Pharmacogenetics: a general review on progress to date

The Role of Pharmacogenomics Studies for Precision Medicine Among Ethiopian Patients and Their Clinical Implications: A Scoping Review

Background

Pharmacogenomics research is currently revolutionizing treatment optimization by discovering molecular markers. Medicines are the cornerstone of treatment for both acute and chronic diseases. Pharmacogenomics associated treatment response varies from 20% to 95%, resulting in from lack of efficacy to serious toxicity. Pharmacogenomics has emerged as a useful tool for therapy optimization and plays a bigger role in clinical care going forward. However, in Africa, in particular in Ethiopia, such studies are scanty and not generalizing. Therefore, the objective of this review was to outline such studies, generating comprehensive evidence and identify studied variants' association with treatment responses in Ethiopian patients.

Methods

The Joanna Briggs Institute's updated 2020 methodological guidelines for conducting and guidance for scoping reviews were used. We meticulously adhered to the systemic review reporting items checklist and scoping review meta-analyses extension.

Results

Two hundred twenty-nine possibly relevant studies were searched. These include: 64, 54, 21, 48 and 42 from PubMed, Scopus, Google Scholar, EMBASE, and manual search, respectively. Seventy-seven duplicate studies were removed. Thirty-nine papers were rejected with justification, whereas 58 studies were qualified for full-text screening. Finally 19 studies were examined. The primary pharmacogene that was found to have a significant influence on the pharmacokinetics of efavirenz was CYP2B6. Drug-induced liver injury has frequently identified toxicity among studied medications.

Conclusion and Future Perspectives

Pharmacogenomics studies in Ethiopian populations are less abundant. The studies conducted focused on infectious diseases, specifically on HAART commonly efavirenz and backbone first-line anti-tuberculosis drugs. There is a high need for further pharmacogenomics research to verify the discrepancies among the studies and for guiding precision medicine. Systematic review and meta-analysis are also recommended for pooled effects of different parameters in pharmacogenomics studies.

Next-Generation sequencing transforming clinical practice and precision medicine

Next-generation sequencing (NGS) has revolutionized the field of genomics and is rapidly transforming clinical diagnosis and precision medicine. This advanced sequencing technology enables the rapid and cost-effective analysis of large-scale genomic data, allowing comprehensive exploration of the genetic landscape of diseases. In clinical diagnosis, NGS has proven to be a powerful tool for identifying disease-causing variants, enabling accurate and early detection of genetic disorders. Additionally, NGS facilitates the identification of novel disease-associated genes and variants, aiding in the development of targeted therapies and personalized treatment strategies. NGS greatly benefits precision medicine by enhancing our understanding of disease mechanisms and enabling the identification of specific molecular markers for disease subtypes, thus enabling tailored medical interventions based on individual characteristics. Furthermore, NGS contributes to the development of non-invasive diagnostic approaches, such as liquid biopsies, which can monitor disease progression and treatment response. The potential of NGS in clinical diagnosis and precision medicine is vast, yet challenges persist in data analysis, interpretation, and protocol standardization. This review highlights NGS applications in disease diagnosis, prognosis, and personalized treatment strategies, while also addressing challenges and future prospects in fully harnessing genomic potential within clinical practice.

Identification of adverse drug reactions that may be related to pharmacogenetics in a public hospital in the South of Brazil

BACKGROUND

Adverse drug reactions (ADRs) are of great concern in clinical practice. Pharmacogenetics can identify individuals and groups at increased risk of developing ADRs, enabling treatment adjustments to improve outcomes. The study aimed to determine the prevalence of ADRs related to drugs with pharmacogenetic evidence level 1A in a public hospital in Southern Brazil.

RESEARCH DESIGN AND METHODS

ADR information was collected from the pharmaceutical registries from 2017 to 2019. Drugs that have pharmacogenetic evidence level 1A were selected. Public genomic databases were used to estimate the genotypes/phenotypes frequency.

RESULTS

During the period, 585 ADRs were spontaneously notified. Most were moderate (76.3%), whereas severe reactions accounted for 33.8%. Additionally, 109 ADRs caused by 41 drugs presented pharmacogenetic evidence level 1A, representing 18.6% of all notified reactions. Depending on the drug-gene pair, up to 35% of individuals from Southern Brazil could be at risk of developing ADRs.

