RESUMO
The emergence and spread of SARS-CoV-2 lineage B.1.1.7, first detected in the United Kingdom, has become a global public health concern because of its increased transmissibility. Over 2,500 COVID-19 cases associated with this variant have been detected in the United States (US) since December 2020, but the extent of establishment is relatively unknown. Using travel, genomic, and diagnostic data, we highlight that the primary ports of entry for B.1.1.7 in the US were in New York, California, and Florida. Furthermore, we found evidence for many independent B.1.1.7 establishments starting in early December 2020, followed by interstate spread by the end of the month. Finally, we project that B.1.1.7 will be the dominant lineage in many states by mid- to late March. Thus, genomic surveillance for B.1.1.7 and other variants urgently needs to be enhanced to better inform the public health response.
Assuntos
Teste para COVID-19 , COVID-19 , Modelos Biológicos , SARS-CoV-2 , COVID-19/genética , COVID-19/mortalidade , COVID-19/transmissão , Feminino , Humanos , Masculino , SARS-CoV-2/genética , SARS-CoV-2/metabolismo , SARS-CoV-2/patogenicidade , Estados Unidos/epidemiologiaRESUMO
Heritability is essential for understanding the biological causes of disease but requires laborious patient recruitment and phenotype ascertainment. Electronic health records (EHRs) passively capture a wide range of clinically relevant data and provide a resource for studying the heritability of traits that are not typically accessible. EHRs contain next-of-kin information collected via patient emergency contact forms, but until now, these data have gone unused in research. We mined emergency contact data at three academic medical centers and identified 7.4 million familial relationships while maintaining patient privacy. Identified relationships were consistent with genetically derived relatedness. We used EHR data to compute heritability estimates for 500 disease phenotypes. Overall, estimates were consistent with the literature and between sites. Inconsistencies were indicative of limitations and opportunities unique to EHR research. These analyses provide a validation of the use of EHRs for genetics and disease research.
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Registros Eletrônicos de Saúde , Doenças Genéticas Inatas/genética , Algoritmos , Bases de Dados Factuais , Relações Familiares , Doenças Genéticas Inatas/patologia , Genótipo , Humanos , Linhagem , Fenótipo , Característica Quantitativa HerdávelRESUMO
Personalized medicine is expected to benefit from combining genomic information with regular monitoring of physiological states by multiple high-throughput methods. Here, we present an integrative personal omics profile (iPOP), an analysis that combines genomic, transcriptomic, proteomic, metabolomic, and autoantibody profiles from a single individual over a 14 month period. Our iPOP analysis revealed various medical risks, including type 2 diabetes. It also uncovered extensive, dynamic changes in diverse molecular components and biological pathways across healthy and diseased conditions. Extremely high-coverage genomic and transcriptomic data, which provide the basis of our iPOP, revealed extensive heteroallelic changes during healthy and diseased states and an unexpected RNA editing mechanism. This study demonstrates that longitudinal iPOP can be used to interpret healthy and diseased states by connecting genomic information with additional dynamic omics activity.
Assuntos
Genoma Humano , Genômica , Medicina de Precisão , Diabetes Mellitus Tipo 2/genética , Feminino , Perfilação da Expressão Gênica , Humanos , Masculino , Metabolômica , Pessoa de Meia-Idade , Mutação , Proteômica , Vírus Sinciciais Respiratórios/isolamento & purificação , Rhinovirus/isolamento & purificaçãoRESUMO
The immune system is a highly complex and dynamic system. Historically, the most common scientific and clinical practice has been to evaluate its individual components. This kind of approach cannot always expose the interconnecting pathways that control immune-system responses and does not reveal how the immune system works across multiple biological systems and scales. High-throughput technologies can be used to measure thousands of parameters of the immune system at a genome-wide scale. These system-wide surveys yield massive amounts of quantitative data that provide a means to monitor and probe immune-system function. New integrative analyses can help synthesize and transform these data into valuable biological insight. Here we review some of the computational analysis tools for high-dimensional data and how they can be applied to immunology.
