Integrative Systems Biology Research Articles

Integrative bioinformatics analysis reveals new prognostic biomarkers of clear cell renal cell carcinoma.

The outcome of clear cell renal cell carcinoma (ccRCC) is still unpredictable. Even with new targeted therapies, the average progression-free survival is dismal. Markers for early detection and progression could improve disease outcome. To identify efficient and hitherto unrecognized pathogenic factors of the disease, we performed a uniquely comprehensive pathway analysis and built a gene interaction network based on large publicly available data sets assembled from 28 publications, comprising a 3-prong approach with high-throughput mRNA, microRNA, and protein expression profiles of 593 ccRCC and 389 normal kidney samples. We validated our results on 2 different data sets of 882 ccRCC and 152 normal tissues. Functional analyses were done by proliferation, migration, and invasion assays following siRNA (small interfering RNA) knockdown. After integration of multilevel data, we identified aryl-hydrocarbon receptor (AHR), grainyhead-like-2 (GRHL2), and KIAA0101 as new pathogenic factors. GRHL2 expression was associated with higher chances for disease relapse and retained prognostic utility after controlling for grade and stage [hazard ratio (HR), 3.47, P = 0.012]. Patients with KIAA0101-positive expression suffered worse disease-free survival (HR, 3.64, P < 0.001), and in multivariate analysis KIAA0101 retained its independent prognostic significance. Survival analysis showed that GRHL2- and KIAA0101-positive patients had significantly lower disease-free survival (P = 0.002 and P < 0.001). We also found that KIAA0101 silencing decreased kidney cancer cell migration and invasion in vitro. Using an integrative system biology approach, we identified 3 novel factors as potential biomarkers (AHR, GRHL2 and KIAA0101) involved in ccRCC pathogenesis and not linked to kidney cancer before.

Systems-based technologies in profiling the stem cell molecular framework for cardioregenerative medicine.

Over the last decade, advancements in stem cell biology have yielded a variety of sources for stem cell-based cardiovascular investigation. Stem cell behavior, whether to maintain its stable state of pluripotency or to prime toward the cardiovascular lineage is governed by a set of coordinated interactions between epigenetic, transcriptional, and translational mechanisms. The science of incorporating genes (genomics), RNA (transcriptomics), proteins (proteomics), and metabolites (metabolomics) data in a specific biological sample is known as systems biology. Integrating systems biology in progression with stem cell biologics can contribute to our knowledge of mechanisms that underlie pluripotency maintenance and guarantee fidelity of cardiac lineage specification. This review provides a brief summarization of OMICS-based strategies including transcriptomics, proteomics, and metabolomics used to understand stem cell fate and to outline molecular processes involved in heart development. Additionally, current efforts in cardioregeneration based on the "one-size-fits-all" principle limit the potential of individualized therapy in regenerative medicine. Here, we summarize recent studies that introduced systems biology into cardiovascular clinical outcomes analysis, allowing for predictive assessment for disease recurrence and patient-specific therapeutic response.

The study of severe cutaneous drug hypersensitivity reactions from a systems biology perspective.

Stevens-Johnson syndrome and toxic epidermal necrolysis are severe hypersensitivity reactions, the majority of which are drug induced. The underlying mechanisms are not fully understood. Here, we review recent findings concerning both mechanistic and genetic factors related to these diseases and propose future approaches to unravel their complexity. Genome-wide association study studies have identified several variants in the human leukocyte antigen region associated with these reactions. These are highly dependent on the population studied and the triggering drug. The T-cell receptor repertoire of the patient is also key. Fas-Fas ligand interactions, perforin and granulysin have also been identified as important players. Furthermore, a high-throughput gene expression study has identified a number of genes that increase in expression in patients during the acute phase of these reactions. We review recent high-throughput studies on these diseases and suggest ways in which the data can be combined and reanalyzed using integrative systems biology techniques. We also suggest future lines of research using recent technology that could shed further light on their underlying mechanisms.

Hitchhiker's guide to multi-dimensional plant pathology.