CONCLUSIONS

Relevant amount of ADRs were related to drugs with pharmacogenetic recommendations on drug labels and/or guidelines. Genetic information could guide and improve clinical outcomes, decreasing ADR incidence and reducing treatment costs.

Clinical pharmacology and pharmacogenetics of prostaglandin analogues in glaucoma

Glaucoma is the main cause of irreversible visual loss worldwide, and comprises a group of progressive, age-related, and chronic optic neuropathies. Prostaglandin analogs are considered a first-line treatment in the management of glaucoma and have the best efficacy in reducing intraocular pressure. When comparing these therapeutic agents between them, long-term therapy with 0.03% bimatoprost is the most effective followed by treatment with 0.005% latanoprost and 0.004% travoprost. The prevalence of adverse events is lower for latanoprost than for other prostaglandin analogs. However, some patients do not respond to the treatment with prostaglandin analogs (non-responders). Intraocular pressure-lowering efficacy differs significantly between individuals partly owing to genetic factors. Rs1045642 in ABCB1, rs4241366 in SLCO2A1, rs9503012 in GMDS, rs10306114 in PTGS1, rs11568658 in MRP4, rs10786455 and rs6686438 in PTGFR were reported to be positive with the response to prostaglandin analogs in patients with glaucoma. A negative association was found between single nucleotide polymorphisms of PTGFR (rs11578155 and rs6672484) and the response to prostaglandin analogs in patients with glaucoma. The current review is an analysis of the information relevant to prostaglandin analog treatments based on previous literatures. It describes in detail the clinical pharmacology and pharmacogenetics of drugs belonging to this therapeutical class to provide a sound pharmacological basis for their proper use in ophthalmological clinical practice.

Pharmacometabolomics-Based Translational Biomarkers: How to Navigate the Data Ocean

Metabolome is the end point of the genome-environment interplay, and enables an important holistic overview of individual adaptability and host responses to environmental, ecological, as well as endogenous changes such as disease. Pharmacometabolomics is the application of metabolome knowledge to decipher the mechanisms of interindividual and intraindividual variations in drug efficacy and safety. Pharmacometabolomics also contributes to prediction of drug treatment outcomes on the basis of baseline (predose) and postdose metabotypes through mathematical modeling. Thus, pharmacometabolomics is a strong asset for a diverse community of stakeholders interested in theory and practice of evidence-based and precision/personalized medicine: academic researchers, public health scholars, health professionals, pharmaceutical, diagnostics, and biotechnology industries, among others. In this expert review, we discuss pharmacometabolomics in four contexts: (1) an interdisciplinary omics tool and field to map the mechanisms and scale of interindividual variability in drug effects, (2) discovery and development of translational biomarkers, (3) advance digital biomarkers, and (4) empower drug repurposing, a field that is increasingly proving useful in the current era of Covid-19. As the applications of pharmacometabolomics are growing rapidly in the current postgenome era, next-generation proteomics and metabolomics follow the example of next-generation sequencing analyses. Pharmacometabolomics can also empower data reliability and reproducibility through multiomics integration strategies, which use each data layer to correct, connect with, and inform each other. Finally, we underscore here that contextual data remain crucial for precision medicine and drug development that stand the test of time and clinical relevance.

Marine Alga Ulva fasciata-Derived Molecules for the Potential Treatment of SARS-CoV-2: An In Silico Approach

SARS-CoV-2 is the causative agent of the COVID-19 pandemic. This in silico study aimed to elucidate therapeutic efficacies against SARS-CoV-2 of phyco-compounds from the seaweed, Ulva fasciata. Twelve phyco-compounds were isolated and toxicity was analyzed by VEGA QSAR. Five compounds were found to be nonmutagenic, noncarcinogenic and nontoxic. Moreover, antiviral activity was evaluated by PASS. Binding affinities of five of these therapeutic compounds were predicted to possess probable biological activity. Fifteen SARS-CoV-2 target proteins were analyzed by the AutoDock Vina program for molecular docking binding energy analysis and the 6Y84 protein was determined to possess optimal binding affinities. The Desmond program from Schrödinger’s suite was used to study high performance molecular dynamic simulation properties for 3,7,11,15-Tetramethyl-2-hexadecen-1-ol—6Y84 for better drug evaluation. The ligand with 6Y84 had stronger binding affinities (−5.9 kcal/mol) over two standard drugs, Chloroquine (−5.6 kcal/mol) and Interferon α-2b (−3.8 kcal/mol). Swiss ADME calculated physicochemical/lipophilicity/water solubility/pharmacokinetic properties for 3,7,11,15-Tetramethyl-2-hexadecen-1-ol, showing that this therapeutic agent may be effective against SARS-CoV-2.

