Drug-related Genes Research Articles

A national-wide survey on clinical implementation of PGx testing into precision therapeutics for Chinese children: a long way before standard clinical practice

BackgroundPharmacogenetics/pharmacogenomics (PGx) focuses on the genetic variation that causes the heterogeneity of pharmacokinetics and drug response among individuals and has the potential to predict individual efficacy and/or side effects. This study aims to investigate and understand the implementation of genetic testing for the personalized medication (GTPM) in children's hospitals in Mainland China.MethodsA survey was conducted on 50 children's hospitals from 31 provinces, municipalities, and autonomous regions across Mainland China, and statistical analysis and recommendations were made.ResultsQuestionnaire response was rate of 76.0% (38/50). Data from 15 hospitals conducting GTPM were included in this study, but only 6 hospitals had offered PGx tests for no less than five drug-related genes, and only 5 hospitals had covered more than ten drugs, which was a small scale overall. 20.0% of the laboratories did not conduct internal quality control, and 33.3% did not participate in inter-laboratory quality assessment. 46.7% of the practitioners did not receive external training. The primary goal for GTPM was to optimize drug dosage in the 15 hospitals, while the main challenge for GTPM was the implementation cost.ConclusionAlthough GTPM in pediatrics has made major progress in Mainland China in recent years, there were still various problems in terms of software, hardware configuration, personnel allocation, business scale, quality control, and result interpretation. This requires joint efforts of health administration, medical insurance departments, researchers, and hospitals to promote and improve GTPM.

Genetic susceptibility and clinical features of CYP2D6 associated with systemic lupus erythematosus in a Chinese population.

This study aims to explore possible susceptibility genes and clinical features for systemic lupus erythematosus (SLE) patients in a Chinese population. Expanding on the results of a prior single-center observational study involving 60 systemic lupus erythematosus patients, a subsequent single-center prospective observational study was conducted on SLE patients undergoing treatment at Nanfang Hospital Affiliated to Southern Medical University from 2021 to 2023. The identification process for drug-related target genes entailed an extensive search across PharmGKB (https://www.pharmgkb.org/), the Clinical Pharmacogenetics Implementation Consortium (CPIC),and PubMed literature databases, to pinpoint common drugs and target single nucleotide polymorphisms(SNPs)for SLE. Blood samples were individually collected and genotyped using MassARRAY® high-throughput nucleic acid mass spectrometry. Genotype frequency differences were assessed through Chi-square tests against both the larger East Asian population as well as kidney transplant recipients. Data collection relied on electronic medical records, encompassing demographic details(age, gender),medication regimens(hormones, NSAIDs, hydroxychloroquine, DMARDs, biologic agents, stomach medications, calcitriol, etc.),laboratory indicators(RF, Anti-CCP antibody, ESR, CRP, anti-ANA antibodies, dsDNA antibodies, anti-SM antibodies, S m. RNP antibodies, A LT, ALB, CR, UA, WBC, PLT, HGB, Ca, K, Glu, CHOL, TG, LDL-C, HDL-C) and lupus activity scores(SLEDAI-2K). Possible disease susceptibility genes were categorized, and SPSS26 software facilitated statistical analyses. The research encompassed a total of 137 SLE patients along with 50 SNPs. After conducting statistical analyses, it emerged that there existed significant disparities in CYP2D6 gene (rs1065852) distribution when compared against allele mutation rates within both East Asian populations (p < .05) and kidney transplant patients(p < .05). Wild-type gene (GG) constituted 14% of cases while mutant gene (GA + AA) constituted 86%. Allele mutation rate (A63.6%) was significantly higher among SLE patients (RR = 0.802; p = .0355). Furthermore, the variant rs1065852 genotype (GA + AA) demonstrated significant associations with lower CRP levels, higher HGB levels, and higher HDL-C levels (p < 0 0.05). The metabolic enzyme CYP2D6 may be used as susceptibility gene for predicting systemic lupus erythematosus and are correlated with CRP and other indicators.

