Global transcriptional analysis of primitive thymocytes reveals accelerated dynamics of T cell specification in fetal stages.

T cell development constitutes a multistage process allowing the dissection of events resulting in cellular commitment and functional specification in a specialized microenvironment. This process is guided by the appropriate expression of regulatory genetic factors like transcriptional activators or repressors which are, in part, dependent on instructive signals of the microenvironment. To date, it remains unclear whether exactly the same genetic mechanism acts in adult compared to fetal T cell development. In order to directly compare T cell commitment during adult and fetal differentiation, we isolated subsequent stages of intrathymic subpopulations starting with early canonical T cell progenitors up to irreversibly committed T cell precursors. The genome-wide analysis revealed several distinct gene clusters with a specific pattern of gene regulation for each subset. The largest cluster contained genes upregulated after transition through the most primitive pool into the next transitory population with a consistently elevated expression of elements associated with ongoing T cell fate specification, like Gata3 and Tcf7, in fetal progenitors. Furthermore, adult and fetal T cell progenitors occupied distinct "transcriptional territories" revealing a precise land map of the progression to final T cell commitment operating in different developmental windows. The presence and/or elevated expression of elements associated with an ongoing establishment of a T cell signature in the most primitive fetal subset is highly suggestive for an extrathymic initiation of T cell specification and underlines the fundamental differences in fetal versus adult lymphopoiesis.

Discriminating active from latent tuberculosis in patients presenting to community clinics.

BACKGROUND: Because of the high global prevalence of latent TB infection (LTBI), a key challenge in endemic settings is distinguishing patients with active TB from patients with overlapping clinical symptoms without active TB but with co-existing LTBI. Current methods are insufficiently accurate. Plasma proteomic fingerprinting can resolve this difficulty by providing a molecular snapshot defining disease state that can be used to develop point-of-care diagnostics. METHODS: Plasma and clinical data were obtained prospectively from patients attending community TB clinics in Peru and from household contacts. Plasma was subjected to high-throughput proteomic profiling by mass spectrometry. Statistical pattern recognition methods were used to define mass spectral patterns that distinguished patients with active TB from symptomatic controls with or without LTBI. RESULTS: 156 patients with active TB and 110 symptomatic controls (patients with respiratory symptoms without active TB) were investigated. Active TB patients were distinguishable from undifferentiated symptomatic controls with accuracy of 87% (sensitivity 84%, specificity 90%), from symptomatic controls with LTBI (accuracy of 87%, sensitivity 89%, specificity 82%) and from symptomatic controls without LTBI (accuracy 90%, sensitivity 90%, specificity 92%). CONCLUSIONS: We show that active TB can be distinguished accurately from LTBI in symptomatic clinic attenders using a plasma proteomic fingerprint. Translation of biomarkers derived from this study into a robust and affordable point-of-care format will have significant implications for recognition and control of active TB in high prevalence settings.

Severe childhood malaria syndromes defined by plasma proteome profiles.

BACKGROUND: Cerebral malaria (CM) and severe malarial anemia (SMA) are the most serious life-threatening clinical syndromes of Plasmodium falciparum infection in childhood. Therefore it is important to understand the pathology underlying the development of CM and SMA, as opposed to uncomplicated malaria (UM). Different host responses to infection are likely to be reflected in plasma proteome-patterns that associate with clinical status and therefore provide indicators of the pathogenesis of these syndromes. METHODS AND FINDINGS: Plasma and comprehensive clinical data for discovery and validation cohorts were obtained as part of a prospective case-control study of severe childhood malaria at the main tertiary hospital of the city of Ibadan, an urban and densely populated holoendemic malaria area in Nigeria. A total of 946 children participated in this study. Plasma was subjected to high-throughput proteomic profiling. Statistical pattern-recognition methods were used to find proteome-patterns that defined disease groups. Plasma proteome-patterns accurately distinguished children with CM and with SMA from those with UM, and from healthy or severely ill malaria-negative children. CONCLUSIONS: We report that an accurate definition of the major childhood malaria syndromes can be achieved using plasma proteome-patterns. Our proteomic data can be exploited to understand the pathogenesis of the different childhood severe malaria syndromes.