AI-guided pipeline for protein-protein interaction drug discovery identifies a SARS-CoV-2 inhibitor.

Protein-protein interactions (PPIs) offer great opportunities to expand the druggable proteome and therapeutically tackle various diseases, but remain challenging targets for drug discovery. Here, we provide a comprehensive pipeline that combines experimental and computational tools to identify and validate PPI targets and perform early-stage drug discovery. We have developed a machine learning approach that prioritizes interactions by analyzing quantitative data from binary PPI assays or AlphaFold-Multimer predictions. Using the quantitative assay LuTHy together with our machine learning algorithm, we identified high-confidence interactions among SARS-CoV-2 proteins for which we predicted three-dimensional structures using AlphaFold-Multimer. We employed VirtualFlow to target the contact interface of the NSP10-NSP16 SARS-CoV-2 methyltransferase complex by ultra-large virtual drug screening. Thereby, we identified a compound that binds to NSP10 and inhibits its interaction with NSP16, while also disrupting the methyltransferase activity of the complex, and SARS-CoV-2 replication. Overall, this pipeline will help to prioritize PPI targets to accelerate the discovery of early-stage drug candidates targeting protein complexes and pathways.

Signaling Transduction Pathways and G-Protein-Coupled Receptors in Different Stages of the Embryonic Diapause Termination Process in Artemia.

Artemia is a widely distributed small aquatic crustacean, renowned for its ability to enter a state of embryonic diapause. The embryonic diapause termination (EDT) is closely linked to environmental cues, but the precise underlying mechanisms remain elusive. In this study, ATAC-seq and RNA-seq sequencing techniques were employed to explore the gene expression profiles in Artemia cysts 30 min after EDT. These profiles were compared with those during diapause and 5 h after EDT. The regulatory mechanisms governing the EDT process were analyzed through Gene Ontology (GO) enrichment analysis of differentially expressed genes. Furthermore, the active G-protein-coupled receptors (GPCRs) were identified through structural analysis. The results unveiled that the signaling transduction during EDT primarily hinges on GPCRs and the cell surface receptor signaling pathway, but distinct genes are involved across different stages. Hormone-mediated signaling pathways and the tachykinin receptor signaling pathway exhibited heightened activity in the '0-30 min' group, whereas the Wnt signaling pathway manifested its function solely in the '30 min-5 h' group. These results imply a complete divergence in the mechanisms of signal regulation during these two stages. Moreover, through structural analysis, five GPCRs operating at different stages of EDT were identified. These findings provide valuable insights into the signal regulation mechanisms governing Artemia diapause.

Reconstruction of Eriocheir sinensis Protein-Protein Interaction Network Based on DGO-SVM Method.

Eriocheir sinensis is an economically important aquatic animal. Its regulatory mechanisms underlying many biological processes are still vague due to the lack of systematic analysis tools. The protein-protein interaction network (PIN) is an important tool for the systematic analysis of regulatory mechanisms. In this work, a novel machine learning method, DGO-SVM, was applied to predict the protein-protein interaction (PPI) in E. sinensis, and its PIN was reconstructed. With the domain, biological process, molecular functions and subcellular locations of proteins as the features, DGO-SVM showed excellent performance in Bombyx mori, humans and five aquatic crustaceans, with 92-96% accuracy. With DGO-SVM, the PIN of E. sinensis was reconstructed, containing 14,703 proteins and 7,243,597 interactions, in which 35,604 interactions were associated with 566 novel proteins mainly involved in the response to exogenous stimuli, cellular macromolecular metabolism and regulation. The DGO-SVM demonstrated that the biological process, molecular functions and subcellular locations of proteins are significant factors for the precise prediction of PPIs. We reconstructed the largest PIN for E. sinensis, which provides a systematic tool for the regulatory mechanism analysis. Furthermore, the novel-protein-related PPIs in the PIN may provide important clues for the mechanism analysis of the underlying specific physiological processes in E. sinensis.

Correlation between d Electrons and the Sweet Spot for the Hydrogen Evolution Reaction: Is Platinum Always the Best Electrocatalyst?

