Multiscale topology in interactomic network: from transcriptome to antiaddiction drug repurposing.

The escalating drug addiction crisis in the United States underscores the urgent need for innovative therapeutic strategies. This study embarked on an innovative and rigorous strategy to unearth potential drug repurposing candidates for opioid and cocaine addiction treatment, bridging the gap between transcriptomic data analysis and drug discovery. We initiated our approach by conducting differential gene expression analysis on addiction-related transcriptomic data to identify key genes. We propose a novel topological differentiation to identify key genes from a protein-protein interaction network derived from DEGs. This method utilizes persistent Laplacians to accurately single out pivotal nodes within the network, conducting this analysis in a multiscale manner to ensure high reliability. Through rigorous literature validation, pathway analysis and data-availability scrutiny, we identified three pivotal molecular targets, mTOR, mGluR5 and NMDAR, for drug repurposing from DrugBank. We crafted machine learning models employing two natural language processing (NLP)-based embeddings and a traditional 2D fingerprint, which demonstrated robust predictive ability in gauging binding affinities of DrugBank compounds to selected targets. Furthermore, we elucidated the interactions of promising drugs with the targets and evaluated their drug-likeness. This study delineates a multi-faceted and comprehensive analytical framework, amalgamating bioinformatics, topological data analysis and machine learning, for drug repurposing in addiction treatment, setting the stage for subsequent experimental validation. The versatility of the methods we developed allows for applications across a range of diseases and transcriptomic datasets.

CovalentInDB 2.0: an updated comprehensive database for structure-based and ligand-based covalent inhibitor design and screening.

The rational design of targeted covalent inhibitors (TCIs) has emerged as a powerful strategy in drug discovery, known for its ability to achieve strong binding affinity and prolonged target engagement. However, the development of covalent drugs is often challenged by the need to optimize both covalent warhead and non-covalent interactions, alongside the limitations of existing compound libraries. To address these challenges, we present CovalentInDB 2.0, an updated online database designed to support covalent drug discovery. This updated version includes 8303 inhibitors and 368 targets, supplemented by 3445 newly added cocrystal structures, providing detailed analyses of non-covalent interactions. Furthermore, we have employed an AI-based model to profile the ligandability of 144 864 cysteines across the human proteome. CovalentInDB 2.0 also features the largest covalent virtual screening library with 2 030 192 commercially available compounds and a natural product library with 105 901 molecules, crucial for covalent drug screening and discovery. To enhance the utility of these compounds, we performed structural similarity analysis and drug-likeness predictions. Additionally, a new user data upload feature enables efficient data contribution and continuous updates. CovalentInDB 2.0 is freely accessible at http://cadd.zju.edu.cn/cidb/.

Concurrent pembrolizumab with AVD for untreated classic Hodgkin lymphoma.

Concurrent administration of pembrolizumab with chemotherapy in untreated classic Hodgkin lymphoma (CHL) has not been studied previously. To investigate this combination, we conducted a single-arm study of concurrent pembrolizumab with AVD (doxorubicin, vinblastine, and dacarbazine; APVD) for untreated CHL. We enrolled 30 patients and met the primary safety end point with no observed significant treatment delays in the first 2 cycles. Twelve patients experienced grade 3 or 4 nonhematologic adverse events (AEs), most commonly febrile neutropenia and infection/sepsis. Grade 3 or 4 immune-related AEs, including alanine aminotransferase elevation and aspartate aminotransferase elevation were observed in 3 patients. One patient experienced an episode of grade 2 colitis and arthritis. Six patients missed at least 1 dose of pembrolizumab because of AEs, primarily grade 2 or higher transaminitis. Among 29 response-evaluable patients, the best overall response rate was 100% and the complete response rate was 90%. With a median follow-up of 2.1 years, the 2-year progression-free survival (PFS) and overall survival were 97% and 100%, respectively. To date, no patient who has withheld or discontinued pembrolizumab because of toxicity has progressed. Clearance of circulating tumor DNA (ctDNA) was associated with superior PFS when measured after cycle 2 and at the end of treatment (EOT). None of the 4 patients with persistent uptake by fluorodeoxyglucose positron emission tomography (PET) at EOT yet negative ctDNA have relapsed to date. Concurrent APVD shows promising safety and efficacy but may yield spurious PET findings in some patients. This trial was registered at www.clinicaltrials.gov as #NCT03331341.