Assuntos
Alergia e Imunologia , Sistema Imunitário , Informática Médica/métodos , Biologia de Sistemas/métodos , Animais , Estudo de Associação Genômica Ampla , Ensaios de Triagem em Larga Escala , Humanos , Análise de Componente Principal , Projetos de PesquisaRESUMO
With the emergence of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) variants that may increase transmissibility and/or cause escape from immune responses, there is an urgent need for the targeted surveillance of circulating lineages. It was found that the B.1.1.7 (also 501Y.V1) variant, first detected in the United Kingdom, could be serendipitously detected by the Thermo Fisher TaqPath COVID-19 PCR assay because a key deletion in these viruses, spike Δ69-70, would cause a "spike gene target failure" (SGTF) result. However, a SGTF result is not definitive for B.1.1.7, and this assay cannot detect other variants of concern (VOC) that lack spike Δ69-70, such as B.1.351 (also 501Y.V2), detected in South Africa, and P.1 (also 501Y.V3), recently detected in Brazil. We identified a deletion in the ORF1a gene (ORF1a Δ3675-3677) in all 3 variants, which has not yet been widely detected in other SARS-CoV-2 lineages. Using ORF1a Δ3675-3677 as the primary target and spike Δ69-70 to differentiate, we designed and validated an open-source PCR assay to detect SARS-CoV-2 VOC. Our assay can be rapidly deployed in laboratories around the world to enhance surveillance for the local emergence and spread of B.1.1.7, B.1.351, and P.1.
Assuntos
COVID-19/virologia , SARS-CoV-2/genética , COVID-19/diagnóstico , COVID-19/genética , Primers do DNA , Humanos , Reação em Cadeia da Polimerase Multiplex/métodos , Mutação , Poliproteínas/genética , Proteínas Virais/genéticaRESUMO
Rationale: Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive, and often fatal disorder. Two U.S. Food and Drug Administration-approved antifibrotic drugs, nintedanib and pirfenidone, slow the rate of decline in lung function, but responses are variable and side effects are common. Objectives: Using an in silico data-driven approach, we identified a robust connection between the transcriptomic perturbations in IPF disease and those induced by saracatinib, a selective Src kinase inhibitor originally developed for oncological indications. Based on these observations, we hypothesized that saracatinib would be effective at attenuating pulmonary fibrosis. Methods: We investigated the antifibrotic efficacy of saracatinib relative to nintedanib and pirfenidone in three preclinical models: 1) in vitro in normal human lung fibroblasts; 2) in vivo in bleomycin and recombinant Ad-TGF-ß (adenovirus transforming growth factor-ß) murine models of pulmonary fibrosis; and 3) ex vivo in mice and human precision-cut lung slices from these two murine models as well as patients with IPF and healthy donors. Measurements and Main Results: In each model, the effectiveness of saracatinib in blocking fibrogenic responses was equal or superior to nintedanib and pirfenidone. Transcriptomic analyses of TGF-ß-stimulated normal human lung fibroblasts identified specific gene sets associated with fibrosis, including epithelial-mesenchymal transition, TGF-ß, and WNT signaling that was uniquely altered by saracatinib. Transcriptomic analysis of whole-lung extracts from the two animal models of pulmonary fibrosis revealed that saracatinib reverted many fibrogenic pathways, including epithelial-mesenchymal transition, immune responses, and extracellular matrix organization. Amelioration of fibrosis and inflammatory cascades in human precision-cut lung slices confirmed the potential therapeutic efficacy of saracatinib in human lung fibrosis. Conclusions: These studies identify novel Src-dependent fibrogenic pathways and support the study of the therapeutic effectiveness of saracatinib in IPF treatment.
