XRCC1 Arg194Trp polymorphism and thyroid cancer

BACKGROUND

Previously published data on the association between the XRCC1 Arg194Trp polymorphism and thyroid cancer (TC) remain controversial.

METHODS

To clarify the association between the XRCC1 Arg194Trp polymorphism and susceptibility to TC, a meta-analysis of case-control studies was conducted. We systematically searched PubMed and CNKI to identify relevant studies. Pooled odds ratios (ORs) of various genetic models were estimated using fixed and random effects models. Heterogeneity was detected by Q-statistic, and the Egger's test was used to evaluate the publication bias.

RESULTS

A total of seven eligible studies for the XRCC1 Arg194Trp polymorphism (1500 patients and 2358 controls) were included in this meta-analysis. The results of our study failed to suggest an association between the Arg194Trp polymorphism and susceptibility of TC. However, in the subgroup analyses by ethnicity, the OR was 0.82 (C allele vs. T allele, 95% CI 0.68-0.98; P = 0.24 for heterogeneity) among the Chinese population. Nevertheless, no significant differences were observed in the Caucasian population in any genetic model.

CONCLUSION

This study suggested that the C allele of XRCC1 had an 18% significantly decreased risk of TC in Chinese, and there were no significant associations among Caucasians under all genetic models.

Flexible multidentate benzyldiamine derivatives with high affinity for β-amyloid in cerebral amyloid angiopathy

Cerebral amyloid angiopathy (CAA) commonly found in the aged is pathologically characterized by β-amyloid (Aβ) deposition in the walls of arteries and capillaries of brain. In this study, four flexible multidentate benzyldiamine derivatives as potential probes for cerebrovascular Aβ deposition were designed and synthesized. In in vitro inhibition assays, the ligands 18-21 displayed high affinities for Aβ aggregates with K_i values of 1.45 ± 0.53 nM, 1.68 ± 0.35 nM, 1.16 ± 0.23 nM and 1.72 ± 0.19 nM, respectively. A significant improvement in the binding affinity over the monomer, compounds 9-12 or benzyldiamine derivatives, demonstrated the applicability of the multidentate approach. The underlying mechanism of these novel Aβ agents was explored by molecular docking technique, which theoretically verified the high affinities of the multidentate benzyldiamine derivatives for Aβ aggregates. Moreover, the molecular masses of the ligands 18-21 are more than 700 Dalton, which are believed to be hardly capable of penetrating blood brain barrier. In this regard, these ligands could be used to distinguish CAA from Alzheimer's disease which is another Aβ-related disorder disease. To convert these ligands to positron emission tomography imaging agents, we attempted to radiosynthesize [¹⁸F]18. Though the radiolabeling was not very successful, the preliminary results suggested that these newly proposed multidentate benzyldiamine derivatives may be used as potential Aβ imaging agents in cerebral amyloid angiopathy.

Tunable Fano Resonance and Enhanced Sensing in a Simple Au/TiO2 Hybrid Metasurface

We investigate Fano resonances and sensing enhancements in a simple Au/TiO₂ hybrid metasurface through the finite-different time-domain (FDTD) simulation and coupled mode theory (CMT) analysis. The results show that the Fano resonance in the proposed simple metasurface is caused by the destructive interaction between the surface plasmon polaritons (SPPs) and the local surface plasmon resonances (LSPRs), the quality factor and dephasing time for the Fano resonance can be effectively tuned by the thickness of Au and TiO₂ structures, the length of each unit in x and y directions, as well as the structural defect. In particular, single Fano resonance splits into multiple Fano resonances caused by a stub-shaped defect, and multiple Fano resonances can be tuned by the size and position of the stub-shaped defect. Moreover, we also find that the sensitivity in the Au/TiO₂ hybrid metasurface with the stub-shaped defect can reach up to 330 nm/RIU and 535 nm/RIU at the Fano resonance 1 and Fano resonance 2, which is more than three times as sensitive in the Au/TiO₂ hybrid metasurface without the stub-shaped defect, and also higher than that in the TiO₂ metasurface reported before. These results may provide further understanding of Fano resonances and guidance for designing ultra-high sensitive refractive index sensors.

