Biomedical Discovery Research Articles

Advances in Hydrogels in Organoids and Organs-on-a-Chip.

Significant advances in materials, microscale technology, and stem cell biology have enabled the construction of 3D tissues and organs, which will ultimately lead to more effective diagnostics and therapy. Organoids and organs-on-a-chip (OOC), evolved from developmental biology and bioengineering principles, have emerged as major technological breakthrough and distinct model systems to revolutionize biomedical research and drug discovery by recapitulating the key structural and functional complexity of human organs in vitro. There is growing interest in the development of functional biomaterials, especially hydrogels, for utilization in these promising systems to build more physiologically relevant 3D tissues with defined properties. The remarkable properties of defined hydrogels as proper extracellular matrix that can instruct cellular behaviors are presented. The recent trend where functional hydrogels are integrated into organoids and OOC systems for the construction of 3D tissue models is highlighted. Future opportunities and perspectives in the development of advanced hydrogels toward accelerating organoids and OOC research in biomedical applications are also discussed.

Detection of DNA 3'-phosphatase activity based on exonuclease III-assisted cascade recycling amplification reaction

DNA 3'-phosphatase is an essential enzyme, which plays a pivotal role in repairing DNA damage. The peculiar activity of DNA 3'-phosphatase has been proved to associate with a variety of human pathologies. Therefore, sensitive determination of DNA 3'-phosphatase is necessary for clinical diagnosis and therapy. Here, we develop a simple, sensitive, and specific fluorescent biosensor including three DNA chains of hairpin DNA1, hairpin DNA2 and fluorescence probe DNA (FP) for detecting the activity of DNA 3'-phosphatase. First, biotin-modified hairpin DNA1 binds with streptavidin-modified magnetic beads (MB) to get MB-DNA1. DNA 3'-phosphatase can hydrolyze phosphate groups on MB-DNA1 to form hydroxyl groups, which leads to the polymerization extension and nicking endonuclease cleavage reaction to obtain the trigger DNA1 fragment (tDNA1). Next, two cyclic amplification reactions are designed. In cycle I, the tDNA1 hybridizes with the hairpin DNA2, which leads the hairpin structure of DNA2 opened and the fluorescence signal of 6-carboxy-fluorescein (FAM) labeled on hairpin DNA2 turned on. This cyclic reaction is amplified by exonuclease III (Exo III). At the same time, the trigger DNA2 fragment (tDNA2) is obtained. In cycle II, similarly, the tDNA2 hybridizes with FP. Thus, the fluorescence signal of FAM labeled on FP released, which multiplies with the fluorescence signal from cycle I. Finally, this strategy is applied to determine two typical DNA 3'-phosphatases including T4 polynucleotide kinase (T4 PNK) and alkaline phosphatase (ALP) with the detection limit (LOD) of 0.0033 and 0.00037 U/mL, respectively. The method provides a promising platform to evaluate the DNA 3'-phosphatase activity in the complicated biological samples and can be potentially applied in the relevant fields such as biomedical research, drug discovery and clinical diagnosis.

Using literature-based discovery to identify candidate genes for the interaction between myocardial infarction and depression

BackgroundA multidirectional relationship has been demonstrated between myocardial infarction (MI) and depression. However, the causal genetic factors and molecular mechanisms underlying this interaction remain unclear. The main purpose of this study was to identify potential candidate genes for the interaction between the two diseases.MethodsUsing a bioinformatics approach and existing gene expression data in the biomedical discovery support system (BITOLA), we defined the starting concept X as “Myocardial Infarction” and end concept Z as “Major Depressive Disorder” or “Depressive disorder”. All intermediate concepts relevant to the “Gene or Gene Product” for MI and depression were searched. Gene expression data and tissue-specific expression of potential candidate genes were evaluated using the Human eFP (electronic Fluorescent Pictograph) Browser, and intermediate concepts were filtered by manual inspection.ResultsOur analysis identified 128 genes common to both the “MI” and “depression” text mining concepts. Twenty-three of the 128 genes were selected as intermediates for this study, 9 of which passed the manual filtering step. Among the 9 genes, LCAT, CD4, SERPINA1, IL6, and PPBP failed to pass the follow-up filter in the Human eFP Browser, due to their low levels in the heart tissue. Finally, four genes (GNB3, CNR1, MTHFR, and NCAM1) remained.ConclusionsGNB3, CNR1, MTHFR, and NCAM1 are putative new candidate genes that may influence the interactions between MI and depression, and may represent potential targets for therapeutic intervention.

