Metabolites of pathogenic microorganisms database (MPMdb) and its seed metabolite applications.

Seed metabolites are the combination of essential compounds required by an organism across various potential environmental conditions. The seed metabolites screening framework based on the network topology approach can capture important biological information of species. This study aims to identify comprehensively the relationship between seed metabolites and pathogenic bacteria. A large-scale data set was compiled, describing the seed metabolite sets and metabolite sets of 124,192 pathogenic strains from 34 genera, by constructing genome-scale metabolic models. The enrichment analysis method was used to screen the specific seed metabolites of each species/genus of pathogenic bacteria. The metabolites of pathogenic microorganisms database (MPMdb) (http://qyzhanglab.hzau.edu.cn/MPMdb/) was established for browsing, searching, predicting, or downloading metabolites and seed metabolites of pathogenic microorganisms. Based on the MPMdb, taxonomic and phylogenetic analyses of pathogenic bacteria were performed according to the function of seed metabolites and metabolites. The results showed that the seed metabolites could be used as a feature for microorganism chemotaxonomy, and they could mirror the phylogeny of pathogenic bacteria. In addition, our screened specific seed metabolites of pathogenic bacteria can be used not only for further tapping the nutritional resources and identifying auxotrophies of pathogenic bacteria but also for designing targeted bactericidal compounds by combining with existing antimicrobial agents.IMPORTANCEMetabolites serve as key communication links between pathogenic microorganisms and hosts, with seed metabolites being crucial for microbial growth, reproduction, external communication, and host infection. However, the large-scale screening of metabolites and the identification of seed metabolites have always been the main technical bottleneck due to the low throughput and costly analysis. Genome-scale metabolic models have become a recognized research paradigm to investigate the metabolic characteristics of species. The developed metabolites of pathogenic microorganisms database in this study is committed to systematically predicting and identifying the metabolites and seed metabolites of pathogenic microorganisms, which could provide a powerful resource platform for pathogenic bacteria research.

Characterization of brain resilience in Alzheimer's disease using polygenic risk scores and further improvement by integrating mitochondria-associated loci.

INTRODUCTION: Identification of high-risk people for Alzheimer's disease (AD) is critical for prognosis and early management. Longitudinal epidemiologic studies have observed heterogeneity in the brain and cognitive aging. Brain resilience was described as above-expected cognitive function. The "resilience" framework has been shown to correlate with individual characteristics such as genetic factors and age. Besides, accumulative evidence has confirmed the association of mitochondria with the pathogenesis of AD. However, it is challenging to assess resilience through genetic metrics, in particular incorporating mitochondria-associated loci. OBJECTIVES: In this paper, we first demonstrated that polygenic risk scores (PRS) could characterize individuals' resilience levels. Then, we indicated that mitochondria-associated loci could improve the performance of PRSs, providing more reliable measurements for the prevention and diagnosis of AD. METHODS: The discovery (N = 1,550) and independent validation samples (N = 2,090) were used to construct nine types of PRSs containing mitochondria-related loci (PRSMT) from both biological and statistical aspects and combined them with known AD risk loci derived from genome-wide association studies (GWAS).Individuals' levels of brain resilience were comprehensively measured by linear regression models using eight pathological characteristics. RESULTS: It was found that PRSs could characterize brain resilience levels (e.g., Pearson correlation test Pmin = 7.96×10-9). Moreover, the performance of PRS models could be efficiently improved by incorporating a small number of mitochondria-related loci (e.g., Pearson correlation test P improved from 1.41×10-3 to 6.09×10-6). PRSs' ability to characterize brain resilience was validated. More importantly, by incorporating some mitochondria-related loci, the performance of PRSs in measuring brain resilience could be significantly improved. CONCLUSION: Our findings imply that mitochondria may play an important role in brain resilience, and targeting mitochondria may open a new door to AD prevention and therapy.

Dissecting Mitochondrial Mechanisms of Alzheimer's Disease Using Gene Dependency Network and Its Implications for Discovering Nutrients Combatting the Disease.

