PD-1 alters T-cell metabolic reprogramming by inhibiting glycolysis and promoting lipolysis and fatty acid oxidation

During activation, T cells undergo metabolic reprogramming, which imprints distinct functional fates. We determined that on PD-1 ligation, activated T cells are unable to engage in glycolysis or amino acid metabolism but have an increased rate of fatty acid β-oxidation (FAO). PD-1 promotes FAO of endogenous lipids by increasing expression of CPT1A, and inducing lipolysis as indicated by elevation of the lipase ATGL, the lipolysis marker glycerol and release of fatty acids. Conversely, CTLA-4 inhibits glycolysis without augmenting FAO, suggesting that CTLA-4 sustains the metabolic profile of non-activated cells. Because T cells utilize glycolysis during differentiation to effectors, our findings reveal a metabolic mechanism responsible for PD-1-mediated blockade of T-effector cell differentiation. The enhancement of FAO provides a mechanistic explanation for the longevity of T cells receiving PD-1 signals in patients with chronic infections and cancer, and for their capacity to be reinvigorated by PD-1 blockade

In vivo gene transfer into rat bone marrow progenitor cells using rSV40 viral vectors

Hematopoietic stem cell (HSC) gene transfer has been attempted almost entirely ex vivo and has been limited by cytokine-induced loss of self-renewal capacity and transplantation-related defects in homing and engraftment. Here, we attempted to circumvent such limitations by injecting vectors directly into the bone marrow (BM) to transduce HSCs in their native environment. Simian virus 40 (SV40)–derived gene delivery vectors were used because they transduce resting CD34+ cells very efficiently. Rats received SV-(Nef-FLAG), carrying FLAG marker epitope—or a control recombinant SV40 (rSV40)—directly into both femoral marrow cavities. Intracellular transgene expression by peripheral blood (PB) or BM cells was detected by cytofluorimetry. An average of 5.3% PB leukocytes expressed FLAG for the entire study—56 weeks. Transgene expression was sustained in multiple cell lineages, including granulocytes (average, 3.3% of leukocytes, 20.4% of granulocytes), CD3+ T lymphocytes (average, 0.53% of leukocytes, 1% of total T cells), and CD45R+ B lymphocytes, indicating gene transfer to long-lived progenitor cells with multilineage capacity. An average of 15% of femoral marrow cells expressed FLAG up to 16.5 months after transduction. Thus, direct intramarrow administration of rSV40s yields efficient gene transfer to rat BM progenitor cells and may be worthy of further investigation

Divergent functions of <i>SNAC4–9</i> and possible mechanisms for tomato adaptation to abiotic stresses

<p>Plants are exposed to all types of abiotic stresses during the process of growth and development, which could adversely affect the productivity and postharvest storage quality of plants. In the current study, the expression of <i>SNAC4–9</i> and the changes of physiological parameters were analyzed in tomato seedlings under various abiotic conditions and hormone treatments. The results demonstrate that all six genes were induced by these stresses at differential induction levels and that <i>SNAC4–9</i> gene expression profiles were likely to be related to the ABA, SA, and MeJA signaling pathways. In addition, all of the stress- and hormone-treated seedlings exhibited significant increases in their proline content and antioxidant enzyme activities. MDA was significantly increased in seedlings exposed to stress and decreased in hormone-treated seedlings. These data collectively suggest that SNAC4–9 might function through ABA, SA, and JA signaling pathways and the regulation of proline and antioxidant systems. This study combines molecular biology and physiology to provide valuable information for further exploring the functional roles of <i>NAC</i> genes in response to environmental stresses and indicates that these genes may exhibit potential for enhancing stress tolerance of transgenic tomato plants and further improving the postharvest storage quality of tomato plants.</p

The Size and Power of Bootstrap Tests for Spatial Dependence in a Linear Regression Model

Spatial bootstrap test, Moran’s I, Monte Carlo, Size distortion, Power, C12, C15, C21, R11,