Myrsini Kaforou

Diagnosis of Childhood Tuberculosis and Host RNA Expression in Africa

BACKGROUND Improved diagnostic tests for tuberculosis in children are needed. We hypothesized that transcriptional signatures of host blood could be used to distinguish tuberculosis from other diseases in African children who either were or were not infected with the human immunodeficiency virus (HIV). METHODS The study population comprised prospective cohorts of children who were undergoing evaluation for suspected tuberculosis in South Africa (655 children), Malawi (701 children), and Kenya (1599 children). Patients were assigned to groups according to whether the diagnosis was culture-confirmed tuberculosis, culture-negative tuberculosis, diseases other than tuberculosis, or latent tuberculosis infection. Diagnostic signatures distinguishing tuberculosis from other diseases and from latent tuberculosis infection were identified from genomewide analysis of RNA expression in host blood. RESULTS We identified a 51-transcript signature distinguishing tuberculosis from other diseases in the South African and Malawian children (the discovery cohort). In the Kenyan children (the validation cohort), a risk score based on the signature for tuberculosis and for diseases other than tuberculosis showed a sensitivity of 82.9% (95% confidence interval [CI], 68.6 to 94.3) and a specificity of 83.6% (95% CI, 74.6 to 92.7) for the diagnosis of culture-confirmed tuberculosis. Among patients with cultures negative for Mycobacterium tuberculosis who were treated for tuberculosis (those with highly probable, probable, or possible cases of tuberculosis), the estimated sensitivity was 62.5 to 82.3%, 42.1 to 80.8%, and 35.3 to 79.6%, respectively, for different estimates of actual tuberculosis in the groups. In comparison, the sensitivity of the Xpert MTB/RIF assay for molecular detection of M. tuberculosis DNA in cases of culture-confirmed tuberculosis was 54.3% (95% CI, 37.1 to 68.6), and the sensitivity in highly probable, probable, or possible cases was an estimated 25.0 to 35.7%, 5.3 to 13.3%, and 0%, respectively; the specificity of the assay was 100%. CONCLUSIONS RNA expression signatures provided data that helped distinguish tuberculosis from other diseases in African children with and those without HIV infection. (Funded by the European Union Action for Diseases of Poverty Program and others).

Brown and white adipose tissues: intrinsic differences in gene expression and response to cold exposure in mice

Brown adipocytes dissipate energy, whereas white adipocytes are an energy storage site. We explored the plasticity of different white adipose tissue depots in acquiring a brown phenotype by cold exposure. By comparing cold-induced genes in white fat to those enriched in brown compared with white fat, at thermoneutrality we defined a “brite” transcription signature. We identified the genes, pathways, and promoter regulatory motifs associated with “browning,” as these represent novel targets for understanding this process. For example, neuregulin 4 was more highly expressed in brown adipose tissue and upregulated in white fat upon cold exposure, and cell studies showed that it is a neurite outgrowth-promoting adipokine, indicative of a role in increasing adipose tissue innervation in response to cold. A cell culture system that allows us to reproduce the differential properties of the discrete adipose depots was developed to study depot-specific differences at an in vitro level. The key transcriptional events underpinning white adipose tissue to brown transition are important, as they represent an attractive proposition to overcome the detrimental effects associated with metabolic disorders, including obesity and type 2 diabetes.

Diagnostic Test Accuracy of a 2-Transcript Host RNA Signature for Discriminating Bacterial vs Viral Infection in Febrile Children