Selective Inhibition of HDAC1 by Macrocyclic Polypeptide for the Treatment of Glioblastoma: A Binding Mechanistic Analysis Based on Molecular Dynamics

Glioblastoma (GBM) is the most common and aggressive intracranial malignant brain tumor, and the abnormal expression of HDAC1 is closely correlated to the progression, recurrence and metastasis of GBM cells, making selective inhibition of HDAC1 a promising strategy for GBM treatments. Among all available selective HDAC1 inhibitors, the macrocyclic peptides have gained great attention due to their remarkable inhibitory selectivity on HDAC1. However, the binding mechanism underlying this selectivity is still elusive, which increases the difficulty of designing and synthesizing the macrocyclic peptide-based anti-GBM drug. Herein, multiple computational approaches were employed to explore the binding behaviors of a typical macrocyclic peptide FK228 in both HDAC1 and HDAC6. Starting from the docking conformations of FK228 in the binding pockets of HDAC1&6, relatively long MD simulation (500 ns) shown that the hydrophobic interaction and hydrogen bonding of E91 and D92 in the Loop2 of HDAC1 with the Cap had a certain traction effect on FK228, and the sub-pocket formed by Loop1 and Loop2 in HDAC1 could better accommodate the Cap group, which had a positive effect on maintaining the active conformation of FK228. While the weakening of the interactions between FK228 and the residues in the Loop2 of HDAC6 during the MD simulation led to the large deflection of FK228 in the binding site, which also resulted in the decrease in the interactions between the Linker region of FK228 and the previously identified key amino acids (H134, F143, H174, and F203). Therefore, the residues located in Loop1 and Loop2 contributed in maintaining the active conformation of FK228, which would provide valuable hints for the discovery and design of novel macrocyclic polypeptide HDAC inhibitors.

Molecular Simulation of Oncostatin M and Receptor (OSM-OSMR) Interaction as a Potential Therapeutic Target for Inflammatory Bowel Disease

Therapeutics targeting cytokines such as the oncostatin M (OSM)-mediated inflammation represent a potential strategy for the treatment of inflammatory bowel disease (IBD). Despite the investigation of the specific role of the interactions between OSM and the receptor (OSMR) in IBD pathogenesis, the 3D structure of the OSM–OSMR complex remains elusive. In this work, the interaction mode between OSM and OSMR at atomic level was predicted by computational simulation approach. The interaction domain of the OSMR was built with the homology modeling method. The near-native structure of the OSM–OSMR complex was obtained by docking, and long-time scale molecular dynamics (MD) simulation in an explicit solvent was further performed to sample the conformations when OSM binds to the OSMR. After getting the equilibrated states of the simulation system, per-residue energy contribution was calculated to characterize the important residues for the OSM–OSMR complex formation. Based on these important residues, eight residues (OSM: Arg100, Leu103, Phe160, and Gln161; OSMR: Tyr214, Ser223, Asp262, and Trp267) were identified as the “hot spots” through computational alanine mutagenesis analysis and verified by additional MD simulation of R100A (one of the identified “hotspots”) mutant. Moreover, six cavities were detected at the OSM–OSMR interface through the FTMap analysis, and they were suggested as important binding sites. The predicted 3D structure of the OSM–OSMR complex and the identified “hot spots” constituting the core of the binding interface provide helpful information in understanding the OSM–OSMR interactions, and the detected sites serve as promising targets in designing small molecules to block the interactions.

