Identification of genome wide host RNA biomarkers for infectious diseases

Existe una predisposición genética en humanos a la susceptibilidad y la gravedad de las enfermedades infecciosas. No todas las personas en contacto cercano con patógenos se infectan y desarrollan la enfermedad, en general, la mayoría de los pacientes muestran síntomas leves o moderados, y solo una minoría desarrolla una enfermedad grave. En la presente tesis nos centramos en el estudio de las firmas de expresión génica ya que el transcriptoma es un puente entre la información contenida dentro de nuestros genes y el fenotipo. Nuestros resultados suponen demuestran el potencial del uso de firmas trascriptómicas del huésped en la práctica clínica como pruebas clínicas para diagnóstico, pronóstico o evaluación de riesgos

A 2-transcript host cell signature distinguishes viral from bacterial diarrhea and it is influenced by the severity of symptoms

Recently, a biomarker signature consisting of 2-transcript host RNAs was proposed for discriminating bacterial from viral infections in febrile children. We evaluated the performance of this signature in a different disease scenario, namely a cohort of Mexican children (n = 174) suffering from acute diarrhea of different infectious etiologies. We first examined the admixed background of the patients, indicating that most of them have a predominantly Native American genetic ancestry with a variable amount of European background (ranging from 0% to 57%). The results confirm that the RNA test can discriminate between viral and bacterial causes of infection (t-test; P-value = 6.94×10−11; AUC = 80%; sensitivity: 68% [95% CI: 55%–79%]; specificity: 84% [95% CI: 78%–90%]), but the strength of the signal differs substantially depending on the causal pathogen, with the stronger signal being that of Shigella (P-value = 3.14 × 10−12; AUC = 89; sensitivity: 70% [95% CI: 57%–83%]; specificity: 100% [95% CI: 100%–100%]). The accuracy of this test improves significantly when excluding mild cases (P-value = 2.13 × 10−6; AUC = 85%; sensitivity: 79% [95% CI: 58%–95%]; specificity: 78% [95% CI: 65%–88%]). The results broaden the scope of previous studies by incorporating different pathogens, variable levels of disease severity, and different ancestral background of patients, and add confirmatory support to the clinical utility of these 2-transcript biomarkers.S

Ancestry patterns inferred from massive RNA-seq data

There is a growing body of evidence suggesting that patterns of gene expression vary within and between human populations. However, the impact of this variation in human diseases has been poorly explored, in part owing to the lack of a standardized protocol to estimate biogeographical ancestry from gene expression studies. Here we examine several studies that provide new solid evidence indicating that the ancestral background of individuals impacts gene expression patterns. Next, we test a procedure to infer genetic ancestry from RNA-seq data in 25 data sets where information on ethnicity was reported. Genome data of reference continental populations retrieved from The 1000 Genomes Project were used for comparisons. Remarkably, only eight out of 25 data sets passed FastQC default filters. We demonstrate that, for these eight population sets, the ancestral background of donors could be inferred very efficiently, even in data sets including samples with complex patterns of admixture (e.g., American-admixed populations). For most of the gene expression data sets of suboptimal quality, ancestral inference yielded odd patterns. The present study thus brings a cautionary note for gene expression studies highlighting the importance to control for the potential confounding effect of ancestral genetic background

A Meta-Analysis of Multiple Whole Blood Gene Expression Data Unveils a Diagnostic Host-Response Transcript Signature for Respiratory Syncytial Virus

Respiratory syncytial virus (RSV) is one of the major causes of acute lower respiratory tract infection worldwide. The absence of a commercial vaccine and the limited success of current therapeutic strategies against RSV make further research necessary. We used a multi-cohort analysis approach to investigate host transcriptomic biomarkers and shed further light on the molecular mechanism underlying RSV-host interactions. We meta-analyzed seven transcriptome microarray studies from the public Gene Expression Omnibus (GEO) repository containing a total of 922 samples, including RSV, healthy controls, coronaviruses, enteroviruses, influenzas, rhinoviruses, and coinfections, from both adult and pediatric patients. We identified > 1500 genes differentially expressed when comparing the transcriptomes of RSV-infected patients against healthy controls. Functional enrichment analysis showed several pathways significantly altered, including immunologic response mediated by RSV infection, pattern recognition receptors, cell cycle, and olfactory signaling. In addition, we identified a minimal 17-transcript host signature specific for RSV infection by comparing transcriptomic profiles against other respiratory viruses. These multi-genic signatures might help to investigate future drug targets against RSV infectionThis study received support from the Instituto de Salud Carlos III: project GePEM (Instituto de Salud Carlos III(ISCIII)/PI16/01478/Cofinanciado FEDER), DIAVIR (Instituto de Salud Carlos III(ISCIII)/DTS19/00049/Cofinanciado FEDER; Proyecto de Desarrollo Tecnológico en Salud) and Resvi-Omics (Instituto de Salud Carlos III(ISCIII)/PI19/0103; 9/Cofinanciado FEDER) given to A.S.; and project ReSVinext (Instituto de Salud Carlos III(ISCIII)/PI16/01569/Cofinanciado FEDER), and Enterogen (Instituto de Salud Carlos III(ISCIII)/ PI19/01090/Cofinanciado FEDER) given to F.M.-T.S

