Sequencing XMET genes to promote genotype-guided risk assessment and precision medicine

High-throughput next generation sequencing (NGS) is a shotgun approach applied in a parallel fashion by which the genome is fragmented and sequenced through small pieces and then analyzed either by aligning to a known reference genome or by de novo assembly without reference genome. This technology has led researchers to conduct an explosion of sequencing related projects in multidisciplinary fields of science. However, due to the limitations of sequencing-based chemistry, length of sequencing reads and the complexity of genes, it is difficult to determine the sequences of some portions of the human genome, leaving gaps in genomic data that frustrate further analysis. Particularly, some complex genes are difficult to be accurately sequenced or mapped because they contain high GC-content and/or low complexity regions, and complicated pseudogenes, such as the genes encoding xenobiotic metabolizing enzymes and transporters (XMETs). The genetic variants in XMET genes are critical to predicate inter-individual variability in drug efficacy, drug safety and susceptibility to environmental toxicity. We summarized and discussed challenges, wet-lab methods, and bioinformatics algorithms in sequencing "complex" XMET genes, which may provide insightful information in the application of NGS technology for implementation in toxicogenomics and pharmacogenomics.

Correction to: Similarities and differences between variants called with human reference genome HG19 or HG38

After publication of this supplement article.

In silico identification of genetic mutations conferring resistance to acetohydroxyacid synthase inhibitors: A case study of Kochia scoparia

Mutations that confer herbicide resistance are a primary concern for herbicide-based chemical control of invasive plants and are often under-characterized structurally and functionally. As the outcome of selection pressure, resistance mutations usually result from repeated long-term applications of herbicides with the same mode of action and are discovered through extensive field trials. Here we used acetohydroxyacid synthase (AHAS) of Kochia scoparia (KsAHAS) as an example to demonstrate that, given the sequence of a target protein, the impact of genetic mutations on ligand binding could be evaluated and resistance mutations could be identified using a biophysics-based computational approach. Briefly, the 3D structures of wild-type (WT) and mutated KsAHAS-herbicide complexes were constructed by homology modeling, docking and molecular dynamics simulation. The resistance profile of two AHAS-inhibiting herbicides, tribenuron methyl and thifensulfuron methyl, was obtained by estimating their binding affinity with 29 KsAHAS (1 WT and 28 mutated) using 6 molecular mechanical (MM) and 18 hybrid quantum mechanical/molecular mechanical (QM/MM) methods in combination with three structure sampling strategies. By comparing predicted resistance with experimentally determined resistance in the 29 biotypes of K. scoparia field populations, we identified the best method (i.e., MM-PBSA with single structure) out of all tested methods for the herbicide-KsAHAS system, which exhibited the highest accuracy (up to 100%) in discerning mutations conferring resistance or susceptibility to the two AHAS inhibitors. Our results suggest that the in silico approach has the potential to be widely adopted for assessing mutation-endowed herbicide resistance on a case-by-case basis.

Similarities and differences between variants called with human reference genome HG19 or HG38

Background

Reference genome selection is a prerequisite for successful analysis of next generation sequencing (NGS) data. Current practice employs one of the two most recent human reference genome versions: HG19 or HG38. To date, the impact of genome version on SNV identification has not been rigorously assessed.

Methods

We conducted analysis comparing the SNVs identified based on HG19 vs HG38, leveraging whole genome sequencing (WGS) data from the genome-in-a-bottle (GIAB) project. First, SNVs were called using 26 different bioinformatics pipelines with either HG19 or HG38. Next, two tools were used to convert the called SNVs between HG19 and HG38. Lastly we calculated conversion rates, analyzed discordant rates between SNVs called with HG19 or HG38, and characterized the discordant SNVs.

Results

The conversion rates from HG38 to HG19 (average 95%) were lower than the conversion rates from HG19 to HG38 (average 99%). The conversion rates varied slightly among the various calling pipelines. Around 1.5% SNVs were discordantly converted between HG19 or HG38. The conversions from HG38 to HG19 had more SNVs which failed conversion and more discordant SNVs than the opposite conversion (HG19 to HG38). Most of the discordant SNVs had low read depth, were low confidence SNVs as defined by GIAB, and/or were predominated by G/C alleles (52% observed versus 42% expected).

Conclusion

A significant number of SNVs could not be converted between HG19 and HG38. Based on careful review of our comparisons, we recommend HG38 (the newer version) for NGS SNV analysis. To summarize, our findings suggest caution when translating identified SNVs between different versions of the human reference genome.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2620-0) contains supplementary material, which is available to authorized users.

