Bedside Back to Bench: Building Bridges between Basic and Clinical Genomic Research

Clinical Utility of Protein Language Models in Resolution of Variants of Uncertain Significance in KCNQ1, KCNH2, and SCN5A Compared With Patch-Clamp Functional Characterization

BACKGROUND

Genetic testing for cardiac channelopathies is the standard of care. However, many rare genetic variants remain classified as variants of uncertain significance (VUS) due to lack of epidemiological and functional data. Whether deep protein language models may aid in VUS resolution remains unknown. Here, we set out to compare how 2 deep protein language models perform at VUS resolution in the 3 most common long-QT syndrome-causative genes compared with the gold-standard patch clamp.

METHODS

A total of 72 rare nonsynonymous VUS (9 KCNQ1, 19 KCNH2, and 50 SCN5A) were engineered by site-directed mutagenesis and expressed in either HEK293 cells or TSA201 cells. Whole-cell patch-clamp technique was used to functionally characterize these variants. The protein language models, evolutionary scale modeling, version 1b and AlphaMissense, were used to predict the variant effect of missense variants and compared with patch clamp.

RESULTS

Considering variants in all 3 genes, the evolutionary scale modeling, version 1b model had a receiver operating characteristic curve-area under the curve of 0.75 (P=0.0003). It had a sensitivity of 88% and a specificity of 50%. AlphaMissense performed well compared with patch-clamp with an receiver operating characteristic curve-area under the curve of 0.85 (P<0.0001), sensitivity of 80%, and specificity of 76%.

CONCLUSIONS

Deep protein language models aid in VUS resolution with high sensitivity but lower specificity. Thus, these tools cannot fully replace functional characterization but can aid in reducing the number of variants that may require functional analysis.

MVAR: A Mouse Variation Registry

The Mouse Variation Registry (MVAR) resource is a scalable registry of mouse single nucleotide variants and small indels and variant annotation. The resource accepts data in standard Variant Call Format (VCF) and assesses the uniqueness of the submitted variants via a canonicalization process. Novel variants are assigned a unique, persistent MVAR identifier; variants that are equivalent to an existing variant in the resource are associated with the existing identifier. Annotations for variant type, molecular consequence, impact, and genomic region in the context of specific transcripts and protein sequences are generated using Ensembl's Variant Effect Predictor (VEP) and Jannovar. Access to the data and annotations in MVAR are supported via an Application Programming Interface (API) and web application. Researchers can search the resource by gene symbol, genomic region, variant (expressed in Human Genome Variation Society syntax), refSNP identifiers, or MVAR identifiers. Tabular search results can be filtered by variant annotations (variant type, molecular consequence, impact, variant region) and viewed according to variant distribution across mouse strains. The registry currently comprises more than 99 million canonical single nucleotide variants for 581 strains of mice. MVAR is accessible from https://mvar.jax.org.

Targeted DNA-seq and RNA-seq of Reference Samples with Short-read and Long-read Sequencing

Next-generation sequencing (NGS) has revolutionized genomic research by enabling high-throughput, cost-effective genome and transcriptome sequencing accelerating personalized medicine for complex diseases, including cancer. Whole genome/transcriptome sequencing (WGS/WTS) provides comprehensive insights, while targeted sequencing is more cost-effective and sensitive. In comparison to short-read sequencing, which still dominates the field due to high speed and cost-effectiveness, long-read sequencing can overcome alignment limitations and better discriminate similar sequences from alternative transcripts or repetitive regions. Hybrid sequencing combines the best strengths of different technologies for a more comprehensive view of genomic/transcriptomic variations. Understanding each technology's strengths and limitations is critical for translating cutting-edge technologies into clinical applications. In this study, we sequenced DNA and RNA libraries of reference samples using various targeted DNA and RNA panels and the whole transcriptome on both short-read and long-read platforms. This study design enables a comprehensive analysis of sequencing technologies, targeting protocols, and library preparation methods. Our expanded profiling landscape establishes a reference point for assessing current sequencing technologies, facilitating informed decision-making in genomic research and precision medicine.

G protein-coupled receptor (GPCR) pharmacogenomics

The field of pharmacogenetics, the investigation of the influence of one or more sequence variants on drug response phenotypes, is a special case of pharmacogenomics, a discipline that takes a genome-wide approach. Massively parallel, next generation sequencing (NGS), has allowed pharmacogenetics to be subsumed by pharmacogenomics with respect to the identification of variants associated with responders and non-responders, optimal drug response, and adverse drug reactions. A plethora of rare and common naturally-occurring GPCR variants must be considered in the context of signals from across the genome. Many fundamentals of pharmacogenetics were established for G protein-coupled receptor (GPCR) genes because they are primary targets for a large number of therapeutic drugs. Functional studies, demonstrating likely-pathogenic and pathogenic GPCR variants, have been integral to establishing models used for in silico analysis. Variants in GPCR genes include both coding and non-coding single nucleotide variants and insertion or deletions (indels) that affect cell surface expression (trafficking, dimerization, and desensitization/downregulation), ligand binding and G protein coupling, and variants that result in alternate splicing encoding isoforms/variable expression. As the breadth of data on the GPCR genome increases, we may expect an increase in the use of drug labels that note variants that significantly impact the clinical use of GPCR-targeting agents. We discuss the implications of GPCR pharmacogenomic data derived from the genomes available from individuals who have been well-phenotyped for receptor structure and function and receptor-ligand interactions, and the potential benefits to patients of optimized drug selection. Examples discussed include the renin-angiotensin system in SARS-CoV-2 (COVID-19) infection, the probable role of chemokine receptors in the cytokine storm, and potential protease activating receptor (PAR) interventions. Resources dedicated to GPCRs, including publicly available computational tools, are also discussed.

Cellular and molecular mechanisms of aspartoacylase and its role in Canavan disease

Canavan disease is an autosomal recessive and lethal neurological disorder, characterized by the spongy degeneration of the white matter in the brain. The disease is caused by a deficiency of the cytosolic aspartoacylase (ASPA) enzyme, which catalyzes the hydrolysis of N-acetyl-aspartate (NAA), an abundant brain metabolite, into aspartate and acetate. On the physiological level, the mechanism of pathogenicity remains somewhat obscure, with multiple, not mutually exclusive, suggested hypotheses. At the molecular level, recent studies have shown that most disease linked ASPA gene variants lead to a structural destabilization and subsequent proteasomal degradation of the ASPA protein variants, and accordingly Canavan disease should in general be considered a protein misfolding disorder. Here, we comprehensively summarize the molecular and cell biology of ASPA, with a particular focus on disease-linked gene variants and the pathophysiology of Canavan disease. We highlight the importance of high-throughput technologies and computational prediction tools for making genotype-phenotype predictions as we await the results of ongoing trials with gene therapy for Canavan disease.