PROTAC-DB 2.0: an updated database of PROTACs.

Proteolysis targeting chimeras (PROTACs), which harness the ubiquitin-proteasome system to selectively induce targeted protein degradation, represent an emerging therapeutic technology with the potential to modulate traditional undruggable targets. Over the past few years, this technology has moved from academia to industry and more than 10 PROTACs have been advanced into clinical trials. However, designing potent PROTACs with desirable drug-like properties still remains a great challenge. Here, we report an updated online database, PROTAC-DB 2.0, which is a repository of structural and experimental data about PROTACs. In this 2nd release, we expanded the number of PROTACs to 3270, which corresponds to a 96% expansion over the first version. Meanwhile, the numbers of warheads (small molecules targeting the proteins of interest), linkers, and E3 ligands (small molecules recruiting E3 ligases) have increased to over 360, 1500 and 80, respectively. In addition, given the importance and the limited number of the crystal target-PROTAC-E3 ternary complex structures, we provide the predicted ternary complex structures for PROTACs with good degradation capability using our PROTAC-Model method. To further facilitate the analysis of PROTAC data, a new filtering strategy based on the E3 ligases is also added. PROTAC-DB 2.0 is available online at http://cadd.zju.edu.cn/protacdb/.

Identification of the ferroptosis-related gene signature and the associated regulation axis in lung cancer and rheumatoid arthritis.

Patients with Rheumatoid arthritis (RA) have an elevated risk of lung cancer compared to the healthy population. However, there are few studies on the relationship between RA and lung adenocarcinoma (LUAD), especially the mechanisms at the genetic level. In this study, we investigated the link between RA and LUAD regarding Ferroptosis-Related Genes. The RNA-seq data of RA (GSE77298 and GSE 82107) and LUAD(GSE75037) in the Gene Expression Omnibus (GEO) database were obtained. 259 ferroptosis-related genes were obtained from the website ( http://www.zhounan.org/ferrdb/ ).The differential genes obtained from the RA and LUAD datasets were intersected with ferroptosis-related genes to obtain the ferroptosis-related differentially expressed genes (FRDEGs). Next, the mRNA-miRNA network was constructed, then Gene Set Enrichment Analysis (GSEA) for target genes were performed. The CIBERSORT algorithm was used to analyze the immune infiltration. Finally, the results were validated using external datasets (GSE89408 and GSE48780) and The Cancer Genome Atlas (TCGA) dataset. We obtained FRDEGs common to LUAD and RA: FANCD2, HELLS, RRM2, G6PD, VLDLR. These five genes play important roles in the progression of RA and LUAD. They also hold great diagnostic value for both diseases. Also, we found that LUAD and RA share common signaling pathways and similar immune mechanisms.

Knowledge-based BERT: a method to extract molecular features like computational chemists.

Molecular property prediction models based on machine learning algorithms have become important tools to triage unpromising lead molecules in the early stages of drug discovery. Compared with the mainstream descriptor- and graph-based methods for molecular property predictions, SMILES-based methods can directly extract molecular features from SMILES without human expert knowledge, but they require more powerful algorithms for feature extraction and a larger amount of data for training, which makes SMILES-based methods less popular. Here, we show the great potential of pre-training in promoting the predictions of important pharmaceutical properties. By utilizing three pre-training tasks based on atom feature prediction, molecular feature prediction and contrastive learning, a new pre-training method K-BERT, which can extract chemical information from SMILES like chemists, was developed. The calculation results on 15 pharmaceutical datasets show that K-BERT outperforms well-established descriptor-based (XGBoost) and graph-based (Attentive FP and HRGCN+) models. In addition, we found that the contrastive learning pre-training task enables K-BERT to 'understand' SMILES not limited to canonical SMILES. Moreover, the general fingerprints K-BERT-FP generated by K-BERT exhibit comparative predictive power to MACCS on 15 pharmaceutical datasets and can also capture molecular size and chirality information that traditional binary fingerprints cannot capture. Our results illustrate the great potential of K-BERT in the practical applications of molecular property predictions in drug discovery.