BACKGROUND: Alzheimer's disease (AD) is the leading cause of dementia, with its prevalence increasing as the global population ages. AD is a multifactorial and intricate neurodegenerative disease with pathological changes varying from person to person. Because the mechanism of AD is highly controversial, effective treatments remain a distant prospect. Currently, one of the most promising hypotheses posits mitochondrial dysfunction as an early event in AD diagnosis and a potential therapeutic target. OBJECTIVE: Here, we adopted a systems medicine strategy to explore the mitochondria-related mechanisms of AD. Then, its implications for discovering nutrients combatting the disease were demonstrated. METHODS: We employed conditional mutual information (CMI) to construct AD gene dependency networks. Furthermore, the GeneRank algorithm was applied to prioritize the gene importance of AD patients and identify potential anti-AD nutrients targeting crucial genes. RESULTS: The results suggested that two highly interconnected networks of mitochondrial ribosomal proteins (MRPs) play an important role in the regulation of AD pathology. The close association between mitochondrial ribosome dysfunction and AD was identified. Additionally, we proposed seven nutrients with potential preventive and ameliorative effects on AD, five of which have been supported by experimental reports. CONCLUSIONS: Our study explored the important regulatory role of MRP genes in AD, which has significant implications for AD prevention and treatment.

Transporter proteins knowledge graph construction and its application in drug development.

Transporters are the main determinant for pharmacokinetics characteristics of drugs, such as absorption, distribution, and excretion of drugs in humans. However, it is difficult to perform drug transporter validation and structure analysis of membrane transporter proteins by experimental methods. Many studies have demonstrated that knowledge graphs (KG) could effectively excavate potential association information between different entities. To improve the effectiveness of drug discovery, a transporter-related KG was constructed in this study. Meanwhile, a predictive frame (AutoInt_KG) and a generative frame (MolGPT_KG) were established based on the heterogeneity information obtained from the transporter-related KG by the RESCAL model. Natural product Luteolin with known transporters was selected to verify the reliability of the AutoInt_KG frame, its ROC-AUC (1:1), ROC-AUC (1:10), PR-AUC (1:1), PR-AUC (1:10) are 0.91, 0.94, 0.91 and 0.78, respectively. Subsequently, the MolGPT_KG frame was constructed to implement efficient drug design based on transporter structure. The evaluation results showed that the MolGPT_KG could generate novel and valid molecules and that these molecules were further confirmed by molecular docking analysis. The docking results showed that they could bind to important amino acids at the active site of the target transporter. Our findings will provide rich information resources and guidance for the further development of the transporter-related drugs.

Evolution-strengthened knowledge graph enables predicting the targetability and druggability of genes.

Identifying promising targets is a critical step in modern drug discovery, with causative genes of diseases that are an important source of successful targets. Previous studies have found that the pathogeneses of various diseases are closely related to the evolutionary events of organisms. Accordingly, evolutionary knowledge can facilitate the prediction of causative genes and further accelerate target identification. With the development of modern biotechnology, massive biomedical data have been accumulated, and knowledge graphs (KGs) have emerged as a powerful approach for integrating and utilizing vast amounts of data. In this study, we constructed an evolution-strengthened knowledge graph (ESKG) and validated applications of ESKG in the identification of causative genes. More importantly, we developed an ESKG-based machine learning model named GraphEvo, which can effectively predict the targetability and the druggability of genes. We further investigated the explainability of the ESKG in druggability prediction by dissecting the evolutionary hallmarks of successful targets. Our study highlights the importance of evolutionary knowledge in biomedical research and demonstrates the potential power of ESKG in promising target identification. The data set of ESKG and the code of GraphEvo can be downloaded from https://github.com/Zhankun-Xiong/GraphEvo.

Systems Genetic Identification of Mitochondrion-Associated Alzheimer's Disease Genes and Implications for Disease Risk Prediction.

