Bioinformatics challenges for personalized medicine

Advanced molecular modeling of proteins: Methods, breakthroughs, and future prospects

The contemporary advancements in molecular modeling of proteins have significantly enhanced our comprehension of biological processes and the functional roles of proteins on a global scale. The application of advanced methodologies, including homology modeling, molecular dynamics simulations, and quantum mechanics/molecular mechanics strategies, has empowered numerous researchers to forecast the behavior of protein macromolecules, elucidate drug-protein interactions, and develop drugs with enhanced precision. This chapter elucidates the advent of deep learning algorithms such as AlphaFold, a notable advancement that has significantly improved the precision of intricate protein structure predictions. The recent advancements have significantly enhanced the precision of protein predictions and expedited drug discovery and development processes. Integrating approaches like multi-scale modeling and hybrid methods incorporating reliable experimental data is anticipated to revolutionize and offer more significant implications for precision medicine and targeted treatments.

Telehealth and Precision Prevention: Bridging the Gap for Individualised Health Strategies

INTRODUCTION

Precision prevention has shown an upsurge in popularity among epidemiologists in both developed and developing countries in the past decade.

OBJECTIVES

Initially practiced in oncology, this approach is increasingly adopted in public health to guard against other common non-communicable diseases (NCDs), such as diabetes and cardiovascular diseases. It aims to tailor preventive measures according to each individual's unique characteristics, such as genomic data, socio-demographic features, environmental factors, and cultural background.

METHODS

Healthcare information technologies, including telehealth and artificial intelligence (AI), have served as a vital catalyst in the expansion of this field in the past decade. Under this framework, real-time contemporaneous clinical data is collected via a wide range of digital health devices, such as telehealth monitors, wearables, etc., and then analyzed by AI or non-AI prediction models, which then generate preventive recommendations.

RESULTS

The utilization of telehealth technologies in the precision prevention of cardiovascular diseases (CVDs) is a very illustrative application. This paper explores these topics as well as certain limitations and unintended consequences (UICs) and outlines telehealth as a core enabler of precision prevention as well as public health.

Bioinformatics in Neonatal/Pediatric Medicine-A Literature Review

Bioinformatics is a scientific field that uses computer technology to gather, store, analyze, and share biological data and information. DNA sequences of genes or entire genomes, protein amino acid sequences, nucleic acid, and protein-nucleic acid complex structures are examples of traditional bioinformatics data. Moreover, proteomics, the distribution of proteins in cells, interactomics, the patterns of interactions between proteins and nucleic acids, and metabolomics, the types and patterns of small-molecule transformations by the biochemical pathways in cells, are further data streams. Currently, the objectives of bioinformatics are integrative, focusing on how various data combinations might be utilized to comprehend organisms and diseases. Bioinformatic techniques have become popular as novel instruments for examining the fundamental mechanisms behind neonatal diseases. In the first few weeks of newborn life, these methods can be utilized in conjunction with clinical data to identify the most vulnerable neonates and to gain a better understanding of certain mortalities, including respiratory distress, bronchopulmonary dysplasia, sepsis, or inborn errors of metabolism. In the current study, we performed a literature review to summarize the current application of bioinformatics in neonatal medicine. Our aim was to provide evidence that could supply novel insights into the underlying mechanism of neonatal pathophysiology and could be used as an early diagnostic tool in neonatal care.

Predicting transcription factor activity using prior biological information

Dysregulation of normal transcription factor activity is a common driver of disease. Therefore, the detection of aberrant transcription factor activity is important to understand disease pathogenesis. We have developed Priori, a method to predict transcription factor activity from RNA sequencing data. Priori has two key advantages over existing methods. First, Priori utilizes literature-supported regulatory information to identify transcription factor-target gene relationships. It then applies linear models to determine the impact of transcription factor regulation on the expression of its target genes. Second, results from a third-party benchmarking pipeline reveals that Priori detects aberrant activity from 124 single-gene perturbation experiments with higher sensitivity and specificity than 11 other methods. We applied Priori and other top-performing methods to predict transcription factor activity from two large primary patient datasets. Our work demonstrates that Priori uniquely discovered significant determinants of survival in breast cancer and identified mediators of drug response in leukemia.

Identifying pathogenic variants related to systemic lupus erythematosus by integrating genomic databases and a bioinformatic approach

Systemic lupus erythematosus (SLE) is an inflammatory-autoimmune disease with a complex multi-organ pathogenesis, and it is known to be associated with significant morbidity and mortality. Various genetic, immunological, endocrine, and environmental factors contribute to SLE. Genomic variants have been identified as potential contributors to SLE susceptibility across multiple continents. However, the specific pathogenic variants that drive SLE remain largely undefined. In this study, we sought to identify these pathogenic variants across various continents using genomic and bioinformatic-based methodologies. We found that the variants rs35677470, rs34536443, rs17849502, and rs13306575 are likely damaging in SLE. Furthermore, these four variants appear to affect the gene expression of NCF2, TYK2, and DNASE1L3 in whole blood tissue. Our findings suggest that these genomic variants warrant further research for validation in functional studies and clinical trials involving SLE patients. We conclude that the integration of genomic and bioinformatic-based databases could enhance our understanding of disease susceptibility, including that of SLE.

Surfaceome Profiling Suggests Potential of Anti-MUC1×EGFR Bispecific Antibody for Breast Cancer Targeted Therapy

Breast cancer (BC) treatment has traditionally been challenging due to tumor heterogeneity. Bispecific antibodies (bsAbs) offer a promising approach for overcoming these challenges by targeting multiple specific epitopes. In the current study, we designed a new bsAb against the most common BC cell surface proteins (SPs). To achieve this, we analyzed RNA-sequencing data to identify differentially expressed genes, which were further evaluated using Gene Ontology enrichment, Hidden Markov Models, clinical trial data, and survival analysis to identify druggable gene-encoding cell SPs. Based on these analyses, we constructed and expressed a bsAb targeting the mucin 1 (MUC1) and epidermal growth factor receptor (EGFR) proteins, which are the dominant druggable gene-encoding cell SPs in BC. The recombinant anti-MUC1×EGFR bsAb demonstrated efficient production and high specificity for MUC1 and EGFR + cell lines and BC tissue. Furthermore, the bsAb significantly reduced the proliferation and migration of BC cells. Our results suggested that simultaneous targeting with bsAbs could be a promising targeted therapy for improving the overall efficacy of BC treatment.

Integrative toxicogenomics: Advancing precision medicine and toxicology through artificial intelligence and OMICs technology

More information about a person's genetic makeup, drug response, multi-omics response, and genomic response is now available leading to a gradual shift towards personalized treatment. Additionally, the promotion of non-animal testing has fueled the computational toxicogenomics as a pivotal part of the next-gen risk assessment paradigm. Artificial Intelligence (AI) has the potential to provid new ways analyzing the patient data and making predictions about treatment outcomes or toxicity. As personalized medicine and toxicogenomics involve huge data processing, AI can expedite this process by providing powerful data processing, analysis, and interpretation algorithms. AI can process and integrate a multitude of data including genome data, patient records, clinical data and identify patterns to derive predictive models anticipating clinical outcomes and assessing the risk of any personalized medicine approaches. In this article, we have studied the current trends and future perspectives in personalized medicine & toxicology, the role of toxicogenomics in connecting the two fields, and the impact of AI on personalized medicine & toxicology. In this work, we also study the key challenges and limitations in personalized medicine, toxicogenomics, and AI in order to fully realize their potential.

