RESUMEN
Personalized treatment of complex diseases has been mostly predicated on biomarker identification of one drug-disease combination at a time. Here, we use a computational approach termed Disruption Networks to generate a data type, contextualized by cell-centered individual-level networks, that captures biology otherwise overlooked when performing standard statistics. This data type extends beyond the "feature level space", to the "relations space", by quantifying individual-level breaking or rewiring of cross-feature relations. Applying Disruption Networks to dissect high-dimensional blood data, we discover and validate that the RAC1-PAK1 axis is predictive of anti-TNF response in inflammatory bowel disease. Intermediate monocytes, which correlate with the inflammatory state, play a key role in the RAC1-PAK1 responses, supporting their modulation as a therapeutic target. This axis also predicts response in rheumatoid arthritis, validated in three public cohorts. Our findings support blood-based drug response diagnostics across immune-mediated diseases, implicating common mechanisms of non-response.
Asunto(s)
Artritis Reumatoide , Enfermedades Inflamatorias del Intestino , Humanos , Infliximab/uso terapéutico , Inhibidores del Factor de Necrosis Tumoral/uso terapéutico , Factor de Necrosis Tumoral alfa , Artritis Reumatoide/tratamiento farmacológico , Enfermedades Inflamatorias del Intestino/tratamiento farmacológicoRESUMEN
Autism spectrum disorder (ASD) is characterized by phenotypic heterogeneity and a complex genetic architecture which includes distinctive epigenetic patterns. We report differential DNA methylation patterns associated with ASD in South African children. An exploratory whole-epigenome methylation screen using the Illumina 450 K MethylationArray identified differentially methylated CpG sites between ASD and controls that mapped to 898 genes (P ≤ 0.05) which were enriched for nine canonical pathways converging on mitochondrial metabolism and protein ubiquitination. Targeted Next Generation Bisulfite Sequencing of 27 genes confirmed differential methylation between ASD and control in our cohort. DNA pyrosequencing of two of these genes, the mitochondrial enzyme Propionyl-CoA Carboxylase subunit Beta (PCCB) and Protocadherin Alpha 12 (PCDHA12), revealed a wide range of methylation levels (9-49% and 0-54%, respectively) in both ASD and controls. Three CpG loci were differentially methylated in PCCB (P ≤ 0.05), while PCDHA12, previously linked to ASD, had two significantly different CpG sites (P ≤ 0.001) between ASD and control. Differentially methylated CpGs were hypomethylated in ASD. Metabolomic analysis of urinary organic acids revealed that three metabolites, 3-hydroxy-3-methylglutaric acid (P = 0.008), 3-methyglutaconic acid (P = 0.018), and ethylmalonic acid (P = 0.043) were significantly elevated in individuals with ASD. These metabolites are directly linked to mitochondrial respiratory chain disorders, with a putative link to PCCB, consistent with impaired mitochondrial function. Our data support an association between DNA methylation and mitochondrial dysfunction in the etiology of ASD. Autism Res 2020, 13: 1079-1093. © 2020 The Authors. Autism Research published by International Society for Autism Research published by Wiley Periodicals, Inc. LAY SUMMARY: Epigenetic changes are chemical modifications of DNA which can change gene function. DNA methylation, a type of epigenetic modification, is linked to autism. We examined DNA methylation in South African children with autism and identified mitochondrial genes associated with autism. Mitochondria are power-suppliers in cells and mitochondrial genes are essential to metabolism and energy production, which are important for brain cells during development. Our findings suggest that some individuals with ASD also have mitochondrial dysfunction.
Asunto(s)
Trastorno del Espectro Autista , Trastorno del Espectro Autista/genética , Metilación de ADN/genética , Epigénesis Genética/genética , Humanos , Mitocondrias/genéticaRESUMEN
Immune responses generally decline with age. However, the dynamics of this process at the individual level have not been characterized, hindering quantification of an individual's immune age. Here, we use multiple 'omics' technologies to capture population- and individual-level changes in the human immune system of 135 healthy adult individuals of different ages sampled longitudinally over a nine-year period. We observed high inter-individual variability in the rates of change of cellular frequencies that was dictated by their baseline values, allowing identification of steady-state levels toward which a cell subset converged and the ordered convergence of multiple cell subsets toward an older adult homeostasis. These data form a high-dimensional trajectory of immune aging (IMM-AGE) that describes a person's immune status better than chronological age. We show that the IMM-AGE score predicted all-cause mortality beyond well-established risk factors in the Framingham Heart Study, establishing its potential use in clinics for identification of patients at risk.