DrugFlow: An AI-Driven One-Stop Platform for Innovative Drug Discovery.

Artificial intelligence (AI)-aided drug design has demonstrated unprecedented effects on modern drug discovery, but there is still an urgent need for user-friendly interfaces that bridge the gap between these sophisticated tools and scientists, particularly those who are less computer savvy. Herein, we present DrugFlow, an AI-driven one-stop platform that offers a clean, convenient, and cloud-based interface to streamline early drug discovery workflows. By seamlessly integrating a range of innovative AI algorithms, covering molecular docking, quantitative structure-activity relationship modeling, molecular generation, ADMET (absorption, distribution, metabolism, excretion and toxicity) prediction, and virtual screening, DrugFlow can offer effective AI solutions for almost all crucial stages in early drug discovery, including hit identification and hit/lead optimization. We hope that the platform can provide sufficiently valuable guidance to aid real-word drug design and discovery. The platform is available at https://drugflow.com.

Assessing the performance of MM/PBSA and MM/GBSA methods. 10. Prediction reliability of binding affinities and binding poses for RNA-ligand complexes.

Ribonucleic acid (RNA)-ligand interactions play a pivotal role in a wide spectrum of biological processes, ranging from protein biosynthesis to cellular reproduction. This recognition has prompted the broader acceptance of RNA as a viable candidate for drug targets. Delving into the atomic-scale understanding of RNA-ligand interactions holds paramount importance in unraveling intricate molecular mechanisms and further contributing to RNA-based drug discovery. Computational approaches, particularly molecular docking, offer an efficient way of predicting the interactions between RNA and small molecules. However, the accuracy and reliability of these predictions heavily depend on the performance of scoring functions (SFs). In contrast to the majority of SFs used in RNA-ligand docking, the end-point binding free energy calculation methods, such as molecular mechanics/generalized Born surface area (MM/GBSA) and molecular mechanics/Poisson Boltzmann surface area (MM/PBSA), stand as theoretically more rigorous approaches. Yet, the evaluation of their effectiveness in predicting both binding affinities and binding poses within RNA-ligand systems remains unexplored. This study first reported the performance of MM/PBSA and MM/GBSA with diverse solvation models, interior dielectric constants (εin) and force fields in the context of binding affinity prediction for 29 RNA-ligand complexes. MM/GBSA is based on short (5 ns) molecular dynamics (MD) simulations in an explicit solvent with the YIL force field; the GBGBn2 model with higher interior dielectric constant (εin = 12, 16 or 20) yields the best correlation (Rp = -0.513), which outperforms the best correlation (Rp = -0.317, rDock) offered by various docking programs. Then, the efficacy of MM/GBSA in identifying the near-native binding poses from the decoys was assessed based on 56 RNA-ligand complexes. However, it is evident that MM/GBSA has limitations in accurately predicting binding poses for RNA-ligand systems, particularly compared with notably proficient docking programs like rDock and PLANTS. The best top-1 success rate achieved by MM/GBSA rescoring is 39.3%, which falls below the best results given by docking programs (50%, PLNATS). This study represents the first evaluation of MM/PBSA and MM/GBSA for RNA-ligand systems and is expected to provide valuable insights into their successful application to RNA targets.

Acylated Inulin as a Potential Shale Hydration Inhibitor in Water Based Drilling Fluids for Wellbore Stabilization.

