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Abstract Background Biological processes are controlled by groups of genes acting in concert. Investigating gene–gene interactions within different cell types can help researchers understand the regulatory mechanisms behind human complex diseases, such as tumors. Methods We collected extensive single-cell RNA-seq data from tumors, involving 563 patients with 44 different tumor types. Through our analysis, we identified various cell types in tumors and created an atlas of different immune cell subsets across different tumor types. Using the SCINET method, we reconstructed interactome networks specific to different cell types. Diverse functional data was then integrated to gain biological insights into the networks, including somatic mutation patterns and gene functional annotation. Additionally, genes with prognostic relevance within the networks were also identified. We also examined cell–cell communications to investigate how gene interactions modulate cell–cell interactions. Results We developed a data portal called CellNetdb for researchers to study cell-type-specific interactome networks. Our findings indicate that these networks can be used to identify genes with topological specificity in different cell types. We also found that prognostic genes can deconvolved into cell types through analyzing network connectivity. Additionally, we identified commonalities and differences in cell-type-specific networks across different tumor types. Our results suggest that these networks can be used to prioritize risk genes. Conclusions This study presented CellNetdb, a comprehensive repository featuring an atlas of cell-type-specific interactome networks across 44 human tumor types. The findings underscore the utility of these networks in delineating the intricacies of tumor microenvironments and advancing the understanding of molecular mechanisms underpinning human tumors.

Plant reproduction is a fundamental process on Earth from the perspective of biodiversity, biomass gain, and crop productivity. It is therefore important to understand the sex determination process, and many researchers are investigating the molecular basis of this phenomenon. However, information on the influence of transcription factors (TFs), genes that encode DNA-binding proteins, on this process is limited, although cucumber is a model plant in this regard. In the present study, based on RNA-seq data for differentially expressed genes (DEGs), we aimed to investigate the regulatory TFs that may influence the metabolic processes in the shoot apex containing the forming flower buds. Therefore, the annotation of the genome of the B10 cucumber line was supplemented with the assigned families of transcription factors. By performing ontology analyses of the DEGs, the processes they participate in were identified, and TFs were located among the results. In addition, TFs that have significantly overrepresented targets among DEGs were detected, and sex-specific interactome network maps were generated, indicating the regulatory TFs based on their effects on DEGs and furthermore, on the processes leading to the formation of different-sex flowers. Among the most overrepresented TF families in the sex comparisons were the NAC, bHLH, MYB, and bZIP families. An interaction network analysis indicated the most abundant families among DEGs' regulatory TFs were MYB, AP2/ERF, NAC, and bZIP, and those with the most significant impact on developmental processes were identified, namely the AP/ERF family, followed by DOF, MYB, MADS, and others. Thus, the networks' central nodes and key regulators were identified with respect to male, female, and hermaphrodite forms. Here, we proposed the first model of the regulatory network of TFs that influences the metabolism of sex development in cucumber. These findings may help us to understand the molecular genetics and functional mechanisms underlying sex determination processes. [ABSTRACT FROM AUTHOR]

Plant reproduction is a fundamental process on Earth from the perspective of biodiversity, biomass gain, and crop productivity. It is therefore important to understand the sex determination process, and many researchers are investigating the molecular basis of this phenomenon. However, information on the influence of transcription factors (TFs), genes that encode DNA-binding proteins, on this process is limited, although cucumber is a model plant in this regard. In the present study, based on RNA-seq data for differentially expressed genes (DEGs), we aimed to investigate the regulatory TFs that may influence the metabolic processes in the shoot apex containing the forming flower buds. Therefore, the annotation of the genome of the B10 cucumber line was supplemented with the assigned families of transcription factors. By performing ontology analyses of the DEGs, the processes they participate in were identified, and TFs were located among the results. In addition, TFs that have significantly overrepresented targets among DEGs were detected, and sex-specific interactome network maps were generated, indicating the regulatory TFs based on their effects on DEGs and furthermore, on the processes leading to the formation of different-sex flowers. Among the most overrepresented TF families in the sex comparisons were the NAC, bHLH, MYB, and bZIP families. An interaction network analysis indicated the most abundant families among DEGs’ regulatory TFs were MYB, AP2/ERF, NAC, and bZIP, and those with the most significant impact on developmental processes were identified, namely the AP/ERF family, followed by DOF, MYB, MADS, and others. Thus, the networks’ central nodes and key regulators were identified with respect to male, female, and hermaphrodite forms. Here, we proposed the first model of the regulatory network of TFs that influences the metabolism of sex development in cucumber. These findings may help us to understand the molecular genetics and functional mechanisms underlying sex determination processes.