The binding mode of vilazodone in the human serotonin transporter elucidated by ligand docking and molecular dynamics simulations

Vilazodone is a novel antidepressant used for the treatment of major depressive disorder (MDD) with a primary action mechanism of inhibiting the human serotonin reuptake transporter (hSERT) and acting as a 5-HT_1A receptor partial agonist. The interaction between vilazodone and the 5-HT_1A receptor has been reported, however, the binding mode of vilazodone in the hSERT remains elusive. In the current study, to elucidate the molecular mechanism of vilazodone binding in the hSERT, the drug and its five analogs were docked into the hSERT crystal structure as initial conformations and were sampled by 400 ns molecular dynamics (MD) simulations. Through the analysis of the profiles of protein-ligand binding free energies, interaction fingerprints, and conformational rearrangements, the binding mode of vilazodone in the hSERT was revealed. As a result, unlike the classical antidepressants located in the S1 site of the hSERT, vilazodone adopted a linear pose in the binding pocket. Its arylpiperazine fragment occupies the central site (S1) and interacts with Y95, D98, I172, Y176, F335, F341, S438, and T439, while the indole fragment extends to the allosteric site (S2) via interacting with the ionic switch (R104/E403) between the two sites. The new insights obtained are not only helpful in understanding the binding mode of vilazodone in the hSERT, but also provide valuable guidance to the discovery of novel antidepressant drugs.

Computational Advances in the Label-free Quantification of Cancer Proteomics Data

BACKGROUND

Due to its ability to provide quantitative and dynamic information on tumor genesis and development by directly profiling protein expression, the proteomics has become intensely popular for characterizing the functional proteins driving the transformation of malignancy, tracing the large-scale protein alterations induced by anticancer drug, and discovering the innovative targets and first-in-class drugs for oncologic disorders.

OBJECTIVE

To quantify cancer proteomics data, the label-free quantification (LFQ) is frequently employed. However, low precision, poor reproducibility and inaccuracy of the LFQ of proteomics data have been recognized as the key "technical challenge" in the discovery of anticancer targets and drugs. In this paper, the recent advances and development in the computational perspective of LFQ in cancer proteomics were therefore systematically reviewed and analyzed.

METHODS

PubMed and Web of Science database were searched for label-free quantification approaches, cancer proteomics and computational advances.

RESULTS

First, a variety of popular acquisition techniques and state-of-the-art quantification tools are systematically discussed and critically assessed. Then, many processing approaches including transformation, normalization, filtering and imputation are subsequently discussed, and their impacts on improving LFQ performance of cancer proteomics are evaluated. Finally, the future direction for enhancing the computation-based quantification technique for cancer proteomics are also proposed.

CONCLUSION

There is a dramatic increase in LFQ approaches in recent year, which significantly enhance the diversity of the possible quantification strategies for studying cancer proteomics.

Integrating the Ribonucleic Acid Sequencing Data From Various Studies for Exploring the Multiple Sclerosis-Related Long Noncoding Ribonucleic Acids and Their Functions

Multiple sclerosis (MS) is a chronic fatal central nervous system (CNS) disease involving in complex immunity dysfunction. Recently, long noncoding RNAs (lncRNAs) were discovered as the important regulatory factors for the pathogenesis of MS. However, these findings often cannot be repeated and confirmed by the subsequent studies. We considered that the small-scale samples or the heterogeneity among various tissues may result in the divergence of the results. Currently, RNA-seq has become a powerful approach to quantify the abundances of lncRNA transcripts. Therefore, we comprehensively collected the MS-related RNA-seq data from a variety of previous studies, and integrated these data using an expression-based meta-analysis to identify the differentially expressed lncRNA between MS patients and controls in whole samples and subgroups. Then, we performed the Jensen-Shannon (JS) divergence and cluster analysis to explore the heterogeneity and expression specificity among various tissues. Finally, we investigated the potential function of identified lncRNAs for MS using weighted gene co-expression network analysis (WGCNA) and gene set enrichment analysis (GSEA), and 5,420 MS-related lncRNAs specifically expressed in the brain tissue were identified. The subgroup analysis found a small heterogeneity of the lncRNA expression profiles between brain and blood tissues. The results of WGCNA and GSEA showed that a potential important function of lncRNAs in MS may be involved in the regulation of ribonucleoproteins and tumor necrosis factor cytokines receptors. In summary, this study provided a strategy to explore disease-related lncRNAs on genome-wide scale, and our findings will be benefit to improve the understanding of MS pathogenesis.