Assuntos
Fibrose Pulmonar Idiopática , Inibidores de Proteínas Quinases , Animais , Humanos , Camundongos , Bleomicina/efeitos adversos , Fibroblastos/metabolismo , Fibrose , Fibrose Pulmonar Idiopática/tratamento farmacológico , Pulmão/patologia , Inibidores de Proteínas Quinases/uso terapêutico , Quinases da Família src/metabolismo , Fator de Crescimento Transformador beta/metabolismoRESUMO
Sepsis is a series of clinical syndromes caused by the immunological response to infection. The clinical evidence for sepsis could typically attribute to bacterial infection or bacterial endotoxins, but infections due to viruses, fungi or parasites could also lead to sepsis. Regardless of the etiology, rapid clinical deterioration, prolonged stay in intensive care units and high risk for mortality correlate with the incidence of sepsis. Despite its prevalence and morbidity, improvement in sepsis outcomes has remained limited. In this comprehensive review, we summarize the current landscape of risk estimation, diagnosis, treatment and prognosis strategies in the setting of sepsis and discuss future challenges. We argue that the advent of modern technologies such as in-depth molecular profiling, biomedical big data and machine intelligence methods will augment the treatment and prevention of sepsis. The volume, variety, veracity and velocity of heterogeneous data generated as part of healthcare delivery and recent advances in biotechnology-driven therapeutics and companion diagnostics may provide a new wave of approaches to identify the most at-risk sepsis patients and reduce the symptom burden in patients within shorter turnaround times. Developing novel therapies by leveraging modern drug discovery strategies including computational drug repositioning, cell and gene-therapy, clustered regularly interspaced short palindromic repeats -based genetic editing systems, immunotherapy, microbiome restoration, nanomaterial-based therapy and phage therapy may help to develop treatments to target sepsis. We also provide empirical evidence for potential new sepsis targets including FER and STARD3NL. Implementing data-driven methods that use real-time collection and analysis of clinical variables to trace, track and treat sepsis-related adverse outcomes will be key. Understanding the root and route of sepsis and its comorbid conditions that complicate treatment outcomes and lead to organ dysfunction may help to facilitate identification of most at-risk patients and prevent further deterioration. To conclude, leveraging the advances in precision medicine, biomedical data science and translational bioinformatics approaches may help to develop better strategies to diagnose and treat sepsis in the next decade.
Assuntos
Medicina de Precisão , Sepse/diagnóstico , Sepse/terapia , Humanos , Prognóstico , Fatores de Risco , Sepse/patologiaRESUMO
MICU1 is a mitochondrial inner membrane protein that inhibits mitochondrial calcium entry; elevated MICU1 expression is characteristic of many cancers, including ovarian cancer. MICU1 induces both glycolysis and chemoresistance and is associated with poor clinical outcomes. However, there are currently no available interventions to normalize aberrant MICU1 expression. Here, we demonstrate that microRNA-195-5p (miR-195) directly targets the 3' UTR of the MICU1 mRNA and represses MICU1 expression. Additionally, miR-195 is under-expressed in ovarian cancer cell lines, and restoring miR-195 expression reestablishes native MICU1 levels and the associated phenotypes. Stable expression of miR-195 in a human xenograft model of ovarian cancer significantly reduces tumor growth, increases tumor doubling times, and enhances overall survival. In conclusion, miR-195 controls MICU1 levels in ovarian cancer and could be exploited to normalize aberrant MICU1 expression, thus reversing both glycolysis and chemoresistance and consequently improving patient outcomes.
