Genomic medicine: time for health-care transformation

A Pharmacogenomics-Based In Silico Investigation of Opioid Prescribing in Post-operative Spine Pain Management and Personalized Therapy

Considering the variability in individual responses to opioids and the growing concerns about opioid addiction, prescribing opioids for postoperative pain management after spine surgery presents significant challenges. Therefore, this study undertook a novel pharmacogenomics-based in silico investigation of FDA-approved opioid medications. The DrugBank database was employed to identify all FDA-approved opioids. Subsequently, the PharmGKB database was utilized to filter through all variant annotations associated with the relevant genes. In addition, the dpSNP ( https://www.ncbi.nlm.nih.gov/snp/ ), a publicly accessible repository, was used. Additional analyses were conducted using STRING-MODEL (version 12), Cytoscape (version 3.10.1), miRTargetLink.2, and NetworkAnalyst (version 3). The study identified 125 target genes of FDA-approved opioids, encompassing 7019 variant annotations. Of these, 3088 annotations were significant and pertained to 78 genes. During variant annotation assessments (VAA), 672 variants remained after filtration. Further in-depth filtration based on variant functions yielded 302 final filtered variants across 56 genes. The Monoamine GPCRs pathway emerged as the most significant signaling pathway. Protein-protein interaction (PPI) analysis revealed a fully connected network comprising 55 genes. Gene-miRNA Interaction (GMI) analysis of these 55 candidate genes identified miR-16-5p as a pivotal miRNA in this network. Protein-Drug Interaction (PDI) assessment showed that multiple drugs, including Ibuprofen, Nicotine, Tramadol, Haloperidol, Ketamine, L-Glutamic Acid, Caffeine, Citalopram, and Naloxone, had more than one interaction. Furthermore, Protein-Chemical Interaction (PCI) analysis highlighted that ABCB1, BCL2, CYP1A2, KCNH2, PTGS2, and DRD2 were key targets of the proposed chemicals. Notably, 10 chemicals, including carbamylhydrazine, tetrahydropalmatine, Terazosin, beta-methylcholine, rubimaillin, and quinelorane, demonstrated dual interactions with the aforementioned target genes. This comprehensive review offers multiple strong, evidence-based in silico findings regarding opioid prescribing in spine pain management, introducing 55 potential genes. The insights from this report can be applied in exome analysis as a pharmacogenomics (PGx) panel for pain susceptibility, facilitating individualized opioid prescribing through genotyping of related variants. The article also points out that African Americans represent an important group that displays a high catabolism of opioids and suggest the need for a personalized therapeutic approach based on genetic information.

Recent evidence and progress for developing precision nursing in symptomatology: A scoping review

AIM

To summarize the omics results of symptomatic research that can help nurses identify intervention targets and design precision interventions for pain, mental health, cognitive impairment, sleep disorder, fatigue, lymphedema, and quality of life, so as to provide a comprehensive summary of help and inspire to precision nursing.

METHODS

CINAHL, PubMed, Web of Science, and ScienceDirect databases were searched. Retrieval time was from January 2012 to December 2021. Symptomatology research applying omics that can be used to guide nurses in designing targeted interventions was included.

RESULTS

Forty-six studies were included in the final review. Symptomatology research that can be integrated with nursing science to develop precision nursing focused on pain, mental health, cognitive impairment, sleep disorder, fatigue, lymphedema, and quality of life. Most studies were related to cognitive impairment (n = 10; 21.74%), pain (n = 9; 19.57%), and mental health (n = 8; 17.39%). Moreover, the included studies involved various omics technologies, such as whole genome, epigenome, transcriptome, proteome, and metabolome.

CONCLUSION

The rapid development of various omic technologies promotes symptomatology research, which can help nurses fully understand the information of patients. Phenotypic characteristics and biomarkers shown in symptomatology research help nurses identify intervention targets and develop individualization interventions, so as to prevent and reduce symptoms and improve the quality of life.

IMPLICATION FOR NURSING AND HEALTH POLICY

This scoping review is the first synthesis of all peer-reviewed literature to summarize and provide important information and references from the omic results of symptomatology studies to develop precision nursing, highlighting the status and development of precision nursing. Nursing education policies should introduce the development and importance of precision nursing. Further research could consider investing more attention in precision nursing. Nursing researchers can carry out some studies applying omics technology to explore more biomarkers, helping guide the formulation of clinical intervention for symptoms.

[Application Status and Prospects of Precision Nursing Under the Concept of Enhanced Recovery After Surgery]

With the development of enhanced recovery after surgery (ERAS), major breakthroughs have been made in this field of study. However, the research fields still need to be continuously expanded to meet the needs of patients. The concept of precision therapy is widely applied in the field of nursing. Under the concept of ERAS, practical studies of applying precision nursing for the perioperative period have already been conducted, exploring such issues as precision nursing assessment, precision nursing intervention design, precision risk prediction model, and information technology to assist precision nursing practice. Research findings have preliminarily validated the safety and effectiveness of applying precision nursing for ERAS in the perioperative period. Herein, we reviewed the reported findings of relevant research published in recent years and identified the following problems in the implementation of precision nursing under the ERAS concept, a lack of implementation standards, challenges concerning the the role of nurses, a lack of high-quality research evidence in the existing literature, and a relevant big data processing platform that China does not have and therefore cannot carry out data sharing, integration, mining, and utilization. We also made suggestions for effective improvement and discussed research prospects. In the future, multidisciplinary collaboration, translational medical research, and the development of various innovative tools are to be strengthened to help improve the quality and effectiveness of nursing care. We hope to provide reference for improving the scientific and targeted implementation of precision nursing for ERAS in the perioperative period.

