The possibility of HIV-1 eradication has been limited by the existence of latently infected cellular reservoirs. Studies to examine control of HIV latency and potential reactivation have been hindered by the small numbers of latently infected cells found in vivo. Major conceptual leaps have been facilitated by the use of latently infected T cell lines and primary cells. However, notable differences exist among cell model systems. Furthermore, screening efforts in specific cell models have identified drug candidates for “anti-latency” therapy, which often fail to reactivate HIV uniformly across different models. Therefore, the activity of a given drug candidate, demonstrated in a particular cellular model, cannot reliably predict its activity in other cell model systems or in infected patient cells, tested ex vivo. This situation represents a critical knowledge gap that adversely affects our ability to identify promising treatment compounds and hinders the advancement of drug testing into relevant animal models and clinical trials. To begin to understand the biological characteristics that are inherent to each HIV-1 latency model, we compared the response properties of five primary T cell models, four J-Lat cell models and those obtained with a viral outgrowth assay using patient-derived infected cells. A panel of thirteen stimuli that are known to reactivate HIV by defined mechanisms of action was selected and tested in parallel in all models. Our results indicate that no single in vitro cell model alone is able to capture accurately the ex vivo response characteristics of latently infected T cells from patients. Most cell models demonstrated that sensitivity to HIV reactivation was skewed toward or against specific drug classes. Protein kinase C agonists and PHA reactivated latent HIV uniformly across models, although drugs in most other classes did not.

Whole blood transcriptional signatures distinguishing active tuberculosis patients from asymptomatic latently infected individuals exist. Consensus has not been achieved regarding the optimal reduced gene sets as diagnostic biomarkers that also achieve discrimination from other diseases. Here we show a blood transcriptional signature of active tuberculosis using RNA-Seq, confirming microarray results, that discriminates active tuberculosis from latently infected and healthy individuals, validating this signature in an independent cohort. Using an advanced modular approach, we utilise the information from the entire transcriptome, which includes overabundance of type I interferon-inducible genes and underabundance of IFNG and TBX21, to develop a signature that discriminates active tuberculosis patients from latently infected individuals or those with acute viral and bacterial infections. We suggest that methods targeting gene selection across multiple discriminant modules can improve the development of diagnostic biomarkers with improved performance. Finally, utilising the modular approach, we demonstrate dynamic heterogeneity in a longitudinal study of recent tuberculosis contacts.

Whole blood transcriptional signatures distinguishing patients with active tuberculosis from asymptomatic latently infected individuals have been described but, no consensus exists for the composition of optimal reduced gene sets as diagnostic biomarkers that also achieve discrimination from other diseases. We have recapitulated a blood transcriptional signature of active tuberculosis using RNA-Seq, previously reported by microarray that discriminates active tuberculosis from latently infected and healthy individuals, also validated in an independent cohort. We show that an advanced modular approach, which preserves and presents a signature of the entire transcriptome, can better discriminate patients with active tuberculosis from both latently infected and acute viral and bacterial infections. We suggest a method of targeted gene selection across modules for constructing diagnostic biomarkers, more representative of the transcriptome that overcomes some limitations of existing techniques. Finally, we utilise the modular approach to demonstrate dynamic heterogeneity in a longitudinal study of recent tuberculosis contacts.Tuberculosis (TB) is the leading cause of global mortality from an infectious disease. In 2016, there were 6.3 million new cases of TB disease and 1.67 million deaths and its diagnosis is problematic 1 . However, clinical disease represents one end of a spectrum of infection states. It is estimated that up to one third of all individuals worldwide have been infected with the causative pathogen, Mycobacterium tuberculosis, but the vast majority remain clinically asymptomatic with no radiological or microbiological evidence for active infection. This is termed latent TB infection (LTBI) and conceptually denotes a state in which M. tuberculosis persists within its host, while maintaining viability with the potential to replicate and cause symptomatic disease. Indeed, LTBI represents the primary reservoir for future incident TB, with 90% of all TB cases estimated to arise from reactivation of existing infection 1,2 . The risk of incident TB arising from existing LTBI is heterogeneous, poorly characterised and modifiable with anti-tuberculous treatment. Modelling studies indicate effective TB prevention to significantly reduce future TB incidence requires policies directed at the identification and treatment of LTBI 3 . However, implementation of mass screening programmes for this purpose are severely constrained by the size of the target population. Transformative advances in diagnostic tools that can effectively stratify TB risk in the LTBI population are therefore implicit to the realisation of systematic screening.The basis for LTBI heterogeneity rests with the limited scope of the tools we have available to identify the state. LTBI is inferred solely through evidence that immune sensitization has occurred, by the tuberculin skin test (TST) or the M. tuberculosis antigen-specific interferon-gamma (IFN-g) release assay (IGRA). Although these tests are both sensitive and specific for identi...