Herein, two Laves intermetallic series, ZrCo1.75M0.25 and NbCo1.75M0.25 (M = Fe, Co, Ni, Ru, Rh, Pd, Os, Ir, and Pt), were synthesized, and their hydrogen evolution reaction (HER) activities were examined to reveal the influence of d electrons to the corresponding HER activities. Owing to the different electronegativity between Zr and Nb (χZr = 1.33; χNb = 1.60), Co and/or M elements receive more electrons in ZrCo1.75M0.25 than that of the Nb one. This leads to the overall weak H adsorption energy (ΔGHad) of ZrCo1.75M0.25 series compared to that of NbCo1.75M0.25 and rationalizes well the superior HER activity of the Rh member compared to that of the Pt one in the ZrCo1.75M0.25 series. Under industrial conditions (333 K, 6.0 M KOH), ZrCo1.75Rh0.25 only requires an overpotential of 110 mV to reach the current density of 500 mA/cm2 and can be operated at high current density over 400 h. This work demonstrates that with a proper combination between elements in intermetallic phases, one can manipulate d electrons of the active metal to be closer to the sweet spot (ΔGHad = 0). The Pt member may no longer exhibit the best HER activity in series, and all elements exhibit the potential to outperform the Pt member in the HER with careful control of the d electron population.

Microscopic Mechanism for Further NO Heterogeneous Reduction by Potassium-Doped Biochar: A DFT Study.

Biomass reburning is an efficient and low-cost way to control nitric oxide (NO), and the abundant potassium (K) element in biomass affects the heterogeneous reaction between NO and biochar. Due to the incomplete simulation of the NO heterogeneous reduction reaction pathway at the molecular level and the unclear catalytic effect of K element in biochar, further research is needed on the possible next reaction and the influencing mechanism of the element. After the products of the existing reaction pathways are referenced, two reasonably simplified biochar structural models are selected as the basic reactants to study the microscopic mechanism for further NO heterogeneous reduction on the biochar surface before and after doping with the K atom based on density functional theory. In studying the two further NO heterogeneous reduction reaction pathways, we find that the carbon monoxide (CO) molecule fragment protrudes from the surface of biochar models with the desorption of N2 at the TS4 transition state, and the two edge types of biochar product models obtained by simulation calculation are Klein edge and ac56 edge observed in the experiment. In studying the catalytic effect of potassium in biochar, we find that the presence of K increases the heat release of adsorption of NO molecules, reduces the energy barrier of the rate-determining step in the nitrogen (N2) generation and desorption process (by 50.88 and 69.97%), and hinders the CO molecule from desorbing from the biochar model surface. Thermodynamic and kinetic analyses also confirm its influence. The study proves that the heterogeneous reduction reaction of four NO molecules on the surface of biochar completes the whole reaction process and provides a basic theoretical basis for the emission of nitrogen oxides (NOx) during biomass reburning.

Potential protective effects of sodium butyrate on glycinin-induced oxidative stress, inflammatory response, and growth inhibition in Cyprinus carpio.

Investigated mitigating effects of sodium butyrate (SB) on the inflammatory response, oxidative stress, and growth inhibition of common carp (Cyprinus carpio) (2.94 ± 0.2 g) are caused by glycinin. Six isonitrogenous and isoenergetic diets were prepared, in which the basal diet was the control diet and the Gly group diet contained 80 g/kg glycinin, while the remaining 4 diets were supplemented with 0.75, 1.50, 2.25, and 3.00 g/kg SB, respectively. The feeding trial lasted for 8 weeks, and the results indicated that supplementing the diet with 1.50-2.25 g/kg of SB significantly improved feed efficiency and alleviated the growth inhibition induced by glycinin. Hepatopancreas and intestinal protease activities and the content of muscle crude protein were significantly decreased by dietary glycinin, but supplement 1.50-2.25 g/kg SB partially reversed this result. SB (1.50-2.25 g/kg) increased the activities of aspartate aminotransferase (AST) and alanine aminotransferase (ALT) in the hepatopancreas and reduced the activities of AST and ALT in the serum. Glycinin significantly reduced immune and antioxidant enzyme activities, whereas 1.50-2.25 g/kg SB reversed these adverse effects. Furthermore, compared with the Gly group, supplement 1.50-2.25 g/kg SB eminently up-regulated the TGF-ß and IL-10 mRNA, and down-regulated the IL-1ß, TNF-α, and NF-κB mRNA in hepatopancreas, mid-intestine (MI), and distal intestine (DI). Meanwhile, supplement 1.50-2.25 g/kg SB activated the Keap1-Nrf2-ARE signaling pathway and upregulate CAT, SOD, and HO-1 mRNA expression in hepatopancreas, MI, and DI. Summarily, glycinin induced inflammatory response, and oxidative stress of common carp ultimately decreased the digestive function and growth performance. SB partially mitigated these adverse effects by activating the Keap1-Nrf2-ARE signaling pathway and inhibiting the NF-κB signaling pathway.

