Motivation Immune cells are important components of the immune system and are crucial for disease initiation, progression, prognosis, and survival. Although several computational methods have been designed for predicting the abundance of immune cells, very few tools are applicable to mouse. Given that mouse is the most widely used animal model in biomedical research, there is an urgent need to develop a precise algorithm for predicting mouse immune cells. Results We developed a tool named ImmuCellAI-mouse (Immune Cell Abundance Identifier for mouse), for estimating the abundance of 36 immune cell (sub)types from gene expression data in a hierarchical strategy of three layers. Reference expression profile and robust marker gene sets of immune cell types were curated. The abundance of cells in three layers was predicted separately by calculating the ssGSEA enrichment score of the expression deviation profile per cell type. Benchmark results showed high accuracy of ImmuCellAI-mouse in predicting most immune cell types, with correlation coefficients between predicted value and real cell proportion of most cell types being larger than 0.8. We applied ImmuCellAI-mouse to a mouse breast tumor dataset and revealed the dynamic change of immune cell infiltration during treatment, which is consistent with the findings of the original study but with more details. We also constructed an online server for ImmuCellAI-mouse, on which users can upload expression matrices for analysis. ImmuCellAI-mouse will be a useful tool for studying the immune microenvironment, cancer immunology, and immunotherapy in mouse models, providing an indispensable supplement for human disease studies. Availability Software is available at http://bioinfo.life.hust.edu.cn/ImmuCellAI-mouse/ Supplementary information Supplementary data are available at Bioinformatics online.
. PI3K integrates the effects of insulin and leptin on large-conductance Ca 2ϩ -activated K ϩ channels in neuropeptide Y neurons of the hypothalamic arcuate nucleus. Am J Physiol Endocrinol Metab 298: E193-E201, 2010. First published August 11, 2009; doi:10.1152/ajpendo.00155.2009.-The adipocyte-derived hormone leptin and the pancreatic -cell-derived hormone insulin function as afferent signals to the hypothalamus in an endocrine feedback loop that regulates body adiposity. They act in hypothalamic centers to modulate the function of specific neuronal subtypes, such as neuropeptide Y (NPY) neurons, by modifying neuronal electrical activity. To investigate the intrinsic activity of these neurons and their responses to insulin and leptin, we used a combination of morphological features and immunocytochemical technique to identify the NPY neurons of hypothalamic arcuate nucleus (ARC) and record whole cell large-conductance Ca 2ϩ -activated potassium (BK) currents on them. We found that both of the hormones increase the peak amplitude of BK currents, shifting the steady-state activation curve to the left. The effect of both insulin and leptin can be prevented by pretreatment with inhibitors of tyrosine kinase and phosphatidylinositol 3-kinase (PI3K) but not MAPK. These data indicate that PI3K-mediated signals are the common regulators of BK channels by insulin and leptin and mediated the two hormones' identical activatory effects on ARC NPY neurons. The effect of insulin and leptin together was similar to that of insulin or leptin alone, and leptin or insulin pretreatment did not lead to insulin-or leptin-sensitizing effects, respectively. These intracellular signaling mechanisms may play key roles in regulating ARC NPY neuron activity and physiological processes such as the control of food intake and body weight, which are under the combined control of insulin and leptin. patch-clamp recording; phosphatidylinositol 3-kinase INSULIN AND LEPTIN ARE HYPOTHESIZED to be "adiposity signals" for the long-term regulation of energy balance in the brain (2,36,40,50). They circulate at levels that are directly proportional to adipose mass and enter the central nervous system (CNS) via saturable receptor-mediated processes (4, 18, 49). The primary CNS target for these adipostats is the arcuate nucleus (ARC), where leptin and insulin receptors are highly expressed (32, 45), and direct administration of either hormone has a potent effect on food intake and body weight (1,28,35). Two specific ARC neuron populations have been strongly implicated in sensing the changes in levels of circulating leptin and insulin and transducing these signals into neuronal outputs (2, 36). They are proopiomelanocortin (POMC)-containing neurons, which are associated with catabolism, and neuropeptide Y (NPY)/agouti gene-related protein (AgRP)-containing neurons, which are associated with anabolism. As adipostatic hormones, insulin and leptin have a similar transcriptional control on these regulatory entities in the hypothalamus. Both of them can inc...
Immune checkpoint blockade (ICB) therapy provides remarkable clinical benefits for multiple cancer types. Much work is currently being conducted to investigate the mechanisms of ICB therapy at the transcriptional level. Integrating the data produced by these studies will help us give more insight into the transcriptomic features of ICB therapy. We collected the transcriptome and clinical data of ICB-treated patient samples from the GEO, ArrayExpress, TCGA, and dbGaP databases. Based on the clinical information, all samples are initially classified into response/non-response or pre-treatment/on-treatment groups. Differential expression, pathway enrichment, and immune cell infiltration analyses are performed between the samples from different groups. We also introduce the Response Score (RS) calculated by integrating the variability degree and the frequency of the dysregulated genes in the responders to evaluate the impact of gene expression on the response. Finally, all the abovementioned contents are integrated into the ICBatlas database. ICBatlas provides the transcriptome features of ICB therapy through the analysis of 1515 ICB-treated samples from 25 studies across nine cancer types. The data in ICBatlas include clinical outcomes, treatment-related genes, biological pathways, and immune cell infiltration. Users can investigate the abovementioned transcriptome features in the Response (R vs. NR) or Treatment (Pre vs. On) modules at the dataset, cancer type, or immune checkpoint level and compare the degree of gene impact on the response in the RS module. ICBatlas is the first database to show the transcriptome features on ICB therapy in human cancers and freely available at http://bioinfo.life.hust.edu.cn/ICBatlas/.
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