Improvement strategy of citrate and biochar assisted nano-palladium/iron composite for effective dechlorination of 2,4-dichlorophenol.

Rapid passivation and aggregation of nanoscale zero-valent iron (nZVI) seriously limit its performance in the remediation of different contaminants from wastewater. To overcome such issues, in the present study, nano-palladium/iron (nPd/Fe) was simultaneously improved by biochar (BC) prepared from discarded peanut shells and green complexing agent sodium citrate (SC). For this purpose, a composite (SC-nPd/Fe@BC) was successfully synthesized to remove 2,4-dichlorophenol (2,4-DCP) from wastewater. In the SC-nPd/Fe@BC, BC acts as a carrier with dispersed nPd/Fe particles to effectively prevent its agglomeration, and increased the specific surface area of the composite, thereby improving the reactivity and stability of nPd/Fe. Characterization results demonstrated that the SC-nPd/Fe@BC composites were well dispersed, and the agglomeration was weakened. The formation of the passivation layer on the surface of the particles was inhibited, and the mechanism of SC and BC improving the reactivity of nPd/Fe was clarified. Different factors were found to influence the reductive dichlorination of 2,4-DCP, including Pd loading, Fe:C, SC addition, temperature, initial pH, and initial pollutant concentration. The dechlorination results revealed that the synergistic effect of the BC and SC made the removal efficiency and dechlorination rate of 2,4-DCP by SC-nPd/Fe@BC reached to 96.0 and 95.6%, respectively, which was better than that of nPd/Fe (removal: 46.2%, dechlorination: 45.3%). Kinetic studies explained that the dechlorination reaction of 2,4-DCP and the data were better represented by the pseudo-first-order kinetic model. The reaction rate constants followed the order of SC-nPd/Fe@BC (0.0264 min-1) > nPd/Fe@BC (0.0089 min-1) > SC-nPd/Fe (0.0081 min-1) > nPd/Fe (0.0043 min-1). Thus, SC-nPd/Fe@BC was capable of efficiently reducing 2,4-DCP and the dechlorination efficiency of BC and SC synergistically assisted composite on 2,4-DCP was much better than that of SC-nPd/Fe, nPd/Fe@BC and nPd/Fe. Findings suggested that SC-nPd/Fe@BC can be promising for efficient treatment of chlorinated pollutants.

Utility of the Morgan Fingerprint in Structure-Based Virtual Ligand Screening.

In modern drug discovery, virtual ligand screening (VLS) is frequently applied to identify possible hits before experimental testing and refinement due to its cost-effective nature for large compound libraries. For decades, efforts have been devoted to developing VLS methods with high accuracy. These include the state-of-the-art FINDSITE suite of approaches FINDSITEcomb2.0, FRAGSITE, and FRAGSITE2 and the meta version FRAGSITEcomb that were developed in our lab. These methods combine ligand homology modeling (LHM), traditional ligand similarity methods, and more recently machine learning approaches to rank ligands and have proven to be superior to most recent deep learning and large language model-based approaches. Here, we describe further improvements to our previous best methods by combining the Morgan fingerprint (MF) with the originally used PubChem fingerprint and FP2 fingerprint. We then benchmarked FINDSITEcomb2.0M, FRAGSITEM, FRAGSITE2M, and the composite meta-approach FRAGSITEcombM. On the 102 target DUD-E set, the 1% enrichment factor (EF1%) and area under the precision-recall curve (AUPR) of FRAGSITEcomb increased from 42.0/0.59 to 47.6/0.72. This 0.72 AUPR is significantly better than that of the state-of-the-art deep learning-based method DenseFS's AUPR of 0.443. An independent test on the 81 targets DEKOIS2.0 set shows that EF1%/AUPR increases from 18.3/0.520 to 23.1/0.683. An ablation investigation shows that the MF contributes to most of the improvement of all four approaches. Thus, the MF is a useful addition to structure-based VLS.