Researchers' Perspectives Regarding Ethical Issues of Biobank Research in the Arab Region

Background: The recent expansion of genomic biobank research in the Arab region in the Middle East North Africa has raised complex ethical and regulatory issues. However, there is a lack of studies regarding the views of Arab researchers involved in such research. We aimed to assess the perceptions and attitudes of Arab researchers regarding these issues in biobank research. Methods: We developed a questionnaire to assess the perceptions and attitudes regarding genetic research of researchers from Egypt, Sudan, Morocco, and Jordan. The questionnaire requested demographic data, perceptions, and attitudes regarding the collection, storage, and use of biospecimens and data, the use of broad consent, data security, data sharing, and community engagement. We used multiple linear regressions to identify predictors of perceptions and attitudes. Results: We recruited 383 researchers. Researchers favored equally the use of broad and tiered consent (44.1% and 39.1%, respectively). Most respondents agreed with the importance of confidentiality protections to ensure data security (91.8%). However, lower percentages were seen regarding the importance of community engagement (64.5%), data sharing with national colleagues and international partners (60.9% and 41.1%, respectively), and biospecimen sharing with national colleagues and international partners (59.9% and 36.2%, respectively). Investigators were evenly split on whether the return of individual research results should depend on the availability or not of a medical intervention that can be offered to address the genetic anomaly (47.5% and 46.4%, respectively). Predictors of attitudes toward biospecimen research included serving on Research Ethics Committees, prior research ethics training, and affiliation with nonacademic institutions. Conclusions: We recommend further exploratory research with researchers regarding the importance of community engagement and to address their concerns about data sharing, with researchers within and outside their countries.

Perry Disease: Bench to Bedside Circulation and a Team Approach

With technological applications, especially in genetic testing, new diseases have been discovered and new disease concepts have been proposed in recent years; however, the pathogenesis and treatment of these rare diseases are not as well established as those of common diseases. To demonstrate the importance of rare disease research, in this paper we focus on our research topic, Perry disease (Perry syndrome). Perry disease is a rare autosomal dominant neurodegenerative disorder clinically characterized by parkinsonism, depression/apathy, weight loss, and respiratory symptoms including central hypoventilation and central sleep apnea. The pathological classification of Perry disease falls under TAR DNA-binding protein 43 (TDP-43) proteinopathies. Patients with Perry disease exhibit DCTN1 mutations, which is the causative gene for the disease; they also show relatively uniform pathological and clinical features. This review summarizes recent findings regarding Perry disease from both basic and clinical perspectives. In addition, we describe technological innovations and outline future challenges and treatment prospects. We discuss the expansion of research from rare diseases to common diseases and the importance of collaboration between clinicians and researchers. Here, we highlight the importance of researching rare diseases as it contributes to a deeper understanding of more common diseases, thereby opening up new avenues for scientific exploration.

Improving Clinical Trials of Antioxidants in Alzheimer's Disease

Maintaining diversity in drug development in research into Alzheimer's disease (AD) is necessary to avoid over-reliance on targeting AD neuropathology. Treatments that reduce or prevent the generation of oxidative stress, frequently cited for its causal role in the aging process and AD, could be useful in at-risk populations or diagnosed AD patients. However, in this review, it is argued that clinical research into antioxidants in AD could provide more useful feedback as to the therapeutic value of the oxidative stress theory of AD. Improving comparability between randomized controlled trials (RCTs) is vital from a waste-reduction and priority-setting point of view for AD clinical research. For as well as attempting to improve meaningful outcomes for patients, RCTs of antioxidants in AD should strive to maximize the extraction of clinically useful information and actionable feedback from trial outcomes. Solutions to maximize information flow from RCTs of antioxidants in AD are offered here in the form of checklist questions to improve ongoing and future trials centered around the following dimensions: adhesion to reporting guidelines like CONSORT, biomarker enrichment, simple tests of treatment, and innovative trial design.

Lynch syndrome, molecular mechanisms and variant classification

Patients with the heritable cancer disease, Lynch syndrome, carry germline variants in the MLH1, MSH2, MSH6 and PMS2 genes, encoding the central components of the DNA mismatch repair system. Loss-of-function variants disrupt the DNA mismatch repair system and give rise to a detrimental increase in the cellular mutational burden and cancer development. The treatment prospects for Lynch syndrome rely heavily on early diagnosis; however, accurate diagnosis is inextricably linked to correct clinical interpretation of individual variants. Protein variant classification traditionally relies on cumulative information from occurrence in patients, as well as experimental testing of the individual variants. The complexity of variant classification is due to (1) that variants of unknown significance are rare in the population and phenotypic information on the specific variants is missing, and (2) that individual variant testing is challenging, costly and slow. Here, we summarise recent developments in high-throughput technologies and computational prediction tools for the assessment of variants of unknown significance in Lynch syndrome. These approaches may vastly increase the number of interpretable variants and could also provide important mechanistic insights into the disease. These insights may in turn pave the road towards developing personalised treatment approaches for Lynch syndrome.

Phenotypic Modulation of Cancer-Associated Antioxidant NQO1 Activity by Post-Translational Modifications and the Natural Diversity of the Human Genome

Human NAD(P)H:quinone oxidoreductase 1 (hNQO1) is a multifunctional and antioxidant stress protein whose expression is controlled by the Nrf2 signaling pathway. hNQO1 dysregulation is associated with cancer and neurological disorders. Recent works have shown that its activity is also modulated by different post-translational modifications (PTMs), such as phosphorylation, acetylation and ubiquitination, and these may synergize with naturally-occurring and inactivating polymorphisms and mutations. Herein, I describe recent advances in the study of the effect of PTMs and genetic variations on the structure and function of hNQO1 and their relationship with disease development in different genetic backgrounds, as well as the physiological roles of these modifications. I pay particular attention to the long-range allosteric effects exerted by PTMs and natural variation on the multiple functions of hNQO1.

Molecular Mechanisms, Genotype-Phenotype Correlations and Patient-Specific Treatments in Inherited Metabolic Diseases

Advances in DNA sequencing technologies are revealing a vast genetic heterogeneity in human population, which may predispose to metabolic alterations if the activity of metabolic enzymes is affected [...].

An atlas of amyloid aggregation: the impact of substitutions, insertions, deletions and truncations on amyloid beta fibril nucleation

Multiplexed assays of variant effects (MAVEs) guide clinical variant interpretation and reveal disease mechanisms. To date, MAVEs have focussed on a single mutation type-amino acid (AA) substitutions-despite the diversity of coding variants that cause disease. Here we use Deep Indel Mutagenesis (DIM) to generate a comprehensive atlas of diverse variant effects for a disease protein, the amyloid beta (Aβ) peptide that aggregates in Alzheimer's disease (AD) and is mutated in familial AD (fAD). The atlas identifies known fAD mutations and reveals that many variants beyond substitutions accelerate Aβ aggregation and are likely to be pathogenic. Truncations, substitutions, insertions, single- and internal multi-AA deletions differ in their propensity to enhance or impair aggregation, but likely pathogenic variants from all classes are highly enriched in the polar N-terminal region of Aβ. This comparative atlas highlights the importance of including diverse mutation types in MAVEs and provides important mechanistic insights into amyloid nucleation.