Shale hydration dispersion and swelling are primary causes of wellbore instability in oil and gas reservoir exploration. In this study, inulin, a fructo-oligosaccharide extracted from Jerusalem artichoke roots, was modified by acylation with three acyl chlorides, and the products (C10-, C12-, and C14-inulin) were investigated for their use as novel shale hydration inhibitors. The inhibition properties were evaluated through the shale cuttings hot-rolling dispersion test, the sodium-based bentonite hydration test, and capillary suction. The three acylated inulins exhibited superb hydration-inhibiting performance at low concentrations, compared to the commonly used inhibitors of KCl and poly (ester amine). An inhibition mechanism was proposed based on surface tension measurements, contact angle measurements, Fourier-transform infrared analysis, and scanning electron microscopy. The acylated inulin reduced the water surface tension significantly, thus, retarding the invasion of water into the shale formation. Then, the acylated inulin was adsorbed onto the shale surface by hydrogen bonding to form a compact, sealed, hydrophobic membrane. Furthermore, the acylated inulins are non-toxic and biodegradable, which meet the increasingly stringent environmental regulations in this field. This method might provide a new avenue for developing high-performance and ecofriendly shale hydration inhibitors.

Molecular characterization of a novel victorivirus isolated from the phytopathogenic fungus Phaeobotryon rhois.

Phaeobotryon rhois is an important pathogenic fungus that causes dieback and canker disease of woody hosts. A novel mycovirus, tentatively named "Phaeobotryon rhois victorivirus 1" (PrVV1), was identified in P. rhois strain SX8-4. The PrVV1 has a double-stranded RNA (dsRNA) genome that is 5,224 base pairs long and contains two open reading frames (ORF1 and ORF2), which overlap at a AUGA sequence. ORF1 encodes a polypeptide of 786 amino acids (aa) that contains the conserved coat protein (CP) domain of victoriviruses, while ORF2, encodes a large polypeptide of 826 aa that contains the conserved RNA-dependent RNA polymerase (RdRp) domain of victoriviruses. Our analysis of genomic structure, homology, and phylogeny indicated that PrVV1 is a novel member of the genus Victorivirus in the family Totiviridae. This is the first report of the complete genome sequence of a victorivirus that infects P. rhois.

CovalentInDB: a comprehensive database facilitating the discovery of covalent inhibitors.

Inhibitors that form covalent bonds with their targets have traditionally been considered highly adventurous due to their potential off-target effects and toxicity concerns. However, with the clinical validation and approval of many covalent inhibitors during the past decade, design and discovery of novel covalent inhibitors have attracted increasing attention. A large amount of scattered experimental data for covalent inhibitors have been reported, but a resource by integrating the experimental information for covalent inhibitor discovery is still lacking. In this study, we presented Covalent Inhibitor Database (CovalentInDB), the largest online database that provides the structural information and experimental data for covalent inhibitors. CovalentInDB contains 4511 covalent inhibitors (including 68 approved drugs) with 57 different reactive warheads for 280 protein targets. The crystal structures of some of the proteins bound with a covalent inhibitor are provided to visualize the protein-ligand interactions around the binding site. Each covalent inhibitor is annotated with the structure, warhead, experimental bioactivity, physicochemical properties, etc. Moreover, CovalentInDB provides the covalent reaction mechanism and the corresponding experimental verification methods for each inhibitor towards its target. High-quality datasets are downloadable for users to evaluate and develop computational methods for covalent drug design. CovalentInDB is freely accessible at http://cadd.zju.edu.cn/cidb/.

The FomYjeF Protein Influences the Sporulation and Virulence of Fusarium oxysporum f. sp. momordicae.

Fusarium oxysporum causes vascular wilt in more than 100 plant species, resulting in massive economic losses. A deep understanding of the mechanisms of pathogenicity and symptom induction by this fungus is necessary to control crop wilt. The YjeF protein has been proven to function in cellular metabolism damage-repair in Escherichia coli and to play an important role in Edc3 (enhancer of the mRNA decapping 3) function in Candida albicans, but no studies have been reported on related functions in plant pathogenic fungi. In this work, we report how the FomYjeF gene in F. oxysporum f. sp. momordicae contributes to conidia production and virulence. The deletion of the FomYjeF gene displayed a highly improved capacity for macroconidia production, and it was shown to be involved in carbendazim's associated stress pathway. Meanwhile, this gene caused a significant increase in virulence in bitter gourd plants with a higher disease severity index and enhanced the accumulation of glutathione peroxidase and the ability to degrade hydrogen peroxide in F. oxysporum. These findings reveal that FomYjeF affects virulence by influencing the amount of spore formation and the ROS (reactive oxygen species) pathway of F. oxysporum f. sp. momordicae. Taken together, our study shows that the FomYjeF gene affects sporulation, mycelial growth, pathogenicity, and ROS accumulation in F. oxysporum. The results of this study provide a novel insight into the function of FomYjeF participation in the pathogenicity of F. oxysporum f. sp. momordicae.