AGPAT3 Deficiency Impairs Adipocyte Differentiation and Leads to a Lean Phenotype in Mice.

Acylglycerophosphate acyltransferases (AGPATs) catalyze the de novo formation of phosphatidic acid to synthesize glycerophospholipids and triglycerides. AGPATs demonstrate unique physiological roles despite a similar biochemical function. AGPAT3 is highly expressed in the testis, kidney, and liver, with intermediate expression in adipose tissue. Loss of Agpat3 is associated with reproductive abnormalities and visual dysfunction. However, the role of AGPAT3 in adipose tissue and whole-body metabolism has not been investigated. We found that male Agpat3-KO mice exhibited reduced body weights with decreased white and brown adipose tissue mass. Such changes were less pronounced in the female Agpat3-KO mice. Agpat3-KO mice have reduced plasma insulin growth factor 1 (IGF1) and insulin levels and diminished circulating lipid metabolites. They manifested intact glucose homeostasis and insulin sensitivity despite a lean phenotype. Agpat3-KO mice maintained an energy balance with normal food intake, energy expenditure, and physical activity, except for increased water intake. Their adaptive thermogenesis was also normal despite reduced brown adipose mass and triglyceride content. Mechanistically, Agpat3 was elevated during mouse and human adipogenesis and enriched in adipocytes. Agpat3-knockdown 3T3-L1 cells and Agpat3-deficient mouse embryonic fibroblasts (MEFs) have impaired adipogenesis in vitro. Interestingly, pioglitazone treatment rescued the adipogenic deficiency in Agpat3 deficient cells. We conclude that AGPAT3 regulates adipogenesis and adipose development. It is possible that adipogenic impairment in Agpat3 deficient cells potentially leads to reduced adipose mass. Findings from this work support the unique role of AGPAT3 in adipose tissue.

Association between monocyte-to-lymphocyte ratio and prostate cancer in the U.S. population: a population-based study.

Introduction: Monocyte-to-lymphocyte ratio (MLR) is a convenient and noninvasive inflammatory biomarker, and inflammation has been reported to be associated with prostate cancer (PCa). Our objective was to ascertain any possible correlation between PCa and MLR. Methods: We utilized data from the 1999-2020 cycles of the National Health and Nutrition Examination Survey (NHANES) regarding MLR and PCa. The independent associations of MLR and other inflammatory biomarkers (platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), system inflammation response index (SIRI), and aggregate index of systemic inflammation (AISI)) with PCa was investigated using weighted multivariate logistic regression and generalized additive models. Receiver operating characteristic (ROC) curves were conducted to evaluate and contrast their diagnostic capabilities. Results: The analysis we conducted comprised 25,367 persons in total. The mean MLR was 0.31 ± 0.14. The prevalence of PCa was 3.1%. A positive association was found between MLR and PCa (OR = 2.28; 95% CI: 1.44, 3.62). According to the interaction tests, age, body mass index (BMI), hypertension, diabetes, and smoking status did not significantly impact the relationship between MLR and PCa (all p for interaction >0.05). ROC analysis showed that MLR had a stronger discriminative ability and accuracy in predicting PCa than other inflammatory biomarkers (NLR, SII, AISI, PLR, and SIRI). Conclusion: MLR might be better than other inflammatory biomarkers (NLR, SIRI, AISI, PLR, and SII) in predicting PCa. American adults who have elevated levels of MLR, NLR, PLR, SII, and AISI should be aware that they have a greater risk of PCa.

circPVT1 Inhibits the Proliferation and Aids in Prediction of the Prognosis of Bladder Cancer.

