Dissection of hubs and bottlenecks in a protein-protein interaction network

Leveraging pleiotropy for the improved treatment of psychiatric disorders

Over 90% of drug candidates fail in clinical trials, while it takes 10-15 years and one billion US dollars to develop a single successful drug. Drug development is more challenging for psychiatric disorders, where disease comorbidity and complex symptom profiles obscure the identification of causal mechanisms for therapeutic intervention. One promising approach for determining more suitable drug candidates in clinical trials is integrating human genetic data into the selection process. Genome-wide association studies have identified thousands of replicable risk loci for psychiatric disorders, and sophisticated statistical tools are increasingly effective at using these data to pinpoint likely causal genes. These studies have also uncovered shared or pleiotropic genetic risk factors underlying comorbid psychiatric disorders. In this article, we argue that leveraging pleiotropic effects will provide opportunities to discover novel drug targets and identify more effective treatments for psychiatric disorders by targeting a common mechanism rather than treating each disease separately.

Combined High-Throughput Proteomics and Random Forest Machine-Learning Approach Differentiates and Classifies Metabolic, Immune, Signaling and ECM Intra-Tumor Heterogeneity of Colorectal Cancer

Colorectal cancer (CRC) is a frequent, worldwide tumor described for its huge complexity, including inter-/intra-heterogeneity and tumor microenvironment (TME) variability. Intra-tumor heterogeneity and its connections with metabolic reprogramming and epithelial-mesenchymal transition (EMT) were investigated with explorative shotgun proteomics complemented by a Random Forest (RF) machine-learning approach. Deep and superficial tumor regions and distant-site non-tumor samples from the same patients (n = 16) were analyzed. Among the 2009 proteins analyzed, 91 proteins, including 23 novel potential CRC hallmarks, showed significant quantitative changes. In addition, a 98.4% accurate classification of the three analyzed tissues was obtained by RF using a set of 21 proteins. Subunit E1 of 2-oxoglutarate dehydrogenase (OGDH-E1) was the best classifying factor for the superficial tumor region, while sorting nexin-18 and coatomer-beta protein (beta-COP), implicated in protein trafficking, classified the deep region. Down- and up-regulations of metabolic checkpoints involved different proteins in superficial and deep tumors. Analogously to immune checkpoints affecting the TME, cytoskeleton and extracellular matrix (ECM) dynamics were crucial for EMT. Galectin-3, basigin, S100A9, and fibronectin involved in TME-CRC-ECM crosstalk were found to be differently variated in both tumor regions. Different metabolic strategies appeared to be adopted by the two CRC regions to uncouple the Krebs cycle and cytosolic glucose metabolism, promote lipogenesis, promote amino acid synthesis, down-regulate bioenergetics in mitochondria, and up-regulate oxidative stress. Finally, correlations with the Dukes stage and budding supported the finding of novel potential CRC hallmarks and therapeutic targets.

Network proteomics of the Lewy body dementia brain reveals presynaptic signatures distinct from Alzheimer's disease

Lewy body dementia (LBD), a class of disorders comprising Parkinson's disease dementia (PDD) and dementia with Lewy bodies (DLB), features substantial clinical and pathological overlap with Alzheimer's disease (AD). The identification of biomarkers unique to LBD pathophysiology could meaningfully advance its diagnosis, monitoring, and treatment. Using quantitative mass spectrometry (MS), we measured over 9,000 proteins across 138 dorsolateral prefrontal cortex (DLPFC) tissues from a University of Pennsylvania autopsy collection comprising control, Parkinson's disease (PD), PDD, and DLB diagnoses. We then analyzed co-expression network protein alterations in those with LBD, validated these disease signatures in two independent LBD datasets, and compared these findings to those observed in network analyses of AD cases. The LBD network revealed numerous groups or "modules" of co-expressed proteins significantly altered in PDD and DLB, representing synaptic, metabolic, and inflammatory pathophysiology. A comparison of validated LBD signatures to those of AD identified distinct differences between the two diseases. Notably, synuclein-associated presynaptic modules were elevated in LBD but decreased in AD relative to controls. We also found that glial-associated matrisome signatures consistently elevated in AD were more variably altered in LBD, ultimately stratifying those LBD cases with low versus high burdens of concurrent beta-amyloid deposition. In conclusion, unbiased network proteomic analysis revealed diverse pathophysiological changes in the LBD frontal cortex distinct from alterations in AD. These results highlight the LBD brain network proteome as a promising source of biomarkers that could enhance clinical recognition and management.