In the development and treatment of many human diseases, the regulatory roles between lncRNAs and miRNAs are important, but much remains unknown about them; moreover, experimental methods for analyzing them are expensive and time-consuming. In this work, we applied a semi-supervised interactome network-based approach to explore and forecast the latent interaction between lncRNAs and miRNAs. We constructed graphs according to the similarity of each of lncRNAs and miRNAs and determined the number of graphlet interaction isomers between nodes in these two graphs. According to the two graphs and the known interactive relationship, we calculated a score for lncRNA–miRNA pairs, as the prediction result. The results showed that the model (LMI-INGI) was reliable in fivefold cross-validation (AUC = 0.8957, PRE = 0.6815, REC = 0.8842, F1 score = 0.7452, AUPR = 0.9213). We also tested the model with data based on the similarity of expression profile and similarity of function for verifying the applicability of LMI-INGI, and the resulting AUC value was 0.9197 and 0.9006, respectively. Compared with the other four algorithms and variable similarity tests, our model successfully demonstrated superiority and good generalizability. LMI-INGI would be helpful in forecasting interactions between lncRNAs and miRNAs. • The computational model made use of semi-supervised interactome network-based approach. • The AUC of 5-fold cross validation (5-fold CV) were significantly better than other previous computational methods. • The similarity of expression profile and similarity of function for verifying the applicability of LMI-INGI were tested. • LMI-INGI would be helpful in forecasting interactions between lncRNAs and miRNAs. [ABSTRACT FROM AUTHOR]

Proteins are essential macromolecules of life that carry out most cellular processes. Since proteins aggregate to perform function, and since protein-protein interaction (PPI) networks model these aggregations, one would expect to uncover new biology from PPI network topology. Hence, using PPI networks to predict protein function and role of protein pathways in disease has received attention. A debate remains open about whether network properties of “biologically central (BC)” genes (i.e., their protein products), such as those involved in aging, cancer, infectious diseases, or signaling and drug-targeted pathways, exhibit some topological centrality compared to the rest of the proteins in the human PPI network.To help resolve this debate, we design new network-based approaches and apply them to get new insight into biological function and disease. We hypothesize that BC genes have a topologically central (TC) role in the human PPI network. We propose two different concepts of topological centrality. We design a new centrality measure to capture complex wirings of proteins in the network that identifies as TC those proteins that reside in dense extended network neighborhoods. Also, we use the notion of domination and find dominating sets (DSs) in the PPI network, i.e., sets of proteins such that every protein is either in the DS or is a neighbor of the DS. Clearly, a DS has a TC role, as it enables efficient communication between different network parts.We find statistically significant enrichment in BC genes of TC nodes and outperform the existing methods indicating that genes involved in key biological processes occupy topologically complex and dense regions of the network and correspond to its “spine” that connects all other network parts and can thus pass cellular signals efficiently throughout the network. To our knowledge, this is the first study that explores domination in the context of PPI networks.

Mitogen-activated protein kinase kinase kinases (MAPKKKs) are pivotal components of MAPK cascades. Pyropia yezoensis, belonging to Rhodophyta, is an important macroalga growing in the intertidal zone of temperate Japan and China. At present, little is known about MAPKKKs and their potential regulatory mechanisms in response to environmental stressors in red algae, particularly with regard to how this gene family retained its ancient characteristics. In this study, we identified 33 MAPKKK genes in P. yezoensis by bioinformatics analysis. Phylogenetic analysis classified them into Raf, MEKK and ZIK groups, which contained conserved kinase motif with minor variations. The number of intron of all PyMAPKKK is few, up to 3. Regulatory elements prediction indicated that the cis-acting elements included many motifs involved in the stress response. Furthermore, expression analysis of PyMAPKKKs under different stress conditions revealed that numerous PyMAPKKK genes were involved in various signaling pathways with different expression patterns. Additionally, 16 PyMAPKKK genes were validated their expression patterns by Realtime-PCR analysis. An interactome network of PyMAPKKK was constructed to show that most of the interacting proteins were serine/threonine protein kinases, MAPKs, and ubiquitin-binding proteins. Our study provided helpful information for understanding the evolution and function of the MAPKKK family in P. yezoensis.

