The Critical Role of AMPKα1 in Regulating Autophagy and Mitochondrial Respiration in IL-15-Stimulated mTORC1Weak Signal-Induced T Cell Memory: An Interplay between Yin (AMPKα1) and Yang (mTORC1) Energy Sensors in T Cell Differentiation.

Two common γ-chain family cytokines IL-2 and IL-15 stimulate the same mammalian target of rapamycin complex-1 (mTORC1) signaling yet induce effector T (TE) and memory T (TM) cell differentiation via a poorly understood mechanism(s). Here, we prepared in vitro IL-2-stimulated TE (IL-2/TE) and IL-15-stimulated TM (IL-15/TM) cells for characterization by flow cytometry, Western blotting, confocal microscopy and Seahorse-assay analyses. We demonstrate that IL-2 and IL-15 stimulate strong and weak mTORC1 signals, respectively, which lead to the formation of CD62 ligand (CD62L)- killer cell lectin-like receptor subfamily G member-1 (KLRG)+ IL-2/TE and CD62L+KLRG- IL-15/TM cells with short- and long-term survival following their adoptive transfer into mice. The IL-15/mTORC1Weak signal activates the forkhead box-O-1 (FOXO1), T cell factor-1 (TCF1) and Eomes transcriptional network and the metabolic adenosine monophosphate-activated protein kinase-α-1 (AMPKα1), Unc-51-like autophagy-activating kinase-1 (ULK1) and autophagy-related gene-7 (ATG7) axis, increasing the expression of mitochondrial regulators aquaporin-9 (AQP9), mitochondrial transcription factor-A (TFAM), peroxisome proliferator-activated receptor-γ coactivator-1α (PGC1α), carnitine palmitoyl transferase-1 (CPT1α), microtubule-associated protein light chain-3 II (LC3II), Complex I and ortic atrophy-1 (OPA1), leading to promoting mitochondrial biogenesis and fatty-acid oxidation (FAO). Interestingly, AMPKα1 deficiency abrogates these downstream responses to IL-15/mTORC1Weak signaling, leading to the upregulation of mTORC1 and hypoxia-inducible factor-1α (HIF-1α), a metabolic switch from FAO to glycolysis and reduced cell survival. Taken together, our data demonstrate that IL-15/mTORC1Weak signaling controls T-cell memory via activation of the transcriptional FOXO1-TCF1-Eomes and metabolic AMPKα1-ULK1-ATG7 pathways, a finding that may greatly impact the development of efficient vaccines and immunotherapies for the treatment of cancer and infectious diseases.

Prosurvival IL-7-Stimulated Weak Strength of mTORC1-S6K Controls T Cell Memory via Transcriptional FOXO1-TCF1-Id3 and Metabolic AMPKα1-ULK1-ATG7 Pathways.

CD8+ memory T (TM) cells play a critical role in immune defense against infection. Two common γ-chain family cytokines, IL-2 and IL-7, although triggering the same mTORC1-S6K pathway, distinctly induce effector T (TE) cells and TM cells, respectively, but the underlying mechanism(s) remains elusive. In this study, we generated IL-7R-/and AMPKα1-knockout (KO)/OTI mice. By using genetic and pharmaceutical tools, we demonstrate that IL-7 deficiency represses expression of FOXO1, TCF1, p-AMPKα1 (T172), and p-ULK1 (S555) and abolishes T cell memory differentiation in IL-7R KO T cells after Listeria monocytogenesis rLmOVA infection. IL-2- and IL-7-stimulated strong and weak S6K (IL-2/S6Kstrong and IL-7/S6Kweak) signals control short-lived IL-7R-CD62L-KLRG1+ TE and long-term IL-7R+CD62L+KLRG1- TM cell formations, respectively. To assess underlying molecular pathway(s), we performed flow cytometry, Western blotting, confocal microscopy, and Seahorse assay analyses by using the IL-7/S6Kweak-stimulated TM (IL-7/TM) and the control IL-2/S6Kstrong-stimulated TE (IL-2/TE) cells. We determine that the IL-7/S6Kweak signal activates transcriptional FOXO1, TCF1, and Id3 and metabolic p-AMPKα1, p-ULK1, and ATG7 molecules in IL-7/TM cells. IL-7/TM cells upregulate IL-7R and CD62L, promote mitochondria biogenesis and fatty acid oxidation metabolism, and show long-term cell survival and functional recall responses. Interestingly, AMPKα1 deficiency abolishes the AMPKα1 but maintains the FOXO1 pathway and induces a metabolic switch from fatty acid oxidation to glycolysis in AMPKα1 KO IL-7/TM cells, leading to loss of cell survival and recall responses. Taken together, our data demonstrate that IL-7-stimulated weak strength of mTORC1-S6K signaling controls T cell memory via activation of transcriptional FOXO1-TCF1-Id3 and metabolic AMPKα1-ULK1-ATG7 pathways. This (to our knowledge) novel finding provides a new mechanism for a distinct IL-2/IL-7 stimulation model in T cell memory and greatly impacts vaccine development.

The Energy Sensor AMPKα1 Is Critical in Rapamycin-Inhibition of mTORC1-S6K-Induced T-cell Memory.