Convolutional neural network-based annotation of bacterial type IV secretion system effectors with enhanced accuracy and reduced false discovery

The type IV bacterial secretion system (SS) is reported to be one of the most ubiquitous SSs in nature and can induce serious conditions by secreting type IV SS effectors (T4SEs) into the host cells. Recent studies mainly focus on annotating new T4SE from the huge amount of sequencing data, and various computational tools are therefore developed to accelerate T4SE annotation. However, these tools are reported as heavily dependent on the selected methods and their annotation performance need to be further enhanced. Herein, a convolution neural network (CNN) technique was used to annotate T4SEs by integrating multiple protein encoding strategies. First, the annotation accuracies of nine encoding strategies integrated with CNN were assessed and compared with that of the popular T4SE annotation tools based on independent benchmark. Second, false discovery rates of various models were systematically evaluated by (1) scanning the genome of Legionella pneumophila subsp. ATCC 33152 and (2) predicting the real-world non-T4SEs validated using published experiments. Based on the above analyses, the encoding strategies, (a) position-specific scoring matrix (PSSM), (b) protein secondary structure & solvent accessibility (PSSSA) and (c) one-hot encoding scheme (Onehot), were identified as well-performing when integrated with CNN. Finally, a novel strategy that collectively considers the three well-performing models (CNN-PSSM, CNN-PSSSA and CNN-Onehot) was proposed, and a new tool (CNN-T4SE, https://idrblab.org/cnnt4se/) was constructed to facilitate T4SE annotation. All in all, this study conducted a comprehensive analysis on the performance of a collection of encoding strategies when integrated with CNN, which could facilitate the suppression of T4SS in infection and limit the spread of antimicrobial resistance.

Epidermal growth factor receptor-extracellular-regulated kinase blockade upregulates TRIM32 signaling cascade and promotes neurogenesis after spinal cord injury

Nerve regeneration is blocked after spinal cord injury (SCI) by a complex myelin-associated inhibitory (MAI) microenvironment in the lesion site; however, the underlying mechanisms are not fully understood. During the process of neural stem cell (NSC) differentiation, pathway inhibitors were added to quantitatively assess the effects on neuronal differentiation. Immunoprecipitation and lentivirus-induced overexpression were used to examine effects in vitro. In vivo, animal experiments and lineage tracing methods were used to identify nascent neurogenesis after SCI. In vitro results indicated that myelin inhibited neuronal differentiation by activating the epidermal growth factor receptor (EGFR)-extracellular-regulated kinase (ERK) signaling cascade. Subsequently, we found that tripartite motif (TRIM) 32, a neuronal fate-determining factor, was inhibited. Moreover, inhibition of EGFR-ERK promoted TRIM32 expression and enhanced neuronal differentiation in the presence of myelin. We further demonstrated that ERK interacts with TRIM32 to regulate neuronal differentiation. In vivo results indicated that EGFR-ERK blockade increased TRIM32 expression and promoted neurogenesis in the injured area, thus enhancing functional recovery after SCI. Our results showed that EGFR-ERK blockade antagonized MAI of neuronal differentiation of NSCs through regulation of TRIM32 by ERK. Collectively, these findings may provide potential new targets for SCI repair.

Protein functional annotation of simultaneously improved stability, accuracy and false discovery rate achieved by a sequence-based deep learning

Functional annotation of protein sequence with high accuracy has become one of the most important issues in modern biomedical studies, and computational approaches of significantly accelerated analysis process and enhanced accuracy are greatly desired. Although a variety of methods have been developed to elevate protein annotation accuracy, their ability in controlling false annotation rates remains either limited or not systematically evaluated. In this study, a protein encoding strategy, together with a deep learning algorithm, was proposed to control the false discovery rate in protein function annotation, and its performances were systematically compared with that of the traditional similarity-based and de novo approaches. Based on a comprehensive assessment from multiple perspectives, the proposed strategy and algorithm were found to perform better in both prediction stability and annotation accuracy compared with other de novo methods. Moreover, an in-depth assessment revealed that it possessed an improved capacity of controlling the false discovery rate compared with traditional methods. All in all, this study not only provided a comprehensive analysis on the performances of the newly proposed strategy but also provided a tool for the researcher in the fields of protein function annotation.