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Citocinas/inmunología , Voluntarios Sanos , Inmunosenescencia/inmunología , Linfocitos/inmunología , Mortalidad , Adulto , Anciano , Anciano de 80 o más Años , Envejecimiento/inmunología , Femenino , Humanos , Individualidad , Estudios Longitudinales , Masculino , Persona de Mediana Edad , Análisis Multivariante , Fenotipo , Modelos de Riesgos Proporcionales , Adulto JovenRESUMEN
OBJECTIVE: Although anti-tumour necrosis factor alpha (anti-TNFα) therapies represent a major breakthrough in IBD therapy, their cost-benefit ratio is hampered by an overall 30% non-response rate, adverse side effects and high costs. Thus, finding predictive biomarkers of non-response prior to commencing anti-TNFα therapy is of high value. DESIGN: We analysed publicly available whole-genome expression profiles of colon biopsies obtained from multiple cohorts of patients with IBD using a combined computational deconvolution-meta-analysis paradigm which allows to estimate immune cell contribution to the measured expression and capture differential regulatory programmes otherwise masked due to variation in cellular composition. Insights from this in silico approach were experimentally validated in biopsies and blood samples of three independent test cohorts. RESULTS: We found the proportion of plasma cells as a robust pretreatment biomarker of non-response to therapy, which we validated in two independent cohorts of immune-stained colon biopsies, where a plasma cellular score from inflamed biopsies was predictive of non-response with an area under the curve (AUC) of 82%. Meta-analysis of the cell proportion-adjusted gene expression data suggested that an increase in inflammatory macrophages in anti-TNFα non-responding individuals is associated with the upregulation of the triggering receptor expressed on myeloid cells 1 (TREM-1) and chemokine receptor type 2 (CCR2)-chemokine ligand 7 (CCL7) -axes. Blood gene expression analysis of an independent cohort, identified TREM-1 downregulation in non-responders at baseline, which was predictive of response with an AUC of 94%. CONCLUSIONS: Our study proposes two clinically feasible assays, one in biopsy and one in blood, for predicting non-response to anti-TNFα therapy prior to initiation of treatment. Moreover, it suggests that mechanism-driven novel drugs for non-responders should be developed.
Asunto(s)
Enfermedades Inflamatorias del Intestino/tratamiento farmacológico , Valor Predictivo de las Pruebas , Factor de Necrosis Tumoral alfa/antagonistas & inhibidores , Biomarcadores/sangre , Biopsia , Humanos , Enfermedades Inflamatorias del Intestino/sangre , Enfermedades Inflamatorias del Intestino/patología , Insuficiencia del TratamientoRESUMEN
Cross-species differences form barriers to translational research that ultimately hinder the success of clinical trials, yet knowledge of species differences has yet to be systematically incorporated in the interpretation of animal models. Here we present Found In Translation (FIT; http://www.mouse2man.org ), a statistical methodology that leverages public gene expression data to extrapolate the results of a new mouse experiment to expression changes in the equivalent human condition. We applied FIT to data from mouse models of 28 different human diseases and identified experimental conditions in which FIT predictions outperformed direct cross-species extrapolation from mouse results, increasing the overlap of differentially expressed genes by 20-50%. FIT predicted novel disease-associated genes, an example of which we validated experimentally. FIT highlights signals that may otherwise be missed and reduces false leads, with no experimental cost.
Asunto(s)
Perfilación de la Expresión Génica , Genómica/métodos , Enfermedades Inflamatorias del Intestino/genética , Aprendizaje Automático , Transcriptoma , Investigación Biomédica Traslacional , Algoritmos , Animales , Estudios de Casos y Controles , Femenino , Humanos , Masculino , Ratones , Persona de Mediana Edad , Transducción de SeñalRESUMEN
Although the function of the mammalian pancreas hinges on complex interactions of distinct cell types, gene expression profiles have primarily been described with bulk mixtures. Here we implemented a droplet-based, single-cell RNA-seq method to determine the transcriptomes of over 12,000 individual pancreatic cells from four human donors and two mouse strains. Cells could be divided into 15 clusters that matched previously characterized cell types: all endocrine cell types, including rare epsilon-cells; exocrine cell types; vascular cells; Schwann cells; quiescent and activated stellate cells; and four types of immune cells. We detected subpopulations of ductal cells with distinct expression profiles and validated their existence with immuno-histochemistry stains. Moreover, among human beta- cells, we detected heterogeneity in the regulation of genes relating to functional maturation and levels of ER stress. Finally, we deconvolved bulk gene expression samples using the single-cell data to detect disease-associated differential expression. Our dataset provides a resource for the discovery of novel cell type-specific transcription factors, signaling receptors, and medically relevant genes.
