Blood Transcriptomic Signature Research Articles

Shared Blood Transcriptomic Signatures between Alzheimer's Disease and Diabetes Mellitus.

Alzheimer’s disease (AD) and diabetes mellitus (DM) are known to have a shared molecular mechanism. We aimed to identify shared blood transcriptomic signatures between AD and DM. Blood expression datasets for each disease were combined and a co-expression network was used to construct modules consisting of genes with similar expression patterns. For each module, a gene regulatory network based on gene expression and protein-protein interactions was established to identify hub genes. We selected one module, where COPS4, PSMA6, GTF2B, GTF2F2, and SSB were identified as dysregulated transcription factors that were common between AD and DM. These five genes were also differentially co-expressed in disease-related tissues, such as the brain in AD and the pancreas in DM. Our study identified gene modules that were dysregulated in both AD and DM blood samples, which may contribute to reveal common pathophysiology between two diseases.

A Neutrophil-Driven Inflammatory Signature Characterizes the Blood Transcriptome Fingerprint of Psoriasis.

Transcriptome profiling approaches have been widely used to investigate the mechanisms underlying psoriasis pathogenesis. Most researchers have measured changes in transcript abundance in skin biopsies; relatively few have examined transcriptome changes in the blood. Although less relevant to the study of psoriasis pathogenesis, blood transcriptome profiles can be readily compared across various diseases. Here, we used a pre-established set of 382 transcriptional modules as a common framework to compare changes in blood transcript abundance in two independent public psoriasis datasets. We then compared the resulting “transcriptional fingerprints” to those obtained for a reference set of 16 pathological or physiological states. The perturbations in blood transcript abundance in psoriasis were relatively subtle compared to the changes we observed in other autoimmune and auto-inflammatory diseases. However, we did observe a consistent pattern of changes for a set of modules associated with neutrophil activation and inflammation; interestingly, this pattern resembled that observed in patients with Kawasaki disease. This similarity between the blood-transcriptome signatures in psoriasis and Kawasaki disease suggests that the immune mechanisms driving their pathogenesis might be partially shared.

Performance of diagnostic and predictive host blood transcriptomic signatures for Tuberculosis disease: A systematic review and meta-analysis.

IntroductionHost blood transcriptomic biomarkers have potential as rapid point-of-care triage, diagnostic, and predictive tests for Tuberculosis disease. We aimed to summarise the performance of host blood transcriptomic signatures for diagnosis of and prediction of progression to Tuberculosis disease; and compare their performance to the recommended World Health Organisation target product profile.MethodsA systematic review and meta-analysis of the performance of host blood mRNA signatures for diagnosing and predicting progression to Tuberculosis disease in HIV-negative adults and adolescents, in studies with an independent validation cohort. Medline, Scopus, Web of Science, and EBSCO libraries were searched for articles published between January 2005 and May 2019, complemented by a search of bibliographies. Study selection, data extraction and quality assessment were done independently by two reviewers. Meta-analysis was performed for signatures that were validated in ≥3 comparable cohorts, using a bivariate random effects model.ResultsTwenty studies evaluating 25 signatures for diagnosis of or prediction of progression to TB disease in a total of 68 cohorts were included. Eighteen studies evaluated 24 signatures for TB diagnosis and 17 signatures met at least one TPP minimum performance criterion. Three diagnostic signatures were validated in clinically relevant cohorts to differentiate TB from other diseases, with pooled sensitivity 84%, 87% and 90% and pooled specificity 79%, 88% and 74%, respectively. Four studies evaluated signatures for progression to TB disease and performance of one signature, assessed within six months of TB diagnosis, met the minimal TPP for a predictive test for progression to TB disease.ConclusionHost blood mRNA signatures hold promise as triage tests for TB. Further optimisation is needed if mRNA signatures are to be used as standalone diagnostic or predictive tests for therapeutic decision-making.