Population Genome Programs across the Middle East and North Africa: Successes, Challenges, and Future Directions

In this review, we discuss the current state of population genome programs (PGPs) conducted in the Middle East and North African (MENA) region. This region has high prevalence of genetic diseases and significant health challenges as well as being a significantly underrepresented population in public genetic databases. The majority of ongoing PGPs represent regions in Europe, North and South America, South Asia, Australia, and Africa, with little to no descriptive information highlighted only on the MENA Region when it comes to genome programs databases, outcomes, or the challenges that MENA region countries may face establishing their own national programs. This review has identified 6 PGPs currently underway in the MENA region, namely in the Kingdom of Saudi Arabia, Qatar, Egypt, the United Arab Emirates, Bahrain, and Iran. Due to the rapidly growing involvement of the MENA region in national-scale genomic data collection, an increase in representation in public genetic databases is to be expected to occur in the near future. Whilst significant progress is being made in some MENA countries, future initiatives as well as ongoing programs will be facing several challenges related to collaboration, finance, infrastructure and institutional data access, data analysis, sustainability, health records, and biobanks. The review also reiterates the need for ensuring ethical and regulated genomic initiatives which can drive developments in personalized medicine treatments to improve patient prognosis and quality of life.

Hope versus reality: Parent expectations of genomic testing

OBJECTIVE

Genomics is increasingly used for diagnostic testing in children. This study describes the expectations of parents whose child received genomic testing and whether or not they were met.

METHODS

A diverse stratified, purposive sample of parents of 22 children in New York City was interviewed using a semi-structured guide. Genomic test results were positive, negative, or uncertain.

RESULTS

Parents expressed their expectations in narrative and numeric fashion. Parents expected that their child's test would have a direct effect on their child's diagnosis. Some believed that results would be definitive, while others recognized testing limitations. Expectations reflected parents' hope to find a diagnosis and led to disappointment when results were uninformative or did not impact clinical management.

CONCLUSION

Results suggest pre-test genetic counseling emphasize the low likelihood of actionable results; however, parents' expectations of genomics' diagnostic capabilities are strongly rooted in their need to end the diagnostic odyssey and may be difficult to manage.

PRACTICE IMPLICATIONS

Parents' hope for a resolution and effective treatment for their child is a powerful context in which genetic counseling is heard. Clinicians who provide genomic testing should continue to acknowledge parents' preconceptions. Additional research in other settings will help understand how to best address and manage parent expectations of genomic medicine.

Milestones of Precision Medicine: An Innovative, Multidisciplinary Overview

Although the concept of precision medicine, in which healthcare is tailored to the molecular and clinical characteristics of each individual, is not new, its implementation in clinical practice has been heterogenous. In some medical specialties, precision medicine has gone from being just a promise to a reality that achieves better patient outcomes. This is a fact if we consider, for example, the great advances made in the genetic diagnosis and subsequent treatment of countless hereditary diseases, such as cystic fibrosis, which have improved the life expectancy of many of the affected children. In the field of oncology, the development of targeted therapies has prolonged the survival of patients with breast, lung, colorectal, melanoma, and hematological malignancies. In other disciplines, clinical milestones are perhaps less well known, but no less important. The current challenge is to expand and generalize the use of technologies that are central to precision medicine, such as massively parallel sequencing, to improve the management (prevention and treatment) of complex conditions such as cardiovascular, kidney, or autoimmune diseases. This process requires investment in specialized expertise, multidisciplinary collaboration, and the nationwide organization of genetic laboratories for diagnosis of specific diseases.

Scalable and Robust Regression Methods for Phenome-Wide Association Analysis on Large-Scale Biobank Data

With the advances in genotyping technologies and electronic health records (EHRs), large biobanks have been great resources to identify novel genetic associations and gene-environment interactions on a genome-wide and even a phenome-wide scale. To date, several phenome-wide association studies (PheWAS) have been performed on biobank data, which provides comprehensive insights into many aspects of human genetics and biology. Although inspiring, PheWAS on large-scale biobank data encounters new challenges including computational burden, unbalanced phenotypic distribution, and genetic relationship. In this paper, we first discuss these new challenges and their potential impact on data analysis. Then, we summarize approaches that are scalable and robust in GWAS and PheWAS. This review can serve as a practical guide for geneticists, epidemiologists, and other medical researchers to identify genetic variations associated with health-related phenotypes in large-scale biobank data analysis. Meanwhile, it can also help statisticians to gain a comprehensive and up-to-date understanding of the current technical tool development.