IMPORTANCE Because clinical features do not reliably distinguish bacterial from viral infection, many children worldwide receive unnecessary antibiotic treatment, while bacterial infection is missed in others. OBJECTIVE To identify a blood RNA expression signature that distinguishes bacterial from viral infection in febrile children. DESIGN, SETTING, AND PARTICIPANTS Febrile children presenting to participating hospitals in the United Kingdom, Spain, the Netherlands, and the United States between 2009-2013 were prospectively recruited, comprising a discovery group and validation group. Each group was classified after microbiological investigation as having definite bacterial infection, definite viral infection, or indeterminate infection. RNA expression signatures distinguishing definite bacterial from viral infection were identified in the discovery group and diagnostic performance assessed in the validation group. Additional validation was undertaken in separate studies of children with meningococcal disease (n = 24) and inflammatory diseases (n = 48) and on published gene expression datasets. EXPOSURES A 2-transcript RNA expression signature distinguishing bacterial infection from viral infection was evaluated against clinical and microbiological diagnosis. MAIN OUTCOMES AND MEASURES Definite bacterial and viral infection was confirmed by culture or molecular detection of the pathogens. Performance of the RNA signature was evaluated in the definite bacterial and viral group and in the indeterminate infection group. RESULTS The discovery group of 240 children (median age, 19 months; 62% male) included 52 with definite bacterial infection, of whom 36 (69%) required intensive care, and 92 with definite viral infection, of whom 32 (35%) required intensive care. Ninety-six children had indeterminate infection. Analysis of RNA expression data identified a 38-transcript signature distinguishing bacterial from viral infection. A smaller (2-transcript) signature (FAM89A and IFI44L) was identified by removing highly correlated transcripts. When this 2-transcript signature was implemented as a disease risk score in the validation group (130 children, with 23 definite bacterial, 28 definite viral, and 79 indeterminate infections; median age, 17 months; 57% male), all 23 patients with microbiologically confirmed definite bacterial infection were classified as bacterial (sensitivity, 100% [95% CI, 100%-100%]) and 27 of 28 patients with definite viral infection were classified as viral (specificity, 96.4% [95% CI, 89.3%-100%]). When applied to additional validation datasets from patients with meningococcal and inflammatory diseases, bacterial infection was identified with a sensitivity of 91.7% (95% CI, 79.2%-100%) and 90.0% (95% CI, 70.0%-100%), respectively, and with specificity of 96.0% (95% CI, 88.0%-100%) and 95.8% (95% CI, 89.6%-100%). Of the children in the indeterminate groups, 46.3% (63/136) were classified as having bacterial infection, although 94.9% (129/136) received antibiotic treatment. CONCLUSIONS AND RELEVANCE This study provides preliminary data regarding test accuracy of a 2-transcript host RNA signature discriminating bacterial from viral infection in febrile children. Further studies are needed in diverse groups of patients to assess accuracy and clinical utility of this test in different clinical settings.

Transcriptomic Profiling in Childhood H1N1/09 Influenza Reveals Reduced Expression of Protein Synthesis Genes

Abstract We compared the blood RNA transcriptome of children hospitalized with influenza A H1N1/09, respiratory syncytial virus (RSV) or bacterial infection, and healthy controls. Compared to controls, H1N1/09 patients showed increased expression of inflammatory pathway genes and reduced expression of adaptive immune pathway genes. This was validated on an independent cohort. The most significant function distinguishing H1N1/09 patients from controls was protein synthesis, with reduced gene expression. Reduced expression of protein synthesis genes also characterized the H1N1/09 expression profile compared to children with RSV and bacterial infection, suggesting that this is a key component of the pathophysiological response in children hospitalized with H1N1/09 infection.

RIP140 Represses the “Brown-in-White” Adipocyte Program Including a Futile Cycle of Triacyclglycerol Breakdown and Synthesis

Receptor-interacting protein 140 (RIP140) is a corepressor of nuclear receptors that is highly expressed in adipose tissues. We investigated the role of RIP140 in conditionally immortal preadipocyte cell lines prepared from white or brown fat depots. In white adipocytes, a large set of brown fat-associated genes was up-regulated in the absence of RIP140. In contrast, a relatively minor role can be ascribed to RIP140 in the control of basal gene expression in differentiated brown adipocytes because significant changes were observed only in Ptgds and Fabp3. The minor role of RIP140 in brown adipocytes correlates with the similar histology and uncoupling protein 1 and CIDEA staining in knockout compared with wild-type brown adipose tissue (BAT). In contrast, RIP140 knockout sc white adipose tissue (WAT) shows increased numbers of multilocular adipocytes with elevated staining for uncoupling protein 1 and CIDEA. Furthermore in a white adipocyte cell line, the markers of BRITE adipocytes, Tbx1, CD137, Tmem26, Cited1, and Epsti1 were repressed in the presence of RIP140 as was Prdm16. Microarray analysis of wild-type and RIP140-knockout white fat revealed elevated expression of genes associated with cold-induced expression or high expression in BAT. A set of genes associated with a futile cycle of triacylglycerol breakdown and resynthesis and functional assays revealed that glycerol kinase and glycerol-3-phosphate dehydrogenase activity as well as [3H]glycerol incorporation were elevated in the absence of RIP140. Thus, RIP140 blocks the BRITE program in WAT, preventing the expression of brown fat genes and inhibiting a triacylglycerol futile cycle, with important implications for energy homeostasis.

