical testing was locally unavailable. Thus, we performed whole genome sequencing (WGS), which identified a likely pathogenic GK nonsense variant, c.1324C>T (p.Gln442Ter), consistent with isolated GKD. Peripheral atherosclerosis and hepatic steatosis were also noted, indicating coexisting cardiometabolic disease. This case highlights isolated GKD as an underrecognized cause of prominent treatment-refractory HTG. In patients with discordant triglyceride results, clear fasting serum, and normal lipoprotein electrophoresis, pseudo-HTG should be considered; genetic evaluation may be informative when glycerol-specific assays are unavailable.

Background: Differential diagnosis between pulmonary arterial hypertension (PAH) and chronic thromboembolic pulmonary hypertension (CTEPH) is crucial for treatment selection, particularly regarding surgical intervention. The prevalence of hereditary thrombophilias in Russian CTEPH patients and the utility of genetic data in differential diagnosis remain understudied. Objectives: To evaluate the prevalence of inherited thrombophilia among patients with CTEPH and PAH, to benchmark the results against published data from other cohorts, and to assess the potential utility of integrating genetic findings and polygenic risk score (PRS) parameters with clinical data for differentiating CTEPH from PAH. Methods: The study included 390 patients (252 PAH, 138 CTEPH). Whole-genome sequencing was performed to analyze classical thrombophilia genes ( PROC, PROS 1 , SERPINC 1 , F 2 , F 5 , ADAMTS 13 , HRG, JAK 2 , PIGA, THBD, F 9 , FGA, FGB, FGG, CALR ) and calculate VTE polygenic risk scores. A meta-analysis of 15 studies on hereditary thrombophilia prevalence in CTEPH was conducted. AutoML algorithm was used to construct diagnostic models. Results: Rare variants in thrombosis-associated genes were significantly more prevalent in patients with CTEPH than in those with PAH (12.3% vs. 1.2%, p < 0.001). Similarly, carriers of the common F5 and F2 thrombophilia variants were more frequent in the CTEPH group (15.9% vs. 6.3%, p = 0.002). Meta-analysis yielded pooled prevalence estimates in CTEPH of 3.0% for antithrombin deficiency, 3.6% for protein S deficiency, 4.7% for protein C deficiency, 7.9% for F5 Leiden, and 5.6% for F2 G20210A; these estimates were consistent with those reported in other populations. The clinical model yielded an ROC AUC of 0.883. The addition of PGS004854 and rare variants in thrombosis-associated genes increased the AUC to 0.951, representing an absolute increase of 0.068 (7.7%; p = 0.026). In the multivariable model, PGS004854 was the strongest positive genetic predictor of CTEPH ( β = 1.869, p < 0.001). Conclusions: The genetic profile of CTEPH patients differs significantly from PAH, with a higher frequency of hereditary thrombotic risk factors. Integrating clinical data with polygenic risk scores and thrombophilia variants improves differential diagnosis accuracy and may optimize the clinical workup.

Machine learning (ML) models integrating genetic and clinical data show promise for personalizing antiplatelet therapy after myocardial infarction (MI). This study aimed to develop an ML model using clinical features and CYP2C19 genotype to predict long-term major adverse cardiac events (MACEs) and guide P2Y12 inhibitor selection. In a prospective observational study of 218 patients with MI undergoing percutaneous coronary intervention, with up to 9-year follow-up for MACEs, we trained and evaluated multiple models incorporating clinical, genetic, and angiographic variables. Uplift modeling principles were applied to assess treatment heterogeneity, and feature importance was analyzed using Shapley Additive Explanations (SHAP). The optimal model (CatBoost with SHAP-based feature selection) achieved an area under the receiver operating characteristic curve of 0.721. Discrimination remained stable under bootstrap resampling and across both infarction presentations. Key prognostic predictors included age, comorbidity index, number of significant coronary lesions, P2Y12 inhibitor type, stent type, and CYP2C19 loss-of-function/gain-of-function variants. Notably, CYP2C19 variants were significant MACE predictors, while drug-eluting stents and ticagrelor were associated with lower predicted long-term risk. These findings suggest that an ML framework incorporating CYP2C19 genotype can stratify long-term MACE risk. This data-driven approach points to the prognostic utility of genetic testing and supports the potential role of ticagrelor, particularly in genetically defined high-risk patients with MI, for optimizing secondary prevention.