Deep learning for predicting toxicity of chemicals: a mini review

Humans and wildlife inhabit a world with panoply of natural and synthetic chemicals. Alarmingly, only a limited number of chemicals have undergone comprehensive toxicological evaluation due to limitations of traditional toxicity testing. High-throughput screening assays provide a higher-speed alternative for conventional toxicity testing. Advancement of high-throughput bioassay technology has greatly increased chemical toxicity data volumes in the past decade, pushing toxicology research into a "big data" era. However, traditional data analysis methods fail to effectively process large data volumes, presenting both a challenge and an opportunity for toxicologists. Deep learning, a machine learning method leveraging deep neural networks (DNNs), is a proven useful tool for building quantitative structure-activity relationship (QSAR) models for toxicity prediction utilizing these new large datasets. In this mini review, a brief technical background on DNNs is provided, and the current state of chemical toxicity prediction models built with DNNs is reviewed. In addition, relevant toxicity data sources are summarized, possible limitations are discussed, and perspectives on DNN utilization in chemical toxicity prediction are given.

Computational prediction models for assessing endocrine disrupting potential of chemicals

Endocrine disrupting chemicals (EDCs) mimic natural hormones and disrupt endocrine function. Humans and wildlife are exposed to EDCs might alter endocrine functions through various mechanisms and lead to an adverse effects. Hence, EDCs identification is important to protect the ecosystem and to promote the public health. Leveraging in-vitro and in-vivo experiments to identify potential EDCs is time consuming and expensive. Hence, quantitative structure-activity relationship is applied to screen the potential EDCs. Here, we summarize the predictive models developed using various algorithms to forecast the binding activity of chemicals to the estrogen and androgen receptors, alpha-fetoprotein, and sex hormone binding globulin.

A review on machine learning methods for in silico toxicity prediction

In silico toxicity prediction plays an important role in the regulatory decision making and selection of leads in drug design as in vitro/vivo methods are often limited by ethics, time, budget, and other resources. Many computational methods have been employed in predicting the toxicity profile of chemicals. This review provides a detailed end-to-end overview of the application of machine learning algorithms to Structure-Activity Relationship (SAR)-based predictive toxicology. From raw data to model validation, the importance of data quality is stressed as it greatly affects the predictive power of derived models. Commonly overlooked challenges such as data imbalance, activity cliff, model evaluation, and definition of applicability domain are highlighted, and plausible solutions for alleviating these challenges are discussed.

Comparative toxicogenomics of three insensitive munitions constituents 2,4-dinitroanisole, nitroguanidine and nitrotriazolone in the soil nematode Caenorhabditis elegans

BACKGROUND

Ecotoxicological studies on the insensitive munitions formulation IMX-101 and its components 2,4-dinitroanisole (DNAN), nitroguanidine (NQ) and nitrotriazolone (NTO) in various organisms showed that DNAN was the main contributor to the overall toxicity of IMX-101 and suggested that the three compounds acted independently. These results motivated this toxicogenomics study to discern toxicological mechanisms for these compounds at the molecular level.

METHODS

Here we used the soil nematode Caenorhabditis elegans, a well-characterized genomics model, as the test organism and a species-specific, transcriptome-wide 44 K-oligo probe microarray for gene expression analysis. In addition to the control treatment, C. elegans were exposed for 24 h to 6 concentrations of DNAN (1.95-62.5 ppm) or NQ (83-2667 ppm) or 5 concentrations of NTO (187-3000 ppm) with ten replicates per treatment. The nematodes were transferred to a clean environment after exposure. Reproduction endpoints (egg and larvae counts) were measured at three time points (i.e., 24-, 48- and 72-h). Gene expression profiling was performed immediately after 24-h exposure to each chemical at the lowest, medium and highest concentrations plus the control with four replicates per treatment.

RESULTS

Statistical analyses indicated that chemical treatment did not significantly affect nematode reproduction but did induce 2175, 378, and 118 differentially expressed genes (DEGs) in NQ-, DNAN-, and NTO-treated nematodes, respectively. Bioinformatic analysis indicated that the three compounds shared both DEGs and DEG-mapped Reactome pathways. Gene set enrichment analysis further demonstrated that DNAN and NTO significantly altered 12 and 6 KEGG pathways, separately, with three pathways in common. NTO mainly affected carbohydrate, amino acid and xenobiotics metabolism while DNAN disrupted protein processing, ABC transporters and several signal transduction pathways. NQ-induced DEGs were mapped to a wide variety of metabolism, cell cycle, immune system and extracellular matrix organization pathways.