Reconstruction of Metabolic-Protein Interaction Integrated Network of Eriocheir sinensis and Analysis of Ecdysone Synthesis.

Integrated networks have become a new interest in genome-scale network research due to their ability to comprehensively reflect and analyze the molecular processes in cells. Currently, none of the integrated networks have been reported for higher organisms. Eriocheir sinensis is a typical aquatic animal that grows through ecdysis. Ecdysone has been identified to be a crucial regulator of ecdysis, but the influence factors and regulatory mechanisms of ecdysone synthesis in E. sinensis are still unclear. In this work, the genome-scale metabolic network and protein-protein interaction network of E. sinensis were integrated to reconstruct a metabolic-protein interaction integrated network (MPIN). The MPIN was used to analyze the influence factors of ecdysone synthesis through flux variation analysis. In total, 236 integrated reactions (IRs) were found to influence the ecdysone synthesis of which 16 IRs had a significant impact. These IRs constitute three ecdysone synthesis routes. It is found that there might be alternative pathways to obtain cholesterol for ecdysone synthesis in E. sinensis instead of absorbing it directly from the feeds. The MPIN reconstructed in this work is the first integrated network for higher organisms. The analysis based on the MPIN supplies important information for the mechanism analysis of ecdysone synthesis in E. sinensis.

Efficacy and safety of Yangxue Qingnao Granules in treatment of migraine: A systematic review and meta-analysis.

BACKGROUND: Yangxue Qingnao Granules (YXQN) is a Chinese patent medicine that has been commonly used in the clinical treatment of migraine. AIM: To assess the efficacy and safety of YXQN alone for the treatment of migraine. METHODS: We searched 10 databases to identify relevant randomized controlled trials (RCTs) published before September 2022. Two review authors independently searched and screened the literature, extracted the data, and assessed the methodological quality of the studies using criteria from ROB 2.0, and analyzed the data using Review Manager 5.4 software. RESULTS: A total of 12 RCTs including 767 participants with migraine met the selection criteria. We divided these studies into comparisons of YXQN with placebo, routine treatment drugs, and other Chinese patent medicines. The meta-analysis showed the following: (1) Efficacy: The YXQN group outperformed the placebo group [relative risk (RR) = 0.29, 95% confidence interval (95%CI): 0.15-0.43, P < 0.00001], routine treatment group (RR = 0.18, 95%CI: 0.09-0.27, P < 0.0001), and Chinese patent medicine group (RR = 0.27, 95%CI: 0.13-0.41, P < 0.001); (2) frequency of headache: There was a significant difference between YXQN vs placebo [mean difference (MD) = -1.25, 95%CI: -1.60 to -0.90, P < 0.00001], routine treatment drugs (MD = -0.85, 95%CI: -1.15 to -0.56, P < 0.00001), and Chinese patent medicine (MD = -0.91, 95%CI: -1.35 to -0.46, P < 0.0001); (3) headache duration: We found great heterogeneity between studies, with no differences between YXQN and placebo (MD = -0.61, 95%CI: -1.53 to -0.31, P = 0.19) and routine treatment drugs (MD = -0.22, 95%CI: -0.89 to 0.46, P < 0.53). YXQN was more effective than other Chinese patent medicines in reducing headache duration (MD = -1.24, 95%CI: -1.70 to -0.77, P < 0.00001); and (4) headache severity: There was no significant difference between YXQN vs placebo (MD = -1.67, 95%CI: -3.52 to 0.19, P = 0.08), routine treatment drugs (MD = -0.53, 95%CI: -2.02 to 0.96, P = 0.68), and other Chinese patent medicines (MD = -0.49, 95%CI: -2.83 to 1.85, P = 0.68). Mild gastrointestinal adverse reactions were reported in three cases. CONCLUSION: This study revealed that YXQN is effective and safe for treatment of migraine.