Edge2vec: Representation learning using edge semantics for biomedical knowledge discovery

BackgroundRepresentation learning provides new and powerful graph analytical approaches and tools for the highly valued data science challenge of mining knowledge graphs. Since previous graph analytical methods have mostly focused on homogeneous graphs, an important current challenge is extending this methodology for richly heterogeneous graphs and knowledge domains. The biomedical sciences are such a domain, reflecting the complexity of biology, with entities such as genes, proteins, drugs, diseases, and phenotypes, and relationships such as gene co-expression, biochemical regulation, and biomolecular inhibition or activation. Therefore, the semantics of edges and nodes are critical for representation learning and knowledge discovery in real world biomedical problems.ResultsIn this paper, we propose the edge2vec model, which represents graphs considering edge semantics. An edge-type transition matrix is trained by an Expectation-Maximization approach, and a stochastic gradient descent model is employed to learn node embedding on a heterogeneous graph via the trained transition matrix. edge2vec is validated on three biomedical domain tasks: biomedical entity classification, compound-gene bioactivity prediction, and biomedical information retrieval. Results show that by considering edge-types into node embedding learning in heterogeneous graphs, edge2vec significantly outperforms state-of-the-art models on all three tasks.ConclusionsWe propose this method for its added value relative to existing graph analytical methodology, and in the real world context of biomedical knowledge discovery applicability.

3D Construction of Shape-Controllable Tissues through Self-Bonding of Multicellular Microcapsules.

Designed microtissues that replicate highly ordered three-dimensional (3D) multicellular in vivo structures have shown huge potential in biomedical research and drug discovery. Through microencapsulation and microfluidic techniques, cell-laden microcapsules have been widely used as pathological or pharmacological models. However, most conventional microtissue construction strategies can only engineer simply predefined microcapsules with monotonous biological components in two dimensions. Here, we propose a flexible 3D microtissue construction method through self-bonding of real-time shape-programmable microcapsules. The microcapsules are prepared by photo-induced electrodeposition of cell-laden alginate hydrogel and flexibly tailored into tissue-specific shapes, sizes, and arbitrary biocomponents. With the local fluidics-guided assembly, the microcapsules are spatially organized into 3D perfectly aligned microtissues. To mimic in vivo intercellular connection, the aligned microcapsules are precoated with fibroblasts to self-bond the adjacent layers into a robust assemblage through fibroblast-extracellular matrix interactions, which highly reproduces the tissue morphogenesis in natural organisms. As a typical complex tissue model, the 3D hepatic lobule was engineered utilizing HepG2 cells seeded into microcapsules with a fibroblast coating, and its biofunction including albumin and urea secretion was improved by nearly two-fold compared with cells seeded without a fibroblast coating. We anticipate that our method will be capable of regenerating more complex multicellular constructs with unprecedented possibilities for future tissue engineering applications.

Future challenges with DNA-encoded chemical libraries in the drug discovery domain

ABSTRACTIntroduction: DNA-encoded chemical libraries (DELs) have come of age and emerged to become a powerful technology platform for ligand discovery in biomedical research and drug discovery. Today, DELs have been widely adopted in the pharmaceutical industry and employed in drug discovery programs worldwide. DELs are capable of interrogating drug targets with an extremely large number of compounds highly efficiently.Area covered: In this review, the authors introduce the history of DELs and provide an overview of the major technological components, including encoding methods, library synthesis, chemistry, selection methods, hit deconvolution strategy, and post-selection data analysis. A brief update on the hit compounds recently discovered from DEL selections against drug targets is also provided. Finally, the authors discuss their views on the present challenges and future directions for the development and application of DELs in drug discovery.Expert opinion: DELs have provided great opportunities for lead compound discovery at an unprecedented scale and efficiency in drug discovery. The key to the future success of DELs as true discovery modalities, rather than just ‘a way to make many compounds,’ is to go beyond physical binding to functional or even phenotypic assays with the capability to probe the biological system.

