Unveiling hidden connections in omics data via pyPARAGON: an integrative hybrid approach for disease network construction.

Network inference or reconstruction algorithms play an integral role in successfully analyzing and identifying causal relationships between omics hits for detecting dysregulated and altered signaling components in various contexts, encompassing disease states and drug perturbations. However, accurate representation of signaling networks and identification of context-specific interactions within sparse omics datasets in complex interactomes pose significant challenges in integrative approaches. To address these challenges, we present pyPARAGON (PAgeRAnk-flux on Graphlet-guided network for multi-Omic data integratioN), a novel tool that combines network propagation with graphlets. pyPARAGON enhances accuracy and minimizes the inclusion of nonspecific interactions in signaling networks by utilizing network rather than relying on pairwise connections among proteins. Through comprehensive evaluations on benchmark signaling pathways, we demonstrate that pyPARAGON outperforms state-of-the-art approaches in node propagation and edge inference. Furthermore, pyPARAGON exhibits promising performance in discovering cancer driver networks. Notably, we demonstrate its utility in network-based stratification of patient tumors by integrating phosphoproteomic data from 105 breast cancer tumors with the interactome and demonstrating tumor-specific signaling pathways. Overall, pyPARAGON is a novel tool for analyzing and integrating multi-omic data in the context of signaling networks. pyPARAGON is available at https://github.com/netlab-ku/pyPARAGON.

Review: Cancer and neurodevelopmental disorders: multi-scale reasoning and computational guide.

The connection and causality between cancer and neurodevelopmental disorders have been puzzling. How can the same cellular pathways, proteins, and mutations lead to pathologies with vastly different clinical presentations? And why do individuals with neurodevelopmental disorders, such as autism and schizophrenia, face higher chances of cancer emerging throughout their lifetime? Our broad review emphasizes the multi-scale aspect of this type of reasoning. As these examples demonstrate, rather than focusing on a specific organ system or disease, we aim at the new understanding that can be gained. Within this framework, our review calls attention to computational strategies which can be powerful in discovering connections, causalities, predicting clinical outcomes, and are vital for drug discovery. Thus, rather than centering on the clinical features, we draw on the rapidly increasing data on the molecular level, including mutations, isoforms, three-dimensional structures, and expression levels of the respective disease-associated genes. Their integrated analysis, together with chromatin states, can delineate how, despite being connected, neurodevelopmental disorders and cancer differ, and how the same mutations can lead to different clinical symptoms. Here, we seek to uncover the emerging connection between cancer, including pediatric tumors, and neurodevelopmental disorders, and the tantalizing questions that this connection raises.

Extracellular Matrix Sulfation in the Tumor Microenvironment Stimulates Cancer Stemness and Invasiveness.

Tumor extracellular matrices (ECM) exhibit aberrant changes in composition and mechanics compared to normal tissues. Proteoglycans (PG) are vital regulators of cellular signaling in the ECM with the ability to modulate receptor tyrosine kinase (RTK) activation via their sulfated glycosaminoglycan (sGAG) side chains. However, their role on tumor cell behavior is controversial. Here, it is demonstrated that PGs are heavily expressed in lung adenocarcinoma (LUAD) patients in correlation with invasive phenotype and poor prognosis. A bioengineered human lung tumor model that recapitulates the increase of sGAGs in tumors in an organotypic matrix with independent control of stiffness, viscoelasticity, ligand density, and porosity, is developed. This model reveals that increased sulfation stimulates extensive proliferation, epithelial-mesenchymal transition (EMT), and stemness in cancer cells. The focal adhesion kinase (FAK)-phosphatidylinositol 3-kinase (PI3K) signaling axis is identified as a mediator of sulfation-induced molecular changes in cells upon activation of a distinct set of RTKs within tumor-mimetic hydrogels. The study shows that the transcriptomic landscape of tumor cells in response to increased sulfation resembles native PG-rich patient tumors by employing integrative omics and network modeling approaches.

SETD3 regulates endoderm differentiation of mouse embryonic stem cells through canonical Wnt signaling pathway.

With self-renewal and pluripotency features, embryonic stem cells (ESCs) provide an invaluable tool to investigate early cell fate decisions. Pluripotency exit and lineage commitment depend on precise regulation of gene expression that requires coordination between transcription (TF) and chromatin factors in response to various signaling pathways. SET domain-containing 3 (SETD3) is a methyltransferase that can modify histones in the nucleus and actin in the cytoplasm. Through an shRNA screen, we previously identified SETD3 as an important factor in the meso/endodermal lineage commitment of mouse ESCs (mESC). In this study, we identified SETD3-dependent transcriptomic changes during endoderm differentiation of mESCs using time-course RNA-seq analysis. We found that SETD3 is involved in the timely activation of the endoderm-related gene network. The canonical Wnt signaling pathway was one of the markedly altered signaling pathways in the absence of SETD3. The assessment of Wnt transcriptional activity revealed a significant reduction in Setd3-deleted (setd3∆) mESCs coincident with a decrease in the nuclear pool of the key TF ß-catenin level, though no change was observed in its mRNA or total protein level. Furthermore, a proximity ligation assay (PLA) found an interaction between SETD3 and ß-catenin. We were able to rescue the differentiation defect by stably re-expressing SETD3 or activating the canonical Wnt signaling pathway by changing mESC culture conditions. Our results suggest that alterations in the canonical Wnt pathway activity and subcellular localization of ß-catenin might contribute to the endoderm differentiation defect of setd3∆ mESCs.

Structural coverage of the human interactome.

Complex biological processes in cells are embedded in the interactome, representing the complete set of protein-protein interactions. Mapping and analyzing the protein structures are essential to fully comprehending these processes' molecular details. Therefore, knowing the structural coverage of the interactome is important to show the current limitations. Structural modeling of protein-protein interactions requires accurate protein structures. In this study, we mapped all experimental structures to the reference human proteome. Later, we found the enrichment in structural coverage when complementary methods such as homology modeling and deep learning (AlphaFold) were included. We then collected the interactions from the literature and databases to form the reference human interactome, resulting in 117 897 non-redundant interactions. When we analyzed the structural coverage of the interactome, we found that the number of experimentally determined protein complex structures is scarce, corresponding to 3.95% of all binary interactions. We also analyzed known and modeled structures to potentially construct the structural interactome with a docking method. Our analysis showed that 12.97% of the interactions from HuRI and 73.62% and 32.94% from the filtered versions of STRING and HIPPIE could potentially be modeled with high structural coverage or accuracy, respectively. Overall, this paper provides an overview of the current state of structural coverage of the human proteome and interactome.