Biomedical Discovery Research Articles

The endogenous circadian clock programs animals to eat at certain times of the 24-hour day: What if we ignore the clock?

The discovery of the molecular mechanisms underlying the circadian clock, which functions in virtually every cell throughout the body to coordinate biological processes to anticipate and better adapt to daily rhythmic changes in the environment, is one of the major biomedical breakthroughs in the 20th century. Twenty years after this breakthrough, the biomedical community is now at a new frontier to incorporate the circadian clock mechanisms into many areas of biomedical research, as studies continue to reveal an important role of the circadian clock in a wide range of biological functions and diseases. A forefront of this exciting area is the research of interactions between the clock and energy metabolism. In this review, we summarize animal and human studies linking disruptions of the circadian clock, either environmental or genetic, to metabolic dysfunctions associated with obesity, diabetes, and other metabolic disorders. We also discuss how these advances in circadian biology may pave the way to revolutionize clinical practice in the era of precision medicine.

Effect of acute intermittent hypoxia on baroreflex sensitivity to decreasing and increasing blood pressures

ObjectivePatients with sleep apnea exhibit impairments in reflex responses to decreasing, but not increasing, arterial blood pressures (BP). Attenuated BP control may contribute to the increased risk of developing hypertension observed in patients with sleep apnea; however the mechanisms involved are not completely understood. Therefore, we aimed to examine the differential effect of acute intermittent hypoxia (IH) on baroreflex sensitivity during decreasing compared to increasing BP. We hypothesized acute IH would attenuate baroreflex sensitivity to decreasing, but not increasing, BP.MethodsHeart rate (HR, electrocardiography), BP (brachial arterial catheter), and muscle sympathetic nerve activity (MSNA, microneurography of the peroneal nerve) were assessed in 14 healthy adults (8M/6F; 32±1 yrs, 26±1 kg/m2). Baroreflex sensitivity was evaluated during decreasing and increasing BP using the modified Oxford technique before and after 30‐min of IH [25‐s hypoxic (0.10 FiO2) exposures alternating with 120‐s room air breathing]. Data were analyzed from the depressor (nitroprusside) and pressor (phenylephrine) phases of the modified Oxford for measures of cardiovagal and sympathetic baroreflex sensitivity, as well as the T50 (diastolic BP with a 50% likelihood of an MSNA burst).ResultsIH resulted in 15 events where oxygen saturation was reduced (SpO2: 98±1% vs 92±2%, p<0.01). Resting normoxic ventilation, carotid chemosensitivity to hypoxia, and MSNA were increased following IH (all p<0.05). HR, systolic BP, diastolic BP, and the T50 were not altered following 30‐min of IH (p‐value range 0.08 to 0.90). Diastolic BP was greater than T50 at baseline (Decreasing BP: p=0.046); however, after IH, diastolic BP was no longer significantly different than the T50 (p=0.52). Cardiovagal baroreflex sensitivity to decreasing and increasing BP differed at baseline (14.0±1.7 vs 22.0±3.3 ms/mmHg, p=0.02) and following IH (11.3±1.1 vs 19.1±2.0 ms/mmHg, p<0.01). Cardiovagal baroreflex sensitivity to decreasing BP tended to be lower following IH (p=0.09). Sympathetic baroreflex sensitivity to decreasing and increasing BP did not differ at baseline (−2.1±0.3 vs −2.5±0.3 %/mmHg; p=0.28) or following IH (−2.6±0.6 vs −3.0±0.4 %/mmHg, p=0.55) and was unaffected by IH (p‐value range 0.20–0.40).ConclusionsAcute IH has a differential effect on baroreflex sensitivity. Specifically, cardiovagal baroreflex sensitivity to decreasing, but not increasing, BP tends to be lower following IH. In contrast, sympathetic baroreflex sensitivity is unaffected by IH. Prior to IH, diastolic BP is greater than the T50, thus promoting lower MSNA. Despite no change in baroreflex sensitivity after IH, diastolic BP is no longer significantly different than the T50 – consistent with higher MSNA following IH.Support or Funding InformationNIH HL130339, Mayo Clinic Center for Biomedical DiscoveryThis abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Piezo2 Mechanosensitive Ion Channel Role in Primary Enterochromaffin (EC) Cell Mechanosensitivity