Filamentous pathogens pose a substantial threat to global food security. One central question in plant pathology is how pathogens cause infection and manage to evade or suppress plant immunity to promote disease. With many technological advances over the past decade, including DNA sequencing technology, an array of new tools has become embedded within the toolbox of next-generation plant pathologists. By employing a multidisciplinary approach plant pathologists can fully leverage these technical advances to answer key questions in plant pathology, aimed at achieving global food security. This review discusses the impact of: cell biology and genetics on progressing our understanding of infection structure formation on the leaf surface; biochemical and molecular analysis to study how pathogens subdue plant immunity and manipulate plant processes through effectors; genomics and DNA sequencing technologies on all areas of plant pathology; and new forms of collaboration on accelerating exploitation of big data. As we embark on the next phase in plant pathology, the integration of systems biology promises to provide a holistic perspective of plant–pathogen interactions from big data and only once we fully appreciate these complexities can we design truly sustainable solutions to preserve our resources.

Micro-RNAs in regenerating lungs: an integrative systems biology analysis of murine influenza pneumonia.

BackgroundTissue regeneration in the lungs is gaining increasing interest as a potential influenza management strategy. In this study, we explored the role of microRNAs, short non-coding RNAs involved in post-transcriptional regulation, during pulmonary regeneration after influenza infection.ResultsWe profiled miRNA and mRNA expression levels following lung injury and tissue regeneration using a murine influenza pneumonia model. BALB/c mice were infected with a sub-lethal dose of influenza A/PR/8(H1N1) virus, and their lungs were harvested at 7 and 15 days post-infection to evaluate the expression of ~300 miRNAs along with ~36,000 genes using microarrays. A global network was constructed between differentially expressed miRNAs and their potential target genes with particular focus on the pulmonary repair and regeneration processes to elucidate the regulatory role of miRNAs in the lung repair pathways. The miRNA arrays revealed a global down-regulation of miRNAs. TargetScan analyses also revealed specific miRNAs highly involved in targeting relevant gene functions in repair such as miR-290 and miR-505 at 7 dpi; and let-7, miR-21 and miR-30 at 15 dpi.ConclusionThe significantly differentially regulated miRNAs are implicated in the activation or suppression of cellular proliferation and stem cell maintenance, which are required during the repair of the damaged lungs. These findings provide opportunities in the development of novel repair strategies in influenza-induced pulmonary injury.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-587) contains supplementary material, which is available to authorized users.

A systems biology analysis of brain microvascular endothelial cell lipotoxicity.

BackgroundNeurovascular inflammation is associated with a number of neurological diseases including vascular dementia and Alzheimer’s disease, which are increasingly important causes of morbidity and mortality around the world. Lipotoxicity is a metabolic disorder that results from accumulation of lipids, particularly fatty acids, in non-adipose tissue leading to cellular dysfunction, lipid droplet formation, and cell death.ResultsOur studies indicate for the first time that the neurovascular circulation also can manifest lipotoxicity, which could have major effects on cognitive function. The penetration of integrative systems biology approaches is limited in this area of research, which reduces our capacity to gain an objective insight into the signal transduction and regulation dynamics at a systems level. To address this question, we treated human microvascular endothelial cells with triglyceride-rich lipoprotein (TGRL) lipolysis products and then we used genome-wide transcriptional profiling to obtain transcript abundances over four conditions. We then identified regulatory genes and their targets that have been differentially expressed through analysis of the datasets with various statistical methods. We created a functional gene network by exploiting co-expression observations through a guilt-by-association assumption. Concomitantly, we used various network inference algorithms to identify putative regulatory interactions and we integrated all predictions to construct a consensus gene regulatory network that is TGRL lipolysis product specific.ConclusionSystem biology analysis has led to the validation of putative lipid-related targets and the discovery of several genes that may be implicated in lipotoxic-related brain microvascular endothelial cell responses. Here, we report that activating transcription factors 3 (ATF3) is a principal regulator of TGRL lipolysis products-induced gene expression in human brain microvascular endothelial cell.

Lynx web services for annotations and systems analysis of multi-gene disorders.

Lynx is a web-based integrated systems biology platform that supports annotation and analysis of experimental data and generation of weighted hypotheses on molecular mechanisms contributing to human phenotypes and disorders of interest. Lynx has integrated multiple classes of biomedical data (genomic, proteomic, pathways, phenotypic, toxicogenomic, contextual and others) from various public databases as well as manually curated data from our group and collaborators (LynxKB). Lynx provides tools for gene list enrichment analysis using multiple functional annotations and network-based gene prioritization. Lynx provides access to the integrated database and the analytical tools via REST based Web Services (http://lynx.ci.uchicago.edu/webservices.html). This comprises data retrieval services for specific functional annotations, services to search across the complete LynxKB (powered by Lucene), and services to access the analytical tools built within the Lynx platform.