DiiS: A Biomedical Data Access Framework for Aiding Data Driven Research Supporting FAIR Principles

Vast amounts of clinical and biomedical research data are produced daily. These data can help enable data driven healthcare through novel biomedical discoveries, improved diagnostics processes, epidemiology, and education. However, finding, and gaining access to these data and relevant metadata that are necessary to achieve these goals remains a challenge. Furthermore, data management and enabling widespread, albeit controlled, use poses a major challenge for data producers. These data sources are often geographically distributed, with diverse characteristics, and are controlled by a host of logistical and legal factors that require appropriate governance and access control guarantees. To overcome these obstacles, a set of guiding principles under the term FAIR has been previously introduced. The primary desirable dataset properties are thus that the data should be Findable, Accessible, Interoperable, and Reusable (FAIR). In this paper, we introduce and describe an abstract framework that models these ideal goals, and could be a step toward supporting data driven research. We also develop a system instantiated on our framework called the Data integration and indexing System (DiiS). The system provides an integration model for making healthcare data available on a global scale. Our research work describes the challenges inhibiting data producers, data stewards, and data brokers in achieving FAIR goals for sharing biomedical data. We attempt to address some of the key challenges through the proposed system. We evaluated our framework using the software architecture testing technique and also looked at how different challenges in data integration are addressed by our system. Our evaluation shows that the DiiS framework is a user friendly data integration system that would greatly contribute to biomedical research.

Sympathetic Discharge Patterns and Neurovascular Transduction Following Acute Intermittent Hypoxia

BackgroundMuscle sympathetic nerve activity (MSNA) is augmented in patients with sleep apnea. This increase in activity is due to increases in firing probability and mean firing rates of individual vasoconstrictor neurons, in addition to multiple within‐burst firing; however, contributing mechanisms and the impact of such firing patterns on peripheral vascular tone are less clear. We examined the effect of acute intermittent hypoxia (IH) on sympathetic neural firing and recruitment strategies and the relationship between changes in MSNA and neurovascular transduction following acute IH. We hypothesized exposure to acute IH would increase MSNA via an increase in action potential (AP) discharge rates and within‐burst firing. We further hypothesized any change in discharge patterns following acute IH would be positively related to a change in neurovascular transduction.MethodsBlood pressure (brachial arterial catheter) and MSNA (microneurography of the peroneal nerve) were assessed in 17 healthy adults (11M/6F; 31 ± 1 yrs) during quiet normoxic rest prior to and following 30‐min of experimental IH [30‐s hypoxic (0.05 FiO2) exposures alternating with 90‐s room air breathing]. AP patterns were studied from the filtered raw MSNA signal using wavelet‐based methodology. The slope of the relationship between diastolic blood pressure (DBP) and the preceding MSNA burst area at a fixed cardiac cycle lag was used to quantify neurovascular transduction.ResultsIH resulted in 15 events where oxygen saturation was reduced (SpO2: 99 ± 1% vs 92 ± 1%, P<0.01). Compared to baseline, multi‐unit MSNA burst incidence (33 ± 4 to 43 ± 5 bursts/100 heart beats), AP incidence (272 ± 50 to 501 ± 112 AP/100 heart beats) and AP content per integrated burst (7 ± 1 to 10 ± 1 AP/burst; 4 ± 1 to 5 ± 1 clusters/burst) were increased following IH (all P<0.05). Neurovascular transduction was not altered following IH (P=0.34); however, there was a positive relationship between the change (Δ) in neurovascular transduction (mmHg/%/s) and changes in AP discharge rate (AP/100 heart beats; R=0.52, P=0.03) and within‐burst firing (AP/burst; R=0.38, P=0.14) following IH.ConclusionsAcute IH increases sympathetic neuronal discharge by increasing within‐burst firing of low‐threshold axons. A positive relationship exists between increases in the neural discharge rates and neurovascular transduction following acute IH, such that those individuals with the greatest increase in AP firing exhibited the greatest increase in transduction of sympathetic activity into blood pressure. These data may have important implications for mechanistic understanding of sympathetic activation in sleep apnea and its down‐stream effects, including the development of hypertension and cardiovascular disease.Support or Funding InformationFunding: NIH HL130339, Mayo Clinic Center for Biomedical Discovery, Mizzou HES PURE, APS UGSRF.This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Polymerase-Extension-Based Selection Method for DNA-Encoded Chemical Libraries against Nonimmobilized Protein Targets.