INTRODUCTIONIntestinal enterochromaffin (EC) cells are the specialized epithelial mechanosensors of the gastrointestinal (GI) epithelium and are functionally and developmentally like Merkel cells in the skin. EC cells release serotonin (5‐HT) in response to mechanical stimuli. Recently, we discovered that like Merkel cells, human and mouse EC cells express the mechanosensitive (MS) ion channel Piezo2. However, little is known about the functional properties of this channel in primary EC cells.AIMTo determine the role of Piezo2 in primary EC cells.METHODSThe enteroendocrine cell transcription factor NeuroD1 was used to mark the late stage of EC cell development and generate NeuroD1‐cre::GCaMP5/tdTomato mouse to identify EC cells and examine their Ca2+ dynamics. We examined colocalization of tdTomato with 5‐HT and Piezo2 by immunohistochemistry. We established and used primary colon epithelium cultures for whole‐cell patch clamp and Ca2+ imaging experiments and mechanically stimulated by membrane displacement with a piezoelectrically driven glass probe or shear flow.RESULTSImmunostaining showed no tdTomato labeling outside of the GI epithelium, and epithelial tdTomato cells were 5‐HT+ and Piezo2+. Mechanical stimulation of primary EC cells by membrane displacement produced inward MS currents (20.8±4.9 pA/pF, Cm=3.5 pF, n=6) with fast activation and inactivation kinetics (τinact 11.1±2.8 ms), steep mechanosensitivity (Boltzmann function fit with a mid‐point of 3.46±0.80 μm and slope 0.79±0.27 μm) and a linear current‐voltage relationship (slope 0.61±0.17 pA/mV, x‐intercept −4.26±1.34 mV, n=5). Shear flow (20 s) transiently increased intracellular calcium ((Ca2+)i) (2.15±0.5 ΔF/F0, upstroke 3–5 s, return to baseline in 60 s, n=12). MS currents were inhibited by the MS ion channel blocker Gd3+ (20.8±4.9 pA/pF versus 5.3±1.9 pA/pF), Piezo channel blocker GsMTx‐4 (20.8±4.9 pA/pF versus 2.5±0.9 pA/pF) and Piezo2 siRNA (20.8±4.9 pA/pF versus 1.1±0.3 pA/pF), but not non‐targeted (NT) siRNA (20.8±4.9 pA/pF versus 16.0±5.2 pA/pF and) (n=4–6 for each condition, p<0.05 for all except NT siRNA). Similarly, the (Ca2+)i responses to force were inhibited by Gd3+ (2.28±0.7 ΔF/F0 to 0.09±0.05 ΔF/F0), GsMTx‐4 (2.0±0.6 ΔF/F0 to 0.3±0.1 ΔF/F0), Piezo2 siRNA (2.15±0.5 ΔF/F0 to 0.08±0.05 ΔF/F0), but not NT siRNA (2.15±0.5 ΔF/F0 to 2.7±0.3 ΔF/F0) (n=4–6, p<0.05 for all except NT siRNA).CONCLUSIONSIn the mouse epithelium, Piezo2+ EC cells were specifically marked by NeuroD1. Primary colonic NeuroD1+ cells had inward and non‐rectifying MS currents that could be blocked by Gd3+, GsMTx‐4 and Piezo2 siRNA. Similarly, shear flow evoked changes in (Ca2+)i that were also blocked by Piezo2 inhibitors and dependent on extracellular Ca2+. These results suggest that the MS ion channel Piezo2 acts as the primary EC cell mechanosensor, whose activation by force leads to MS ionic current and (Ca2+)i increase that may mediate mechanically‐induced 5‐HT release.Support or Funding InformationThis work was supported by NIH K08 DK106456, AGA Research Scholar Award, Mayo Clinic Center for Biomedical Discovery, Mayo Clinic Center for Cell Signaling in Gastroenterology (P30DK084567) and DK52766.This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Developing the Transdisciplinary Aging Research Agenda: New Developments in Big Data.

Background: Advances in big data analytics can enable more effective and efficient research processes, with important implications for aging research. Translating these new potentialities to research outcomes, however, remains a challenge, as exponentially increasing big data availability is yet to translate into a commensurate era of ‘big knowledge,’ or exponential increases in biomedical breakthroughs. Some argue that big data analytics heralds a new era associated with the ‘end of theory.’ According to this perspective, correlation supersedes causation, and science will ultimately advance without theory and hypotheses testing. On the other hand, others argue that theory cannot be subordinate to data, no matter how comprehensive data coverage may ultimately become.Objective: Given these two tensions, namely (i) between exponential increases in data that have not translated into exponential increases in biomedical research outputs; and (ii) between the promise of comprehensive data coverage and inductive data-driven modes of enquiry versus theory-driven deductive modes, this critical review seeks to offer useful perspectives of big data analytics and to derive certain theoretical implications for aging research.Method: This work offers a critical review of theory and literature relating big data to aging research. Result: The rise of big data provides important insights into the theory development process itself, highlighting potential for holistic theoretical assemblage to ultimately enable near real time research capability. Conclusion: Big data may represent a new paradigm of aging research that can dramatically increase the rate of scientific breakthroughs, but innovative theory development remains key to this potential.