Energy sensors mTORC1 and AMPKα1 regulate T-cell metabolism and differentiation, while rapamycin (Rapa)-inhibition of mTORC1 (RIM) promotes T-cell memory. However, the underlying pathway and the role of AMPKα1 in Rapa-induced T-cell memory remain elusive. Using genetic and pharmaceutical tools, we demonstrate that Rapa promotes T-cell memory in mice in vivo post Listeria monocytogenesis rLmOVA infection and in vitro transition of effector T (TE) to memory T (TM) cells. IL-2- and IL-2+Rapa-stimulated T [IL-2/T and IL-2(Rapa+)/T] cells, when transferred into mice, differentiate into short-term IL-7R-CD62L-KLRG1+ TE and long-lived IL-7R+CD62L+KLRG1- TM cells, respectively. To assess the underlying pathways, we performed Western blotting, confocal microscopy and Seahorse-assay analyses using IL-2/T and IL-2(Rapa+)/T-cells. We determined that IL-2(Rapa+)/T-cells activate transcription FOXO1, TCF1 and Eomes and metabolic pAMPKα1(T172), pULK1(S555) and ATG7 molecules and promote mitochondrial biogenesis and fatty-acid oxidation (FAO). We found that rapamycin-treated AMPKα-deficient AMPKα1-KO IL-2(Rapa+)/TM cells up-regulate transcription factor HIF-1α and induce a metabolic switch from FAO to glycolysis. Interestingly, despite the rapamycin treatment, AMPKα-deficient TM cells lost their cell survival capacity. Taken together, our data indicate that rapamycin promotes T-cell memory via transcriptional FOXO1-TCF1-Eomes programs and AMPKα1-ULK1-ATG7 metabolic axis, and that AMPKα1 plays a critical role in RIM-induced T-cell memory.

Molecular characterization of an MLL1 fusion and its role in chromosomal instability.

Chromosomal rearrangements involving the mixed-lineage leukemia (MLL1) gene are common in a unique group of acute leukemias, with more than 100 fusion partners in this malignancy alone. However, do these fusions occur or have a role in solid tumors? We performed extensive network analyses of MLL1-fusion partners in patient datasets, revealing that multiple MLL1-fusion partners exhibited significant interactions with the androgen-receptor signaling pathway. Further exploration of tumor sequence data from TCGA predicts the presence of MLL1 fusions with truncated SET domain in prostate tumors. To investigate the physiological relevance of MLL1 fusions in solid tumors, we engineered a truncated version of MLL1 by fusing it with one of its known fusion partners, ZC3H13, to use as a model system. Functional characterization with cell-based assays revealed that MLL1-ZC3H13 fusion induced chromosomal instability, affected mitotic progression, and enhanced tumorsphere formation. The MLL1-ZC3H13 chimera consistently increased the expression of a cancer stem cell marker (CD44); in addition, we detected potential collateral lethality between DOT1L and MLL1 fusions. Our work reveals that MLL1 fusions are likely prevalent in solid tumors and exhibit a potential pro-tumorigenic role.

Enhancing the throughput and multiplexing capabilities of next generation sequencing for efficient implementation of pooled shRNA and CRISPR screens.

Next generation sequencing is becoming the method of choice for functional genomic studies that use pooled shRNA or CRISPR libraries. A key challenge in sequencing these mixed-oligo libraries is that they are highly susceptible to hairpin and/or heteroduplex formation. This results in polyclonal, low quality, and incomplete reads and reduces sequencing throughput. Unfortunately, this challenge is significantly magnified in low-to-medium throughput bench-top sequencers as failed reads significantly perturb the maximization of sequence coverage and multiplexing capabilities. Here, we report a methodology that can be adapted to maximize the coverage on a bench-top, Ion PGM System for smaller shRNA libraries with high efficiency. This ligation-based, half-shRNA sequencing strategy minimizes failed sequences and is also equally amenable to high-throughput sequencers for increased multiplexing. Towards this, we also demonstrate that our strategy to reduce heteroduplex formation improves multiplexing capabilities of pooled CRISPR screens using Illumina NextSeq 500. Overall, our method will facilitate sequencing of pooled shRNA or CRISPR libraries from genomic DNA and maximize sequence coverage.

Comparative analysis of differential network modularity in tissue specific normal and cancer protein interaction networks.

BACKGROUND: Large scale understanding of complex and dynamic alterations in cellular and subcellular levels during cancer in contrast to normal condition has facilitated the emergence of sophisticated systemic approaches like network biology in recent times. As most biological networks show modular properties, the analysis of differential modularity between normal and cancer protein interaction networks can be a good way to understand cancer more significantly. Two aspects of biological network modularity e.g. detection of molecular complexes (potential modules or clusters) and identification of crucial nodes forming the overlapping modules have been considered in this regard. METHODS: In the current study, the computational analysis of previously published protein interaction networks (PINs) has been conducted to identify the molecular complexes and crucial nodes of the networks. Protein molecules involved in ten major cancer signal transduction pathways were used to construct the networks based on expression data of five tissues e.g. bone, breast, colon, kidney and liver in both normal and cancer conditions. MCODE (molecular complex detection) and ModuLand methods have been used to identify the molecular complexes and crucial nodes of the networks respectively. RESULTS: In case of all tissues, cancer PINs show higher level of clustering (formation of molecular complexes) than the normal ones. In contrast, lower level modular overlapping is found in cancer PINs than the normal ones. Thus a proposition can be made regarding the formation of some giant nodes in the cancer networks with very high degree and resulting in reduced overlapping among the network modules though the predicted molecular complex numbers are higher in cancer conditions. CONCLUSION: The study predicts some major molecular complexes that might act as the important regulators in cancer progression. The crucial nodes identified in this study can be potential drug targets to combat cancer.