Towards precision medicine: interrogating the human genome to identify drug pathways associated with potentially functional, population-differentiated polymorphisms

The discovery of archaea: from observed anomaly to consequential restructuring of the phylogenetic tree

Observational and experimental discoveries of new factual entities such as objects, systems, or processes, are major contributors to some advances in the life sciences. Yet, whereas discovery of theories was extensively deliberated by philosophers of science, very little philosophical attention was paid to the discovery of factual entities. This paper examines historical and philosophical aspects of the experimental discovery by Carl Woese of archaea, prokaryotes that comprise one of the three principal domains of the phylogenetic tree. Borrowing Kuhn's terminology, this discovery of a major biological entity was made during a 'normal science' project of building molecular taxonomy for prokaryotes. Unexpectedly, however, an observed anomaly instigated the discovery of archaea. Substantiation of the existence of the new archaeal entity and consequent reconstruction of the phylogenetic tree prompted replacement of a long-held model of a prokarya and eukarya bipartite tree of life by a new model of a tripartite tree comprising of bacteria, archaea, and eukarya. This paper explores the history and philosophical implications of the progression of Woese's project from normal science to anomaly-instigated model-changing discovery. It is also shown that the consequential discoveries of RNA splicing and of ribozymes were similarly prompted by unexpected irregularities during normal science activities. It is thus submitted that some discoveries of factual biological entities are triggered by unforeseen observational or experimental anomalies.

Mutagenic primer-based novel multiplex PCR-RFLP technique to genotype BECN1 SNPs rs10512488 and rs11552192

BACKGROUND

Single Nucleotide Polymorphisms (SNPs) in candidate autophagy gene BECN1 could influence its functions thereby autophagy process. BECN1 noncoding SNPs were found to be significantly associated with neurodegenerative disease and type 2 diabetes mellitus. This study aimed to develop a simultaneous genotyping technique for two BECN1 SNPs (rs10512488 and rs11552192).

METHODS

A mutagenic primer-based approach was used to introduce a NdeI restriction site to genotype rs10512488 by Artificial-Restriction Fragment Length Polymorphism (A-RFLP) along with rs11552192 by Polymerase Chain Reaction (PCR)-RFLP. Multiplexing PCR and restriction digestion reactions were set up for simultaneous genotyping of both SNPs in 100 healthy individuals. Genotypic and allele frequencies were manually calculated, and the Hardy-Weinberg Equilibrium was assessed using the chi-square test.

RESULTS

We successfully developed PCR and RFLP conditions for the amplification and restriction digestion of both SNPs within the same tube for genotyping. The results of genotyping by newly developed multiplexing PCR-RFLP technique were concordant with the genotypes obtained by Sanger sequencing of samples. Allelic frequencies of rs10512488 obtained were 0.15 (A) and 0.85 (G), whereas allelic frequencies of rs11552192 were 0.16 (T) and 0.84 (A).

CONCLUSION

The newly developed technique is rapid, cost-effective and time-saving for large-scale applications compared to sequencing methods and would play an important role in low-income settings. For the first time, allelic frequencies of rs10512488 and rs11552192 were reported among the North Indian population.

The Potential of Single Nucleotide Polymorphisms (SNPs) as Biomarkers and Their Association with the Increased Risk of Coronary Heart Disease: A Systematic Review