Hemochromatosis classification: update and recommendations by the BIOIRON Society

Hemochromatosis (HC) is a genetically heterogeneous disorder in which uncontrolled intestinal iron absorption may lead to progressive iron overload (IO) responsible for disabling and life-threatening complications such as arthritis, diabetes, heart failure, hepatic cirrhosis, and hepatocellular carcinoma. The recent advances in the knowledge of pathophysiology and molecular basis of iron metabolism have highlighted that HC is caused by mutations in at least 5 genes, resulting in insufficient hepcidin production or, rarely, resistance to hepcidin action. This has led to an HC classification based on different molecular subtypes, mainly reflecting successive gene discovery. This scheme was difficult to adopt in clinical practice and therefore needs revision. Here we present recommendations for unambiguous HC classification developed by a working group of the International Society for the Study of Iron in Biology and Medicine (BIOIRON Society), including both clinicians and basic scientists during a meeting in Heidelberg, Germany. We propose to deemphasize the use of the molecular subtype criteria in favor of a classification addressing both clinical issues and molecular complexity. Ferroportin disease (former type 4a) has been excluded because of its distinct phenotype. The novel classification aims to be of practical help whenever a detailed molecular characterization of HC is not readily available.

Functional Studies of Genetic Variants Associated with Human Diseases in Notch Signaling-Related Genes Using Drosophila

Rare variants in the many genes related to Notch signaling cause diverse Mendelian diseases that affect myriad organ systems. In addition, genome- and exome-wide association studies have linked common and rare variants in Notch-related genes to common diseases and phenotypic traits. Moreover, somatic mutations in these genes have been observed in many types of cancer, some of which are classified as oncogenic and others as tumor suppressive. While functional characterization of some of these variants has been performed through experimental studies, the number of "variants of unknown significance" identified in patients with diverse conditions keeps increasing as high-throughput sequencing technologies become more commonly used in the clinic. Furthermore, as disease gene discovery efforts identify rare variants in human genes that have yet to be linked to a disease, the demand for functional characterization of variants in these "genes of unknown significance" continues to increase. In this chapter, we describe a workflow to functionally characterize a rare variant in a Notch signaling related gene that was found to be associated with late-onset Alzheimer's disease. This pipeline involves informatic analysis of the variant of interest using diverse human and model organism databases, followed by in vivo experiments in the fruit fly Drosophila melanogaster. The protocol described here can be used to study variants that affect amino acids that are not conserved between human and fly. By "humanizing" the almondex gene in Drosophila with mutant alleles and heterologous genomic rescue constructs, a missense variant in TM2D3 (TM2 Domain Containing 3) was shown to be functionally damaging. This, and similar approaches, greatly facilitate functional interpretations of genetic variants in the human genome and propel personalized medicine.

TiMEG: an integrative statistical method for partially missing multi-omics data

Multi-omics data integration is widely used to understand the genetic architecture of disease. In multi-omics association analysis, data collected on multiple omics for the same set of individuals are immensely important for biomarker identification. But when the sample size of such data is limited, the presence of partially missing individual-level observations poses a major challenge in data integration. More often, genotype data are available for all individuals under study but gene expression and/or methylation information are missing for different subsets of those individuals. Here, we develop a statistical model TiMEG, for the identification of disease-associated biomarkers in a case-control paradigm by integrating the above-mentioned data types, especially, in presence of missing omics data. Based on a likelihood approach, TiMEG exploits the inter-relationship among multiple omics data to capture weaker signals, that remain unidentified in single-omic analysis or common imputation-based methods. Its application on a real tuberous sclerosis dataset identified functionally relevant genes in the disease pathway.

Characterization of the Testis-specific LINC01016 Gene Reveals Isoform-specific Roles in Controlling Biological Processes

Long noncoding RNAs (lncRNAs) have emerged as critical regulators of biological processes. However, the aberrant expression of an isoform from the same lncRNA gene could lead to RNA with altered functions due to changes in their conformations, leading to diseases. Here, we describe a detailed characterization of the gene that encodes long intergenic non-protein-coding RNA 01016 (LINC01016, also known as LncRNA1195) with a focus on its structure, exon usage, and expression in human and macaque tissues. In this study we show that it is among the highly expressed lncRNAs in the testis, exclusively conserved among nonhuman primates, suggesting its recent evolution and is processed into 12 distinct RNAs in testis, cervix, and uterus tissues. Further, we integrate de novo annotation of expressed LINC01016 transcripts and isoform-dependent gene expression analyses to show that human LINC01016 is a multiexon gene, processed through differential exon usage with isoform-specific roles. Furthermore, in cervical, testicular, and uterine cancers, LINC01016 isoforms are differentially expressed, and their expression is predictive of survival in these cancers. This study has revealed an essential aspect of lncRNA biology, rarely associated with coding RNAs, that lncRNA genes are precisely processed to generate isoforms with distinct biological roles in specific tissues.

Pan-cancer proteogenomic investigations identify post-transcriptional kinase targets

Identifying genomic alterations of cancer proteins has guided the development of targeted therapies, but proteomic analyses are required to validate and reveal new treatment opportunities. Herein, we develop a new algorithm, OPPTI, to discover overexpressed kinase proteins across 10 cancer types using global mass spectrometry proteomics data of 1,071 cases. OPPTI outperforms existing methods by leveraging multiple co-expressed markers to identify targets overexpressed in a subset of tumors. OPPTI-identified overexpression of ERBB2 and EGFR proteins correlates with genomic amplifications, while CDK4/6, PDK1, and MET protein overexpression frequently occur without corresponding DNA- and RNA-level alterations. Analyzing CRISPR screen data, we confirm expression-driven dependencies of multiple currently-druggable and new target kinases whose expressions are validated by immunochemistry. Identified kinases are further associated with up-regulated phosphorylation levels of corresponding signaling pathways. Collectively, our results reveal protein-level aberrations-sometimes not observed by genomics-represent cancer vulnerabilities that may be targeted in precision oncology.

Modeling transcriptional regulation of model species with deep learning

To enable large-scale analyses of transcription regulation in model species, we developed DeepArk, a set of deep learning models of the cis-regulatory activities for four widely studied species: Caenorhabditis elegans, Danio rerio, Drosophila melanogaster, and Mus musculus DeepArk accurately predicts the presence of thousands of different context-specific regulatory features, including chromatin states, histone marks, and transcription factors. In vivo studies show that DeepArk can predict the regulatory impact of any genomic variant (including rare or not previously observed) and enables the regulatory annotation of understudied model species.

MAPS: machine-assisted phenotype scoring enables rapid functional assessment of genetic variants by high-content microscopy

BACKGROUND

Genetic testing is widely used in evaluating a patient's predisposition to hereditary diseases. In the case of cancer, when a functionally impactful mutation (i.e. genetic variant) is identified in a disease-relevant gene, the patient is at elevated risk of developing a lesion in their lifetime. Unfortunately, as the rate and coverage of genetic testing has accelerated, our ability to assess the functional status of new variants has fallen behind. Therefore, there is an urgent need for more practical, streamlined and cost-effective methods for classifying variants.

RESULTS

To directly address this issue, we designed a new approach that uses alterations in protein subcellular localization as a key indicator of loss of function. Thus, new variants can be rapidly functionalized using high-content microscopy (HCM). To facilitate the analysis of the large amounts of imaging data, we developed a new software toolkit, named MAPS for machine-assisted phenotype scoring, that utilizes deep learning to extract and classify cell-level features. MAPS helps users leverage cloud-based deep learning services that are easy to train and deploy to fit their specific experimental conditions. Model training is code-free and can be done with limited training images. Thus, MAPS allows cell biologists to easily incorporate deep learning into their image analysis pipeline. We demonstrated an effective variant functionalization workflow that integrates HCM and MAPS to assess missense variants of PTEN, a tumor suppressor that is frequently mutated in hereditary and somatic cancers.