Assuntos
Proteínas de Transporte de Cátions , MicroRNAs , Neoplasias Ovarianas , Proteínas de Ligação ao Cálcio/genética , Proteínas de Ligação ao Cálcio/metabolismo , Proteínas de Transporte de Cátions/genética , Proteínas de Transporte de Cátions/metabolismo , Linhagem Celular Tumoral , Proliferação de Células/genética , Feminino , Regulação Neoplásica da Expressão Gênica , Glicólise/genética , Humanos , MicroRNAs/genética , MicroRNAs/metabolismo , Proteínas de Transporte da Membrana Mitocondrial/metabolismo , Neoplasias Ovarianas/genéticaRESUMO
Using a systems biology approach to prioritize potential points of intervention in ovarian cancer, we identified the lysine rich coiled-coil 1 (KRCC1), as a potential target. High-grade serous ovarian cancer patient tumors and cells express significantly higher levels of KRCC1 which correlates with poor overall survival and chemoresistance. We demonstrate that KRCC1 is predominantly present in the chromatin-bound nuclear fraction, interacts with HDAC1, HDAC2, and with the serine-threonine phosphatase PP1CC. Silencing KRCC1 inhibits cellular plasticity, invasive properties, and potentiates apoptosis resulting in reduced tumor growth. These phenotypes are associated with increased acetylation of histones and with increased phosphorylation of H2AX and CHK1, suggesting the modulation of transcription and DNA damage that may be mediated by the action of HDAC and PP1CC, respectively. Hence, we address an urgent need to develop new targets in cancer.
Assuntos
Dano ao DNA , Peptídeos e Proteínas de Sinalização Intracelular , Proteínas de Neoplasias , Neoplasias Ovarianas , Transcrição Gênica , Linhagem Celular Tumoral , Feminino , Histona Desacetilase 1/genética , Histona Desacetilase 1/metabolismo , Histona Desacetilase 2/genética , Histona Desacetilase 2/metabolismo , Histonas/genética , Histonas/metabolismo , Humanos , Peptídeos e Proteínas de Sinalização Intracelular/genética , Peptídeos e Proteínas de Sinalização Intracelular/metabolismo , Proteínas de Neoplasias/genética , Proteínas de Neoplasias/metabolismo , Neoplasias Ovarianas/genética , Neoplasias Ovarianas/metabolismo , Neoplasias Ovarianas/patologia , Neoplasias Ovarianas/terapia , Fosforilação , Fatores de RiscoRESUMO
Precision medicine can utilize new techniques in order to more effectively translate research findings into clinical practice. In this article, we first explore the limitations of traditional study designs, which stem from (to name a few): massive cost for the assembly of large patient cohorts; non-representative patient data; and the astounding complexity of human biology. Second, we propose that harnessing electronic health records and mobile device biometrics coupled to longitudinal data may prove to be a solution to many of these problems by capturing a 'real world' phenotype. We envision that future biomedical research utilizing more precise approaches to patient care will utilize continuous and longitudinal data sources.
Assuntos
Big Data , Registros Eletrônicos de Saúde/tendências , Medicina de Precisão/tendências , Pesquisa Translacional Biomédica/tendências , Bancos de Espécimes Biológicos , Humanos , Estudos Longitudinais , FenótipoRESUMO
Gaining knowledge and actionable insights from complex, high-dimensional and heterogeneous biomedical data remains a key challenge in transforming health care. Various types of data have been emerging in modern biomedical research, including electronic health records, imaging, -omics, sensor data and text, which are complex, heterogeneous, poorly annotated and generally unstructured. Traditional data mining and statistical learning approaches typically need to first perform feature engineering to obtain effective and more robust features from those data, and then build prediction or clustering models on top of them. There are lots of challenges on both steps in a scenario of complicated data and lacking of sufficient domain knowledge. The latest advances in deep learning technologies provide new effective paradigms to obtain end-to-end learning models from complex data. In this article, we review the recent literature on applying deep learning technologies to advance the health care domain. Based on the analyzed work, we suggest that deep learning approaches could be the vehicle for translating big biomedical data into improved human health. However, we also note limitations and needs for improved methods development and applications, especially in terms of ease-of-understanding for domain experts and citizen scientists. We discuss such challenges and suggest developing holistic and meaningful interpretable architectures to bridge deep learning models and human interpretability.