Human genetic analyses and epidemiological studies showed a potential association between several types of gene polymorphism and the development of coronary heart disease (CHD). Many studies on this pertinent topic need to be investigated further to reach an evidence-based conclusion. Therefore, in this current review, we describe several types of gene polymorphisms that are potentially linked to CHD. A systematic review using the databases EBSCO, PubMed, and ScienceDirect databases was searched until October of 2022 to find relevant studies on the topic of gene polymorphisms on risk factors for CHD, especially for the factors associated with single nucleotide polymorphisms (SNPs). The risk of bias and quality assessment was evaluated by Joanna Briggs Institute (JBI) guidelines. From keyword search results, a total of 6243 articles were identified, which were subsequently narrowed to 14 articles using prespecified inclusion criteria. The results suggested that there were 33 single nucleotide polymorphisms (SNPs) that can potentially increase the risk factors and clinical symptoms of CHD. This study also indicated that gene polymorphisms had a potential role in increasing CHD risk factors that were causally associated with atherosclerosis, increased homocysteine, immune/inflammatory response, Low-Density Lipoprotein (LDL), arterial lesions, and reduction of therapeutic effectiveness. In conclusion, the findings of this study indicate that SNPs may increase risk factors for CHD and SNPs show different effects between individuals. This demonstrates that knowledge of SNPs on CHD risk factors can be used to develop biomarkers for diagnostics and therapeutic response prediction to decide successful therapy and become the basis for defining personalized medicine in future.

Robust SNP-based prediction of rheumatoid arthritis through machine-learning-optimized polygenic risk score

BACKGROUND

The popular statistics-based Genome-wide association studies (GWAS) have provided deep insights into the field of complex disorder genetics. However, its clinical applicability to predict disease/trait outcomes remains unclear as statistical models are not designed to make predictions. This study employs statistics-free machine-learning (ML)-optimized polygenic risk score (PRS) to complement existing GWAS and bring the prediction of disease/trait outcomes closer to clinical application. Rheumatoid Arthritis (RA) was selected as a model disease to demonstrate the robustness of ML in disease prediction as RA is a prevalent chronic inflammatory joint disease with high mortality rates, affecting adults at the economic prime. Early identification of at-risk individuals may facilitate measures to mitigate the effects of the disease.

METHODS

This study employs a robust ML feature selection algorithm to identify single nucleotide polymorphisms (SNPs) that can predict RA from a set of training data comprising RA patients and population control samples. Thereafter, selected SNPs were evaluated for their predictive performances across 3 independent, unseen test datasets. The selected SNPs were subsequently used to generate PRS which was also evaluated for its predictive capacity as a sole feature.

RESULTS

Through robust ML feature selection, 9 SNPs were found to be the minimum number of features for excellent predictive performance (AUC > 0.9) in 3 independent, unseen test datasets. PRS based on these 9 SNPs was significantly associated with (P < 1 × 10^-16) and predictive (AUC > 0.9) of RA in the 3 unseen datasets. A RA ML-PRS calculator of these 9 SNPs was developed ( https://xistance.shinyapps.io/prs-ra/ ) to facilitate individualized clinical applicability. The majority of the predictive SNPs are protective, reside in non-coding regions, and are either predicted to be potentially functional SNPs (pfSNPs) or in high linkage disequilibrium (r2 > 0.8) with un-interrogated pfSNPs.

CONCLUSIONS

These findings highlight the promise of this ML strategy to identify useful genetic features that can robustly predict disease and amenable to translation for clinical application.

CYP2A6 and GABRA2 Gene Polymorphisms are Associated With Dexmedetomidine Drug Response

Background: Dexmedetomidine is a commonly used clinical sedative; however, the drug response varies among individuals. Thus, the purpose of this study was to explore the association between dexmedetomidine response and gene polymorphisms related to drug-metabolizing enzymes and drug response (CYP2A6, UGT2B10, UGT1A4, ADRA2A, ADRA2B, ADRA2C, GABRA1, GABRB2, and GLRA1).

Methods: This study was a prospective cohort study. A total of 194 female patients aged 18–60 years, American Society of Anesthesiologists (ASA) score I-II, who underwent laparoscopy at the Third Xiangya Hospital of Central South University, were included. The sedative effect was assessed every 2 min using the Ramsay score, and the patient’s heart rate decrease within 20 min was recorded. Peripheral blood was collected from each participant to identify genetic variants in the candidate genes of metabolic and drug effects using the Sequenom MassARRAY^® platform. Furthermore, additional peripheral blood samples were collected from the first 99 participants at multiple time points after dexmedetomidine infusion to perform dexmedetomidine pharmacokinetic analysis by Phoenix^® WinNonlin 7.0 software.