CONCLUSIONS

This paper presents a new way to rapidly assess variant function using cloud deep learning. Since most tumor suppressors have well-defined subcellular localizations, our approach could be widely applied to functionalize variants of uncertain significance and help improve the utility of genetic testing.

Preclinical models of pancreatic ductal adenocarcinoma: challenges and opportunities in the era of precision medicine

Pancreatic ductal adenocarcinoma (PDAC) is an extremely lethal malignancy, with an average 5-year survival rate of 9% (Siegel RL, Miller KD, Jemal A. Ca Cancer J Clin. 2019;69(1):7-34). The steady increase in mortality rate indicates limited efficacy of the conventional regimen. The heterogeneity of PDAC calls for personalized treatment in clinical practice, which requires the construction of a preclinical system for generating patient-derived models. Currently, the lack of high-quality preclinical models results in ineffective translation of novel targeted therapeutics. This review summarizes applications of commonly used models, discusses major difficulties in PDAC model construction and provides recommendations for integrating workflows for precision medicine.

Naturally-Occurring Rare Mutations Cause Mild to Catastrophic Effects in the Multifunctional and Cancer-Associated NQO1 Protein

The functional and pathological implications of the enormous genetic diversity of the human genome are mostly unknown, primarily due to our unability to predict pathogenicity in a high-throughput manner. In this work, we characterized the phenotypic consequences of eight naturally-occurring missense variants on the multifunctional and disease-associated NQO1 protein using biophysical and structural analyses on several protein traits. Mutations found in both exome-sequencing initiatives and in cancer cell lines cause mild to catastrophic effects on NQO1 stability and function. Importantly, some mutations perturb functional features located structurally far from the mutated site. These effects are well rationalized by considering the nature of the mutation, its location in protein structure and the local stability of its environment. Using a set of 22 experimentally characterized mutations in NQO1, we generated experimental scores for pathogenicity that correlate reasonably well with bioinformatic scores derived from a set of commonly used algorithms, although the latter fail to semiquantitatively predict the phenotypic alterations caused by a significant fraction of mutations individually. These results provide insight into the propagation of mutational effects on multifunctional proteins, the implementation of in silico approaches for establishing genotype-phenotype correlations and the molecular determinants underlying loss-of-function in genetic diseases.

Strategic vision for improving human health at The Forefront of Genomics

Starting with the launch of the Human Genome Project three decades ago, and continuing after its completion in 2003, genomics has progressively come to have a central and catalytic role in basic and translational research. In addition, studies increasingly demonstrate how genomic information can be effectively used in clinical care. In the future, the anticipated advances in technology development, biological insights, and clinical applications (among others) will lead to more widespread integration of genomics into almost all areas of biomedical research, the adoption of genomics into mainstream medical and public-health practices, and an increasing relevance of genomics for everyday life. On behalf of the research community, the National Human Genome Research Institute recently completed a multi-year process of strategic engagement to identify future research priorities and opportunities in human genomics, with an emphasis on health applications. Here we describe the highest-priority elements envisioned for the cutting-edge of human genomics going forward-that is, at 'The Forefront of Genomics'.

Towards Accurate Genotype-Phenotype Correlations in the CYP2D6 Gene

Establishing accurate and large-scale genotype-phenotype correlations and predictions of individual response to pharmacological treatments are two of the holy grails of Personalized Medicine. These tasks are challenging and require an integrated knowledge of the complex processes that regulate gene expression and, ultimately, protein functionality in vivo, the effects of mutations/polymorphisms and the different sources of interindividual phenotypic variability. A remarkable example of our advances in these challenging tasks is the highly polymorphic CYP2D6 gene, which encodes a cytochrome P450 enzyme involved in the metabolization of many of the most marketed drugs (including SARS-Cov-2 therapies such as hydroxychloroquine). Since the introduction of simple activity scores (AS) over 10 years ago, its ability to establish genotype-phenotype correlations on the drug metabolizing capacity of this enzyme in human population has provided lessons that will help to improve this type of score for this, and likely many other human genes and proteins. Multidisciplinary research emerges as the best approach to incorporate additional concepts to refine and improve such functional/activity scores for the CYP2D6 gene, as well as for many other human genes associated with simple and complex genetic diseases.

ZMAT2 in Humans and Other Primates: A Highly Conserved and Understudied Gene

Recent advances in genetics present unique opportunities for enhancing our understanding of human physiology and disease predisposition through detailed analysis of gene structure, expression, and population variation via examination of data in publicly accessible genome and gene expression repositories. Yet, the vast majority of human genes remain understudied. Here, we show the scope of these genomic and genetic resources by evaluating ZMAT2, a member of a 5-gene family that through May 2020 had been the focus of only 4 peer-reviewed scientific publications. Using analysis of information extracted from public databases, we show that human ZMAT2 is a 6-exon gene and find that it exhibits minimal genetic variation in human populations and in disease states, including cancer. We further demonstrate that the gene and its encoded protein are highly conserved among nonhuman primates and define a cohort of ZMAT2 pseudogenes in the marmoset genome. Collectively, our investigations illustrate how complementary use of genomic, gene expression, and population genetic resources can lead to new insights about human and mammalian biology and evolution, and when coupled with data supporting key roles for ZMAT2 in keratinocyte differentiation and pre-RNA splicing argue that this gene is worthy of further study.

Immune Response Resetting as a Novel Strategy to Overcome SARS-CoV-2-Induced Cytokine Storm

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19), which rapidly became a pandemic of global proportions. Sepsis is commonly present with high lethality in the severe forms of the disease. The virus-induced cytokine storm puts the immune system in overdrive at the expense of the pathogen-specific immune response and is likely to underlie the most advanced COVID-19 clinical features, including sepsis-related multiple organ dysfunction as well as the pathophysiological changes found in the lungs. We review the major therapeutic strategies that have been considered for sepsis and might be amenable to repurposing for COVID-19. We also discuss two different immunization strategies that have the potential to confer antiviral heterologous protection: innate-induced trained immunity and adaptive-induced immune response resetting.

Definition and diagnosis of cerebral palsy in genetic studies: a systematic review

AIM

To conduct a systematic review of phenotypic definition and case ascertainment in published genetic studies of cerebral palsy (CP) to inform guidelines for the reporting of such studies.

METHOD

Inclusion criteria comprised genetic studies of candidate genes, with CP as the outcome, published between 1990 and 2019 in the PubMed, Embase, and BIOSIS Citation Index databases.

RESULTS

Fifty-seven studies met the inclusion criteria. We appraised how CP was defined, the quality of information on case ascertainment, and compliance with international consensus guidelines. Seven studies (12%) were poorly described, 33 studies (58%) gave incomplete information, and 17 studies (30%) were well described. Missing key information precluded determining how many studies complied with the definition by Rosenbaum et al. Only 18 out of 57 studies (32%) were compliant with the Surveillance of Cerebral Palsy in Europe (SCPE) international guidelines on defining CP.

