The ability to identify and characterize not only the protein-protein interactions but also their internal modular organization through network analysis is fundamental for understanding the mechanisms of biological processes at the molecular level. Indeed, the detection of the network communities can enhance our understanding of the molecular basis of disease pathology, and promote drug discovery and disease treatment in personalized medicine. This work gives an overview of recent computational methods for the detection of protein complexes and functional modules in protein-protein interaction networks, also providing a focus on some of its applications. We propose a systematic reformulation of frequently adopted taxonomies for these methods, also proposing new categories to keep up with the most recent research. We review the literature of the last five years (2017-2021) and provide links to existing data and software resources. Finally, we survey recent works exploiting module identification and analysis, in the context of a variety of disease processes for biomarker identification and therapeutic target detection. Our review provides the interested reader with an up-to-date and self-contained view of the existing research, with links to state-of-the-art literature and resources, as well as hints on open issues and future research directions in complex detection and its applications.

High-Grade Serous Ovarian Carcinoma (HGSC) is the most incidental and lethal subtype of epithelial ovarian cancer (EOC) with a high mortality rate of nearly 65%. Recent findings aimed at understanding the pathogenesis of HGSC have attributed its principal source as the Fallopian Tube (FT). To further comprehend the exact mechanism of carcinogenesis, which is still less known, we performed a transcriptome analysis comparing FT and HGSC. Our study aims at exploring new players involved in the development of HGSC from FT, along with their signaling network, and we chose to focus on non-coding RNAs. Non-coding RNAs (ncRNAs) are increasingly observed to be the major regulators of several cellular processes and could have key functions as biological markers, as well as even a therapeutic approach. The most physiologically relevant and significantly dysregulated non-coding RNAs were identified bioinformatically. After analyzing the trend in HGSC and other cancers, MAGI2-AS3 was observed to be an important player in EOC. We assessed its tumor-suppressive role in EOC by means of various assays. Further, we mapped its signaling pathway using its role as a miRNA sponge to predict the miRNAs binding to MAGI2AS3 and showed it experimentally. We conclude that MAGI2-AS3 acts as a tumor suppressor in EOC, specifically in HGSC by sponging miR-15-5p, miR-374a-5p and miR-374b-5p, and altering downstream signaling of certain mRNAs through a ceRNA network.

Long non-coding RNAs (lncRNAs) are increasingly being identified as crucial regulators in pathologies like cancer. High-grade serous ovarian carcinoma (HGSC) is the most common subtype of ovarian cancer (OC), one of the most lethal gynecological malignancies. LncRNAs, especially in cancers such as HGSC, could play a valuable role in diagnosis and even therapy. From RNA-sequencing analysis performed between an OC cell line, SKOV3, and a Fallopian Tube (FT) cell line, FT194, an important long non-coding RNA, HAND2 Anti sense RNA 1 (HAND2-AS1), was observed to be significantly downregulated in OCs when compared to FT. Its downregulation in HGSC was validated in different datasets and in a panel of HGSC cell lines. Furthermore, this study shows that the downregulation of HAND2-AS1 is caused by promoter hypermethylation in HGSC and behaves as a tumor suppressor in HGSC cell lines. Since therapeutic relevance is of key importance in HGSC research, for the first time, HAND2-AS1 upregulation was demonstrated to be one of the mechanisms through which HDAC inhibitor Panobinostat could be used in a strategy to increase HGSC cells’ sensitivity to chemotherapeutic agents currently used in clinical trials. To unravel the mechanism by which HAND2-AS1 exerts its role, an in silico mRNA network was constructed using mRNAs whose expressions were positively and negatively correlated with this lncRNA in HGSC. Finally, a putative ceRNA network with possible miRNA targets of HAND2-AS1 and their mRNA targets was constructed, and the enriched Gene Ontology (GO) biological processes and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were identified.

The anteroposterior axial identity of motor neurons (MNs) determines their functionality and vulnerability to neurodegeneration. Thus, it is a crucial parameter in the design of strategies aiming to produce MNs from human pluripotent stem cells (hPSCs) for regenerative medicine/disease modelling applications. However, the in vitro generation of posterior MNs corresponding to the thoracic/lumbosacral spinal cord has been challenging. Although the induction of cells resembling neuromesodermal progenitors (NMPs), the bona fide precursors of the spinal cord, offers a promising solution, the progressive specification of posterior MNs from these cells is not well defined. Here, we determine the signals guiding the transition of human NMP-like cells toward thoracic ventral spinal cord neurectoderm. We show that combined WNT-FGF activities drive a posterior dorsal pre-/early neural state, whereas suppression of TGFβ-BMP signalling pathways promotes a ventral identity and neural commitment. Based on these results, we define an optimised protocol for the generation of thoracic MNs that can efficiently integrate within the neural tube of chick embryos. We expect that our findings will facilitate the comparison of hPSC-derived spinal cord cells of distinct axial identities.

The ever-increasing importance of structured data in different applications, especially in the biomedical field, has driven the need for reducing its complexity through projections into a more manageable space. The latest methods for learning features on graphs focus mainly on the neighborhood of nodes and edges. Methods capable of providing a representation that looks beyond the single node neighborhood are kernel graphs. However, they produce handcrafted features unaccustomed with a generalized model. To reduce this gap, in this work we propose a neural embedding framework, based on probability distribution representations of graphs, named Netpro2vec . The goal is to look at basic node descriptions other than the degree, such as those induced by the Transition Matrix and Node Distance Distribution. Netpro2vec provides embeddings completely independent from the task and nature of the data. The framework is evaluated on synthetic and various real biomedical network datasets through a comprehensive experimental classification phase and is compared to well-known competitors.