Building Diverse Careers in Clinical and Translational Research: Evaluation of a Certificate Program in Translational Research.

The Certificate Program in Translational Research (CPTR) at the Georgia Clinical and Translational Science Alliance provides PhD students, post-doctoral fellows and faculty with didactic, mentored, and experiential training in clinical and translational research. Quantitative evaluation includes tracking trainee competency, publications, grants and careers in clinical and translational research. Qualitative evaluation includes interviews with trainees about program experiences. The CPTR provided knowledge and skills in clinical and translational research through coursework, clinical rotation, and collaboration with interdisciplinary scientists. Trainees reported increased confidence in 22 program competencies. Trainees have published more than 290 peer-reviewed articles and received over four million dollars in grants from the NIH, over $15 from the U.S. Department of Defense, and more than $300,000 from foundations. Trainees who completed the program remained in clinical and translational research. Programs like the CPTR are needed to train investigators to advance biomedical discoveries into population health.

DataMed - an open source discovery index for finding biomedical datasets.

ObjectiveFinding relevant datasets is important for promoting data reuse in the biomedical domain, but it is challenging given the volume and complexity of biomedical data. Here we describe the development of an open source biomedical data discovery system called DataMed, with the goal of promoting the building of additional data indexes in the biomedical domain.Materials and MethodsDataMed, which can efficiently index and search diverse types of biomedical datasets across repositories, is developed through the National Institutes of Health–funded biomedical and healthCAre Data Discovery Index Ecosystem (bioCADDIE) consortium. It consists of 2 main components: (1) a data ingestion pipeline that collects and transforms original metadata information to a unified metadata model, called DatA Tag Suite (DATS), and (2) a search engine that finds relevant datasets based on user-entered queries. In addition to describing its architecture and techniques, we evaluated individual components within DataMed, including the accuracy of the ingestion pipeline, the prevalence of the DATS model across repositories, and the overall performance of the dataset retrieval engine.Results and ConclusionOur manual review shows that the ingestion pipeline could achieve an accuracy of 90% and core elements of DATS had varied frequency across repositories. On a manually curated benchmark dataset, the DataMed search engine achieved an inferred average precision of 0.2033 and a precision at 10 (P@10, the number of relevant results in the top 10 search results) of 0.6022, by implementing advanced natural language processing and terminology services. Currently, we have made the DataMed system publically available as an open source package for the biomedical community.

Multi-label Deep Learning for Gene Function Annotation in Cancer Pathways

The war on cancer is progressing globally but slowly as researchers around the world continue to seek and discover more innovative and effective ways of curing this catastrophic disease. Organizing biological information, representing it, and making it accessible, or biocuration, is an important aspect of biomedical research and discovery. However, because maintaining sophisticated biocuration is highly resource dependent, it continues to lag behind the continually being generated biomedical data. Another critical aspect of cancer research, pathway analysis, has proven to be an efficient method for gaining insight into the underlying biology associated with cancer. We propose a deep-learning-based model, Stacked Denoising Autoencoder Multi-Label Learning (SdaMLL), for facilitating gene multi-function discovery and pathway completion. SdaMLL can capture intermediate representations robust to partial corruption of the input pattern and generate low-dimensional codes superior to conditional dimension reduction tools. Experimental results indicate that SdaMLL outperforms existing classical multi-label algorithms. Moreover, we found some gene functions, such as Fused in Sarcoma (FUS, which may be part of transcriptional misregulation in cancer) and p27 (which we expect will become a member viral carcinogenesis), that can be used to complete the related pathways. We provide a visual tool (https://www.keaml.cn/gpvisual) to view the new gene functions in cancer pathways.

Engineering Human Neural Tissue by 3D Bioprinting.