Background: Circular RNA PVT1 (circPVT1) is aberrantly expressed in several cancers, but its functional role and clinical relevance in bladder urothelial carcinoma (BLCA) remain unknown. This study aimed to identify the expression level of circPVT1 in BLCA and investigated its functional relevance with BLCA progression both in vitro and in vivo. Methods: GEPIA, UALCAN, and OncoLnc were referred to presented data. Quantitative real-time PCR (qPCR) was used for the measurement of transnational expression of genes in BLCA specimens and cell lines. Immunohistochemistry (IHC) and fluorescence in situ hybridization analysis (FISH) assays were performed to detect HER2 amplification, Pearson's correlation analysis to analyze the correlation between circPVT1 expression and clinical characteristics, Cox regression and K-M survival analyses to analyze prognostic factors. A nomogram was constructed for predicting prognosis. The proliferation of cells was measured by CCK-8 and colony formation assay, and the proliferation in vivo was evaluated using nude mouse models. qPCR was used to detect the expression of proliferation-related genes. Results: circPVT1 was but mRNA PVT1 was not significantly overexpressed in BLCA. A high circPVT1 expression was associated with a better survival and negative HER2, but not with age, gender, and T stage. circPVT1 was an independent prognostic factor for the overall survival of BLCA patients. Knocking down circPVT1 promoted BLCA proliferation in vitro and in vivo. Knocking down circPVT1 upregulated ERBB2, MKI67, and PCNA expression and downregulated TP53 expression, but exerted no influence on CCND1 and CCNB1 expression. Conclusion: circPVT1 is a tumor suppressor and novel prognostic biomarker for BLCA.

FRAGSITE2: A structure and fragment-based approach for virtual ligand screening.

Protein function annotation and drug discovery often involve finding small molecule binders. In the early stages of drug discovery, virtual ligand screening (VLS) is frequently applied to identify possible hits before experimental testing. While our recent ligand homology modeling (LHM)-machine learning VLS method FRAGSITE outperformed approaches that combined traditional docking to generate protein-ligand poses and deep learning scoring functions to rank ligands, a more robust approach that could identify a more diverse set of binding ligands is needed. Here, we describe FRAGSITE2 that shows significant improvement on protein targets lacking known small molecule binders and no confident LHM identified template ligands when benchmarked on two commonly used VLS datasets: For both the DUD-E set and DEKOIS2.0 set and ligands having a Tanimoto coefficient (TC) < 0.7 to the template ligands, the 1% enrichment factor (EF1% ) of FRAGSITE2 is significantly better than those for FINDSITEcomb2.0 , an earlier LHM algorithm. For the DUD-E set, FRAGSITE2 also shows better ROC enrichment factor and AUPR (area under the precision-recall curve) than the deep learning DenseFS scoring function. Comparison with the RF-score-VS on the 76 target subset of DEKOIS2.0 and a TC < 0.99 to training DUD-E ligands, FRAGSITE2 has double the EF1% . Its boosted tree regression method provides for more robust performance than a deep learning multiple layer perceptron method. When compared with the pretrained language model for protein target features, FRAGSITE2 also shows much better performance. Thus, FRAGSITE2 is a promising approach that can discover novel hits for protein targets. FRAGSITE2's web service is freely available to academic users at http://sites.gatech.edu/cssb/FRAGSITE2.

Targeted suppression of mTORC2 reduces seizures across models of epilepsy.

Epilepsy is a neurological disorder that poses a major threat to public health. Hyperactivation of mTOR complex 1 (mTORC1) is believed to lead to abnormal network rhythmicity associated with epilepsy, and its inhibition is proposed to provide some therapeutic benefit. However, mTOR complex 2 (mTORC2) is also activated in the epileptic brain, and little is known about its role in seizures. Here we discover that genetic deletion of mTORC2 from forebrain neurons is protective against kainic acid-induced behavioral and EEG seizures. Furthermore, inhibition of mTORC2 with a specific antisense oligonucleotide robustly suppresses seizures in several pharmacological and genetic mouse models of epilepsy. Finally, we identify a target of mTORC2, Nav1.2, which has been implicated in epilepsy and neuronal excitability. Our findings, which are generalizable to several models of human seizures, raise the possibility that inhibition of mTORC2 may serve as a broader therapeutic strategy against epilepsy.