CONCLUSION

Despite the absence of significant effects on apical reproduction endpoints, DNAN, NTO and NQ caused significant alterations in gene expression and pathways at 1.95 ppm, 187 ppm and 83 ppm, respectively. This study provided supporting evidence that the three chemicals may exert independent toxicity by acting on distinct molecular targets and pathways.

Quantum chemical simulations revealed the toxicokinetic mechanisms of organic phosphorus flame retardants catalyzed by P450 enzymes

The metabolic fate and toxicokinetics of organic phosphorus flame retardants catalyzed by cytochrome P450 enzymes (CYPs) are here investigated by in silico simulations, leveraging an active center model to mimic the CYPs, triphenyl phosphate (TPHP), tris(2-butoxyethyl) phosphate and tris(1,3-dichloro-2-propyl) phosphate as substrates. Our calculations elucidated key main pathways and predicted products, which were corroborated by current in vitro data. Results showed that alkyl OPFRs are eliminated faster than aryl and halogenated alkyl-substituted OPFRs. In addition, we discovered a proton shuttle pathway for aryl hydroxylation of TPHP and P = O bond-assisted H-transfer mechanisms (rather than nonenzymatic hydrolysis) that lead to O-dealkylation/dearylation of phosphotriesters.

Target-specific toxicity knowledgebase (TsTKb): a novel toolkit for in silico predictive toxicology

As the number of man-made chemicals increases at an unprecedented pace, efforts of quickly screening and accurately evaluating their potential adverse biological effects have been hampered by prohibitively high costs of in vivo/vitro toxicity testing. While it is unrealistic and unnecessary to test every uncharacterized chemical, it remains a major challenge to develop alternative in silico tools with high reliability and precision in toxicity prediction. To address this urgent need, we have developed a novel mode-of-action-guided, molecular modeling-based, and machine learning-enabled modeling approach for in silico chemical toxicity prediction. Here we introduce the core element of this approach, Target-specific Toxicity Knowledgebase (TsTKb), which consists of two main components: Chemical Mode of Action (ChemMoA) database and a suite of prediction model libraries.

Application of genome analysis strategies in the clinical testing for pediatric diseases

Next‐generation sequencing (NGS) is being used in clinical testing. Government authorities in both China and the United States are overseeing the clinical application of NGS instruments and reagents. In addition, the US Association for Molecular Pathology and the College of American Pathologists have jointly released a guidance to standardize the analysis and interpretation of NGS data involved in clinical testing. At present, the analysis strategies and pipelines for NGS data related to the clinical detection of pediatric disease are similar to those used for adult diseases. However, for rare pediatric diseases without linkage to known genetic variants, it is currently difficult to detect the relevant pathogenic genes using NGS technology. Additionally, it is challenging to identify novel pathogenic genes of familial pediatric tumors. Therefore, characterization of the pathogenic genes associated with above diseases is important for the diagnosis and treatment of rare diseases in children. This article introduces the general pipelines for NGS data analyses of diseases and elucidates data analysis strategies for the pathogenic genes of rare pediatric diseases and familial pediatric tumors.

In Silico Pharmacoepidemiologic Evaluation of Drug-Induced Cardiovascular Complications Using Combined Classifiers

Drug-induced cardiovascular complications are the most common adverse drug events and account for the withdrawal or severe restrictions on the use of multitudinous postmarketed drugs. In this study, we developed new in silico models for systematic identification of drug-induced cardiovascular complications in drug discovery and postmarketing surveillance. Specifically, we collected drug-induced cardiovascular complications covering the five most common types of cardiovascular outcomes (hypertension, heart block, arrhythmia, cardiac failure, and myocardial infarction) from four publicly available data resources: Comparative Toxicogenomics Database, SIDER, Offsides, and MetaADEDB. Using these databases, we developed a combined classifier framework through integration of five machine-learning algorithms: logistic regression, random forest, k-nearest neighbors, support vector machine, and neural network. The totality of models included 180 single classifiers with area under receiver operating characteristic curves (AUC) ranging from 0.647 to 0.809 on 5-fold cross-validations. To develop the combined classifiers, we then utilized a neural network algorithm to integrate the best four single classifiers for each cardiovascular outcome. The combined classifiers had higher performance with an AUC range from 0.784 to 0.842 compared to single classifiers. Furthermore, we validated our predicted cardiovascular complications for 63 anticancer agents using experimental data from clinical studies, human pluripotent stem cell-derived cardiomyocyte assays, and literature. The success rate of our combined classifiers reached 87%. In conclusion, this study presents powerful in silico tools for systematic risk assessment of drug-induced cardiovascular complications. This tool is relevant not only in early stages of drug discovery but also throughout the life of a drug including clinical trials and postmarketing surveillance.