Transcriptomic profile of adverse neurodevelopmental outcomes after neonatal encephalopathy

A rapid and early diagnostic test to identify the encephalopathic babies at risk of adverse outcome may accelerate the development of neuroprotectants. We examined if a whole blood transcriptomic signature measured soon after birth, predicts adverse neurodevelopmental outcome eighteen months after neonatal encephalopathy. We performed next generation sequencing on whole blood ribonucleic acid obtained within six hours of birth from the first 47 encephalopathic babies recruited to the Hypothermia for Encephalopathy in Low and middle-income countries (HELIX) trial. Two infants with blood culture positive sepsis were excluded, and the data from remaining 45 were analysed. A total of 855 genes were significantly differentially expressed between the good and adverse outcome groups, of which RGS1 and SMC4 were the most significant. Biological pathway analysis adjusted for gender, trial randomisation allocation (cooling therapy versus usual care) and estimated blood leukocyte proportions revealed over-representation of genes from pathways related to melatonin and polo-like kinase in babies with adverse outcome. These preliminary data suggest that transcriptomic profiling may be a promising tool for rapid risk stratification in neonatal encephalopathy. It may provide insights into biological mechanisms and identify novel therapeutic targets for neuroprotection.

Blood transcriptomic discrimination of bacterial and viral infections in the emergency department: a multi-cohort observational validation study

BackgroundThere is an urgent need to develop biomarkers that stratify risk of bacterial infection in order to support antimicrobial stewardship in emergency hospital admissions.MethodsWe used computational machine learning to derive a rule-out blood transcriptomic signature of bacterial infection (SeptiCyte™ TRIAGE) from eight published case-control studies. We then validated this signature by itself in independent case-control data from more than 1500 samples in total, and in combination with our previously published signature for viral infections (SeptiCyte™ VIRUS) using pooled data from a further 1088 samples. Finally, we tested the performance of these signatures in a prospective observational cohort of emergency department (ED) patients with fever, and we used the combined SeptiCyte™ signature in a mixture modelling approach to estimate the prevalence of bacterial and viral infections in febrile ED patients without microbiological diagnoses.ResultsThe combination of SeptiCyte™ TRIAGE with our published signature for viral infections (SeptiCyte™ VIRUS) discriminated bacterial and viral infections in febrile ED patients, with a receiver operating characteristic area under the curve of 0.95 (95% confidence interval 0.90–1), compared to 0.79 (0.68–0.91) for WCC and 0.73 (0.61–0.86) for CRP. At pre-test probabilities 0.35 and 0.72, the combined SeptiCyte™ score achieved a negative predictive value for bacterial infection of 0.97 (0.90–0.99) and 0.86 (0.64–0.96), compared to 0.90 (0.80–0.94) and 0.66 (0.48–0.79) for WCC and 0.88 (0.69–0.95) and 0.60 (0.31–0.72) for CRP. In a mixture modelling approach, the combined SeptiCyte™ score estimated that 24% of febrile ED cases receiving antibacterials without a microbiological diagnosis were due to viral infections. Our analysis also suggested that a proportion of patients with bacterial infection recovered without antibacterials.ConclusionsBlood transcriptional biomarkers offer exciting opportunities to support precision antibacterial prescribing in ED and improve diagnostic classification of patients without microbiologically confirmed infections.

Understanding Human Cerebral Malaria through a Blood Transcriptomic Signature: Evidences for Erythrocyte Alteration, Immune/Inflammatory Dysregulation, and Brain Dysfunction.

