Biology subjects

Murgas, K. A.

Publications and source records attributed to Murgas, K. A..

8 recordsLinked to original sources

bioRxiv2024

ORCO: Ollivier-Ricci Curvature-Omics: an unsupervised method for analyzing robustness in biological systems

Although recent advanced sequencing technologies have improved the resolution of genomic and proteomic data to better characterize molecular phenotypes, efficient computational tools to analyze and interpret the large-scale omic data are still needed. To address this, we have developed a network-based bioinformatic tool called Ollivier-Ricci curvature-omics (ORCO). ORCO incorporates gene interaction information with omic data into a biological network, and computes Ollivier-Ricci curvature (ORC) values for individual interactions. ORC, an edge-based measure, indicates network robustness and captures global gene signaling changes in functional cooperation using a consistent information passing measure, thereby helping identify therapeutic targets and regulatory modules in biological systems. This tool can be applicable to any data that can be represented as a network. ORCO is an open-source Python package and publicly available on GitHub at https://github.com/aksimhal/ORC-Omics.

bioinformatics↗

bioRxiv2023

Multi-Scale Geometric Network Analysis Identifies Melanoma Immunotherapy Response Gene Modules

Melanoma response to immune-modulating therapy remains incompletely characterized at the molecular level. In this study, we assess melanoma immunotherapy response using a multi-scale network approach to identify gene modules with coordinated gene expression in response to treatment. Using gene expression data of melanoma before and after treatment with nivolumab, we modeled gene expression changes in a correlation network and measured a key network geometric property, dynamic Ollivier-Ricci curvature, to distinguish critical edges within the network and reveal multi-scale treatment-response gene communities. Analysis identified six distinct gene modules corresponding to sets of genes interacting in response to immunotherapy. One module alone, overlapping with the nuclear factor kappa-B pathway (NFKB), was associated with improved patient survival and a positive clinical response to immunotherapy. This analysis demonstrates the usefulness of dynamic Ollivier-Ricci curvature as a general method for identifying information-sharing gene modules in cancer.

bioinformatics↗

bioRxiv2023

Growth Dynamics of Ductal Carcinoma in Situ Recapitulate Normal Breast Development

Ductal carcinoma in situ (DCIS) and invasive breast cancer share many morphologic, proteomic, and genomic alterations. Yet in contrast to invasive cancer, many DCIS tumors do not progress and may remain indolent over decades. To better understand the heterogenous nature of this disease, we reconstructed the growth dynamics of 18 DCIS tumors based on the geo-spatial distribution of their somatic mutations. The somatic mutation topographies revealed that DCIS is multiclonal and consists of spatially discontinuous subclonal lesions. Here we show that this pattern of spread is consistent with a new Comet model of DCIS tumorigenesis, whereby multiple subclones arise early and nucleate the buds of the growing tumor. The discontinuous, multiclonal growth of the Comet model is analogous to the branching morphogenesis of normal breast development that governs the rapid expansion of the mammary epithelium during puberty. The branching morphogenesis-like dynamics of the proposed Comet model diverges from the canonical model of clonal evolution, and better explains observed genomic spatial data. Importantly, the Comet model allows for the clinically relevant scenario of extensive DCIS spread, without being subjected to the selective pressures of subclone competition that promote the emergence of increasingly invasive phenotypes. As such, the normal cell movement inferred during DCIS growth provides a new explanation for the limited risk of progression in DCIS and adds biologic rationale for ongoing clinical efforts to reduce DCIS overtreatment.

cancer biology↗

bioRxiv2023

Mapping the Single-cell Differentiation Landscape of Osteosarcoma

The genetic intratumoral heterogeneity observed in human osteosarcomas (OS) poses challenges for drug development and the study of cell fate, plasticity, and differentiation, processes linked to tumor grade, cell metastasis, and survival. To pinpoint errors in OS differentiation, we transcriptionally profiled 31,527 cells from a tissue-engineered model that directs MSCs toward adipogenic and osteoblastic fates. Incorporating pre-existing chondrocyte data, we applied trajectory analysis and non-negative matrix factorization (NMF) to generate the first human mesenchymal differentiation atlas. This roadmap served as a reference to delineate the cellular composition of morphologically complex OS tumors and quantify each cells lineage commitment. Projecting a bulk RNA-seq OS dataset onto this roadmap unveiled a correlation between a stem-like transcriptomic phenotype and poorer survival outcomes. Our study takes the critical first step in accurately quantifying OS differentiation and lineage, a prerequisite to better understanding global differentiation bottlenecks that might someday be targeted therapeutically. Statement of SignificanceOS treatment kills proliferating cells without addressing the root disease cause: dysregulated differentiation. Constructing a roadmap of mesenchymal differentiation enabled us to identify gene modules linked to OS cell fate and patient survival. This ability to characterize and quantify cancer cell fate enables a novel, differentiation-based drug-screening strategy.