AtNHR2A (Arabidopsis thaliana nonhost resistance 2A) and AtNHR2B (Arabidopsis thaliana nonhost resistance 2B) are two proteins that participate in nonhost resistance, a broad-spectrum mechanism of plant immunity that protects plants against the majority of potential pathogens. AtNHR2A and AtNHR2B are localized to the cytoplasm, chloroplasts, and other subcellular compartments of unknown identity. The multiple localizations of AtNHR2A and AtNHR2B suggest that these two proteins are highly dynamic and versatile, likely participating in multiple biological processes. In spite of their importance, the specific functions of AtNHR2A and AtNHR2B have not been elucidated. Thus, to aid in the functional characterization of these two proteins and identify the biological processes in which these proteins operate, we used immunoprecipitation coupled with mass spectrometry (IP-MS) to identify proteins interacting with AtNHR2A and AtNHR2B and to generate their interactome network. Further validation of three of the identified proteins provided new insights into specific pathways and processes related to plant immunity where AtNHR2A and AtNHR2B participate. Moreover, the comprehensive analysis of the AtNHR2A- and AtNHR2B-interacting proteins using published empirical information revealed that the functions of AtNHR2A and AtNHR2B are not limited to plant immunity but encompass other biological processes.

AtNHR2A (Arabidopsis thaliana nonhost resistance 2A) and AtNHR2B (Arabidopsis thaliana nonhost resistance 2B) are two proteins that participate in nonhost resistance, a broad-spectrum mechanism of plant immunity that protects plants against the majority of potential pathogens. AtNHR2A and AtNHR2B are localized to the cytoplasm, chloroplasts, and other subcellular compartments of unknown identity. The multiple localizations of AtNHR2A and AtNHR2B suggest that these two proteins are highly dynamic and versatile, likely participating in multiple biological processes. In spite of their importance, the specific functions of AtNHR2A and AtNHR2B have not been elucidated. Thus, to aid in the functional characterization of these two proteins and identify the biological processes in which these proteins operate, we used immunoprecipitation coupled with mass spectrometry (IP-MS) to identify proteins interacting with AtNHR2A and AtNHR2B and to generate their interactome network. Further validation of three of the identified proteins provided new insights into specific pathways and processes related to plant immunity where AtNHR2A and AtNHR2B participate. Moreover, the comprehensive analysis of the AtNHR2A- and AtNHR2B-interacting proteins using published empirical information revealed that the functions of AtNHR2A and AtNHR2B are not limited to plant immunity but encompass other biological processes. [ABSTRACT FROM AUTHOR]

Many notions and concepts for network analysis, including the shortest path approach, came to systems biology from the theory of graphs — the field of mathematics that studies graphs. We studied the relationship between the shortest paths and a biologically meaningful molecular path between vertices in human molecular interaction networks. We analyzed the sets of the shortest paths in the human interactome derived from HPRD and HIPPIE databases between all possible combinations of start and end proteins in eight signaling pathways in the KEGG database — NF-kappa B, MAPK, Jak-STAT, mTOR, ErbB, Wnt, TGF-beta, and the signaling part of the apoptotic process. We investigated whether the shortest paths match the canonical paths. We studied whether centrality of vertices and paths in the subnetworks induced by the shortest paths can highlight vertices and paths that are part of meaningful molecular paths. We found that the shortest paths match canonical counterparts only for canonical paths of length 2 or 3 interactions. The shortest paths match longer canonical counterparts with shortcuts or substitutions by protein complex members. We found that high centrality vertices are part of the canonical paths for up to 80% of the canonical paths depending on the database and the length. [ABSTRACT FROM AUTHOR]

