Macrophages from Rosa26-Integrated Cas9-Expressing C57BL/6J Mice Have a Putative TRIF-Mediated Defect in the TLR-3/4 Signaling.

In this study, we report that the TLR4 ligand, LPS, and TLR3 ligand polyinosinic:polycytidylic acid failed to activate IRF3 or STAT1 in bone marrow-derived macrophages (BMMs) isolated from two independently generated lines of Rosa26-integrated Cas9-expressing C57BL/6J (B6) mice. RNA-sequencing analysis reveals that hundreds to thousands of genes including IFN-stimulated genes were differentially expressed in BMMs from these Cas9 strains compared with B6 upon LPS stimulation. Furthermore, the NF-κB signaling axis and TRIF-mediated necroptosis were also strongly reduced in response to LPS and polyinosinic:polycytidylic acid. In contrast, there were no defects in the responses of BMMs to ligands of the RIG-I, STING, TLR2, TLR9, and IFN receptors. Defects in TLR3 and TLR4 signaling were observed in mice with the B6 but not 129 background, and when Cas9 was integrated at the Rosa26 but not H11 locus. However, integration at the Rosa26 site, CAG promoter-driven Cas9 or eGFP were not individually sufficient to cause the defect. Taken together, the results of this study suggest a putative TRIF-mediated defect in TLR-3/4 signaling in BMMs from commercially available and widely used B6-Cas9-expressing mice.

Author Correction: Cooperative nutrient accumulation sustains growth of mammalian cells.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Genome-wide CRISPR screen identifies host dependency factors for influenza A virus infection.

Host dependency factors that are required for influenza A virus infection may serve as therapeutic targets as the virus is less likely to bypass them under drug-mediated selection pressure. Previous attempts to identify host factors have produced largely divergent results, with few overlapping hits across different studies. Here, we perform a genome-wide CRISPR/Cas9 screen and devise a new approach, meta-analysis by information content (MAIC) to systematically combine our results with prior evidence for influenza host factors. MAIC out-performs other meta-analysis methods when using our CRISPR screen as validation data. We validate the host factors, WDR7, CCDC115 and TMEM199, demonstrating that these genes are essential for viral entry and regulation of V-type ATPase assembly. We also find that CMTR1, a human mRNA cap methyltransferase, is required for efficient viral cap snatching and regulation of a cell autonomous immune response, and provides synergistic protection with the influenza endonuclease inhibitor Xofluza.

Streamlined Protocol for Deep Proteomic Profiling of FAC-sorted Cells and Its Application to Freshly Isolated Murine Immune Cells.

Proteomic profiling describes the molecular landscape of proteins in cells immediately available to sense, transduce, and enact the appropriate responses to extracellular queues. Transcriptional profiling has proven invaluable to our understanding of cellular responses; however, insights may be lost as mounting evidence suggests transcript levels only moderately correlate with protein levels in steady state cells. Mass spectrometry-based quantitative proteomics is a well-suited and widely used analytical tool for studying global protein abundances. Typical proteomic workflows are often limited by the amount of sample input that is required for deep and quantitative proteome profiling. This is especially true if the cells of interest need to be purified by fluorescence-activated cell sorting (FACS) and one wants to avoid ex vivo culturing. To address this need, we developed an easy to implement, streamlined workflow that enables quantitative proteome profiling from roughly 2 µg of protein input per experimental condition. Utilizing a combination of facile cell collection from cell sorting, solid-state isobaric labeling and multiplexing of peptides, and small-scale fractionation, we profiled the proteomes of 12 freshly isolated, primary murine immune cell types. Analyzing half of the 3e5 cells collected per cell type, we quantified over 7000 proteins across 12 key immune cell populations directly from their resident tissues. We show that low input proteomics is precise, and the data generated accurately reflects many aspects of known immunology, while expanding the list of cell-type specific proteins across the cell types profiled. The low input proteomics methods we developed are readily adaptable and broadly applicable to any cell or sample types and should enable proteome profiling in systems previously unattainable.

Histidine catabolism is a major determinant of methotrexate sensitivity.

The chemotherapeutic drug methotrexate inhibits the enzyme dihydrofolate reductase1, which generates tetrahydrofolate, an essential cofactor in nucleotide synthesis2. Depletion of tetrahydrofolate causes cell death by suppressing DNA and RNA production3. Although methotrexate is widely used as an anticancer agent and is the subject of over a thousand ongoing clinical trials4, its high toxicity often leads to the premature termination of its use, which reduces its potential efficacy5. To identify genes that modulate the response of cancer cells to methotrexate, we performed a CRISPR-Cas9-based screen6,7. This screen yielded FTCD, which encodes an enzyme-formimidoyltransferase cyclodeaminase-that is required for the catabolism of the amino acid histidine8, a process that has not previously been linked to methotrexate sensitivity. In cultured cancer cells, depletion of several genes in the histidine degradation pathway markedly decreased sensitivity to methotrexate. Mechanistically, histidine catabolism drains the cellular pool of tetrahydrofolate, which is particularly detrimental to methotrexate-treated cells. Moreover, expression of the rate-limiting enzyme in histidine catabolism is associated with methotrexate sensitivity in cancer cell lines and with survival rate in patients. In vivo dietary supplementation of histidine increased flux through the histidine degradation pathway and enhanced the sensitivity of leukaemia xenografts to methotrexate. The histidine degradation pathway markedly influences the sensitivity of cancer cells to methotrexate and may be exploited to improve methotrexate efficacy through a simple dietary intervention.

Monitoring T cell-dendritic cell interactions in vivo by intercellular enzymatic labelling.

