Within germinal centers (GCs), complex and highly orchestrated molecular programs must balance proliferation, somatic hypermutation (SHM) and selection to both provide effective humoral immunity and to protect against genomic instability and neoplastic transformation. In contrast to this complexity, GC B cells are canonically divided into two principal populations, dark zone (DZ) and light zone (LZ) cells. We now demonstrate that following selection in the LZ, B cells migrated to specialized sites within the canonical DZ that contained tingible body macrophages (TBMs) and were sites of ongoing cell division. Proliferating DZ (DZp) cells then transited into the larger DZ to become differentiating DZ (DZd) cells before re-entering the LZ. Multidimensional analysis revealed distinct molecular programs in each population commensurate with observed compartmentization of non-compatable functions. These data provide a new three-cell population model that both orders critical GC functions and reveals essential molecular programs of humoral adaptive immunity.

Regulatory T (Treg) cells are critical mediators of immune tolerance whose activity depends upon T cell receptor (TCR) and mTORC1 kinase signaling, but the mechanisms that dictate functional activation of these pathways are incompletely understood. Here, we showed that amino acids license Treg cell function by priming and sustaining TCR-induced mTORC1 activity. mTORC1 activation was induced by amino acids, especially arginine and leucine, accompanied by the dynamic lysosomal localization of the mTOR and Tsc complexes. Rag and Rheb GTPases were central regulators of amino aciddependent mTORC1 activation in effector Treg (eTreg) cells. Mice bearing RagA-RagB-or Rheb1-Rheb2-deficient Treg cells developed a fatal autoimmune disease and had reduced eTreg cell accumulation and function. RagA-RagB regulated mitochondrial and lysosomal fitness, while Rheb1-Rheb2 enforced eTreg cell suppressive gene signature. Together, these findings reveal a crucial requirement of amino acid signaling for licensing and sustaining mTORC1 activation and functional programming of Treg cells.

The development of high-resolution liquid chromatography (LC) is essential for improving the sensitivity and throughput of mass spectrometry (MS)-based proteomics. Here we present systematic optimization of a long gradient LC–MS/MS platform to enhance protein identification from a complex mixture. The platform employed an in-house fabricated, reverse-phase long column (100 μm × 150 cm, 5 μm C18 beads) coupled to Q Exactive MS. The column was capable of achieving a peak capacity of ∼700 in a 720 min gradient of 10–45% acetonitrile. The optimal loading level was ∼6 μg of peptides, although the column allowed loading as many as 20 μg. Gas-phase fractionation of peptide ions further increased the number of peptide identification by ∼10%. Moreover, the combination of basic pH LC prefractionation with the long gradient LC–MS/MS platform enabled the identification of 96 127 peptides and 10 544 proteins at 1% protein false discovery rate in a post-mortem brain sample of Alzheimer’s disease. Because deep RNA sequencing of the same specimen suggested that ∼16 000 genes were expressed, the current analysis covered more than 60% of the expressed proteome. Further improvement strategies of the LC/LC–MS/MS platform were also discussed.

BackgroundNeoepitopes derived from tumor-specific somatic mutations are promising targets for immunotherapy in childhood cancers. However, the potential for such therapies in targeting these epitopes remains uncertain due to a lack of knowledge of the neoepitope landscape in childhood cancer. Studies to date have focused primarily on missense mutations without exploring gene fusions, which are a major class of oncogenic drivers in pediatric cancer.MethodsWe developed an analytical workflow for identification of putative neoepitopes based on somatic missense mutations and gene fusions using whole-genome sequencing data. Transcriptome sequencing data were incorporated to interrogate the expression status of the neoepitopes.ResultsWe present the neoepitope landscape of somatic alterations including missense mutations and oncogenic gene fusions identified in 540 childhood cancer genomes and transcriptomes representing 23 cancer subtypes. We found that 88% of leukemias, 78% of central nervous system tumors, and 90% of solid tumors had at least one predicted neoepitope. Mutation hotspots in KRAS and histone H3 genes encode potential epitopes in multiple patients. Additionally, the ETV6-RUNX1 fusion was found to encode putative neoepitopes in a high proportion (69.6%) of the pediatric leukemia harboring this fusion.ConclusionsOur study presents a comprehensive repertoire of potential neoepitopes in childhood cancers, and will facilitate the development of immunotherapeutic approaches designed to exploit them. The source code of the workflow is available at GitHub (https://github.com/zhanglabstjude/neoepitope).Electronic supplementary materialThe online version of this article (doi:10.1186/s13073-017-0468-3) contains supplementary material, which is available to authorized users.

Sir-The policy on release of unpublished data from large genome centres has generated considerable discussion and some confusion, as your Editorial "Sacrifice for the greater good?" makes plain (Nature 421, 875; 2003). In our view, data sets from large, centralized, expensive genome data-collection projects should be freely available to the entire scientific community, immediately and with no restrictions or conditions. Our position is that pre-publication release of large genome data sets is a special case, and not a principle that should be applied "throughout the world of biology", as was asserted in your editorial. Large genome sequencing has become an expensive, factory-style operation, in which economies of scale can only be realized by very large centres. Large data production centres, established and supported by the scientific community, represent a different model of science from traditional investigator-initiated projects. We argue that they need to operate under different rules. The broader scientific community supports the highly centralized model represented by the US large-scale genome centres (funded via the National Institutes of Health and the Department of Energy) on the condition that everyone in this community gets equal access to the data. If this is the case, everyone wins: large data sets are generated at the lowest cost and greatest speed, and scientific work progresses on multiple fronts without delay. In contrast, if genome centres restrict their data and get preferential access to it, then some members of the community will no longer support monopolistic funding models (in which large centres sequence one genome after another without peer review of each project). Instead, they will demand the right to compete with these empires, especially for the most scientifically desirable genomes. Other scientists, especially bioinformaticians, will seek to relocate to the centres to gain the advantage of early data access. Data restrictions will therefore promote factionalization where we should be seeking efficiencies of scale, and centralization where we should be promoting diversity. We agree with your editorial that the proposed new policy, recently released for comment by the US genome centres (see www.genome.gov/page.cfm?pageID= 10506537), is ambiguous. It states that genome sequence data "should be available