Drug Target Genes Research Articles

Identification of the Mechanism of Matrine Combined with Glycyrrhizin for Hepatocellular Carcinoma Treatment through Network Pharmacology and Bioinformatics Analysis.

Matrine and glycyrrhizin are representative active ingredients of traditional Chinese medicine (TCM) used in clinical practice. Studies have demonstrated that matrine has antitumor pharmacological effects and that glycyrrhizin protects liver function. However, the potential bioactive compounds and mechanisms remain unknown, as well as whether they have synergistic effects in killing cancer cells and protecting liver cells. To investigate the synergistic effects and mechanism of matrine combined with glycyrrhizin in hepatocellular carcinoma (HCC) treatment, we used both network pharmacology and bioinformatics analyses. First, the chemical gene interaction information of matrine and glycyrrhizin was obtained from the PubChem database. The pathogenic genes of HCC were accessed from five public databases. The RNA sequencing data and clinical information of HCC patients were downloaded from The Cancer Genome Atlas (TCGA). Next, the overlapping genes among the potential targets of matrine and glycyrrhizin and HCC-related targets were determined using bioinformatics analysis. We constructed the drug-target interaction network. Prognosis-associated genes were acquired through the univariate Cox regression model and Lasso-Cox regression model. The results were verified by the International Cancer Genome Consortium (ICGC) database. Finally, we predicted the immune function of the samples. The drug-target interaction network consisted of 10 matrine and glycyrrhizin targets. We selected a Lasso-Cox regression model consisting of 3 differentially expressed genes (DEGs) to predict the efficacy of the combination in HCC. Subsequently, we successfully predicted the overall survival of HCC patients using the constructed prognostic model and investigated the correlation of the immune response. Matrine and glycyrrhizin have synergistic effects on HCC. The model we obtained consisted of three drug-target genes by Lasso-Cox regression analysis. The model independently predicted the combined effect of matrine and glycyrrhizin in HCC treatment and OS, which will be helpful for guiding clinical treatment. The prognostic model was correlated with the immune cells and immune checkpoints of patients, which had an adjuvant effect on HCC immunotherapy. Matrine and glycyrrhizin can have therapeutic effects on HCC by promoting the production or enhancing the core gene activity in the drug network and improving the immune system function of patients.

Drug-target genes and their spontaneous mutations associated with resistance to first-line, second-line, third-line, novel and repurposed anti-tuberculosis drugs in Mycobacterium tuberculosis resistant strains

Drug-resistant tuberculosis is a threat to the control of tuberculosis globally, it develops mainly due to mutations in target genes of (MTB). Mutations in the rpoB gene confer resistance to rifampicin (RIF). The most frequent mutations conferring resistance to RIF include; Ser531Leu, Asp516Val, and His526Asp. Isoniazid resistance (INHr) occur most frequently due to mutations in the and its promoter. Most frequent mutation in is Ser315Thr 1, while in inhA include; Thr8Cys, Ala16Gly, and Cys15Thr. Mutations in , and genes confer resistance to ethambutol. 70% of mutations in the gene occur in codon 306, 406, or 497 and include; Met306Leu, Gly43Cys, and Ser412Pro. Mutations in the , and genes mediate resistance to pyrazinamide. Frequent mutations in A include; Tyr64Ser, Phe94Ala, and Trp68Gly. MTB resistance to streptomycin (STR) occur due to mutations in the , B, and L genes. Mutations (Ala80Pro), and L (Lys43Arg) confer resistance to STR. Fluoroquinolone resistance is mediated via mutations in the A and B genes. The most common mutations in the A gene include; Gly88Cys, Ala90Val, and Ser91Pro. While those in the gyrB gene include; Glu540Val, and Asn538Asp. Mutations in the rrs and promoter region cause resistance to the kanamycin and amikacin. While mutations in the and A cause resistance to capreomycin and viomycin. Common mutations in include; Cys1402Thr, Ala1401Gly, and Gly1484Thr. While mutations in the include; Cys12Thr, Gly10Ala, and Gly37Thr.Detection of drug-target genes and their mutations has therapeutic and diagnostic value.