Assuntos
Aprendizado Profundo , Atenção à Saúde/organização & administração , Biologia Computacional , Mineração de Dados , Diagnóstico por Imagem , Registros Eletrônicos de Saúde , Genômica , Humanos , TelemedicinaRESUMO
Increase in global population and growing disease burden due to the emergence of infectious diseases (Zika virus), multidrug-resistant pathogens, drug-resistant cancers (cisplatin-resistant ovarian cancer) and chronic diseases (arterial hypertension) necessitate effective therapies to improve health outcomes. However, the rapid increase in drug development cost demands innovative and sustainable drug discovery approaches. Drug repositioning, the discovery of new or improved therapies by reevaluation of approved or investigational compounds, solves a significant gap in the public health setting and improves the productivity of drug development. As the number of drug repurposing investigations increases, a new opportunity has emerged to understand factors driving drug repositioning through systematic analyses of drugs, drug targets and associated disease indications. However, such analyses have so far been hampered by the lack of a centralized knowledgebase, benchmarking data sets and reporting standards. To address these knowledge and clinical needs, here, we present RepurposeDB, a collection of repurposed drugs, drug targets and diseases, which was assembled, indexed and annotated from public data. RepurposeDB combines information on 253 drugs [small molecules (74.30%) and protein drugs (25.29%)] and 1125 diseases. Using RepurposeDB data, we identified pharmacological (chemical descriptors, physicochemical features and absorption, distribution, metabolism, excretion and toxicity properties), biological (protein domains, functional process, molecular mechanisms and pathway cross talks) and epidemiological (shared genetic architectures, disease comorbidities and clinical phenotype similarities) factors mediating drug repositioning. Collectively, RepurposeDB is developed as the reference database for drug repositioning investigations. The pharmacological, biological and epidemiological principles of drug repositioning identified from the meta-analyses could augment therapeutic development.
Assuntos
Biologia Computacional/métodos , Bases de Dados Factuais , Doença , Descoberta de Drogas , Reposicionamento de Medicamentos , Proteínas/metabolismo , Humanos , Epidemiologia Molecular , Proteínas/genéticaRESUMO
MOTIVATION: Radiologists have used algorithms for Computer-Aided Diagnosis (CAD) for decades. These algorithms use machine learning with engineered features, and there have been mixed findings on whether they improve radiologists' interpretations. Deep learning offers superior performance but requires more training data and has not been evaluated in joint algorithm-radiologist decision systems. RESULTS: We developed the Computer-Aided Note and Diagnosis Interface (CANDI) for collaboratively annotating radiographs and evaluating how algorithms alter human interpretation. The annotation app collects classification, segmentation, and image captioning training data, and the evaluation app randomizes the availability of CAD tools to facilitate clinical trials on radiologist enhancement. AVAILABILITY AND IMPLEMENTATION: Demonstrations and source code are hosted at (https://candi.nextgenhealthcare.org), and (https://github.com/mbadge/candi), respectively, under GPL-3 license. SUPPLEMENTARY INFORMATION: Supplementary material is available at Bioinformatics online.