Atovaquone and Pibrentasvir Inhibit the SARS-CoV-2 Endoribonuclease and Restrict Infection In Vitro but Not In Vivo.

The emergence of SARS-CoV-1 in 2003 followed by MERS-CoV and now SARS-CoV-2 has proven the latent threat these viruses pose to humanity. While the SARS-CoV-2 pandemic has shifted to a stage of endemicity, the threat of new coronaviruses emerging from animal reservoirs remains. To address this issue, the global community must develop small molecule drugs targeting highly conserved structures in the coronavirus proteome. Here, we characterized existing drugs for their ability to inhibit the endoribonuclease activity of the SARS-CoV-2 non-structural protein 15 (nsp15) via in silico, in vitro, and in vivo techniques. We have identified nsp15 inhibition by the drugs pibrentasvir and atovaquone which effectively inhibit SARS-CoV-2 and HCoV-OC43 at low micromolar concentrations in cell cultures. Furthermore, atovaquone, but not pibrentasvir, is observed to modulate HCoV-OC43 dsRNA and infection in a manner consistent with nsp15 inhibition. Although neither pibrentasvir nor atovaquone translate to clinical efficacy in a murine prophylaxis model of SARS-CoV-2 infection, atovaquone may serve as a basis for the design of future nsp15 inhibitors.

Association between weight-adjusted-waist index and chronic kidney disease: a cross-sectional study.

AIMS: We aimed to investigate the potential association between weight-adjusted-waist index (WWI) and chronic kidney disease (CKD). DESIGN AND METHODS: This research examined data collected from the National Health and Nutrition Examination Survey (NHANES) spanning from 1999 to 2020. CKD was defined as the low estimated glomerular filtration rate (eGFR) or the existence of albuminuria (urinary albumin-to-creatinine ratio (ACR) ≥ 30mg/g). Low-eGFR was described as eGFR < 60 mL/min/1.73m2. The associations between WWI with CKD, albuminuria, and low-eGFR were examined using generalized additive models and weighted multivariable logistic regression models. We also analyzed the associations of other obesity indicators with CKD, albuminuria, and low-eGFR, including body mass index (BMI), waist-to-height ratio (WHtR), waist circumference(WC), height, and weight. The receiver operating characteristic (ROC) curves were used to assess and compare their diagnostic abilities. RESULTS: Males made up 48.26% of the total 40,421 individuals that were recruited. The prevalences of CKD, albuminuria, and low-eGFR were 16.71%, 10.97%, and 7.63%, respectively. WWI was found to be positively linked with CKD (OR = 1.42; 95% CI: 1.26, 1.60). A nonlinear connection between WWI and CKD was found using smooth curve fitting. Additionally, a higher prevalence of albuminuria is linked to a higher level of WWI (OR = 1.60; 95% CI: 1.40, 1.82). Different stratifications did not substantially influence the connection between WWI and CKD, albuminuria, and low-eGFR, according to subgroup analysis and interaction tests. We observed higher height was related to higher low-eGFR prevalence (OR = 1.05; 95% CI: 1.03, 1.06). ROC analysis revealed that WWI had the best discrimination and accuracy for predicting CKD and albuminuria compared to other obesity indicators (BMI, WHTR, WC, height and weight). In addition, height had the highest area under the curve (AUC) value for predicting low-eGFR. CONCLUSION: WWI is the best obesity indicator to predict CKD and albuminuria compared to other obesity indicators (BMI, WHTR, WC, height, and weight). WWI and CKD and albuminuria were found to be positively correlated. Furthermore, height had the strongest ability to predict low-eGFR. Therefore, the importance of WWI and height in assessing kidney health in US adults should be emphasized.

Crosstalk of disulfidptosis-related subtypes, establishment of a prognostic signature and immune infiltration characteristics in bladder cancer based on a machine learning survival framework.

