Biology subjects

DeMeo, D.

Publications and source records attributed to DeMeo, D..

6 recordsLinked to original sources

bioRxiv2025

Sex differences in thyroid aging and their implications in thyroid disorders: insights from gene regulatory networks

Thyroid disorders are more prevalent in females than males, with sex differences in disease risk varying by age, e.g. although risk of anaplastic thyroid carcinoma (ATC) increases with age for both sexes, females are diagnosed at younger ages than males. In contrast, Hashimotos thyroiditis (HT) is more common in ages 30-50, with females having higher risk. These patterns suggest that in thyroid, gene regulatory processes evolve with age in a sex-specific manner. To characterize these sex differences in thyroid aging, we estimated and analyzed individual-specific gene regulatory networks using BONOBO and PANDA algorithms. We found that regulatory patterns of pathways involved in cell proliferation, immune response, and metabolic processes vary nonlinearly with age, with sex-specific differences in direction and rate. Females show two inflection points around ages 40 and 60, when regulatory patterns show significant change for most pathways, consistent with changes in targeting by estrogen and androgen receptors. Investigating gene regulatory patterns in HT and ATC, we observed that disease-relevant immune and metabolic pathways changed with age in the same direction as they change in ATC. In contrast, in HT, disease-related regulatory patterns are in the opposite direction of aging. In age groups where HT is most diagnosed, regulatory patterns in females more closely resembled disease states than males, highlighting sex-biased regulations role in elevated disease risk in females. Our findings demonstrate immune response and metabolic processes are regulated in age- and sex-specific manner, potentially driving variation in disease risk, emphasizing the need for tailored screening and prevention strategies. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/684890v3_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@cd93aeorg.highwire.dtl.DTLVardef@9a913aorg.highwire.dtl.DTLVardef@1b4a862org.highwire.dtl.DTLVardef@19adca8_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2025

Identifying Sex Differences in Lung Adenocarcinoma Using Multi-Omics Integrative Protein Signaling Networks

Lung adenocarcinoma (LUAD) exhibits differences between the sexes in incidence, prognosis, and therapy, suggesting underexplored molecular mechanisms. We conducted an integrative multi-omics analysis using the Clinical Proteomic Tumor Analysis Consortium (CPTAC) and The Cancer Genome Atlas (TCGA) datasets to contrast transcriptomes and proteomes between sexes. We used TIGER to analyze TCGA-LUAD expression data and found sex-biased activity of transcription factors (TFs); we used PTM-SEA with CPTAC-LUAD proteomics data and found sex-biased kinase activity. We combined these to construct a kinase-TF signaling network and discovered druggable pathways linked to cancer-related processes. We also found significant sex biases in clinically relevant TFs and kinases, including NR3C1, AR, and AURKA. Using the PRISM drug screening database, we identified potential sex-specific drugs, such as glucocorticoid receptor agonists and aurora kinase inhibitors. Our findings emphasize the importance of considering sex and using multi-omics network methods to discover personalized cancer therapies.

cancer biology↗

bioRxiv2023

Chitinase 3-like-1 (CHI3L1) Inhibits Innate Anti-Tumor and Tissue Remodeling ImmuneResponses by Regulating CD47-SIRPα and CD24-Siglec10-Mediated Phagocytosis

Innate immune responses such as phagocytosis are critically linked to the generation of adaptive immune responses against the neoantigens in cancer and the efferocytosis that is essential for homeostasis in diseases characterized by lung injury, inflammation, and remodeling as in Chronic Obstructive Pulmonary Disease (COPD). Chitinase 3-like-1 (CHI3L1) is induced in many cancers where it inhibits adaptive immune responses by stimulating immune checkpoint molecules (ICPs) and portends a poor prognosis. CHI3L1 is also induced in COPD where it regulates epithelial cell death. Here we demonstrate that pulmonary melanoma metastasis inhibits macrophage phagocytosis by stimulating the CD47-SIRP and CD24-Siglec10 phagocytosis checkpoint pathways while inhibiting macrophage "eat me" signals from calreticulin and HMGB1. We also demonstrate that these effects on macrophage phagocytosis are mediated by CHI3L1 stimulation of the SHP-1 and SHP-2 phosphatases and the inhibition of the accumulation and phosphorylation of cytoskeleton-regulating non-muscle myosin IIa. This inhibition of innate immune responses like phagocytosis provides a mechanistic explanation for the ability of CHI3L1 to stimulate ICPs and inhibit adaptive immune responses in cancer and diseases like COPD. The ability of CHI3L1 to simultaneously inhibit innate immune responses, stimulate ICPs, inhibit T cell co-stimulation, and regulate a number of other oncogenic and inflammation pathways suggest that CHI3L1-targeted therapeutics are promising interventions in cancer, COPD and other disorders.