Background Cerebral malaria (CM), a reversible encephalopathy affecting young children, is a medical emergency requiring rapid clinical assessment and treatment. However, understanding of the genes/proteins and the biological pathways involved in the disease outcome is still limited. Methods We have performed a whole transcriptomic analysis of blood samples from Malian children with CM or uncomplicated malaria (UM). Hierarchical clustering and pathway, network, and upstream regulator analyses were performed to explore differentially expressed genes (DEGs). We validated gene expression for 8 genes using real-time quantitative PCR (RT-qPCR). Plasma levels were measured for IP-10/CXCL10 and IL-18. Results A blood RNA signature including 538 DEGs (∣FC | ≥2.0, adjusted P value ≤ 0.01) allowed to discriminate between CM and UM. Ingenuity Pathway Analysis (IPA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) revealed novel genes and biological pathways related to immune/inflammatory responses, erythrocyte alteration, and neurodegenerative disorders. Gene expressions of CXCL10, IL12RB2, IL18BP, IL2RA, AXIN2, and NET were significantly lower in CM whereas ARG1 and SLC6A9 were higher in CM compared to UM. Plasma protein levels of IP-10/CXCL10 were significantly lower in CM than in UM while levels of IL-18 were higher. Interestingly, among children with CM, those who died from a complication of malaria tended to have higher concentrations of IP-10/CXCL10 and IFN-γ than those who recovered. Conclusions This study identified some new factors and mechanisms that play crucial roles in CM and characterized their respective biological pathways as well as some upstream regulators.

Nutrigenomic Effects of Long-Term Grape Pomace Supplementation in Dairy Cows

Simple SummaryThe aim of this study was to evaluate the effect of grape pomace (GP), the polyphenol-rich agricultural by-product, on dairy cows’ whole-blood transcriptome, milk production and composition. Twelve lactating Holstein-Friesian cows were randomly assigned to two groups; the first received a GP-supplemented diet for 60 days (group GP), whereas the second was given only a basal diet (CTR). The results reveal 40 protein-coding genes differentially expressed in the GP group when compared with the CTR group, but no effects were noticed on milk production, concentrations of crude protein, fat, casein, lactose and urea, or somatic cell count. Compared to CTR, GP had a transcriptomic signature mainly reflecting a reinforced immunogenic response.The increasing demand for more animal products put pressure on improving livestock production efficiency and sustainability. In this context, advanced animal nutrition studies appear indispensable. Here, the effect of grape pomace (GP), the polyphenol-rich agricultural by-product, was evaluated on Holstein-Friesian cows’ whole-blood transcriptome, milk production and composition. Two experimental groups were set up. The first one received a basal diet and served as a control, while the second one received a 7.5% GP-supplemented diet for a total of 60 days. Milk production and composition were not different between the group; however, the transcriptome analysis revealed a total of 40 genes significantly affected by GP supplementation. Among the most interesting down-regulated genes, we found the DnaJ heat-shock protein family member A1 (DNAJA1), the mitochondrial fission factor (MFF), and the impact RWD domain protein (IMPACT) genes. The gene set enrichment analysis evidenced the positive enrichment of ‘interferon alpha (IFN-α) and IFN-γ response’, ‘IL6-JAK-STAT3 signaling’ and ‘complement’ genes. Moreover, the functional analysis denoted positive enrichment of the ‘response to protozoan’ and ‘negative regulation of viral genome replication’ biological processes. Our data provide an overall view of the blood transcriptomic signature after a 60-day GP supplementation in dairy cows which mainly reflects a GP-induced immunomodulatory effect.

Blood transcriptomic biomarker as a surrogate of ischemic brain gene expression.

ObjectivesBlood biomarkers for cerebral tissue ischemia are lacking. The goal was to identify a blood transcriptomic signature jointly identified in the ischemic brain.MethodsA nonhuman primate model with middle cerebral artery (MCA) territory infarction was used to study gene expression by microarray during acute ischemic cerebral stroke in the brain and the blood. Brain samples were collected in the infarcted and contralateral non‐infarcted cortex as well as blood samples before and after occlusion. Gene expression was compared between the two brain locations to find differentially expressed genes. The expressions of these genes were then compared in the blood pre‐ and post‐occlusion.ResultsHierarchical clustering of brain expression data revealed strong independent clustering of ischemic and nonischemic brain samples. The top five enriched, up‐regulated gene sets in the brain were TNF α signaling, apoptosis, P53 pathway, hypoxia, and UV response up. A comparison of differentially expressed genes in the brain and blood revealed a significant overlap of gene expression patterns. Stringent analysis of blood expression data from pre‐ and post‐occlusion samples in each monkey identified nine genes highly differentially expressed in both the brain and the blood. Many of these up‐regulated genes belong to pathways involved in cell death and DNA damage repair.InterpretationCommon gene expression profile can be identified in the brain and blood and clearly differentiates ischemic from nonischemic conditions. Therefore, specific blood transcriptomic signature may represent a surrogate for brain ischemic gene expression.