Whole genome re-sequencing reveals the genetic diversity and evolutionary patterns of Eucommia ulmoides.

Eucommia ulmoides (E. ulmoides) is a deciduous perennial tree belonging to the order Garryales, and is known as "living fossil" plant, along with ginkgo (Ginkgo biloba), metaspaca (Metasequoia glyptostroboides) and dove tree (Davidia involucrata Baill). However, the genetic diversity and population structure of E. ulmoides are still  ambiguous nowdays. In this study, we re-sequenced the genomes of 12 E. ulmoides accessions from different major climatic geography regions in China to elucidate the genetic diversity, population structure and evolutionary pattern. By integration of phylogenetic analysis, principal component analysis and population structure analysis based on a number of high-quality SNPs, a total of 12 E. ulmoides accessions were clustered into four different groups. This result is consistent with their geographical location except for group samples from Shanghai and Hunan province. E. ulmoides accessions from Hunan province exhibited a closer genetic relationship with E. ulmoides accessions from Shanghai in China compared with other regions, which is also supported by the result of population structure analyses. Genetic diversity analysis further revealed that E. ulmoides samples in Shanghai and Hunan province were with higher genetic diversity than those in other regions in this study. In addition, we treated the E. ulmoides materials from Shanghai and Hunan province as group A, and the other materials from other places as group B, and then analyzed the evolutionary pattern of E. ulmoides. The result showed the significant differentiation (Fst = 0.1545) between group A and group B. Some candidate highly divergent genome regions were identified in group A by selective sweep analyses, and the function analysis of candidate genes in these regions showed that biological regulation processes could be correlated with the Eu-rubber biosynthesis. Notably, nine genes were identified from selective sweep regions. They were involved in the Eu-rubber biosynthesis and expressed in rubber containing tissues. The genetic diversity research and evolution model of E. ulmoides were preliminarily explored in this study, which laid the foundation for the protection of germplasm resources and the development and utilization of multipurpose germplasm resources in the future.

Ligustrazine protects against chronic hypertensive glaucoma in rats by inhibiting autophagy via the PI3K-Akt/mTOR pathway.

PURPOSE: Glaucoma is a leading cause of global irreversible blindness, and characterized by the progressive loss of retinal ganglion cells (RGCs). Ligustrazine (TMP) is a natural product that has shown beneficial effects on various diseases. This study aimed to determine whether ligustrazine produces a therapeutic effect on glaucoma and to investigate its underlying mechanisms. METHODS: A rat chronic hypertensive glaucoma model was induced by episcleral vein cauterization (EVC). Adult Sprague-Dawley (SD) rats were intraperitoneally administered TMP at a dose of 80 mg/kg once a day, from two days before EVC to one month after EVC. To elucidate the role of the mammalian target of rapamycin (mTOR) and phosphoinositide 3-kinase (PI3K), TMP-treated experimental rats were co-treated with the mTOR inhibitor rapamycin (5 mg/kg) or the PI3K inhibitor Ly294002 (10 mg/kg). The intraocular pressure (IOP) of the experimental and control rats was measured every six days. Retinal cells were examined by hematoxylin-eosin and terminal deoxynucleotidyltransferase-mediated biotinylated UTP nick end labeling (TUNEL) staining, as well as transmission electron microscopy. Immunohistochemistry and western blot analysis were performed to measure proteins involved in apoptosis and autophagy. RESULTS: Ligustrazine protected retinal cells from death in experimental glaucoma rats, which was not due to the lowering of IOP, but could be attributable to direct suppression of retinal cell apoptosis. In glaucoma rats, autophagy was markedly activated in retina cells, as evidenced by increased numbers of autophagosomes and the expression of autophagy-related proteins (ATG5 and LC3-II/I). Notably, such alterations in glaucoma rats were almost completely reversed by ligustrazine. The suppressive effects of ligustrazine on apoptosis and autophagy of retina cells were markedly attenuated by the mTOR inhibitor rapamycin or the PI3K inhibitor Ly294002. Additionally, ligustrazine significantly increased the protein levels of phosphorylated PI3K (p-PI3K), protein kinase B (p-Akt), and mTOR (p-mTOR) in glaucoma rats, whereas such increases were attenuated by rapamycin or Ly294002. CONCLUSIONS: These results demonstrate that ligustrazine is protective in experimental glaucoma by inhibiting autophagy via the activation of the PI3K-Akt/mTOR pathway, providing compelling evidence that ligustrazine is potentially therapeutic for patients with glaucoma.