Cumulative evidence has revealed the association between mitochondrial dysfunction and Alzheimer's disease (AD). Because the number of mitochondrial genes is very limited, the mitochondrial pathogenesis of AD must involve certain nuclear genes. In this study, we employed systems genetic methods to identify mitochondrion-associated nuclear genes that may participate in the pathogenesis of AD. First, we performed a mitochondrial genome-wide association study (MiWAS, n = 809) to identify mitochondrial single-nucleotide polymorphisms (MT-SNPs) associated with AD. Then, epistasis analysis was performed to examine interacting SNPs between the mitochondrial and nuclear genomes. Weighted co-expression network analysis (WGCNA) was applied to transcriptomic data from the same sample (n = 743) to identify AD-related gene modules, which were further enriched by mitochondrion-associated genes. Using hub genes derived from these modules, random forest models were constructed to predict AD risk in four independent datasets (n = 743, n = 542, n = 161, and n = 540). In total, 9 potentially significant MT-SNPs and 14,340 nominally significant MT-nuclear interactive SNPs were identified for AD, which were validated by functional analysis. A total of 6 mitochondrion-related modules involved in AD pathogenesis were found by WGCNA, from which 91 hub genes were screened and used to build AD risk prediction models. For the four independent datasets, these models perform better than those derived from AD genes identified by genome-wide association studies (GWASs) or differential expression analysis (DeLong's test, p < 0.05). Overall, through systems genetics analyses, mitochondrion-associated SNPs/genes with potential roles in AD pathogenesis were identified and preliminarily validated, illustrating the power of mitochondrial genetics in AD pathogenesis elucidation and risk prediction.

Facilitating Antiviral Drug Discovery Using Genetic and Evolutionary Knowledge.

Over the course of human history, billions of people worldwide have been infected by various viruses. Despite rapid progress in the development of biomedical techniques, it is still a significant challenge to find promising new antiviral targets and drugs. In the past, antiviral drugs mainly targeted viral proteins when they were used as part of treatment strategies. Since the virus mutation rate is much faster than that of the host, such drugs feature drug resistance and narrow-spectrum antiviral problems. Therefore, the targeting of host molecules has gradually become an important area of research for the development of antiviral drugs. In recent years, rapid advances in high-throughput sequencing techniques have enabled numerous genetic studies (such as genome-wide association studies (GWAS), clustered regularly interspersed short palindromic repeats (CRISPR) screening, etc.) for human diseases, providing valuable genetic and evolutionary resources. Furthermore, it has been revealed that successful drug targets exhibit similar genetic and evolutionary features, which are of great value in identifying promising drug targets and discovering new drugs. Considering these developments, in this article the authors propose a host-targeted antiviral drug discovery strategy based on knowledge of genetics and evolution. We first comprehensively summarized the genetic, subcellular location, and evolutionary features of the human genes that have been successfully used as antiviral targets. Next, the summarized features were used to screen novel druggable antiviral targets and to find potential antiviral drugs, in an attempt to promote the discovery of new antiviral drugs.

Agent Clustering Strategy Based on Metabolic Flux Distribution and Transcriptome Expression for Novel Drug Development.

The network module-based method has been used for drug repositioning. The traditional drug repositioning method only uses the gene characteristics of the drug but ignores the drug-triggered metabolic changes. The metabolic network systematically characterizes the connection between genes, proteins, and metabolic reactions. The differential metabolic flux distribution, as drug metabolism characteristics, was employed to cluster the agents with similar MoAs (mechanism of action). In this study, agents with the same pharmacology were clustered into one group, and a total of 1309 agents from the CMap database were clustered into 98 groups based on differential metabolic flux distribution. Transcription factor (TF) enrichment analysis revealed the agents in the same group (such as group 7 and group 26) were confirmed to have similar MoAs. Through this agent clustering strategy, the candidate drugs which can inhibit (Japanese encephalitis virus) JEV infection were identified. This study provides new insights into drug repositioning and their MoAs.

Development of specific and selective bactericide by introducing exogenous metabolite of pathogenic bacteria.