Background: Bladder cancer (BLCA) is the most common malignancy of the urinary tract. On the other hand, disulfidptosis, a mechanism of disulfide stress-induced cell death, is closely associated with tumorigenesis and progression. Here, we investigated the impact of disulfidptosis-related genes (DRGs) on the prognosis of BLCA, identified various DRG clusters, and developed a risk model to assess patient prognosis, immunological profile, and treatment response. Methods: The expression and mutational characteristics of four DRGs were first analyzed in bulk RNA-Seq and single-cell RNA sequencing data, IHC staining identified the role of DRGs in BLCA progression, and two DRG clusters were identified by consensus clustering. Using the differentially expressed genes (DEGs) from these two clusters, we transformed ten machine learning algorithms into more than 80 combinations and finally selected the best algorithm to construct a disulfidptosis-related prognostic signature (DRPS). We based this selection on the mean C-index of three BLCA cohorts. Furthermore, we explored the differences in clinical characteristics, mutational landscape, immune cell infiltration, and predicted efficacy of immunotherapy between high and low-risk groups. To visually depict the clinical value of DRPS, we employed nomograms. Additionally, we verified whether DRPS predicts response to immunotherapy in BLCA patients by utilizing the Tumour Immune Dysfunction and Rejection (TIDE) and IMvigor 210 cohorts. Results: In the integrated cohort, we identified several DRG clusters and DRG gene clusters that differed significantly in overall survival (OS) and tumor microenvironment. After the integration of clinicopathological features, DRPS showed robust predictive power. Based on the median risk score associated with disulfidptosis, BLCA patients were divided into low-risk (LR) and high-risk (HR) groups, with patients in the LR group having a better prognosis, a higher tumor mutational load and being more sensitive to immunotherapy and chemotherapy. Conclusion: Our study, therefore, provides a valuable tool to further guide clinical management and tailor the treatment of BLCA patients, offering new insights into individualized treatment.

USP7 Inhibits Osteoclastogenesis via Dual Effects of Attenuating TRAF6/TAK1 Axis and Stimulating STING Signaling.

Ubiquitination is a reversible post-translational modification implicated in cell differentiation, homeostasis, and organ development. Several deubiquitinases (DUBs) decrease protein ubiquitination through the hydrolysis of ubiquitin linkages. However, the role of DUBs in bone resorption and formation is still unclear. In this study, we identified DUB ubiquitin-specific protease 7 (USP7) as a negative regulator of osteoclast formation. USP7 combines with tumor necrosis factor receptor-associated factor 6 (TRAF6) and inhibits its ubiquitination by impairing the Lys63-linked polyubiquitin chain. Such impairment leads to the suppression of receptor activator of NF-κB ligand (RANKL)-mediated nuclear factor-κB (NF-κB) and mitogen-activated protein kinases (MAPKs) activation without affecting TRAF6 stability. USP7 also protects the stimulator of interferon genes (STING) against degradation, inducing interferon-ß (IFN-ß) expression in osteoclast formation, thereby inhibiting osteoclastogenesis cooperatively with the classical TRAF6 pathway. Furthermore, USP7 inhibition accelerates osteoclast differentiation and bone resorption both in vitro and in vivo. Contrarily, USP7 overexpression impairs osteoclast differentiation and bone resorption in vitro and in vivo. Additionally, in ovariectomy (OVX) mice, USP7 levels are lower than those in sham-operated mice, suggesting that USP7 plays a role in osteoporosis. Altogether, our data reveal the dual effect of USP7-mediated TRAF6 signal transduction and USP7-mediated protein degradation of STING in osteoclast formation.

Metabolite asymmetric dimethylarginine (ADMA) functions as a destabilization enhancer of SOX9 mediated by DDAH1 in osteoarthritis.