DARG: An integrated knowledge base for analyzing addictive drug related genes

DARG: An integrated knowledge base for analyzing addictive drug related genes

The effectiveness of personalized medication based on drug-related genes, for schizophre-nia patients with resistance to traditional drugs.

The effectiveness of personalized medication based on drug-related genes, for schizophre-nia patients with resistance to traditional drugs.

Pharmacovariome scanning using whole pharmacogene resequencing coupled with deep computational analysis and machine learning for clinical pharmacogenomics

BackgroundThis pilot study aims to identify and functionally assess pharmacovariants in whole exome sequencing data. While detection of known variants has benefited from pharmacogenomic-dedicated bioinformatics tools before, in this paper we have tested novel deep computational analysis in addition to artificial intelligence as possible approaches for functional analysis of unknown markers within less studied drug-related genes.MethodsPharmacovariants from 1800 drug-related genes from 100 WES data files underwent (a) deep computational analysis by eight bioinformatic algorithms (overall containing 23 tools) and (b) random forest (RF) classifier as the machine learning (ML) approach separately. ML model efficiency was calculated by internal and external cross-validation during recursive feature elimination. Protein modelling was also performed for predicted highly damaging variants with lower frequencies. Genotype–phenotype correlations were implemented for top selected variants in terms of highest possibility of being damaging.ResultsFive deleterious pharmacovariants in the RYR1, POLG, ANXA11, CCNH, and CDH23 genes identified in step (a) and subsequent analysis displayed high impact on drug-related phenotypes. Also, the utilization of recursive feature elimination achieved a subset of 175 malfunction pharmacovariants in 135 drug-related genes that were used by the RF model with fivefold internal cross-validation, resulting in an area under the curve of 0.9736842 with an average accuracy of 0.9818 (95% CI: 0.89, 0.99) on predicting whether a carrying individuals will develop adverse drug reactions or not. However, the external cross-validation of the same model indicated a possible false positive result when dealing with a low number of observations, as only 60 important variants in 49 genes were displayed, giving an AUC of 0.5384848 with an average accuracy of 0.9512 (95% CI: 0.83, 0.99).ConclusionWhile there are some technologies for functionally assess not-interpreted pharmacovariants, there is still an essential need for the development of tools, methods, and algorithms which are able to provide a functional prediction for every single pharmacovariant in both large-scale datasets and small cohorts. Our approaches may bring new insights for choosing the right computational assessment algorithms out of high throughput DNA sequencing data from small cohorts to be used for personalized drug therapy implementation.

LM-DTI: a tool of predicting drug-target interactions using the node2vec and network path score methods.

Introduction: Drug-target interaction (DTI) prediction is a key step in drug function discovery and repositioning. The emergence of large-scale heterogeneous biological networks provides an opportunity to identify drug-related target genes, which led to the development of several computational methods for DTI prediction. Methods: Considering the limitations of conventional computational methods, a novel tool named LM-DTI based on integrated information related to lncRNAs and miRNAs was proposed, which adopted the graph embedding (node2vec) and the network path score methods. First, LM-DTI innovatively constructed a heterogeneous information network containing eight networks composed of four types of nodes (drug, target, lncRNA, and miRNA). Next, the node2vec method was used to obtain feature vectors of drug as well as target nodes, and the path score vector of each drug-target pair was calculated using the DASPfind method. Finally, the feature vectors and path score vectors were merged and input into the XGBoost classifier to predict potential drug-target interactions. Results and Discussion: The 10-fold cross validations evaluate the classification accuracies of the LM-DTI. The prediction performance of LM-DTI in AUPR reached 0.96, which showed a significant improvement compared with those of conventional tools. The validity of LM-DTI has also been verified by manually searching literature and various databases. LM-DTI is scalable and computing efficient; thus representing a powerful drug relocation tool that can be accessed for free at http://www.lirmed.com:5038/lm_dti.

SARS-CoV-2 induces "cytokine storm" hyperinflammatory responses in RA patients through pyroptosis.