The widespread and repeated use of broad-spectrum bactericides has led to an increase in resistance. Developing novel broad-spectrum bactericides cannot solve the resistance problem, and may even aggravate it. The design of specific and selective bactericides has become urgent. A specific bactericidal design strategy was proposed by introducing exogenous metabolites in this study. This strategy was used to optimize two known antibacterial agents, luteolin (M) and Isoprothiolane (D), against Xoo. Based on the prodrug principles, target compound MB and DB were synthesized by combing M or D with exogenous metabolites, respectively. Bactericidal activity test results demonstrated that while the antibacterial ability of target compounds was significantly improved, their selectivity was also well enhanced by the introducing of exogenous metabolites. Comparing with the original compound, the antibacterial activity of target compound was significantly increased 92.0% and 74.5%, respectively. The optimized target compounds were more easily absorbed, and the drug application concentrations were much lower than those of the original agents, which would greatly reduce environmental pollution and relieve resistance risk. Our proposed strategy is of great significance for exploring the specific and selective bactericides against other pathogens.

Genome-wide epistasis analysis for Alzheimer's disease and implications for genetic risk prediction.

BACKGROUND: Single-nucleotide polymorphisms (SNPs) identified by genome-wide association studies only explain part of the heritability of Alzheimer's disease (AD). Epistasis has been considered as one of the main causes of "missing heritability" in AD. METHODS: We performed genome-wide epistasis screening (N = 10,389) for the clinical diagnosis of AD using three popularly adopted methods. Subsequent analyses were performed to eliminate spurious associations caused by possible confounding factors. Then, candidate genetic interactions were examined for their co-expression in the brains of AD patients and analyzed for their association with intermediate AD phenotypes. Moreover, a new approach was developed to compile the epistasis risk factors into an epistasis risk score (ERS) based on multifactor dimensional reduction. Two independent datasets were used to evaluate the feasibility of ERSs in AD risk prediction. RESULTS: We identified 2 candidate genetic interactions with PFDR <  0.05 (RAMP3-SEMA3A and NSMCE1-DGKE/C17orf67) and another 5 genetic interactions with PFDR <  0.1. Co-expression between the identified interactions supported the existence of possible biological interactions underlying the observed statistical significance. Further association of candidate interactions with intermediate phenotypes helps explain the mechanisms of neuropathological alterations involved in AD. Importantly, we found that ERSs can identify high-risk individuals showing earlier onset of AD. Combined risk scores of SNPs and SNP-SNP interactions showed slightly but steadily increased AUC in predicting the clinical status of AD. CONCLUSIONS: In summary, we performed a genome-wide epistasis analysis to identify novel genetic interactions potentially implicated in AD. We found that ERS can serve as an indicator of the genetic risk of AD.

Genome-wide pathogenesis interpretation using a heat diffusion-based systems genetics method and implications for gene function annotation.

BACKGROUND: Genetics is best dedicated to interpreting pathogenesis and revealing gene functions. The past decade has witnessed unprecedented progress in genetics, particularly in genome-wide identification of disorder variants through Genome-Wide Association Studies (GWAS) and Phenome-Wide Association Studies (PheWAS). However, it is still a great challenge to use GWAS/PheWAS-derived data to elucidate pathogenesis. METHODS: In this study, we used HotNet2, a heat diffusion-based systems genetics algorithm, to calculate the networks for disease genes obtained from GWAS and PheWAS, with an attempt to get deeper insights into disease pathogenesis at a molecular level. RESULTS: Through HotNet2 calculation, significant networks for 202 (for GWAS) and 167 (for PheWAS) types of diseases were identified and evaluated, respectively. The GWAS-derived disease networks exhibit a stronger biomedical relevance than PheWAS counterparts. Therefore, the GWAS-derived networks were used for pathogenesis interpretation by integrating the accumulated biomedical information. As a result, the pathogenesis for 64 diseases was elucidated in terms of mutation-caused abnormal transcriptional regulation, and 47 diseases were preliminarily interpreted in terms of mutation-caused varied protein-protein interactions. In addition, 3,802 genes (including 46 function-unknown genes) were assigned with new functions by disease network information, some of which were validated through mice gene knockout experiments. CONCLUSIONS: Systems genetics algorithm HotNet2 can efficiently establish genotype-phenotype links at the level of biological networks. Compared with original GWAS/PheWAS results, HotNet2-calculated disease-gene associations have stronger biomedical significance, hence provide better interpretations for the pathogenesis of genome-wide variants, and offer new insights into gene functions as well. These results are also helpful in drug development.