Osteoarthritis (OA) is a degenerative disease with a series of metabolic changes accompanied by many altered enzymes. Here, we report that the down-regulated dimethylarginine dimethylaminohydrolase-1 (DDAH1) is accompanied by increased asymmetric dimethylarginine (ADMA) in degenerated chondrocytes and in OA samples. Global or chondrocyte-conditional knockout of ADMA hydrolase DDAH1 accelerated OA development in mice. ADMA induces the degeneration and senescence of chondrocytes and reduces the extracellular matrix deposition, thereby accelerating OA progression. ADMA simultaneously binds to SOX9 and its deubiquitinating enzyme USP7, blocking the deubiquitination effects of USP7 on SOX9 and therefore leads to SOX9 degradation. The ADMA level in synovial fluids of patients with OA is increased and has predictive value for OA diagnosis with good sensitivity and specificity. Therefore, activating DDAH1 to reduce ADMA level might be a potential therapeutic strategy for OA treatment.

Neddylation is required for perinatal cardiac development through stimulation of metabolic maturation.

Cardiac maturation is crucial for postnatal cardiac development and is increasingly known to be regulated by a series of transcription factors. However, post-translational mechanisms regulating this process remain unclear. Here we report the indispensable role of neddylation in cardiac maturation. Mosaic deletion of NAE1, an essential enzyme for neddylation, in neonatal hearts results in the rapid development of cardiomyopathy and heart failure. NAE1 deficiency disrupts transverse tubule formation, inhibits physiological hypertrophy, and represses fetal-to-adult isoform switching, thus culminating in cardiomyocyte immaturation. Mechanistically, we find that neddylation is needed for the perinatal metabolic transition from glycolytic to oxidative metabolism in cardiomyocytes. Further, we show that HIF1α is a putative neddylation target and that inhibition of neddylation accumulates HIF1α and impairs fatty acid utilization and bioenergetics in cardiomyocytes. Together, our data show neddylation is required for cardiomyocyte maturation through promoting oxidative metabolism in the developing heart.

Association between neutrophil-to-lymphocyte ratio and diabetic kidney disease in type 2 diabetes mellitus patients: a cross-sectional study.

Aims: This investigation examined the possibility of a relationship between neutrophil-to-lymphocyte ratio (NLR) and diabetic kidney disease (DKD) in type 2 diabetes mellitus (T2DM) patients. Methods: Adults with T2DM who were included in the National Health and Nutrition Examination Survey (NHANES) between 1999 and 2020 were the subjects of the current cross-sectional investigation. Low estimated glomerular filtration rate (eGFR) (< 60 mL/min/1.73 m2) or albuminuria (urinary albumin-to-creatinine ratio (ACR) ≥ 30 mg/g) in T2DM patients were the diagnostic criteria for DKD. Weighted multivariable logistic regression models and generalized additive models were used to investigate the independent relationships between NLR levels with DKD, albuminuria, and low-eGFR. Additionally, we examined the relationships between DKD, albuminuria, and low-eGFR with other inflammatory markers, such as the aggregate index of systemic inflammation (AISI), systemic immune-inflammation index (SII), system inflammation response index (SIRI), and platelet-to-lymphocyte ratio (PLR) and monocyte-to-lymphocyte ratio (MLR). Their diagnostic capabilities were evaluated and contrasted using receiver operating characteristic (ROC) curves. Results: 44.65% of the 7,153 participants who were recruited for this study were males. DKD, albuminuria, and low-eGFR were prevalent in 31.76%, 23.08%, and 14.55% of cases, respectively. Positive correlations were seen between the NLR with the prevalences of DKD, albuminuria, and low-eGFR. Subgroup analysis and interaction tests revealed that the associations of NLR with DKD, albuminuria, and low-eGFR were not significantly different across populations. In addition, MLR, SII and SIRI showed positive associations with the prevalence of DKD. ROC analysis discovered that when compared to other inflammatory markers (MLR, PLR, SII, SIRI, and AISI), NLR may demonstrate more discriminatory power and accuracy in assessing the risk of DKD, albuminuria, and low-eGFR. Conclusion: Compared to other inflammatory markers (MLR, PLR, SII, SIRI, and AISI), NLR may serve as the more effective potential inflammatory marker for identifying the risk of DKD, albuminuria, and low-eGFR in US T2DM patients. T2DM patients with elevated levels of NLR, MLR, SII, and SIRI should be closely monitored for their potential risk to renal function.