Safety of beta-blocker and calcium channel blocker antihypertensive drugs in pregnancy: a Mendelian randomization study

BackgroundBeta-blocker (BB) and calcium channel blocker (CCB) antihypertensive drugs are commonly used in pregnancy. However, data on their relative impact on maternal and foetal outcomes are limited. We leveraged genetic variants mimicking BB and CCB antihypertensive drugs to investigate their effects on risk of pre-eclampsia, gestational diabetes and birthweight using the Mendelian randomization paradigm.MethodsGenetic association estimates for systolic blood pressure (SBP) were extracted from summary data of a genome-wide association study (GWAS) on 757,601 participants. Uncorrelated single-nucleotide polymorphisms (SNPs) associated with SBP (p < 5 × 10−8) in BB and CCB drug target gene regions were selected as proxies for drug target perturbation. Genetic association estimates for the outcomes were extracted from GWASs on 4743 cases and 136,325 controls (women without a hypertensive disorder in pregnancy) for pre-eclampsia or eclampsia, 7676 cases and 130,424 controls (women without any pregnancy-related morbidity) for gestational diabetes, and 155,202 women (who have given birth at least once) for birthweight of the first child. All studies were in European ancestry populations. Mendelian randomization estimates were generated using the two-sample inverse-variance weighted model.ResultsAlthough not reaching the conventional threshold for statistical significance, genetically-proxied BB was associated with reduced risk of pre-eclampsia (OR per 10 mmHg SBP reduction 0.27, 95%CI 0.06–1.19, p = 0.08) and increased risk of gestational diabetes (OR per 10 mmHg SBP reduction 2.01, 95%CI 0.91–4.42, p = 0.08), and significantly associated with lower birthweight of first child (beta per 10 mmHg SBP reduction − 0.27, 95%CI − 0.39 to − 0.15, p = 1.90 × 10−5). Genetically-proxied CCB was associated with reduced risk of pre-eclampsia and eclampsia (OR 0.62, 95%CI 0.43–0.89, p = 9.33 × 10−3), and was not associated with gestational diabetes (OR 1.05, 95% CI 0.76–1.45, p = 0.76) or changes in birthweight of first child (beta per 10 mmHg SBP reduction 0.02, 95%CI − 0.04–0.07, p = 0.54).ConclusionsWhile BB and CCB antihypertensive drugs may both be efficacious for lowering blood pressure in pregnancy, this genetic evidence suggests that BB use may lower birthweight. Conversely, CCB use may reduce risk of pre-eclampsia and eclampsia without impacting gestational diabetes risk or birthweight. These data support further study on the effects of BBs on birthweight.

A Tumor‐Organoid‐Based Precision Medicine Platform for the Prediction of Drug Sensitivity of Colorectal Cancer

AbstractInvestigation of clinical actionable targets is crucial for the individual treatment of patients with advanced colorectal cancer (CRC). A complete platform integrating the molecular profiling and prediction of drug reactivity is currently scarce. An integrated precision medicine approach which combines high‐throughput sequencing platform with microfluidic organoid drug testing system is established. Capture sequencing identifies 6 drug targetable genes and 13 candidate drugs for a stage III colorectal cancer patient. The drug sensitivity is confirmed by using patient‐derived organoids (PDOs) model and microfluidic drug testing system. With this integrated precision medicine approach, the optimal drugs including Afatinib, Dacomitinib, and Osimertinib are successfully identified. The platform provides a practical method to guide the clinical therapies for colorectal cancer patients.

Polygenic enrichment distinguishes disease associations of individual cells in single-cell RNA-seq data.

