The accumulation of myeloid-derived suppressor cells (MDSC) in tumor-bearing hosts is a hallmark of malignancy-associated inflammation and a major mediator for the induction of T cell suppression in cancer. MDSC can be divided phenotypically into granulocytic (G-MDSC) and monocytic (Mo-MDSC) subgroups. Several mechanisms mediate the induction of T cell anergy by MDSC; however, the specific role of these pathways in the inhibitory activity of MDSC subpopulations remains unclear. Therefore, we aimed to determine the effector mechanisms by which subsets of tumor-infiltrating MDSC block T cell function. We found that G-MDSC had a higher ability to impair proliferation and expression of effector molecules in activated T cells, as compared to Mo-MDSC. Interestingly, both MDSC subgroups inhibited T cells through nitric oxide (NO)-related pathways, but expressed different effector inhibitory mechanisms. Specifically, G-MDSC impaired T cells through the production of peroxynitrites (PNT), while Mo-MDSC suppressed by the release of NO. The production of PNT in G-MDSC depended on the expression of gp91phox and endothelial NO synthase (eNOS), while inducible NO synthase (iNOS) mediated the generation of NO in Mo-MDSC. Deletion of eNOS and gp91phox or scavenging of PNT blocked the suppressive function of G-MDSC and induced anti-tumoral effects, without altering Mo-MDSC inhibitory activity. Furthermore, NO-scavenging or iNOS knockdown prevented Mo-MDSC function, but did not affect PNT production or suppression by G-MDSC. These results suggest that MDSC subpopulations utilize independent effector mechanisms to regulate T cell function. Inhibition of these pathways is expected to specifically block MDSC subsets and overcome immune suppression in cancer.

Enzymatic depletion of the non-essential amino acid L-Arginine (L-Arg) in cancer patients by the administration of a pegylated form of the catabolic enzyme arginase I (peg-Arg I) has shown some promise as a therapeutic approach. However, L-Arg deprivation also suppresses T-cell responses in tumors. In this study, we sought to reconcile these observations by conducting a detailed analysis of the effects of peg-Arg I on normal T-cells. Strikingly, we found that peg-Arg I blocked proliferation and cell cycle progression in normal activated T-cells without triggering apoptosis or blunting T-cell activation. These effects were associated with an inhibition of aerobic glycolysis in activated T-cells, but not with significant alterations in mitochondrial oxidative respiration, which thereby regulated survival of T-cells exposed to peg-Arg I. Further mechanistic investigations showed that addition of citrulline, a metabolic precursor for L-Arg, rescued the anti-proliferative effects of peg-Arg I on T-cells in vitro. Moreover, serum levels of citrulline increased after in vivo administration of peg-Arg I. In support of the hypothesis that peg-Arg I acted indirectly to block T-cell responses in vivo, peg-Arg I inhibited T-cell proliferation in mice by inducing accumulation of myeloid-derived suppressor cells (MDSC). MDSC induction by peg-Arg I occurred through the general control non-repressed-2 eIF2α kinase. Moreover, we found that peg-Arg I enhanced the growth of tumors in mice in a manner that correlated with higher MDSC numbers. Taken together, our results highlight the risks of the L-Arg-depleting therapy for cancer treatment and suggest a need for co-targeting MDSC in such therapeutic settings.