Efficacy Evaluation of Treatment of Psoriasis Via Narrow Band-Ultraviolet Radiation

Introduction: Psoriasis is a common autoimmune skin disease associated with genetically influenced chronic inflammation accompanied by remitting and deteriorating scaly skin. T-cell targeted biologics, IL-17 inhibitors, IL-12/IL-23 inhibitors, TNF-α inhibitors, PDE4 inhibitors, and ultraviolet (UV) radiation are applied to treat psoriasis. Efficacy evaluation of narrow band UVB (NB-UVB) radiation was the aim of this study. Methods: Data were extracted from Gene Expression Omnibus (GEO) and were pre-evaluated via the GEO2R program. The significant differentially expressed genes (DEGs) were included in the protein-protein interaction (PPI) network analysis. The hubs, bottlenecks, and hub-bottleneck DEGs were introduced as central genes. Activation, inhibition, and expression relationship between central genes were assessed to explore the critical individuals. Results: Among 513 analyzed significant DEGs, 22 hub-bottleneck genes were identified. Further analysis revealed that FN1, STAT3, HIF1A, IL1B, P4HB, SOD2, MMP2, and STAT1 were the crucial genes in psoriasis samples targeted by NB-UVB radiation. Conclusion: In conclusion, NB-UVB radiation as a treatment targets critical genes in peri-lesion skin tissue biopsy of psoriasis patients via a complicated mechanism. This therapeutic method downregulates STAT3, HIF1A, IL1B, and P4HB to treat psoriasis but downregulates STAT1 and SOD2 and upregulates MMP2 and FN1 to develop disease.

Apoplastomes of contrasting cacao genotypes to witches' broom disease reveals differential accumulation of PR proteins

Witches' broom disease (WBD) affects cocoa trees (Theobroma cacao L.) and is caused by the fungus Moniliophthora perniciosa that grows in the apoplast in its biotrophic phase and later progresses into the tissues, causing serious losses in the production of cocoa beans. Therefore, the apoplast of T. cacao can provide important defense responses during the interaction with M. perniciosa. In this work, the protein profile of the apoplast of the T. cacao genotypes Catongo, susceptible to WBD, and CCN-51, resistant one, was evaluated. The leaves of T. cacao were collected from asymptomatic plants grown in a greenhouse (GH) and from green witches' brooms grown under field (FD) conditions for extraction of apoplastic washing fluid (AWF). AWF was used in proteomic and enzymatic analysis. A total of 14 proteins were identified in Catongo GH and six in Catongo FD, with two proteins being common, one up-accumulated, and one down-accumulated. In CCN-51, 19 proteins were identified in the GH condition and 13 in FD, with seven proteins being common, one up-accumulated, and six down-accumulated. Most proteins are related to defense and stress in both genotypes, with emphasis on pathogenesis-related proteins (PR): PR-2 (β-1,3-glucanases), PR-3 and PR-4 (chitinases), PR-5 (thaumatine), PR-9 (peroxidases), and PR-14 (lipid transfer proteins). Furthermore, proteins from microorganisms were detected in the AWF. The enzymatic activities of PR-3 showed a significant increase (p < 0.05) in Catongo GH and PR-2 activity (p < 0.01) in CCN-51 FD. The protein profile of the T. cacao apoplastome offers insight into the defense dynamics that occur in the interaction with the fungus M. perniciosa and offers new insights in exploring future WBD control strategies.

A Tiny Viral Protein, SARS-CoV-2-ORF7b: Functional Molecular Mechanisms

This study presents the interaction with the human host metabolism of SARS-CoV-2 ORF7b protein (43 aa), using a protein-protein interaction network analysis. After pruning, we selected from BioGRID the 51 most significant proteins among 2753 proven interactions and 1708 interactors specific to ORF7b. We used these proteins as functional seeds, and we obtained a significant network of 551 nodes via STRING. We performed topological analysis and calculated topological distributions by Cytoscape. By following a hub-and-spoke network architectural model, we were able to identify seven proteins that ranked high as hubs and an additional seven as bottlenecks. Through this interaction model, we identified significant GO-processes (5057 terms in 15 categories) induced in human metabolism by ORF7b. We discovered high statistical significance processes of dysregulated molecular cell mechanisms caused by acting ORF7b. We detected disease-related human proteins and their involvement in metabolic roles, how they relate in a distorted way to signaling and/or functional systems, in particular intra- and inter-cellular signaling systems, and the molecular mechanisms that supervise programmed cell death, with mechanisms similar to that of cancer metastasis diffusion. A cluster analysis showed 10 compact and significant functional clusters, where two of them overlap in a Giant Connected Component core of 206 total nodes. These two clusters contain most of the high-rank nodes. ORF7b acts through these two clusters, inducing most of the metabolic dysregulation. We conducted a co-regulation and transcriptional analysis by hub and bottleneck proteins. This analysis allowed us to define the transcription factors and miRNAs that control the high-ranking proteins and the dysregulated processes within the limits of the poor knowledge that these sectors still impose.

