Dysregulation of microRNA in cholangiocarcinoma identified through a meta-analysis of microRNA profiling

BACKGROUND In the past decades,the potential of microRNA(miRNA)in cancer diagnostics and prognostics has gained a lot of interests.In this study,a meta-analysis was conducted upon the pooled miRNA microarray data of cholangiocarcinoma(CCA).AIM To identify differentially expressed(DE)miRNAs and perform functional analyses in order to gain insights to understanding miRNA-target interactions involved in tumorigenesis pathways of CCA.METHODS Raw data from 8 CCA miRNA microarray datasets,consisting of 443 samples in total,were integrated and statistically analyzed to identify DE miRNAs via comparison of levels of miRNA expression between CCA and normal bile duct samples using t-tests(P<0.001).The 10-fold cross validation was performed in order to increase the robustness of the t-test results.Our data showed 70 up-regulated and 48 down-regulated miRNAs in CCA. GeneOntology and pathway enrichment analyses revealed that mRNA targets of DEmiRNAs were significantly involved in several biological processes. The mostprominent dysregulated pathways included phosphatidylinositol-3 kinases/Akt,mitogen-activated protein kinase and Ras signaling pathways.CONCLUSIONDE miRNAs found in our meta-analysis revealed dysregulation in major cancerpathways involved in the development of CCA. These results indicated thenecessity of understanding the miRNA-target interactions and the significance ofdysregulated miRNAs in terms of diagnostics and prognostics of cancers.

Single-cell RNA-sequencing and subcellular spatial transcriptomics facilitate the translation of liver microphysiological systems for regulatory application

Reducing the use of animal models in drug development and safety assessment has long been supported by the U.S.Food and Drug Administration(FDA).The report by Royal Society for the Prevention of Cruelty to Animals indicates that in 2020,experiments involved the use of over 100 million animals,with the United States leading the list by utilizing 20 million animals.Beyond ethical considerations associated with animal testing and the costs in terms of time and money,animal models are not always effective in predicting human reactions to drug exposure.While animal testing has been the traditional method for assessing the safety and efficacy of drugs.

Pitfall of genome-wide association studies: Sources of inconsistency in genotypes and their effects

Personalized medicine will improve heath outcomes and patient satisfaction. However, implementing personalized medicine based on individuals’ biological information is far from simple, requiring genetic biomarkers that are mainly developed and used by the pharmaceutical companies for selecting those patients who benefit more, or have less risk of adverse drug reactions, from a particular drug. Genome-wide Association Studies (GWAS) aim to identify genetic variants across the human genome that might be utilized as genetic biomarkers for diagnosis and prognosis. During the last several years, high-density genotyping SNP arrays have facilitated GWAS that successfully identified common genetic variants associated with a variety of phenotypes. However, each of the identified genetic variants only explains a very small fraction of the underlying genetic contribution to the studied phenotypic trait. The replication studies demonstrated that only a small portion of associated loci in the initial GWAS can be replicated, even within the same populations. Given the complexity of GWAS, multiple sources of Type I (false positive) and Type II (false negative) errors exist. The inconsistency in genotypes that caused either by the genotypeing experiment or by genotype calling process is a major source of the false GWAS findings. Accurate and reproducible genotypes are paramount as inconsistency in genotypes can lead to an inflation of false associations. This article will review the sources of inconsistency in genotypes and discuss its effect in GWAS findings.

Homology Model and Ligand Binding Interactions of the Extracellular Domain of the Human α4β2 Nicotinic Acetylcholine Receptor

Addiction to nicotine, and possibly other tobacco constituents, is a major factor that contributes to the difficulties smokers face when attempting to quit smoking. Amongst the various subtypes of nicotinic acetylcholine receptors (nAChRs), the α4β2 subtype plays an important role in mediating the addiction process. The characterization of human α4β2-ligand binding interactions provides a molecular framework for understanding ligand-receptor interactions, rendering insights into mechanisms of nicotine addiction and may furnish a tool for efficiently identifying ligands that can bind the nicotine receptor. Therefore, we constructed a homology model of human α4β2 nAChR and performed molecular docking and molecular dynamics (MD) simulations to elucidate the potential human α4β2-ligand binding modes for eleven compounds known to bind to this receptor. Residues V96, L97 and F151 of the α4 subunit and L111, F119 and F121 of the β2 subunit were found to be involved in hydrophobic interactions while residues S153 and W154 of the α4 subunit were involved in the formation of hydrogen bonds between the receptor and respective ligands. The homology model and its eleven ligand-bound structures will be used to develop a virtual screening program for identifying tobacco constituents that are potentially addictive.