INTERPRETATION

Limited compliance with international consensus guidelines on phenotypic definition and mediocre reporting of CP case ascertainment hinders the comparison of results among genetic studies of CP (including meta-analyses), thereby limiting the quality, interpretability, and generalizability of study findings. Compliance with the SCPE guidelines is important for ongoing gene discovery efforts in CP, given the potential for misclassification of unrelated neurological conditions as CP.

Human CRY1 variants associate with attention deficit/hyperactivity disorder

Attention deficit/hyperactivity disorder (ADHD) is a common and heritable phenotype frequently accompanied by insomnia, anxiety, and depression. Here, using a reverse phenotyping approach, we report heterozygous coding variations in the core circadian clock gene cryptochrome 1 in 15 unrelated multigenerational families with combined ADHD and insomnia. The variants led to functional alterations in the circadian molecular rhythms, providing a mechanistic link to the behavioral symptoms. One variant, CRY1Δ11 c.1657+3A>C, is present in approximately 1% of Europeans, therefore standing out as a diagnostic and therapeutic marker. We showed by exome sequencing in an independent cohort of patients with combined ADHD and insomnia that 8 of 62 patients and 0 of 369 controls carried CRY1Δ11. Also, we identified a variant, CRY1Δ6 c.825+1G>A, that shows reduced affinity for BMAL1/CLOCK and causes an arrhythmic phenotype. Genotype-phenotype correlation analysis revealed that this variant segregated with ADHD and delayed sleep phase disorder (DSPD) in the affected family. Finally, we found in a phenome-wide association study involving 9438 unrelated adult Europeans that CRY1Δ11 was associated with major depressive disorder, insomnia, and anxiety. These results defined a distinctive group of circadian psychiatric phenotypes that we propose to designate as "circiatric" disorders.

Childhood rare lung disease in the 21st century: "-omics" technology advances accelerating discovery

Childhood rare lung diseases comprise a large number of heterogeneous respiratory disorders that are individually rare but are collectively associated with substantial morbidity, mortality, and healthcare resource utilization. Although the genetic mechanisms for several of these disorders have been elucidated, the pathogenesis mechanisms for others remain poorly understood and treatment options remain limited. Childhood rare lung diseases are enriched for genetic etiologies; identification of the disease mechanisms underlying these rare disorders can inform the biology of normal human lung development and has implications for the treatment of more common respiratory diseases in children and adults. Advances in "-omics" technology, such as genomic sequencing, clinical phenotyping, biomarker discovery, genome editing, in vitro and model organism disease modeling, single-cell analyses, cellular imaging, and high-throughput drug screening have enabled significant progress for diagnosis and treatment of rare childhood lung diseases. The most striking example of this progress has been realized for patients with cystic fibrosis for whom effective, personalized therapies based on CFTR genotype are now available. In this chapter, we focus on recent technology advances in childhood rare lung diseases, acknowledge persistent challenges, and identify promising new technologies that will impact not only biological discovery, but also improve diagnosis, therapies, and survival for children with these rare disorders.

Structure and expression of the long noncoding RNA gene MIR503 in humans and non-human primates

Recent technical and other advances in genomics provide unique opportunities to improve our understanding of human physiology and disease predisposition through a detailed analysis of gene structure and expression by examining data in public genome and gene-expression repositories. Yet, the vast majority of human genes remain understudied. This is particularly true of genes for long noncoding RNAs (lncRNAs). Here, we describe the detailed characterization of MIR503HG, a lncRNA gene found on the X chromosome in humans. Using information extracted from public databases, we show that human MIR503HG is a 5-exon gene, and that it is highly conserved among 5 non-human primates spanning over 85 million years ago of evolutionary diversification. MIR503HG is transcribed and processed into multiple distinct RNAs in each of these species through differential exon use and alternative RNA splicing, with a higher abundance of transcripts being found in reproductive tissues, especially during the early stages of ovary and testis development, indicating a possible role in reproductive biology. Furthermore, in select reproductive system cancers, MIR503HG transcripts are downregulated, with higher levels of RNA expression being associated with clinical outcomes. Collectively, these investigations show how the use of genomic, gene expression, and other genetic resources can lead to new insights about human biology and disease, and argue that MIR503HG is worthy of additional study.

A Bayesian method to estimate variant-induced disease penetrance

A major challenge emerging in genomic medicine is how to assess best disease risk from rare or novel variants found in disease-related genes. The expanding volume of data generated by very large phenotyping efforts coupled to DNA sequence data presents an opportunity to reinterpret genetic liability of disease risk. Here we propose a framework to estimate the probability of disease given the presence of a genetic variant conditioned on features of that variant. We refer to this as the penetrance, the fraction of all variant heterozygotes that will present with disease. We demonstrate this methodology using a well-established disease-gene pair, the cardiac sodium channel gene SCN5A and the heart arrhythmia Brugada syndrome. From a review of 756 publications, we developed a pattern mixture algorithm, based on a Bayesian Beta-Binomial model, to generate SCN5A penetrance probabilities for the Brugada syndrome conditioned on variant-specific attributes. These probabilities are determined from variant-specific features (e.g. function, structural context, and sequence conservation) and from observations of affected and unaffected heterozygotes. Variant functional perturbation and structural context prove most predictive of Brugada syndrome penetrance.

Precision medicine - networks to the rescue

Genetic variants are often not predictive of the phenotypic outcome. Individuals carrying the same pathogenic variant, associated with Mendelian or complex disease, can manifest to different extents, from severe-to-mild to no disease. Improving the accuracy of predicted clinical manifestations of genetic variants has emerged as one of the biggest challenges in precision medicine, which can only be addressed by understanding the mechanisms underlying genotype-phenotype relationships. Efforts to understand the molecular basis of these relationships have identified complex systems of interacting biomolecules that underlie cellular function. Here, we review recent advances in how modeling cellular systems as networks of interacting proteins has fueled identification of disease-associated processes, delineation of underlying molecular mechanisms, and prediction of the pathogenicity of variants. This review is intended to be inspiring for clinicians, geneticists, and network biologists alike who aim to jointly advance our understanding of human disease and accelerate progress toward precision medicine.

Variations of type IV collagen-encoding genes in patients with histological diagnosis of focal segmental glomerulosclerosis

Focal segmental glomerulosclerosis (FSGS), an important cause of end-stage kidney disease (ESKD), covers a spectrum of clinicopathological syndromes sharing a common glomerular lesion, based on an injury of podocytes caused by diverse insults to glomeruli. Although it is well expressed in many reports that the term FSGS is not useful and applicable to a single disease, particularly in genetic studies, FSGS continues to be used as a single clinical diagnosis. Distinguishing genetic forms of FSGS is important for the treatment and overall prognosis because secondary forms of FSGS, produced by rare pathogenic variations in podocyte genes, are not good candidates for immunosuppressive treatment. Over the past decade, several next generation sequencing (NGS) methods have been used to investigate the patients with steroid resistance nephrotic syndrome (SRNS) or FSGS. Pathogenic variants in COL4A3, COL4A4, or COL4A5 genes have been frequently identified in patients with histologic diagnosis of FSGS. The contribution of these mostly heterozygous genetic variations in FSGS pathogenesis and the clinical course of patients with these variations have not been well characterized. This review emphasizes the importance of appropriate approach in selection and diagnosis of cases and interpretation of the genetic data in these studies and suggests a detailed review of existing clinical variant databases using newly available population genetic data.