Detection of Tuberculosis Recurrence, Diagnosis and Treatment Response by a Blood Transcriptomic Risk Signature in HIV-Infected Persons on Antiretroviral Therapy.

HIV-infected individuals are at high risk of tuberculosis disease and those with prior tuberculosis episodes are at even higher risk of disease recurrence. A non-sputum biomarker that identifies individuals at highest tuberculosis risk would allow targeted microbiological testing and appropriate treatment and also guide need for prolonged therapy. We determined the utility of a previously developed whole blood transcriptomic correlate of risk (COR) signature for (1) predicting incident recurrent tuberculosis, (2) tuberculosis diagnosis and (3) its potential utility for tuberculosis treatment monitoring in HIV-infected individuals. We retrieved cryopreserved blood specimens from three previously completed clinical studies and measured the COR signature by quantitative microfluidic real-time-PCR. The signature differentiated recurrent tuberculosis progressors from non-progressors within 3 months of diagnosis with an area under the Receiver-operating characteristic (ROC) curve (AUC) of 0.72 (95% confidence interval (CI), 0.58–0.85) amongst HIV-infected individuals on antiretroviral therapy (ART). Twenty-five of 43 progressors (58%) were asymptomatic at microbiological diagnosis and thus had subclinical disease. The signature showed excellent diagnostic discrimination between HIV-uninfected tuberculosis cases and controls (AUC 0.97; 95%CI 0.94–1). Performance was lower in HIV-infected individuals (AUC 0.83; 95%CI 0.81–0.96) and signature scores were directly associated with HIV viral loads. Tuberculosis treatment response in HIV-infected individuals on ART with a new recurrent tuberculosis diagnosis was also assessed. Signature scores decreased significantly during treatment. However, pre-treatment scores could not differentiate between those who became sputum negative before and after 2 months. Direct application of the unmodified blood transcriptomic COR signature detected subclinical and active tuberculosis by blind validation in HIV-infected individuals. However, prognostic performance for recurrent tuberculosis, and performance as diagnostic and as treatment monitoring tool in HIV-infected persons was inferior to published results from HIV-negative cohorts. Our results suggest that performance of transcriptomic signatures comprising interferon stimulated genes are negatively affected in HIV-infected individuals, especially in those with incompletely suppressed viral loads.

Performance of host blood transcriptomic signatures for diagnosing and predicting progression to tuberculosis disease in HIV-negative adults and adolescents: a systematic review protocol

IntroductionOne-quarter of the global population, including the majority of adults in tuberculosis (TB) endemic countries, are estimated to be Mycobacterium tuberculosis (MTB) infected. An estimated 10 million new TB cases...

Early blood transcriptomic signature predicts patients’ outcome after out-of-hospital cardiac arrest