Integrative analyses of hepatic differentially expressed genes and blood biomarkers during the peripartal period between dairy cows overfed or restricted-fed energy prepartum.

Using published dairy cattle liver transcriptomics dataset along with novel blood biomarkers of liver function, metabolism, and inflammation we have attempted an integrative systems biology approach applying the classical functional enrichment analysis using DAVID, a newly-developed Dynamic Impact Approach (DIA), and an upstream gene network analysis using Ingenuity Pathway Analysis (IPA). Transcriptome data was generated from experiments evaluating the impact of prepartal plane of energy intake [overfed (OF) or restricted (RE)] on liver of dairy cows during the peripartal period. Blood biomarkers uncovered that RE vs. OF led to greater prepartal liver distress accompanied by a low-grade inflammation and larger proteolysis (i.e., higher haptoglobin, bilirubin, and creatinine). Post-partum the greater bilirubinaemia and lipid accumulation in OF vs. RE indicated a large degree of liver distress. The re-analysis of microarray data revealed that expression of >4,000 genes was affected by diet × time. The bioinformatics analysis indicated that RE vs. OF cows had a liver with a greater lipid and amino acid catabolic capacity both pre- and post-partum while OF vs. RE cows had a greater activation of pathways/functions related to triglyceride synthesis. Furthermore, RE vs. OF cows had a larger (or higher capacity to cope with) ER stress likely associated with greater protein synthesis/processing, and a higher activation of inflammatory-related functions. Liver in OF vs. RE cows had a larger cell proliferation and cell-to-cell communication likely as a response to the greater lipid accumulation. Analysis of upstream regulators indicated a pivotal role of several lipid-related transcription factors (e.g., PPARs, SREBPs, and NFE2L2) in priming the liver of RE cows to better face the early postpartal metabolic and inflammatory challenges. An all-encompassing dynamic model was proposed based on the findings.

From high-throughput structural bioinformatics to integrative systems biology: NETTAB 2014

The NETTAB 2014 Workshop will be held in Torino, by the Molecular Biotechnology Center, from October 16 to 17, 2014. The workshop is aimed at discussing how to manage and elaborate structural and high-throughput proteomics data so that it may be integrated with information from other life sciences disciplines in the context of Systems Biology. The workshop title, which is “From high-throughput structural bioinformatics to integrative systems biology”, reflects this scope. The topics of the workshop will relate to methods, tools, applications, and perspectives on structural bioinformatics, proteomics and integrative systems biology.

Genomics to systems biology in animal and veterinary sciences: Progress, lessons and opportunities

Livestock genomics has gone through a paradigm shift since the advent of genome sequencing that includes Genome-Wide Association Studies (GWAS), Whole Genome Predictions (WGP) and Genomic Selection (GS). Beginning with a brief review of current progress and challenges in livestock GWAS, WGP and GS, opportunities for next generation methods are introduced that unravel the underlying systems genetics of complex traits and provide biologically meaningful and accurate predictions. Genome-Wide Epistasis Association (GWEA) and Weighted Interaction SNP Hub (WISH) network methods are introduced here to unravel complex trait genetics. These methods effectively address the problems of GWAS that have no ability to model and analyze genome-wide genetic interactions and thus do not capture any epistatic variance that could explain part of the missing heritability. Further, the Systems genomic BLUP (sgBLUP) prediction method is introduced in this paper as a next generation WGP or GS tool that can account for and differentiate SNPs with known biological roles in the phenotypic or disease outcomes and potentially increase the accuracy of prediction. It is emphasized that tools that link genetic variants to their functions, pathways and other biological roles will become even more important in the future. These tools include FunctSNP, Postgwas and NCBI2R which are briefly discussed. Genome-Wide Gene Expression (Transcriptomics) analyses using RNAseq technology are briefly discussed with some examples including results from our own pig experiments. In the last part of this review, systems genetics and systems biology approaches are introduced that involve joint modeling and analyses of multi-omics data types from genomics through transcriptomics (microarray and RNAseq), metabolomics to proteomics. It is shown using published studies that these systems approaches are valuable and powerful compared to stand-alone genomic methods in identifying key causal and highly predictive genetic variants for complex traits as well as in building up complex genetic regulatory networks. In all sections, some applications of next generation/-omics methods in livestock species (e.g. feed efficiency, growth, weight gain, fertility and disease resistance in cattle, pigs and sheep) are provided with references to relevant software and tools. In conclusion, this paper reviewed the current progress, lessons and challenges in livestock genomics and its ongoing transition to and opportunities for integrative systems genetics and systems biology in animal and veterinary sciences. Most of these integrative systems genetics and systems biology tools and methods presented here are equally applicable to plant and human genetics and systems biology.