A Simple Screening Approach To Prioritize Genes for Functional Analysis Identifies a Role for Interferon Regulatory Factor 7 in the Control of Respiratory Syncytial Virus Disease

Making the most of “big data” is one of the core challenges of current biology. There is a large array of heterogeneous data sets of host gene responses to infection, but these data sets do not inform us about gene function and require specialized skill sets and training for their utilization. Here we describe an approach that combines and simplifies these data sets, distilling this information into a single list of genes commonly upregulated in response to infection with RSV as a model pathogen. Many of the genes on the list have unknown functions in RSV disease. We validated the gene list with new clinical, in vitro, and in vivo data. This approach allows the rapid selection of genes of interest for further, more-detailed studies, thus reducing time and costs. Furthermore, the approach is simple to use and widely applicable to a range of diseases. ABSTRACT Greater understanding of the functions of host gene products in response to infection is required. While many of these genes enable pathogen clearance, some enhance pathogen growth or contribute to disease symptoms. Many studies have profiled transcriptomic and proteomic responses to infection, generating large data sets, but selecting targets for further study is challenging. Here we propose a novel data-mining approach combining multiple heterogeneous data sets to prioritize genes for further study by using respiratory syncytial virus (RSV) infection as a model pathogen with a significant health care impact. The assumption was that the more frequently a gene is detected across multiple studies, the more important its role is. A literature search was performed to find data sets of genes and proteins that change after RSV infection. The data sets were standardized, collated into a single database, and then panned to determine which genes occurred in multiple data sets, generating a candidate gene list. This candidate gene list was validated by using both a clinical cohort and in vitro screening. We identified several genes that were frequently expressed following RSV infection with no assigned function in RSV control, including IFI27, IFIT3, IFI44L, GBP1, OAS3, IFI44, and IRF7. Drilling down into the function of these genes, we demonstrate a role in disease for the gene for interferon regulatory factor 7, which was highly ranked on the list, but not for IRF1, which was not. Thus, we have developed and validated an approach for collating published data sets into a manageable list of candidates, identifying novel targets for future analysis. IMPORTANCE Making the most of “big data” is one of the core challenges of current biology. There is a large array of heterogeneous data sets of host gene responses to infection, but these data sets do not inform us about gene function and require specialized skill sets and training for their utilization. Here we describe an approach that combines and simplifies these data sets, distilling this information into a single list of genes commonly upregulated in response to infection with RSV as a model pathogen. Many of the genes on the list have unknown functions in RSV disease. We validated the gene list with new clinical, in vitro, and in vivo data. This approach allows the rapid selection of genes of interest for further, more-detailed studies, thus reducing time and costs. Furthermore, the approach is simple to use and widely applicable to a range of diseases.

Diagnosis of Bacterial Infection Using a 2-Transcript Host RNA Signature in Febrile Infants 60 Days or Younger

Distinguishing children with potentially life-threatening bacterial infections from febrile children with viral infections remains a major challenge. Herberg and colleagues,1 in a preliminary, cross-sectional study of 370 febrile children (aged <17 years) in Europe and the United States, reported that children with bacterial infection may be characterized by the difference in blood RNA expression values of 2 genes. In a recent study, Mahajan and colleagues2 reported a 66-transcript blood RNA signature that distinguished bacterial from viral infection in 279 febrile infants younger than 60 days. Young infants are at high risk of bacterial infection; diagnosis is difficult and prompt treatment important. To provide further validation of the 2-gene signature and to evaluate its performance in the infant population, we applied the signature to the RNA expression data of Mahajan et al.

Understanding immune protection against tuberculosis using RNA expression profiling.

A major limitation in the development and testing of new tuberculosis (TB) vaccines is the current inadequate understanding of the nature of the immune response required for protection against either infection with Mycobacterium tuberculosis (MTB) or progression to disease. Genome wide RNA expression analysis has provided a new tool with which to study the inflammatory and immunological response to mycobacteria. To explore how currently available transcriptomic data might be used to understand the basis of protective immunity to MTB, we analysed and reviewed published RNA expression studies to (1) identify a “susceptible” immune response in patients with acquired defects in the interferon gamma pathway; (2) identify the “failing” transcriptomic response in patients with TB as compared with latent TB infection (LTBI); and (3) identify elements of the “protective” response in healthy latently infected and healthy uninfected individuals.