Results: Carriers of the minor allele (C) of CYP2A6 rs28399433 had lower metabolic enzyme efficiency and higher plasma concentrations of dexmedetomidine. In addition, the participants were divided into dexmedetomidine sensitive or dexmedetomidine tolerant groups based on whether they had a Ramsay score of at least four within 20 min, and CYP2A6 rs28399433 was identified to have a significant influence on the dexmedetomidine sedation sensitivity by logistic regression with Plink software [p = 0.003, OR (95% CI): 0.27 (0.11–0.65)]. C allele carriers were more sensitive to the sedative effects of dexmedetomidine than A allele carriers. GABRA2 rs279847 polymorphism was significantly associated with the degree of the heart rate decrease. In particular, individuals with the GG genotype had a 4-fold higher risk of heart rate abnormality than carriers of the T allele (OR = 4.32, 95% CI: 1.96–9.50, p = 0.00027).

Conclusion:CYP2A6 rs28399433 polymorphism affects the metabolic rate of dexmedetomidine and is associated with susceptibility to the sedative effects of dexmedetomidine; GABRA2 rs279847 polymorphism is significantly associated with the degree of the heart rate decrease.

Open Structural Data in Precision Medicine

Three-dimensional protein structural data at the molecular level are pivotal for successful precision medicine. Such data are crucial not only for discovering drugs that act to block the active site of the target mutant protein but also for clarifying to the patient and the clinician how the mutations harbored by the patient work. The relative paucity of structural data reflects their cost, challenges in their interpretation, and lack of clinical guidelines for their utilization. Rapid technological advancements in experimental high-resolution structural determination increasingly generate structures. Computationally, modeling algorithms, including molecular dynamics simulations, are becoming more powerful, as are compute-intensive hardware, particularly graphics processing units, overlapping with the inception of the exascale era. Accessible, freely available, and detailed structural and dynamical data can be merged with big data to powerfully transform personalized pharmacology. Here we review protein and emerging genome high-resolution data, along with means, applications, and examples underscoring their usefulness in precision medicine. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 5 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Ali ME, Abuduhier FM, Ullah MF. Clinical Implications of Krüpple-like Transcription Factor KLF-14 and Certain Micro-RNA (miR-27a, miR-196a2, miR-423) Gene Variations as a Risk Factor in the Genetic Predisposition to PCOS

Polycystic ovary syndrome (PCOS) is a disorder with a symptomatic manifestation of an array of metabolic and endocrine impairments. PCOS has a relatively high prevalence rate among young women of reproductive age and is a risk factor for some severe metabolic diseases such as T2DM, insulin insensitivity, and obesity, while the most dominant endocrine malfunction is an excess of testosterone showing hyperandrogenism and hirsutism. MicroRNAs have been implicated as mediators of metabolic diseases including obesity and insulin resistance, as these can regulate multiple cellular pathways such as insulin signaling and adipogenesis. Genome-wide association studies during the last few years have also linked the Krüpple-like family of transcription factors such as KLF14, which contribute in mechanisms of mammalian gene regulation, with certain altered metabolic traits and risk of atherosclerosis and type-2 DM. This study has characterized the biochemical and endocrine parameters in PCOS patients with a comprehensive serum profiling in comparison to healthy controls and further examined the influence of allelic variations for miRNAs 27a (rs895819 A > G), 196a2 (rs11614913 C > T), 423 (rs6505162C > A), and transcription factor KLF14 (rs972283 A > G) gene polymorphism on the risk and susceptibility to PCOS. The experimental protocol included amplification refractory mutation-specific (ARMS)-PCR to detect and determine the presence of these polymorphic variants in the study subjects. The results in this case−control study showed that most of the serum biomarkers, both biochemical and endocrine, that were analyzed in the study demonstrated statistically significant alterations in PCOS patients, including lipids (LDL, HDL, cholesterol), T2DM markers (fasting glucose, free insulin, HOMA-IR), and hormones (FSH, LH, testosterone, and progesterone). The distribution of Krüppel-like factor 14 rs972283 G > A, miR-27a rs895819 A > G, and miR-196a-2 rs11614913 C > T genotypes analyzed within PCOS patients and healthy controls in the considered population was significant (p < 0.05), except for miR-423 rs6505162 C > A genotypes (p > 0.05). The study found that in the codominant model, KLF14-AA was strongly associated with greater PCOS susceptibility (OR 2.35, 95% CI = 1.128 to 4.893, p < 0.022), miR-27a-GA was linked to an enhanced PCOS susceptibility (OR 2.06, 95% CI = 1.165 to 3.650, p < 0.012), and miR-196a-CT was associated with higher PCOS susceptibility (OR 2.06, 95% CI = 1.191 to 3.58, p < 0.009). Moreover, allele A of KLF-14 and allele T of miR-196a2 were strongly associated with PCOS susceptibility in the considered population.