Beyond first-line non-anthracycline-based chemotherapy for extranodal NK/T-cell lymphoma: clinical outcome and current perspectives on salvage therapy for patients after first relapse and progression of disease

Background

Current standard treatment, including non-anthracycline-based chemotherapy and optimal combining of radiotherapy, has dramatically improved outcomes of patients with extranodal natural killer/T-cell lymphoma (ENKTL) during the last decade. This study was conducted to investigate the clinical outcome of ENKTL patients with relapsed or progressive disease after initial current standard therapy.

Patients and methods

We retrospectively reviewed patients diagnosed with ENKTL at six centers in four countries (China, France, Singapore, and South Korea) from 1997 to 2015 and analyzed 179 patients who had relapsed or progressed after initial current standard therapy.

Results

After a median follow-up of 58.6 months (range 27.9-89.2), the median second progression-free survival (PFS) was 4.1 months [95% confidence interval (CI) 3.04-5.16] and overall survival (OS) was 6.4 months (95% CI 4.36-8.51). Multivariate Cox-regression analysis revealed that elevated lactate dehydrogenase, multiple extranodal sites (≥2), and presence of B symptoms were associated with inferior OS (P < 0.05). OS and PFS were significantly different according to both prognostic index of natural killer lymphoma (PINK) and PINK-E (Epstein-Barr virus) models. Salvage chemotherapy with l-asparaginase (l-Asp)-based regimens showed a significantly better clinical benefit to response rate and PFS, although it did not lead to OS improvement. First use of l-Asp in the salvage setting and l-Asp rechallenge at least 6 months after initial treatment were the best candidates for salvage l-Asp containing chemotherapy.

Conclusions

Most patients with relapsed or refractory ENKTL had poor prognosis with short survival. Further studies are warranted to determine the optimal treatment of patients with relapsed or refractory ENKTL.

Structural Changes Due to Antagonist Binding in Ligand Binding Pocket of Androgen Receptor Elucidated Through Molecular Dynamics Simulations

When a small molecule binds to the androgen receptor (AR), a conformational change can occur which impacts subsequent binding of co-regulator proteins and DNA. In order to accurately study this mechanism, the scientific community needs a crystal structure of the Wild type AR (WT-AR) ligand binding domain, bound with antagonist. To address this open need, we leveraged molecular docking and molecular dynamics (MD) simulations to construct a structure of the WT-AR ligand binding domain bound with antagonist bicalutamide. The structure of mutant AR (Mut-AR) bound with this same antagonist informed this study. After molecular docking analysis pinpointed the suitable binding orientation of a ligand in AR, the model was further optimized through 1 μs of MD simulations. Using this approach, three molecular systems were studied: (1) WT-AR bound with agonist R1881, (2) WT-AR bound with antagonist bicalutamide, and (3) Mut-AR bound with bicalutamide. Our structures were very similar to the experimentally determined structures of both WT-AR with R1881 and Mut-AR with bicalutamide, demonstrating the trustworthiness of this approach. In our model, when WT-AR is bound with bicalutamide, Val716/Lys720/Gln733, or Met734/Gln738/Glu897 move and thus disturb the positive and negative charge clumps of the AF2 site. This disruption of the AF2 site is key for understanding the impact of antagonist binding on subsequent co-regulator binding. In conclusion, the antagonist induced structural changes in WT-AR detailed in this study will enable further AR research and will facilitate AR targeting drug discovery.

Gene-Activated Matrices for Cartilage Defect Reparation

A cartilage defect has a very limited ability to repair itself spontaneously due to the shortage of blood. Many attempts have been made to restore the integrity of cartilage in clinical and experimental studies. Recently, tissue engineering has emerged as a new protocol for lost tissue regeneration. Meanwhile, the defect-repairing environment can be improved by gene therapy methods. Gene-activated matrices (GAM) fabricated with biomaterials and plasmids fill the cartilage defects to restore the integrity of joint surface, facilitating repair cell adhesion and proliferation as well as the synthesis of extracelluar matrix. And they also serve as a local gene delivery system, inducing therapeutic agent expression at the repair site. In the present study, we fabricated two- and three-dimensional matrices from chitosan and gelatin, then added a plasmid DNA encoding transforming growth factors-β1 (TGF-β1) for cartilage defect regeneration. First, we demonstrated primary chondrocytes could maintain their biological characteristics and secrete therapeutic proteins when they were cultured onto GAM in vitro. Subsequently we inserted three-dimensional GAM into cartilage defects of rabbit knee joints. With the results of the new cartilage tissue formation, we came to the conclusion that GAM was helpful for new tissue production and this therapeutic protocol provided a cheap, simple, and effective method for cartilage defect reparation.