The role of CYP2C19 genotyping to guide antiplatelet therapy following ischemic stroke or transient ischemic attack

Introduction

Clopidogrel is an antiplatelet agent recommended for secondary prevention of ischemic stroke (IS) and transient ischemic attack (TIA). Conversion of clopidogrel to its active metabolite by hepatic cytochrome P450-2C19 (CYP2C19) is essential for the inhibition of the P2Y12 receptor and subsequent platelet aggregation to prevent thrombotic events. CYP2C19 is highly polymorphic, with over 30 loss of function (LoF) alleles. This review considers whether there is sufficient data to support genotype guided antiplatelet therapy after stroke.

Areas covered

A systematic literature review retrieved articles, which describe the interaction between CYP2C19 genotype and clinical outcomes following IS or TIA when treated with clopidogrel. The review documents efforts to identify optimal antiplatelet regimens and explores the value genotype guided antiplatelet therapy. The work outlines the contemporary understanding of clopidogrel metabolism and appraises evidence linking CYP2C19 LoF variants with attenuated platelet inhibition and poorer outcomes.

Expert opinion

There is good evidence that CYP2C19 LoF allele carriers of Han-Chinese ancestry have increased risk for further vascular events following TIA or IS when treated with clopidogrel. The evidence base is less certain in other populations. The expansion of pharmacogenetics into routine clinical practice will facilitate further research and help tailor other aspects of secondary prevention.

Open problems in human trait genetics

Genetic studies of human traits have revolutionized our understanding of the variation between individuals, and yet, the genetics of most traits is still poorly understood. In this review, we highlight the major open problems that need to be solved, and by discussing these challenges provide a primer to the field. We cover general issues such as population structure, epistasis and gene-environment interactions, data-related issues such as ancestry diversity and rare genetic variants, and specific challenges related to heritability estimates, genetic association studies, and polygenic risk scores. We emphasize the interconnectedness of these problems and suggest promising avenues to address them.

The reference liver-CYP450 and UGT enzymes in healthy donor and metastatic livers: the impact of genotype

Background

Hepatic enzymes involved in drug metabolism vary markedly in expression, abundance and activity, which affects individual susceptibility to drugs and toxicants. The present study aimed to compare mRNA expression and protein abundance of the most pharmacologically relevant drug-metabolizing enzymes in two main sources of the control liver samples that are used as the reference, i.e. organ donor livers and non-tumorous tissue from metastatic livers. An association analysis of the most common genetic variants with mRNA and protein levels was also performed.

Methods

The CYP450 and UGT enzymes (CYP1A2, CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, CYP2E1, CYP3A4, CYP3A5, UGT1A1, UGT1A3, UGT2B7 and UGT2B15) were analyzed for mRNA (qPCR) and protein abundance (LC–MS/MS) in healthy donors (n = 11) and metastatic (n = 13) livers. Genotyping was performed by means of TaqMan assays and pyrosequencing.

Results

Significantly higher protein abundance in the metastatic livers was observed in case of CYP2C9, CYP2D6, and UGT2B7, and for UGT1A3 the difference was only significant at mRNA level. For all the enzymes except CYP2E1 some significant correlation between mRNA and protein content was observed, and for UGT1A1 an inverse correlation with age was noted. CYP2C19, CYP3A5 and CYP2D6 were significantly affected by genotype.

Conclusion

The selection of a control group for the study on drug-metabolizing enzymes (e.g. in pathological states) may possibly affect its conclusions on differences in mRNA and protein content. Genotyping for common functional variants of CYP450 enzymes should be performed in all studies on drug-metabolizing enzymes.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43440-021-00337-w.