Molecular characterization of a novel partitivirus isolated from the phytopathogenic fungus Aplosporella javeedii.

Aplosporella javeedii is a pathogenic fungus that causes canker and dieback of jujube in China. In this study, we report a new mycovirus, Aplosporella javeedii partitivirus 1 (AjPV1), isolated from A. javeedii strain NX55-3. The AjPV1 genome contains two double-stranded RNA elements (dsRNA1 and dsRNA2). The size of dsRNA1 is 2,360 bp, and it encodes a putative RNA-dependent RNA polymerase (RdRp), while dsRNA2 is 2,301 bp in length and encodes a putative capsid protein (CP). The sequences of RdRp and CP have significant similarity to those of members of the family Partitiviridae. Sequence alignment and phylogenetic analysis showed that AjPV1 is a new member of the family Partitiviridae that is related to members of the genus Betapartitivirus. To our knowledge, AjPV1 is the first mycovirus reported from A. javeedii.

The gap between public perceptions and monitoring indicators of environmental quality in Beijing.

This paper explores the differences between public perceptions and existing monitoring indicators in environmental quality in Beijing. The results reveal, existing indicators do not accurately reflect public perceptions of the environment. Some environmental problems, such as fluffy catkins, are not reflected in the existing indicators, yet have a relatively large influence on public perception. The policymakers and public's variegated understanding of the environment, the lag period of public perceptions of environmental quality change, and lack of standards and monitoring for emerging problems are the three main reasons that resulted in a deviation of monitoring indicators from the public perceptions. Resultantly, to improve the level of environmental governance, it is necessary to take relevant measures that reduce the difference between public perceptions and monitoring indicators. Residents should be surveyed regularly to obtain their public perceptions, and the selection of indicators should not be limited to environmental pollution. In particular, some indicators based on public perception should be developed to complement the existing environmental monitoring protocol. In addition, due to the emerging environmental problems, quality standards and monitoring systems should be updated regularly.

Hv1-deficiency protects ß cells from glucotoxicity through regulation of NOX4 level.

High glucose (HG)-induced oxidative stress contributes to the dysfunction of pancreatic ß cells in diabetes. The voltage-gated proton channel Hv1 has been proposed to support reactive oxygen species (ROS) production during respiratory bursts. However, the effect of Hv1 on glucotoxicity in pancreatic ß cells is not clear yet. In this study, we examined the protective effects of Hv1-deficiency in HG cultured ß cells. Following 48 h of treatment with 30 mM high glucose, Hv1 KO ß cells showed higher cell viability, lower cell apoptosis and a more stable insulin gene expression level compared to WT ß cells. In both control and HG cultured ß cells, deficiency of Hv1 decreased the glucose- and PMA-induced ROS production. Finally, HG incubation led to NOX4 upregulation in WT ß cells, which could be inhibited by HV1 deficiency. In conclusion, Hv1-deficiency prevents the HG treatment-induced NOX4 upregulation and protects ß cells from glucotoxicity.

Assessing the performance of the MM/PBSA and MM/GBSA methods. 10. Impacts of enhanced sampling and variable dielectric model on protein-protein Interactions.