Identification of Potential Biomarkers and Small Molecule Drugs for Bisphosphonate-Related Osteonecrosis of the Jaw (BRONJ): An Integrated Bioinformatics Study Using Big Data

This study aimed to identify potential molecular mechanisms and therapeutic targets for bisphosphonate-related osteonecrosis of the jaw (BRONJ), a rare but serious side effect of bisphosphonate therapy. This study analyzed a microarray dataset (GSE7116) of multiple myeloma patients with BRONJ (n = 11) and controls (n = 10), and performed gene ontology, a pathway enrichment analysis, and a protein-protein interaction network analysis. A total of 1481 differentially expressed genes were identified, including 381 upregulated and 1100 downregulated genes, with enriched functions and pathways related to apoptosis, RNA splicing, signaling pathways, and lipid metabolism. Seven hub genes (FN1, TNF, JUN, STAT3, ACTB, GAPDH, and PTPRC) were also identified using the cytoHubba plugin in Cytoscape. This study further screened small-molecule drugs using CMap and verified the results using molecular docking methods. This study identified 3-(5-(4-(Cyclopentyloxy)-2-hydroxybenzoyl)-2-((3-hydroxybenzo[d]isoxazol-6-yl) methoxy) phenyl) propanoic acid as a potential drug treatment and prognostic marker for BRONJ. The findings of this study provide reliable molecular insight for biomarker validation and potential drug development for the screening, diagnosis, and treatment of BRONJ. Further research is needed to validate these findings and develop an effective biomarker for BRONJ.

Development and Optimization of an Unbiased, Metagenomics-Based Pathogen Detection Workflow for Infectious Disease and Biosurveillance Applications

Rapid, specific, and sensitive identification of microbial pathogens is critical to infectious disease diagnosis and surveillance. Classical culture-based methods can be applied to a broad range of pathogens but have long turnaround times. Molecular methods, such as PCR, are time-effective but are not comprehensive and may not detect novel strains. Metagenomic shotgun next-generation sequencing (NGS) promises specific identification and characterization of any pathogen (viruses, bacteria, fungi, and protozoa) in a less biased way. Despite its great potential, NGS has yet to be widely adopted by clinical microbiology laboratories due in part to the absence of standardized workflows. Here, we describe a sample-to-answer workflow called PanGIA (Pan-Genomics for Infectious Agents) that includes simplified, standardized wet-lab procedures and data analysis with an easy-to-use bioinformatics tool. PanGIA is an end-to-end, multi-use workflow that can be used for pathogen detection and related applications, such as biosurveillance and biothreat detection. We performed a comprehensive survey and assessment of current, commercially available wet-lab technologies and open-source bioinformatics tools for each workflow component. The workflow includes total nucleic acid extraction from clinical human whole blood and environmental microbial forensic swabs as sample inputs, host nucleic acid depletion, dual DNA and RNA library preparation, shotgun sequencing on an Illumina MiSeq, and sequencing data analysis. The PanGIA workflow can be completed within 24 h and is currently compatible with bacteria and viruses. Here, we present data from the development and application of the clinical and environmental workflows, enabling the specific detection of pathogens associated with bloodstream infections and environmental biosurveillance, without the need for targeted assay development.

Assessment of Diagnosis, Prognosis and Immune Infiltration Response to the Expression of the Ferroptosis-Related Molecule HAMP in Clear Cell Renal Cell Carcinoma

BACKGROUND

Hepcidin antimicrobial peptide (HAMP) is a key factor in maintaining iron metabolism, which may induce ferroptosis when upregulated. However, its prognostic value and relation to immune infiltrating cells remains unclear.

METHODS

This study analyzed the expression levels of HAMP in the Oncomine, Timer and Ualcan databases, and examined its prognostic potential in KIRC with R programming. The Timer and GEPIA databases were used to estimate the correlations between HAMP and immune infiltration and the markers of immune cells. The intersection genes and the co-expression PPI network were constructed via STRING, R programming and GeneMANIA, and the hub genes were selected with Cytoscape. In addition, we analyzed the gene set enrichment and GO/KEGG pathways by GSEA.

RESULTS

Our study revealed higher HAMP expression levels in tumor tissues including KIRC, which were related to poor prognosis in terms of OS, DSS and PFI. The expression of HAMP was positively related to the immune infiltration level of macrophages, Tregs, etc., corresponding with the immune biomarkers. Based on the intersection genes, we constructed the PPI network and used the 10 top hub genes. Further, we performed a pathway enrichment analysis of the gene sets, including Huntington's disease, the JAK-STAT signaling pathway, ammonium ion metabolic process, and so on.

CONCLUSION

In summary, our study gave an insight into the potential prognosis of HAMP, which may act as a diagnostic biomarker and therapeutic target related to immune infiltration in KIRC.

ELIMINATOR: essentiality analysis using multisystem networks and integer programming

A gene is considered as essential when it is indispensable for cells to grow and replicate in a certain environment. However, gene essentiality is not a structural property but rather a contextual one, which depends on the specific biological conditions affecting the cell. This circumstantial essentiality of genes is what brings the attention of scientist since we can identify genes essential for cancer cells but not essential for healthy cells. This same contextuality makes their identification extremely challenging. Huge experimental efforts such as Project Achilles where the essentiality of thousands of genes is measured together with a plethora of molecular data (transcriptomics, copy number, mutations, etc.) in over one thousand cell lines can shed light on the causality behind the essentiality of a gene in a given environment. Here, we present an in-silico method for the identification of patient-specific essential genes using constraint-based modelling (CBM). Our method expands the ideas behind traditional CBM to accommodate multisystem networks. In essence, it first calculates the minimum number of lowly expressed genes required to be activated by the cell to sustain life as defined by a set of requirements; and second, it performs an exhaustive in-silico gene knockout to find those that lead to the need of activating additional lowly expressed genes. We validated the proposed methodology using a set of 452 cancer cell lines derived from the Cancer Cell Line Encyclopedia where an exhaustive experimental large-scale gene knockout study using CRISPR (Achilles Project) evaluates the impact of each removal. We also show that the integration of different essentiality predictions per gene, what we called Essentiality Congruity Score, reduces the number of false positives. Finally, we explored our method in a breast cancer patient dataset, and our results showed high concordance with previous publications. These findings suggest that identifying genes whose activity is fundamental to sustain cellular life in a patient-specific manner is feasible using in-silico methods. The patient-level gene essentiality predictions can pave the way for precision medicine by identifying potential drug targets whose deletion can induce death in tumour cells.

Assessment of deep learning and transfer learning for cancer prediction based on gene expression data

BACKGROUND

Machine learning is now a standard tool for cancer prediction based on gene expression data. However, deep learning is still new for this task, and there is no clear consensus about its performance and utility. Few experimental works have evaluated deep neural networks and compared them with state-of-the-art machine learning. Moreover, their conclusions are not consistent.