The coronavirus disease (COVID-19) is a pandemic disease that threatens worldwide public health, and rheumatoid arthritis (RA) is the most common autoimmune disease. COVID-19 and RA are each strong risk factors for the other, but their molecular mechanisms are unclear. This study aims to investigate the biomarkers between COVID-19 and RA from the mechanism of pyroptosis and find effective disease-targeting drugs. We obtained the common gene shared by COVID-19, RA (GSE55235), and pyroptosis using bioinformatics analysis and then did the principal component analysis(PCA). The Co-genes were evaluated by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and ClueGO for functional enrichment, the protein-protein interaction (PPI) network was built by STRING, and the k-means machine learning algorithm was employed for cluster analysis. Modular analysis utilizing Cytoscape to identify hub genes, functional enrichment analysis with Metascape and GeneMANIA, and NetworkAnalyst for gene-drug prediction. Network pharmacology analysis was performed to identify target drug-related genes intersecting with COVID-19, RA, and pyroptosis to acquire Co-hub genes and construct transcription factor (TF)-hub genes and miRNA-hub genes networks by NetworkAnalyst. The Co-hub genes were validated using GSE55457 and GSE93272 to acquire the Key gene, and their efficacy was assessed using receiver operating curves (ROC); SPEED2 was then used to determine the upstream pathway. Immune cell infiltration was analyzed using CIBERSORT and validated by the HPA database. Molecular docking, molecular dynamics simulation, and molecular mechanics-generalized born surface area (MM-GBSA) were used to explore and validate drug-gene relationships through computer-aided drug design. COVID-19, RA, and pyroptosis-related genes were enriched in pyroptosis and pro-inflammatory pathways(the NOD-like receptor family pyrin domain containing 3 (NLRP3) inflammasome complex, death-inducing signaling complex, regulation of interleukin production), natural immune pathways (Network map of SARS-CoV-2 signaling pathway, activation of NLRP3 inflammasome by SARS-CoV-2) and COVID-19-and RA-related cytokine storm pathways (IL, nuclear factor-kappa B (NF-κB), TNF signaling pathway and regulation of cytokine-mediated signaling). Of these, CASP1 is the most involved pathway and is closely related to minocycline. YY1, hsa-mir-429, and hsa-mir-34a-5p play an important role in the expression of CASP1. Monocytes are high-caspase-1-expressing sentinel cells. Minocycline can generate a highly stable state for biochemical activity by docking closely with the active region of caspase-1. Caspase-1 is a common biomarker for COVID-19, RA, and pyroptosis, and it may be an important mediator of the excessive inflammatory response induced by SARS-CoV-2 in RA patients through pyroptosis. Minocycline may counteract cytokine storm inflammation in patients with COVID-19 combined with RA by inhibiting caspase-1 expression.

Personalized antihypertensive treatment guided by pharmacogenomics in China.

The implementation of genotyping for anti-hypertensive drugs in clinical practice remains a challenge. We conducted this study to analyze the distribution of polymorphisms of antihypertensive drug-related genes in Changsha County in China and compare the clinical effectiveness of genotype-guided and clinical experience-guided antihypertensive therapy in hypertensive individuals. A total of 9,933 essential hypertensive participants from Changsha County were consecutively enrolled in our study, and 7 genetic polymorphic loci (CYP2D6*10, ADRB1, CYP2C9*3, AGTR1, ACE, CYP3A5*3 and NPPA) were detected by a polymerase chain reaction (PCR)-fluorescence probe. From an available sample of 660 hypertensive participants, 495 cases were randomly identified by genotype-guided therapy and 165 cases by clinical experience-guided therapy. We performed 24-hour ambulatory blood pressure (BP) monitoring on each of these cases, pre- and post-intervention. In the enrolled 9,933 cases, the mutation frequencies of CYP2C9*3, ADRB1(1165G>C), AGTR1(1166A>C), CYP2D6*10, ACE(I/D), CYP3A5*3 and NPPA(2238T>C) were 4.41%, 74.60%, 5.55%, 57.08%, 30.94%, 69.03% and 1.19%, respectively. In both genotype-guided and clinical experience-guided groups, the comparisons of intra-group pre-and post-treatments showed significant decreases in diastolic blood pressure (DBP) (P<0.01) and significant increases in the control rate of BP (47.1% vs. 38.6% and 37.5% vs. 33.9%, P<0.05) in response to adjusted antihypertensive agents. Correspondingly, the extent of the reduction of systolic blood pressure (SBP; 3.52±11.72 vs. 0.92±9.14 mmHg), the extent of the increase in the rate of BP control (8.5% vs. 3.6%) and the percentage rate of decrease of grades 2 and 3 hypertensive individuals were more significant in the genotype-guided group than that in the clinical experience-guided group (P<0.01). While prescribing anti-hypertensive drugs, appropriate dosage and type adjustments should be made according to the gene mutation frequency and individual circumstances. Pharmacogenomics-guided personalized treatment of hypertensive patients is likely to be a more effective strategy, especially in those with significantly elevated SBP.