Single-cell RNA sequencing (scRNA-seq) provides unique insights into the pathology and cellular origin of disease. We introduce single-cell disease relevance score (scDRS), an approach that links scRNA-seq with polygenic disease risk at single-cell resolution, independent of annotated cell types. scDRS identifies cells exhibiting excess expression across disease-associated genes implicated by genome-wide association studies (GWASs). We applied scDRS to 74 diseases/traits and 1.3 million single-cell gene-expression profiles across 31 tissues/organs. Cell-type-level results broadly recapitulated known cell-type-disease associations. Individual-cell-level results identified subpopulations of disease-associated cells not captured by existing cell-type labels, including T cell subpopulations associated with inflammatory bowel disease, partially characterized by their effector-like states; neuron subpopulations associated with schizophrenia, partially characterized by their spatial locations; and hepatocyte subpopulations associated with triglyceride levels, partially characterized by their higher ploidy levels. Genes whose expression was correlated with the scDRS score across cells (reflecting coexpression with GWAS disease-associated genes) were strongly enriched for gold-standard drug target and Mendelian disease genes.

Genomic variants-driven drug repurposing for tuberculosis by utilizing the established bioinformatic-based approach

A major challenge in translating genomic variants of Tuberculosis (TB) into clinical implementation is to integrate the disease-associated variants and facilitate drug discovery through the concept of genomic-driven drug repurposing. Here, we utilized two established genomic databases, namely a Genome-Wide Association Study (GWAS) and a Phenome-Wide Association Study (PheWAS) to identify the genomic variants associated with TB disease and further utilize them for drug-targeted genes. We evaluated 3.425 genomic variants associated with TB disease which overlapped with 200 TB-associated genes. To prioritize the biological TB risk genes, we devised an in-silico pipeline and leveraged an established bioinformatics method based on six functional annotations (missense mutation, cis-eQTL, biological process, cellular component, molecular function, and KEGG molecular pathway analysis). Interestingly, based on the six functional annotations that we applied, we discovered that 14 biological TB risk genes are strongly linked to the deregulation of the biological TB risk genes. Hence, we demonstrated that 12 drug target genes overlapped with 40 drugs for other indications and further suggested that the drugs may be repurposed for the treatment of TB. We highlighted that CD44, CCR5, CXCR4, and C3 are highly promising proposed TB targets since they are connected to SELP and HLA-B, which are biological TB risk genes with high systemic scores on functional annotations. In sum, the current study shed light on the genomic variants involved in TB pathogenesis as the biological TB risk genes and provided empirical evidence that the genomics of TB may contribute to drug discovery.

Characterizing the Mechanisms of Metalaxyl, Bronopol and Copper Sulfate against Saprolegnia parasitica Using Modern Transcriptomics.

Saprolegniasis, which is caused by Saprolegnia parasitica, leads to considerable economic losses. Recently, we showed that metalaxyl, bronopol and copper sulfate are good antimicrobial agents for aquaculture. In the current study, the efficacies of metalaxyl, bronopol and copper sulfate are evaluated by in vitro antimicrobial experiments, and the mechanism of action of these three antimicrobials on S. parasitica is explored using transcriptome technology. Finally, the potential target genes of antimicrobials on S. parasitica are identified by protein–protein interaction network analysis. Copper sulfate had the best inhibitory effect on S. parasitica, followed by bronopol. A total of 1771, 723 and 2118 DEGs upregulated and 1416, 319 and 2161 DEGs downregulated S. parasitica after three drug treatments (metalaxyl, bronopol and copper sulfate), separately. Additionally, KEGG pathway analysis also determined that there were 17, 19 and 13 significantly enriched metabolic pathways. PPI network analysis screened out three important proteins, and their corresponding genes were SPRG_08456, SPRG_03679 and SPRG_10775. Our results indicate that three antimicrobials inhibit S. parasitica growth by affecting multiple biological functions, including protein synthesis, oxidative stress, lipid metabolism and energy metabolism. Additionally, the screened key genes can be used as potential target genes of chemical antimicrobial drugs for S. parasitica.

Interactome overlap between risk genes of epilepsy and targets of anti-epileptic drugs.