BackgroundEarly prognostication is a major challenge after out-of-hospital cardiac arrest (OHCA). AimsWe hypothesized that a genome-wide analysis of blood gene expression could offer new prognostic tools and lines of research. MethodsSixty-nine patients were enrolled from an ancillary study of the clinical trial NCT00999583 that tested the effect of erythropoietin (EPO) after OHCA. Blood samples were collected in comatose survivors of OHCA at hospital admission and 1 and 3 days after resuscitation. Gene expression profiles were analyzed (Illumina HumanHT-12 V4 BeadChip; >34,000 genes). Patients were classified into two categories representing neurological favorable outcome (cerebral performance category [CPC] = 1-2) vs unfavorable outcome (CPC > 2) at Day 60 after OHCA. Differential and functional enrichment analyses were performed to compare transcriptomic profiles between these two categories. ResultsAmong the 69 enrolled patients, 33 and 36 patients were treated or not by EPO, respectively. Among them, 42% had a favorable neurological outcome in both groups. EPO did not affect the transcriptomic response at Day-0 and 1 after OHCA. In contrast, 76 transcripts differed at Day-0 between patients with unfavorable vs favorable neurological outcome. This signature persisted at Day-1 after OHCA. Functional enrichment analysis revealed a down-regulation of adaptive immunity with concomitant up-regulation of innate immunity and inflammation in patients with unfavorable vs favorable neurological outcome. The transcription of many genes of the HLA family was decreased in patients with unfavorable vs favorable neurological outcome. Concomitantly, neutrophil activation and inflammation were observed. Up-stream regulators analysis showed the implication of numerous factors involved in cell cycle and damages. A logistic regression including a set of genes allowed a reliable prediction of the clinical outcomes (specificity = 88%; Hit Rate = 83%). ConclusionsA transcriptomic signature involving a counterbalance between adaptive and innate immune responses is able to predict neurological outcome very early after hospital admission after OHCA. This deserves confirmation in a larger population.

Blood-based dynamic genomic signature for obsessive-compulsive disorder.

No biologically based diagnostic criteria are in clinical use today for obsessive-compulsive disorder (OCD), schizophrenia, and major depressive disorder (MDD), which are defined with reference to Diagnostic and Statistical Manual clinical symptoms alone. However, these disorders cannot always be well distinguished on clinical grounds and may also be comorbid. A biological blood-based dynamic genomic signature that can differentiate among OCD, MDD, and schizophrenia would therefore be of great utility. This study enrolled 77 patients with OCD, 67 controls with no psychiatric illness, 39 patients with MDD, and 40 with schizophrenia. An OCD-specific gene signature was identified using blood gene expression analysis to construct a predictive model of OCD that can differentiate this disorder from healthy controls, MDD, and schizophrenia using a logistic regression algorithm. To verify that the genes selected were not derived as a result of chance, the algorithm was tested twice. First, the algorithm was used to predict the cohort with true disease/control status and second, the algorithm predicted the cohort with disease/control status randomly reassigned (null set). A six-gene panel (COPS7A, FKBP1A, FIBP, TP73-AS1, SDF4, and GOLGA8A) discriminated patients with OCD from healthy controls, MDD, and schizophrenia in the training set (with an area under the receiver-operating-characteristic curve of 0.938; accuracy, 86%; sensitivity, 88%; and specificity, 85%). Our findings indicate that a blood transcriptomic signature can distinguish OCD from healthy controls, MDD, and schizophrenia. This finding further confirms the feasibility of using dynamic blood-based genomic signatures in psychiatric disorders and may provide a useful tool for clinical staff engaged in OCD diagnosis and decision making.

Blood transcriptomic markers of Mycobacterium tuberculosis load in sputum.

Peripheral blood transcriptome signatures that distinguish active pulmonary tuberculosis (TB) from control groups have been reported, but correlations of these signatures with sputum mycobacterial load are incompletely defined. We assessed the performance of published TB transcriptomic signatures in Haiti, and identified transcriptomic biomarkers of TB bacterial load in sputum as measured by Xpert® MTB/RIF molecular testing. People in Port au Prince, Haiti, with untreated pulmonary TB (n = 51) formed the study cohort: 19 people with low and 32 with high sputum Mycobacterium tuberculosis load. Peripheral whole blood transcriptomes were generated using RNA sequencing. Twenty of the differentially expressed transcripts in TB vs. no TB were differentially expressed in people with low vs. high sputum mycobacterial loads. The difference between low and high bacterial load groups was independent of radiographic severity. In a published data set of transcriptomic response to anti-tuberculosis treatment, this 20-gene subset was more treatment-responsive at 6 months than the full active TB signature. We identified genes whose transcript levels in the blood distinguish active TB with high vs. low M. tuberculosis loads in the sputum. These transcripts may reveal mechanisms of mycobacterial control of M. tuberculosis during active infection, as well as identifying potential biomarkers for bacterial response to anti-tuberculosis treatment.