Bioinformatics-driven discovery of rational combination for overcoming EGFR-mutant lung cancer resistance to EGFR therapy.

Non-small-cell lung cancer (NSCLC) is the leading cause of cancer death in the United States. Targeted tyrosine kinase inhibitors (TKIs) directed against the epidermal growth factor receptor (EGFR) have been widely and successfully used in treating NSCLC patients with activating EGFR mutations. Unfortunately, the duration of response is short-lived, and all patients eventually relapse by acquiring resistance mechanisms. We performed an integrative systems biology approach to determine essential kinases that drive EGFR-TKI resistance in cancer cell lines. We used a series of bioinformatics methods to analyze and integrate the functional genetics screen and RNA-seq data to identify a set of kinases that are critical in survival and proliferation in these TKI-resistant lines. By connecting the essential kinases to compounds using a novel kinase connectivity map (K-Map), we identified and validated bosutinib as an effective compound that could inhibit proliferation and induce apoptosis in TKI-resistant lines. A rational combination of bosutinib and gefitinib showed additive and synergistic effects in cancer cell lines resistant to EGFR TKI alone. We have demonstrated a bioinformatics-driven discovery roadmap for drug repurposing and development in overcoming resistance in EGFR-mutant NSCLC, which could be generalized to other cancer types in the era of personalized medicine. K-Map can be accessible at: http://tanlab.ucdenver.edu/kMap. Supplementary data are available at Bioinformatics online.

Manganese-containing Prussian blue nanoparticles for imaging of pediatric brain tumors.

Pediatric brain tumors (PBTs) are a leading cause of death in children. For an improved prognosis in patients with PBTs, there is a critical need to develop molecularly-specific imaging agents to monitor disease progression and response to treatment. In this paper, we describe manganese-containing Prussian blue nanoparticles as agents for molecular magnetic resonance imaging (MRI) and fluorescence-based imaging of PBTs. Our core-shell nanoparticles consist of a core lattice structure that incorporates and retains paramagnetic Mn2+ ions, and generates MRI contrast (both negative and positive). The biofunctionalized shell is comprised of fluorescent avidin, which serves the dual purpose of enabling fluorescence imaging and functioning as a platform for the attachment of biotinylated ligands that target PBTs. The surfaces of our nanoparticles are modified with biotinylated antibodies targeting neuron-glial antigen 2 or biotinylated transferrin. Both neuron-glial antigen 2 and the transferrin receptor are protein markers overexpressed in PBTs. We describe the synthesis, biofunctionalization, and characterization of these multimodal nanoparticles. Further, we demonstrate the MRI and fluorescence imaging capabilities of manganese-containing Prussian blue nanoparticles in vitro. Finally, we demonstrate the potential of these nanoparticles as PBT imaging agents by measuring their organ and brain biodistribution in an orthotopic mouse model of PBTs using ex vivo fluorescence imaging.

The human gut microbiota: a dynamic interplay with the host from birth to senescence settled during childhood.

The microbiota "organ" is the central bioreactor of the gastrointestinal tract, populated by a total of 10(14) bacteria and characterized by a genomic content (microbiome), which represents more than 100 times the human genome. The microbiota plays an important role in child health by acting as a barrier against pathogens and their invasion with a highly dynamic modality, exerting metabolic multistep functions and stimulating the development of the host immune system, through well-organized programming, which influences all of the growth and aging processes. The advent of "omics" technologies (genomics, proteomics, metabolomics), characterized by complex technological platforms and advanced analytical and computational procedures, has opened new avenues to the knowledge of the gut microbiota ecosystem, clarifying some aspects on the establishment of microbial communities that constitute it, their modulation and active interaction with external stimuli as well as food, within the host genetic variability. With a huge interdisciplinary effort and an interface work between basic, translational, and clinical research, microbiologists, specialists in "-omics" disciplines, and clinicians are now clarifying the role of the microbiota in the programming process of several gut-related diseases, from the physiological symbiosis to the microbial dysbiosis stage, through an integrated systems biology approach.