PHEVIR: an artificial intelligence algorithm that predicts the molecular role of pathogens in complex human diseases.

Infectious diseases are known to cause a wide variety of post-infection complications. However, it's been challenging to identify which diseases are most associated with a given pathogen infection. Using the recently developed LeMeDISCO approach that predicts comorbid diseases associated with a given set of putative mode of action (MOA) proteins and pathogen-human protein interactomes, we developed PHEVIR, an algorithm which predicts the corresponding human disease comorbidities of 312 viruses and 57 bacteria. These predictions provide an understanding of the molecular bases of complications and means of identifying appropriate drug targets to treat them. As an illustration of its power, PHEVIR is applied to identify putative driver pathogens and corresponding human MOA proteins for Type 2 diabetes, atherosclerosis, Alzheimer's disease, and inflammatory bowel disease. Additionally, we explore the origins of the oncogenicity/oncolyticity of certain pathogens and the relationship between heart disease and influenza. The full PHEVIR database is available at https://sites.gatech.edu/cssb/phevir/ .

Hepatic neddylation deficiency triggers fatal liver injury via inducing NF-κB-inducing kinase in mice.

The conjugation of neural precursor cell expressed, developmentally downregulated 8 (NEDD8) to target proteins, termed neddylation, participates in many cellular processes and is aberrant in various pathological diseases. Its relevance to liver function and failure remains poorly understood. Herein, we show dysregulated expression of NAE1, a regulatory subunit of the only NEDD8 E1 enzyme, in human acute liver failure. Embryonic- and adult-onset deletion of NAE1 in hepatocytes causes hepatocyte death, inflammation, and fibrosis, culminating in fatal liver injury in mice. Hepatic neddylation deficiency triggers oxidative stress, mitochondrial dysfunction, and hepatocyte reprogramming, potentiating liver injury. Importantly, NF-κB-inducing kinase (NIK), a serine/Thr kinase, is a neddylation substrate. Neddylation of NIK promotes its ubiquitination and degradation. Inhibition of neddylation conversely causes aberrant NIK activation, accentuating hepatocyte damage and inflammation. Administration of N-acetylcysteine, a glutathione surrogate and antioxidant, mitigates liver failure caused by hepatic NAE1 deletion in adult male mice. Therefore, hepatic neddylation is important in maintaining postnatal and adult liver homeostasis, and the identified neddylation targets/pathways provide insights into therapeutically intervening acute liver failure.

LeMeDISCO is a computational method for large-scale prediction & molecular interpretation of disease comorbidity.

To understand the origin of disease comorbidity and to identify the essential proteins and pathways underlying comorbid diseases, we developed LeMeDISCO (Large-Scale Molecular Interpretation of Disease Comorbidity), an algorithm that predicts disease comorbidities from shared mode of action proteins predicted by the artificial intelligence-based MEDICASCY algorithm. LeMeDISCO was applied to predict the occurrence of comorbid diseases for 3608 distinct diseases. Benchmarking shows that LeMeDISCO has much better comorbidity recall than the two molecular methods XD-score (44.5% vs. 6.4%) and the SAB score (68.6% vs. 8.0%). Its performance is somewhat comparable to the phenotype method-based Symptom Similarity Score, 63.7% vs. 100%, but LeMeDISCO works for far more cases and its large comorbidity recall is attributed to shared proteins that can help provide an understanding of the molecular mechanism(s) underlying disease comorbidity. The LeMeDISCO web server is available for academic users at: http://sites.gatech.edu/cssb/LeMeDISCO .