RNA-Seq Data-Mining Allows the Discovery of Two Long Non-Coding RNA Biomarkers of Viral Infection in Humans

There is a growing interest in unraveling gene expression mechanisms leading to viral host invasion and infection progression. Current findings reveal that long non-coding RNAs (lncRNAs) are implicated in the regulation of the immune system by influencing gene expression through a wide range of mechanisms. By mining whole-transcriptome shotgun sequencing (RNA-seq) data using machine learning approaches, we detected two lncRNAs (ENSG00000254680 and ENSG00000273149) that are downregulated in a wide range of viral infections and different cell types, including blood monocluclear cells, umbilical vein endothelial cells, and dermal fibroblasts. The efficiency of these two lncRNAs was positively validated in different viral phenotypic scenarios. These two lncRNAs showed a strong downregulation in virus-infected patients when compared to healthy control transcriptomes, indicating that these biomarkers are promising targets for infection diagnosis. To the best of our knowledge, this is the very first study using host lncRNAs biomarkers for the diagnosis of human viral infectionsThis study received support from the Instituto de Salud Carlos III: project GePEM (Instituto de Salud Carlos III(ISCIII)/PI16/01478/Cofinanciado FEDER), DIAVIR (Instituto de Salud Carlos III(ISCIII)/DTS19/00049/Cofinanciado FEDER; Proyecto de Desarrollo Tecnológico en Salud) and Resvi-Omics (Instituto de Salud Carlos III(ISCIII)/PI19/01039/Cofinanciado FEDER) and project BI-BACVIR (PRIS-3; Agencia de Conocimiento en Salud (ACIS)—Servicio Gallego de Salud (SERGAS)—Xunta de Galicia; Spain) given to A.S.; and project ReSVinext (Instituto de Salud Carlos III(ISCIII)/PI16/01569/Cofinanciado FEDER), and Enterogen (Instituto de Salud Carlos III(ISCIII)/ PI19/01090/Cofinanciado FEDER) given to F.M.-TS

A qPCR expression assay of IFI44L gene differentiates viral from bacterial infections in febrile children

The diagnosis of bacterial infections in hospital settings is currently performed using bacterial culture from sterile site, but they are lengthy and limited. Transcriptomic biomarkers are becoming promising tools for diagnosis with potential applicability in clinical settings. We evaluated a RT-qPCR assay for a 2-transcript host expression signature (FAM89A and IFI44L genes) inferred from microarray data that allow to differentiate between viral and bacterial infection in febrile children. This assay was able to discriminate viral from bacterial infections (P-value = 1.04 × 10-4; AUC = 92.2%; sensitivity = 90.9%; specificity = 85.7%) and showed very high reproducibility regardless of the reference gene(s) used to normalize the data. Unexpectedly, the monogenic IFI44L expression signature yielded better results than those obtained from the 2-transcript test (P-value = 3.59 × 10-5; AUC = 94.1%; sensitivity = 90.9%; specificity = 92.8%). We validated this IFI44L signature in previously published microarray and whole-transcriptome data from patients affected by different types of viral and bacterial infections, confirming that this gene alone differentiates between both groups, thus saving time, effort, and costs. Herein, we demonstrate that host expression microarray data can be successfully translated into a fast, highly accurate and relatively inexpensive in vitro assay that could be implemented in the clinical routine.Instituto de Salud Carlos IIIXunta de Galicia. Consellería de SanidadeFondo de Investigación SanitariaI+D+I y Fondos FEDE

Phylogeographic and genome-wide investigations of Vietnam ethnic groups reveal signatures of complex historical demographic movements