MiRNA expression profile in non‐human primate in response to Yersinia pestis infection (1138.3)

miRNA expression profile in non‐human primate in response to Yersinia pestis infection George Dimitrov, Candace Moyler, Aarti Gautam, Raina Kumar, Rasha Hammamieh and Marti Jett Integrative Systems Biology Program, US Army Center for Environmental Health Research 568 Doughten Drive, Fort Detrick, MD 21702‐5010 MicroRNAs (miRNAs) are small non‐coding RNA molecules, 20‐30 nucleotides in size, that suppress gene expression at both transcriptional and post‐transcriptional levels. Recent studies indicate that miRNAs play significant roles in pathogen‐host interactions. Yersinia pestis is a gram‐negative obligate pathogen and the causative agent of plague, which throughout history has had a devastating effect on human population. We carried out miRNA profiling studies in tissues and blood samples collected from African green monkeys (Cercopithecus aethiops) exposed to aerosolized Y. pestis CO92. These studies enabled us to examine and identify the tissue‐specific differences in small RNA expression profiles during pathogen‐host interactions, and to detect their regulation patterns during the progression of disease. For this study, we used a small RNA Illumina deep sequencing method. Total yields of clean sequencing reads ranging from 17009208 to 11126920 were obtained after removing adaptor contamination and quality filtering. The size distribution of small RNAs (miRNA) ranged from 20 to 24 nt, which accounted for 38 percent of the total reads. The miRNA precursor and mature miRNA expression profiles were created. These will allow us to study the expression profile for tissue‐specific miRNA across different time points.Grant Funding Source: TMTI0029_09_WR_T from the Department of Defense

Strategies for coordinating experimentation and modeling in integrative systems biology

In this paper, we provide a novel analysis of the affordances and trade-offs of different strategies for integrating model-building and experimentation in integrative systems biology. Unimodal strategies rely on collaboration between experimenters and modelers in distinct laboratories. In a bimodal strategy modelers perform their own experiments. Each option has advantages and challenges. In the case of the labs we studied, the choice of strategy often depends on preferences held by lab directors as to the strategy that best achieves certain philosophical objectives concerning what they see as the aims of modeling in systems biology and the epistemic standards to apply to it. We identify an important connection between philosophical divisions in systems biology and the structure of research in systems biology. Better knowledge of these strategies and their philosophical motivations provide insight behind the diversifying structure of the field, and can help lab directors understand the challenges their researchers face, as well as the training and lab organization options available.

Applicability of Computational Systems Biology in Toxicology

Systems biology as a research field has emerged within the last few decades. Systems biology, often defined as the antithesis of the reductionist approach, integrates information about individual components of a biological system. In integrative systems biology, large data sets from various sources and databases are used to model and predict effects of chemicals on, for instance, human health. In toxicology, computational systems biology enables identification of important pathways and molecules from large data sets; tasks that can be extremely laborious when performed by a classical literature search. However, computational systems biology offers more advantages than providing a high-throughput literature search; it may form the basis for establishment of hypotheses on potential links between environmental chemicals and human diseases, which would be very difficult to establish experimentally. This is possible due to the existence of comprehensive databases containing information on networks of human protein-protein interactions and protein-disease associations. Experimentally determined targets of the specific chemical of interest can be fed into these networks to obtain additional information that can be used to establish hypotheses on links between the chemical and human diseases. Such information can also be applied for designing more intelligent animal/cell experiments that can test the established hypotheses. Here, we describe how and why to apply an integrative systems biology method in the hypothesis-generating phase of toxicological research.