Genetic Variants and Drug Efficacy in Tuberculosis: A Step toward Personalized Therapy

Tuberculosis (TB) continues to be a major infectious disease affecting individuals worldwide. Current TB treatment strategy recommends the standard short-course chemotherapy regimen containing first-line drug, i.e., isoniazid, rifampicin, pyrazinamide, and ethambutol to treat patients suffering from drug-susceptible TB. Although Mycobacterium tuberculosis, the causing agent, is susceptible to drugs, some patients do not respond to the treatment or treatment may result in serious adverse reactions. Many studies revealed that anti-TB drug-related toxicity is associated with genetic variations, and these variations may also influence attaining maximum drug concentration. Thus, inter-individual diversities play a characteristic role by influencing the genes involved in drug metabolism pathways. The development of pharmacogenomics could bring a revolution in the field of treatment, and the understanding of germline variants may give rise to optimized targeted treatments and refine the response to standard therapy. In this review, we briefly introduced the field of pharmacogenomics with the evolution in genetics and discussed the pharmacogenetic impact of genetic variations on genes involved in the activities, such as anti-TB drug transportation, metabolism, and gene regulation.

Artificial-Intelligence-Assisted Discovery of Genetic Factors for Precision Medicine of Antiplatelet Therapy in Diabetic Peripheral Artery Disease

An increased risk of cardiovascular events was identified in patients with peripheral artery disease (PAD). Clopidogrel is one of the most widely used antiplatelet medications. However, there are heterogeneous outcomes when clopidogrel is used to prevent cardiovascular events in PAD patients. Here, we use an artificial intelligence (AI)-assisted methodology to identify genetic factors potentially involved in the clopidogrel-resistant mechanism, which is currently unclear. Several discoveries can be pinpointed. Firstly, a high proportion (>50%) of clopidogrel resistance was found among diabetic PAD patients in Taiwan. Interestingly, our result suggests that platelet function test-guided antiplatelet therapy appears to reduce the post-interventional occurrence of major adverse cerebrovascular and cardiac events in diabetic PAD patients. Secondly, AI-assisted genome-wide association study of a single-nucleotide polymorphism (SNP) database identified a SNP signature composed of 20 SNPs, which are mapped into 9 protein-coding genes (SLC37A2, IQSEC1, WASHC3, PSD3, BTBD7, GLIS3, PRDM11, LRBA1, and CNR1). Finally, analysis of the protein connectivity map revealed that LRBA, GLIS3, BTBD7, IQSEC1, and PSD3 appear to form a protein interaction network. Intriguingly, the genetic factors seem to pinpoint a pathway related to endocytosis and recycling of P2Y12 receptor, which is the drug target of clopidogrel. Our findings reveal that a combination of AI-assisted discovery of SNP signatures and clinical parameters has the potential to develop an ethnic-specific precision medicine for antiplatelet therapy in diabetic PAD patients.