Sequence-specific amperometric detection based on a double-probe mode and enzyme-mediated multiple signal electrocatalysis for the double-stranded DNA of PML/RARα-related fusion gene

A new electrochemical DNA biosensor based on double-probe mode and enzyme-mediated multiple signal electrocatalysis is constructed for the highly sensitive determination of double-stranded (ds-) PML/RARα fusion gene. Through the ingenious design of two groups of detection probes, including two thiolated capture probes anchored on dual standalone detection units integrated into one customized gold electrode and four biotinylated reporter probes, hybridizing with different segments of the same target single-stranded DNA (ssDNA) simultaneously, the hybridization efficiency between the probes and target is improved by preventing the reannealing of the two separate target ssDNA. Compared with a single reporter probe, this method can dramatically increase the amount of biotin and introduce numerous streptavidin-labelled horseradish peroxidase (HRP), thereby significantly amplifying electrochemical signals with low background signals. The combination of the dual-probe mode, multiple signal amplification strategy, and the inherent electrocatalytic activity of the HRP results in the prominent electrochemical sensing performance in detecting large-fragment target dsDNA with a detection limit as low as 71 fM. Furthermore, taking advantage of the new detection strategy, polymerase chain reaction (PCR) products and enzyme-digested PCR products from NB4 cells can be effectively analysed, showing great promise for the development of a new class of point-of-care platforms for disease-/drug-related genes.

Equus roundworms (Parascaris univalens) are undergoing rapid divergence while genes involved in metabolic as well as anthelminic resistance are under positive selection

BackgroundThe evolution of parasites is often directly affected by the host's environment. Studies on the evolution of the same parasites in different hosts are of great interest and are highly relevant to our understanding of divergence.MethodsHere we performed whole-genome sequencing of Parascaris univalens from different Equus hosts (horses, zebras and donkeys). Phylogenetic and selection analyses were performed to study the divergence and adaptability of P. univalens.ResultsAt the genetic level, multiple lines of evidence indicate that P. univalens is mainly separated into two clades (horse-derived and zebra & donkey-derived). This divergence began 300–1000 years ago, and we found that most of the key enzymes related to glycolysis were under strong positive selection in zebra & donkey-derived roundworms, whereas the lipid-related metabolic system was under positive selection in horse-derived roundworms, indicating that the adaptive evolution of metabolism has occurred over the past few centuries. In addition, we found that some drug-related genes showed a significantly higher degree of selection in diverse populations.ConclusionsThis work reports the adaptive evolution and divergence trend of P. univalens in different hosts for the first time. Its results indicate that the divergence of P. univalens is a continuous, dynamic process. Furthermore, the continuous monitoring of the effects of differences in nutritional and drug histories on the rapid evolution of roundworms is conducive to further understanding host-parasite interactions.