RESULTS

We extensively evaluate the deep learning approach on 22 cancer prediction tasks based on gene expression data. We measure the impact of the main hyper-parameters and compare the performances of neural networks with the state-of-the-art. We also investigate the effectiveness of several transfer learning schemes in different experimental setups.

CONCLUSION

Based on our experimentations, we provide several recommendations to optimize the construction and training of a neural network model. We show that neural networks outperform the state-of-the-art methods only for very large training set size. For a small training set, we show that transfer learning is possible and may strongly improve the model performance in some cases.

Bioinformatic Challenges Detecting Genetic Variation in Precision Medicine Programs

Precision medicine programs to identify clinically relevant genetic variation have been revolutionized by access to increasingly affordable high-throughput sequencing technologies. A decade of continual drops in per-base sequencing costs means it is now feasible to sequence an individual patient genome and interrogate all classes of genetic variation for < $1,000 USD. However, while advances in these technologies have greatly simplified the ability to obtain patient sequence information, the timely analysis and interpretation of variant information remains a challenge for the rollout of large-scale precision medicine programs. This review will examine the challenges and potential solutions that exist in identifying predictive genetic biomarkers and pharmacogenetic variants in a patient and discuss the larger bioinformatic challenges likely to emerge in the future. It will examine how both software and hardware development are aiming to overcome issues in short read mapping, variant detection and variant interpretation. It will discuss the current state of the art for genetic disease and the remaining challenges to overcome for complex disease. Success across all types of disease will require novel statistical models and software in order to ensure precision medicine programs realize their full potential now and into the future.

Determining the adjusted initial treatment dose of warfarin anticoagulant medicine using kernel-based support vector regression

BACKGROUND AND OBJECTIVE

A novel research field in bioinformatics is pharmacogenomics and the corresponding applications of artificial intelligence tools. Pharmacogenomics is the study of the relationship between genotype and responses to medical measures such as drug use. One of the most effective drugs is warfarin anticoagulant, but determining its initial treatment dose is challenging. Mistakes in the determination of the initial treatment dose can result directly in patient death.

METHODS

Some of the most successful techniques for estimating the initial treatment dose are kernel-based methods. However, all the available studies use pre-defined and constant kernels that might not necessarily address the problem's intended requirements. The present study seeks to define and present a new computational kernel extracted from a data set. This process aims to utilize all the data-related statistical features to generate a dose determination tool proportional to the data set with minimum error rate. The kernel-based version of the least square support vector regression estimator was defined. Through this method, a more appropriate approach was proposed for predicting the adjusted dose of warfarin.

RESULTS AND CONCLUSION

This paper benefits from the International Warfarin Pharmacogenomics Consortium (IWPC) Database. The results obtained in this study demonstrate that the support vector regression with the proposed new kernel can successfully estimate the ideal dosage of warfarin for approximately 68% of patients.

Emerging Machine Learning Techniques for Modelling Cellular Complex Systems in Alzheimer's Disease

We live in the big data era in the biomedical field, where machine learning has a very important contribution to the interpretation of complex biological processes and diseases, since it has the potential to create predictive models from multidimensional data sets. Part of the application of machine learning in biomedical science is to study and model complex cellular systems such as biological networks. In this context, the study of complex diseases, such as Alzheimer's diseases (AD), benefits from established methodologies of network science and machine learning as they offer algorithmic tools and techniques that can address the limitations and challenges of modeling and studying cellular AD-related networks. In this paper we analyze the opportunities and challenges at the intersection of machine learning and network biology and whether this can affect the biological interpretation and clarification of diseases. Specifically, we focus on GRN techniques which through omics data and the use of machine learning techniques can construct a network that captures all the information at the molecular level for the disease under study. We record the emerging machine learning techniques that are focus on ensemble tree-based techniques in the area of classification and regression. Their potential for unraveling the complexity of model cellular systems in complex diseases, such as AD, offers the opportunity for novel machine learning methodologies to decipher the mechanisms of the various AD processes.

A Domain-Adaptable Heterogeneous Information Integration Platform: Tourism and Biomedicine Domains

In recent years, information integration systems have become very popular in mashup-type applications. Information sources are normally presented in an individual and unrelated fashion, and the development of new technologies to reduce the negative effects of information dispersion is needed. A major challenge is the integration and implementation of processing pipelines using different technologies promoting the emergence of advanced architectures capable of processing such a number of diverse sources. This paper describes a semantic domain-adaptable platform to integrate those sources and provide high-level functionalities, such as recommendations, shallow and deep natural language processing, text enrichment, and ontology standardization. Our proposed intelligent domain-adaptable platform (IDAP) has been implemented and tested in the tourism and biomedicine domains to demonstrate the adaptability, flexibility, modularity, and utility of the platform. Questionnaires, performance metrics, and A/B control groups’ evaluations have shown improvements when using IDAP in learning environments.

Analysis and Interpretation of the Impact of Missense Variants in Cancer

Large scale genome sequencing allowed the identification of a massive number of genetic variations, whose impact on human health is still unknown. In this review we analyze, by an in silico-based strategy, the impact of missense variants on cancer-related genes, whose effect on protein stability and function was experimentally determined. We collected a set of 164 variants from 11 proteins to analyze the impact of missense mutations at structural and functional levels, and to assess the performance of state-of-the-art methods (FoldX and Meta-SNP) for predicting protein stability change and pathogenicity. The result of our analysis shows that a combination of experimental data on protein stability and in silico pathogenicity predictions allowed the identification of a subset of variants with a high probability of having a deleterious phenotypic effect, as confirmed by the significant enrichment of the subset in variants annotated in the COSMIC database as putative cancer-driving variants. Our analysis suggests that the integration of experimental and computational approaches may contribute to evaluate the risk for complex disorders and develop more effective treatment strategies.

aScan: A Novel Method for the Study of Allele Specific Expression in Single Individuals

In diploid organisms, two copies of each allele are normally inherited from parents. Paternal and maternal alleles can be regulated and expressed unequally, which is referred to as allele-specific expression (ASE). In this work, we present aScan, a novel method for the identification of ASE from the analysis of matched individual genomic and RNA sequencing data. By performing extensive analyses of both real and simulated data, we demonstrate that aScan can correctly identify ASE with high accuracy and sensitivity in different experimental settings. Additionally, by applying our method to a small cohort of individuals that are not included in publicly available databases of human genetic variation, we outline the value of possible applications of ASE analysis in single individuals for deriving a more accurate annotation of "private" low-frequency genetic variants associated with regulatory effects on transcription. All in all, we believe that aScan will represent a beneficial addition to the set of bioinformatics tools for the analysis of ASE. Finally, while our method was initially conceived for the analysis of RNA-seq data, it can in principle be applied to any quantitative NGS assay for which matched genotypic and expression data are available. AVAILABILITY: aScan is currently available in the form of an open source standalone software package at: https://github.com/Federico77z/aScan/. aScan version 1.0.3, available at https://github.com/Federico77z/aScan/releases/tag/1.0.3, has been used for all the analyses included in this manuscript. A Docker image of the tool has also been made available at https://github.com/pmandreoli/aScanDocker.