Aanti-epileptic drugs have been used for treating epilepsy for decades, meanwhile, more than one hundred genes have been identified to be associated with risk of epilepsy; however, the interaction mechanism between anti-epileptic drugs and risk genes of epilepsy was still not clearly understood. In this study, we systematically explored the interaction of epilepsy risk genes and anti-epileptic drug targets through a network-based approach. Our results revealed that anti-epileptic drug targets were significantly over-represented in risk genes of epilepsy with 17 overlapping genes and P-value = 2.2 ×10 -16. We identified a significantly localized PPI network with 55 epileptic risk genes and 94 anti-epileptic drug target genes, and network overlap analysis showed significant interactome overlap between risk genes and drug targets with P-value = 0.04. Besides, genes from PPI network were significantly enriched in the co-expression network of epilepsy with 22 enriched genes and P-value = 1.3 ×10 -15; meanwhile, cell type enrichment analysis indicated genes in this network were significantly enriched in 4 brain cell types (Interneuron, Medium Spiny Neuron, CA1 pyramidal Neuron, and Somatosensory pyramidal Neuron). These results provide evidence for significant interactions between epilepsy risk genes and anti-epileptic drug targets from the perspective of network biology.

Lung adenocarcinoma-related target gene prediction and drug repositioning.

Lung cancer is the leading cause of cancer deaths globally, and lung adenocarcinoma (LUAD) is the most common type of lung cancer. Gene dysregulation plays an essential role in the development of LUAD. Drug repositioning based on associations between drug target genes and LUAD target genes are useful to discover potential new drugs for the treatment of LUAD, while also reducing the monetary and time costs of new drug discovery and development. Here, we developed a pipeline based on machine learning to predict potential LUAD-related target genes through established graph attention networks (GATs). We then predicted potential drugs for the treatment of LUAD through gene coincidence-based and gene network distance-based methods. Using data from 535 LUAD tissue samples and 59 precancerous tissue samples from The Cancer Genome Atlas, 48,597 genes were identified and used for the prediction model building of the GAT. The GAT model achieved good predictive performance, with an area under the receiver operating characteristic curve of 0.90. 1,597 potential LUAD-related genes were identified from the GAT model. These LUAD-related genes were then used for drug repositioning. The gene overlap and network distance with the target genes were calculated for 3,070 drugs and 672 preclinical compounds approved by the US Food and Drug Administration. At which, bromoethylamine was predicted as a novel potential preclinical compound for the treatment of LUAD, and cimetidine and benzbromarone were predicted as potential therapeutic drugs for LUAD. The pipeline established in this study presents new approach for developing targeted therapies for LUAD.

Mutation patterns of resistance genes for macrolides, aminoglycosides, and rifampicin in nontuberculous mycobacteria isolates from Kenya

Background: Nontuberculous mycobacteria (NTM) treatment constitutes a macrolide-based antibiotic regimen in combination with aminoglycosides for Rapid-Growing mycobacteria (RGM), and rifampicin for Slow-Growing mycobacteria (SGM). Mutations in the anti-NTM drug target regions promote NTM evolution to mutant strains that are insusceptible to NTM drugs leading to treatment failure. We, therefore, described the mutation patterns of anti-NTM drug target genes including rrl, rrs, and rpoB in NTM isolates from Kenya. Methods: We carried out a cross-sectional study that included 122 NTM obtained from the sputum of symptomatic tuberculosis-negative patients in Kenya. All the 122 NTM underwent targeted sequencing of the rrl gene. The 54 RGM were also sequenced for rrs, and the 68 SGM were sequenced for rpoB genes using ABI 3730XL analyzer. The obtained sequences were aligned to their wild-type reference sequences for each gene using Geneious then mutations were identified. Pearson chi-square at 95% confidence interval tested the association of NTM to mutation patterns for each gene. Results: Twenty-eight (23%) of the NTM were resistant to at least one of the antibiotics used in the macrolide-based treatment. Twelve (10.4%) of NTM were macrolide resistant, with 7(58.3%) of RGM and 5(41.7%) of SGM having mutations in the rrl gene. For ten (83.3%) NTM, mutations were found at position 2058, while for two (16.6%) NTM, mutations were found at position 2059. Six (11.1%) of the 54 RGM exhibited mutations in the aminoglycoside target gene rrs at location 1408. Ten (14.7%) of the 68 SGM were resistant to rifampicin, with 40 percent having mutations at codon 531 in the rpoB gene. Conclusion: We demonstrated a significant level of drug resistance for macrolides, aminoglycosides and rifampicin in NTM isolated from symptomatic TB negative patients in Kenya.