Simultaneous Improvement in the Precision, Accuracy, and Robustness of Label-free Proteome Quantification by Optimizing Data Manipulation Chains

The label-free proteome quantification (LFQ) is multistep workflow collectively defined by quantification tools and subsequent data manipulation methods that has been extensively applied in current biomedical, agricultural, and environmental studies. Despite recent advances, in-depth and high-quality quantification remains extremely challenging and requires the optimization of LFQs by comparatively evaluating their performance. However, the evaluation results using different criteria (precision, accuracy, and robustness) vary greatly, and the huge number of potential LFQs becomes one of the bottlenecks in comprehensively optimizing proteome quantification. In this study, a novel strategy, enabling the discovery of the LFQs of simultaneously enhanced performance from thousands of workflows (integrating 18 quantification tools with 3,128 manipulation chains), was therefore proposed. First, the feasibility of achieving simultaneous improvement in the precision, accuracy, and robustness of LFQ was systematically assessed by collectively optimizing its multistep manipulation chains. Second, based on a variety of benchmark datasets acquired by various quantification measurements of different modes of acquisition, this novel strategy successfully identified a number of manipulation chains that simultaneously improved the performance across multiple criteria. Finally, to further enhance proteome quantification and discover the LFQs of optimal performance, an online tool (https://idrblab.org/anpela/) enabling collective performance assessment (from multiple perspectives) of the entire LFQ workflow was developed. This study confirmed the feasibility of achieving simultaneous improvement in precision, accuracy, and robustness. The novel strategy proposed and validated in this study together with the online tool might provide useful guidance for the research field requiring the mass-spectrometry-based LFQ technique.

Recent Technological Advances in the Mass Spectrometry-based Nanomedicine Studies: An Insight from Nanoproteomics

Nanoscience becomes one of the most cutting-edge research directions in recent years since it is gradually matured from basic to applied science. Nanoparticles (NPs) and nanomaterials (NMs) play important roles in various aspects of biomedicine science, and their influences on the environment have caused a whole range of uncertainties which require extensive attention. Due to the quantitative and dynamic information provided for human proteome, mass spectrometry (MS)-based quantitative proteomic technique has been a powerful tool for nanomedicine study. In this article, recent trends of progress and development in the nanomedicine of proteomics were discussed from quantification techniques and publicly available resources or tools. First, a variety of popular protein quantification techniques including labeling and label-free strategies applied to nanomedicine studies are overviewed and systematically discussed. Then, numerous protein profiling tools for data processing and postbiological statistical analysis and publicly available data repositories for providing enrichment MS raw data information sources are also discussed.

A critical assessment of the feature selection methods used for biomarker discovery in current metaproteomics studies

Microbial community (MC) has great impact on mediating complex disease indications, biogeochemical cycling and agricultural productivities, which makes metaproteomics powerful technique for quantifying diverse and dynamic composition of proteins or peptides. The key role of biostatistical strategies in MC study is reported to be underestimated, especially the appropriate application of feature selection method (FSM) is largely ignored. Although extensive efforts have been devoted to assessing the performance of FSMs, previous studies focused only on their classification accuracy without considering their ability to correctly and comprehensively identify the spiked proteins. In this study, the performances of 14 FSMs were comprehensively assessed based on two key criteria (both sample classification and spiked protein discovery) using a variety of metaproteomics benchmarks. First, the classification accuracies of those 14 FSMs were evaluated. Then, their abilities in identifying the proteins of different spiked concentrations were assessed. Finally, seven FSMs (FC, LMEB, OPLS-DA, PLS-DA, SAM, SVM-RFE and T-Test) were identified as performing consistently superior or good under both criteria with the PLS-DA performing consistently superior. In summary, this study served as comprehensive analysis on the performances of current FSMs and could provide a valuable guideline for researchers in metaproteomics.