Functional and Structural Abnormalities in the Pain Network of Generalized Anxiety Disorder Patients with Pain Symptoms.

Pain symptoms significantly impact the well-being and work capacity of individuals with generalized anxiety disorder (GAD), and hinder treatment and recovery. Despite existing literature focusing on the neural substrate of pain and anxiety separately, further exploration is needed to understand the possible neuroimaging mechanisms of the pain symptoms in GAD patients. We recruited 73 GAD patients and 75 matched healthy controls (HC) for clinical assessments, as well as resting-state functional and structural magnetic resonance imaging scans. We defined a pain-related network through a published meta-analysis, including the insula, thalamus, periaqueductal gray, prefrontal cortex, anterior cingulate cortex, amygdala, and hippocampus. Subsequently, we conducted the regional homogeneity (ReHo) and the gray matter volume (GMV) within the pain-related network. Correlation analysis was then employed to explore associations between abnormal regions and self-reported outcomes, assessed using the Patient Health Questionnaire-15 (PHQ-15) and pain scores. We observed significantly increased ReHo in the bilateral insula but decreased GMV in the bilateral thalamus of GAD compared to HC. Further correlation analysis revealed a positive correlation between ReHo of the left anterior insula and pain scores in GAD patients, while a respective negative correlation between GMV of the bilateral thalamus and PHQ-15 scores. In summary, GAD patients exhibit structural and functional abnormalities in pain-related networks. The enhanced ReHo in the left anterior insula is correlated with pain symptoms, which might be a crucial brain region of pain symptoms in GAD.

Development and validation of a deep interpretable network for continuous acute kidney injury prediction in critically ill patients.

Early detection of acute kidney injury (AKI) may provide a crucial window of opportunity to prevent further injury, which helps improve clinical outcomes. This study aimed to develop a deep interpretable network for continuously predicting the 24-hour AKI risk in real-time and evaluate its performance internally and externally in critically ill patients. A total of 21,163 patients' electronic health records sourced from Beth Israel Deaconess Medical Center (BIDMC) were first included in building the model. Two external validation populations included 3025 patients from the Philips eICU Research Institute and 2625 patients from Zhongda Hospital Southeast University. A total of 152 intelligently engineered predictors were extracted on an hourly basis. The prediction model referred to as DeepAKI was designed with the basic framework of squeeze-and-excitation networks with dilated causal convolution embedded. The integrated gradients method was utilized to explain the prediction model. When performed on the internal validation set (3175 [15 %] patients from BIDMC) and the two external validation sets, DeepAKI obtained the area under the curve of 0.799 (95 % CI 0.791-0.806), 0.763 (95 % CI 0.755-0.771) and 0.676 (95 % CI 0.668-0.684) for continuousAKI prediction, respectively. For model interpretability, clinically relevant important variables contributing to the model prediction were informed, and individual explanations along the timeline were explored to show how AKI risk arose. The potential threats to generalisability in deep learning-based models when deployed across health systems in real-world settings were analyzed.

Dynamic decline of lymphocytes predicts extracorporeal membrane oxygenation-related infections: a retrospective observational study.