When a set of genes are identified to be related to a disease, say through gene expression analysis, it is common to examine the average distance among their protein products in the human interactome as a measure of biological relatedness of these genes. The reasoning for this is that, genes associated with a disease would tend to be functionally related, and that functionally related genes would be closely connected to each other in the interactome. Typically, average shortest path length (ASPL) of disease genes (although referred to as genes in the context of disease-associations, the interactions are among protein-products of these genes) is compared to ASPL of randomly selected genes or to ASPL in a randomly permuted network. We examined whether the ASPL of a set of genes is indeed a good measure of biological relatedness or whether it is simply a characteristic of the degree distribution of those genes. We examined the ASPL of genes sets of some disease and pathway associations and compared them to ASPL of three types of randomly selected control sets: uniform selection, from entire proteome, degree-matched selection, and random permutation of the network. We found that disease associated genes and their degree-matched random genes have comparable ASPL. In other words, ASPL is a characteristic of the degree of the genes and the network topology, and not that of functional coherence. [ABSTRACT FROM AUTHOR]

Because of its adaptability to high-throughput approaches and a low operating cost, the yeast two-hybrid (Y2H) assay remains the most widely used one for high-throughput protein-protein interactions (PPI) mapping experiments. Here we provide a detailed protocol for a liquid culture-based high-throughput binary protein-protein Y2H screen pipeline of a pool of 50 proteins used as baits against a collection of ~12,000 Arabidopsis proteins encoded by sequence-verified open reading frames (ORFs)., (© 2023. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Abstract: The idea that multi-scale dynamic complex systems formed by interacting macromolecules and metabolites, cells, organs and organisms underlie some of the most fundamental aspects of life was proposed by a few visionaries half a century ago. We are witnessing a powerful resurgence of this idea made possible by the availability of nearly complete genome sequences, ever improving gene annotations and interactome network maps, the development of sophisticated informatic and imaging tools, and importantly, the use of engineering and physics concepts such as control and graph theory. Alongside four other fundamental “great ideas” as suggested by Sir Paul Nurse, namely, the gene, the cell, the role of chemistry in biological processes, and evolution by natural selection, systems-level understanding of “What is Life” may materialize as one of the major ideas of biology. [Copyright &y& Elsevier]

• Inferred microbial interactome networks in anaerobic NA-degrading consortia. • Sulfate-reducing networks were more complexity than nitrate-reducing networks. • Differences between microbial networks can be ascribed to electron acceptors. • Predicted potential syntrophic associations in anaerobic NA-degrading consortia. Naphthenic acids (NAs) can be syntrophically metabolized by indigenous microbial communities in pristine sediments beneath oil sands tailings ponds. Syntrophy is an essential determinant of the microbial interactome, however, the interactome network in anaerobic NAs-degrading consortia has not been previously addressed due to complexity and resistance of NAs. To evaluate the impact of electron acceptors on topology of interactome networks, we inferred two microbial interactome networks for anaerobic NAs-degrading consortia under nitrate- and sulfate-reducing conditions. The complexity of the network was higher under sulfate-reducing conditions than nitrate-reducing conditions. Differences in the taxonomic composition between the two modules implies that different potential syntrophic interactions exist in each network. We inferred the presence of the same syntrophic microorganisms, from genera Bellilinea, Longilinea, and Litorilinea , initiating the metabolism in both networks, but within each network, we predicted unique syntrophic associations that have not been reported. Electron acceptor has a large effect on the interactome networks for anaerobic NAs-degrading consortia, offers insight into an unrecognized dimension of these consortia. These results provide a novel approach for exploring potential syntrophic relationships in biodegrading processes to help cost-effectively remove NAs in oil sands tailings ponds. [ABSTRACT FROM AUTHOR]

Comprehensive identification of direct, physical interactions between biological macromolecules, such as protein-protein, protein-DNA, and protein-RNA interactions, is critical for our understanding of the function of gene products as well as the global organization and interworkings of various molecular machines within the cell. The accurate and comprehensive detection of direct interactions, however, remains a huge challenge due to the inherent structural complexity arising from various post-transcriptional and translational modifications coupled with huge heterogeneity in concentration, affinity, and subcellular location differences existing for any interacting molecules. This has created a need for developing multiple orthogonal and complementary assays for detecting various types of biological interactions. In this introduction, we discuss the methods developed for measuring different types of molecular interactions with an emphasis on direct protein-protein interactions, critical issues for generating high-quality interactome datasets, and the insights into biological networks and human diseases that current interaction mapping efforts provide. Further, we will discuss what future might lie ahead for the continued evolution of two-hybrid methods and the role of interactomics for expanding the advancement of biomedical science.