immunology↗

bioRxiv2022

The Network Zoo: a multilingual package for the inference and analysis of biological networks

Inference and analysis of cellular biological networks requires software tools that integrate multi-omic data from various sources. The Network Zoo (netZoo; netzoo.github.io) is an open-source software suite to model biological networks, including context-specific gene regulatory networks and multi-omics partial correlation networks, to conduct differential analyses, estimate community structure, and model the transitions between biological states. The netZoo builds on our ongoing development of network methods, harmonizing the implementations in various computing languages (R, Python, MATLAB, and C) and between methods to allow a better integration of these tools into analytical pipelines. To demonstrate the value of this integrated toolkit, we analyzed the multi-omic data from the Cancer Cell Line Encyclopedia (CCLE) by inferring gene regulatory networks for each cancer cell line and associating network features with other phenotypic attributes such as drug sensitivity. This allowed us to identify transcription factors that play a critical role in both drug resistance and cancer development in melanoma. We also used netZoo to build a pan-cancer, multi-tiered CCLE map and used it to identify known metabolic hallmarks of cancer and to estimate novel context-specific elements that mediate post-transcriptional regulation. Because the netZoo tools are open-source and there is a growing community of both users and developers, we built an ecosystem to support community contributions, share use cases, and visualize networks online. As additional data types become available and our suite of methods grows, we will expand "the zoo" to incorporate an increasingly sophisticated collection of tools for network inference and analysis.

genomics↗

bioRxiv2022

A Statistical Physics Approach for Disease Module Detection

Extensive evidence indicates that the pathobiological processes of a complex disease are associated with perturbation within specific disease neighborhoods of the human protein-protein interaction (PPI) network (a.k.a. the interactome), often referred to as the disease module. Many computational methods have been developed to integrate the interactome and omics profiles to extract context-dependent disease modules. Yet, existing methods all have fundamental limitations in terms of rigor and/or efficiency. Here, we developed a statistical physics approach based on the random-field Ising model (RFIM) for disease module detection, which is both mathematically rigorous and computationally efficient. We applied our RFIM approach with genome-wide association studies (GWAS) of six complex diseases to examine its performance for disease module detection. We found that our RFIM approach outperforms existing methods in terms of computational efficiency, connectivity of disease modules, and robustness to the interactome incompleteness.

systems biology↗

bioRxiv2018

DNA methylation modules associate with incident cardiovascular disease and cumulative risk factor exposure

1 AbstractO_ST_ABSBackgroundC_ST_ABSEpigenome-wide association studies using DNA methylation have the potential to uncover novel biomarkers and mechanisms of cardiovascular disease (CVD) risk. However, the direction of causation for these associations is not always clear, and investigations to-date have generally failed to replicate at the level of individual loci. Here, we undertook module- and region-based DNA methylation analyses of incident CVD in the Womens Health Initiative (WHI) and Framingham Heart Study Offspring Cohort (FHS) in order to find more robust epigenetic biomarkers for cardiovascular risk.\n\nMethods and FindingsWe applied weighted gene correlation network analysis (WGCNA) and the Comb-p algorithm to find methylation modules and regions associated with incident CVD in the WHI dataset. We discovered two modules whose activation correlated with CVD risk and replicated across cohorts. One of these modules was enriched for development-related processes and overlaps strongly with epigenetic aging sites. For the other, we showed preliminary evidence for monocyte-specific effects and statistical links to cumulative exposure to traditional cardiovascular risk factors. Additionally, we found three regions (associated with the genes SLC9A1, SLC1A5, and TNRC6C) whose methylation associates with CVD risk.\n\nConclusionsIn sum, we present several epigenetic associations with incident CVD that reveal disease mechanisms related to development and monocyte biology. Furthermore, we show that epigenetic modules may act as a molecular readout of cumulative cardiovascular risk factor exposure, with implications for the improvement of clinical risk prediction.

genomics↗