Local lung epithelial IL-6TS activation in the absence of type 2 airway inflammation defines a novel subset of asthmatic patients and might drive airway inflammation and epithelial dysfunction in these patients.

Understanding how immune challenges elicit different responses is critical for diagnosing and deciphering immune regulation. Using a modular strategy to interpret the complex transcriptional host response in mouse models of infection and inflammation, we show a breadth of immune responses in the lung. Lung immune signatures are dominated by either IFN-γ and IFN-inducible, IL-17-induced neutrophil- or allergy-associated gene expression. Type I IFN and IFN-γ-inducible, but not IL-17- or allergy-associated signatures, are preserved in the blood. While IL-17-associated genes identified in lung are detected in blood, the allergy signature is only detectable in blood CD4 + effector cells. Type I IFN-inducible genes are abrogated in the absence of IFN-γ signaling and decrease in the absence of IFNAR signaling, both independently contributing to the regulation of granulocyte responses and pathology during Toxoplasma gondii infection. Our framework provides an ideal tool for comparative analyses of transcriptional signatures contributing to protection or pathogenesis in disease.

Although mouse infection models have been extensively used to study the host response to Mycobacterium tuberculosis , their validity in revealing determinants of human TB resistance and disease progression has been heavily debated. Here, we show that the modular transcriptional signature in the blood of susceptible mice infected with a clinical isolate of M. tuberculosis resembles that of active human tuberculosis disease, with a dominance of a type I IFN response and neutrophil activation and recruitment, together with a loss in B lymphocyte, NK and T cell effector responses. In addition, resistant but not susceptible strains of mice show increased lung B, NK and T cell effector responses in the lung upon infection. Importantly, the blood signature of active disease shared by mice and humans is also evident in latent tuberculosis progressors before diagnosis suggesting that these responses both predict and contribute to the pathogenesis of progressive M. tuberculosis infection.

Linking gender-specific differences to the molecular etiology of obesity has been largely based on genomic and transcriptomic evidence lacking endophenotypic insight and is not applicable to the extracellular fluid compartments, or the milieu intérieur, of the human body. To address this need, this study profiled the whole serum proteomes of age-matched nondiabetic overweight and obese females (n = 28) and males (n = 31) using a multiplex design with pooled biological and technical replicates. To bypass basic limitations of immunodepletion-based strategies, subproteome enrichment by size-exclusion chromatography (SuPrE-SEC) followed by iTRAQ 2D-LC-nESI-FTMS analysis was used. The study resulted in the reproducible analysis of 2472 proteins (peptide FDR < 5%, q < 0.05). A total of 248 proteins exhibited significant modulation between men and women (p < 0.05) that mapped to pathways associated with β-estradiol, lipid and prostanoid metabolism, vitamin D function, immunity/inflammation, and the complement and coagulation cascades. This novel endophenotypic signature of gender-specific differences in whole serum confirmed and expanded the results of previous physiologic and pharmacologic studies exploring sexual dimorphism at the genomic and transcriptomic level in tissues and cells. Conclusively, the multifactorial and pleiotropic nature of human obesity exhibits sexual dimorphism in the circulating proteome of importance to clinical study design.

Asthma arises from the complex interplay of inflammatory pathways in diverse cell types and tissues. We sought to undertake a comprehensive transcriptomic assessment of the epithelium and airway T cells that remain understudied in asthma and investigate interactions between multiple cells and tissues. Epithelial brushings and flow-sorted CD3 1 T cells from sputum and BAL were obtained from healthy subjects (n = 19) and patients with asthma (mild, moderate, and severe asthma; n = 46). Gene expression was assessed using Affymetrix HT HG-U133 1 PM GeneChips, and results were validated by real-time quantitative PCR. In the epithelium, IL-13 response genes (POSTN, SERPINB2, and CLCA1), mast cell mediators (CPA3 and TPSAB1), inducible nitric oxide synthase, and cystatins (CST1, CST2, and CST4) were upregulated in mild asthma, but, except for cystatins, were suppressed by corticosteroids in moderate asthma. In severe asthma-with predominantly neutrophilic phenotype-several distinct processes were upregulated, including neutrophilia (TCN1 and MMP9), mucins, and oxidative stress responses. The majority of the disease signature was evident in sputum T cells in severe asthma, where 267 genes were differentially regulated compared with health, highlighting compartmentalization of inflammation. This signature included IL-17-inducible chemokines (CXCL1, CXCL2, CXCL3, IL8, and CSF3) and chemoattractants for neutrophils (IL8, CCL3, and LGALS3), T cells, and monocytes. A protein interaction network in severe asthma highlighted signatures of responses to bacterial infections across tissues (CEACAM5, CD14, and TLR2), including Toll-like receptor signaling. In conclusion, the activation of innate immune pathways in the airways suggests that activated T cells may be driving neutrophilic inflammation and steroid-insensitive IL-17 response in severe asthma.