The territory of present-day Vietnam was the cradle of one of the world’s earliest civilizations, and one of the first world regions to develop agriculture. We analyzed the mitochondrial DNA (mtDNA) complete control region of six ethnic groups and the mitogenomes from Vietnamese in The 1000 Genomes Project (1000G). Genome-wide data from 1000G (~55k SNPs) were also investigated to explore different demographic scenarios. All Vietnamese carry South East Asian (SEA) haplotypes, which show a moderate geographic and ethnic stratification, with the Mong constituting the most distinctive group. Two new mtDNA clades (M7b1a1f1 and F1f1) point to historical gene flow between the Vietnamese and other neighboring countries. Bayesian-based inferences indicate a time-deep and continuous population growth of Vietnamese, although with some exceptions. The dramatic population decrease experienced by the Cham 700 years ago (ya) fits well with the Nam tiến (“southern expansion”) southwards from their original heartland in the Red River Delta. Autosomal SNPs consistently point to important historical gene flow within mainland SEA, and add support to a main admixture event occurring between Chinese and a southern Asian ancestral composite (mainly represented by the Malay). This admixture event occurred ~800 ya, again coinciding with the Nam tiến.This study received support from the Instituto de Salud Carlos III (Proyecto de Investigación en Salud, Acción Estratégica en Salud: project GePEM ISCIII/PI16/01478/Cofinanciado FEDER) (AS) and project ReSVinext ISCIII/PI16/01569/Cofinanciado FEDER (FMT); Consellería de Sanidade, Xunta de Galicia (RHI07/2-intensificación actividad investigadora, PS09749 and 10PXIB918184PR), Instituto de Salud Carlos III (Intensificación de la actividad investigadora 2007–2012, PI16/01569), Fondo de Investigación Sanitaria (FIS; PI070069/PI1000540) del plan nacional de I+D+I and “fondos FEDER” (FMT), and 2016-PG071 Consolidación e Estructuración REDES 2016GI-1344 G3VIP (Grupo Gallego de Genética Vacunas Infecciones y Pediatría, ED341D R2016/021) (AS and FMT)S

Plasma lipid profiles discriminate bacterial from viral infection in febrile children

Fever is the most common reason that children present to Emergency Departments. Clinical signs and symptoms suggestive of bacterial infection are often non-specific, and there is no definitive test for the accurate diagnosis of infection. The 'omics' approaches to identifying biomarkers from the host-response to bacterial infection are promising. In this study, lipidomic analysis was carried out with plasma samples obtained from febrile children with confirmed bacterial infection (n = 20) and confirmed viral infection (n = 20). We show for the first time that bacterial and viral infection produces distinct profile in the host lipidome. Some species of glycerophosphoinositol, sphingomyelin, lysophosphatidylcholine and cholesterol sulfate were higher in the confirmed virus infected group, while some species of fatty acids, glycerophosphocholine, glycerophosphoserine, lactosylceramide and bilirubin were lower in the confirmed virus infected group when compared with confirmed bacterial infected group. A combination of three lipids achieved an area under the receiver operating characteristic (ROC) curve of 0.911 (95% CI 0.81 to 0.98). This pilot study demonstrates the potential of metabolic biomarkers to assist clinicians in distinguishing bacterial from viral infection in febrile children, to facilitate effective clinical management and to the limit inappropriate use of antibiotics

Identification of regulatory variants associated with genetic susceptibility to meningococcal disease

Non-coding genetic variants play an important role in driving susceptibility to complex diseases but their characterization remains challenging. Here, we employed a novel approach to interrogate the genetic risk of such polymorphisms in a more systematic way by targeting specific regulatory regions relevant for the phenotype studied. We applied this method to meningococcal disease susceptibility, using the DNA binding pattern of RELA - a NF-kB subunit, master regulator of the response to infection - under bacterial stimuli in nasopharyngeal epithelial cells. We designed a custom panel to cover these RELA binding sites and used it for targeted sequencing in cases and controls. Variant calling and association analysis were performed followed by validation of candidate polymorphisms by genotyping in three independent cohorts. We identified two new polymorphisms, rs4823231 and rs11913168, showing signs of association with meningococcal disease susceptibility. In addition, using our genomic data as well as publicly available resources, we found evidences for these SNPs to have potential regulatory effects on ATXN10 and LIF genes respectively. The variants and related candidate genes are relevant for infectious diseases and may have important contribution for meningococcal disease pathology. Finally, we described a novel genetic association approach that could be applied to other phenotypes

Identification of regulatory variants associated with genetic susceptibility to meningococcal disease.

Non-coding genetic variants play an important role in driving susceptibility to complex diseases but their characterization remains challenging. Here, we employed a novel approach to interrogate the genetic risk of such polymorphisms in a more systematic way by targeting specific regulatory regions relevant for the phenotype studied. We applied this method to meningococcal disease susceptibility, using the DNA binding pattern of RELA - a NF-kB subunit, master regulator of the response to infection - under bacterial stimuli in nasopharyngeal epithelial cells. We designed a custom panel to cover these RELA binding sites and used it for targeted sequencing in cases and controls. Variant calling and association analysis were performed followed by validation of candidate polymorphisms by genotyping in three independent cohorts. We identified two new polymorphisms, rs4823231 and rs11913168, showing signs of association with meningococcal disease susceptibility. In addition, using our genomic data as well as publicly available resources, we found evidences for these SNPs to have potential regulatory effects on ATXN10 and LIF genes respectively. The variants and related candidate genes are relevant for infectious diseases and may have important contribution for meningococcal disease pathology. Finally, we described a novel genetic association approach that could be applied to other phenotypes