Consistent gene signature of schizophrenia identified by a novel feature selection strategy from comprehensive sets of transcriptomic data

The etiology of schizophrenia (SCZ) is regarded as one of the most fundamental puzzles in current medical research, and its diagnosis is limited by the lack of objective molecular criteria. Although plenty of studies were conducted, SCZ gene signatures identified by these independent studies are found highly inconsistent. As one of the most important factors contributing to this inconsistency, the feature selection methods used currently do not fully consider the reproducibility among the signatures discovered from different datasets. Therefore, it is crucial to develop new bioinformatics tools of novel strategy for ensuring a stable discovery of gene signature for SCZ. In this study, a novel feature selection strategy (1) integrating repeated random sampling with consensus scoring and (2) evaluating the consistency of gene rank among different datasets was constructed. By systematically assessing the identified SCZ signature comprising 135 differentially expressed genes, this newly constructed strategy demonstrated significantly enhanced stability and better differentiating ability compared with the feature selection methods popular in current SCZ research. Based on a first-ever assessment on methods’ reproducibility cross-validated by independent datasets from three representative studies, the new strategy stood out among the popular methods by showing superior stability and differentiating ability. Finally, 2 novel and 17 previously reported transcription factors were identified and showed great potential in revealing the etiology of SCZ. In sum, the SCZ signature identified in this study would provide valuable clues for discovering diagnostic molecules and potential targets for SCZ.

What Makes Species Productive of Anti-Cancer Drugs? Clues from Drugs’ Species Origin, Druglikeness, Target and Pathway

Background:

Despite the substantial contribution of natural products to the FDA drug approval list, the discovery of anti-cancer drugs from the huge amount of species on the planet remains looking for a needle in a haystack. Objective: Drug-productive clusters in the phylogenetic tree are thus proposed to narrow the searching scope by focusing on much smaller amount of species within each cluster, which enable prioritized and rational bioprospecting for novel drug-like scaffolds. However, the way anti-cancer nature-derived drugs distribute in phylogenetic tree has not been reported, and it is oversimplified to just focus anti-cancer drug discovery on the drug-productive clusters, since the number of species in each cluster remains too large to be managed.

Objective:

Drug-productive clusters in the phylogenetic tree are thus proposed to narrow the searching scope by focusing on much smaller amount of species within each cluster, which enable prioritized and rational bioprospecting for novel drug-like scaffolds. However, the way anti-cancer nature-derived drugs distribute in phylogenetic tree has not been reported, and it is oversimplified to just focus anti-cancer drug discovery on the drug-productive clusters, since the number of species in each cluster remains too large to be managed.

Methods:

In this study, 260 anti-cancer drugs approved in the past 70 years were comprehensively analyzed by hierarchical clustering of phylogenetic distribution.

Results:

207 out of these 260 drugs were derived from or inspired by the natural products isolated from 58 species. Phylogenetic distribution of those drugs further revealed that nature-derived anti-cancer drugs originated mostly from drug-productive families that tend to be clustered rather than scattered on the phylogenetic tree. Moreover, based on their productivity, drug-producing species were categorized into productive (CPS), newly emerging (CNS) and lessproductive (CLS). Statistical significances in druglikeness between drugs from CPS and CLS were observed, and drugs from CNS were found to share similar drug-like properties to those from CPS.

Conclusion:

This finding indicated a great raise in drug approval standard, which suggested us to focus bioprospecting on the species yielding multiple drugs and keeping productive for long period of time.

Genome-wide identification and analysis of the eQTL lncRNAs in multiple sclerosis based on RNA-seq data