Asunto(s)
Transcriptoma , Animales , Diferenciación Celular , Perfilación de la Expresión Génica , Regulación del Desarrollo de la Expresión Génica , Humanos , Islotes Pancreáticos , Ratones , Páncreas , Páncreas Exocrino , Análisis de la Célula Individual , Factores de TranscripciónRESUMEN
PI3K activity determines positive and negative selection of B cells, a key process for immune tolerance and B cell maturation. Activation of PI3K is balanced by phosphatase and tensin homolog (Pten), the PI3K's main antagonistic phosphatase. Yet, the extent of feedback regulation between PI3K activity and Pten expression during B cell development is unclear. Here, we show that PI3K control of this process is achieved post-transcriptionally by an axis composed of a transcription factor (c-Myc), a microRNA (miR17-92), and Pten. Enhancing activation of this axis through overexpression of miR17-92 reconstitutes the impaired PI3K activity for positive selection in CD19-deficient B cells and restores most of the B cell developmental impairments that are evident in CD19-deficient mice. Using a genetic approach of deletion and complementation, we show that the c-Myc/miR17-92/Pten axis critically controls PI3K activity and the sensitivity of immature B cells to negative selection imposed by activation-induced cell death.
Asunto(s)
Antígenos CD19/genética , Linfocitos B/fisiología , Fosfatidilinositol 3-Quinasas/metabolismo , Transducción de Señal , Animales , Antígenos CD19/metabolismo , Muerte Celular , Células Cultivadas , ARN Helicasas DEAD-box/genética , ARN Helicasas DEAD-box/metabolismo , Prueba de Complementación Genética , Heterocigoto , Ratones Endogámicos C57BL , Ratones Transgénicos , MicroARNs/metabolismo , Fosfohidrolasa PTEN/metabolismo , Proteínas Proto-Oncogénicas c-myc/metabolismo , Ribonucleasa III/genética , Ribonucleasa III/metabolismoRESUMEN
Systems approaches have been used to describe molecular signatures driving immunity to influenza vaccination in humans. Whether such signatures are similar across multiple seasons and in diverse populations is unknown. We applied systems approaches to study immune responses in young, elderly, and diabetic subjects vaccinated with the seasonal influenza vaccine across five consecutive seasons. Signatures of innate immunity and plasmablasts correlated with and predicted influenza antibody titers at 1 month after vaccination with >80% accuracy across multiple seasons but were not associated with the longevity of the response. Baseline signatures of lymphocyte and monocyte inflammation were positively and negatively correlated, respectively, with antibody responses at 1 month. Finally, integrative analysis of microRNAs and transcriptomic profiling revealed potential regulators of vaccine immunity. These results identify shared vaccine-induced signatures across multiple seasons and in diverse populations and might help guide the development of next-generation vaccines that provide persistent immunity against influenza.
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Anticuerpos Antivirales/genética , Vacunas contra la Influenza/inmunología , Gripe Humana/prevención & control , Transcriptoma/inmunología , Adulto , Anciano , Anticuerpos Antivirales/sangre , Femenino , Citometría de Flujo , Humanos , Masculino , Persona de Mediana Edad , Análisis de Secuencia por Matrices de Oligonucleótidos , Estaciones del Año , Análisis de SistemasRESUMEN
West Nile virus (WNV) infection is usually asymptomatic but can cause severe neurological disease and death, particularly in older patients, and how individual variations in immunity contribute to disease severity is not yet defined. Animal studies identified a role for several immunity-related genes that determine the severity of infection. We have integrated systems-level transcriptional and functional data sets from stratified cohorts of subjects with a history of WNV infection to define whether these markers can distinguish susceptibility in a human population. Transcriptional profiles combined with immunophenotyping of primary cells identified a predictive signature of susceptibility that was detectable years after acute infection (67% accuracy), with the most prominent alteration being decreased IL1B induction following ex vivo infection of macrophages with WNV. Deconvolution analysis also determined a significant role for CXCL10 expression in myeloid dendritic cells. This systems analysis identified markers of pathogenic mechanisms and offers insights into potential therapeutic strategies.
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Susceptibilidad a Enfermedades , Biología de Sistemas/métodos , Virus del Nilo Occidental/inmunología , Adulto , Anciano , Anciano de 80 o más Años , Quimiocina CXCL10/biosíntesis , Quimiocina CXCL10/inmunología , Femenino , Perfilación de la Expresión Génica , Marcadores Genéticos , Humanos , Inmunofenotipificación , Interleucina-1beta/biosíntesis , Interleucina-1beta/inmunología , Masculino , Persona de Mediana Edad , Transcripción Genética , Adulto JovenRESUMEN
The quanta unit of the immune system is the cell, yet analyzed samples are often heterogeneous with respect to cell subsets which can mislead result interpretation. Experimentally, researchers face a difficult choice whether to profile heterogeneous samples with the ensuing confounding effects, or a priori focus on a few cell subsets of interest, potentially limiting new discoveries. An attractive alternative solution is to extract cell subset-specific information directly from heterogeneous samples via computational deconvolution techniques, thereby capturing both cell-centered and whole system level context. Such approaches are capable of unraveling novel biology, undetectable otherwise. Here we review the present state of available deconvolution techniques, their advantages and limitations, with a focus on blood expression data and immunological studies in general.