Assuntos
Algoritmos , Software , Aprendizado Profundo , Humanos , Aprendizado de Máquina , Redes Neurais de ComputaçãoRESUMO
MOTIVATION: Electronic health records (EHRs) are quickly becoming omnipresent in healthcare, but interoperability issues and technical demands limit their use for biomedical and clinical research. Interactive and flexible software that interfaces directly with EHR data structured around a common data model (CDM) could accelerate more EHR-based research by making the data more accessible to researchers who lack computational expertise and/or domain knowledge. RESULTS: We present PatientExploreR, an extensible application built on the R/Shiny framework that interfaces with a relational database of EHR data in the Observational Medical Outcomes Partnership CDM format. PatientExploreR produces patient-level interactive and dynamic reports and facilitates visualization of clinical data without any programming required. It allows researchers to easily construct and export patient cohorts from the EHR for analysis with other software. This application could enable easier exploration of patient-level data for physicians and researchers. PatientExploreR can incorporate EHR data from any institution that employs the CDM for users with approved access. The software code is free and open source under the MIT license, enabling institutions to install and users to expand and modify the application for their own purposes. AVAILABILITY AND IMPLEMENTATION: PatientExploreR can be freely obtained from GitHub: https://github.com/BenGlicksberg/PatientExploreR. We provide instructions for how researchers with approved access to their institutional EHR can use this package. We also release an open sandbox server of synthesized patient data for users without EHR access to explore: http://patientexplorer.ucsf.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Assuntos
Registros Eletrônicos de Saúde , Software , Computadores , Bases de Dados Factuais , Humanos , Estudos Observacionais como AssuntoRESUMO
MicroRNAs are recognized as important regulators of many facets of physiological brain function while also being implicated in the pathogenesis of several neurological disorders. Dysregulation of miR155 is widely reported across a variety of neurodegenerative conditions, including Alzheimer's disease (AD), Parkinson's disease, amyotrophic lateral sclerosis, and traumatic brain injury. In previous work, we observed that experimentally validated miR155 gene targets were consistently enriched among genes identified as differentially expressed across multiple brain tissue and disease contexts. In particular, we found that human herpesvirus-6A (HHV-6A) suppressed miR155, recapitulating reports of miR155 inhibition by HHV-6A in infected T-cells, thyrocytes, and natural killer cells. In earlier studies, we also reported the effects of constitutive deletion of miR155 on accelerating the accumulation of Aß deposits in 4-month-old APP/PSEN1 mice. Herein, we complete the cumulative characterization of transcriptomic, electrophysiological, neuropathological, and learning behavior profiles from 4-, 8- and 10-month-old WT and APP/PSEN1 mice in the absence or presence of miR155. We also integrated human post-mortem brain RNA-sequences from four independent AD consortium studies, together comprising 928 samples collected from six brain regions. We report that gene expression perturbations associated with miR155 deletion in mouse cortex are in aggregate observed to be concordant with AD-associated changes across these independent human late-onset AD (LOAD) data sets, supporting the relevance of our findings to human disease. LOAD has recently been formulated as the clinicopathological manifestation of a multiplex of genetic underpinnings and pathophysiological mechanisms. Our accumulated data are consistent with such a formulation, indicating that miR155 may be uniquely positioned at the intersection of at least four components of this LOAD "multiplex": (1) innate immune response pathways; (2) viral response gene networks; (3) synaptic pathology; and (4) proamyloidogenic pathways involving the amyloid ß peptide (Aß).
Assuntos
Doença de Alzheimer/genética , Encéfalo/patologia , MicroRNAs/genética , Transcriptoma/genética , Doença de Alzheimer/patologia , Peptídeos beta-Amiloides/metabolismo , Animais , Encéfalo/metabolismo , Modelos Animais de Doenças , Redes Reguladoras de Genes/genética , Humanos , Camundongos Transgênicos , Doenças do Sistema Nervoso/patologia , Placa Amiloide/patologiaRESUMO
Integrative gene network approaches enable new avenues of exploration that implicate causal genes in sporadic late-onset Alzheimer's disease (LOAD) pathogenesis, thereby offering novel insights for drug-discovery programs. We previously constructed a probabilistic causal network model of sporadic LOAD and identified TYROBP/DAP12, encoding a microglial transmembrane signaling polypeptide and direct adapter of TREM2, as the most robust key driver gene in the network. Here, we show that absence of TYROBP/DAP12 in a mouse model of AD-type cerebral Aß amyloidosis (APPKM670/671NL/PSEN1Δexon9) recapitulates the expected network characteristics by normalizing the transcriptome of APP/PSEN1 mice and repressing the induction of genes involved in the switch from homeostatic microglia to disease-associated microglia (DAM), including Trem2, complement (C1qa, C1qb, C1qc, and Itgax), Clec7a and Cst7. Importantly, we show that constitutive absence of TYROBP/DAP12 in the amyloidosis mouse model prevented appearance of the electrophysiological and learning behavior alterations associated with the phenotype of APPKM670/671NL/PSEN1Δexon9 mice. Our results suggest that TYROBP/DAP12 could represent a novel therapeutic target to slow, arrest, or prevent the development of sporadic LOAD. These data establish that the network pathology observed in postmortem human LOAD brain can be faithfully recapitulated in the brain of a genetically manipulated mouse. These data also validate our multiscale gene networks by demonstrating how the networks intersect with the standard neuropathological features of LOAD.