The pathogenesis of multiple sclerosis (MS) is significantly regulated by long noncoding RNAs (lncRNAs), the expression of which is substantially influenced by a number of MS-associated risk single nucleotide polymorphisms (SNPs). It is thus hypothesized that the dysregulation of lncRNA induced by genomic variants may be one of the key molecular mechanisms for the pathology of MS. However, due to the lack of sufficient data on lncRNA expression and SNP genotypes of the same MS patients, such molecular mechanisms underlying the pathology of MS remain elusive. In this study, a bioinformatics strategy was applied to obtain lncRNA expression and SNP genotype data simultaneously from 142 samples (51 MS patients and 91 controls) based on RNA-seq data, and an expression quantitative trait loci (eQTL) analysis was conducted. In total, 2383 differentially expressed lncRNAs were identified as specifically expressing in brain-related tissues, and 517 of them were affected by SNPs. Then, the functional characterization, secondary structure changes and tissue and disease specificity of the cis-eQTL SNPs and lncRNA were assessed. The cis-eQTL SNPs were substantially and specifically enriched in neurological disease and intergenic region, and the secondary structure was altered in 17.6% of all lncRNAs in MS. Finally, the weighted gene coexpression network and gene set enrichment analyses were used to investigate how the influence of SNPs on lncRNAs contributed to the pathogenesis of MS. As a result, the regulation of lncRNAs by SNPs was found to mainly influence the antigen processing/presentation and mitogen-activated protein kinases (MAPK) signaling pathway in MS. These results revealed the effectiveness of the strategy proposed in this study and give insight into the mechanism (SNP-mediated modulation of lncRNAs) underlying the pathology of MS.

Prediction of the binding mode and resistance profile for a dual-target pyrrolyl diketo acid scaffold against HIV-1 integrase and reverse-transcriptase-associated ribonuclease H

The rapid emergence of drug-resistant variants is one of the most common causes of highly active antiretroviral therapeutic (HAART) failure in patients infected with HIV-1. Compared with the existing HAART, the recently developed pyrrolyl diketo acid scaffold targeting both HIV-1 integrase (IN) and reverse transcriptase-associated ribonuclease H (RNase H) is an efficient approach to counteract the failure of anti-HIV treatment due to drug resistance. However, the binding mode and potential resistance profile of these inhibitors with important mechanistic principles remain poorly understood. To address this issue, an integrated computational method was employed to investigate the binding mode of inhibitor JMC6F with HIV-1 IN and RNase H. By using per-residue binding free energy decomposition analysis, the following residues: Asp64, Thr66, Leu68, Asp116, Tyr143, Gln148 and Glu152 in IN, Asp443, Glu478, Trp536, Lys541 and Asp549 in RNase H were identified as key residues for JMC6F binding. And then computational alanine scanning was carried to further verify the key residues. Moreover, the resistance profile of the currently known major mutations in HIV-1 IN and 2 mutations in RNase H against JMC6F was predicted by in silico mutagenesis studies. The results demonstrated that only three mutations in HIV-1 IN (Y143C, Q148R and N155H) and two mutations in HIV-1 RNase H (Y501R and Y501W) resulted in a reduction of JMC6F potency, thus indicating their potential role in providing resistance to JMC6F. These data provided important insights into the binding mode and resistance profile of the inhibitors with a pyrrolyl diketo acid scaffold in HIV-1 IN and RNase H, which would be helpful for the development of more effective dual HIV-1 IN and RNase H inhibitors.

Assessing the Effectiveness of Direct Data Merging Strategy in Long-Term and Large-Scale Pharmacometabonomics

Because of the extended period of clinic data collection and huge size of analyzed samples, the long-term and large-scale pharmacometabonomics profiling is frequently encountered in the discovery of drug/target and the guidance of personalized medicine. So far, integration of the results (ReIn) from multiple experiments in a large-scale metabolomic profiling has become a widely used strategy for enhancing the reliability and robustness of analytical results, and the strategy of direct data merging (DiMe) among experiments is also proposed to increase statistical power, reduce experimental bias, enhance reproducibility and improve overall biological understanding. However, compared with the ReIn, the DiMe has not yet been widely adopted in current metabolomics studies, due to the difficulty in removing unwanted variations and the inexistence of prior knowledges on the performance of the available merging methods. It is therefore urgently needed to clarify whether DiMe can enhance the performance of metabolic profiling or not. Herein, the performance of DiMe on 4 pairs of benchmark datasets was comprehensively assessed by multiple criteria (classification capacity, robustness and false discovery rate). As a result, integration/merging-based strategies (ReIn and DiMe) were found to perform better under all criteria than those strategies based on single experiment. Moreover, DiMe was discovered to outperform ReIn in classification capacity and robustness, while the ReIn showed superior capacity in controlling false discovery rate. In conclusion, these findings provided valuable guidance to the selection of suitable analytical strategy for current metabolomics.