A peripheral blood transcriptomic signature predicts autoantibody development in infants at risk of type 1 diabetes.

Autoimmune-mediated destruction of pancreatic islet β cells results in type 1 diabetes (T1D). Serum islet autoantibodies usually develop in genetically susceptible individuals in early childhood before T1D onset, with multiple islet autoantibodies predicting diabetes development. However, most at-risk children remain islet-antibody negative, and no test currently identifies those likely to seroconvert. We sought a genomic signature predicting seroconversion risk by integrating longitudinal peripheral blood gene expression profiles collected in high-risk children included in the BABYDIET and DIPP cohorts, of whom 50 seroconverted. Subjects were followed for 10 years to determine time of seroconversion. Any cohort effect and the time of seroconversion were corrected to uncover genes differentially expressed (DE) in seroconverting children. Gene expression signatures associated with seroconversion were evident during the first year of life, with 67 DE genes identified in seroconverting children relative to those remaining antibody negative. These genes contribute to T cell-, DC-, and B cell-related immune responses. Near-birth expression of ADCY9, PTCH1, MEX3B, IL15RA, ZNF714, TENM1, and PLEKHA5, along with HLA risk score predicted seroconversion (AUC 0.85). The ubiquitin-proteasome pathway linked DE genes and T1D susceptibility genes. Therefore, a gene expression signature in infancy predicts risk of seroconversion. Ubiquitination may play a mechanistic role in diabetes progression.

Existing blood transcriptional classifiers accurately discriminate active tuberculosis from latent infection in individuals from south India

Several studies have identified blood transcriptomic signatures that can distinguish active from latent Tuberculosis (TB). The purpose of this study was to assess how well these existing gene profiles classify TB disease in a South Indian population. RNA sequencing was performed on whole blood PAXgene samples collected from 28 TB patients and 16 latently TB infected (LTBI) subjects enrolled as part of an ongoing household contact study. Differential gene expression and clustering analyses were performed and compared with explicit predictive testing of TB and LTBI individuals based on established gene signatures. We observed strong predictive performance of TB disease states based on expression of known gene sets (ROC AUC 0.9007–0.9879). Together, our findings indicate that previously reported classifiers generated from different ethnic populations can accurately discriminate active TB from LTBI in South Indian patients. Future work should focus on converting existing gene signatures into a universal TB gene signature for diagnosis, monitoring TB treatment, and evaluating new drug regimens.

Blood transcriptomic diagnosis of pulmonary and extrapulmonary tuberculosis.

BACKGROUND. Novel rapid diagnostics for active tuberculosis (TB) are required to overcome the time delays and inadequate sensitivity of current microbiological tests that are critically dependent on sampling the site of disease. Multiparametric blood transcriptomic signatures of TB have been described as potential diagnostic tests. We sought to identify the best transcript candidates as host biomarkers for active TB, extend the evaluation of their specificity by comparison with other infectious diseases, and to test their performance in both pulmonary and extrapulmonary TB.METHODS. Support vector machine learning, combined with feature selection, was applied to new and previously published blood transcriptional profiles in order to identify the minimal TB‑specific transcriptional signature shared by multiple patient cohorts including pulmonary and extrapulmonary TB, and individuals with and without HIV-1 coinfection.RESULTS. We identified and validated elevated blood basic leucine zipper transcription factor 2 (BATF2) transcript levels as a single sensitive biomarker that discriminated active pulmonary and extrapulmonary TB from healthy individuals, with receiver operating characteristic (ROC) area under the curve (AUC) scores of 0.93 to 0.99 in multiple cohorts of HIV-1–negative individuals, and 0.85 in HIV-1–infected individuals. In addition, we identified and validated a potentially novel 4-gene signature comprising CD177, haptoglobin, immunoglobin J chain, and galectin 10 that discriminated active pulmonary and extrapulmonary TB from other febrile infections, giving ROC AUCs of 0.94 to 1.CONCLUSIONS. Elevated blood BATF2 transcript levels provide a sensitive biomarker that discriminates active TB from healthy individuals, and a potentially novel 4-gene transcriptional signature differentiates between active TB and other infectious diseases in individuals presenting with fever.FUNDING. MRC, Wellcome Trust, Rosetrees Trust, British Lung Foundation, NIHR.