The Zmat2 gene in non-mammalian vertebrates: Organizational simplicity within a divergent locus in fish

ZMAT2 is among the least-studied of mammalian proteins and genes, even though it is the ortholog of Snu23, a protein involved in pre-mRNA splicing in yeast. Here we have used data from genomic and gene expression repositories to examine the Zmat2 gene and locus in 8 terrestrial vertebrates, 10 ray-finned fish, and 1 lobe-finned fish representing > 500 million years of evolutionary diversification. The analyses revealed that vertebrate Zmat2 genes are similar to their mammalian counterparts, as in 16/19 species studied they contain 6 exons, and in 18/19 encode a single conserved protein. However, unlike in mammals, no Zmat2 pseudogenes were identified in these vertebrates, although an expressed Zmat2 paralog was characterized in flycatcher that resembled a DNA copy of a processed and retro-transposed mRNA, and thus could be a proto-pseudogene captured during its evolutionary journey from active to inert. The Zmat2 locus in terrestrial vertebrates, and in spotted gar and coelacanth, also shares additional genes with its mammalian counterparts, including Histidyl-tRNA synthetase (Hars), Hars2, and others, but these are absent from the Zmat2 locus in teleost fish, in which Stem-loop-binding protein 2 (Slbp2) and Lymphocyte cytosolic protein 2a (Lcp2a) are present instead. Taken together, these observations argue that a recognizable Zmat2 was present in the earliest vertebrate ancestors, and postulate that during chromosomal tetraploidization and subsequent re-diploidization during modern teleost evolution, the duplicated Zmat2 gene was retained and the original lost. This study also highlights how information from genomic resources can be leveraged to reveal new biologically significant insights.

TraPS-VarI: Identifying genetic variants altering phosphotyrosine based signalling motifs

Patient stratification and individualized therapeutic strategies rely on the established knowledge of genotype-specific molecular and cellular alterations of biological and therapeutic significance. Whilst almost all approved drugs have been developed based on the Reference Sequence protein database (RefSeq), the latest genome sequencing studies establish the substantial prevalence of non-synonymous genetic mutations in the general population, including stop-insertion and frame shift mutations within the coding regions of membrane proteins. While the availability of individual genotypes are becoming increasingly common, the biological and clinical interpretations of mutations among individual genomes is largely lagging behind. Lately, transmembrane proteins of haematopoietic (myeloid and lymphoid) derived immune cells have attracted much attention as important targets for cancer immunotherapies. As such, the signalling properties of haematological transmembrane receptors rely on the membrane-proximal phosphotyrosine based sequence motifs (TBSMs) such as ITAM (immunoreceptor tyrosine-based activation motif), ITIM (immunoreceptor tyrosine-based inhibition motif) and signal transducer and activator of transcription 3 (STAT3)-recruiting YxxQ motifs. However, mutations that alter the coding regions of transmembrane proteins, resulting in either insertion or deletion of crucial signal modulating TBSMs, remains unknown. To conveniently identify individual cell line-specific or patient-specific membrane protein altering mutations, we present the Transmembrane Protein Sequence Variant Identifier (TraPS-VarI). TraPS-VarI is an annotation tool for accurate mapping of the effect of an individual's mutation in the transmembrane protein sequence, and to identify the prevalence of TBSMs. TraPS-VarI is a biologist and clinician-friendly algorithm with a web interface and an associated database browser (https://www.traps-vari.org/).

Regulation of gene expression by growth hormone

Growth hormone (GH) plays a pivotal role in many physiological processes in humans, and in other mammalian and non-mammalian vertebrate species, through actions on somatic growth, tissue development and repair, and intermediary metabolism. This review will focus on mechanisms of GH actions on gene expression, primarily from the perspective of the genes that encode proteins stimulated by GH to regulate somatic growth, especially insulin-like growth factor 1 (IGF-I), but also others that are induced or repressed by GH. Topics to be discussed will include a brief overview of GH-mediated signal transduction pathways and how these cascades alter the functions of responsive transcription factors, with a specific focus on STAT5B, a key member of the signal transducers and activators of transcription family, characterization of essential GH-regulated genes, and elucidation of mechanisms of their regulation from biochemical, genetic, and genomic perspectives.

Variant effect predictions capture some aspects of deep mutational scanning experiments

BACKGROUND

Deep mutational scanning (DMS) studies exploit the mutational landscape of sequence variation by systematically and comprehensively assaying the effect of single amino acid variants (SAVs; also referred to as missense mutations, or non-synonymous Single Nucleotide Variants - missense SNVs or nsSNVs) for particular proteins. We assembled SAV annotations from 22 different DMS experiments and normalized the effect scores to evaluate variant effect prediction methods. Three trained on traditional variant effect data (PolyPhen-2, SIFT, SNAP2), a regression method optimized on DMS data (Envision), and a naïve prediction using conservation information from homologs.

RESULTS

On a set of 32,981 SAVs, all methods captured some aspects of the experimental effect scores, albeit not the same. Traditional methods such as SNAP2 correlated slightly more with measurements and better classified binary states (effect or neutral). Envision appeared to better estimate the precise degree of effect. Most surprising was that the simple naïve conservation approach using PSI-BLAST in many cases outperformed other methods. All methods captured beneficial effects (gain-of-function) significantly worse than deleterious (loss-of-function). For the few proteins with multiple independent experimental measurements, experiments differed substantially, but agreed more with each other than with predictions.

CONCLUSIONS

DMS provides a new powerful experimental means of understanding the dynamics of the protein sequence space. As always, promising new beginnings have to overcome challenges. While our results demonstrated that DMS will be crucial to improve variant effect prediction methods, data diversity hindered simplification and generalization.

Variants of uncertain significance in the era of high-throughput genome sequencing: a lesson from breast and ovary cancers

The promising expectations about personalized medicine have opened the path to routine large-scale sequencing and increased the importance of genetic counseling for hereditary cancers, among which hereditary breast and ovary cancers (HBOC) have a major impact. High-throughput sequencing, or Next-Generation Sequencing (NGS), has improved cancer patient management, ameliorating diagnosis and treatment decisions. In addition to its undeniable clinical utility, NGS is also unveiling a large number of variants that we are still not able to clearly define and classify, the variants of uncertain significance (VUS), which account for about 40% of total variants. At present, VUS use in the clinical context is challenging. Medical reports may omit this kind of data and, even when included, they limit the clinical utility of genetic information. This has prompted the scientific community to seek easily applicable tests to accurately classify VUS and increase the amount of usable information from NGS data. In this review, we will focus on NGS and classification systems for VUS investigation, with particular attention on HBOC-related genes and in vitro functional tests developed for ameliorating and accelerating variant classification in cancer.

Zmat2 in mammals: conservation and diversification among genes and Pseudogenes

BACKGROUND

Recent advances in genetics and genomics present unique opportunities for enhancing our understanding of mammalian biology and evolution through detailed multi-species comparative analysis of gene organization and expression. Yet, of the more than 20,000 protein coding genes found in mammalian genomes, fewer than 10% have been examined in any detail. Here we elucidate the power of data available in publicly-accessible genomic and genetic resources by querying them to evaluate Zmat2, a minimally studied gene whose human ortholog has been implicated in spliceosome function and in keratinocyte differentiation.