Non-parametric individual treatment effect estimation for survival data with random forests

MOTIVATION

Personalized medicine often relies on accurate estimation of a treatment effect for specific subjects. This estimation can be based on the subject's baseline covariates but additional complications arise for a time-to-event response subject to censoring. In this paper, the treatment effect is measured as the difference between the mean survival time of a treated subject and the mean survival time of a control subject. We propose a new random forest method for estimating the individual treatment effect with survival data. The random forest is formed by individual trees built with a splitting rule specifically designed to partition the data according to the individual treatment effect. For a new subject, the forest provides a set of similar subjects from the training dataset that can be used to compute an estimation of the individual treatment effect with any adequate method.

RESULTS

The merits of the proposed method are investigated with a simulation study where it is compared to numerous competitors, including recent state-of-the-art methods. The results indicate that the proposed method has a very good and stable performance to estimate the individual treatment effects. Two examples of application with a colon cancer data and breast cancer data show that the proposed method can detect a treatment effect in a sub-population even when the overall effect is small or nonexistent.

AVAILABILITY AND IMPLEMENTATION

The authors are working on an R package implementing the proposed method and it will be available soon. In the meantime, the code can be obtained from the first author at sami.tabib@hec.ca.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Investigating the Role of Missense SNPs on ALDH 1A1 mediated pharmacokinetic resistance to cyclophosphamide

Cyclophosphamide (CP) is a well-known anti-cancer drug, which exerts its therapeutic effect by DNA cross-linking, both within and between DNA strands. Earlier, a single dose of CP was enough for an effective treatment however due to overexpression of ALDH 1A1 in cancer cells and consequent drug inactivation, the quality of treatment suffered a lot. Drug inactivation via Drug Metabolizing Enzyme (DME) like Aldehyde dehydrogenase 1A1 (ALDH 1A1) is one of the resistance mechanism which is least considered and somewhat overlooked. The current study focused on investigating the impact of missense SNPs on ALDH 1A1 mediated pharmacokinetic resistance to CP. To achieve this aim, we selected 14 missense SNPs from the large pool of SNPs database. The stability of the mutants corresponding to selected SNPs was then determined using web-based tools like I-Mutant, CUPSAT, Maestro-web, STRUM, Eris, SDM, DUET, I-Stable. The obtained results from the mentioned web tools were later validated by molecular dynamic simulations. Furthermore, to find out the optimal range in terms of geometrical parameters and binding affinity for a molecule to be a good substrate for ALDH 1A1, some well-reported substrates of ALDH1A1 were pooled from the literature. Subsequently, similar parameters were calculated for each aldophosphamide (Active metabolite of CP) - mutant complexes to determine if these parameters lie within the optimal range. Based on this analyses population which is most or least susceptible to resistance was suggested. Our results demonstrated that the population group corresponding to rs11554423 (Gly125Arg) and rs763363983 (Val460Leu) mutation may be least vulnerable to CP resistance. Whereas, the population corresponding to rs1049981 (Asn121Ser) and rs774967243 (Val295Leu) SNPs may be most vulnerable to CP resistance.

Fido-SNP: the first webserver for scoring the impact of single nucleotide variants in the dog genome

As the amount of genomic variation data increases, tools that are able to score the functional impact of single nucleotide variants become more and more necessary. While there are several prediction servers available for interpreting the effects of variants in the human genome, only few have been developed for other species, and none were specifically designed for species of veterinary interest such as the dog. Here, we present Fido-SNP the first predictor able to discriminate between Pathogenic and Benign single-nucleotide variants in the dog genome. Fido-SNP is a binary classifier based on the Gradient Boosting algorithm. It is able to classify and score the impact of variants in both coding and non-coding regions based on sequence features within seconds. When validated on a previously unseen set of annotated variants from the OMIA database, Fido-SNP reaches 88% overall accuracy, 0.77 Matthews correlation coefficient and 0.91 Area Under the ROC Curve.

A spatial neighborhood methodology for computing and analyzing lymph node carcinoma similarity in precision medicine

Precision medicine seeks to tailor therapy to the individual patient, based on statistical correlates from patients who are similar to the one under consideration. These correlates can and should go beyond genetics, and in general, beyond tabular or array data that can be easily represented computationally and compared. For example, in many types of cancer, cancer treatment and toxicity depend in large measure on the spatial disease spread-e.g., metastasizes to regional lymph nodes in head and neck cancer. However, there is currently a lack of methodology for integrating spatial information when considering patient similarity. We present a novel modeling methodology for the comparison of cancer patients within a cohort, based on the spatial spread of the lymph nodes affected in each patient. The method uses a topological map, bigrams, and hierarchical clustering to group patients based on their similarity. We compare this approach against a nonspatial (categorical) similarity approach where patients are binned solely by their affected nodes. We present similarity results on a 582 head and neck cancer patient cohort, along with two visual abstractions for analysis of the results, and we present clinician feedback. Our novel methodology partitions a patient cohort into clinically meaningful groups more susceptible to treatment side-effects. Such spatially-aware similarity approaches can help maximize the effectiveness of each patient's treatment.

A comparison of random-field-theory and false-discovery-rate inference results in the analysis of registered one-dimensional biomechanical datasets

Background

The inflation of falsely rejected hypotheses associated with multiple hypothesis testing is seen as a threat to the knowledge base in the scientific literature. One of the most recently developed statistical constructs to deal with this problem is the false discovery rate (FDR), which aims to control the proportion of the falsely rejected null hypotheses among those that are rejected. FDR has been applied to a variety of problems, especially for the analysis of 3-D brain images in the field of Neuroimaging, where the predominant form of statistical inference involves the more conventional control of false positives, through Gaussian random field theory (RFT). In this study we considered FDR and RFT as alternative methods for handling multiple testing in the analysis of 1-D continuum data. The field of biomechanics has recently adopted RFT, but to our knowledge FDR has not previously been used to analyze 1-D biomechanical data, nor has there been a consideration of how FDR vs. RFT can affect biomechanical interpretations.

Methods

We reanalyzed a variety of publicly available experimental datasets to understand the characteristics which contribute to the convergence and divergence of RFT and FDR results. We also ran a variety of numerical simulations involving smooth, random Gaussian 1-D data, with and without true signal, to provide complementary explanations for the experimental results.

Results

Our results suggest that RFT and FDR thresholds (the critical test statistic value used to judge statistical significance) were qualitatively identical for many experimental datasets, but were highly dissimilar for others, involving non-trivial changes in data interpretation. Simulation results clarified that RFT and FDR thresholds converge as the true signal weakens and diverge when the signal is broad in terms of the proportion of the continuum size it occupies. Results also showed that, while sample size affected the relation between RFT and FDR results for small sample sizes (<15), this relation was stable for larger sample sizes, wherein only the nature of the true signal was important.

Discussion

RFT and FDR thresholds are both computationally efficient because both are parametric, but only FDR has the ability to adapt to the signal features of particular datasets, wherein the threshold lowers with signal strength for a gain in sensitivity. Additional advantages and limitations of these two techniques as discussed further. This article is accompanied by freely available software for implementing FDR analyses involving 1-D data and scripts to replicate our results.