Whole Exome Sequencing Study in a Family with Type 2 Diabetes Mellitus

Background

Type 2 diabetes mellitus (T2DM) is characterized by β cell decline in the pancreas and insulin resistance. This study aimed to investigate the possible pathogenic gene mutation sites of T2DM patients using whole exome sequencing.

Materials and Methods

We recruited a Chinese family with 3-generation history of diabetes. The whole blood genomic DNA of seven members of the family was extracted and sent for whole exome sequencing. Biological information was analyzed with in silico prediction methods, including significance analysis of single nucleotide polymorphism (SNP)/Indel site, and analysis of specific SNP/Indel proteins and their potential mechanisms.

Results

Six out of seven members of the family were diagnosed with diabetes. All DNA samples (23 kb) met quality requirements of library construction. Clean reads of each sample demonstrated high Q20 and Q30 (>80%), indicating good sequencing quality of sequencing data. A total of 130,693 SNPs and 15,928 Indels were found in DNA samples. A total of 22 significant SNPs and Indel mutation sites located on 19 genes were obtained, including ZCCHC3, SYN2, RPL14, SRRD, AMD1, CAMKK2, ZNF787, RNF157, NPIPB15, ALG3, KIAA0040, MAST2, ESRRA, C8orf58, PNLIPRP1, DACH1, MACC1, CAPN9 and DMKN. An rs2305205 mutation of PNLIPRP1 gene and an rs778701848 mutation of CAMKK2 gene may be associated with the pathogenesis of T2DM in this family.

Conclusion

Exons of these diabetic patients demonstrated an rs2305205 mutation in PNLIPRP1 gene and an rs778701848 mutation in CAMKK2 gene. These two mutations might promote T2DM occurrence through reducing sensitivity of peripheral tissue to insulin and reducing insulin secretion.

Genetic diversity of 'Very Important Pharmacogenes' in two South-Asian populations

OBJECTIVES

Reliable identification of population-specific variants is important for building the single nucleotide polymorphism (SNP) profile. In this study, genomic variation using allele frequency differences of pharmacologically important genes for Gujarati Indians in Houston (GIH) and Indian Telugu in the U.K. (ITU) from the 1000 Genomes Project vis-à-vis global population data was studied to understand its role in drug response.

METHODS

Joint genotyping approach was used to derive variants of GIH and ITU independently. SNPs of both these populations with significant allele frequency variation (minor allele frequency ≥ 0.05) with super-populations from the 1000 Genomes Project and gnomAD based on Chi-square distribution with p-value of ≤ 0.05 and Bonferroni's multiple adjustment tests were identified. Population stratification and fixation index analysis was carried out to understand genetic differentiation. Functional annotation of variants was carried out using SnpEff, VEP and CADD score.

RESULTS

Population stratification of VIP genes revealed four clusters viz., single cluster of GIH and ITU, one cluster each of East Asian, European, African populations and Admixed American was found to be admixed. A total of 13 SNPs belonging to ten pharmacogenes were identified to have significant allele frequency variation in both GIH and ITU populations as compared to one or more super-populations. These SNPs belong to VKORC1 (rs17708472, rs2359612, rs8050894) involved in Vitamin K cycle, cytochrome P450 isoforms CYP2C9 (rs1057910), CYP2B6 (rs3211371), CYP2A2 (rs4646425) and CYP2A4 (rs4646440); ATP-binding cassette (ABC) transporter ABCB1 (rs12720067), DPYD1 (rs12119882, rs56160474) involved in pyrimidine metabolism, methyltransferase COMT (rs9332377) and transcriptional factor NR1I2 (rs6785049). SNPs rs1544410 (VDR), rs2725264 (ABCG2), rs5215 and rs5219 (KCNJ11) share high fixation index (≥ 0.5) with either EAS/AFR populations. Missense variants rs1057910 (CYP2C9), rs1801028 (DRD2) and rs1138272 (GSTP1), rs116855232 (NUDT15); intronic variants rs1131341 (NQO1) and rs115349832 (DPYD) are identified to be 'deleterious'.