Role of Common Genetic Variants for Drug-Resistance to Specific Anti-Seizure Medications

Objective: Resistance to anti-seizure medications (ASMs) presents a significant hurdle in the treatment of people with epilepsy. Genetic markers for resistance to individual ASMs could support clinicians to make better-informed choices for their patients. In this study, we aimed to elucidate whether the response to individual ASMs was associated with common genetic variation. Methods: A cohort of 3,649 individuals of European descent with epilepsy was deeply phenotyped and underwent single nucleotide polymorphism (SNP)-genotyping. We conducted genome-wide association analyses (GWASs) on responders to specific ASMs or groups of functionally related ASMs, using non-responders as controls. We performed a polygenic risk score (PRS) analyses based on risk variants for epilepsy and neuropsychiatric disorders and ASM resistance itself to delineate the polygenic burden of ASM-specific drug resistance. Results: We identified several potential regions of interest but did not detect genome-wide significant loci for ASM-specific response. We did not find polygenic risk for epilepsy, neuropsychiatric disorders, and drug-resistance associated with drug response to specific ASMs or mechanistically related groups of ASMs. Significance: This study could not ascertain the predictive value of common genetic variants for ASM responder status. The identified suggestive loci will need replication in future studies of a larger scale.

Role of Pharmacogenetics in Adverse Drug Reactions: An Update towards Personalized Medicine

Adverse drug reactions (ADRs) are an important and frequent cause of morbidity and mortality. ADR can be related to a variety of drugs, including anticonvulsants, anaesthetics, antibiotics, antiretroviral, anticancer, and antiarrhythmics, and can involve every organ or apparatus. The causes of ADRs are still poorly understood due to their clinical heterogeneity and complexity. In this scenario, genetic predisposition toward ADRs is an emerging issue, not only in anticancer chemotherapy, but also in many other fields of medicine, including hemolytic anemia due to glucose-6-phosphate dehydrogenase (G6PD) deficiency, aplastic anemia, porphyria, malignant hyperthermia, epidermal tissue necrosis (Lyell's Syndrome and Stevens-Johnson Syndrome), epilepsy, thyroid diseases, diabetes, Long QT and Brugada Syndromes. The role of genetic mutations in the ADRs pathogenesis has been shown either for dose-dependent or for dose-independent reactions. In this review, we present an update of the genetic background of ADRs, with phenotypic manifestations involving blood, muscles, heart, thyroid, liver, and skin disorders. This review aims to illustrate the growing usefulness of genetics both to prevent ADRs and to optimize the safe therapeutic use of many common drugs. In this prospective, ADRs could become an untoward "stress test," leading to new diagnosis of genetic-determined diseases. Thus, the wider use of pharmacogenetic testing in the work-up of ADRs will lead to new clinical diagnosis of previously unsuspected diseases and to improved safety and efficacy of therapies. Improving the genotype-phenotype correlation through new lab techniques and implementation of artificial intelligence in the future may lead to personalized medicine, able to predict ADR and consequently to choose the appropriate compound and dosage for each patient.

DeepCDR: a hybrid graph convolutional network for predicting cancer drug response

MOTIVATION

Accurate prediction of cancer drug response (CDR) is challenging due to the uncertainty of drug efficacy and heterogeneity of cancer patients. Strong evidences have implicated the high dependence of CDR on tumor genomic and transcriptomic profiles of individual patients. Precise identification of CDR is crucial in both guiding anti-cancer drug design and understanding cancer biology.

RESULTS

In this study, we present DeepCDR which integrates multi-omics profiles of cancer cells and explores intrinsic chemical structures of drugs for predicting CDR. Specifically, DeepCDR is a hybrid graph convolutional network consisting of a uniform graph convolutional network and multiple subnetworks. Unlike prior studies modeling hand-crafted features of drugs, DeepCDR automatically learns the latent representation of topological structures among atoms and bonds of drugs. Extensive experiments showed that DeepCDR outperformed state-of-the-art methods in both classification and regression settings under various data settings. We also evaluated the contribution of different types of omics profiles for assessing drug response. Furthermore, we provided an exploratory strategy for identifying potential cancer-associated genes concerning specific cancer types. Our results highlighted the predictive power of DeepCDR and its potential translational value in guiding disease-specific drug design.

AVAILABILITY AND IMPLEMENTATION

DeepCDR is freely available at https://github.com/kimmo1019/DeepCDR.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Germline Variants That Affect Tumor Progression

Germline variants have a rich history of being studied in the context of cancer risk. Emerging studies now suggest that germline variants contribute not only to cancer risk but to tumor progression as well. In this opinion article, we discuss the initial discoveries associating germline variants with patient outcome and the mechanisms by which germline variants affect molecular pathways. Germline variants affect molecular pathways through amino acid changes, alteration of splicing patterns or expression of genes, influencing the selection for somatic mutations, and causing genome-wide mutational enrichment. These molecular alterations can lead to tumor phenotypes that become clinically apparent such as metastasis, alterations to the immune microenvironment, and modulation of therapeutic response. Overall, the growing body of evidence suggests that germline variants play a larger role in tumor progression than has been previously appreciated and that germline variation holds substantial potential for improving personalized medicine and patient outcomes.