A Data Driven Multi-Layer Framework of Pervasive Information Computing System for eHealthcare

In the last decade, significant advancements in telecommunications and informatics have seen which incredibly boost mobile communications, wireless networks, and pervasive computing. It enables healthcare applications to increase human livelihood. Furthermore, it seems feasible to continuous observation of patients and elderly individuals for their wellbeing. Such pervasive arrangements enable medical experts to analyse current patient status, minimise reaction time, increase livelihood, scalability, and availability. There is found plenty of remote patient monitoring model in literature, and most of them are designed with limited scope. Most of them are lacking to give an overall unified, complete model which talk about all state-of-the-art functionalities. In this regard, remote patient monitoring systems (RPMS's) play important roles through wearable devices to monitor the patient's physiological condition. RPMS also enables the capture of related videos, images, and frames. RPMS do not mean to enable only capturing various sorts of patient-related information, but it also must facilitate analytics, transformation, security, alerts, accessibility, etc. In this view, RPMS must ensure some broad issues like, wearability, adaptability, interoperability, integration, security, and network efficiency. This article proposes a data-driven multi-layer architecture for pervasively remote patient monitoring that incorporates these issues. The system has been classified into five fundamental layers: the data acquisition layer, the data pre-processing layer, the network and data transfer layer, the data management layer and the data accessing layer. It enables patient care at real-time using the network infrastructure efficiently. A detailed discussion on various security issues have been carried out. Moreover, standard deviation-based data reduction and a machine-learning-based data access policy is also proposed.

Assessing computational predictions of the phenotypic effect of cystathionine-beta-synthase variants

Accurate prediction of the impact of genomic variation on phenotype is a major goal of computational biology and an important contributor to personalized medicine. Computational predictions can lead to a better understanding of the mechanisms underlying genetic diseases, including cancer, but their adoption requires thorough and unbiased assessment. Cystathionine-beta-synthase (CBS) is an enzyme that catalyzes the first step of the transsulfuration pathway, from homocysteine to cystathionine, and in which variations are associated with human hyperhomocysteinemia and homocystinuria. We have created a computational challenge under the CAGI framework to evaluate how well different methods can predict the phenotypic effect(s) of CBS single amino acid substitutions using a blinded experimental data set. CAGI participants were asked to predict yeast growth based on the identity of the mutations. The performance of the methods was evaluated using several metrics. The CBS challenge highlighted the difficulty of predicting the phenotype of an ex vivo system in a model organism when classification models were trained on human disease data. We also discuss the variations in difficulty of prediction for known benign and deleterious variants, as well as identify methodological and experimental constraints with lessons to be learned for future challenges.

PMut: a web-based tool for the annotation of pathological variants on proteins, 2017 update

We present here a full update of the PMut predictor, active since 2005 and with a large acceptance in the field of predicting Mendelian pathological mutations. PMut internal engine has been renewed, and converted into a fully featured standalone training and prediction engine that not only powers PMut web portal, but that can generate custom predictors with alternative training sets or validation schemas. PMut Web portal allows the user to perform pathology predictions, to access a complete repository of pre-calculated predictions, and to generate and validate new predictors. The default predictor performs with good quality scores (MCC values of 0.61 on 10-fold cross validation, and 0.42 on a blind test with SwissVar 2016 mutations). The PMut portal is freely accessible at http://mmb.irbbarcelona.org/PMut. A complete help and tutorial is available at http://mmb.irbbarcelona.org/PMut/help.

PhD-SNPg: a webserver and lightweight tool for scoring single nucleotide variants

One of the major challenges in human genetics is to identify functional effects of coding and non-coding single nucleotide variants (SNVs). In the past, several methods have been developed to identify disease-related single amino acid changes but only few tools are able to score the impact of non-coding variants. Among the most popular algorithms, CADD and FATHMM predict the effect of SNVs in non-coding regions combining sequence conservation with several functional features derived from the ENCODE project data. Thus, to run CADD or FATHMM locally, the installation process requires to download a large set of pre-calculated information. To facilitate the process of variant annotation we develop PhD-SNP^g, a new easy-to-install and lightweight machine learning method that depends only on sequence-based features. Despite this, PhD-SNP^g performs similarly or better than more complex methods. This makes PhD-SNP^g ideal for quick SNV interpretation, and as benchmark for tool development. Availability: PhD-SNP^g is accessible at http://snps.biofold.org/phd-snpg.

Pathogenicity and functional impact of non-frameshifting insertion/deletion variation in the human genome

Differentiation between phenotypically neutral and disease-causing genetic variation remains an open and relevant problem. Among different types of variation, non-frameshifting insertions and deletions (indels) represent an understudied group with widespread phenotypic consequences. To address this challenge, we present a machine learning method, MutPred-Indel, that predicts pathogenicity and identifies types of functional residues impacted by non-frameshifting insertion/deletion variation. The model shows good predictive performance as well as the ability to identify impacted structural and functional residues including secondary structure, intrinsic disorder, metal and macromolecular binding, post-translational modifications, allosteric sites, and catalytic residues. We identify structural and functional mechanisms impacted preferentially by germline variation from the Human Gene Mutation Database, recurrent somatic variation from COSMIC in the context of different cancers, as well as de novo variants from families with autism spectrum disorder. Further, the distributions of pathogenicity prediction scores generated by MutPred-Indel are shown to differentiate highly recurrent from non-recurrent somatic variation. Collectively, we present a framework to facilitate the interrogation of both pathogenicity and the functional effects of non-frameshifting insertion/deletion variants. The MutPred-Indel webserver is available at http://mutpred.mutdb.org/.

Estimating classification accuracy in positive-unlabeled learning: characterization and correction strategies

Accurately estimating performance accuracy of machine learning classifiers is of fundamental importance in biomedical research with potentially societal consequences upon the deployment of bestperforming tools in everyday life. Although classification has been extensively studied over the past decades, there remain understudied problems when the training data violate the main statistical assumptions relied upon for accurate learning and model characterization. This particularly holds true in the open world setting where observations of a phenomenon generally guarantee its presence but the absence of such evidence cannot be interpreted as the evidence of its absence. Learning from such data is often referred to as positive-unlabeled learning, a form of semi-supervised learning where all labeled data belong to one (say, positive) class. To improve the best practices in the field, we here study the quality of estimated performance in positive-unlabeled learning in the biomedical domain. We provide evidence that such estimates can be wildly inaccurate, depending on the fraction of positive examples in the unlabeled data and the fraction of negative examples mislabeled as positives in the labeled data. We then present correction methods for four such measures and demonstrate that the knowledge or accurate estimates of class priors in the unlabeled data and noise in the labeled data are sufficient for the recovery of true classification performance. We provide theoretical support as well as empirical evidence for the efficacy of the new performance estimation methods.