Deep learning architectures for multi-label classification of intelligent health risk prediction

BACKGROUND

Multi-label classification of data remains to be a challenging problem. Because of the complexity of the data, it is sometimes difficult to infer information about classes that are not mutually exclusive. For medical data, patients could have symptoms of multiple different diseases at the same time and it is important to develop tools that help to identify problems early. Intelligent health risk prediction models built with deep learning architectures offer a powerful tool for physicians to identify patterns in patient data that indicate risks associated with certain types of chronic diseases.

RESULTS

Physical examination records of 110,300 anonymous patients were used to predict diabetes, hypertension, fatty liver, a combination of these three chronic diseases, and the absence of disease (8 classes in total). The dataset was split into training (90%) and testing (10%) sub-datasets. Ten-fold cross validation was used to evaluate prediction accuracy with metrics such as precision, recall, and F-score. Deep Learning (DL) architectures were compared with standard and state-of-the-art multi-label classification methods. Preliminary results suggest that Deep Neural Networks (DNN), a DL architecture, when applied to multi-label classification of chronic diseases, produced accuracy that was comparable to that of common methods such as Support Vector Machines. We have implemented DNNs to handle both problem transformation and algorithm adaption type multi-label methods and compare both to see which is preferable.

CONCLUSIONS

Deep Learning architectures have the potential of inferring more information about the patterns of physical examination data than common classification methods. The advanced techniques of Deep Learning can be used to identify the significance of different features from physical examination data as well as to learn the contributions of each feature that impact a patient's risk for chronic diseases. However, accurate prediction of chronic disease risks remains a challenging problem that warrants further studies.

Endocrine Disrupting Chemicals Mediated through Binding Androgen Receptor Are Associated with Diabetes Mellitus

Endocrine disrupting chemicals (EDCs) can mimic natural hormone to interact with receptors in the endocrine system and thus disrupt the functions of the endocrine system, raising concerns on the public health. In addition to disruption of the endocrine system, some EDCs have been found associated with many diseases such as breast cancer, prostate cancer, infertility, asthma, stroke, Alzheimer’s disease, obesity, and diabetes mellitus. EDCs that binding androgen receptor have been reported associated with diabetes mellitus in in vitro, animal, and clinical studies. In this review, we summarize the structural basis and interactions between androgen receptor and EDCs as well as the associations of various types of diabetes mellitus with the EDCs mediated through androgen receptor binding. We also discuss the perspective research for further understanding the impact and mechanisms of EDCs on the risk of diabetes mellitus.

Individualized network-based drug repositioning infrastructure for precision oncology in the panomics era

Advances in next-generation sequencing technologies have generated the data supporting a large volume of somatic alterations in several national and international cancer genome projects, such as The Cancer Genome Atlas and the International Cancer Genome Consortium. These cancer genomics data have facilitated the revolution of a novel oncology drug discovery paradigm from candidate target or gene studies toward targeting clinically relevant driver mutations or molecular features for precision cancer therapy. This focuses on identifying the most appropriately targeted therapy to an individual patient harboring a particularly genetic profile or molecular feature. However, traditional experimental approaches that are used to develop new chemical entities for targeting the clinically relevant driver mutations are costly and high-risk. Drug repositioning, also known as drug repurposing, re-tasking or re-profiling, has been demonstrated as a promising strategy for drug discovery and development. Recently, computational techniques and methods have been proposed for oncology drug repositioning and identifying pharmacogenomics biomarkers, but overall progress remains to be seen. In this review, we focus on introducing new developments and advances of the individualized network-based drug repositioning approaches by targeting the clinically relevant driver events or molecular features derived from cancer panomics data for the development of precision oncology drug therapies (e.g. one-person trials) to fully realize the promise of precision medicine. We discuss several potential challenges (e.g. tumor heterogeneity and cancer subclones) for precision oncology. Finally, we highlight several new directions for the precision oncology drug discovery via biotherapies (e.g. gene therapy and immunotherapy) that target the 'undruggable' cancer genome in the functional genomics era.