RESULTS

We find extensive conservation in coding regions and overall structure of Zmat2 in 18 mammals representing 13 orders and spanning ~ 165 million years of evolutionary development, and in their encoded proteins. We identify a tandem duplication in the Zmat2 gene and locus in opossum, but not in other monotremes, marsupials, or other mammals, indicating that this event occurred subsequent to the divergence of these species from one another. We also define a collection of Zmat2 pseudogenes in half of the mammals studied, and suggest based on phylogenetic analysis that they each arose independently in the recent evolutionary past.

CONCLUSIONS

Mammalian Zmat2 genes and ZMAT2 proteins illustrate conservation of structure and sequence, along with the development and diversification of pseudogenes in a large fraction of species. Collectively, these observations also illustrate how the focused identification and interpretation of data found in public genomic and gene expression resources can be leveraged to reveal new insights of potentially high biological significance.

Bridging Genomics to Phenomics at Atomic Resolution through Variation Spatial Profiling

To understand the impact of genome sequence variation (the genotype) responsible for biological diversity and human health (the phenotype) including cystic fibrosis and Alzheimer's disease, we developed a Gaussian-process-based machine learning (ML) approach, variation spatial profiling (VSP). VSP uses a sparse collection of known variants found in the population that perturb the protein fold to define unknown variant function based on the emergent general principle of spatial covariance (SCV). SCV quantitatively captures the role of proximity in genotype-to-phenotype spatial-temporal relationships. Phenotype landscapes generated through SCV provide a platform that can be used to describe the functional properties that drive sequence-to-function-to-structure design of the polypeptide fold at atomic resolution. We provide proof of principle that SCV can enable the use of population-based genomic platforms to define the origins and mechanism of action of genotype-to-phenotype transformations contributing to the health and disease of an individual.

Computational and cellular studies reveal structural destabilization and degradation of MLH1 variants in Lynch syndrome

Defective mismatch repair leads to increased mutation rates, and germline loss-of-function variants in the repair component MLH1 cause the hereditary cancer predisposition disorder known as Lynch syndrome. Early diagnosis is important, but complicated by many variants being of unknown significance. Here we show that a majority of the disease-linked MLH1 variants we studied are present at reduced cellular levels. We show that destabilized MLH1 variants are targeted for chaperone-assisted proteasomal degradation, resulting also in degradation of co-factors PMS1 and PMS2. In silico saturation mutagenesis and computational predictions of thermodynamic stability of MLH1 missense variants revealed a correlation between structural destabilization, reduced steady-state levels and loss-of-function. Thus, we suggest that loss of stability and cellular degradation is an important mechanism underlying many MLH1 variants in Lynch syndrome. Combined with analyses of conservation, the thermodynamic stability predictions separate disease-linked from benign MLH1 variants, and therefore hold potential for Lynch syndrome diagnostics.

Clinical genetic testing in endocrinology: Current concepts and contemporary challenges

Recent advances in DNA sequencing technology have led to an unprecedented period of disease-gene discovery offering many new opportunities for genetic testing in the clinical setting. Endocrinology has seen a rapid expansion in the taxonomy of monogenic disorders, which can be detected by an expanding portfolio of genetic tests in both diagnostic and predictive settings. Successful testing relies on many factors including the ability to identify those at increased risk of genetic disease in the busy clinic as well as a working knowledge of the various testing platforms and their limitations. The clinical utility of a given test is dependent upon many factors, which include the reliability of the genetic testing platform, the accuracy of the test result interpretation and knowledge of disease penetrance and expression. The increasing adoption of "high-content" genetic testing based on next-generation sequencing (NGS) to diagnose hereditary endocrine disorders brings a number of challenges including the potential for uncertain test results and/or genetic findings unrelated to the indication for testing. Therefore, it is increasingly important that the clinician is aware of the current evolution in genetic testing, and understands the different settings in which it may be employed. This review provides an overview of the genetic testing workflow, focusing on each of the major components required for successful testing in adult and paediatric endocrine settings. In addition, the challenges of variant interpretation are highlighted, as are issues related to informed consent, prenatal diagnosis and predictive testing. Finally, the future directions of genetic testing relevant to endocrinology are discussed.

Next-Generation Sequencing and the Clinical Oncology Workflow: Data Challenges, Proposed Solutions, and a Call to Action

PURPOSE

Next-generation sequencing (NGS) of tumor and germline DNA is foundational for precision oncology, with rapidly expanding diagnostic, prognostic, and therapeutic implications. Although few question the importance of NGS in modern oncology care, the process of gathering primary molecular data, integrating it into electronic health records, and optimally using it as part of a clinical workflow remains far from seamless. Numerous challenges persist around data standards and interoperability, and clinicians frequently face difficulties in managing the growing amount of genomic knowledge required to care for patients and keep up to date.

METHODS

This review provides a descriptive analysis of genomic data workflows for NGS data in clinical oncology and issues that arise from the inconsistent use of standards for sharing data across systems. Potential solutions are described.

RESULTS

NGS technology, especially for somatic genomics, is well established and widely used in routine patient care, quality measurement, and research. Available genomic knowledge bases play an evolving role in patient management but lack harmonization with one another. Questions about their provenance and timeliness of updating remain. Potentially useful standards for sharing genomic data, such as HL7 FHIR and mCODE, remain primarily in the research and/or development stage. Nonetheless, their impact will likely be seen as uptake increases across care settings and laboratories. The specific use case of ASCO CancerLinQ, as a clinicogenomic database, is discussed.

CONCLUSION

Because the electronic health records of today seem ill suited for managing genomic data, other solutions are required, including universal data standards and applications that use application programming interfaces, along with a commitment on the part of sequencing laboratories to consistently provide structured genomic data for clinical use.

High-Throughput Functional Evaluation of KCNQ1 Decrypts Variants of Unknown Significance

BACKGROUND

The explosive growth in known human gene variation presents enormous challenges to current approaches for variant classification that have implications for diagnosis and treatment of many genetic diseases. For disorders caused by mutations in cardiac ion channels as in congenital arrhythmia syndromes, in vitro electrophysiological evidence has high value in discriminating pathogenic from benign variants, but these data are often lacking because assays are cost, time, and labor intensive.

METHODS

We implemented a strategy for performing high-throughput functional evaluations of ion channel variants that repurposed an automated electrophysiological recording platform developed previously for drug discovery.

RESULTS

We demonstrated the success of this approach by evaluating 78 variants in KCNQ1, a major gene involved in genetic disorders of cardiac arrhythmia susceptibility. We benchmarked our results with traditional electrophysiological approaches and observed a high level of concordance. This strategy also enabled studies of dominant-negative behavior of variants exhibiting severe loss-of-function. Overall, our results provided functional data useful for reclassifying >65% of the studied KCNQ1 variants.

CONCLUSIONS

Our results illustrate an efficient and high-throughput paradigm linking genotype to function for a human cardiac ion channel that will enable data-driven classification of large numbers of variants and create new opportunities for precision medicine.