cancer biology↗

bioRxiv2023

Cancer heterogeneity is defined by normal cellular trade-offs

Cancer transcriptional patterns exhibit both shared and unique features across diverse cancer types, but whether these patterns are sufficient to characterize the full breadth of tumor phenotype heterogeneity remains an open question. We hypothesized that cancer transcriptional diversity mirrors patterns in normal tissues optimized for distinct functional tasks. Starting with normal tissue transcriptomic profiles, we use non-negative matrix factorization to derive six distinct transcriptomic phenotypes, called archetypes, which combine to describe both normal tissue patterns and variations across a broad spectrum of malignancies. We show that differential enrichment of these signatures correlates with key tumor characteristics, including overall patient survival and drug sensitivity, independent of clinically actionable DNA alterations. Additionally, we show that in HR+/HER2-breast cancers, metastatic tumors adopt transcriptomic signatures consistent with the invaded tissue. Broadly, our findings suggest that cancer often arrogates normal tissue transcriptomic characteristics as a component of both malignant progression and drug response. This quantitative framework provides a strategy for connecting the diversity of cancer phenotypes and could potentially help manage individual patients.

cancer biology↗

bioRxiv2022

Beyond Pairwise Interactions: Higher-Order Dynamics in Protein Interaction Networks

Protein interactions form a complex dynamic system that shapes cell phenotype and function; in this regard, network analysis is a powerful tool for studying the dynamics of cellular processes. Graph-based models are limited, however, in that these models consider only pairwise relationships. Higher-order interactions are well-characterized in biology, including protein complex formation and feedback or feedforward loops. These higher-order relationships are better represented by a hypergraph as a generalized network model. Here, we present an approach to analyzing dynamic gene expression data using a hypergraph model and quantify network heterogeneity via Forman-Ricci curvature. We observe, on a global level, increased network curvature in pluripotent stem cells and cancer cells. Further, we use local curvature to conduct pathway analysis in a melanoma dataset, finding increased curvature in several oncogenic pathways and decreased curvature in tumor suppressor pathways. We compare this approach to a graph-based model and a differential gene expression approach.

systems biology↗

bioRxiv2021

Quantifying Cellular Pluripotency and Pathway Robustness through Forman-Ricci Curvature

In stem cell biology, cellular pluripotency describes the capacity of a given cell to differentiate into multiple cell types. From a statistical physics perspective, entropy provides a statistical measure of randomness and has been demonstrated as a way to quantitate pluripotency when considering biological gene networks. Furthermore, recent theoretical work has established a relationship between Ricci curvature (a geometric measure of "flatness") and entropy (also related to robustness), which one can exploit to link the geometric quantity of curvature to the statistical quantity of entropy. Therefore, this study seeks to explore Ricci curvature in biological gene networks as a descriptor of pluripotency and robustness among gene pathways. Here, we investigate Forman-Ricci curvature, a combinatorial discretization of Ricci curvature, along with network entropy, to explore the relationship of the two quantities as they occur in gene networks. First, we demonstrate our approach on an experiment of stem cell gene expression data. As expected, we find Ricci curvature directly correlates with network entropy, suggesting Ricci curvature could serve as an indicator for cellular pluripotency much like entropy. Second, we measure Forman-Ricci curvature in a dataset of cancer and non-cancer cells from melanoma patients. We again find Ricci curvature is increased in the cancer state, reflecting increased pluripotency or "stemness". Further, we locally examine curvature on the gene level to identify several genes and gene pathways with known relevance to melanoma. In turn, we conclude Forman-Ricci curvature provides valuable biological information related to pluripotency and pathway functionality. In particular, the advantages of this geometric approach are promising for extension to higher-order topological structures in order to represent more complex features of biological systems.

systems biology↗

bioRxiv2019

Unique spatial integration in mouse primary visual cortex and higher visual areas

Neurons in the visual system integrate over a wide range of spatial scales. This diversity is thought to enable both local and global computations. To understand how spatial information is encoded across the mouse visual system, we use two-photon imaging to measure receptive fields in primary visual cortex (V1) and three downstream higher visual areas (HVAs): LM (lateromedial), AL (anterolateral) and PM (posteromedial). We find significantly larger receptive field sizes and less surround suppression in PM than in V1 or the other HVAs. Unlike other visual features studied in this system, specialization of spatial integration in PM cannot be explained by specific projections from V1 to the HVAs. Instead, our data suggests that distinct connectivity within PM may support the areas unique ability to encode global features of the visual scene, whereas V1, LM and AL may be more specialized for processing local features.

neuroscience↗