DNA-encoded chemical libraries (DELs) have become an important ligand discovery technology in biomedical research and drug discovery. DELs can be comprised of hundreds of millions to billions of candidate molecules and provide outstanding chemical diversity for discovering novel ligands and inhibitors for a large variety of biological targets. However, in most cases, DELs are selected against purified and immobilized proteins based on binding affinity. The development and application of DELs to more complex biological targets requires selection methods compatible with nonimmobilized and unpurified proteins. Here, we describe an approach using polymerase-based extension and target-directed photo-cross-linking and its application to the interrogation of a solution-phase protein target, carbonic anhydrase II.

Patient data discovery platforms as enablers of biomedical and translational research: A systematic review.

The global shift from paper health records to electronic ones has led to an impressive growth of biomedical digital data along the past two decades. Exploring and extracting knowledge from these data has the potential to enhance translational research and lead to positive outcomes for the population's health and healthcare. The aim of this study was to conduct a systematic review to identify software platforms that enable discovery, secondary use and interoperability of biomedical data. Additionally, we aim evaluating the identified solutions in terms of clinical interest and main healthcare-related outcomes. A systematic search of the scientific literature published and indexed in Pubmed between January 2014 and September 2018 was performed. Inclusion criteria were as follows: relevance for the topic of biomedical data discovery, English language, and free full text. To increase the recall, we developed a semi-automatic and incremental methodology to retrieve articles that cite one or more of the previous set. A total number of 500 candidate papers were retrieved through this methodology. Of these, 85 were eligible for abstract assessment. Finally, 37 studies qualified for a full-text review, and 20 provided enough information for the study objectives. This study revealed that biomedical discovery platforms are both a current necessity and a significantly innovative agent in the area of healthcare. The outcomes that were identified, in terms of scientific publications, clinical studies and research collaborations stand as evidence.

Utilization of the CRISPR-Cas9 Gene Editing System to Dissect Neuroinflammatory and Neuropharmacological Mechanisms in Parkinson's Disease.

Chronic and debilitating neurodegenerative diseases, such as Parkinson's disease (PD), impose an immense medical, emotional, and economic burden on patients and society. Due to a complex interaction between genetic and environmental risk factors, the etiology of PD remains elusive. However, the cumulative evidence emerging from clinical and experimental research over the last several decades has identified mitochondrial dysfunction, oxidative stress, neuroinflammation, and dysregulated protein degradation as the main drivers of PD neurodegeneration. The genome-editing system CRISPR (clustered regularly interspaced short palindromic repeats) has recently transformed the field of biotechnology and biomedical discovery and is poised to accelerate neurodegenerative disease research. It has been leveraged to generate PD animal models, such as Parkin, DJ-1, and PINK1 triple knockout miniature pigs. CRISPR has also allowed the deeper understanding of various PD gene interactions, as well as the identification of novel apoptotic pathways associated with neurodegenerative processes in PD. Furthermore, its application has been used to dissect neuroinflammatory pathways involved in PD pathogenesis, such as the PKCδ signaling pathway, as well as the roles of novel compensatory or protective pathways, such as Prokineticin-2 signaling. This review aims to highlight the historical milestones in the evolution of this technology and attempts to illustrate its transformative potential in unraveling disease mechanisms as well as in the development of innovative treatment strategies for PD. Graphical Abstract.

A Smart Imaging Workflow for Organ-Specific Screening in a Cystic Kidney Zebrafish Disease Model.