Bioprinting provides an opportunity to produce three-dimensional (3D) tissues for biomedical research and translational drug discovery, toxicology, and tissue replacement. Here we describe a method for fabricating human neural tissue by 3D printing human neural stem cells with a bioink, and subsequent gelation of the bioink for cell encapsulation, support, and differentiation to functional neurons and supporting neuroglia. The bioink uniquely comprises the polysaccharides alginate, water-soluble carboxymethyl-chitosan, and agarose. Importantly, the method could be adapted to fabricate neural and nonneural tissues from other cell types, with the potential to be applied for both research and clinical product development.

BioBroker: Knowledge Discovery Framework for Heterogeneous Biomedical Ontologies and Data

A large number of ontologies have been introduced by the biomedical community in recent years. Knowledge discovery for entity identification from ontology has become an important research area, and it is always interesting to discovery how associations are established to connect concepts in a single ontology or across multiple ontologies. However, due to the exponential growth of biomedical big data and their complicated associations, it becomes very challenging to detect key associations among entities in an inefficient dynamic manner. Therefore, there exists a gap between the increasing needs for association detection and large volume of biomedical ontologies. In this paper, to bridge this gap, we presented a knowledge discovery framework, the BioBroker, for grouping entities to facilitate the process of biomedical knowledge discovery in an intelligent way. Specifically, we developed an innovative knowledge discovery algorithm that combines a graph clustering method and an indexing technique to discovery knowledge patterns over a set of interlinked data sources in an efficient way. We have demonstrated capabilities of the BioBroker for query execution with a use case study on a subset of the Bio2RDF life science linked data.

Finding relevant biomedical datasets: the UC San Diego solution for the bioCADDIE Retrieval Challenge.

The number and diversity of biomedical datasets grew rapidly in the last decade. A large number of datasets are stored in various repositories, with different formats. Existing dataset retrieval systems lack the capability of cross-repository search. As a result, users spend time searching datasets in known repositories, and they typically do not find new repositories. The biomedical and healthcare data discovery index ecosystem (bioCADDIE) team organized a challenge to solicit new indexing and searching strategies for retrieving biomedical datasets across repositories. We describe the work of one team that built a retrieval pipeline and examined its performance. The pipeline used online resources to supplement dataset metadata, automatically generated queries from users’ free-text questions, produced high-quality retrieval results and achieved the highest inferred Normalized Discounted Cumulative Gain among competitors. The results showed that it is a promising solution for cross-database, cross-domain and cross-repository biomedical dataset retrieval. Database URL: https://github.com/w2wei/dataset_retrieval_pipeline

The BioDARPA Concept

The dramatic development of biomedical technologies inevitably raises a question of the increasing gap between basic biomedical discoveries and their use in clinical practice. Created in 1958 with the same purpose, DARPA has had to bridging the gap in defense technologies (mostly in rocket and radar sciences). Today we can talk about the relevance of the idea of biomedical DARPA (BioDARPA) as a concept of organizational design for priority research and development of emerging biotechnologies.

Nitric Oxide, Normal Science, and Lessons Learned by a Marginally Prepared Mind.

The discovery in the late 1970s that the vascular endothelium plays a major role in regulating the caliber of blood vessels caused a minor paradigm shift in thinking about the cardiovascular system. That the gas nitric oxide (NO) was central to this discovery was unexpected and set the stage for a broader series of insights related to gaseous signaling molecules in biological systems. Over the last 25 years, the author's lab has attempted to understand what NO does in alive, awake, exercising humans. This article recounts six lessons learned along the way, about luck, reductionism, and the translation of biomedical discoveries to therapy, as well as about philosophical questions related to big versus small science and curiosity-driven versus goal-directed approaches. The article also poses a fundamental question: would NO in specific and gaseous signaling molecules more generally have been discovered by sequencing, Big Data, and hypothesis-free science?

User needs analysis and usability assessment of DataMed - a biomedical data discovery index.

ObjectiveTo present user needs and usability evaluations of DataMed, a Data Discovery Index (DDI) that allows searching for biomedical data from multiple sources.Materials and MethodsWe conducted 2 phases of user studies. Phase 1 was a user needs analysis conducted before the development of DataMed, consisting of interviews with researchers. Phase 2 involved iterative usability evaluations of DataMed prototypes. We analyzed data qualitatively to document researchers’ information and user interface needs.ResultsBiomedical researchers’ information needs in data discovery are complex, multidimensional, and shaped by their context, domain knowledge, and technical experience. User needs analyses validate the need for a DDI, while usability evaluations of DataMed show that even though aggregating metadata into a common search engine and applying traditional information retrieval tools are promising first steps, there remain challenges for DataMed due to incomplete metadata and the complexity of data discovery.DiscussionBiomedical data poses distinct problems for search when compared to websites or publications. Making data available is not enough to facilitate biomedical data discovery: new retrieval techniques and user interfaces are necessary for dataset exploration. Consistent, complete, and high-quality metadata are vital to enable this process.ConclusionWhile available data and researchers’ information needs are complex and heterogeneous, a successful DDI must meet those needs and fit into the processes of biomedical researchers. Research directions include formalizing researchers’ information needs, standardizing overviews of data to facilitate relevance judgments, implementing user interfaces for concept-based searching, and developing evaluation methods for open-ended discovery systems such as DDIs.