MicroRNAs are frequently dysregulated in cancer, yet how their tissue specificity is remodeled during malignant transformation remains poorly characterized. Here, we systematically quantified the tissue specificity of miRNAs across normal (GTEx) and cancer (TCGA) tissues using the Tau index as the primary metric and the Gini index for independent validation. Our analysis revealed a negative association between tissue specificity in healthy tissues and changes in specificity during malignant transformation, indicating that miRNAs with higher tissue specificity in normal tissues tend to undergo greater loss of specificity in cancer. To robustly define dysregulation, we combined two independent analyses: a binomial test over per-project differential expression across 17 matched normal tissues within the TCGA cohort, and a TCGA–GTEx pan-tissue comparison of mean expression. The change in specificity (ΔTau) separated up- from downregulated miRNAs, showing moderate agreement with the binomial signal and a strong correlation with the expression-based contrast. Finally, we identified six miRNAs that lose tissue specificity upon transformation while remaining consistently upregulated (miR-519a-5p, miR-512-3p, miR-522-3p, miR-105-5p, miR-935, miR-1269a). Functional analysis of their experimentally validated and predicted targets showed significant enrichment for converging on core oncogenic programs. Collectively, integrating specificity dynamics with dysregulation evidence pinpoints candidate miRNAs with coordinated, cancer-relevant regulatory roles and highlights those with favorable tissue-specificity profiles that may warrant further investigation as potential therapeutic candidates.

The role of cancer-associated fibroblasts (CAFs) in glioblastoma remains unclear, as their existence in the brain tumor microenvironment is still debated, given that the normal brain parenchyma is devoid of fibroblasts. It is unclear whether cells described as CAFs represent a distinct stromal population or a transcriptional state of perivascular cells such as pericytes. The aim of this study was to determine the identity, origin, and functional relevance of CAFs in glioblastoma. We analyzed 54 single-cell RNA sequencing datasets together with 88 bulk RNA sequencing samples. We identified a continuous transcriptional spectrum linking endothelial cells, pericytes, and CAFs, supporting pericytes as the most likely source of CAFs in glioblastoma. We further derived and validated robust CAF- and pericyte-specific gene signatures, enabling clear separation of these populations across cohorts. Reproducible CAF-associated ligand–receptor interactions were enriched in angiogenesis and immune modulation pathways. In bulk RNA-seq data, both CAF signature scoring and deconvolution consistently demonstrated increased CAF abundance in IDH-wildtype gliomas and further enrichment after chemoradiotherapy, while selective CYP1B1 expression in CAFs suggested a potential association with therapy-induced tumor adaptation. Overall, CAFs represent a distinct, pericyte-related stromal population in glioblastoma with conserved transcriptional and signaling programs. High CAF signature scores were associated with poorer overall and progression-free survival and were enriched in IDH-wildtype and post-chemoradiotherapy gliomas, suggesting a role for CAFs in therapy-associated remodeling of the tumor microenvironment in aggressive disease.

Aim . To evaluate the contribution of polygenic risk scores to a short-term mortality prediction model in patients with dilated cardiomyopathy (DCM). M aterial and methods . The study included 137 patients with DCM. The mean age was 56,8±13,5 years; 115 (83,9%) were men. The median follow-up period was 17 [13; 22] months. During the follow-up period, 26 patients (19,0%) died. To identify predictors of short-term all-cause mortality, a classification tree model was constructed based on the chi-squared automatic interaction detection (CHAID) algorithm, supplemented by polygenic risk scores (PRS) for DCM and heart failure (HF). The model prognostic value was assessed using ROC analysis with area under the curve (AUC) calculation. R e sults . In the constructed model, the most significant predictors of short-term all-cause mortality were parenteral furosemide administration during the index hospitalization, high genetic risk according to the PGS005079 HF scale (cutoff value of 0,42), a ferritin level Conclusion . Short-term all-cause mortality prognosis in patients with dilated cardiomyopathy (DCM) is associated with the need for parenteral loop diuretics during the index hospitalization, decreased ferritin levels, a high genetic predisposition to heart failure (as assessed by the PGS005079 PGS), and signs of more severe heart failure decompensation involving the systemic circulation.

Deep learning methods have become methods of choice in the analysis of genomic data. The performance of deep learning models depends on the information available for training. A growing trend in deep learning applications involves leveraging multi-omics data - spanning genomics, transcriptomics, epigenomics, proteomics, metabolomics, and other domains. When a deep learning model trained on omics data achieves high performance, the important question is to define factors that contribute to model's predictive power. Explainable AI (XAI) methods can be categorized as model-aware and model-agnostic. Model-agnostic approaches, which rely on combinatorial feature perturbations to assess impact, are often computationally prohibitive for deep learning models. To address this, we developed OmiXAI, a pipeline integrating ensemble model-aware XAI methods for deep learning models trained on explicit omics feature matrices. Our framework incorporates gradient-based techniques - including Integrated Gradients, InputXGradients, Guided Backpropagation, and Deconvolution (for CNNs and GNNs) - as well as Saliency Maps and GNNExplainer (specifically for GNNs). We evaluated OmiXAI on a case study of functional genomic element prediction using interval-aligned epigenomic features, demonstrating its efficacy through feature importance analysis and benchmarking of XAI methods. Notably, OmiXAI enabled feature engineering, reducing the critical feature set from almost 2,000 to just 50. Its modular design allows seamless integration of additional attribution methods, ensuring adaptability beyond omics to diverse problem domains. While testing the ensemble approach we benchmarked individual XAI methods and discuss their drawbacks and limitations. OmiXAI is freely available at https://github.com/aameliig/OmiXAI.

