Biology subjects

Rodin, A. S.

Publications and source records attributed to Rodin, A. S..

9 recordsLinked to original sources

bioRxiv2026

Extrachromosomal DNA as a Causal Instrument for Spatial Multi-Omics

Spatial transcriptomics, multiplex imaging, and computational pathology now map tissue organization at cellular resolution, but the analyses applied to these data remain correlational. Clustering and co-occurrence statistics describe which features appear together; they cannot say which feature drives the others. We propose a framework for causal inference in spatial multi-omics built on a specific feature of extrachromosomal DNA (ecDNA). ecDNA carries no centromere; it does not attach to the mitotic spindle and partitions randomly to daughter cells at division. Two neighboring cells in the same microenvironment can therefore inherit very different oncogene copy numbers for reasons unrelated to local signaling. Together, these cell-intrinsic randomization properties of ecDNA provide the framework for ecDNA copy number serving as an instrumental variable (IV) separating the effects of oncogene dosage from the downstream cellular effects. In this study, we formalize this within a structural causal model, implement two-stage least squares estimation with sibling-comparison and falsification diagnostics, and provide sensitivity analyses for residual confounding. To benchmark these methods against ground truth, we built CAUSANTA, an agent-based simulator that generates spatial tissue with a known causal graph and stochastic ecDNA inheritance. The framework recovers known oncogene-to-phenotype effects from simulated data, and we outline its application to real tumor sections, including multi-instrument settings where independent ecDNA species carrying different oncogenes enable factorial designs within a single tumor.

cancer biology↗

bioRxiv2026

Conserved Residues in the Gα interface show subtype specificity in Gβγ coupling

Although the structural basis of selective G-protein coupling to G protein coupled receptors (GPCRs) is well characterized, the mechanisms underlying selective interactions between distinct G subtypes (Gs, Gi, Gq, and G12/13) and G{beta}{gamma} remain poorly understood. While conserved residues in G subtypes are often assumed to have similar functions, they may instead modulate coupling selectivity by altering the frequency and stability of contacts at the G:G{beta}{gamma} interface. Using molecular dynamics (MD) simulations combined with the interpretable machine learning method, Bayesian Network Model (BNM), and protein-protein proximity (BRET) assays, we show that conserved residues in the two closely related Gi/o and Gq/11 subfamilies contribute differentially to G{beta}{gamma} coupling. These conserved residue "hotspots" on Gi and Gq produced divergent functional effects on G{beta}{gamma} coupling, indicating that conservation does not ensure functional equivalence. These findings suggest that local microenvironment and paralog-specific allosteric coupling shape how conserved interface residues contribute to protein-protein coupling. The framework provides a systematic approach for dissecting subtype-specific mechanisms, with implications for drug design and for annotating the functional relevance of disease-associated variants. The computational methods used here are broadly applicable to other homologous protein families.

molecular biology↗

bioRxiv2025

Protein Frustration Reveals Active Sites in Co-Evolved GPCR:G Protein Complexes and in Engineered Targeted Degrader Complexes

The folded structure of a protein is understood to be an optimal energy state. However, previous studies have shown that certain amino acid residue positions that play a critical role in protein function are often in a suboptimal energy state or "frustrated". Here, we leverage over 1200 three-dimensional structures of G protein-coupled receptors (GPCRs) to demonstrate that residues at the interface between GPCR and its ligand or G protein contain a higher density of frustrated residues compared to other structural regions in the receptor. Likewise, the G subunit of the trimeric G proteins shows multiple clusters of highly frustrated residues on its surface that overlap with their effector protein (G{beta}{gamma}, RGS, Adenyl cyclase, Ric8) binding interfaces. Compared to the co-evolved GPCR:G protein complexes, engineered protein complexes, such as those facilitated by molecular degraders, show a much greater density of highly frustrated residues in the degrader interface. Our study highlights the use of protein frustration as an invaluable tool to evaluate both native protein-protein interfaces and design strategies to facilitate engineered protein complexes.

biophysics↗

bioRxiv2025

Lymphocytes and monocytes undergo swift suppression of IL-10R, IL-6R, and IL-2Rβγ signaling under high concentrations of different cytokines