Protein supersaturation powers innate immune signaling

Innate immunity protects us in youth but turns against us as we age. The reason for this tradeoff is unclear. Seeking a thermodynamic basis, we focused on death fold domains (DFDs), whose ordered polymerization has been stoichiometrically linked to innate immune signal amplification. We hypothesized that soluble ensembles of DFDs function as phase change batteries that store energy via supersaturation and subsequently release it through nucleated polymerization. Using imaging and FRET-based cytometry to characterize the phase behaviors of all 109 human DFDs, we found that the hubs of innate immune signaling networks encode large nucleation barriers that are intrinsically insulated from cross-pathway activation. We showed via optogenetics that supersaturation drives signal amplification and that the inflammasome is constitutively supersaturated in vivo. Our findings reveal that the soluble "inactive" states of adaptor DFDs function as essential, yet impermanent, kinetic barriers to inflammatory cell death, suggesting a thermodynamic driving force for aging.

Network Proteomics of the Lewy Body Dementia Brain Reveals Presynaptic Signatures Distinct from Alzheimer's Disease

Lewy body dementia (LBD), a class of disorders comprising Parkinson's disease dementia (PDD) and dementia with Lewy bodies (DLB), features substantial clinical and pathological overlap with Alzheimer's disease (AD). The identification of biomarkers unique to LBD pathophysiology could meaningfully advance its diagnosis, monitoring, and treatment. Using quantitative mass spectrometry (MS), we measured over 9,000 proteins across 138 dorsolateral prefrontal cortex (DLPFC) tissues from a University of Pennsylvania autopsy collection comprising control, Parkinson's disease (PD), PDD, and DLB diagnoses. We then analyzed co-expression network protein alterations in those with LBD, validated these disease signatures in two independent LBD datasets, and compared these findings to those observed in network analyses of AD cases. The LBD network revealed numerous groups or "modules" of co-expressed proteins significantly altered in PDD and DLB, representing synaptic, metabolic, and inflammatory pathophysiology. A comparison of validated LBD signatures to those of AD identified distinct differences between the two diseases. Notably, synuclein-associated presynaptic modules were elevated in LBD but decreased in AD relative to controls. We also found that glial-associated matrisome signatures consistently elevated in AD were more variably altered in LBD, ultimately stratifying those LBD cases with low versus high burdens of concurrent beta-amyloid deposition. In conclusion, unbiased network proteomic analysis revealed diverse pathophysiological changes in the LBD frontal cortex distinct from alterations in AD. These results highlight the LBD brain network proteome as a promising source of biomarkers that could enhance clinical recognition and management.

Hubs and Bottlenecks in Protein-Protein Interaction Networks

Protein-protein interaction networks (PPINs) represent the physical interactions among proteins in a cell. These interactions are critical in all cellular processes, including signal transduction, metabolic regulation, and gene expression. In PPINs, centrality measures are widely used to identify the most critical nodes. The two most commonly used centrality measures in networks are degree and betweenness centralities. Degree centrality is the number of connections a node has in the network, and betweenness centrality is the measure of the extent to which a node lies on the shortest paths between pairs of other nodes in the network. In PPINs, proteins with high degree and betweenness centrality are referred to as hubs and bottlenecks respectively. Hubs and bottlenecks are topologically and functionally essential proteins that play crucial roles in maintaining the network's structure and function. This article comprehensively reviews essential literature on hubs and bottlenecks, including their properties and functions.

Combined Salivary Proteome Profiling and Machine Learning Analysis Provides Insight into Molecular Signature for Autoimmune Liver Diseases Classification

Autoimmune hepatitis (AIH) and primary biliary cholangitis (PBC) are autoimmune liver diseases that target the liver and have a wide spectrum of presentation. A global overview of quantitative variations on the salivary proteome in presence of these two pathologies is investigated in this study. The acid-insoluble salivary fraction of AIH and PBC patients, and healthy controls (HCs), was analyzed using a gel-based bottom-up proteomic approach combined with a robust machine learning statistical analysis of the dataset. The abundance of Arginase, Junction plakoglobin, Desmoplakin, Hexokinase-3 and Desmocollin-1 decreased, while that of BPI fold-containing family A member 2 increased in AIHp compared to HCs; the abundance of Gelsolin, CD14, Tumor-associated calcium signal transducer 2, Clusterin, Heterogeneous nuclear ribonucleoproteins A2/B1, Cofilin-1 and BPI fold-containing family B member 2 increased in PBCp compared to HCs. The abundance of Hornerin decreased in both AIHp and PBCp with respect to HCs and provided an area under the ROC curve of 0.939. Machine learning analysis confirmed the feasibility of the salivary proteome to discriminate groups of subjects based on AIH or PBC occurrence as previously suggested by our group. The topology-based functional enrichment analysis performed on these potential salivary biomarkers highlights an enrichment of terms mostly related to the immune system, but also with a strong involvement in liver fibrosis process and with antimicrobial activity.