Background: Limited data are available regarding the current microbiological characteristics of extracorporeal membrane oxygenation (ECMO)-related infections in intensive care units (ICUs) in China. This retrospective study aimed to determine the epidemiology, risk factors and impact on the outcome of ECMO-related infections. Methods: A retrospective observational study from January 2014 to December 2019 was performed, and adult patients receiving ECMO support for more than 48 hours were included in this study. The primary outcome was the incidence rate of ECMO-related infection. Clinical data were recorded, and risk factors associated with an increased risk of ECMO-related infection were analyzed. Results: A total of 174 adult patients who received ECMO and underwent ECMO for 1,670 days were included in this study. Forty-six patients (26.4%) developed ECMO-related infections, corresponding to 27.5 first episodes/1,000 ECMO days. The most common ECMO-related infection observed was ventilator-associated pneumonia (VAP). Infected patients had longer durations of mechanical ventilation {20.2 [interquartile range (IQR), 12.6, 30.7] vs. 9.0 (IQR, 5.8, 14.7) days, P<0.001}, ECMO support [11.6 (IQR, 8.1, 17.3) vs. 7.6 (IQR, 5.6, 9.7) days, P<0.001] and hospital stays (28.2±20.7 vs. 22.0±15.6 days, P<0.001). The factors independently associated with ECMO-related infection were a dynamic decrease in lymphocyte count [adjusted odds ratio (OR) =3.578, 95% confidence interval (CI): 2.175-4.906, P<0.001] and ECMO duration (adjusted OR =1.207, 95% CI: 1.096-1.330, P<0.001). Compared to patients without infection, infected patients had greater hospital mortality (39.1% vs. 78.3%, P<0.001) and 90-day mortality (40.6% vs. 87.0%, P<0.001). ECMO-related infections were associated with worse outcomes (adjusted Kaplan-Meier curve, log rank test P<0.001). Conclusions: Patients supported by ECMO had a high risk of developing ECMO-related infection. The most common ECMO-related infection observed was VAP. A dynamic decrease in lymphocyte counts was significantly associated with an increased risk of ECMO-related infection.

Widespread variation in molecular interactions and regulatory properties among transcription factor isoforms.

Most human Transcription factors (TFs) genes encode multiple protein isoforms differing in DNA binding domains, effector domains, or other protein regions. The global extent to which this results in functional differences between isoforms remains unknown. Here, we systematically compared 693 isoforms of 246 TF genes, assessing DNA binding, protein binding, transcriptional activation, subcellular localization, and condensate formation. Relative to reference isoforms, two-thirds of alternative TF isoforms exhibit differences in one or more molecular activities, which often could not be predicted from sequence. We observed two primary categories of alternative TF isoforms: "rewirers" and "negative regulators", both of which were associated with differentiation and cancer. Our results support a model wherein the relative expression levels of, and interactions involving, TF isoforms add an understudied layer of complexity to gene regulatory networks, demonstrating the importance of isoform-aware characterization of TF functions and providing a rich resource for further studies.

Lactobacillus rhamnosus GG ameliorates hyperuricemia in a novel model.

Hyperuricemia (HUA) is a metabolic syndrome caused by abnormal purine metabolism. Although recent studies have noted a relationship between the gut microbiota and gout, whether the microbiota could ameliorate HUA-associated systemic purine metabolism remains unclear. In this study, we constructed a novel model of HUA in geese and investigated the mechanism by which Lactobacillus rhamnosus GG (LGG) could have beneficial effects on HUA. The administration of antibiotics and fecal microbiota transplantation (FMT) experiments were used in this HUA goose model. The effects of LGG and its metabolites on HUA were evaluated in vivo and in vitro. Heterogeneous expression and gene knockout of LGG revealed the mechanism of LGG. Multi-omics analysis revealed that the Lactobacillus genus is associated with changes in purine metabolism in HUA. This study showed that LGG and its metabolites could alleviate HUA through the gut-liver-kidney axis. Whole-genome analysis, heterogeneous expression, and gene knockout of LGG enzymes ABC-type multidrug transport system (ABCT), inosine-uridine nucleoside N-ribohydrolase (iunH), and xanthine permease (pbuX) demonstrated the function of nucleoside degradation in LGG. Multi-omics and a correlation analysis in HUA patients and this goose model revealed that a serum proline deficiency, as well as changes in Collinsella and Lactobacillus, may be associated with the occurrence of HUA. Our findings demonstrated the potential of a goose model of diet-induced HUA, and LGG and proline could be promising therapies for HUA.