A Review of the Important Role of CYP2D6 in Pharmacogenomics

Cytochrome P450 2D6 (CYP2D6) is a critical pharmacogene involved in the metabolism of ~20% of commonly used drugs across a broad spectrum of medical disciplines including psychiatry, pain management, oncology and cardiology. Nevertheless, CYP2D6 is highly polymorphic with single-nucleotide polymorphisms, small insertions/deletions and larger structural variants including multiplications, deletions, tandem arrangements, and hybridisations with non-functional CYP2D7 pseudogenes. The frequency of these variants differs across populations, and they significantly influence the drug-metabolising enzymatic function of CYP2D6. Importantly, altered CYP2D6 function has been associated with both adverse drug reactions and reduced drug efficacy, and there is growing recognition of the clinical and economic burdens associated with suboptimal drug utilisation. To date, pharmacogenomic clinical guidelines for at least 48 CYP2D6-substrate drugs have been developed by prominent pharmacogenomics societies, which contain therapeutic recommendations based on CYP2D6-predicted categories of metaboliser phenotype. Novel algorithms to interpret CYP2D6 function from sequencing data that consider structural variants, and machine learning approaches to characterise the functional impact of novel variants, are being developed. However, CYP2D6 genotyping is yet to be implemented broadly into clinical practice, and so further effort and initiatives are required to overcome the implementation challenges and deliver the potential benefits to the bedside.

Pharmacogenetics of Statin-Induced Myotoxicity

Statins, a class of lipid-lowering medications, have been a keystone treatment in cardiovascular health. However, adverse effects associated with statin use impact patient adherence, leading to statin discontinuation. Statin-induced myotoxicity (SIM) is one of the most common adverse effects, prevalent across all ages, genders, and ethnicities. Although certain demographic cohorts carry a higher risk, the impaired quality of life attributed to SIM is significant. The pathogenesis of SIM remains to be fully elucidated, but it is clear that SIM is multifactorial. These factors include drug-drug interactions, renal or liver dysfunction, and genetics. Genetic-inferred risk for SIM was first reported by a landmark genome-wide association study, which reported a higher risk of SIM with a polymorphism in the SLCO1B1 gene. Since then, research associating genetic factors with SIM has expanded widely and has become one of the foci in the field of pharmacogenomics. This review provides an update on the genetic risk factors associated with SIM.

Pharmacogenomics to Predict Tumor Therapy Response: A Focus on ATP-Binding Cassette Transporters and Cytochromes P450

Pharmacogenomics is an evolving tool of precision medicine. Recently, due to the introduction of next-generation sequencing and projects generating "Big Data", a plethora of new genetic variants in pharmacogenes have been discovered. Cancer resistance is a major complication often preventing successful anticancer treatments. Pharmacogenomics of both somatic mutations in tumor cells and germline variants may help optimize targeted treatments and improve the response to conventional oncological therapy. In addition, integrative approaches combining copy number variations and long noncoding RNA profiling with germline and somatic variations seem to be a promising approach as well. In pharmacology, expression and enzyme activity are traditionally the more studied aspects of ATP-binding cassette transporters and cytochromes P450. In this review, we briefly introduce the field of pharmacogenomics and the advancements driven by next-generation sequencing and outline the possible roles of genetic variation in the two large pharmacogene superfamilies. Although the evidence needs further substantiation, somatic and copy number variants as well as rare variants and common polymorphisms in these genes could all affect response to cancer therapy. Regulation by long noncoding RNAs has also been shown to play a role. However, in all these areas, more comprehensive studies on larger sets of patients are needed.

Group-based pharmacogenetic prediction: is it feasible and do current NHS England ethnic classifications provide appropriate data?