不相交主成分分析(PCA)和遗传算法(GA)用于差异表达基因的识别

建立了一种基于不相交主成分分析(Disjoint PCA)和遗传算法(GA)的特征变量选择方法,并用于从基因表达谱(Gene expression profiles)数据中识别差异表达的基因.在该方法中,用不相交主成分分析评估基因组在区分两类不同样品时的区分能力;用GA寻找区分能力最强的基因组;所识别基因的偶然相关性用统计方法评估.由于该方法考虑了基因间的协同作用更接近于基因的生物过程,从而使所识别的基因具有更好的差异表达能力.将该方法应用于肝细胞癌(HCC)样品的基因芯片数据分析,结果表明,所识别的基因具有较强的区分能力,优于常用的基因芯片显著性分析(Significance analysis of microarrays,SAM)方法.

Critical role of bioinformatics in translating huge amounts of next-generation sequencing data into personalized medicine

Realizing personalized medicine requires integrating diverse data types with bioinformatics.The most vital data are genomic information for individuals that are from advanced next-generation sequencing(NGS) technologies at present.The technologies continue to advance in terms of both decreasing cost and sequencing speed with concomitant increase in the amount and complexity of the data.The prodigious data together with the requisite computational pipelines for data analysis and interpretation are stressors to IT infrastructure and the scientists conducting the work alike.Bioinformatics is increasingly becoming the rate-limiting step with numerous challenges to be overcome for translating NGS data for personalized medicine.We review some key bioinformatics tasks,issues,and challenges in contexts of IT requirements,data quality,analysis tools and pipelines,and validation of biomarkers.

Erratum to:Critical role of bioinformatics in translating huge amounts of next-generation sequencing data into personalized medicine

The following statement should be included in the manuscript：The views presented in this article do not necessarily reflect those of the US Food and Drug Administration.

Studies on abacavir-induced hypersensitivity reaction:a successful example of translation of pharmacogenetics to personalized medicine

Abacavir is an effective nucleoside analog reverse transcriptase inhibitor used to treat human immunodeficiency virus(HIV) infected patients.Its main side effect is hypersensitivity reaction(HSR).The incidence of the HSR is associated with ethnicity among patients exposed to abacavir,and retrospective and prospective studies show a significantly increased risk of abacavir-induced HSR in human leukocyte antigen(HLA)-B*57:01-carrying patients.Immunological studies indicated that abacavir interacts specifically with HLA-B*57:01 and changed the binding specificity between the HLA molecule and the HLA-presented endogenous peptide repertoire,leading to a systemic autoimmune reaction.HLA-B*57:01 screening,combined with patch testing,had clinically predictive value and cost-effective impact in reducing the incidence of abacavir-induced HSR regardless of the HLA-B*57:01 prevalence in the population.Therefore,the US Food and Drug Administration(FDA) and international HIV treatment guidelines recommend a routine HLA-B*57:01 screening prior to abacavir treatment to decrease false positive diagnosis and prevent abacavir-induced HSR.The studies of abacavir-induced HSR and the implementation of the HLA-B*57:01 screening in the clinic represent a successful example of the use of pharmacogenetics for personalized diagnosis and therapy.

Erratum to:Studies on abacavir-induced hypersensitivity reaction:a successful example of translation of pharmacogenetics to personalized medicine

The following statement should be included in the manuscript：The views presented in this article do not necessarily reflect those of the US Food and Drug Administration.

Redefinition of Fatty Liver Disease from NAFLD to MAFLD through the Lens of Drug Development and Regulatory Science

Metabolic(dysfunction)-associated fatty liver disease(MAFLD)affects a third of the population and is a leading cause of liver-related death.Since no effective treatments exist,novel approaches to drug development are required.Unfortunately,outdated terminology and definitions of the disease are hampering efforts to develop new drugs and treatments.An international consensus panel has put forth an influential proposal for the disease to be renamed from nonalcoholic fatty liver disease(NAFLD)to MAFLD,includ-ing a proposal for how the disease should be diagnosed.As allies with the many stakeholders in MAFLD care―including patients,patients’advocates,clinicians,researchers,nurse and allied health groups,regional societies,and others―we are aware of the negative consequences of the NAFLD term and definition.We share the sense of urgency for change and will act in new ways to achieve our goals.Although there is much work to be done to overcome clinical inertia and reverse worrisome recent trends,the MAFLD initiative provides a firm foundation to build on.It provides a roadmap for moving for-ward toward more efficient care and affordable,sustainable drug and device innovation in MAFLD care.We hope it will bring promising new opportunities for a brighter future for MAFLD care and improve care and outcomes for patients of one of the globe’s largest and costliest public health burdens.From this viewpoint,we have revisited this initiative through the perspectives of drug development and regulatory science.

Network Pharmacology for Traditional Chinese Medicine Research: Methodologies and Applications

The inception of network pharmacology comes from the advance in ＂multi-target, multi-drug＂ paradigm and opens up a new field for pharmaceutical science. Traditional Chinese medicine （TCM） is well-known for its use of medicinal herb combinations to treat the functional disorders induced by diseases through a holistic view, which naturally followed the principal of network pharmacology. In this review, the methodologies of network pharmacology in TCM studies were summarized. Specifically, the methodologies for network construction and network analysis were discussed in detail by following several TCM study cases. The perspectives for TCM network pharmacology were also provided.