The JAK-STAT signaling pathway is fundamental for immune system regulation. It involves phosphorylation of several types of STAT proteins in response to binding of cytokines to immune cell receptors. Traditionally, the immune signaling studies focus on measuring the levels of phosphorylated STATs (pSTATs) following individual cytokine application. We developed an experimental approach, based on multiparametric flow cytometry, to simultaneously measure the levels of five pSTATs after 15 minutes of cell treatment with high doses of individual cytokines and their paired combinations. Analysis of our experimental data involving peripheral blood mononuclear cells from healthy donors reveals systematic suppression of IL-10R, IL-6R, and IL-2R{beta}{gamma} signaling in T cells, B cells, NK cells, and monocytes. This suppression is mediated by at least all tested cytokines that do not induce relevant pSTATs by themselves. Remarkably, the cytokines with negligible own signaling do act as prominent selective signaling suppressors. In contrast, the signaling of IFNAR, IFNGR, IL-4R, and IL-2R{beta}{gamma} remains largely unaffected by co-application of other cytokines. We propose that this pattern of signaling suppression represents an evolutionary developed mechanism enhancing the promptness, specialization, and efficiency of the immune response, while increased concentration of cytokines serves as a danger signal of inefficient response. We hypothesize that selective signaling suppression arises from the differential sensitivity of conformations of cytokine-receptor complexes to the increase of cell surface tension and stiffness, which is caused by effects following the binding of cytokines to membrane-associated molecules, including glycocalyx elements. While the rewiring of immune cell signaling should represent a powerful evolutionary tool for augmentation of adaptive response, it should also lead to the prolonged suppression of counteracting signaling pathways, culminating in cytokine release syndrome and contributing to autoimmune diseases.

immunology↗

bioRxiv2025

Temporally Resolved and Interpretable Machine Learning Model of GPCR Conformational Transition

Identifying target-specific drugs remains a challenge in pharmacology, especially for highly homologous proteins such as dopamine receptors D2R and D3R. Differences in target-specific cryptic druggable sites for such receptors arise from the distinct conformational ensembles underlying their dynamic behavior. While Molecular Dynamics (MD) simulations has emerged as a powerful tool for dissecting protein dynamics, the sheer volume of MD data requires scalable and unbiased data analysis strategies to pinpoint residue communities regulating conformational state ensembles. We have developed the Dynamically Resolved Universal Model for BayEsiAn network Tracking (DRUMBEAT) interpretable machine learning algorithm and validated it by identifying residue communities that enable the deactivation of the {beta}2-adrenergic receptor. Further, upon analyzing dopamine receptor dynamics we identified distinct and non-conserved residue communities around the contacts F1704.62_F172ECL2 and S1464.38_G14134.56 that are specific to D3R conformational transitions compared to D2R. This information can be tapped to design subtype-specific drugs for neuropsychiatric and substance use disorders.

biochemistry↗

bioRxiv2023

Systems profiling reveals recurrently dysregulated cytokine signaling responses in ER+ breast cancer patients' blood

Cytokines mediate cell-to-cell communication across the immune system and therefore are critical to immunosurveillance in cancer and other diseases. Several cytokines show dysregulated abundance or signaling responses in breast cancer, associated with the disease and differences in survival and progression. Cytokines operate in a coordinated manner to affect immune surveillance and regulate one another, necessitating a systems approach for a complete picture of this dysregulation. Here, we profiled cytokine signaling responses of peripheral immune cells from breast cancer patients as compared to healthy controls in a multidimensional manner across ligands, cell populations, and responsive pathways. We find alterations in cytokine responsiveness across pathways and cell types that are best defined by integrated signatures across dimensions. Alterations in the abundance of a cytokines cognate receptor do not explain differences in responsiveness. Rather, alterations in baseline signaling and receptor abundance suggesting immune cell reprogramming are associated with altered responses. These integrated features suggest a global reprogramming of immune cell communication in breast cancer. Significance StatementWhile individual cytokine responses have previously been observed to be altered in breast cancer, cytokine signaling responses are tightly interconnected in a way that has not been previously characterized. Here, we profile cytokine signaling responses and find alterations that are shared across both pathways and cell types. The signatures across these measurements better define the alterations and point to a broad immunosuppression response. HighlightsO_LIBaseline and post-stimulation cytokine signaling profiles differ between healthy donors and breast cancer patients. C_LIO_LIChanges in cytokine response are not explained by differences in abundance of the cognate receptor C_LIO_LIFeatures of signaling response and receptor abundance dysregulation are coordinated across patients C_LIO_LIIntegrated patterns of dysregulation in breast cancer patients share features of Th17 like-response as well as regulatory-like B and CD8+ T cells C_LI