CONCLUSIONS

Analysis of SNPs pertaining to pharmacogenes in GIH and ITU populations using population structure, fixation index and allele frequency variation provides a premise for understanding the role of genetic diversity in drug response in Asian Indians.

Validation of a multi-gene qPCR-based pharmacogenomics panel across major ethnic groups in Singapore and Indonesia

Aim: The clinical utility of pharmacogenomics (PGx) has been gaining traction alongside growing evidence that adverse drug reactions (ADRs) have significant genetic associations. Nala PGx Core^® is a multi-gene qPCR-based panel of 20 allele variants, comprising 18 SNPs and two CYP2D6 copy number markers across four pharmacogenes - CYP2C9, CYP2C19, CYP2D6 and SLCO1B1. Methods: In this study, we validated the performance of Nala PGx Core^® against benchmark methods, on the Singaporean and Indonesian populations. Results & conclusion: Nala PGx Core^® demonstrated robust and accurate genotyping when compared with other established benchmarks. Furthermore, the panel successfully characterized alleles of clinical relevance, such as CYP2D6*10 and CYP2D6*36, across major ethnic groups present of Singapore and Indonesia, suggesting its potential for adoption in clinical workflows regionally.

Rates of genetic testing in patients prescribed drugs with pharmacogenomic information in FDA-approved labeling

This study examined rates of genetic testing in two cohorts of publicly insured individuals who have newly prescribed medication with FDA pharmacogenomic labeling guidance. Genetic testing was rare (4.4% and 10.5% in Medicaid and Medicare cohorts, respectively) despite the fact that all participants selected were taking medications that contained pharmacogenomic labeling information. When testing was conducted it was typically done before the initial use of a target medication. Factors that emerged as predictors of the likelihood of undergoing genetic testing included White ethnicity (vs. Black), female gender, and age. Cost analyses indicated higher expenditures in groups receiving genetic testing vs. matched comparators with no genetic testing, as well as disparities between proactively and reactively tested groups (albeit in opposite directions across cohorts). Results are discussed in terms of the possible reasons for the low base rate of testing, mechanisms of increased cost, and barriers to dissemination and implementation of these tests.

Robust Performance of Potentially Functional SNPs in Machine Learning Models for the Prediction of Atorvastatin-Induced Myalgia

Statins can cause muscle symptoms resulting in poor adherence to therapy and increased cardiovascular risk. We hypothesize that combinations of potentially functional SNPs (pfSNPs), rather than individual SNPs, better predict myalgia in patients on atorvastatin. This study assesses the value of potentially functional single nucleotide polymorphisms (pfSNPs) and employs six machine learning algorithms to identify the combination of SNPs that best predict myalgia.

Methods: Whole genome sequencing of 183 Chinese, Malay and Indian patients from Singapore was conducted to identify genetic variants associated with atorvastatin induced myalgia. To adjust for confounding factors, demographic and clinical characteristics were also examined for their association with myalgia. The top factor, sex, was then used as a covariate in the whole genome association analyses. Variants that were highly associated with myalgia from this and previous studies were extracted, assessed for potential functionality (pfSNPs) and incorporated into six machine learning models. Predictive performance of a combination of different models and inputs were compared using the average cross validation area under ROC curve (AUC). The minimum combination of SNPs to achieve maximum sensitivity and specificity as determined by AUC, that predict atorvastatin-induced myalgia in most, if not all the six machine learning models was determined.