Integrating molecular networks with genetic variant interpretation for precision medicine

More reliable and cheaper sequencing technologies have revealed the vast mutational landscapes characteristic of many phenotypes. The analysis of such genetic variants has led to successful identification of altered proteins underlying many Mendelian disorders. Nevertheless the simple one‐variant one‐phenotype model valid for many monogenic diseases does not capture the complexity of polygenic traits and disorders. Although experimental and computational approaches have improved detection of functionally deleterious variants and important interactions between gene products, the development of comprehensive models relating genotype and phenotypes remains a challenge in the field of genomic medicine. In this context, a new view of the pathologic state as significant perturbation of the network of interactions between biomolecules is crucial for the identification of biochemical pathways associated with complex phenotypes. Seminal studies in systems biology combined the analysis of genetic variation with protein–protein interaction networks to demonstrate that even as biological systems evolve to be robust to genetic variation, their topologies create disease vulnerabilities. More recent analyses model the impact of genetic variants as changes to the “wiring” of the interactome to better capture heterogeneity in genotype–phenotype relationships. These studies lay the foundation for using networks to predict variant effects at scale using machine‐learning or algorithmic approaches. A wealth of databases and resources for the annotation of genotype–phenotype relationships have been developed to support developments in this area. This overview describes how study of the molecular interactome has generated insights linking the organization of biological systems to disease mechanism, and how this information can enable precision medicine.

This article is categorized under:

Translational, Genomic, and Systems Medicine > Translational Medicine

Biological Mechanisms > Cell Signaling

Models of Systems Properties and Processes > Mechanistic Models

Analytical and Computational Methods > Computational Methods

An Intelligent Multi-Objective Framework of Pervasive Information Computing

This article describes how electronic healthcare has been the key application of pervasive computing innovations to enhance healthcare quality and protect human lives. Specific patients of constant sicknesses and elderly individuals, by and large, may oblige continuous observing of their wellbeing status wherever they are. In this regard, remote patient monitoring technology plays the various important role through wearable devices to monitor patient's physiological figures. But, this must ensure some broad issues like, wearability, adaptability, interoperability, integration, security, and network efficiency. This article proposes a data-driven multi-layer architecture for pervasively remote patient monitoring that incorporates aforesaid issues. It enables the patient's care at the real time and supports anywhere and anytime requirement with using network infrastructure efficiently.

BIOINFORMATIC SEARCH OF CRISPR/CAS SYSTEM STRUCTURES IN GENOME OF PCT281 PLASMID OF BACILLUS THURINGIENSIS SUBSP. CHINENSIS STRAIN CT-43

Background. CRISPR/Cas systems loci are one of the functionally important patterns in bacterial genome which perform the role of “adaptive immune defense” from foreign nucleic acids. The study of CRISPR/Cas systems structure in genomes of plasmids and phages provide new information about the evolution of this systems in bacterial hosts.

Aims. A search of CRISPR/Cas systems structures in pCT281 plasmid from Bacillus thuringiensis subsp. chinensis strain CT-43 using bioinformatic methods.

Materials and methods. Search studies using bioinformatics methods were performed with the genome of pCT281 plasmid of B. thuringiensis subsp. chinensis strain CT-43 from the RefSeq database. To search for the CRISPR/Cas system structure MacSyFinder (ver. 1.0.5) and three combined algorithms were used: CRISPRFinder; PILER-CR; CRISPR Recognition Tool (CRT). The consensus repeat sequence was generated in WebLogo 3.

Results and discussion. In pCT281 plasmid we detected one locus of CRISPR/Cas system of the type I-C which contains 2 CRISPR-cassettes and 4 cas-genes located between them. The CRISPR-cassette 1 includes 10 spacers from 32 to 35 bp and 11 repeats 32bp in length. 5 spacers (33–35 bp) separated by 6 repeats 32 bp in length were detected in the CRISPR-cassette 2.

Conclusions. The bioinformatic methods used in this study enable to conduct a search of CRISPR/Cas systems structures in plasmid genomes. The presence of the CRISPR-Cas locus in pCT281 plasmid confirms a possible transfer of this system from the nucleoid to this plasmid. The detected spacers provide information about phages this bacteria was encountered.

A systematic review on the genetics of male infertility in the era of next-generation sequencing

OBJECTIVES

To identify the role of next-generation sequencing (NGS) in male infertility, as advances in NGS technologies have contributed to the identification of novel genes responsible for a wide variety of human conditions and recently has been applied to male infertility, allowing new genetic factors to be discovered.

MATERIALS AND METHODS

PubMed was searched for combinations of the following terms: 'exome', 'genome', 'panel', 'sequencing', 'whole-exome sequencing', 'whole-genome sequencing', 'next-generation sequencing', 'azoospermia', 'oligospermia', 'asthenospermia', 'teratospermia', 'spermatogenesis', and 'male infertility', to identify studies in which NGS technologies were used to discover variants causing male infertility.

RESULTS

Altogether, 23 studies were found in which the primary mode of variant discovery was an NGS-based technology. These studies were mostly focused on patients with quantitative sperm abnormalities (non-obstructive azoospermia and oligospermia), followed by morphological and motility defects. Combined, these studies uncover variants in 28 genes causing male infertility discovered by NGS methods.

CONCLUSIONS

Male infertility is a condition that is genetically heterogeneous, and therefore remarkably amenable to study by NGS. Although some headway has been made, given the high incidence of this condition despite its detrimental effect on reproductive fitness, there is significant potential for further discoveries.

Implementing a web-based introductory bioinformatics course for non-bioinformaticians that incorporates practical exercises

A recent scientific discipline, bioinformatics, defined as using informatics for the study of biological problems, is now a requirement for the study of biological sciences. Bioinformatics has become such a powerful and popular discipline that several academic institutions have created programs in this field, allowing students to become specialized. However, biology students who are not involved in a bioinformatics program also need a solid toolbox of bioinformatics software and skills. Therefore, we have developed a completely online bioinformatics course for non-bioinformaticians, entitled "BIF-1901 Introduction à la bio-informatique et à ses outils (Introduction to bioinformatics and bioinformatics tools)," given by the Department of Biochemistry, Microbiology, and Bioinformatics of Université Laval (Quebec City, Canada). This course requires neither a bioinformatics background nor specific skills in informatics. The underlying main goal was to produce a completely online up-to-date bioinformatics course, including practical exercises, with an intuitive pedagogical framework. The course, BIF-1901, was conceived to cover the three fundamental aspects of bioinformatics: (1) informatics, (2) biological sequence analysis, and (3) structural bioinformatics. This article discusses the content of the modules, the evaluations, the pedagogical framework, and the challenges inherent to a multidisciplinary, fully online course. © 2017 by The International Union of Biochemistry and Molecular Biology, 46(1):31-38, 2018.

Cohort-specific imputation of gene expression improves prediction of warfarin dose for African Americans

Background

Genome-wide association studies are useful for discovering genotype–phenotype associations but are limited because they require large cohorts to identify a signal, which can be population-specific. Mapping genetic variation to genes improves power and allows the effects of both protein-coding variation as well as variation in expression to be combined into “gene level” effects.

Methods

Previous work has shown that warfarin dose can be predicted using information from genetic variation that affects protein-coding regions.

Here, we introduce a method that improves dose prediction by integrating tissue-specific gene expression. In particular, we use drug pathways and expression quantitative trait loci knowledge to impute gene expression—on the assumption that differential expression of key pathway genes may impact dose requirement. We focus on 116 genes from the pharmacokinetic and pharmacodynamic pathways of warfarin within training and validation sets comprising both European and African-descent individuals.