Abstract LB548: Genomic analysis of antineoplastic drug-related genetic variations based on large-scale population sequencing

Abstract Purpose: Cancer has become the second leading cause of death in the world. In 2020, there were 19.29 million new cancer patients and a total of 10 million deaths. Drug are the most popular ways to control the development of cancer, but the effectiveness of anti-tumor drugs is only about 25%. Drug efficacy is affected by many factors, among which the most critical is genetic variants. Genomic analysis based on large-scale population sequencing was performed to calculate the mutation frequency of antineoplastic drug-related genetic variations and systematically analyze general descriptions and population differences. Methods: Information of antineoplastic drugs is available from FDA and CFDA, and gene list comes from DrugBank, PharmGKB, FDA biomarker list and clinical guidelines. The maximum likelihood method was used to estimate the population mutation frequency of a single locus, and the calculation process was completed on Ali Cloud platform. GATK, ANNOVAR, PROVEAN, SIFT and other software were used for quality control, annotation and function prediction of mutations. Perl, Python, R and other computer languages were used for statistical analysis. Results: we collect 206,604 samples from mainland China for analysis, and identified 104 antineoplastic drugs and 517 drug-related genes at the same time. There were 201,774 genetic variations, most of which were intron mutations, exon mutations account for about 1% of all mutations. We found 41,955 novel mutations, which unrecorded in dbSNP, accounting for about 20.8%. Most of these mutations were rare mutations with frequencies less than 1%, subsequent prediction of mutation function also indicated that a large proportion of these mutations were deleterious. These results suggest that rare mutations may also play an important role in the use of antineoplastic drugs, which has previously been underestimated. The mutation frequency distribution of different types of genes suggests that transporters have a greater impact on personalized medicine during the use of antineoplastic drugs in China. By analyzing the genetic variation of important metabolic enzymes, we found that mutations such as UGT1A1*6, DPYD*9A, CYP2D6*2 and *10 have a high frequency and high detection probability in the Chinese population. Therefore, when patients use drugs metabolized by related metabolic enzymes, genetic testing should be done in advance to prevent the occurrence of adverse reactions. Regional analysis revealed that the overall differences among provinces in China were small, but the differences were large compared with other ethnic groups. Conclusion: Results of large-scale population sequencing reveal distribution and discipline of genetic variation of antineoplastic drug-related genes in Chinese population, further confirm the importance of precision medicine in cancer, and illustrate that the use of antineoplastic drugs can also be affected by ethnic differences. Citation Format: Yan Zhan, Ji-Ye Yin. Genomic analysis of antineoplastic drug-related genetic variations based on large-scale population sequencing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr LB548.

Clinical and Gene Features of SARS-CoV-2-Positive Recurrence in Patients Recovered From COVID-19.

There are still frequent reports that a number of recovered coronavirus disease 2019 (COVID-19) patients following discharge have re-detectable positive (RP) results by RT-PCR. Understanding the clinical and molecular characteristics of RP patients may have implications for curbing the COVID-19 pandemic. In this study, 318 COVID-19 convalescent patients, including 59 RP patients and 259 non-RP (NRP) patients, were enrolled. Among RP patients, women accounted for a significantly high proportion (67.8%), and the titers of IgG and IgM antibodies in this group were also significantly high. Differentially expressed genes (DEGs), including 692 upregulated and 383 downregulated genes, overlapped in two public GEO datasets containing RP and NRP blood cell samples. Enrichment analysis indicated that these DEGs were related to several key signaling pathways, such as viral infection, immune activation, and inflammatory responses. Importantly, 59 indicator genes constituting the core network exhibited high diagnostic values and were correlated with markers of different immune cells. Among these, 12 drug-related genes were associated with the RP results. Our work suggests that, in addition to clinically available features, blood cell transcriptome sequencing can be performed to obtain gene signatures for diagnosis of RP patients.

Exploring the mechanism and experimental verification of puerarin in the treatment of endometrial carcinoma based on network pharmacology and bioinformatics analysis