Enhanced sampling has been extensively used to capture the conformational transitions in protein folding, but it attracts much less attention in the studies of protein-protein recognition. In this study, we evaluated the impact of enhanced sampling methods and solute dielectric constants on the overall accuracy of the molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) and molecular mechanics/generalized Born surface area (MM/GBSA) approaches for the protein-protein binding free energy calculations. Here, two widely used enhanced sampling methods, including aMD and GaMD, and conventional molecular dynamics (cMD) simulations with two AMBER force fields (ff03 and ff14SB) were used to sample the conformations for 21 protein-protein complexes. The MM/PBSA and MM/GBSA calculation results illustrate that the standard MM/GBSA based on the cMD simulations yields the best Pearson correlation (rp = -0.523) between the predicted binding affinities and the experimental data, which is much higher than that given by MM/PBSA (rp = -0.212). Two enhanced sampling methods (aMD and GaMD) are indeed more efficient for conformational sampling, but they did not improve the binding affinity predictions for protein-protein systems, suggesting that the aMD or GaMD sampling (at least in short timescale simulations) may not be a good choice for the MM/PBSA and MM/GBSA predictions of protein-protein complexes. The solute dielectric constant of 1.0 is recommended to MM/GBSA, but a higher solute dielectric constant is recommended to MM/PBSA, especially for the systems with higher polarity on the protein-protein binding interfaces. Then, a preliminary assessment of the MM/GBSA calculations based on a variable dielectric generalized Born (VDGB) model was conducted. The results highlight the potential power of VDGB in the free energy predictions for protein-protein systems, but more thorough studies should be done in the future.

Association of a common variant of SYNPO2 gene with increased risk of serous epithelial ovarian cancer.

In China, the majority of ovarian cancer patients (80%-90%) are women who are diagnosed with epithelial ovarian cancer. The SYNPO2 gene has recently been reported to be associated with epithelial ovarian cancer in Europeans. To investigate the association of common variants of SYNPO2 gene with epithelial ovarian cancer in Han Chinese individuals, we designed a case-control study with 719 epithelial ovarian cancer patients and 1568 unrelated healthy controls of Han Chinese descent. A total of 49 tagging single-nucleotide polymorphisms were genotyped; single-single-nucleotide polymorphism association, imputation, and haplotypic association analyses were performed. The single-nucleotide polymorphism rs17329882 was found to be strongly associated with serous epithelial ovarian cancer and with ages ≤49 years, consistent with the pre-menopausal status of analyzed epithelial ovarian cancer cases. Odds ratios and 95% confidence intervals provided evidence of the risk effects of the C allele of the single-nucleotide polymorphism on epithelial ovarian cancer. Imputation analyses also confirmed the results with a similar pattern. Additionally, haplotype analyses indicated that the haplotype block that contained rs17329882 was significantly associated with epithelial ovarian cancer risk, specifically with the serous epithelial ovarian cancer subtype. In conclusion, our results show that SYNPO2 gene plays an important role in the etiology of epithelial ovarian cancer, suggesting that this gene may be a potential genetic modifier for developing epithelial ovarian cancer.

TGF-ß Stimulates Endochondral Differentiation after Denervation.

Transforming growth factor beta (TGF-ß) is a multifunctional protein that induces gene expression of cartilage-specific molecules, but its exact role in the process of chondrogenesis is unclear. Because recent studies suggest that TGF-ß can facilitate chondrogenic precursor cells differentiating into chondrocytes, we sought to determine whether TGF-ß prevents denervation-induced reduction of endochondral bone formation in an experimental model. Mice were treated daily with recombinant human TGF-ß1 (rhTGF-ß1) for 3 weeks. We found that rhTGF-ß1 not only prevented denervation-induced reduction of gene expression of type II collagen, type X collagen, aggrecan, Indian hedgehog, and parathyroid hormone-related peptide, but also synergized endochondral differentiation. These results demonstrate that short-term systemic administration of TGF-ß substantially prevents denervation-induced reduction of endochondral bone formation via stimulating endochondral differentiation. Potential therapeutic applications will be pursued in further studies that address the molecular biological mechanism of TGF-ß on endochodral bone formation after denervation in animal models.