Results

We build gene-tissue signatures associated with warfarin dose in a cohort-specific manner and identify a signature of 11 gene-tissue pairs that significantly augments the International Warfarin Pharmacogenetics Consortium dosage-prediction algorithm in both populations.

Conclusions

Our results demonstrate that imputed expression can improve dose prediction and bridge population-specific compositions.

MATLAB code is available at https://github.com/assafgo/warfarin-cohort

Electronic supplementary material

The online version of this article (doi:10.1186/s13073-017-0495-0) contains supplementary material, which is available to authorized users.

Personalized medicine-a modern approach for the diagnosis and management of hypertension

The main goal of treating hypertension is to reduce blood pressure to physiological levels and thereby prevent risk of cardiovascular disease and hypertension-associated target organ damage. Despite reductions in major risk factors and the availability of a plethora of effective antihypertensive drugs, the control of blood pressure to target values is still poor due to multiple factors including apparent drug resistance and lack of adherence. An explanation for this problem is related to the current reductionist and ‘trial-and-error’ approach in the management of hypertension, as we may oversimplify the complex nature of the disease and not pay enough attention to the heterogeneity of the pathophysiology and clinical presentation of the disorder. Taking into account specific risk factors, genetic phenotype, pharmacokinetic characteristics, and other particular features unique to each patient, would allow a personalized approach to managing the disease. Personalized medicine therefore represents the tailoring of medical approach and treatment to the individual characteristics of each patient and is expected to become the paradigm of future healthcare. The advancement of systems biology research and the rapid development of high-throughput technologies, as well as the characterization of different –omics, have contributed to a shift in modern biological and medical research from traditional hypothesis-driven designs toward data-driven studies and have facilitated the evolution of personalized or precision medicine for chronic diseases such as hypertension.

Comprehensive benchmarking of SNV callers for highly admixed tumor data

Precision medicine attempts to individualize cancer therapy by matching tumor-specific genetic changes with effective targeted therapies. A crucial first step in this process is the reliable identification of cancer-relevant variants, which is considerably complicated by the impurity and heterogeneity of clinical tumor samples. We compared the impact of admixture of non-cancerous cells and low somatic allele frequencies on the sensitivity and precision of 19 state-of-the-art SNV callers. We studied both whole exome and targeted gene panel data and up to 13 distinct parameter configurations for each tool. We found vast differences among callers. Based on our comprehensive analyses we recommend joint tumor-normal calling with MuTect, EBCall or Strelka for whole exome somatic variant calling, and HaplotypeCaller or FreeBayes for whole exome germline calling. For targeted gene panel data on a single tumor sample, LoFreqStar performed best. We further found that tumor impurity and admixture had a negative impact on precision, and in particular, sensitivity in whole exome experiments. At admixture levels of 60% to 90% sometimes seen in pathological biopsies, sensitivity dropped significantly, even when variants were originally present in the tumor at 100% allele frequency. Sensitivity to low-frequency SNVs improved with targeted panel data, but whole exome data allowed more efficient identification of germline variants. Effective somatic variant calling requires high-quality pathological samples with minimal admixture, a consciously selected sequencing strategy, and the appropriate variant calling tool with settings optimized for the chosen type of data.

Blind prediction of deleterious amino acid variations with SNPs&GO

SNPs&GO is a machine learning method for predicting the association of single amino acid variations (SAVs) to disease, considering protein functional annotation. The method is a binary classifier that implements a support vector machine algorithm to discriminate between disease-related and neutral SAVs. SNPs&GO combines information from protein sequence with functional annotation encoded by gene ontology (GO) terms. Tested in sequence mode on more than 38,000 SAVs from the SwissVar dataset, our method reached 81% overall accuracy and an area under the receiving operating characteristic curve of 0.88 with low false-positive rate. In almost all the editions of the Critical Assessment of Genome Interpretation (CAGI) experiments, SNPs&GO ranked among the most accurate algorithms for predicting the effect of SAVs. In this paper, we summarize the best results obtained by SNPs&GO on disease-related variations of four CAGI challenges relative to the following genes: CHEK2 (CAGI 2010), RAD50 (CAGI 2011), p16-INK (CAGI 2013), and NAGLU (CAGI 2016). Result evaluation provides insights about the accuracy of our algorithm and the relevance of GO terms in annotating the effect of the variants. It also helps to define good practices for the detection of deleterious SAVs.

Towards personalized medicine of colorectal cancer

Efforts in colorectal cancer (CRC) research aim to improve early detection and treatment for metastatic stages which could translate into better prognosis of this disease. One of the major challenges that hinder these efforts is the heterogeneous nature of CRC and involvement of diverse molecular pathways. New large-scale 'omics' technologies are making it possible to generate, analyze and interpret biological data from molecular determinants of CRC. The developments of sophisticated computational analyses would allow information from different omics platforms to be integrated, thus providing new insights into the biology of CRC. Together, these technological advances and an improved mechanistic understanding might allow CRC to be clinically managed at the level of the individual patient. This review provides an account of the current challenges in CRC management and an insight into how new technologies could allow the development of personalized medicine for CRC.

Working toward precision medicine: Predicting phenotypes from exomes in the Critical Assessment of Genome Interpretation (CAGI) challenges

Precision medicine aims to predict a patient's disease risk and best therapeutic options by using that individual's genetic sequencing data. The Critical Assessment of Genome Interpretation (CAGI) is a community experiment consisting of genotype-phenotype prediction challenges; participants build models, undergo assessment, and share key findings. For CAGI 4, three challenges involved using exome-sequencing data: Crohn's disease, bipolar disorder, and warfarin dosing. Previous CAGI challenges included prior versions of the Crohn's disease challenge. Here, we discuss the range of techniques used for phenotype prediction as well as the methods used for assessing predictive models. Additionally, we outline some of the difficulties associated with making predictions and evaluating them. The lessons learned from the exome challenges can be applied to both research and clinical efforts to improve phenotype prediction from genotype. In addition, these challenges serve as a vehicle for sharing clinical and research exome data in a secure manner with scientists who have a broad range of expertise, contributing to a collaborative effort to advance our understanding of genotype-phenotype relationships.

Barriers to clinical adoption of next-generation sequencing: a policy Delphi panel's solutions

Aim

Identify solutions to the most important policy barriers to the clinical adoption of next-generation sequencing.

Materials & methods

Four-round modified policy Delphi with a multistakeholder panel of 48 experts. The panel deliberated policy solutions to (previously reported) challenges deemed most important to address.

Results

The group advocated using consensus panels to promote consistency in payer policies and to standardize test reporting, and favored making genomic data-sharing a condition of regulatory clearance, certification, or accreditation processes. They were split on the role of US FDA.

Conclusion

Panelists found common ground on solutions for health plan coverage policy consistency, data-sharing, and standardizing reporting, but were sharply divided on the role of the FDA in mitigating risks to patients.