Endometrial carcinoma is one of the two cancers with rising mortality and morbidity in recent years. In the light of many controversies about its treatment, it is urgent to construct a new prognostic model and to find out new therapeutic directions. As a small drug molecule widely used in clinical treatment and experimental research in China, puerarin has recently been proven to have obvious anti-cancer effects in multiple cancer cells. In this study, bioinformatics analysis and experimental validation were used to explore the potential mechanism of puerarin for endometrial carcinoma and construct a prognostic model. A total of 22 drug-related differential genes were found by constructing a database of drug targets and disease genes. The protein–protein interaction network was constructed for GO and KEGG enrichment analysis to initially explore the potential mechanism of its therapeutic effects. To construct the prognostic model, validation was performed by risk regression analysis and LASSO analysis. Finally, two prognostic genes—PIM1 and BIRC5 were determined to establish high and low risk groups. Kaplan–Meier analysis displayed a higher survival rate in the low-risk group than in the high-risk group. ROC curves indicated the stable and good effect in prediction (one-year AUC is 0.626; two-year AUC is 0.620; three-year AUC is 0.623). The interrelationship between immunity and its disease was explored by immune infiltration analysis. Finally, the potential effect of puerarin on endometrial carcinoma cells was further verified by experiments.

Identification of Candidate Therapeutic Genes for More Precise Treatment of Esophageal Squamous Cell Carcinoma and Adenocarcinoma.

The standard therapy administered to patients with advanced esophageal cancer remains uniform, despite its two main histological subtypes, namely esophageal squamous cell carcinoma (SCC) and esophageal adenocarcinoma (AC), are being increasingly considered to be different. The identification of potential drug target genes between SCC and AC is crucial for more effective treatment of these diseases, given the high toxicity of chemotherapy and resistance to administered medications. Herein we attempted to identify and rank differentially expressed genes (DEGs) in SCC vs. AC using ensemble feature selection methods. RNA-seq data from The Cancer Genome Atlas and the Fudan-Taizhou Institute of Health Sciences (China). Six feature filters algorithms were used to identify DEGs. We built robust predictive models for histological subtypes with the random forest (RF) classification algorithm. Pathway analysis also be performed to investigate the functional role of genes. 294 informative DEGs (87 of them are newly discovered) have been identified. The areas under receiver operator curve (AUC) were higher than 99.5% for all feature selection (FS) methods. Nine genes (i.e., ERBB3, ATP7B, ABCC3, GALNT14, CLDN18, GUCY2C, FGFR4, KCNQ5, and CACNA1B) may play a key role in the development of more directed anticancer therapy for SCC and AC patients. The first four of them are drug targets for chemotherapy and immunotherapy of esophageal cancer and involved in pharmacokinetics and pharmacodynamics pathways. Research identified novel DEGs in SCC and AC, and detected four potential drug targeted genes (ERBB3, ATP7B, ABCC3, and GALNT14) and five drug-related genes.

Identification of an Aerococcus urinaeequi isolate by whole genome sequencing and average nucleotide identity analysis

Identification and classification of microorganisms is one of the most important but difficult and challenging issues in microbiology. Whole genome sequencing (WGS), which can give a thorough understanding for the genome of bacteria strain, has been universally used for studying bacterial classification, evolution, and drug-related resistant genes. We in this study aimed to identify a Gram-positive, microaerophilic, catalase-negative cocci strain named AV208, which has shown resistance to vancomycin, by whole genome's average nucleotide identity (ANI) and high-throughput sequencing technology. The AV208 strain was identified by following commercially available identification systems, including API 20 Strep system and Vitek 2 Compact Gram-positive identification system for biochemical phenotypic test. Matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF-MS) and 16S rRNA gene sequencing were used for confirmation identification. The whole genome of AV208 was sequenced by using high throughput sequencing technology and ANI between AV208, and its phylogenetic neighbours were analysed by the Orthologous Average Nucleotide Identity Tool (OAT) software. Polymerase chain reaction (PCR) and DNA sequencing were used to investigate the potential molecular mechanism for vancomycin resistance. The AV208 strain was isolated from an ascites sample from a patient with chronic kidney disease who showed extensive resistance to the drugs detected, such as vancomycin with MIC >256 μg/mL. With combination of biochemical phenotypic test, MALDI-TOF-MS and 16S rRNA gene sequencing, the AV208 strain was tentatively identified as an Aercoccus viridans. By using complete genome sequence, we found a 96.24% ANI between strain AV208 and Aerococcus urinaeequi CCUG 28094T, which was higher than that with A. viridans CCUG4311T (94.9%). The consistency of 16S rRNA sequence of strain AV208 was 100% with A. urinaeequi CCUG 28094T and 99.9% with A. viridans CCUG4311T, with only one base difference between them. PCR and sequencing for van genes revealed that AV208 was positive for the vanA gene. A Tn1546 transposon-like structure with vanA gene was found in the genome, which was predicted locating in plasmid, causing vancomycin resistance phenotypes. Average nucleotide identity analysis based on whole genome sequence is an accurate and effective method for identification of bacteria, especially for strains that are not discernible by existing methods such as Aerococcus.