Assuntos
Proteínas Adaptadoras de Transdução de Sinal/deficiência , Doença de Alzheimer/metabolismo , Peptídeos beta-Amiloides/metabolismo , Amiloidose/metabolismo , Proteínas de Membrana/deficiência , Proteínas Adaptadoras de Transdução de Sinal/genética , Proteínas Adaptadoras de Transdução de Sinal/metabolismo , Doença de Alzheimer/genética , Peptídeos beta-Amiloides/genética , Precursor de Proteína beta-Amiloide/genética , Amiloidose/genética , Animais , Encéfalo/metabolismo , Modelos Animais de Doenças , Feminino , Redes Reguladoras de Genes , Humanos , Masculino , Proteínas de Membrana/genética , Proteínas de Membrana/metabolismo , Camundongos , Camundongos Endogâmicos C57BL , Camundongos Knockout , Patologia Molecular/métodos , Fenótipo , Placa Amiloide/patologia , TranscriptomaRESUMO
This article was originally published under standard licence, but has now been made available under a CC BY 4.0 license. The PDF and HTML versions of the paper have been modified accordingly.
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To date, no large scale, systematic description of the blood serum proteome has been performed in inflammatory bowel disease (IBD) patients. By using microarray technology, a more complete description of the blood proteome of IBD patients is feasible. It may help to achieve a better understanding of the disease. We analyzed blood serum profiles of 1128 proteins in IBD patients of European descent (84 Crohn's Disease (CD) subjects and 88 Ulcerative Colitis (UC) subjects) as well as 15 healthy control subjects, and linked protein variability to patient age (all cohorts) and genetic components (genotype data generated from CD patients). We discovered new, previously unreported aging-associated proteomic traits (such as serum Albumin level), confirmed previously reported results from different tissues (i.e., upregulation of APOE with aging), and found loss of regulation of MMP7 in CD patients. In carrying out a genome wide genotype-protein association study (proteomic Quantitative Trait Loci, pQTL) within the CD patients, we identified 41 distinct proteomic traits influenced by cis pQTLs (underlying SNPs are referred to as pSNPs). Significant overlaps between pQTLs and cis eQTLs corresponding to the same gene were observed and in some cases the QTL were related to inflammatory disease susceptibility. Importantly, we discovered that serum protein levels of MST1 (Macrophage Stimulating 1) were regulated by SNP rs3197999 (p = 5.96E-10, FDR<5%), an accepted GWAS locus for IBD. Filling the knowledge gap of molecular mechanisms between GWAS hits and disease susceptibility requires systematically dissecting the impact of the locus at the cell, mRNA expression, and protein levels. The technology and analysis tools that are now available for large-scale molecular studies can elucidate how alterations in the proteome driven by genetic polymorphisms cause or provide protection against disease. Herein, we demonstrated this directly by integrating proteomic and pQTLs with existing GWAS, mRNA expression, and eQTL datasets to provide insights into the biological processes underlying IBD and pinpoint causal genetic variants along with their downstream molecular consequences.