Inter-individual variation of drug metabolising enzymes (DMEs) leads to variable efficacy of many drugs and even adverse drug responses. Consequently, it would be desirable to test variants of many DMEs before drug treatment. Inter-ethnic differences in frequency mean that the choice of SNPs to test may vary across population groups. Here we examine the utility of testing representative groups as a way of assessing what variants might be tested. We show that publicly available population information is potentially useful for determining loci for pre-treatment genetic testing, and for determining the most prevalent risk haplotypes in defined groups. However, we also show that the NHS England classifications have limitations for grouping for these purposes, in particular for people of African descent. We conclude: (1) genotyping of hospital patients and people from the hospital catchment area confers no advantage over using samples from appropriate existing ethnic group collections or publicly available data, (2) given the current NHS England Black African grouping, a decision as to whether to test, would have to apply to all patients of recent Black African ancestry to cover reported risk alleles and (3) the current scarcity of available genome and drug effect data from Africans is a problem for both testing and treatment decisions.

Structural Biology in the Multi-Omics Era

Rapid developments in cryogenic electron microscopy have opened new avenues to probe the structures of protein assemblies in their near native states. Recent studies have begun applying single -particle analysis to heterogeneous mixtures, revealing the potential of structural-omics approaches that combine the power of mass spectrometry and electron microscopy. Here we highlight advances and challenges in sample preparation, data processing, and molecular modeling for handling increasingly complex mixtures. Such advances will help structural-omics methods extend to cellular-level models of structural biology.

Predicting and Understanding the Human Microbiome's Impact on Pharmacology

Our bodies each possess a unique and dynamic collection of microbes and viruses, collectively the 'microbiome', with distinct metabolic capacities from our human cells. Unforeseen modification of drugs by the microbiome can drastically alter their clinical effectiveness, with the most dramatic cases leading to fatal drug interactions. Pharmaceuticals can be activated, deactivated, toxified, or release metabolites that alter the 'canonical' pharmacokinetics of the drug. Thus, predicting and characterizing microbe-drug interactions is necessary to develop and implement personalized drug administration protocols and, more broadly, to improve drug safety and efficacy. In this review, we focus on microbiome-driven alterations to drug pharmacokinetics and provide a research framework for pharmacologists interested in characterizing microbiome interactions with any drug of interest.

Toward a comprehensive view of cancer immune responsiveness: a synopsis from the SITC workshop

Tumor immunology has changed the landscape of cancer treatment. Yet, not all patients benefit as cancer immune responsiveness (CIR) remains a limitation in a considerable proportion of cases. The multifactorial determinants of CIR include the genetic makeup of the patient, the genomic instability central to cancer development, the evolutionary emergence of cancer phenotypes under the influence of immune editing, and external modifiers such as demographics, environment, treatment potency, co-morbidities and cancer-independent alterations including immune homeostasis and polymorphisms in the major and minor histocompatibility molecules, cytokines, and chemokines. Based on the premise that cancer is fundamentally a disorder of the genes arising within a cell biologic process, whose deviations from normality determine the rules of engagement with the host's response, the Society for Immunotherapy of Cancer (SITC) convened a task force of experts from various disciplines including, immunology, oncology, biophysics, structural biology, molecular and cellular biology, genetics, and bioinformatics to address the complexity of CIR from a holistic view. The task force was launched by a workshop held in San Francisco on May 14-15, 2018 aimed at two preeminent goals: 1) to identify the fundamental questions related to CIR and 2) to create an interactive community of experts that could guide scientific and research priorities by forming a logical progression supported by multiple perspectives to uncover mechanisms of CIR. This workshop was a first step toward a second meeting where the focus would be to address the actionability of some of the questions identified by working groups. In this event, five working groups aimed at defining a path to test hypotheses according to their relevance to human cancer and identifying experimental models closest to human biology, which include: 1) Germline-Genetic, 2) Somatic-Genetic and 3) Genomic-Transcriptional contributions to CIR, 4) Determinant(s) of Immunogenic Cell Death that modulate CIR, and 5) Experimental Models that best represent CIR and its conversion to an immune responsive state. This manuscript summarizes the contributions from each group and should be considered as a first milestone in the path toward a more contemporary understanding of CIR. We appreciate that this effort is far from comprehensive and that other relevant aspects related to CIR such as the microbiome, the individual's recombined T cell and B cell receptors, and the metabolic status of cancer and immune cells were not fully included. These and other important factors will be included in future activities of the taskforce. The taskforce will focus on prioritization and specific actionable approach to answer the identified questions and implementing the collaborations in the follow-up workshop, which will be held in Houston on September 4-5, 2019.

