T cell ontogeny: Organ location of maturing populations as defined by surface antigen markers is similar in neonates and adults

Earlier studies have suggested that splenic T cell populations in nursling mice (<18 d of age) have Lyt cell surface antigens that identify them as less mature than their adult counterparts. Studies presented here, however, demonstrate that the expression of the Thy-1, Lyt-1, Lyt-2, and Lyt-3 T cell antigens is virtually identical in 14-d-old and adult T cell populations even though at 14 d, T cells constitute <10% of the total spleen cell population. Because the expression of these antigens on the immature (cortical) thymocyte population differs substantially from their expression on peripheral T cells, the majority of splenic T cells as judged by these criteria is similar in nurslings and adults. Very few cells in the neonatal thymus 4 h after birth correspond, in terms of antigen expression, to the more mature (medullary) thymocyte population of adults, but such cells develop rapidly during the first few days of life. They are present, therefore, sufficiently early to serve as the immediate source of peripheral T cells, as they apparently do in the adult. This then suggests that the locations for the major T cell maturational events are established within the first 2 wk of life of the mouse and maintained as such thereafter. The use of monoclonal antibodies and quantitative immunofluorescence analysis in our studies probably explains the differences between our findings and those reported previously, which relied on cytotoxic depletion by alloantisera and complement to estimate the frequencies of cells carrying the Lyt differentiation antigens in nurslings

Effects of N-acetyl-cysteine on endothelial function and inflammation in patients with type 2 diabetes mellitus

Endothelial dysfunction has been associated with premature vascular disease. There is increasing data that N-acetyl-cysteine (NAC) may prevent or improve endothelial dysfunction. The aim of this study was to assess the effects of NAC on endothelial function in patients with type 2 diabetes mellitus, a population at high risk for endothelial dysfunction. Twenty-four patients with diabetes mellitus were assigned randomly to initial therapy with either 900 mg NAC or placebo twice daily in a double-blind, cross-over study design. Flowmediated vasodilation (FMD) of the brachial artery was assessed at baseline, after four weeks of therapy, after a four-week wash-out period, and after another four weeks on the opposite treatment. Plasma and red blood cell glutathione levels and high-sensitivity C-reactive protein (CRP) were measured at all four visits. At baseline, FMD was moderately impaired (3.7±2.9%). There was no significant change in FMD after four weeks of NAC therapy as compared to placebo (0.1±3.6% vs. 1.2±4.2%). Similarly, there was no significant change in glutathione levels. However, median CRP decreased from 2.35 to 2.14 mg/L during NAC therapy (p=0.04), while it increased from 2.24 to 2.65 mg/L with placebo. No side effects were noted during the treatment period. In this double-blind, randomized cross-over study, four weeks of oral NAC therapy failed to improve endothelial dysfunction in patients with diabetes mellitus. However, NAC therapy decreased CRP levels, suggesting that this compound may have some efficacy in reducing systemic inflammation

Learning Signaling Network Structures with Sparsely Distributed Data

Flow cytometric measurement of signaling protein abundances has proved particularly useful for elucidation of signaling pathway structure. The single cell nature of the data ensures a very large dataset size, providing a statistically robust dataset for structure learning. Moreover, the approach is easily scaled to many conditions in high throughput. However, the technology suffers from a dimensionality constraint: at the cutting edge, only about 12 protein species can be measured per cell, far from sufficient for most signaling pathways. Because the structure learning algorithm (in practice) requires that all variables be measured together simultaneously, this restricts structure learning to the number of variables that constitute the flow cytometer's upper dimensionality limit. To address this problem, we present here an algorithm that enables structure learning for sparsely distributed data, allowing structure learning beyond the measurement technology's upper dimensionality limit for simultaneously measurable variables. The algorithm assesses pairwise (or n-wise) dependencies, constructs “Markov neighborhoods” for each variable based on these dependencies, measures each variable in the context of its neighborhood, and performs structure learning using a constrained search.Leukemia & Lymphoma Society of AmericaNational Institutes of Health (U.S.) (grant AI06584)National Institutes of Health (U.S.) (grant GM68762)Burroughs Wellcome FundNational Institutes of Health (U.S.) (grant N01-HV-28183)National Institutes of Health (U.S.) (U19 AI057229)National Institutes of Health (U.S.) (2P01 AI36535)National Institutes of Health (U.S.) (U19 AI062623)National Institutes of Health (U.S.) (R01-AI065824)National Institutes of Health (U.S.) (2P01 CA034233-22A1)National Institutes of Health (U.S.) (HHSN272200700038C)National Institutes of Health (U.S.) (NCI grant U54 RFA-CA-05-024)National Institutes of Health (U.S.) (LLS grant 7017-6