The zebrafish is being increasingly used in biomedical research and drug discovery to conduct large-scale compound screening. However, there is a lack of accessible methodologies to enable automated imaging and scoring of tissue-specific phenotypes at enhanced resolution. Here, we present the development of an automated imaging pipeline to identify chemical modifiers of glomerular cyst formation in a zebrafish model for human cystic kidney disease. Morpholino-mediated knockdown of intraflagellar transport protein Ift172 in Tg(wt1b:EGFP) embryos was used to induce large glomerular cysts representing a robustly scorable phenotypic readout. Compound-treated embryos were consistently aligned within the cavities of agarose-filled microplates. By interfacing feature detection algorithms with automated microscopy, a smart imaging workflow for detection, centring and zooming in on regions of interests was established, which enabled the automated capturing of standardised higher resolution datasets of pronephric areas. High-content screening datasets were processed and analysed using custom-developed heuristic algorithms implemented in common open-source image analysis software. The workflow enables highly efficient profiling of entire compound libraries and scoring of kidney-specific morphological phenotypes in thousands of zebrafish embryos. The demonstrated toolset covers all the aspects of a complex whole organism screening assay and can be adapted to other organs, specimens or applications.

MeSHProbeNet: a self-attentive probe net for MeSH indexing.

MEDLINE is the primary bibliographic database maintained by National Library of Medicine (NLM). MEDLINE citations are indexed with Medical Subject Headings (MeSH), which is a controlled vocabulary curated by the NLM experts. This greatly facilitates the applications of biomedical research and knowledge discovery. Currently, MeSH indexing is manually performed by human experts. To reduce the time and monetary cost associated with manual annotation, many automatic MeSH indexing systems have been proposed to assist manual annotation, including DeepMeSH and NLM's official model Medical Text Indexer (MTI). However, the existing models usually rely on the intermediate results of other models and suffer from efficiency issues. We propose an end-to-end framework, MeSHProbeNet (formerly named as xgx), which utilizes deep learning and self-attentive MeSH probes to index MeSH terms. Each MeSH probe enables the model to extract one specific aspect of biomedical knowledge from an input article, thus comprehensive biomedical information can be extracted with different MeSH probes and interpretability can be achieved at word level. MeSH terms are finally recommended with a unified classifier, making MeSHProbeNet both time efficient and space efficient. MeSHProbeNet won the first place in the latest batch of Task A in the 2018 BioASQ challenge. The result on the last test set of the challenge is reported in this paper. Compared with other state-of-the-art models, such as MTI and DeepMeSH, MeSHProbeNet achieves the highest scores in all the F-measures, including Example Based F-Measure, Macro F-Measure, Micro F-Measure, Hierarchical F-Measure and Lowest Common Ancestor F-measure. We also intuitively show how MeSHProbeNet is able to extract comprehensive biomedical knowledge from an input article.

Clinical data quality: a data life cycle perspective

Clinical data is the staple of modern learning health systems. It promises to accelerate biomedical discovery and improves the efficiency of clinical and translational research but is also fraught with significant data quality issues. This paper aims to provide a life cycle perspective of clinical data quality issues along with recommendations for establishing appropriate expectations for research based on real-world clinical data and best practices for reusing clinical data as a secondary data source.

Socially Accountable Academic Health Centers: Pursuing a Quadripartite Mission.

Academic health centers (AHCs) in the United States have had a leading role in educating the medical workforce, generating new biomedical knowledge, and providing tertiary and quaternary clinical care. Yet the health status of the U.S. population lags behind almost every other developed world economy. One reason is that the health care system is not organized optimally to address the major driver of health status, the social determinants of health (SDOH). The United States' overall poor health status is a reflection of dramatic disparities in health that exist between communities and population groups, and these are associated with variations in the underlying SDOH. Improving health status in the United States thus requires a fundamental reengineering of the health delivery system to address SDOH more explicitly and systematically. AHCs' tripartite mission, which has served so well in the past, is no longer sufficient to position AHCs to lead and resolve the intractable drivers of poor health status, such as unfair and unjust health disparities, health inequities, or differences in a population's SDOH.AHCs enjoy broad public support and have an opportunity-and an obligation-to lead in improving the nation's health. This Perspective proposes a new framework for AHCs to expand on their traditional tripartite mission of education, research, and clinical care to include explicitly a fourth mission of social accountability. Through this fourth mission, comprehensive community engagement can be undertaken, addressing SDOH and measuring the health impact of interventions by using a deliberate structure and process, yielding defined outcomes.