Genetics, genomics and metabolomics: new insights into maternal metabolism during pregnancy

Maternal glucose metabolism during pregnancy differs from the non-gravid state to allow the mother to meet her own and the growing fetus's energy needs. New insights into the mechanisms underlying maternal metabolism during pregnancy are being gained through the use of new 'omics' technologies. This review focuses on the application of genetics/genomics and metabolomics to the study of maternal metabolism during pregnancy. Following the identification of susceptibility genes for Type 2 diabetes through genome-wide association studies, association has been demonstrated of some Type 2 diabetes susceptibility genes with gestational diabetes mellitus, suggesting that the genetic architecture of Type 2 diabetes and gestational diabetes are, in part, similar. More recent genome-wide association studies examining maternal metabolism during pregnancy have demonstrated overlap of genes associated with metabolic traits in the gravid and non-gravid population, as well as genes that appear to be relatively unique to pregnancy. Metabolomics has also been used to profile the metabolic state of women during pregnancy through the multiplexed measurement of many low molecular weight metabolites. Measurement of amino acids and conventional metabolites have demonstrated changes in mothers with higher insulin resistance and glucose similar to changes in non-gravid, insulin-resistant populations, suggesting similarities in the metabolic profile characteristic of insulin resistance and hyperglycaemia in pregnant and non-pregnant populations. Metabolomics and genomics are but a few of the now available high-throughput 'omics' technologies. Future studies that integrate data from multiple technologies will allow an integrated systems biology approach to maternal metabolism during pregnancy.

Integrative radiation systems biology

Maximisation of the ratio of normal tissue preservation and tumour cell reduction is the main concept of radiotherapy alone or combined with chemo-, immuno- or biologically targeted therapy. The foremost parameter influencing this ratio is radiation sensitivity and its modulation towards a more efficient killing of tumour cells and a better preservation of normal tissue at the same time is the overall aim of modern therapy schemas. Nevertheless, this requires a deep understanding of the molecular mechanisms of radiation sensitivity in order to identify its key players as potential therapeutic targets. Moreover, the success of conventional approaches that tried to statistically associate altered radiation sensitivity with any molecular phenotype such as gene expression proofed to be somewhat limited since the number of clinically used targets is rather sparse. However, currently a paradigm shift is taking place from pure frequentistic association analysis to the rather holistic systems biology approach that seeks to mathematically model the system to be investigated and to allow the prediction of an altered phenotype as the function of one single or a signature of biomarkers. Integrative systems biology also considers the data from different molecular levels such as the genome, transcriptome or proteome in order to partially or fully comprehend the causal chain of molecular mechanisms. An example for the application of this concept currently carried out at the Clinical Cooperation Group “Personalized Radiotherapy in Head and Neck Cancer” of the Helmholtz-Zentrum München and the LMU Munich is described. This review article strives for providing a compact overview on the state of the art of systems biology, its actual challenges, potential applications, chances and limitations in radiation oncology research working towards improved personalised therapy concepts using this relatively new methodology.

Use of Systems Biology Approaches to Analysis of Genome-Wide Association Studies of Myocardial Infarction and Blood Cholesterol in the Nurses' Health Study and Health Professionals’ Follow-Up Study

With the advance of genome-wide association studies and newly identified SNP (single-nucleotide polymorphism) associations with complex disease, important discoveries have emerged focusing not only on individual genes but on disease-associated pathways and gene sets. The authors used prospective myocardial infarction case-control studies nested in the Nurses’ Health and Health Professionals Follow-Up Studies to investigate genetic variants associated with myocardial infarction or LDL, HDL, triglycerides, adiponectin and apolipoprotein B (apoB). Using these case-control studies to illustrate an integrative systems biology approach, the authors applied SNP set enrichment analysis to identify gene sets where expression SNPs representing genes from these sets show enrichment in their association with endpoints of interest. The authors also explored an aggregate score approach. While power limited one’s ability to detect significance for association of individual loci with myocardial infarction, the authors found significance for loci associated with LDL, HDL, apoB and triglycerides, replicating previous observations. Applying SNP set enrichment analysis and risk score methods, the authors also found significance for three gene sets and for aggregate scores associated with myocardial infarction as well as for loci-related to cardiovascular risk factors, supporting the use of these methods in practice.

Is There Really a Need to Adjust Gentamicin Dosing in Neonates Treated With Hypothermia?

*Division of Pediatric Clinical Pharmacology, Children's National Medical Center, Washington, D.C. Departments of †Pediatrics, ‡Pharmacology & Physiology, and §Integrative Systems Biology, The George Washington University School of Medicine and Health Sciences, Washington, D.C. The authors declare no conflicts of interest.