High-throughput imaging of zebrafish embryos using a linear-CCD-based flow imaging system.

High-throughput imaging and screening is essential for biomedical research and drug discovery using miniature model organisms such as zebrafish. This study introduces a high-speed imaging system which illuminates zebrafish embryos flowing through a capillary tube with a sheet of light and captures them using a linear charge-coupled device (CCD). This system can image dozens of zebrafish embryos per second. An image algorithm was developed to recognize each embryo and to perform automatic analysis. We distinguished dead and living embryos according to the gray level distribution and conducted statistics of morphological characteristics of embryos at different growing stages.

Spineless solutions: The potential of invertebrate animal models for advancing science in the developing world.

Creating and expanding research and development (RD fostering research requires different approaches. Many Asian and Latin American countries have implemented national scholarship programmes to send students, junior and senior researchers abroad for scientific training in an effort to create qualified personnel so as to jumpstart local R&D upon their return. As a result, the publication rates of South American countries have doubled during the past decade, but still account for only 4% of the global publication volume [1], mainly owing to limited funding. > Adopting research strategies based on […] invertebrate models […] could thereby provide a boost for research in developing and middle‐income countries. It is therefore necessary to adopt additional, cost‐efficient strategies to produce reliable data, within the funding constraints, that contribute to building a viable research infrastructure. In terms of biomedical research and drug discovery, this could involve investing into cheaper alternatives to rodent models for in vivo testing of drug candidates, such as invertebrate species, to decrease cost and time, and to stimulate new research areas and high‐throughput (HTS) screening. Adopting research strategies based on HTS of invertebrate models, which are already benefiting research elsewhere, could thereby provide a boost for research in developing and middle‐income countries. ### Animal models in biomedical research In 2013, middle‐income countries invested on average 1.36% of their combined GDP into research and development, compared to 1.70% in the United Kingdom or 2.72% in the United States [2]. In addition, the financial crash of 2007 has greatly affected the budget allocation for research in developing countries where the majority of funding for …

Mirror-image polymerase chain reaction

The construction of mirror-image biological systems may open the next frontier for biomedical technology development and discovery. Here we have designed and chemically synthesized a mutant version of the thermostable Sulfolobus solfataricus P2 DNA polymerase IV (Dpo4) consisting of d-amino acids. With a total peptide length of 358 amino acid residues, it is the largest chemically synthesized d-amino acid protein reported to date. We show that the d-polymerase is able to amplify a 120-bp l-DNA sequence coding for the Escherichia coli 5S ribosomal RNA gene rrfB by mirror-image polymerase chain reaction, and that both the natural and mirror-image systems operate with strict chiral specificity. The development of efficient miPCR systems may lead to many practical applications, such as mirror-image systematic evolution of ligands by exponential enrichment for the selection of therapeutically promising nuclease-resistant l-nucleic acid aptamers.

A collaborative filtering-based approach to biomedical knowledge discovery.

The increase in publication rates makes it challenging for an individual researcher to stay abreast of all relevant research in order to find novel research hypotheses. Literature-based discovery methods make use of knowledge graphs built using text mining and can infer future associations between biomedical concepts that will likely occur in new publications. These predictions are a valuable resource for researchers to explore a research topic. Current methods for prediction are based on the local structure of the knowledge graph. A method that uses global knowledge from across the knowledge graph needs to be developed in order to make knowledge discovery a frequently used tool by researchers. We propose an approach based on the singular value decomposition (SVD) that is able to combine data from across the knowledge graph through a reduced representation. Using cooccurrence data extracted from published literature, we show that SVD performs better than the leading methods for scoring discoveries. We also show the diminishing predictive power of knowledge discovery as we compare our predictions with real associations that appear further into the future. Finally, we examine the strengths and weaknesses of the SVD approach against another well-performing system using several predicted associations. All code and results files for this analysis can be accessed at https://github.com/jakelever/knowledgediscovery. sjones@bcgsc.ca. Supplementary data are available at Bioinformatics online.