Transcriptomic effects of propranolol and primidone converge on molecular pathways relevant to essential tremor

Essential tremor (ET) is one of the most common movement disorders, affecting nearly 5% of individuals over 65 years old. Despite this, few genetic risk loci for ET have been identified. Recent advances in pharmacogenomics have previously been useful to identify disease related molecular targets. Notably, gene expression has proven to be quite successful for the inference of drug response in cell models. We sought to leverage this approach in the context of ET where many patients are responsive to two drugs: propranolol and primidone. In this study, cerebellar DAOY and neural progenitor cells were treated for 5 days with clinical concentrations of propranolol and primidone, after which RNA-sequencing was used to identify convergent differentially expressed genes across treatments. Propranolol was found to affect the expression of genes previously associated with ET and other movement disorders such as TRAPPC11. Pathway enrichment analysis of these convergent drug-targeted genes identified multiple terms related to calcium signaling, endosomal sorting, axon guidance, and neuronal morphology. Furthermore, genes targeted by ET drugs were enriched within cell types having high expression of ET-related genes in both cortical and cerebellar tissues. Altogether, our results highlight potential cellular and molecular mechanisms associated with tremor reduction and identify relevant genetic biomarkers for drug-responsiveness in ET.

The Resistance Mechanism of Mycoplasma bovis From Yaks in Tibet to Fluoroquinolones and Aminoglycosides.

Mycoplasma bovis (M. bovis) is one of the important pathogens for yaks. Aminoglycosides and fluoroquinolones are frequently used medications for the treatment of M. bovis. Drug-resistant strains were inevitable with the abuse of antibiotics. The resistance of M. bovis to aminoglycosides was related to the base mutations in drug target genes. Amino acid mutations at the quinolone resistance-determining region (QRDR) in gyrA, gyrB, parC, and parE conferred resistance to fluoroquinolones. In order to investigate the resistance mechanism of M. bovis from yaks in Tibet to aminoglycosides and fluoroquinolones, six frequently used antibiotics and ten clinical M. bovis strains were administered for a drug sensitivity test for in vitro-induced highly resistant strains, a drug stable-resistance test, cross-resistance test, and analysis of target gene mutations. The results showed that the clinical strains of M. bovis from yaks in Tibet had varying degrees of resistance to fluoroquinolones and aminoglycosides. The mechanism of resistance to fluoroquinolones and aminoglycosides was identified preliminarily for M. bovis from yaks: the single-site base mutation mediated the resistance of M. bovis from yaks and both base mutations led to highly resistant strains (aminoglycosides: rrs3 and rrs4; fluoroquinolones: gyrA and parC). The active efflux system results of M. bovis showed that there was no active efflux system based on fluoroquinolones and aminoglycosides expressed in M. bovis from yaks. The research could provide a reference for clinical treatment of M. bovis.

Uncontrolled transposition following RNAi loss causes hypermutation and antifungal drug resistance in clinical isolates of Cryptococcus neoformans.

Cryptococcus neoformans infections cause approximately 15% of AIDS-related deaths owing to a combination of limited antifungal therapies and drug resistance. A collection of clinical and environmental C. neoformans isolates were assayed for increased mutation rates via fluctuation analysis, and we identified two hypermutator C. neoformans clinical isolates with increased mutation rates when exposed to the combination of rapamycin and FK506. Sequencing of drug target genes found that Cnl1 transposon insertions conferred the majority of resistance to rapamycin and FK506 and could also independently cause resistance to 5-fluoroorotic acid and the clinically relevant antifungal 5-flucytosine. Whole-genome sequencing revealed both hypermutator genomes harbour a nonsense mutation in the RNA-interference component ZNF3 and hundreds of Cnl1 elements organized into massive subtelomeric arrays on each of the fourteen chromosomes. Quantitative trait locus mapping in 28 progeny derived from a cross between a hypermutator and wild-type identified a locus associated with hypermutation that included znf3. CRISPR editing of the znf3 nonsense mutation abolished hypermutation and restored small-interfering-RNA production. We conclude that hypermutation and drug resistance in these clinical isolates result from RNA-interference loss and accumulation of Cnl1 elements.