EDACO, a derivative of myricetin, inhibits the differentiation of Gaoyou duck embryonic osteoclasts in vitro

1. This study determined the effects of (E)-3-(2-(4-(3-(2,4-dimethoxyphenyl)acryloyl)phenoxy)ethoxy)-5,7-dimethoxy-2-(3,4,5-trimethoxyphenyl)-4H-chromen-4-one (EDACO) on the differentiation of Gaoyou duck embryonic osteoclasts cultured in vitro. 2. Bone marrow mononuclear cells (BM-MNC) were collected from 23-d-old Gaoyou duck embryos and induced by macrophage colony-stimulating factor and receptor activator of nuclear factor κB ligand in the presence of EDACO at different concentrations (i.e. 10, 20, 40, 80 and 160 µM). Tartrate-resistant acid phosphatase (TRAP) staining and resorption ability determination were conducted. 3. Results suggested that EDACO suppressed the shaping of positive multinucleated cells and the number of TRAP-positive cells in the 20, 40, 80 and 160 μM EDACO groups was significantly decreased (P < 0.05 or P < 0.01). Besides, the absorption activity of differentiated duck embryonic osteoclasts was significantly inhibited (P < 0.05) in both 80 and 160 μM EDACO groups. 4. Overall, EDACO can inhibit the differentiation of BM-MNC into mature osteoclasts in duck embryos.1.

[The clinical characteristics of 346 patients with IgG4-related disease]

Objective: To analyze the clinical characteristics of IgG₄-related disease (IgG₄-RD)so as to improve the understanding of IgG₄-RD in China. Methods: IgG₄-RD patients were recruited from Peking Union Medical College Hospital between January 2011 and January 2016. All patients were followed-up for more than 6 months. The demographic characteristics, symptoms, organ involvements, laboratory examinations and treatment efficacy were evaluated and analyzed. Results: A total of 346 patients were finally enrolled, including 230 males (66.5%) and 116 females (33.5%). The mean age of disease onset was (53.8±14.2) years old. The mostly common involved organs were lymph nodes (56.4%) and submandibular glands (52.6%). Other affected organs and manifestations included: swelling of the lacrimal glands (46.5%), autoimmune pancreatitis (38.4%), pulmonary involvement (28.0%), sclerosing cholangitis (25.4%), naso-sinusitis (23.4%), parotid gland swelling (21.7%), retroperitoneal fibrosis (19.9%), large arteries involvement (9.5%), kidney involvement (obstructive nephropathy caused by retroperitoneal fibrosis was excluded) (6.9%), skin lesions (6.4%). Rare features consisted of thyroid glands, pituitary glands, gastrointestinal tract, pachymeningitis, pericardium, sclerosing mediastinitis and orchitis. The majority of patients had multi-organ involvement, such as 74.3% patients with 3 and more, 18.2% and 7.5% patients with 2 and single organ involvement respectively. The average IgG₄-RD responder index (IgG₄-RD RI) was 13.21±5.70. History of allergy was found in 172 (49.7%) patients. As to the laboratory tests, elevated serum IgG₄ levels were confirmed in 285 (94.1%) patients, which was positively correlated with IgG₄-RD RI. There were 33.5% patients receiving monotherapy of glucocorticoid, 52.6% treated with glucocorticoids combined with immunosuppressive agents, 4.9% patients with immunosuppressant only, and 9.0% patients with mild disease not receiving medication. The majority (336, 97.1%) patients improved the above regimens. Conclusion: IgG₄-RD is a systemic fibro-inflammatory disease with multiple organ involvement. The mostly common involved organs include lymph node, submandibular glands, and pancreas. Glucocorticoids and immunosuppressive agents were effective for IgG₄-RD.