Biology subjects

Litvinov, D.

Publications and source records attributed to Litvinov, D..

4 recordsLinked to original sources

bioRxiv2026

AI-based separation of malignant cell- and microenvironment-specific gene expression from bulk RNA sequencing enhances biomarker interpretation

Bulk RNA sequencing (RNA-seq)-based gene expression analysis is a promising tool for personalized cancer diagnostics, disease monitoring, and treatment decision-making. However, its clinical utility is limited by interference from non-malignant tumor microenvironment cells, which can dominate transcript data in low-purity tumors. While cell deconvolution methods like Kassandra can predict digital cell percentages from bulk RNA-seq, approaches for delineating the gene expression contribution of tumor compartments remain limited. To overcome this limitation, we developed Helenus, a machine-learning-based tool that separates gene expression between malignant and non-malignant cells. Trained on over 200 million synthetic RNA profiles representing diverse tumor types and purities, Helenus demonstrated high accuracy in separating gene expression origin. Helenus also uncovered true genomic-RNA correlations such as copy number alterations and the expression of therapeutic antibody-drug conjugate targets specifically on tumor cells. Helenus provides critical insights into tumor biology and immunotherapy response by precisely identifying biomarker expressions, paving the way for more effective personalized cancer care. SignificanceHelenus extracts gene expression profiles of cancerous and non-cancerous compartments of tumor biopsies from bulk RNA-seq data, enabling the determination of how the expression of specific genes affects malignancy and tumor immunity.

cancer biology↗

bioRxiv2026

Stoic: Fast and accurate protein stoichiometry prediction

MotivationProtein complexes are central to cellular function, but experimental determination of their structures remains challenging. Structure prediction methods require prior knowledge of stoichiometry - the number of copies of each protein entity within a complex. Current approaches rely on computationally expensive brute-force methods that run structure prediction on multiple stoichiometry combinations, often with limited accuracy. ResultsWe introduce Stoic, a method that uses protein language model embeddings to predict protein complex stoichiometry. Our approach learns to identify interface residues that participate in protein-protein interactions, rather than relying on global sequence features. By integrating these interface-aware embeddings into a graph neural network, Stoic achieves fast and accurate stoichiometry prediction for both homomeric and heteromeric targets. AvailabilitySource code for inference and training along with web versions are available in the repository at https://github.com/PickyBinders/stoic. Contactjanani.durairaj@unibas.ch

bioinformatics↗

bioRxiv2024

Surforama: interactive exploration of volumetric data by leveraging 3D surfaces

MotivationVisualization and annotation of segmented surfaces is of paramount importance for studying membrane proteins in their native cellular environment by cryogenic electron tomography (cryo-ET). Yet, analyzing membrane proteins and their organization is challenging due to their small sizes and the need to consider local context constrained to the membrane surface. ResultsTo interactively visualize, annotate, and analyze proteins in cellular context from cryo-ET data, we have developed Surforama, a Python package and napari plugin. For interactive visualization of membrane proteins in tomograms, Surforama renders the local densities projected on the surface of the segmentations. Suforama additionally provides tools to annotate and analyze particles on the membrane surfaces. Finally, for compatibility with other tools in the cryo-ET analysis ecosystem, results can be exported as RELION-formatted STAR files. As a demonstration, we performed subtomogram averaging and neighborhood analysis of photosystem II proteins in thylakoid membranes from the green alga Chlamydomonas reinhardtii. Availability and implementationPython package, code and examples are available at: https://github.com/cellcanvas/surforama

bioinformatics↗

bioRxiv2023

Melanin found in wheat spike husks

Melanin is the dark polymer pigment found in all kingdoms of life. Plant melanin, formed through the oxidation and polymerization of phenolic compounds, does not contain nitrogen, however it possesses similar properties with melanin of animal, fungal or bacterial origin. Melanin in plants is usually found in seed coats or fruit pericarp and is ascribed mechanical barrier or some other protective functions. Wild and formerly locally cultivated wheat species, like Persian wheat (Triticum carthlicum Nevski) frequently exhibit black color of spike husks and awns. The pigment causing it and the biological purpose of this coloration was not clarified before. In this paper using standard chemical extraction procedures for anthocyanins and melanin, ultraviolet-visible-near-infrared spectroscopy and hyperspectral imaging, we prove that the black color of Persian wheat spikes is caused by melanin but not anthocyanins. Also, we show that the dark pigment in husks and awns is located in epiderma and subepidermal sclerenchyma cells, that implies melanin potentially to have mechanical-enhancing and protection function. Other possible functions of melanin in cereals are discussed.

plant biology↗