The DisGeNET cytoscape app: Exploring and visualizing disease genomics data

Thanks to the unbiased exploration of genomic variants at large scale, hundreds of thousands of disease-associated loci have been uncovered. In parallel, network-based approaches have proven to be essential to understand the molecular mechanisms underlying human diseases. The use of these approaches has been boosted by the abundance of information about disease associated genes and variants, high quality human interactomics data, and the emergence of new types of omics data. The DisGeNET Cytoscape App combines the capabilities of Cytoscape with those of DisGeNET, a knowledge platform based on a comprehensive catalogue of disease-associated genes and variants. The DisGeNET Cytoscape App contains functions to query, analyze, and visualize different network representations of the gene-disease and variant-disease associations available in DisGeNET. It supports a wide variety of applications through its query and filter functionalities, including the annotation of foreign networks generated by other apps or uploaded by the user. The new release of the DisGeNET Cytoscape App has been designed to support Cytoscape 3.x and incorporates novel distinctive features such as visualization and analysis of variant-disease networks, disease enrichment analysis for genes and variants, and analytic support through Cytoscape Automation. Moreover, the DisGeNET Cytoscape App features an API to access its core functionalities via the REST protocol fostering the development of reproducible and scalable analysis workflows based on DisGeNET data.

How to design a national genomic project-a systematic review of active projects

An increasing number of countries are investing efforts to exploit the human genome, in order to improve genetic diagnostics and to pave the way for the integration of precision medicine into health systems. The expected benefits include improved understanding of normal and pathological genomic variation, shorter time-to-diagnosis, cost-effective diagnostics, targeted prevention and treatment, and research advances.We review the 41 currently active individual national projects concerning their aims and scope, the number and age structure of included subjects, funding, data sharing goals and methods, and linkage with biobanks, medical data, and non-medical data (exposome). The main aims of ongoing projects were to determine normal genomic variation (90%), determine pathological genomic variation (rare disease, complex diseases, cancer, etc.) (71%), improve infrastructure (59%), and enable personalized medicine (37%). Numbers of subjects to be sequenced ranges substantially, from a hundred to over a million, representing in some cases a significant portion of the population. Approximately half of the projects report public funding, with the rest having various mixed or private funding arrangements. 90% of projects report data sharing (public, academic, and/or commercial with various levels of access) and plan on linking genomic data and medical data (78%), existing biobanks (44%), and/or non-medical data (24%) as the basis for enabling personal/precision medicine in the future.Our results show substantial diversity in the analysed categories of 41 ongoing national projects. The overview of current designs will hopefully inform national initiatives in designing new genomic projects and contribute to standardisation and international collaboration.

The Pharmacogenetics of Rituximab: Potential Implications for Anti-CD20 Therapies in Multiple Sclerosis

There are a broad range of disease-modifying therapies (DMTs) available in relapsing-remitting multiple sclerosis (RRMS), but limited biomarkers exist to personalise DMT choice. All DMTs, including monoclonal antibodies such as rituximab and ocrelizumab, are effective in preventing relapses and preserving neurological function in MS. However, each agent harbours its own risk of therapeutic failure or adverse events. Pharmacogenetics, the study of the effects of genetic variation on therapeutic response or adverse events, could improve the precision of DMT selection. Pharmacogenetic studies of rituximab in MS patients are lacking, but pharmacogenetic markers in other rituximab-treated autoimmune conditions have been identified. This review will outline the wider implications of pharmacogenetics and the mechanisms of anti-CD20 agents in MS. We explore the non-MS rituximab literature to characterise pharmacogenetic variants that could be of prognostic relevance in those receiving rituximab, ocrelizumab or other monoclonal antibodies for MS.

Decoding Plant-Environment Interactions That Influence Crop Agronomic Traits

To ensure food security in the face of increasing global demand due to population growth and progressive urbanization, it will be crucial to integrate emerging technologies in multiple disciplines to accelerate overall throughput of gene discovery and crop breeding. Plant agronomic traits often appear during the plants' later growth stages due to the cumulative effects of their lifetime interactions with the environment. Therefore, decoding plant-environment interactions by elucidating plants' temporal physiological responses to environmental changes throughout their lifespans will facilitate the identification of genetic and environmental factors, timing and pathways that influence complex end-point agronomic traits, such as yield. Here, we discuss the expected role of the life-course approach to monitoring plant and crop health status in improving crop productivity by enhancing the understanding of plant-environment interactions. We review recent advances in analytical technologies for monitoring health status in plants based on multi-omics analyses and strategies for integrating heterogeneous datasets from multiple omics areas to identify informative factors associated with traits of interest. In addition, we showcase emerging phenomics techniques that enable the noninvasive and continuous monitoring of plant growth by various means, including three-dimensional phenotyping, plant root phenotyping, implantable/injectable sensors and affordable phenotyping devices. Finally, we present an integrated review of analytical technologies and applications for monitoring plant growth, developed across disciplines, such as plant science, data science and sensors and Internet-of-things technologies, to improve plant productivity.