CRISPR-Based Tools in Immunity.

CRISPR technology has opened a new era of genome interrogation and genome engineering. Discovered in bacteria, where it protects against bacteriophage by cleaving foreign nucleic acid sequences, the CRISPR system has been repurposed as an adaptable tool for genome editing and multiple other applications. CRISPR's ease of use, precision, and versatility have led to its widespread adoption, accelerating biomedical research and discovery in human cells and model organisms. Here we review CRISPR-based tools and discuss how they are being applied to decode the genetic circuits that control immune function in health and disease. Genetic variation in immune cells can affect autoimmune disease risk, infectious disease pathogenesis, and cancer immunotherapies. CRISPR provides unprecedented opportunities for functional mechanistic studies of coding and noncoding genome sequence function in immunity. Finally, we discuss the potential of CRISPR technology to engineer synthetic cellular immunotherapies for a wide range of human diseases.

Machine Learning and Integrative Analysis of Biomedical Big Data.

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues.

Reimagining the research-practice relationship: policy recommendations for informatics-enabled evidence-generation across the US health system.

The widespread adoption and use of electronic health records and their use to enable learning health systems (LHS) holds great promise to accelerate both evidence-generating medicine (EGM) and evidence-based medicine (EBM), thereby enabling a LHS. In 2016, AMIA convened its 10th annual Policy Invitational to discuss issues key to facilitating the EGM-EBM paradigm at points-of-care (nodes), across organizations (networks), and to ensure viability of this model at scale (sustainability). In this article, we synthesize discussions from the conference and supplements those deliberations with relevant context to inform ongoing policy development. Specifically, we explore and suggest public policies needed to facilitate EGM-EBM activities on a national scale, particularly those policies that can enable and improve clinical and health services research at the point-of-care, accelerate biomedical discovery, and facilitate translation of findings to improve the health of individuals and populations.

ROMOP: a light-weight R package for interfacing with OMOP-formatted electronic health record data.

ObjectivesElectronic health record (EHR) data are increasingly used for biomedical discoveries. The nature of the data, however, requires expertise in both data science and EHR structure. The Observational Medical Outcomes Partnership (OMOP) common data model (CDM) standardizes the language and structure of EHR data to promote interoperability of EHR data for research. While the OMOP CDM is valuable and more attuned to research purposes, it still requires extensive domain knowledge to utilize effectively, potentially limiting more widespread adoption of EHR data for research and quality improvement.Materials and methodsWe have created ROMOP: an R package for direct interfacing with EHR data in the OMOP CDM format.ResultsROMOP streamlines typical EHR-related data processes. Its functions include exploration of data types, extraction and summarization of patient clinical and demographic data, and patient searches using any CDM vocabulary concept.ConclusionROMOP is freely available under the Massachusetts Institute of Technology (MIT) license and can be obtained from GitHub (http://github.com/BenGlicksberg/ROMOP). We detail instructions for setup and use in the Supplementary Materials. Additionally, we provide a public sandbox server containing synthesized clinical data for users to explore OMOP data and ROMOP (http://romop.ucsf.edu).

A High-Throughput System for Transient and Stable Protein Production in Mammalian Cells.

Recombinant protein expression and purification is an essential component of biomedical research and drug discovery. Advances in automation and laboratory robotics have enabled the development of highly parallel and rapid processes for cell culture and protein expression, purification, and analysis. Human embryonic kidney (HEK) cells and Chinese hamster ovary (CHO) cells have emerged as the standard host cell workhorses for producing recombinant secreted mammalian proteins by using both transient and stable production strategies. In this chapter we describe a fully automated custom platform, Protein Expression and Purification Platform (PEPP), used for transient protein production from HEK cells and stable protein production from CHO cells. Central to PEPP operation is a suite of custom robotic and instrumentation platforms designed and built at GNF, custom cell culture ware, and custom scheduling software referred to as Runtime. The PEPP platform enables cost-effective, facile, consistent production of proteins at quantities and quality useful for early stage drug discovery tasks such as screening, bioassays, protein engineering, and analytics.