MCL1 Enhances the Survival of CD8+ Memory T Cells after Viral Infection

Viral infection results in the generation of massive numbers of activated effector CD8+ T cells that recognize viral components. Most of these are short-lived effector T cells (SLECs) that die after clearance of the virus. However, a small proportion of this population survives and forms antigen-specific memory precursor effector cells (MPECs), which ultimately develop into memory cells. These can participate in a recall response upon reexposure to antigen even at protracted times postinfection. Here, antiapoptotic myeloid cell leukemia 1 (MCL1) was found to prolong survival upon T cell stimulation, and mice expressing human MCL1 as a transgene exhibited a skewing in the proportion of CD8+ T cells, away from SLECs toward MPECs, during the acute phase of vaccinia virus infection. A higher frequency and total number of antigen-specific CD8+ T cells were observed in MCL1 transgenic mice. These findings show that MCL1 can shape the makeup of the CD8+ T cell response, promoting the formation of long-term memory

Assessment of Geothermal Resources in the North Jiangsu Basin, East China, Using Monte Carlo Simulation

Geothermal energy has been recognized as an important clean renewable energy. Accurate assessment of geothermal resources is an essential foundation for their development and utilization. The North Jiangsu Basin (NJB), located in the Lower Yangtze Craton, is shaped like a wedge block of an ancient plate boundary and large-scale carbonate thermal reservoirs are developed in the deep NJB. Moreover, the NJB exhibits a high heat flow background because of its extensive extension since the Late Mesozoic. In this study, we used the Monte Carlo method to evaluate the geothermal resources of the main reservoir shallower than 10 km in the NJB. Compared with the volumetric method, the Monte Carlo method takes into account the variation mode and uncertainties of the input parameters. The simulation results show that the geothermal resources of the sandstone thermal reservoir in the shallow NJB are very rich, with capacities of (6.6–12) × 1020 J (mean 8.6 × 1020 J), (5.1–16) × 1020 J (mean 9.1 × 1020 J), and (3.2–11) × 1020 J (mean 6.6 × 1020 J) for the Yancheng, Sanduo and Dai’nan sandstone reservoir, respectively. In addition, the capacity of the geothermal resource of the carbonate thermal reservoir in the deep NJB is far greater than the former, reaching (9.9–15) × 1021 J (mean 12 × 1021 J). The results indicate capacities of a range value of (1.2–1.7) × 1021 J (mean 1.4 × 1022 J) for the whole NJB (<10 km)

Automated Flow Synthesis of Tumor Neoantigen Peptides for Personalized Immunotherapy

High-throughput genome sequencing and computation have enabled rapid identification of targets for personalized medicine, including cancer vaccines. Synthetic peptides are an established mode of cancer vaccine delivery, but generating the peptides for each patient in a rapid and affordable fashion remains difficult. High-throughput peptide synthesis technology is therefore urgently needed for patient-specific cancer vaccines to succeed in the clinic. Previously, we developed automated flow peptide synthesis technology that greatly accelerates the production of synthetic peptides. Herein, we show that this technology permits the synthesis of high-quality peptides for personalized medicine. Automated flow synthesis produces 30-mer peptides in less than 35 minutes and 15- to 16-mer peptides in less than 20 minutes. The purity of these peptides is comparable with or higher than the purity of peptides produced by other methods. This work illustrates how automated flow synthesis technology can enable customized peptide therapies by accelerating synthesis and increasing purity. We envision that implementing this technology in clinical settings will greatly increase capacity to generate clinical-grade peptides on demand, which is a key step in reaching the full potential of personalized vaccines for the treatment of cancer and other diseases.National Science Foundation (Grant 1122374)National Institutes of Health (Grants R21-CA216772-01A1 and NCI-1RO1CA155010-02)National Cancer Institute (Grants R21 CA216772-01A1 and SPORE-2P50CA101942-11A1)NHLBI (Grant 5R01HL103532-03