Integrating Network Pharmacology, Molecular Docking, and Experimental Validation to Investigate the Mechanism of (-)-Guaiol Against Lung Adenocarcinoma.

BackgroundLung adenocarcinoma (LUAD) is the most common type of lung cancer, which poses a serious threat to human life and health. -(−)Guaiol, an effective ingredient of many medicinal herbs, has been shown to have a high potential for tumor interference and suppression. However, knowledge of pharmacological mechanisms is still lacking adequate identification or interpretation.Material/MethodsThe genes of LUAD patients collected from TCGA were analyzed using limma and WGCNA. In addition, targets of (−)-Guaiol treating LUAD were selected through a prediction network. Venn analysis was then used to visualize the overlapping genes, which were further condensed using the PPI network. GO and KEGG analyses were performed sequentially, and the essential targets were evaluated and validated using molecular docking. In addition, cell-based verification, including the CCK-8 assay, cell death assessment, apoptosis analysis, and western blot, was performed to determine the mechanism of action of (−)-Guaiol.ResultsThe genes included 959 differentially-expressed genes, 6075 highly-correlated genes, and 480 drug-target genes. Through multivariate analysis, 23 hub genes were identified and functional enrichment analyses revealed that the PI3K/Akt signaling pathway was the most significant. Experiment results showed that -(−)Guaiol can inhibit LUAD cell growth and induce apoptosis. Additional evidence suggested that the PI3K/Akt signaling pathway established an inseparable role in the antitumor processes of -(−)Guaiol, which is consistent with network pharmacology results.ConclusionsOur results show that the effect of (−)-Guaiol in LUAD treatment involves the PI3K/Akt signaling pathway, providing a useful reference and medicinal value in the treatment of LUAD.

In vitro anti-trypanosomal effect of ivermectin on Trypanosoma evansi by targeting multiple metabolic pathways.

High cytotoxicity and increasing resistance reports of existing chemotherapeutic agents against T. evansi have raised the demand for novel, potent, and high therapeutic index molecules for the treatment of surra in animals. In this regard, repurposing approach of drug discovery has provided an opportunity to explore the therapeutic potential of existing drugs against new organism. With this objective, the macrocyclic lactone representative, ivermectin, has been investigated for the efficacy against T. evansi in the axenic culture medium. To elucidate the potential target of ivermectin in T. evansi, mRNA expression profile of 13 important drug target genes has been studied at 12, 24, and 48 h interval. In the in vitro growth inhibition assay, ivermectin inhibited T. evansi growth and multiplication significantly (p < 0.001) with IC50 values of 13.82 μM, indicating potent trypanocidal activity. Cytotoxicity assays on equine peripheral blood mononuclear cells (PBMCs) and Vero cell line showed that ivermectin affected the viability of cells with a half-maximal cytotoxic concentration (CC50) at 17.48 and 22.05 μM, respectively. Data generated showed there was significant down-regulation of hexokinase (p < 0.001), ESAG8 (p < 0.001), aurora kinase (p < 0.001), casein kinase 1 (p < 0.001), topoisomerase II (p < 0.001), calcium ATPase 1 (p < 0.001), ribonucleotide reductase I (p < 0.05), and ornithine decarboxylase (p < 0.01). The mRNA expression of oligopeptidase B remains refractory to the exposure of the ivermectin. The arginine kinase 1 and ribonucleotide reductase II showed up-regulation on treatment with ivermectin. The ivermectin was found to affect glycolytic pathways, ATP-dependent calcium ATPase, cellular kinases, and other pathway involved in proliferation and maintenance of internal homeostasis of T. evansi. These data imply that intervention with alternate strategies like nano-formulation, nano-carriers, and nano-delivery or identification of ivermectin homologs with low cytotoxicity and high bioavailability can be explored in the future as an alternate treatment for surra in animals.Graphical abstract

Integrative Bioinformatics Analysis Reveals CHEK1 and UBE2C as Luminal A Breast Cancer Subtype Biomarkers.