An analysis of the mechanism underlying photocatalytic disinfection based on integrated metabolic networks and transcriptional data.

Photocatalytic disinfection has long been used to combat pathogenic bacteria. However, the specific mechanism underlying photocatalytic disinfection and its corresponding targets remain unclear. In this study, an analysis of the potential mechanism underlying photocatalytic disinfection was performed based on integrated metabolic networks and transcriptional data. Two sets of RNA-seq data (wild type and a photocatalysis-resistant mutant mediated by titanium dioxide (TiO2)) were processed to constrain the genome scale metabolic models (GSMM) of E. coli. By analyzing the metabolic network, the differential metabolic flux of every reaction was computed in constrained GSMM, and several significantly differential metabolic fluxes in reactions were extracted and analyzed. Most of these reactions were involved in the transmembrane transport of substances and occurred on the inner membrane or were an important component of the cell membrane. These results, which are consistent with the reported information, validated our analysis process. In addition, our work also identified other new and valuable metabolic pathways, such as the reaction ALCD2x, which has a great effect on the energy production process under bacterial anaerobic conditions. The DHAK reaction is also related to the metabolic process of ATP. These reactions with large differential metabolic fluxes merit further research. Additionally, to provide a strategy to address photocatalysis-resistant mutant bacteria, a metabolic compensation analysis was also performed. The metabolic compensation analysis results provided suggestions for a combined method that can effectively combat resistant bacteria. This method could also be used to explore the mechanisms of drug resistance in other microorganisms.

Identification of NUDT5 Inhibitors From Approved Drugs.

Recent studies have revealed the important role of NUDT5 in estrogen signaling and breast cancer, but research on the corresponding targeted therapy has just started. Drug repositioning strategy can effectively reduce the time and economic resources spent on drug discovery. To find novel inhibitors of NUDT5, we investigated the previously identified connectivity map-based drug association models and found eighteen FDA approved drugs as candidates. The molecular docking and molecular dynamic simulation were performed and revealed that fourteen organic drugs have the potential to bind the NUDT5 target. Eight representative drugs were selected to perform the cell line viability inhibition analysis, and the results showed that seven of them were able to suppress MCF7 breast cancer cells. Two drugs, nomifensine and isoconazole, showed lower IC50 than the known antiestrogens raloxifene and tamoxifen, and they deserve further pharmacodynamic investigations to test their feasibility for use as NUDT5 inhibitors.

Using a Heat Diffusion Model to Detect Potential Drug Resistance Genes of Mycobacterium tuberculosis.

BACKGROUND: Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), is one of the oldest known and most dangerous diseases. Although the spread of TB was controlled in the early 20th century using antibiotics and vaccines, TB has again become a threat because of increased drug resistance. There is still a lack of effective treatment regimens for a person who is already infected with multidrug-resistant Mtb (MDR-Mtb) or extensively drug-resistant Mtb (XDRMtb). In the past decades, many research groups have explored the drug resistance profiles of Mtb based on sequence data by GWAS, which identified some mutations that were significantly linked with drug resistance, and attempted to explain the resistance mechanisms. However, they mainly focused on several significant mutations in drug targets (e.g. rpoB, katG). Some genes which are potentially associated with drug resistance may be overlooked by the GWAS analysis. OBJECTIVE: In this article, our motivation is to detect potential drug resistance genes of Mtb using a heat diffusion model. METHODS: All sequencing data, which contained 127 samples of Mtb, i.e. 34 ethambutol-, 65 isoniazid-, 53 rifampicin- and 45 streptomycin-resistant strains. The raw sequence data were preprocessed using Trimmomatic software and aligned to the Mtb H37Rv reference genome using Bowtie2. From the resulting alignments, SAMtools and VarScan were used to filter sequences and call SNPs. The GWAS was performed by the PLINK package to obtain the significant SNPs, which were mapped to genes. The P-values of genes calculated by GWAS were transferred into a heat vector. The heat vector and the Mtb protein-protein interactions (PPI) derived from the STRING database were inputted into the heat diffusion model to obtain significant subnetworks by HotNet2. Finally, the most significant (P < 0.05) subnetworks associated with different phenotypes were obtained. To verify the change of binding energy between the drug and target before and after mutation, the method of molecular dynamics simulation was performed using the AMBER software. RESULTS: We identified significant subnetworks in rifampicin-resistant samples. Excitingly, we found rpoB and rpoC, which are drug targets of rifampicin. From the protein structure of rpoB, the mutation location was extremely close to the drug binding site, with a distance of only 3.97 Å. Molecular dynamics simulation revealed that the binding energy of rpoB and rifampicin decreased after D435V mutation. To a large extent, this mutation can influence the affinity of drug-target binding. In addition, topA and pyrG were reported to be linked with drug resistance, and might be new TB drug targets. Other genes that have not yet been reported are worth further study. CONCLUSION: Using a heat diffusion model in combination with GWAS results and protein-protein interactions, the significantly mutated subnetworks in rifampicin-resistant samples were found. The subnetwork not only contained the known targets of rifampicin (rpoB, rpoC), but also included topA and pyrG, which are potentially associated with drug resistance. Together, these results offer deeper insights into drug resistance of Mtb, and provides potential drug targets for finding new antituberculosis drugs.