Consumer Health Informatics: Promoting Patient Self-care Management of Illnesses and Health

Consumer health informatics (CHI) is propelling important changes for medical providers and the lives of patients through information and communications technology. Independently, medical consumers seek, collect, and use health information for decision making. However, when constructing a CHI-based medical platform, high technology must be applied in a fully understandable and usable format for both health care providers and consumers. This study examines the present status of CHI and its effect on medical consumers. For the development of CHI, we discuss the need for tailored health communications and capacity building with chronic patients at the medical center. First, empowerment is a key characteristic needed for medical consumer health care management. However, promoting patient self-care management of illnesses and health is necessary to create conjugation where cooperation with medical service providers is possible. Also, establishing a health care delivery system that will support cooperation is necessary. Second, tailored health communications can uniquely construct the health information of patients, which prevents unnecessary or excessive information from leading patients to confused and inappropriate decisions. Ultimately, through the present environment of health communication, the innovation of a consumer health care information system has become the tide of the times and the positive effect of improved health can be expected.

Single-Nucleotide Polymorphism of PPARγ, a Protein at the Crossroads of Physiological and Pathological Processes

Genome polymorphisms are responsible for phenotypic differences between humans and for individual susceptibility to genetic diseases and therapeutic responses. Non-synonymous single-nucleotide polymorphisms (nsSNPs) lead to protein variants with a change in the amino acid sequence that may affect the structure and/or function of the protein and may be utilized as efficient structural and functional markers of association to complex diseases. This study is focused on nsSNP variants of the ligand binding domain of PPARγ a nuclear receptor in the superfamily of ligand inducible transcription factors that play an important role in regulating lipid metabolism and in several processes ranging from cellular differentiation and development to carcinogenesis. Here we selected nine nsSNPs variants of the PPARγ ligand binding domain, V290M, R357A, R397C, F360L, P467L, Q286P, R288H, E324K, and E460K, expressed in cancer tissues and/or associated with partial lipodystrophy and insulin resistance. The effects of a single amino acid change on the thermodynamic stability of PPARγ, its spectral properties, and molecular dynamics have been investigated. The nsSNPs PPARγ variants show alteration of dynamics and tertiary contacts that impair the correct reciprocal positioning of helices 3 and 12, crucially important for PPARγ functioning.

In silico Mapping of Protein Unfolding Mutations for Inherited Disease

The effect of disease-causing missense mutations on protein folding is difficult to evaluate. To understand this relationship, we developed the unfolding mutation screen (UMS) for in silico evaluation of the severity of genetic perturbations at the atomic level of protein structure. The program takes into account the protein-unfolding curve and generates propensities using calculated free energy changes for every possible missense mutation at once. These results are presented in a series of unfolding heat maps and a colored protein 3D structure to show the residues critical to the protein folding and are available for quick reference. UMS was tested with 16 crystal structures to evaluate the unfolding for 1391 mutations from the ProTherm database. Our results showed that the computational accuracy of the unfolding calculations was similar to the accuracy of previously published free energy changes but provided a better scale. Our residue identity control helps to improve protein homology models. The unfolding predictions for proteins involved in age-related macular degeneration, retinitis pigmentosa, and Leber's congenital amaurosis matched well with data from previous studies. These results suggest that UMS could be a useful tool in the analysis of genotype-to-phenotype associations and next-generation sequencing data for inherited diseases.

-Omic and Electronic Health Record Big Data Analytics for Precision Medicine

OBJECTIVE

Rapid advances of high-throughput technologies and wide adoption of electronic health records (EHRs) have led to fast accumulation of -omic and EHR data. These voluminous complex data contain abundant information for precision medicine, and big data analytics can extract such knowledge to improve the quality of healthcare.

METHODS

In this paper, we present -omic and EHR data characteristics, associated challenges, and data analytics including data preprocessing, mining, and modeling.

RESULTS

To demonstrate how big data analytics enables precision medicine, we provide two case studies, including identifying disease biomarkers from multi-omic data and incorporating -omic information into EHR.

CONCLUSION

Big data analytics is able to address -omic and EHR data challenges for paradigm shift toward precision medicine.

SIGNIFICANCE

Big data analytics makes sense of -omic and EHR data to improve healthcare outcome. It has long lasting societal impact.

Barriers to clinical adoption of next generation sequencing: Perspectives of a policy Delphi panel

This research aims to inform policymakers by engaging expert stakeholders to identify, prioritize, and deliberate the most important and tractable policy barriers to the clinical adoption of next generation sequencing (NGS). A 4-round Delphi policy study was done with a multi-stakeholder panel of 48 experts. The first 2 rounds of online questionnaires (reported here) assessed the importance and tractability of 28 potential barriers to clinical adoption of NGS across 3 major policy domains: intellectual property, coverage and reimbursement, and FDA regulation. We found that: 1) proprietary variant databases are seen as a key challenge, and a potentially intractable one; 2) payer policies were seen as a frequent barrier, especially a perceived inconsistency in standards for coverage; 3) relative to other challenges considered, FDA regulation was not strongly perceived as a barrier to clinical use of NGS. Overall the results indicate a perceived need for policies to promote data-sharing, and a desire for consistent payer coverage policies that maintain reasonably high standards of evidence for clinical utility, limit testing to that needed for clinical care decisions, and yet also flexibly allow for clinician discretion to use genomic testing in uncertain circumstances of high medical need.

Protein function in precision medicine: deep understanding with machine learning

Precision medicine and personalized health efforts propose leveraging complex molecular, medical and family history, along with other types of personal data toward better life. We argue that this ambitious objective will require advanced and specialized machine learning solutions. Simply skimming some low-hanging results off the data wealth might have limited potential. Instead, we need to better understand all parts of the system to define medically relevant causes and effects: how do particular sequence variants affect particular proteins and pathways? How do these effects, in turn, cause the health or disease-related phenotype? Toward this end, deeper understanding will not simply diffuse from deeper machine learning, but from more explicit focus on understanding protein function, context-specific protein interaction networks, and impact of variation on both.

Metabolomics of autism spectrum disorders: early insights regarding mammalian-microbial cometabolites

INTRODUCTION

Autism spectrum disorders (ASD) are a group of neurodevelopmental disorders consisting of delayed or impaired language development and difficulties in social interactions. The very high degree of phenotypic heterogeneity in ASD originates from the interaction between environmental risk factors and susceptible genetic loci, leading to epigenetic DNA methylation. Advances in system biology are becoming strategic for implementing knowledge on the ASD aetiology and for the early diagnosis of the disease after birth.

AREAS COVERED

We overhauled the value of either targeted or untargeted metabolomics studies in autism for identifying the most relevant metabolic pathways and key metabolites implicated in the disease, with special emphasis to mammalian-microbial metabolites. The most discriminant metabolites in ASD belong to amino acid metabolism, antioxidant status, nicotinic acid metabolism, and mitochondrial metabolism. Expert commentary: Most published studies point out the role of metabolites derived from the gut microbiota: they can modulate the behavioral phenotype of the autistic children, greatly influencing host metabolic pathways and the immune system, shaping the individual susceptibility to the disease. Pitfalls and caveats in metabolomics results across studies have been additionally recognized and discussed leading to the conclusion that metabolomics studies in ASD are far to be definitive and univocal.