In light of the limited number of targetable oncogenic drivers in breast cancer (BRCA), it is important to identify effective and druggable gene targets for the treatment of this devastating disease. Herein, the GSE102484 dataset containing expression profiling data from 683 BRCA patients was re-analyzed using weighted gene co-expression network analysis (WGCNA). The yellow module with the highest correlation to BRCA progression was screened out, followed by functional enrichment analysis and establishment of a protein–protein interaction (PPI) network. After further validation through survival analysis and expression evaluation, CHEK1 and UBE2C were finally identified as hub genes related to the progression of BRCA, especially the luminal A breast cancer subtype. Notably, both hub genes were found to be dysregulated in multiple types of immune cells and closely correlated with tumor infiltration, as revealed by Tumor Immune Estimation Resource (TIMER) along with other bioinformatic tools. Construction of transcription factors (TF)-hub gene network further confirmed the existence of 11 TFs which could regulate both hub genes simultaneously. Our present study may facilitate the invention of targeted therapeutic drugs and provide novel insights into the understanding of the mechanism beneath the progression of BRCA.

A CRISPR-guided mutagenic DNA polymerase strategy for the detection of antibiotic-resistant mutations in M. tuberculosis

A sharp increase in multidrug-resistant tuberculosis (MDR-TB) threatens human health. Spontaneous mutation in essential gene confers an ability of Mycobacterium tuberculosis resistance to anti-TB drugs. However, conventional laboratory strategies for identification and prediction of the mutations in this slowly growing species remain challenging. Here, by combining XCas9 nickase and the error-prone DNA polymerase A from M. tuberculosis, we constructed a CRISPR-guided DNA polymerase system, CAMPER, for effective site-directed mutagenesis of drug-target genes in mycobacteria. CAMPER was able to generate mutagenesis of all nucleotides at user-defined loci, and its bidirectional mutagenesis at nick sites allowed editing windows with lengths up to 80 nucleotides. Mutagenesis of drug-targeted genes in Mycobacterium smegmatis and M. tuberculosis with this system significantly increased the fraction of the antibiotic-resistant bacterial population to a level approximately 60- to 120-fold higher than that in unedited cells. Moreover, this strategy could facilitate the discovery of the mutation conferring antibiotic resistance and enable a rapid verification of the growth phenotype-mutation genotype association. Our data demonstrate that CAMPER facilitates targeted mutagenesis of genomic loci and thus may be useful for broad functions such as resistance prediction and development of novel TB therapies.

Differential Gene Analysis of Trastuzumab in Breast Cancer Based on Network Pharmacology and Medical Images.

The purpose of this study was to use network pharmacology, biomedical images and molecular docking technology in the treatment of breast cancer to investigate the feasible therapeutic targets and mechanisms of trastuzumab. In the first place, we applied pubchem swisstarget (http://www.swisstargetprediction.ch/), (https://pubchem.ncbi.nlm.nih.gov/) pharmmapper (http://lilab-ecust.cn/pharmmapper/), and the batman-tcm (http://bionet.ncpsb.org.cn/batman-tcm/) database to collect the trastuzumab targets. Then, in NCBI-GEO, breast cancer target genes were chosen (https://www.ncbi.nlm.nih.gov/geo/). The intersection regions of drug and disease target genes were used to draw a Venn diagram. Through Cytoscape 3.7.2 software, and the STRING database, we then formed a protein-protein interaction (PPI) network. Besides, we concluded KEGG pathway analysis and Geen Ontology analysis by using ClueGO in Cytospace. Finally, the top 5 target proteins in the PPI network to dock with trastuzumab were selected. After screening trastuzumab and breast cancer in databases separately, we got 521 target genes of the drug and 1,464 target genes of breast cancer. The number of overlapping genes was 54. PPI network core genes include GAPDH, MMP9, CCNA2, RRM2, CHEK1, etc. GO analysis indicated that trastuzumab treats breast cancer through abundant biological processes, especially positive regulation of phospholipase activity, linoleic acid metabolic process, and negative regulation of endothelial cell proliferation. The molecular function is NADP binding and the cellular component is tertiary granule lumen. The results of KEGG enrichment analysis exhibited four pathways related to the formation and cure of breast cancer, containing Drug metabolism, Glutathione metabolism, Pyrimidine metabolism and PPAR signaling pathway. Molecular docking showed that trastuzumab has good binding abilities with five core target proteins (GAPDH, MMP9, CCNA2, RRM2, CHEK1). This study, through network pharmacology and molecular docking, provides new pieces of evidence and ideas to understand how trastuzumab treats breast cancer at the gene level.