Potential Targets of Actein Identified by Systems Chemical Biology Methods.

Actein is the main active ingredient of medicinal plant Cimicifuga racemosa (L.) Nutt, which has been reported to have various pharmacological effects, but the mechanism of actein remains undetermined. In this study, systems chemical biology methods were used to predict the targets and elucidate the pharmacological mechanisms of actein. First, 54 gene co-expression modules were obtained by biclustering. Then, the top 1 % agents with the highest regulatory similarity were screened out to be highly functionally similar to actein. Finally, the results of molecular docking and molecular dynamics simulation showed that actein has a stronger interaction with eight targets than original ligands. It suggests that the antipsychotic effect of actein probably occurs by targeting the key residues of the eight receptors, which are compatible with previously reported information. This study not only provides predicted targets of actein, but also a new method for exploring the mechanisms of other natural products in drug discovery.

Drug Repurposing for Japanese Encephalitis Virus Infection by Systems Biology Methods.

Japanese encephalitis is a zoonotic disease caused by the Japanese encephalitis virus (JEV). It is mainly epidemic in Asia with an estimated 69,000 cases occurring per year. However, no approved agents are available for the treatment of JEV infection, and existing vaccines cannot control various types of JEV strains. Drug repurposing is a new concept for finding new indication of existing drugs, and, recently, the concept has been used to discover new antiviral agents. Identifying host proteins involved in the progress of JEV infection and using these proteins as targets are the center of drug repurposing for JEV infection. In this study, based on the gene expression data of JEV infection and the phenome-wide association study (PheWAS) data, we identified 286 genes that participate in the progress of JEV infection using systems biology methods. The enrichment analysis of these genes suggested that the genes identified by our methods were predominantly related to viral infection pathways and immune response-related pathways. We found that bortezomib, which can target these genes, may have an effect on the treatment of JEV infection. Subsequently, we evaluated the antiviral activity of bortezomib using a JEV-infected mouse model. The results showed that bortezomib can lower JEV-induced lethality in mice, alleviate suffering in JEV-infected mice and reduce the damage in brains caused by JEV infection. This work provides an agent with new indication to treat JEV infection.

Identification of Non-Electrophilic Nrf2 Activators from Approved Drugs.

Oxidative damage can lead to a wide range of diseases. Nrf2 is an important transcription factor that regulates many of the cytoprotective enzymes involved in the oxidative stress response. Therefore, targeting the regulation of Nrf2 activation is one logical and effective strategy to prevent or lower the risk of oxidative stress-related diseases. Until now, most research has focused on electrophilic indirect Nrf2 activators, but the risk of 'off-target' effects may be associated with these activators. To find novel small non-electrophilic modulators of Nrf2, we started from chemical agents derived from a connectivity map (cMap) and identified 22 non-electrophilic potential Nrf2-activating drugs through a drug repositioning tactic. By determining the expression changes of antioxidant genes in MCF7 cells that were treated with the potential Nrf2 activators using quantitative real-time polymerase chain reaction RT-PCR (real-time polymerase chain reaction) (qRT-PCR), astemizole was found to have a greater scale of upregulating antioxidant genes NQO1, HO-1, and GCLM than the positive control d,l-sulforaphane, although the testing concentration was lower than that of the control. Astemizole is a good potential redox regulator and deserves more pharmacodynamic experimentation to test and verify its feasibility for use as an Nrf2 activator.