Implications of the Essential Role of Small Molecule Ligand Binding Pockets in Protein-Protein Interactions.

Protein-protein interactions (PPIs) and protein-metabolite interactions play a key role in many biochemical processes, yet they are often viewed as being independent. However, the fact that small molecule drugs have been successful in inhibiting PPIs suggests a deeper relationship between protein pockets that bind small molecules and PPIs. We demonstrate that 2/3 of PPI interfaces, including antibody-epitope interfaces, contain at least one significant small molecule ligand binding pocket. In a representative library of 50 distinct protein-protein interactions involving hundreds of mutations, >75% of hot spot residues overlap with small molecule ligand binding pockets. Hence, ligand binding pockets play an essential role in PPIs. In representative cases, evolutionary unrelated monomers that are involved in different multimeric interactions yet share the same pocket are predicted to bind the same metabolites/drugs; these results are confirmed by examples in the PDB. Thus, the binding of a metabolite can shift the equilibrium between monomers and multimers. This implicit coupling of PPI equilibria, termed "metabolic entanglement", was successfully employed to suggest novel functional relationships among protein multimers that do not directly interact. Thus, the current work provides an approach to unify metabolomics and protein interactomics.

Mode-pairing quantum key distribution.

Quantum key distribution - the establishment of information-theoretically secure keys based on quantum physics - is mainly limited by its practical performance, which is characterised by the dependence of the key rate on the channel transmittance R(η). Recently, schemes based on single-photon interference have been proposed to improve the key rate to [Formula: see text] by overcoming the point-to-point secret key capacity bound with interferometers. Unfortunately, all of these schemes require challenging global phase locking to realise a stable long-arm single-photon interferometer with a precision of approximately 100 nm over fibres that are hundreds of kilometres long. Aiming to address this problem, we propose a mode-pairing measurement-device-independent quantum key distribution scheme in which the encoded key bits and bases are determined during data post-processing. Using conventional second-order interference, this scheme can achieve a key rate of [Formula: see text] without global phase locking when the local phase fluctuation is mild. We expect this high-performance scheme to be ready-to-implement with off-the-shelf optical devices.

Identification of Critical Biomarkers and Immune Infiltration in Rheumatoid Arthritis Based on WGCNA and LASSO Algorithm.

Rheumatoid arthritis(RA) is the most common inflammatory arthritis, and a significant cause of morbidity and mortality. RA patients' synovial inflammation contains a variety of genes and signalling pathways that are poorly understood. It was the goal of this research to discover the major biomarkers related to the course of RA and how they connect to immune cell infiltration. The Gene Expression Omnibus was used to download gene microarray data. Differential expression analysis, weighted gene co-expression network analysis (WGCNA), and least absolute shrinkage and selection operator (LASSO) regression were used to identify hub markers for RA. Single-sample GSEA was used to examine the infiltration levels of 28 immune cells and their connection to hub gene markers. The hub genes' expression in RA-HFLS and HFLS cells was verified by RT-PCR. The CCK-8 assay was applied to determine the roles of hub genes in RA. In this study, we identified 21 differentially expressed genes (DEGs) in RA. WGCNA yielded two co-expression modules, one of which exhibited the strongest connection with RA. Using a combination of differential genes, a total of 6 intersecting genes was discovered. Six hub genes were identified as possible biomarkers for RA after a lasso analysis was performed on the data. Three hub genes, CKS2, CSTA, and LY96, were found to have high diagnostic value using ROC curve analysis. They were shown to be closely related to the concentrations of several immune cells. RT-PCR confirmed that the expressions of CKS2, CSTA and LY96 were distinctly upregulated in RA-HFLS cells compared with HFLS cells. More importantly, knockdown of CKS2 suppressed the proliferation of RA-HFLS cells. Overall, to help diagnose and treat RA, it's expected that CKS2, CSTA, and LY96 will be available, and the aforementioned infiltration of immune cells may have a significant impact on the onset and progression of the disease.