Predicting active tuberculosis progression by RNA analysis.

www.thelancet.com Published online March 23, 2016 http://dx.doi.org/10.1016/S0140-6736(16)00165-3 1 WHO’s goals for tuberculosis c ontrol after 2015 are to reduce the number of deaths by 95% and the number of new cases by 90% by 2035. These ambitious goals will require development of new approaches for preventing emergence of active tuberculosis from the vast pool of individuals with latent tuberculosis infection. Latent tuberculosis infection describes individuals who have been infected with Mycobacterium tuberculosis, but in whom the infection has been contained by the immune response. Latent tuberculosis infection is defi ned by tuberculin skin test or in-vitro T-cell reactivity to mycobacterial antigens. A third of the world’s population have latent tuberculosis infection, but only 5–10% progress to active disease. Progression is most common in the years soon after infection and in young children, those with HIV infection, people who have had renal dialysis, or those with other immunesuppressive conditions. Patients who progress from latent tuberculosis infection to active disease might be symptomatic and infectious for months before tuberculosis is diagnosed. Thus, the epidemic is sustained by emergence of new cases of tuberculosis from the 2 billion people with latent tuberculosis infection and onward infection of their contacts. Antimycobacterial drugs are eff ective in reducing the risk of progression from latent tuberculosis infection to active tuberculosis. However, routine treatment of all latently infected individuals in high-burden countries is not feasible because of the large numbers who would require treatment. Additionally the prolonged courses of antimycobacterial drugs needed, the potential toxic eff ects of the drugs, and the danger of increasing drug resistance through inadvertent treatment of active tuberculosis cases with prophylactic rather than therapeutic regimens are prohibitive to population-level treatment. A test that identifi es individuals with latent tuberculosis infection who are at risk of progression would transform tuberculosis control, enabling targeted treatment of the population at risk. In The Lancet, Daniel Zak and colleagues report the results of a multicentre study investigating blood RNA expression to predict progression from latent tuberculosis infection to active disease. 6363 South African adolescents were screened by skin testing or interferon gamma release assay to identify those with latent tuberculosis infection, who were then followed up for 2 years. Sequential blood samples from 153 latent tuberculosis infection cases who either progressed to active tuberculosis or from controls who did not progress by the end of the study were analysed by RNA sequencing to identify genes that were diff erentially expressed between the two groups. A 16 gene signature was identifi ed that discriminated progressors from non-progressors, which was then validated using quantitative real-time PCR, fi rst in the adolescent cohort, and then in independent Gambian and South African cohorts of 4466 screened household contacts of sputum-smear positive cases of tuberculosis. The 16 gene signature predicted progression to tuberculosis in the adolescent cohort in the 12 months preceding tuberculosis diagnosis, with sensitivity of 66·1% and specifi city of 80·6%. In the household contact cohorts the signature had a sensitivity of 53·7% and a specifi city of 82·8% for progression to tuberculosis within 12 months. Predictive performance of the signature in both cohorts decreased with increasing time to diagnosis. The signature not only identifi ed progression from latent tuberculosis infection to active disease; it also distinguished active from latent tuberculosis infection with high sensitivity when applied to published tuberculosis RNA expression data. As the authors fi rst identifi ed RNA expression diff erences between progressors and non-progressors using blood samples taken closest to time of tuberculosis diagnosis, it is Predicting active tuberculosis progression by RNA analysis

Whole Blood Gene Expression Reveals Specific Transcriptome Changes in Neonatal Encephalopathy

Background: Variable responses to hypothermic neuroprotection are related to the clinical heterogeneity of encephalopathic babies; hence better disease stratification may facilitate the development of individualized neuroprotective therapies. Objectives: We examined if whole blood gene expression analysis can identify specific transcriptome profiles in neonatal encephalopathy. Material and Methods: We performed next-generation sequencing on whole blood RNA from 12 babies with neonatal encephalopathy and 6 time-matched healthy term babies. Genes significantly differentially expressed between encephalopathic and control babies were identified. This set of genes was then compared to the host RNA response in septic neonates and subjected to pathway analysis. Results: We identified 950 statistically significant genes discriminating perfectly between healthy controls and neonatal encephalopathy. The major pathways in neonatal encephalopathy were axonal guidance signaling (p = 0.0009), granulocyte adhesion and diapedesis (p = 0.003), IL-12 signaling and production in macrophages (p = 0.003), and hypoxia-inducible factor 1α signaling (p = 0.004). There were only 137 genes in common between neonatal encephalopathy and bacterial sepsis sets. Conclusion: Babies with neonatal encephalopathy have striking differences in gene expression profiles compared with healthy control and septic babies. Gene expression profiles may be useful for disease stratification and for developing personalized neuroprotective therapies.