Bioinformatics Identification of Drug Resistance-Associated Gene Pairs in Mycobacterium tuberculosis.

Tuberculosis is a chronic infectious disease caused by Mycobacterium tuberculosis (Mtb). Due to the extensive use of anti-tuberculosis drugs and the development of mutations, the emergence and spread of multidrug-resistant tuberculosis is recognized as one of the most dangerous threats to global tuberculosis control. Some single mutations have been identified to be significantly linked with drug resistance. However, the prior research did not take gene-gene interactions into account, and the emergence of transmissible drug resistance is connected with multiple genetic mutations. In this study we use the bioinformatics software GBOOST (The Hong Kong University, Clear Water Bay, Kowloon, Hong Kong, China) to calculate the interactions of Single Nucleotide Polymorphism (SNP) pairs and identify gene pairs associated with drug resistance. A large part of the non-synonymous mutations in the drug target genes that were included in the screened gene pairs were confirmed by previous reports, which lent sound solid credits to the effectiveness of our method. Notably, most of the identified gene pairs containing drug targets also comprise Pro-Pro-Glu (PPE) family proteins, suggesting that PPE family proteins play important roles in the drug resistance of Mtb. Therefore, this study provides deeper insights into the mechanisms underlying anti-tuberculosis drug resistance, and the present method is useful for exploring the drug resistance mechanisms for other microorganisms.

An integrated approach of bioassay and molecular docking to study the dihydroxylation mechanism of pyrene by naphthalene dioxygenase in Rhodococcus sp. ustb-1.

Naphthalene dioxygenase (NDO) is the initial enzyme catalyzing the biodegradation of aromatic compounds, and it plays a key role in microbial remediation of polluting sites. In this study, Rhodococcus sp. ustb-1 derived from crude oil was selected to investigate the biodegradation characters and dihydroxylation mechanism of pyrene by an integrated approach of bioassay and molecular docking. The biodegradation experiment proved that the strain ustb-1 shows high effective biodegradability to pyrene with a 70.8% degradation on the 28th day and the metabolite pyrene cis-4,5-dihydrodiol was found. The results of molecular docking indicated that the regions surrounding pyrene are defined by hydrophobic amino acids which are favorable for the binding of dioxygen molecule at C4 and C5 positions of pyrene in a side-on mode. The binding positions of dioxygen are in agreement with the mass spectral analysis of the metabolite pyrene cis-4,5-dihydrodiol. In summary, this study provides a promising explanation for the possible binding behavior between pyrene and active site of NDO.

[Analyze and compare metabolic pathways of Bacillus cereus group].

A large number of data and information was obtained from genome sequencing and high-throughput genomic studies, use of the information to study metabolic networks become a new hotspot in biological research. This article compared different methods to reconstruct metabolic networks and analyzed the advantages and disadvantages of each methods, and then introduced some researches about carbohydrate metabolism pathways, amino acid metabolic pathways, and energy metabolism pathways of 9 strains of Bacillus cereus, 6 strains of B. anthracis,,6 strain of B. thuringiensis, and finds out their similarities and characteristics. These three strains have some necessary metabolic pathways, such as glycolysis, tri-carboxylic acid cycle, alanine metabolism, histidine metabolism, and energy metabolism, but they may have some specific pathways. B cereus has higher efficiency in utilizing monosaccharide, B. anthracis is rich in degradation and transport pathways of amino acids. A glutamate metabolic bypass way exists in B. thuringiensis. Analysis of metabolic pathways provides a new way to study and use food toxin, anthrax toxin, and insecticidal toxin of these strains in future.