cancer biology↗

bioRxiv2020

Synthetic data generation with probabilistic Bayesian Networks

Bayesian Network (BN) modeling is a prominent and increasingly popular computational systems biology method. It aims to construct probabilistic networks from the large heterogeneous biological datasets that reflect the underlying networks of biological relationships. Currently, a variety of strategies exist for evaluating BN methodology performance, ranging from utilizing artificial benchmark datasets and models, to specialized biological benchmark datasets, to simulation studies that generate synthetic data from predefined network models. The latter is arguably the most comprehensive approach; however, existing implementations are typically limited by their reliance on the SEM (structural equation modeling) framework, which includes many explicit and implicit assumptions that may be unrealistic in a typical biological data analysis scenario. In this study, we develop an alternative, purely probabilistic, simulation framework that more appropriately fits with real biological data and biological network models. In conjunction, we also expand on our current understanding of the theoretical notions of causality and dependence / conditional independence in BNs and the Markov Blankets within.

systems biology↗

bioRxiv2020

Dissecting Response to Cancer Immunotherapy by Applying Bayesian Network Analysis to Flow Cytometry Data

Cancer immunotherapy, specifically immune checkpoint blockade therapy, has been found to be effective in the treatment of metastatic cancers. However, only a subset of patients achieve clinical responses. Consequently, elucidating immune system-related pre-treatment biomarkers that are predictive with respect to sustained clinical response is a major research priority. Another research priority is evaluating changes in the immune system before and after treatment in responders and non-responders. Our group has been studying immune signaling networks as an accurate reflection of the global immune state. Flow cytometry (FACS, Fluorescence-activated cell sorting) data characterizing immune signaling in peripheral blood mononuclear cells (PBMC) from gastroesophageal adenocarcinoma (GEA) patients were used to analyze changes in immune signaling networks in this setting. Here, we describe a novel computational pipeline developed by us to perform secondary analyses of FACS data using systems biology / machine learning techniques and concepts. It is centered around comparative Bayesian network analyses of immune signaling networks and is capable of detecting true positive signals that conventional methods (such as FlowJo manual gating) might miss. Future studies are planned to validate and follow up immune markers (and combinations / interactions thereof) associated with clinical responses that were identified by this computational pipeline.

systems biology↗

bioRxiv2019

New analysis framework incorporating mixed mutual information and scalable Bayesian networks for multimodal high dimensional genomic and epigenomic cancer data

We propose a novel two-stage analysis strategy to discover candidate genes associated with the particular cancer outcomes in large multimodal genomic cancers databases, such as The Cancer Genome Atlas (TCGA). During the first stage, we use mixed mutual information to perform variable selection; during the second stage, we use scalable Bayesian network (BN) modeling to identify candidate genes and their interactions. Two crucial features of the proposed approach are (i) the ability to handle mixed data types (continuous and discrete, genomic, epigenomic, etc.), and (ii) a flexible boundary between the variable selection and network modeling stages --- the boundary that can be adjusted in accordance with the investigators BN software scalability and hardware implementation. These two aspects result in high generalizability of the proposed analytical framework. We apply the above strategy to three different TCGA datasets (LGG, Brain Lower Grade Glioma; HNSC, Head and Neck Squamous Cell Carcinoma; STES, Stomach and Esophageal Carcinoma), linking multimodal molecular information (SNPs, mRNA expression, DNA methylation) to two clinical outcome variables (tumor status and patient survival). We identify 11 candidate genes, of which 6 have already been directly implicated in the cancer literature. One novel LGG prognostic factor suggested by our analysis, methylation of TMPRSS11F type II transmembrane serine protease, presents intriguing direction for the follow-up studies.

genomics↗