Interactions between different cell types are essential for multiple biological processes, including immunity, embryonic development and neuronal signalling. Although the dynamics of cell-cell interactions can be monitored in vivo by intravital microscopy, this approach does not provide any information on the receptors and ligands involved or enable the isolation of interacting cells for downstream analysis. Here we describe a complementary approach that uses bacterial sortase A-mediated cell labelling across synapses of immune cells to identify receptor-ligand interactions between cells in living mice, by generating a signal that can subsequently be detected ex vivo by flow cytometry. We call this approach for the labelling of 'kiss-and-run' interactions between immune cells 'Labelling Immune Partnerships by SorTagging Intercellular Contacts' (LIPSTIC). Using LIPSTIC, we show that interactions between dendritic cells and CD4+ T cells during T-cell priming in vivo occur in two distinct modalities: an early, cognate stage, during which CD40-CD40L interactions occur specifically between T cells and antigen-loaded dendritic cells; and a later, non-cognate stage during which these interactions no longer require prior engagement of the T-cell receptor. Therefore, LIPSTIC enables the direct measurement of dynamic cell-cell interactions both in vitro and in vivo. Given its flexibility for use with different receptor-ligand pairs and a range of detectable labels, we expect that this approach will be of use to any field of biology requiring quantification of intercellular communication.

Cooperative nutrient accumulation sustains growth of mammalian cells.

The coordination of metabolic processes to allow increased nutrient uptake and utilization for macromolecular synthesis is central for cell growth. Although studies of bulk cell populations have revealed important metabolic and signaling requirements that impact cell growth on long time scales, whether the same regulation influences short-term cell growth remains an open question. Here we investigate cell growth by monitoring mass accumulation of mammalian cells while rapidly depleting particular nutrients. Within minutes following the depletion of glucose or glutamine, we observe a growth reduction that is larger than the mass accumulation rate of the nutrient. This indicates that if one particular nutrient is depleted, the cell rapidly adjusts the amount that other nutrients are accumulated, which is consistent with cooperative nutrient accumulation. Population measurements of nutrient sensing pathways involving mTOR, AKT, ERK, PKA, MST1, or AMPK, or pro-survival pathways involving autophagy suggest that they do not mediate this growth reduction. Furthermore, the protein synthesis rate does not change proportionally to the mass accumulation rate over these time scales, suggesting that intracellular metabolic pools buffer the growth response. Our findings demonstrate that cell growth can be regulated over much shorter time scales than previously appreciated.

Disruption of the Rag-Ragulator Complex by c17orf59 Inhibits mTORC1.

mTORC1 controls key processes that regulate cell growth, including mRNA translation, ribosome biogenesis, and autophagy. Environmental amino acids activate mTORC1 by promoting its recruitment to the cytosolic surface of the lysosome, where its kinase is activated downstream of growth factor signaling. mTORC1 is brought to the lysosome by the Rag GTPases, which are tethered to the lysosomal membrane by Ragulator, a lysosome-bound scaffold. Here, we identify c17orf59 as a Ragulator-interacting protein that regulates mTORC1 activity through its interaction with Ragulator at the lysosome. The binding of c17orf59 to Ragulator prevents Ragulator interaction with the Rag GTPases, both in cells and in vitro, and decreases Rag GTPase lysosomal localization. Disruption of the Rag-Ragulator interaction by c17orf59 impairs mTORC1 activation by amino acids by preventing mTOR from reaching the lysosome. By disrupting the Rag-Ragulator interaction to inhibit mTORC1, c17orf59 expression may represent another mechanism to modulate nutrient sensing by mTORC1.

RagA, but not RagB, is essential for embryonic development and adult mice.

The mechanistic target of rapamycin complex 1 (mTORC1) integrates cues from growth factors and nutrients to control metabolism. In contrast to the growth factor input, genetic disruption of nutrient-dependent activation of mTORC1 in mammals remains unexplored. We engineered mice lacking RagA and RagB genes, which encode the GTPases responsible for mTORC1 activation by nutrients. RagB has limited expression, and its loss shows no effects on mammalian physiology. RagA deficiency leads to E10.5 embryonic death, loss of mTORC1 activity, and severe growth defects. Primary cells derived from these mice exhibit no regulation of mTORC1 by nutrients and maintain high sensitivity to growth factors. Deletion of RagA in adult mice is lethal. Upon RagA loss, a myeloid population expands in peripheral tissues. RagA-specific deletion in liver increases cellular responses to growth factors. These results show the essentiality of nutrient sensing for mTORC1 activity in mice and its suppression of PI3K/Akt signaling.

Ragulator is a GEF for the rag GTPases that signal amino acid levels to mTORC1.

The mTOR Complex 1 (mTORC1) pathway regulates cell growth in response to numerous cues, including amino acids, which promote mTORC1 translocation to the lysosomal surface, its site of activation. The heterodimeric RagA/B-RagC/D GTPases, the Ragulator complex that tethers the Rags to the lysosome, and the v-ATPase form a signaling system that is necessary for amino acid sensing by mTORC1. Amino acids stimulate the binding of guanosine triphosphate to RagA and RagB but the factors that regulate Rag nucleotide loading are unknown. Here, we identify HBXIP and C7orf59 as two additional Ragulator components that are required for mTORC1 activation by amino acids. The expanded Ragulator has nucleotide exchange activity toward RagA and RagB and interacts with the Rag heterodimers in an amino acid- and v-ATPase-dependent fashion. Thus, we provide mechanistic insight into how mTORC1 senses amino acids by identifying Ragulator as a guanine nucleotide exchange factor (GEF) for the Rag GTPases.