Biology subjects

Krymova, E.

Publications and source records attributed to Krymova, E..

4 recordsLinked to original sources

bioRxiv2026

Decoding molecular programs that define macrophage responses to tumor-derived cues

Tumor-associated macrophages (TAMs) comprise functionally diverse states that can suppress anti-tumor immunity and promote tumor progression, yet the tumor microenvironmental cues and signaling programs that generate these states remain incompletely defined. Here, we systematically stimulate primary human monocyte-derived macrophages with a panel of cytokines and metabolites abundant in the tumor microenvironment (TME), and profile their transcriptomic and phosphoproteomic responses to resolve stimulus-specific molecular programs. We observe that potassium (K+) and adenosine (Ado) stimulation, which accumulate in necrotic tumor cores, downregulate antigen-presentation genes and their master regulator CIITA. K+ stimulation results in the upregulated fibronectin 1 expression, associated with immunosuppressive, metastasis-promoting TAM subsets. Ado induces upregulated expression of tryptophan (Trp) catabolism genes, myeloid checkpoints and metallothioneins (MTs). Although MT-high TAM states have been recurrently observed across tumor single cell RNA sequencing studies, their function remains poorly defined. We show that elevated MT expression in tumor tissue is associated with shorter overall survival. By aligning in vitro transcriptomes with single-cell RNA sequencing (scRNA-seq) signatures from a pan-cancer TAM atlas, we identify significant similarities between several in vitro states and clinically observed TAM populations, with Ado-stimulated macrophages closely resembling a MT-expressing TAM cluster. Overall, this work provides a systematic molecular context linking tumor microenvironmental cues to clinically relevant TAM states and offers a framework for recapitulating their functions in vitro. STATEMENT OF SIGNIFICANCEThis study explores how cytokines and metabolites from the tumor microenvironment shape macrophage molecular phenotypes and lead to the upregulation of clinically relevant marker genes and recapitulation of functional states of interest.

immunology↗

bioRxiv2025

The lipidomic architecture of the mouse brain

Lipids are fundamental components of the brain, crucial for synaptic transmission and signal propagation. Altered brain lipid composition is associated with common and rare neuropathologies, yet, the spatial organization of the mammalian brain lipidome remains insufficiently characterized compared to other modalities1-8. Here, we mapped the membrane lipid architecture of the adult mouse brain at micrometric scale, across sexes, and during pregnancy. This Lipid Brain Atlas reveals that lipids define a fine-grained biochemical structure that aligns with functional anatomy. Membrane lipid spatial heterogeneity clusters into territories, which we termed lipizones. Lipizones partially mirror cell type territories, but also capture distal axon terminals. Through lipizones, (i) we reveal the organizing principles of the gray matter lipidome, related to connectivity and cytoarchitecture; (ii) we discover a new axis of oligodendrocyte heterogeneity in the white matter; (iii) and we find biochemical zonation in the choroid plexus and in the ventricular walls. We show that this lipidomic architecture can adapt to changing physiological needs. In the brain of pregnant females, the white matter is metabolically activated and the outer cortex is reorganized. These results are a foundational resource (https://lbae-v2.epfl.ch/), poised to reshape our understanding of lipids in brain development, physiology, and pathology.

systems biology↗

bioRxiv2025

Evaluation of deep learning approaches for high-resolution chromatin accessibility prediction from genomic sequence

Fine-grained prediction of chromatin accessibility from DNA sequence is a foundational step in modeling gene expression changes resulting from sequence variants. Yet, few methods operate at the resolution necessary to capture subtle effects of single-nucleotide changes. Furthermore, it remains unclear which architectural components--such as residual connections, normalization strategies, or attention mechanisms--drive performance in these high-resolution predictions. To address these knowledge gaps, we systematically evaluate classic architectural choices and introduce ConvNeXt V2 blocks, originally developed for computer vision, as high-resolution feature extractors in deep learning models for genomic data. Integrated into diverse architectures--CNNs, LSTMs, dilated CNNs, and transformers--ConvNeXt V2 blocks consistently improve performance, leading to similar prediction accuracy across these different model types. This reveals that early feature extraction, rather than downstream architecture, is the primary determinant of prediction accuracy. A comprehensive evaluation of these models on ATAC-seq signal prediction at 4 bp resolution in a cell type-specific manner identifies the ConvNeXtbased dilated CNN as the most robust performer, better preserving the signals shape. Our codebase and benchmarks provide practical tools for high-resolution chromatin modeling.

genomics↗

bioRxiv2024

UniversalEPI: Harnessing Attention Mechanisms to Decode Chromatin Interactions in Rare and Unexplored Cell Types

Enhancer-promoter interactions (EPIs) play a central role in gene regulation, but experimental techniques such as Hi-C for mapping these interactions remain costly and labor-intensive. Computational methods have been developed to predict EPIs in silico from DNA sequence and chromatin information; however, there are major challenges with the generalizability and accuracy of predictions by existing methods across cell types and conditions unseen during model training. We developed and validated UniversalEPI, an attention-based deep ensemble model that predicts EPIs up to 2 Mb apart using only DNA sequence and chromatin accessibility (ATAC-seq) data. Unlike models that reconstruct full Hi-C contact maps, UniversalEPI focuses on biologically relevant, sparse chromatin interactions between accessible regulatory elements. It generalizes across both bulk and single-cell ATAC-seq-derived pseudo-bulk datasets, delivering state-of-the-art performance while using fewer input modalities than existing approaches. By modeling predictive uncertainty, UniversalEPI enables statistically robust differential analysis of chromatin interactions across conditions. We demonstrate its utility by tracking dynamic EPIs during human macrophage activation and identifying regulatory differences between cancer cell states in esophageal adenocarcinoma. By providing precalculated Hi-C predictions for 157 ENCODE datasets, UniversalEPI expands the scope and applicability of in silico 3D genome modeling for studying gene regulation in development and disease.

genomics↗