Results: Through whole genome association analyses using sex as a covariate, a larger proportion of pfSNPs compared to non-pf SNPs were found to be highly associated with myalgia. Although none of the individual SNPs achieved genome wide significance in univariate analyses, machine learning models identified a combination of 15 SNPs that predict myalgia with good predictive performance (AUC >0.9). SNPs within genes identified in this study significantly outperformed SNPs within genes previously reported to be associated with myalgia. pfSNPs were found to be more robust in predicting myalgia, outperforming non-pf SNPs in the majority of machine learning models tested.

Conclusion: Combinations of pfSNPs that were consistently identified by different machine learning models to have high predictive performance have good potential to be clinically useful for predicting atorvastatin-induced myalgia once validated against an independent cohort of patients.

Co-expression of drug metabolizing cytochrome P450 enzymes and estrogen receptor alpha (ESR1) in human liver: racial differences and the regulatory role of ESR1

OBJECTIVES

The function and expression of cytochrome P450 (CYP) drug metabolizing enzymes is highly variable, greatly affecting drug exposure, and therapeutic outcomes. The expression of these enzymes is known to be controlled by many transcription factors (TFs), including ligand-free estrogen receptor alpha (ESR1, in the absence of estrogen). However, the relationship between the expression of ESR1, other TFs, and CYP enzymes in human liver is still unclear.

METHODS

Using real-time PCR, we quantified the mRNA levels of 12 CYP enzymes and nine TFs in 246 human liver samples from European American (EA, n = 133) and African American (AA, n = 113) donors.

RESULTS

Our results showed higher expression levels of ESR1 and six CYP enzymes in EA than in AA. Partial least square regression analysis showed that ESR1 is the top-ranking TF associating with the expression of eight CYP enzymes, six of which showed racial difference in expression. Conversely, four CYP enzymes without racial difference in expression did not have ESR1 as a top-ranking TF. These results indicate that ESR1 may contribute to variation in CYP enzyme expression between these two ancestral backgrounds.

CONCLUSIONS

These results are consistent with our previous study showing ESR1 as a master regulator for the expression of several CYP enzymes. Therefore, factors affecting ESR1 expression may have broad influence on drug metabolism through altered expression of CYP enzymes.

Stem Cells for Next Level Toxicity Testing in the 21st Century

The call for a paradigm change in toxicology from the United States National Research Council in 2007 initiates awareness for the invention and use of human-relevant alternative methods for toxicological hazard assessment. Simple 2D in vitro systems may serve as first screening tools, however, recent developments infer the need for more complex, multicellular organotypic models, which are superior in mimicking the complexity of human organs. In this review article most critical organs for toxicity assessment, i.e., skin, brain, thyroid system, lung, heart, liver, kidney, and intestine are discussed with regards to their functions in health and disease. Embracing the manifold modes-of-action how xenobiotic compounds can interfere with physiological organ functions and cause toxicity, the need for translation of such multifaceted organ features into the dish seems obvious. Currently used in vitro methods for toxicological applications and ongoing developments not yet arrived in toxicity testing are discussed, especially highlighting the potential of models based on embryonic stem cells and induced pluripotent stem cells of human origin. Finally, the application of innovative technologies like organs-on-a-chip and genome editing point toward a toxicological paradigm change moves into action.

The Laboratory Domestication of Zebrafish: From Diverse Populations to Inbred Substrains