The use of genomic variants to drive drug repurposing for chronic hepatitis B

BackgroundOne of the main challenges in personalized medicine is to establish and apply a large number of variants from genomic databases into clinical diagnostics and further facilitate genome-driven drug repurposing. By utilizing biological chronic hepatitis B infection (CHB) risk genes, our study proposed a systematic approach to use genomic variants to drive drug repurposing for CHB. MethodThe genomic variants were retrieved from the Genome-Wide Association Study (GWAS) and Phenome-Wide Association Study (PheWAS) databases. Then, the biological CHB risk genes crucial for CHB progression were prioritized based on the scoring system devised with five strict functional annotation criteria. A score of ≥ 2 were categorized as the biological CHB risk genes and further shed light on drug target genes for CHB treatments. Overlapping druggable targets were identified using two drug databases (DrugBank and Drug-Gene Interaction Database (DGIdb)). ResultsA total of 44 biological CHB risk genes were screened based on the scoring system from five functional annotation criteria. Interestingly, we found 6 druggable targets that overlapped with 18 drugs with status of undergoing clinical trials for CHB, and 9 druggable targets that overlapped with 20 drugs undergoing preclinical investigations for CHB. Eight druggable targets were identified, overlapping with 25 drugs that can potentially be repurposed for CHB. Notably, CD40 and HLA-DPB1 were identified as promising targets for CHB drug repurposing based on the target scores. ConclusionThrough the integration of genomic variants and a bioinformatic approach, our findings suggested the plausibility of CHB genomic variant-driven drug repurposing for CHB.

Functional regulations between genetic alteration-driven genes and drug target genes acting as prognostic biomarkers in breast cancer

Differences in genetic molecular features including mutation, copy number alterations and DNA methylation, can explain interindividual variability in response to anti-cancer drugs in cancer patients. However, identifying genetic alteration-driven genes and characterizing their functional mechanisms in different cancer types are still major challenges for cancer studies. Here, we systematically identified functional regulations between genetic alteration-driven genes and drug target genes and their potential prognostic roles in breast cancer. We identified two mutation and copy number-driven gene pairs (PARP1-ACSL1 and PARP1-SRD5A3), three DNA methylation-driven gene pairs (PRLR-CDKN1C, PRLR-PODXL2 and PRLR-SRD5A3), six gene pairs between mutation-driven genes and drug target genes (SLC19A1-SLC47A2, SLC19A1-SRD5A3, AKR1C3-SLC19A1, ABCB1-SRD5A3, NR3C2-SRD5A3 and AKR1C3-SRD5A3), and four copy number-driven gene pairs (ADIPOR2-SRD5A3, CASP12-SRD5A3, SLC39A11-SRD5A3 and GALNT2-SRD5A3) that all served as prognostic biomarkers of breast cancer. In particular, RARP1 was found to be upregulated by simultaneous copy number amplification and gene mutation. Copy number deletion and downregulated expression of ACSL1 and upregulation of SRD5A3 both were observed in breast cancers. Moreover, copy number deletion of ACSL1 was associated with increased resistance to PARP inhibitors. PARP1-ACSL1 pair significantly correlated with poor overall survival in breast cancer owing to the suppression of the MAPK, mTOR and NF-kB signaling pathways, which induces apoptosis, autophagy and prevents inflammatory processes. Loss of SRD5A3 expression was also associated with increased sensitivity to PARP inhibitors. The PARP1-SRD5A3 pair significantly correlated with poor overall survival in breast cancer through regulating androgen receptors to induce cell proliferation. These results demonstrate that genetic alteration-driven gene pairs might serve as potential biomarkers for the prognosis of breast cancer and facilitate the identification of combination therapeutic targets for breast cancers.