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Studying protein interaction networks of all proteins in an organism (“interactomes”) remains one of the major challenges in modern biomedicine. Such information is crucial to understanding cellular pathways and developing effective therapies for the treatment of human diseases. Over the past two decades, diverse biochemical, genetic, and cell biological methods have been developed to map interactomes. In this review, we highlight basic principles of interactome mapping. Specifically, we discuss the strengths and weaknesses of individual assays, how to select a method appropriate for the problem being studied, and provide general guidelines for carrying out the necessary follow‐up analyses. In addition, we discuss computational methods to predict, map, and visualize interactomes, and provide a summary of some of the most important interactome resources. We hope that this review serves as both a useful overview of the field and a guide to help more scientists actively employ these powerful approaches in their research. Graphical Abstract A practical guide to the fundamentals of protein interaction network mapping, providing information to help researchers make effective use of proteomics approaches. A range of new and well‐established experimental and computational methods and resources are covered.

To characterize how protein-protein interaction (PPI) networks change, we quantified the relative PPI abundance of 1.6 million protein pairs in the yeast Saccharomyces cerevisiae across nine growth conditions, with replication, for a total of 44 million measurements. Our multi-condition screen identified 13,764 pairwise PPIs, a threefold increase over PPIs identified in one condition. A few ‘immutable’ PPIs are present across all conditions, while most ‘mutable’ PPIs are rarely observed. Immutable PPIs aggregate into highly connected ‘core’ network modules, with most network remodeling occurring within a loosely connected ‘accessory’ module. Mutable PPIs are less likely to co-express, co-localize, and be explained by simple mass action kinetics, and more likely to contain proteins with intrinsically disordered regions, implying that environment-dependent association and binding is critical to cellular adaptation. Our results show that protein interactomes are larger than previously thought and contain highly dynamic regions that reorganize to drive or respond to cellular changes.

Mitochondria, the membrane-bound cell organelles that supply most of the energy needed for cell function, are highly regulated, dynamic organelles bearing the ability to alter both form and functionality rapidly to maintain normal physiological events and challenge stress to the cell. This amazingly vibrant movement and distribution of mitochondria within cells is controlled by the highly coordinated interplay between mitochondrial dynamic processes and fission and fusion events, as well as mitochondrial quality-control processes, mainly mitochondrial autophagy (also known as mitophagy). Fusion connects and unites neighboring depolarized mitochondria to derive a healthy and distinct mitochondrion. In contrast, fission segregates damaged mitochondria from intact and healthy counterparts and is followed by selective clearance of the damaged mitochondria via mitochondrial specific autophagy, i.e., mitophagy. Hence, the mitochondrial processes encompass all coordinated events of fusion, fission, mitophagy, and biogenesis for sustaining mitochondrial homeostasis. Accumulated evidence strongly suggests that mitochondrial impairment has already emerged as a core player in the pathogenesis, progression, and development of various human diseases, including cardiovascular ailments, the leading causes of death globally, which take an estimated 17.9 million lives each year. The crucial factor governing the fission process is the recruitment of dynamin-related protein 1 (Drp1), a GTPase that regulates mitochondrial fission, from the cytosol to the outer mitochondrial membrane in a guanosine triphosphate (GTP)-dependent manner, where it is oligomerized and self-assembles into spiral structures. In this review, we first aim to describe the structural elements, functionality, and regulatory mechanisms of the key mitochondrial fission protein, Drp1, and other mitochondrial fission adaptor proteins, including mitochondrial fission 1 (Fis1), mitochondrial fission factor (Mff), mitochondrial dynamics 49 (Mid49), and mitochondrial dynamics 51 (Mid51). The core area of the review focuses on the recent advances in understanding the role of the Drp1-mediated mitochondrial fission adaptor protein interactome to unravel the missing links of mitochondrial fission events. Lastly, we discuss the promising mitochondria-targeted therapeutic approaches that involve fission, as well as current evidence on Drp1-mediated fission protein interactions and their critical roles in the pathogeneses of cardiovascular diseases (CVDs).

Background Protein–protein interaction (PPI) prediction is an important task towards the understanding of many bioinformatics functions and applications, such as predicting protein functions, gene-disease associations and disease-drug associations. However, many previous PPI prediction researches do not consider missing and spurious interactions inherent in PPI networks. To address these two issues, we define two corresponding tasks, namely missing PPI prediction and spurious PPI prediction, and propose a method that employs graph embeddings that learn vector representations from constructed Gene Ontology Annotation (GOA) graphs and then use embedded vectors to achieve the two tasks. Our method leverages on information from both term–term relations among GO terms and term-protein annotations between GO terms and proteins, and preserves properties of both local and global structural information of the GO annotation graph. Results We compare our method with those methods that are based on information content (IC) and one method that is based on word embeddings, with experiments on three PPI datasets from STRING database. Experimental results demonstrate that our method is more effective than those compared methods. Conclusion Our experimental results demonstrate the effectiveness of using graph embeddings to learn vector representations from undirected GOA graphs for our defined missing and spurious PPI tasks.

Non-steroidal anti-inflammatory drugs (NSAIDs) are used commonly but can cause foregut symptoms, peptic ulcer disease and small bowel enteropathy. Such iatrogenic injury can be complicated by gastrointestinal bleeding and perforation. Limiting NSAID use or co-administration with proton pump inhibitors (PPIs) reduce dyspepsia, peptic ulcer disease and rates of complications. Selective cyclo-oxygenase (COX)-2 inhibitors are as effective as adding PPIs in preventing upper and lower gastrointestinal complications. COX-2 inhibitors are suggested in those with high cardiovascular risk and the addition of PPI in those with high risk of bleeding. Where required, COX-2 inhibitor monotherapy may be preferred in unexplained iron deficiency anaemia.