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Células Sanguíneas/inmunología , Biología Computacional/métodos , Animales , Células Sanguíneas/química , Expresión Génica , Humanos , Sistema InmunológicoRESUMEN
UNLABELLED: Gene expression data are typically generated from heterogeneous biological samples that are composed of multiple cell or tissue types, in varying proportions, each contributing to global gene expression. This heterogeneity is a major confounder in standard analysis such as differential expression analysis, where differences in the relative proportions of the constituent cells may prevent or bias the detection of cell-specific differences. Computational deconvolution of global gene expression is an appealing alternative to costly physical sample separation techniques and enables a more detailed analysis of the underlying biological processes at the cell-type level. To facilitate and popularize the application of such methods, we developed CellMix, an R package that incorporates most state-of-the-art deconvolution methods, into an intuitive and extendible framework, providing a single entry point to explore, assess and disentangle gene expression data from heterogeneous samples. AVAILABILITY AND IMPLEMENTATION: The CellMix package builds on R/BioConductor and is available from http://web.cbio.uct.ac.za/â¼renaud/CRAN/web/CellMix. It is currently being submitted to BioConductor. The package's vignettes notably contain additional information, examples and references.
Asunto(s)
Perfilación de la Expresión Génica/métodos , Programas Informáticos , Línea Celular Transformada , HumanosRESUMEN
Heterogeneity in sample composition is an inherent issue in many gene expression studies and, in many cases, should be taken into account in the downstream analysis to enable correct interpretation of the underlying biological processes. Typical examples are infectious diseases or immunology-related studies using blood samples, where, for example, the proportions of lymphocyte sub-populations are expected to vary between cases and controls. Nonnegative Matrix Factorization (NMF) is an unsupervised learning technique that has been applied successfully in several fields, notably in bioinformatics where its ability to extract meaningful information from high-dimensional data such as gene expression microarrays has been demonstrated. Very recently, it has been applied to biomarker discovery and gene expression deconvolution in heterogeneous tissue samples. Being essentially unsupervised, standard NMF methods are not guaranteed to find components corresponding to the cell types of interest in the sample, which may jeopardize the correct estimation of cell proportions. We have investigated the use of prior knowledge, in the form of a set of marker genes, to improve gene expression deconvolution with NMF algorithms. We found that this improves the consistency with which both cell type proportions and cell type gene expression signatures are estimated. The proposed method was tested on a microarray dataset consisting of pure cell types mixed in known proportions. Pearson correlation coefficients between true and estimated cell type proportions improved substantially (typically from about 0.5 to approximately 0.8) with the semi-supervised (marker-guided) versions of commonly used NMF algorithms. Furthermore known marker genes associated with each cell type were assigned to the correct cell type more frequently for the guided versions. We conclude that the use of marker genes improves the accuracy of gene expression deconvolution using NMF and suggest modifications to how the marker gene information is used that may lead to further improvements.
Asunto(s)
Algoritmos , Biología Computacional/métodos , Perfilación de la Expresión Génica/métodos , Expresión Génica , Modelos Genéticos , Análisis de Secuencia por Matrices de Oligonucleótidos/métodos , Línea Celular Tumoral , Análisis por Conglomerados , Marcadores Genéticos , Humanos , Análisis MultivarianteRESUMEN
BACKGROUND: Nonnegative Matrix Factorization (NMF) is an unsupervised learning technique that has been applied successfully in several fields, including signal processing, face recognition and text mining. Recent applications of NMF in bioinformatics have demonstrated its ability to extract meaningful information from high-dimensional data such as gene expression microarrays. Developments in NMF theory and applications have resulted in a variety of algorithms and methods. However, most NMF implementations have been on commercial platforms, while those that are freely available typically require programming skills. This limits their use by the wider research community. RESULTS: Our objective is to provide the bioinformatics community with an open-source, easy-to-use and unified interface to standard NMF algorithms, as well as with a simple framework to help implement and test new NMF methods. For that purpose, we have developed a package for the R/BioConductor platform. The package ports public code to R, and is structured to enable users to easily modify and/or add algorithms. It includes a number of published NMF algorithms and initialization methods and facilitates the combination of these to produce new NMF strategies. Commonly used benchmark data and visualization methods are provided to help in the comparison and interpretation of the results. CONCLUSIONS: The NMF package helps realize the potential of Nonnegative Matrix Factorization, especially in bioinformatics, providing easy access to methods that have already yielded new insights in many applications. Documentation, source code and sample data are available from CRAN.