The 2017 Network Tools and Applications in Biology (NETTAB) workshop: aims, topics and outcomes

The 17th International NETTAB workshop was held in Palermo, Italy, on October 16-18, 2017. The special topic for the meeting was "Methods, tools and platforms for Personalised Medicine in the Big Data Era", but the traditional topics of the meeting series were also included in the event. About 40 scientific contributions were presented, including four keynote lectures, five guest lectures, and many oral communications and posters. Also, three tutorials were organised before and after the workshop. Full papers from some of the best works presented in Palermo were submitted for this Supplement of BMC Bioinformatics. Here, we provide an overview of meeting aims and scope. We also shortly introduce selected papers that have been accepted for publication in this Supplement, for a complete presentation of the outcomes of the meeting.

Reducing Diagnostic Errors Worldwide Through Diagnostic Management Teams

A major challenge facing most countries is the growing cost of healthcare. Laboratory testing costs constitute approximately 3% of all clinical costs, while waste of funds due to inappropriate admissions to clinical departments is reported to be as high as 15%. A frequently used approach to save money in healthcare is random reduction of laboratory budgets, focusing on decreasing the number of unnecessary laboratory tests. The World Health Assembly has approached this problem by publishing a list of essential in vitro diagnostic tests, to achieve a global rationalization of the problem. A much more thoughtful strategy to reducing healthcare expenditure is to improve the efficiency of the diagnostic process. Decreasing the time to a correct diagnosis provides considerable financial and clinical benefits. Additionally, reducing both overutilization and underutilization of laboratory tests while achieving the correct diagnosis is of great benefit to challenged healthcare budgets. Examining the situation in the United States and Italy, this review presents an opportunity for reducing diagnostic error and increasing the efficiency of diagnostic testing worldwide. One approach taken to achieve major savings in healthcare in the United States, which can be applied in Italy and other countries, is the creation of “diagnostic management teams,” comprising experts in specialty areas of medicine, primarily based in the clinical laboratory, who can advise physicians on the selection of necessary tests and the interpretation of complex test results.

Regulatory Architecture of the Neuronal Cacng2/Tarpγ2 Gene Promoter: Multiple Repressive Domains, a Polymorphic Regulatory Short Tandem Repeat, and Bidirectional Organization with Co-regulated lncRNAs

CACNG2 (TARPγ2, Stargazin) is a multi-functional regulator of excitatory neurotransmission and has been implicated in the pathological processes of several brain diseases. Cacng2 function is dependent upon expression level, but currently, little is known about the molecular mechanisms that control expression of this gene. To address this deficit and investigate disease-related gene variants, we have cloned and characterized the rat Cacng2 promoter and have defined three major features: (i) multiple repressive domains that include an array of RE-1 silencing transcription factor (REST) elements, and a calcium regulatory element-binding factor (CaRF) element, (ii) a (poly-GA) short tandem repeat (STR), and (iii) bidirectional organization with expressed lncRNAs. Functional activity of the promoter was demonstrated in transfected neuronal cell lines (HT22 and PC12), but although selective removal of REST and CaRF domains was shown to enhance promoter-driven transcription, the enhanced Cacng2 promoter constructs were still about fivefold weaker than a comparable rat Synapsin-1 promoter sequence. Direct evidence of REST activity at the Cacng2 promoter was obtained through co-transfection with an established dominant-negative REST (DNR) construct. Investigation of the GA-repeat STR revealed polymorphism across both animal strains and species, and size variation was also observed in absence epilepsy disease model cohorts (Genetic Absence Epilepsy Rats, Strasbourg [GAERS] and non-epileptic control [NEC] rats). These data provide evidence of a genotype (STR)-phenotype correlation that may be unique with respect to proximal gene regulatory sequence in the demonstrated absence of other promoter, or 3' UTR variants in GAERS rats. However, although transcriptional regulatory activity of the STR was demonstrated in further transfection studies, we did not find a GAERS vs. NEC difference, indicating that this specific STR length variation may only be relevant in the context of other (Cacna1h and Kcnk9) gene variants in this disease model. Additional studies revealed further (bidirectional) complexity at the Cacng2 promoter, and we identified novel, co-regulated, antisense rat lncRNAs that are paired with Cacng2 mRNA. These studies have provided novel insights into the organization of a synaptic protein gene promoter, describing multiple repressive and modulatory domains that can mediate diverse regulatory inputs.