Role of Common-Gamma Chain Cytokines in NK Cell Development and Function: Perspectives for Immunotherapy

NK cells are components of the innate immunity system and play an important role as a first-line defense mechanism against viral infections and in tumor immune surveillance. Their development and their functional activities are controlled by several factors among which cytokines sharing the usage of the common cytokine-receptor gamma chain play a pivotal role. In particular, IL-2, IL-7, IL-15, and IL-21 are the members of this family predominantly involved in NK cell biology. In this paper, we will address their role in NK cell ontogeny, regulation of functional activities, development of specialized cell subsets, and acquisition of memory-like functions. Finally, the potential application of these cytokines as recombinant molecules to NK cell-based immunotherapy approaches will be discussed

Divergent targets of glycolysis and oxidative phosphorylation result in additive effects of metformin and starvation in colon and breast cancer

Emerging evidence demonstrates that targeting energy metabolism is a promising strategy to fight cancer. Here we show that combining metformin and short-term starvation markedly impairs metabolism and growth of colon and breast cancer. The impairment in glycolytic flux caused by starvation is enhanced by metformin through its interference with hexokinase II activity, as documented by measurement of 18F-fluorodeoxyglycose uptake. Oxidative phosphorylation is additively compromised by combined treatment: metformin virtually abolishes Complex I function; starvation determines an uncoupled status of OXPHOS and amplifies the activity of respiratory Complexes II and IV thus combining a massive ATP depletion with a significant increase in reactive oxygen species. More importantly, the combined treatment profoundly impairs cancer glucose metabolism and virtually abolishes lesion growth in experimental models of breast and colon carcinoma. Our results strongly suggest that energy metabolism is a promising target to reduce cancer progression

Discovery of a novel glucose metabolism in cancer: The role of endoplasmic reticulum beyond glycolysis and pentose phosphate shunt

Cancer metabolism is characterized by an accelerated glycolytic rate facing reduced activity of oxidative phosphorylation. This "Warburg effect" represents a standard to diagnose and monitor tumor aggressiveness with (18)F-fluorodeoxyglucose whose uptake is currently regarded as an accurate index of total glucose consumption. Studying cancer metabolic response to respiratory chain inhibition by metformin, we repeatedly observed a reduction of tracer uptake facing a marked increase in glucose consumption. This puzzling discordance brought us to discover that (18)F-fluorodeoxyglucose preferentially accumulates within endoplasmic reticulum by exploiting the catalytic function of hexose-6-phosphate-dehydrogenase. Silencing enzyme expression and activity decreased both tracer uptake and glucose consumption, caused severe energy depletion and decreased NADPH content without altering mitochondrial function. These data document the existence of an unknown glucose metabolism triggered by hexose-6-phosphate-dehydrogenase within endoplasmic reticulum of cancer cells. Besides its basic relevance, this finding can improve clinical cancer diagnosis and might represent potential target for therapy

Drosophila evolution over space and time (DEST):A new population genomics resource

Drosophila melanogaster is a leading model in population genetics and genomics, and a growing number of whole-genome datasets from natural populations of this species have been published over the last years. A major challenge is the integration of disparate datasets, often generated using different sequencing technologies and bioinformatic pipelines, which hampers our ability to address questions about the evolution of this species. Here we address these issues by developing a bioinformatics pipeline that maps pooled sequencing (Pool-Seq) reads from D. melanogaster to a hologenome consisting of fly and symbiont genomes and estimates allele frequencies using either a heuristic (PoolSNP) or a probabilistic variant caller (SNAPE-pooled). We use this pipeline to generate the largest data repository of genomic data available for D. melanogaster to date, encompassing 271 previously published and unpublished population samples from over 100 locations in > 20 countries on four continents. Several of these locations have been sampled at different seasons across multiple years. This dataset, which we call Drosophila Evolution over Space and Time (DEST), is coupled with sampling and environmental meta-data. A web-based genome browser and web portal provide easy access to the SNP dataset. We further provide guidelines on how to use Pool-Seq data for model-based demographic inference. Our aim is to provide this scalable platform as a community resource which can be easily extended via future efforts for an even more extensive cosmopolitan dataset. Our resource will enable population geneticists to analyze spatio-temporal genetic patterns and evolutionary dynamics of D. melanogaster populations in unprecedented detail.DrosEU is funded by a Special Topic Networks (STN) grant from the European Society for Evolutionary Biology (ESEB). MK (M. Kapun) was supported by the Austrian Science Foundation (grant no. FWF P32275); JG by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (H2020-ERC-2014-CoG-647900) and by the Spanish Ministry of Science and Innovation (BFU-2011-24397); TF by the Swiss National Science Foundation (SNSF grants PP00P3_133641, PP00P3_165836, and 31003A_182262) and a Mercator Fellowship from the German Research Foundation (DFG), held as a EvoPAD Visiting Professor at the Institute for Evolution and Biodiversity, University of Münster; AOB by the National Institutes of Health (R35 GM119686); MK (M. Kankare) by Academy of Finland grant 322980; VL by Danish Natural Science Research Council (FNU) grant 4002-00113B; FS Deutsche Forschungsgemeinschaft (DFG) grant STA1154/4-1, Project 408908608; JP by the Deutsche Forschungsgemeinschaft Projects 274388701 and 347368302; AU by FPI fellowship (BES-2012-052999); ET Israel Science Foundation (ISF) grant 1737/17; MSV, MSR and MJ by a grant from the Ministry of Education, Science and Technological Development of the Republic of Serbia (451-03-68/2020-14/200178); AP, KE and MT by a grant from the Ministry of Education, Science and Technological Development of the Republic of Serbia (451-03-68/2020-14/200007); and TM NSERC grant RGPIN-2018-05551.Peer reviewe