Using MARRVEL v1.2 for Bioinformatics Analysis of Human Genes and Variant Pathogenicity

One of the greatest challenges in the bioinformatic analysis of human sequencing data is identifying which variants are pathogenic. Numerous databases and tools have been generated to address this difficulty. However, these many useful data and tools are broadly dispersed, requiring users to search for their variants of interest through human genetic databases, variant function prediction tools, and model organism databases. To solve this problem, we collected data and observed workflows of human geneticists, clinicians, and model organism researchers to carefully select and display valuable information that facilitates the evaluation of whether a variant is likely to be pathogenic. This program, Model organism Aggregated Resources for Rare Variant ExpLoration (MARRVEL) v1.2, allows users to collect relevant data from 27 public sources for further efficient bioinformatic analysis of the pathogenicity of human variants. © 2019 by John Wiley & Sons, Inc.

The clinical imperative for inclusivity: Race, ethnicity, and ancestry (REA) in genomics

The Clinical Genome Resource (ClinGen) Ancestry and Diversity Working Group highlights the need to develop guidance on race, ethnicity, and ancestry (REA) data collection and use in clinical genomics. We present quantitative and qualitative evidence to characterize: (1) acquisition of REA data via clinical laboratory requisition forms, and (2) information disparity across populations in the Genome Aggregation Database (gnomAD) at clinically relevant sites ascertained from annotations in ClinVar. Our requisition form analysis showed substantial heterogeneity in clinical laboratory ascertainment of REA, as well as marked incongruity among terms used to define REA categories. There was also striking disparity across REA populations in the amount of information available about clinically relevant variants in gnomAD. European ancestral populations constituted the majority of observations (55.8%), allele counts (59.7%), and private alleles (56.1%) in gnomAD at 550 loci with "pathogenic" and "likely pathogenic" expert-reviewed variants in ClinVar. Our findings highlight the importance of implementing and supporting programs to increase diversity in genome sequencing and clinical genomics, as well as measuring uncertainty around population-level datasets that are used in variant interpretation. Finally, we suggest the need for a standardized REA data collection framework to be developed through partnerships and collaborations and adopted across clinical genomics.

Biophysical and Mechanistic Models for Disease-Causing Protein Variants

The rapid decrease in DNA sequencing cost is revolutionizing medicine and science. In medicine, genome sequencing has revealed millions of missense variants that change protein sequences, yet we only understand the molecular and phenotypic consequences of a small fraction. Within protein science, high-throughput deep mutational scanning experiments enable us to probe thousands of variants in a single, multiplexed experiment. We review efforts that bring together these topics via experimental and computational approaches to determine the consequences of missense variants in proteins. We focus on the role of changes in protein stability as a driver for disease, and how experiments, biophysical models, and computation are providing a framework for understanding and predicting how changes in protein sequence affect cellular protein stability.

The insulin-like growth factor 2 gene in mammals: Organizational complexity within a conserved locus

The secreted protein, insulin-like growth factor 2 (IGF2), plays a central role in fetal and prenatal growth and development, and is regulated at the genetic level by parental imprinting, being expressed predominantly from the paternally derived chromosome in mice and humans. Here, IGF2/Igf2 and its locus has been examined in 19 mammals from 13 orders spanning ~166 million years of evolutionary development. By using human or mouse DNA segments as queries in genome analyses, and by assessing gene expression using RNA-sequencing libraries, more complexity was identified within IGF2/Igf2 than was annotated previously. Multiple potential 5’ non-coding exons were mapped in most mammals and are presumably linked to distinct IGF2/Igf2 promoters, as shown for several species by interrogating RNA-sequencing libraries. DNA similarity was highest in IGF2/Igf2 coding exons; yet, even though the mature IGF2 protein was conserved, versions of 67 or 70 residues are produced secondary to species-specific maintenance of alternative RNA splicing at a variable intron-exon junction. Adjacent H19 was more divergent than IGF2/Igf2, as expected in a gene for a noncoding RNA, and was identified in only 10/19 species. These results show that common features, including those defining IGF2/Igf2 coding and several non-coding exons, were likely present at the onset of the mammalian radiation, but that others, such as a putative imprinting control region 5’ to H19 and potential enhancer elements 3’ to H19, diversified with speciation. This study also demonstrates that careful analysis of genomic and gene expression repositories can provide new insights into gene structure and regulation.

Preclinical Modelling of PDA: Is Organoid the New Black?

Pancreatic ductal adenocarcinoma (PDA) is a malignancy of the exocrine pancreas with the worst prognosis among all solid tumours, and soon to become the second leading cause of cancer-related deaths. A more comprehensive understanding of the molecular mechanisms underlying this disease is crucial to the development of diagnostic tools as well as to the identification of more effective therapies. High-frequency mutations in PDA occur in “undruggable” genes, and molecular subtyping based on bulk transcriptome analysis does not yet nominate valid therapeutic intervention strategies. Genome-wide sequencing studies have also demonstrated a considerable intra- and inter-patient’s genetic heterogeneity, which further complicate this dire scenario. More than in other malignancies, functionalization of the PDA genome and preclinical modelling at the individual patient level appear necessary to substantially improve survival rates for pancreatic cancer patients. Traditional human PDA models, including monolayer cell cultures and patient-derived xenografts, have certainly led to valuable biological insights in the past years. However, those model systems suffer from several limitations that have contributed to the lack of concordance between preclinical and clinical studies for PDA. Pancreatic ductal organoids have recently emerged as a reliable culture system to establish models from both normal and neoplastic pancreatic tissues. Pancreatic organoid cultures can be efficiently generated from small tissue biopsies, which opens up the possibility of longitudinal studies in individual patients. A proof-of-concept study has demonstrated that patient-derived PDA organoids are able to predict responses to conventional chemotherapy. The use of this three-dimensional culture system has already improved our understanding of PDA biology and promises to implement precision oncology by enabling the alignment of preclinical and clinical platforms to guide therapeutic intervention in PDA.

A physician-scientist preceptorship in clinical and translational research enhances training and mentorship

BACKGROUND

Dual degree program MD/PhD candidates typically train extensively in basic science research and in clinical medicine, but often receive little formal experience or mentorship in clinical and translational research.

METHODS

To address this educational and curricular gap, the University of Wisconsin Medical Scientist Training Program partnered with the University of Wisconsin Institute for Clinical and Translational Research to create a new physician-scientist preceptorship in clinical and translational research. This six-week apprentice-style learning experience-guided by a physician-scientist faculty mentor-integrates both clinical work and a translational research project, providing early exposure and hands-on experience with clinically oriented research and the integrated career of a physician-scientist. Five years following implementation, we retrospectively surveyed students and faculty members to determine the outcomes of this preceptorship.

RESULTS

Over five years, 38 students and 36 faculty members participated in the physician-scientist preceptorship. Based on student self-assessments (n = 29, response rate 76%), the course enhanced competency in conducting translational research and understanding regulation of clinical research among other skills. Mentor assessments (n = 17, response rate 47%) supported the value of the preceptorship in these same areas. Based on work during the preceptorship, half of the students produced a peer-reviewed publication or a meeting abstract. At least eleven peer-reviewed manuscripts were generated. The preceptorship also provided a structure for physician-scientist mentorship in the students' clinical specialty of choice.

CONCLUSION

The physician-scientist preceptorship provides a new curricular model to address the gap of clinical research training and provides for mentorship of physician-scientists during medical school. Future work will assess the long-term impact of this course on physician-scientist career trajectories.