Partial least squares (PLS) and factorial regression (FR) are statistical models that incorporate external environmental and/or cultivar variables for studying and interpreting genotype × environment interaction (GEl). The Additive Main effect and Multiplicative Interaction (AMMI) model uses only the phenotypic response variable of interest; however, if information on external environmental (or genotypic) variables is available, this can be regressed on the environmental (or genotypic) scores estimated from AMMI and superimposed on the AMMI biplot. The objectives of this study with two wheat [Triticum turgidum (L.) var. durum] field trials were (i) to compare the results of PLS, FR, and AMMI on the basis of external environmental (and cultivar) variables, (ii) to examine whether procedures based PLS , FR, and AMMI identify the same or a different subset of cultivar and/or environmental covariables that influence GEI for grain yield, and (iii) to find multiple FR models that include environmental and cultivar covariables and their cross products that explain a large proportion of GEI with relatively few degrees of freedom. Results for the first trial showed that AMMI, PLS, and FR identified similar cultivar and environmental variables that explained a large proportion of the cultivar × year interaction. Results for the second wheat trial showed good correspondence between PLS and FR for 23 environmental covariables. For both trials, PLS and FR complement each other and the AMMI and PLS biplots offered similar interpretations of the GEl. The FR analysis can be used to confirm these results and to obtain even more parsimonious descriptions of the GEL M ULTI-ENVIRONMENT TRIALS play an important role in selecting the best cultivars (or agronomic practices) to be used in future years at different locations and in assessing a cultivar's stability across environments before its commercial release. When the performance of cultivars is compared across sites, several cultivar attributes are considered, of which grain yield is one of the most important. Cultivars grown in multi-environment trials react differently to environmental changes.

ObjectivesHousehold contacts (HHCs) of pulmonary tuberculosis patients are at high risk of Mycobacterium tuberculosis infection and early disease development. Identification of individuals at risk of tuberculosis disease is a desirable goal for tuberculosis control. Interferon-gamma release assays (IGRAs) using specific M. tuberculosis antigens provide an alternative to tuberculin skin testing (TST) for infection detection. Additionally, the levels of IFNγ produced in response to these antigens may have prognostic value. We estimated the prevalence of M. tuberculosis infection by IGRA and TST in HHCs and their source population (SP), and assessed whether IFNγ levels in HHCs correlate with tuberculosis development.MethodsA cohort of 2060 HHCs was followed for 2–3 years after exposure to a tuberculosis case. Besides TST, IFNγ responses to mycobacterial antigens: CFP, CFP-10, HspX and Ag85A were assessed in 7-days whole blood cultures and compared to 766 individuals from the SP in Medellín, Colombia. Isoniazid prophylaxis was not offered to child contacts because Colombian tuberculosis regulations consider it only in children under 5 years, TST positive without BCG vaccination.ResultsUsing TST 65.9% of HHCs and 42.7% subjects from the SP were positive (OR 2.60, p<0.0001). IFNγ response to CFP-10, a biomarker of M. tuberculosis infection, tested positive in 66.3% HHCs and 24.3% from the SP (OR = 6.07, p<0.0001). Tuberculosis incidence rate was 7.0/1000 person years. Children <5 years accounted for 21.6% of incident cases. No significant difference was found between positive and negative IFNγ responders to CFP-10 (HR 1.82 95% CI 0.79–4.20 p = 0.16). However, a significant trend for tuberculosis development amongst high HHC IFNγ producers was observed (trend Log rank p = 0.007).DiscussionCFP-10-induced IFNγ production is useful to establish tuberculosis infection prevalence amongst HHC and identify those at highest risk of disease. The high tuberculosis incidence amongst children supports administration of chemoprohylaxis to child contacts regardless of BCG vaccination.

The partial least squares (PLS) regression method relates genotype ✕ environment interaction effects (GEI) as dependent variables (Y) to external environmental (or cultivar) variables as the explanatory variables (X) in one single estimation procedure. We applied PLS regression to two wheat data sets with the objective of determining the most relevant cultivar and environmental variables that explained grain yield GEI. One data set had two field experiments, one includingseven durum wheat (Triticum turgidum L. var. durum ) cultivars and the other, seven bread wheat (Triticum aestivum L.) cultivars, both tested for 6 yr. In durum wheat cultivars, sun hours per day in December, February, and March as well as maximum temperature in March were related to the factor that explained more than 39% of GEI, while in bread wheat cultivars, minimum temperature in December and January as well as sun hours per day in January and February were the environmental variables related to the factor that explained the largest portion (>41%) of GEI. The second data set had eight bread wheat cultivars evaluated in 21 low relative humidity (RH) environments and 12 high RH environments. For both low and high RH environments, results indicated that relative performance of cultivars is influenced by differential sensitivity to minimum temperatures during the spike growth period. The PLS method was effective in detecting environmental and cultivar explanatory variables associated with factors that explained large portions of GEI.