An Expanded Evaluation of Protein Function Prediction Methods Shows an Improvement In Accuracy

Background: A major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, assessing methods for protein function prediction and tracking progress in the field remain challenging. Results: We conducted the second critical assessment of functional annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. We evaluated 126 methods from 56 research groups for their ability to predict biological functions using Gene Ontology and gene-disease associations using Human Phenotype Ontology on a set of 3681 proteins from 18 species. CAFA2 featured expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis compared the best methods from CAFA1 to those of CAFA2. Conclusions: The top-performing methods in CAFA2 outperformed those from CAFA1. This increased accuracy can be attributed to a combination of the growing number of experimental annotations and improved methods for function prediction. The assessment also revealed that the definition of top-performing algorithms is ontology specific, that different performance metrics can be used to probe the nature of accurate predictions, and the relative diversity of predictions in the biological process and human phenotype ontologies. While there was methodological improvement between CAFA1 and CAFA2, the interpretation of results and usefulness of individual methods remain context-dependent

An expanded evaluation of protein function prediction methods shows an improvement in accuracy

Background: A major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, assessing methods for protein function prediction and tracking progress in the field remain challenging. Results: We conducted the second critical assessment of functional annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. We evaluated 126 methods from 56 research groups for their ability to predict biological functions using Gene Ontology and gene-disease associations using Human Phenotype Ontology on a set of 3681 proteins from 18 species. CAFA2 featured expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis compared the best methods from CAFA1 to those of CAFA2. Conclusions: The top-performing methods in CAFA2 outperformed those from CAFA1. This increased accuracy can be attributed to a combination of the growing number of experimental annotations and improved methods for function prediction. The assessment also revealed that the definition of top-performing algorithms is ontology specific, that different performance metrics can be used to probe the nature of accurate predictions, and the relative diversity of predictions in the biological process and human phenotype ontologies. While there was methodological improvement between CAFA1 and CAFA2, the interpretation of results and usefulness of individual methods remain context-dependent. Keywords: Protein function prediction, Disease gene prioritizationpublishedVersio

Recombinant IL-21 and anti-CD4 antibodies cooperate in syngeneic neuroblastoma immunotherapy and mediate long-lasting immunity

IL-21 is an immune-enhancing cytokine, which showed promising results in cancer immunotherapy. We previously observed that the administration of anti-CD4 cell-depleting antibody strongly enhanced the anti-tumor effects of an IL-21-engineered neuroblastoma (NB) cell vaccine. Here, we studied the therapeutic effects of a combination of recombinant (r) IL-21 and anti-CD4 monoclonal antibodies (mAb) in a syngeneic model of disseminated NB. Subcutaneous rIL-21 therapy at 0.5 or 1 \u3bcg/dose (at days 2, 6, 9, 13 and 15 after NB induction) had a limited effect on NB development. However, coadministration of rIL-21 at the two dose levels and a cell-depleting anti-CD4 mAb cured 28 and 70 % of mice, respectively. Combined immunotherapy was also effective if started 7 days after NB implant, resulting in a 30 % cure rate. Anti-CD4 antibody treatment efficiently depleted CD4(+) CD25(high) Treg cells, but alone had limited impact on NB. Combination immunotherapy by anti-CD4 mAb and rIL-21 induced a CD8(+) cytotoxic T lymphocyte response, which resulted in tumor eradication and long-lasting immunity. CD4(+) T cells, which re-populated mice after combination immunotherapy, were required for immunity to NB antigens as indicated by CD4(+) T cell depletion and re-challenge experiments. In conclusion, these data support a role for regulatory CD4(+) T cells in a syngeneic NB model and suggest that rIL-21 combined with CD4(+) T cell depletion reprograms CD4(+) T cells from immune regulatory to anti-tumor functions. These observations open new perspectives for the use of IL-21-based immunotherapy in conjunction with transient CD4(+) T cell depletion, in human metastatic NB

Metformin inhibits cell cycle progression of B-cell chronic lymphocytic leukemia cells.

B-cell chronic lymphocytic leukemia (CLL) was believed to result from clonal accumulation of resting apoptosis-resistant malignant B lymphocytes. However, it became increasingly clear that CLL cells undergo, during their life, iterative cycles of re-activation and subsequent clonal expansion. Drugs interfering with CLL cell cycle entry would be greatly beneficial in the treatment of this disease. 1, 1-Dimethylbiguanide hydrochloride (metformin), the most widely prescribed oral hypoglycemic agent, inexpensive and well tolerated, has recently received increased attention for its potential antitumor activity. We wondered whether metformin has apoptotic and anti-proliferative activity on leukemic cells derived from CLL patients. Metformin was administered in vitro either to quiescent cells or during CLL cell activation stimuli, provided by classical co-culturing with CD40L-expressing fibroblasts. At doses that were totally ineffective on normal lymphocytes, metformin induced apoptosis of quiescent CLL cells and inhibition of cell cycle entry when CLL were stimulated by CD40-CD40L ligation. This cytostatic effect was accompanied by decreased expression of survival- and proliferation-associated proteins, inhibition of signaling pathways involved in CLL disease progression and decreased intracellular glucose available for glycolysis. In drug combination experiments, metformin lowered the apoptotic threshold and potentiated the cytotoxic effects of classical and novel antitumor molecules. Our results indicate that, while CLL cells after stimulation are in the process of building their full survival and cycling armamentarium, the presence of metformin affects this process