Molecular mechanism of Si-Miao-Yong-An decoction in the treatment of diabetic foot microcirculation and network pharmacology information analysis results

OBJECTIVE: This study aims to explore the effect and mechanism of Si-Miao-Yong-An decoction (SMYAD) on diabetic foot by simulating the microenvironment of diabetic foot in vitro, and further verify these results using bioinformatics analysis technology, in order to provide a basis for the treatment of diabetic foot with traditional Chinese medicine. MATERIALS AND METHODS: Human umbilical vein endothelial cells (HUVECs) were cultured under hypoxia to simulate the microcirculation of diabetic foot. Then, these were administered with SMYAD for intervention. Afterward, the cell viability was detected by MTT assay, the apoptosis was detected by flow cytometry, and the STAT3 signaling pathway and Bcl-2 and Bax protein expression were detected by Western blot. Next, TCMSP was used to determine the chemical composition and target gene information of the SMYAD, and the GeneCards database was used to search for the disease-related target gene information of diabetic foot. Furthermore, the Venny 2.1 online software was used to screen and obtain the drug–disease common target genes for the SMYAD and diabetic foot. The common target genes were entered into the STRING database for retrieval, in order to construct the network diagram for protein-related action, and the R software was used to analyze the pathway enrichment, in order to explore the mechanism of the SMYAD in the treatment of diabetic foot microcirculation. RESULTS: Compared with the control group, the SMYAD increased the activity of HUVECs cultured in the hypoxia state but decreased the apoptosis rate. Furthermore, the protein expression of p-STAT3 and Bcl-2 increased, and the protein expression of Bax decreased (P &lt; 0.05). A total of 235 drug-related target genes were found by screening, and 4553 target genes related to diabetic foot were obtained. The Venny software analysis revealed that there were 171 drug–disease interaction target genes. The STRING database and the GO and KEGG functional enrichment analysis revealed that STAT3, AKT, MAPK, and other proteins were involved, and that these may be correlated to the mechanism of the SMYAD in the treatment of diabetic foot microcirculation. CONCLUSION: SMYAD can affect the expression of Bcl-2 and Bax genes by regulating the activity of the STAT3 signaling pathway, playing a role in the treatment of diabetic foot.

Ibuprofen induced Stevens-Johnson syndrome and liver injury in children: a case report

Stevens-Johnson syndrome (SJS) is a disorder that causes severe damage to the skin and mucous membranes with bullous and erosive properties. Drug-induced liver injury (DILI) is closely related to non-steroidal anti-inflammatory drugs (such as ibuprofen). Liver injury caused by ibuprofen is often related to overdose, and liver injury caused by normal dose is rare, and there are individual differences in different situations. In this case, a child developed SJS and acute liver injury after treatment with ibuprofen suspension. We described the characteristics of related adverse reactions induced by ibuprofen, and analyzed the relationship between SJS caused by the drug and related drug genes. Glucocorticoids and antihistamines were used to treat dermatitis, reduced glutathione (GSH) to protect the liver and plasma exchange detoxification. Finally, the patient’s dermatitis healed and the liver injury was significantly improved. Many studies have suggested that DILI may be related to human leukocyte antigen (HLA) genotyping. The detection of drug-related genes revealed that the SJS and liver damage caused by ibuprofen might have been related to the positive HLA-B*5801. This article suggests that attention should be paid to checking liver function indicators after taking ibuprofen, and genetic screening can be used to reduce the risk of gene-related adverse reactions when necessary.