INTRODUCTION:In the last few years, numerous new potassium-competitive acid blocker (P-CAB)-based randomized controlled trials (RCTs) concerning the first-line regimens for Helicobacter pylori infection treatment from various countries have been published. However, no network meta-analysis (NWM) exists, which examines the comparative efficacy and safety of P-CAB-based dual, triple, and quadruple treatments, and, therefore, in this NWM, we examined this matter comparing efficacy and safety of these P-CAB-based regimens.METHODS:Databases were searched for identification, screening, eligibility, and inclusion of relevant RCTs. Extracted data were entered into a Bayesian NWM, and the ranking order for each regimen was evaluated by means of the surface under the cumulative ranking area values.RESULTS:Twenty-five eligible RCTs were included with 7,605 patients randomized to 6 first-line regimens, i.e. P-CAB dual therapy, P-CAB triple therapy, P-CAB quadruple therapy, PPI dual therapy, PPI triple therapy, and PPI quadruple therapy. The surface under the cumulative ranking area values (%) for these 6 regimens were 92.7, 62.5, 33.9, 75.1, 19.4, and 16.3, respectively. The comparative effectiveness ranking showed that P-CAB dual therapy regimen ranked first for efficacy and last for adverse effects and had the best profile for integrated efficacy-safety.DISCUSSION:In this NWM concerning the comparative efficacy and safety of P-CAB-based dual, triple, and quadruple regimens for the first-line H. pylori infection treatment, the overall results showed that P-CAB-based dual treatment ranked first for efficacy with the best-integrated efficacy-safety profile. This is of importance, since the dual regimens overcome the crucial issue of clarithromycin resistance. Consequently, these findings are expected to be useful in helping clinical decision making and future guidelines.

Over the last few decades, we have witnessed growing interest from both academic and industrial laboratories in peptides as possible therapeutics. Bioactive peptides have a high potential to treat various diseases with specificity and biological safety. Compared to small molecules, peptides represent better candidates as inhibitors (or general modulators) of key protein–protein interactions. In fact, undruggable proteins containing large and smooth surfaces can be more easily targeted with the conformational plasticity of peptides. The discovery of bioactive peptides, working against disease-relevant protein targets, generally requires the high-throughput screening of large libraries, and in silico approaches are highly exploited for their low-cost incidence and efficiency. The present review reports on the potential challenges linked to the employment of peptides as therapeutics and describes computational approaches, mainly structure-based virtual screening (SBVS), to support the identification of novel peptides for therapeutic implementations. Cutting-edge SBVS strategies are reviewed along with examples of applications focused on diverse classes of bioactive peptides (i.e., anticancer, antimicrobial/antiviral peptides, peptides blocking amyloid fiber formation).

Gastric cancer remains one of the leading cancers in the world with a high mortality, particularly in East Asia. Helicobacter pylori infection accounts for the majority of the noncardia gastric cancers by triggering gastric inflammation and subsequent neoplastic progression. Eradication of H. pylori can reduce, but not totally eliminate, subsequent risk of developing gastric cancer. Proton-pump inhibitors (PPIs) are one of the most widely prescribed medications worldwide. With their profound gastric-acid suppression, there are concerns about a possible carcinogenic role in gastric cancer, due to induced hypergastrinemia, gastric atrophy and bacterial overgrowth in the stomach. While randomized clinical trials to establish causality between long-term PPI use and gastric cancer are lacking, current evidence based on observational studies suggests PPIs are associated with an increased gastric cancer risk. However, opinions on causality remain divergent due to unmeasured and possible residual confounding in various studies. Our recent study has showed that even after H. pylori eradication, long-term PPI use is still associated with an increased risk of gastric cancer by more than twofold. Hence, long-term PPIs should be used judiciously after considering individual’s risk–benefit profile, particularly among those with history of H. pylori infection. Further well-designed prospective studies are warranted to confirm the potential role of PPIs in gastric cancer according to baseline gastric histology and its interaction with other chemopreventive agents like aspirin, statins and metformin.

Background: Vacuolar ATPase (V-ATPase) is involved in cancer development. The use of proton pump inhibitors (PPIs) as V-ATPase inhibitors has been reported to enhance the effectiveness of chemotherapy in certain cancers. This study aimed to evaluate the effect of PPIs on chemotherapy for esophageal cancer. Methods: To investigate the effects of PPIs on esophageal cancer cells, human KYSE50 and 70 cells were plated and 3 PPIs (lansoprazole, esomeprazole, vonoprazan) were added at various concentrations with 5-Fluorouracil (5-FU) to the corresponding cells for a cell viability assay. To investigate the effects of PPI treatment on patients undergoing 5-FU-based therapy in the clinical setting, we retrospectively analyzed the clinical outcomes and chemotherapy-related adverse events in 40 esophageal cancer patients who received 5-FU chemotherapy in our hospital between May 2013 and April 2017. Results: In the viability assays, all PPIs significantly enhanced the cytotoxic effect of 5-FU on the two esophageal cancer cell lines. In the clinical study, PPI-treated patients showed better overall survival (OS) than patients managed without PPI treatment. A multivariate analysis revealed that PPI treatment was independently associated with OS (p = 0.009, HR, 0.33; 95% CI, 0.12–0.76). Conclusions: PPI treatment may safely enhance chemosensitivity in esophageal cancer patients.