Longitudinal analysis of sarcoidosis blood transcriptomic signatures and disease outcomes.

Previously, we demonstrated concordance in differentially expressed genes in sarcoidosis blood and lung, implicating shared dysfunction of specific immune pathways. In the present study, we hypothesised that expression levels of candidate genes in sarcoidosis blood could predict and track with disease outcomes longitudinally. We applied Ingenuity Pathway Analysis to a cross-sectional derivation microarray dataset (n=38) to identify canonical pathways and candidate genes associated with sarcoidosis. In a separate longitudinal sarcoidosis cohort (n=103), we serially measured 48 candidate gene transcripts, and assessed their relation to disease chronicity and severity. In the cross-sectional derivation study, pathway analysis showed upregulation of genes related to interferon signalling and the role of pattern recognition receptors, and downregulation of T-cell receptor (TCR) signalling pathways in sarcoidosis. In the longitudinal cohort, factor analysis confirmed coregulation of genes marking these pathways and identified CXCL9 as an additional candidate pathway. CXCL9 and TCR factors discriminated between chronic versus nonprogressive disease, and CXCL9 predicted disease outcomes longitudinally. Interferon factor was similarly increased in both disease phenotypes. Factors associated with lung function decline included decreased TCR factor and increased CXCL9. These findings demonstrate blood transcriptomic signatures reflecting TCR signalling and CXCL9 predict sarcoidosis chronicity and correlate with disease severity longitudinally.

S59 Dramatic decline in plasma small RNA concentration in HIV-infected and uninfected individuals receiving anti-tuberculosis therapy: a putative biomarker of treatment response

Non-coding RNA molecules, particularly miRNA, regulate translation of mRNA and have been found to target expression of genes important in immune response, such as IFN-γ. Differences in blood transcriptome signatures...

Blood Transcriptomic Biomarkers of Alzheimer's Disease Patients Treated with EHT 0202

Monitoring the genomic expression of patients in clinical trials for Alzheimer's disease (AD) can assist trial design and treatment response analysis. Here, we report on the identification in AD patients of blood-based transcriptomic signatures associated with treatment response of EHT 0202, a new compound with potential disease-modifying and symptomatic properties, in a 3-month, placebo-controlled, Phase IIA study aimed at determining the clinical safety, tolerability, and exploratory efficacy of EHT 0202 (40 and 80 mg bid) as adjunctive therapy to one cholinesterase inhibitor in mild to moderate AD patients. Genome-wide transcriptomic profiling was performed on blood samples taken prior to treatment and at study completion in a subpopulation of 60 AD patients selected as either the 10 worst disease decliners or the 10 best improvers of each treatment group, using ADAS-Cog scores as measure of disease severity. In the patients responding to EHT 0202, a pre-treatment (baseline) transcriptomic signature showed activation of pathways related to AD, CNS disorders, diabetes, inflammation, and autoimmunity, while a post-treatment signature indicated reduced activation of these pathways with induced metabolic and transcription stimulation. This pilot study demonstrates the utility of blood transcriptomic signatures used as biomarkers for predicting patient response or monitoring efficacy, for an administered therapeutic drug in a complex disease such as AD. For EHT 0202 or other AD drugs, such biomarkers may help to improve strategies to better identify appropriate patient populations for treatment, understand the drug mechanism of efficacy, and/or clarify the inherent subjectivity in most clinical endpoints used in this disease.

P4‐290: Blood transcriptomic signatures of Alzheimer's patients treated with a novel Alzheimer's disease drug, EHT 0202, allow identification of novel potential biomarkers correlated with treatment response and efficacy

P4‐290: Blood transcriptomic signatures of Alzheimer's patients treated with a novel Alzheimer's disease drug, EHT 0202, allow identification of novel potential biomarkers correlated with treatment response and efficacy