Graph Convolutional Network for Drug Response Prediction Using Gene Expression Data

Genomic profiles of cancer patients such as gene expression have become a major source to predict responses to drugs in the era of personalized medicine. As large-scale drug screening data with cancer cell lines are available, a number of computational methods have been developed for drug response prediction. However, few methods incorporate both gene expression data and the biological network, which can harbor essential information about the underlying process of the drug response. We proposed an analysis framework called DrugGCN for prediction of Drug response using a Graph Convolutional Network (GCN). DrugGCN first generates a gene graph by combining a Protein-Protein Interaction (PPI) network and gene expression data with feature selection of drug-related genes, and the GCN model detects the local features such as subnetworks of genes that contribute to the drug response by localized filtering. We demonstrated the effectiveness of DrugGCN using biological data showing its high prediction accuracy among the competing methods.

Clade-specific chromosomal rearrangements and loss of subtelomeric adhesins in Candida auris.

Candida auris is an emerging fungal pathogen of rising concern due to global spread, the ability to cause healthcare-associated outbreaks, and antifungal resistance. Genomic analyses revealed that early contemporaneously detected cases of C. auris were geographically stratified into four major clades. While Clades I, III, and IV are responsible for ongoing outbreaks of invasive and multidrug-resistant infections, Clade II, also termed the East Asian clade, consists primarily of cases of ear infection, is often susceptible to all antifungal drugs, and has not been associated with outbreaks. Here, we generate chromosome-level assemblies of twelve isolates representing the phylogenetic breadth of these four clades and the only isolate described to date from Clade V. This Clade V genome is highly syntenic with those of Clades I, III, and IV, although the sequence is highly divergent from the other clades. Clade II genomes appear highly rearranged, with translocations occurring near GC-poor regions, and large subtelomeric deletions in most chromosomes, resulting in a substantially different karyotype. Rearrangements and deletion lengths vary across Clade II isolates, including two from a single patient, supporting ongoing genome instability. Deleted subtelomeric regions are enriched in Hyr/Iff-like cell-surface proteins, novel candidate cell wall proteins, and an ALS-like adhesin. Cell wall proteins from these families and other drug-related genes show clade-specific signatures of selection in Clades I, III, and IV. Subtelomeric dynamics and the conservation of cell surface proteins in the clades responsible for global outbreaks causing invasive infections suggest an explanation for the different phenotypes observed between clades.

Genetic Diversity of Drug-Related Genes in Native Americans of the Brazilian Amazon.

IntroductionThe genetic admixture of the Brazilian population has considerable relevance to the implementation of the principles of pharmacogenomics (PGx), as it may compromise the extrapolation of data obtained in more homogeneous world populations.PurposeThis study aims to investigate a panel of 117 polymorphisms in 35 pharmacogenes, which contains label recommendations or clinical evidence by international drug regulatory agencies, in Amazonian Native American populations, and compare the results obtained with continental population data from the 1000 Genomes Project Consortium.Patients and MethodsThe study population is composed of 109 Native American individuals from three Brazilian Amazon groups. The genotyping of the PGx polymorphisms was performed by allelic discrimination using TaqMan® OpenArray Genotyping with a panel of 120 customized assays on the QuantStudio™ 12K Flex Real-Time PCR System.ResultsStatistical differences within the Native American populations were observed regarding both genotypes and phenotypes of some genes of the CYP family. The discriminant analysis of principal components (DAPCs) between the NAM group and the continental populations of the 1000 Genomes Project resulted in the clustering of the three Native American populations. Additionally, in general, the NAM group was determined to be closely situated between East Asia, America, and South Asia groups, which enabled us to infer a genetic similarity between these populations. The DAPC analysis further demonstrated that eight polymorphisms and six polymorphisms were more relevant in differentiating the NAM from the continental populations and the NAM populations among themselves, respectively.ConclusionSome investigated polymorphisms show differences among world populations, particularly with populations of European origin, for whom precision medicine protocols are primarily designed. The accumulated knowledge regarding these variations may assist in the design of specific protocols for Native American populations and populations admixed with them.