Team science and the physician-scientist in the age of grand health challenges.

Despite remarkable advances in medical research, clinicians face daunting challenges from new diseases, variations in patient responses to interventions, and increasing numbers of people with chronic health problems. The gap between biomedical research and unmet clinical needs can be addressed by highly talented interdisciplinary investigators focused on translational bench-to-bedside medicine. The training of talented physician-scientists comfortable with forming and participating in multidisciplinary teams that address complex health problems is a top national priority. Challenges, methods, and experiences associated with physician-scientist training and team building were explored at a workshop held at the Second International Conference on One Medicine One Science (iCOMOS 2016), April 24-27, 2016, in Minneapolis, Minnesota. A broad range of scientists, regulatory authorities, and health care experts determined that critical investments in interdisciplinary training are essential for the future of medicine and healthcare delivery. Physician-scientists trained in a broad, nonlinear, cross-disciplinary manner are and will be essential members of science teams in the new age of grand health challenges and the birth of precision medicine. Team science approaches have accomplished biomedical breakthroughs once considered impossible, and dedicated physician-scientists have been critical to these achievements. Together, they translate into the pillars of academic growth and success.

Selecting a DNA-Encoded Chemical Library against Non-immobilized Proteins Using a "Ligate-Cross-Link-Purify" Strategy.

DNA-encoded chemical libraries (DELs) have recently emerged and become an important technology platform in biomedical research and drug discovery. DELs containing large numbers of compounds can be prepared and selected against biological targets to discover novel ligands and inhibitors. In practice, DELs are usually selected against purified and immobilized proteins using the typical "bind-wash-elute" protocol; however, selection methods compatible with non-immobilized proteins would be able to greatly expand the target scope of DELs beyond purified proteins to more-complex and biologically relevant targets. Using a novel "ligate-cross-link-purify" strategy, we report here a method capable of selecting DELs against unmodified and non-immobilized protein targets. In addition, this method has shown excellent capability in identifying binders with moderate and weak affinities.

Rational confederation of genes and diseases: NGS interpretation via GeneCards, MalaCards and VarElect

BackgroundA key challenge in the realm of human disease research is next generation sequencing (NGS) interpretation, whereby identified filtered variant-harboring genes are associated with a patient’s disease phenotypes. This necessitates bioinformatics tools linked to comprehensive knowledgebases. The GeneCards suite databases, which include GeneCards (human genes), MalaCards (human diseases) and PathCards (human pathways) together with additional tools, are presented with the focus on MalaCards utility for NGS interpretation as well as for large scale bioinformatic analyses.ResultsVarElect, our NGS interpretation tool, leverages the broad information in the GeneCards suite databases. MalaCards algorithms unify disease-related terms and annotations from 69 sources. Further, MalaCards defines hierarchical relatedness—aliases, disease families, a related diseases network, categories and ontological classifications. GeneCards and MalaCards delineate and share a multi-tiered, scored gene-disease network, with stringency levels, including the definition of elite status—high quality gene-disease pairs, coming from manually curated trustworthy sources, that includes 4500 genes for 8000 diseases. This unique resource is key to NGS interpretation by VarElect. VarElect, a comprehensive search tool that helps infer both direct and indirect links between genes and user-supplied disease/phenotype terms, is robustly strengthened by the information found in MalaCards. The indirect mode benefits from GeneCards’ diverse gene-to-gene relationships, including SuperPaths—integrated biological pathways from 12 information sources. We are currently adding an important information layer in the form of “disease SuperPaths”, generated from the gene-disease matrix by an algorithm similar to that previously employed for biological pathway unification. This allows the discovery of novel gene-disease and disease–disease relationships. The advent of whole genome sequencing necessitates capacities to go beyond protein coding genes. GeneCards is highly useful in this respect, as it also addresses 101,976 non-protein-coding RNA genes. In a more recent development, we are currently adding an inclusive map of regulatory elements and their inferred target genes, generated by integration from 4 resources.ConclusionsMalaCards provides a rich big-data scaffold for in silico biomedical discovery within the gene-disease universe. VarElect, which depends significantly on both GeneCards and MalaCards power, is a potent tool for supporting the interpretation of wet-lab experiments, notably NGS analyses of disease. The GeneCards suite has thus transcended its 2-decade role in biomedical research, maturing into a key player in clinical investigation.