Assuntos
Envelhecimento/sangue , Predisposição Genética para Doença , Doenças Inflamatórias Intestinais/sangue , Proteoma/metabolismo , Adulto , Biomarcadores/sangue , Estudos de Casos e Controles , Feminino , Fator de Crescimento de Hepatócito/sangue , Ensaios de Triagem em Larga Escala , Humanos , Doenças Inflamatórias Intestinais/epidemiologia , Doenças Inflamatórias Intestinais/genética , Masculino , Pessoa de Meia-Idade , Polimorfismo de Nucleotídeo Único , Proteoma/genética , Proteínas Proto-Oncogênicas/sangue , Locos de Características QuantitativasRESUMO
The tens of thousands of industrial and synthetic chemicals released into the environment have an unknown but potentially significant capacity to interfere with neurodevelopment. Consequently, there is an urgent need for systematic approaches that can identify disruptive chemicals. Little is known about the impact of environmental chemicals on critical periods of developmental neuroplasticity, in large part, due to the challenge of screening thousands of chemicals. Using an integrative bioinformatics approach, we systematically scanned 2001 environmental chemicals and identified 50 chemicals that consistently dysregulate two transcriptional signatures of critical period plasticity. These chemicals included pesticides (e.g., pyridaben), antimicrobials (e.g., bacitracin), metals (e.g., mercury), anesthetics (e.g., halothane), and other chemicals and mixtures (e.g., vehicle emissions). Application of a chemogenomic enrichment analysis and hierarchical clustering across these diverse chemicals identified two clusters of chemicals with one that mimicked an immune response to pathogen, implicating inflammatory pathways and microglia as a common chemically induced neuropathological process. Thus, we established an integrative bioinformatics approach to systematically scan thousands of environmental chemicals for their ability to dysregulate molecular signatures relevant to critical periods of development.
Assuntos
Encéfalo/crescimento & desenvolvimento , Monitoramento Ambiental/métodos , Poluentes Ambientais/análise , Imunidade/genética , Plasticidade Neuronal/genética , Transcriptoma/genética , Animais , Encéfalo/metabolismo , Biologia Computacional , Perfilação da Expressão Gênica , Genômica , Camundongos Endogâmicos C57BLRESUMO
Sleep quality has been directly linked to cognitive function, quality of life, and a variety of serious diseases across many clinical domains. Standard methods for assessing sleep involve overnight studies in hospital settings, which are uncomfortable, expensive, not representative of real sleep, and difficult to conduct on a large scale. Recently, numerous commercial digital devices have been developed that record physiological data, such as movement, heart rate, and respiratory rate, which can act as a proxy for sleep quality in lieu of standard electroencephalogram recording equipment. The sleep-related output metrics from these devices include sleep staging and total sleep duration and are derived via proprietary algorithms that utilize a variety of these physiological recordings. Each device company makes different claims of accuracy and measures different features of sleep quality, and it is still unknown how well these devices correlate with one another and perform in a research setting. In this pilot study of 21 participants, we investigated whether sleep metric outputs from self-reported sleep metrics (SRSMs) and four sensors, specifically Fitbit Surge (a smart watch), Withings Aura (a sensor pad that is placed under a mattress), Hexoskin (a smart shirt), and Oura Ring (a smart ring), were related to known cognitive and psychological metrics, including the n-back test and Pittsburgh Sleep Quality Index (PSQI). We analyzed correlation between multiple device-related sleep metrics. Furthermore, we investigated relationships between these sleep metrics and cognitive scores across different timepoints and SRSM through univariate linear regressions. We found that correlations for sleep metrics between the devices across the sleep cycle were almost uniformly low, but still significant (P < 0.05). For cognitive scores, we found the Withings latency was statistically significant for afternoon and evening timepoints at P = 0.016 and P = 0.013. We did not find any significant associations between SRSMs and PSQI or cognitive scores. Additionally, Oura Ring's total sleep duration and efficiency in relation to the PSQI measure was statistically significant at P = 0.004 and P = 0.033, respectively. These findings can hopefully be used to guide future sensor-based sleep research.