We know from human genetic studies that practically all aspects of biology are strongly influenced by the genetic background, as reflected in the advent of “personalized medicine.” Yet, with few exceptions, this is not taken into account when using laboratory populations as animal model systems for research in these fields. Laboratory strains of zebrafish (Danio rerio) are widely used for research in vertebrate developmental biology, behavior, and physiology, for modeling diseases, and for testing pharmaceutic compounds in vivo. However, all of these strains are derived from artificial bottleneck events and therefore are likely to represent only a fraction of the genetic diversity present within the species. Here, we use restriction site-associated DNA sequencing to genetically characterize wild populations of zebrafish from India, Nepal, and Bangladesh, and to compare them to previously published data on four common laboratory strains. We measured nucleotide diversity, heterozygosity, and allele frequency spectra, and find that wild zebrafish are much more diverse than laboratory strains. Further, in wild zebrafish, there is a clear signal of GC-biased gene conversion that is missing in laboratory strains. We also find that zebrafish populations in Nepal and Bangladesh are most distinct from all other strains studied, making them an attractive subject for future studies of zebrafish population genetics and molecular ecology. Finally, isolates of the same strains kept in different laboratories show a pattern of ongoing differentiation into genetically distinct substrains. Together, our findings broaden the basis for future genetic, physiological, pharmaceutic, and evolutionary studies in Danio rerio.

Identification of ancestry proportions in admixed groups across the Americas using clinical pharmacogenomic SNP panels

We evaluated the performance of three PGx panels to estimate biogeographical ancestry: the DMET panel, and the VIP and Preemptive PGx panels described in the literature. Our analysis indicate that the three panels capture quite well the individual variation in admixture proportions observed in recently admixed populations throughout the Americas, with the Preemptive PGx and DMET panels performing better than the VIP panel. We show that these panels provide reliable information about biogeographic ancestry and can be used to guide the implementation of PGx clinical decision-support (CDS) tools. We also report that using these panels it is possible to control for the effects of population stratification in association studies in recently admixed populations, as exemplified with a warfarin dosing GWA study in a sample from Brazil.

Covalently Mercurated Molecular Beacon for Discriminating the Canonical Nucleobases

A highly nucleobase-discriminating metalated nucleoside analogue, 3-fluoro-2-mercuri-6-methylaniline, was incorporated into an oligonucleotide molecular beacon. Fluorescence emission spectra were measured after the addition of four different complementary strands, in which the nucleobase opposite the metalated analogue varies. The fluorescence results showed a clear binding selectivity at room temperature, in the order G>T>C>A. The selectivity is based on the different affinities between the metalated nucleoside analogue and the canonical nucleobases. The synthesized probe is capable of robust discrimination between the two purine as well as the two pyrimidine bases by fluorescence at room temperature, and more sophisticated temperature analysis allows clear separation of every canonical nucleobase. The probe would, hence, be a suitable method for the detection of single nucleotide polymorphisms.

PPARG (Pro12Ala) genetic variant and risk of T2DM: a systematic review and meta-analysis

Type 2 diabetes mellitus (T2DM) is a complex disease caused by the interaction between genetic and environmental factors. A growing number of evidence suggests that the peroxisome proliferator-activated receptor gamma (PPARG) gene plays a major role in T2DM development. Meta-analysis of genetic association studies is an efficient tool to gain a better understanding of multifactorial diseases and potentially to provide valuable insights into gene-disease interactions. The present study was focused on assessing the association between Pro12Ala variation in the PPARG and T2DM risk through a comprehensive meta-analysis. We searched PubMed, WoS, Embase, Scopus and ProQuest from 1990 to 2017. The fixed-effect or random-effect model was used to evaluate the pooled odds ratios (ORs) and 95% confidence intervals (CIs) depending on the heterogeneity among studies. The sources of heterogeneity and publication bias among the included studies were assessed using I² statistics and Egger's tests. A total of 73 studies, involving 62,250 cases and 69,613 controls were included. The results showed that the minor allele (G) of the rs1801282 variant was associated with the decreased risk of T2DM under different genetic models. Moreover, the protective effect of minor allele was detected to be significantly more in some ethnicities including the European (18%), East Asian (20%), and South East Asian (18%). And the reduction of T2DM risk in Ala12 carriers was stronger in individuals from North Europe rather than Central and South Europe. Our findings indicated that the rs1801282 variant may contribute to decrease of T2DM susceptibility in different ancestries.