The p150 isoform of the double-stranded RNA editing enzyme ADAR1 binds Z-DNA and Z-RNA through the conserved winged helix-turn-helix Zα domain. Here, we describe an inverse computational design strategy to map protein interactors of Zα. We used RFdiffusion and ProteinMPNN to generate ∼10,000 synthetic binders optimized for the Zα recognition surface, then used their sequences as structural templates for BLASTp searches against the human proteome. Multi-stage screening of ∼1,200 candidate regions from 298 proteins via ColabFold pDockQ identified 79 candidates for high-resolution AlphaFold3 modeling, which revealed the m6A reader YTHDC1 as the top-ranked interactor. AlphaFold3 predicts that a glutamate-rich poly-E disordered region of YTHDC1 (residues 199–254) docks into the basic recognition pocket of Zα through a charge-complementary mechanism that mimics the phosphate backbone of Z-RNA. Microsecond molecular dynamics simulations confirmed stability of the binary ADAR1p150–YTHDC1 complex, with the Zα–poly-E interface maintaining RMSD below 3 Å throughout. Ternary complex simulations showed that dsRNA acts as a co-anchoring scaffold stabilizing simultaneous engagement of both proteins in a catalytically dormant conformation. YTHDC1 localizes to transcription-associated YT bodies where nascent RNAs undergo m6A modification and negative supercoiling promotes Z-DNA formation, suggesting that YTHDC1 recruits ADAR1p150 to promote editing of intron-containing substrates prior to splicing. Short Statement: ADAR1 can edit RNAs after they are made, changing the message they carry and removing double-stranded RNAs that can activate inflammatory responses. Using AI-driven protein design, we discovered that ADAR1 physically interacts with YTHDC1, a protein that recognizes newly made self-RNAs. The interaction allows ADAR to edit RNAs as they are made. This unexpected connection between two fundamental RNA modification systems opens new avenues for understanding and potentially targeting autoimmune disorders and cancers driven by the mis-editing of RNA transcripts.

The role of cancer associated fibroblasts (CAFs) in glioblastoma remains unclear, as their existence in the brain tumor microenvironment is still debated, given that the normal brain parenchyma is devoid of fibroblasts. It is unclear whether cells described as CAFs represent a distinct stromal population or a transcriptional state of perivascular cells such as pericytes. The aim of this study was to determine the identity, origin, and functional relevance of CAFs in glioblastoma. We analyzed 54 single cell RNA sequencing datasets together with 88 bulk RNA sequencing samples. We identified a continuous transcriptional spectrum linking endothelial cells, pericytes, and CAFs, supporting pericytes as the most likely source of CAFs in glioblastoma. We further derived and validated robust CAF and pericyte specific gene signatures, enabling clear separation of these populations across cohorts. Reproducible CAF associated ligand receptor interactions were enriched in angiogenesis and immune modulation pathways. In bulk RNA seq data, both CAF signature scoring and deconvolution consistently demonstrated increased CAF abundance in IDH wildtype gliomas and further enrichment after chemoradiotherapy, while selective CYP1B1 expression in CAFs suggested a potential association with therapy-induced tumor adaptation. Overall, CAFs represent a distinct, pericyte related stromal population in glioblastoma with conserved transcriptional and signaling programs. High CAF signature scores were associated with poorer overall and progression-free survival and were enriched in IDH wildtype and post chemoradiotherapy gliomas, suggesting a role for CAFs in therapy-associated remodeling of the tumor microenvironment in aggressive disease.

MicroRNAs (miRNAs) act as crucial post-transcriptional regulators of large gene networks, and their aberrant expression drives key oncogenic processes such as epithelial-mesenchymal transition (EMT), angiogenesis, immune evasion, and metastasis. Oncogenic miRNAs that lose tissue specificity during malignant transformation represent promising therapeutic targets, as their restricted expression in healthy organs could minimize off-target effects. To identify these candidates, this study performed a comprehensive pan-cancer analysis integrating tissue-specificity profiles of healthy tissues from the GTEx project with tumor data from the TCGA, TARGET, CGCI, and CPTAC cohorts. By combining profiling with differential expression analysis between tumor and matched normal samples, cross-cohort integration revealed that malignant transformation is characterized by a widespread loss of tissue-specific miRNA expression. Among these altered patterns, a cluster of nine oncomiRs was identified: miR-105-5p, miR-1269a, miR-196a-5p, miR-9-5p, miR-96-5p, miR-210-3p, miR-301b-3p, miR-592, and miR-135b-5p. These specific miRNAs were significantly and recurrently upregulated across various solid tumors. Functional enrichment analysis of their experimentally validated targets demonstrated a clear convergence on shared oncogenic pathways, particularly those governing hypoxia response, PI3K/AKT signaling, EMT, angiogenesis, and immune modulation.