Combination of vegetable soup and glucan demonstrates synergistic effects on macrophage-mediated immune responses

Vegetable soup (VS), a plant-based functional food, has been used as a traditional folk medicine and is attracting attention for its ability to enhance the immune response. β-Glucan, a well-established and effective immunomodulator, has synergistic effects when used in combination with some bioactive compounds. In the present study, we aimed to evaluate the synergistic immunomodulatory effects of the combination of VS and β-glucan on macrophage-mediated immune responses. β-Glucan was demonstrated to synergistically enhance the VS-stimulated immune response, including the production of interleukin-6, tumor necrosis factor-α, and nitric oxide, mainly through the mitogen-activated protein kinase pathway in macrophages. In addition, this combination has the potential for further development in functional foods with immune-enhancing activity.Supplementary InformationThe online version contains supplementary material available at 10.1007/s10068-021-00888-x.     

Microenvironment and related genes predict outcomes of patients with cervical cancer: evidence from TCGA and bioinformatic analysis

Cervical cancer (CESC) is one of the most common cancers and affects the female genital tract. Consistent HPV infection status has been determined to be a vital cause of tumorigenesis. HPV infection may induce changes to the immune system and limit the host’s immune response. Immunotherapy is therefore essential to improving the overall survival of both locally advanced and recurrent CESC patients. Using 304 relevant samples from TCGA, we assessed immune cell function in CESC patients to better understand the status of both tumor micro-environment cells and immune cells in CESC. Functional enrichment analysis, pathway enrichment analysis, and PPI network construction were performed to explore the differentially expressed genes (DEGs). The analysis identified 425 DEGs, which included 295 up-regulated genes and 130 down-regulated genes. We established that upregulation of CCL5 was correlated with significantly better survival, meaning that CCL5 expression could serve as a novel prognostic biomarker for CESC patients. We further focused on CCL5 as a hub gene in CESC, as it had significant correlations with increased numbers of several types of immune cells. Cell-type fractions of M1 macrophages were significantly higher in the high-immune-scores group, which was associated with better overall survival. Finally, we concluded that CCL5 is a promising prognostic biomarker for CESC, as well as a novel chemotherapeutic target.     

Functions of ULK1 in autophagy and non-autophagy pathways and its implications in human physiology and disease

ULK1 (unc-51 like autophagy activating kinase 1), a mammalian serine/threonine kinase, is a key component of autophagy initiation complex and helps to induce all types of autophagy. Canonical autophagy is a process in which, through the interactions of a series of autophagy-related proteins, damaged organelles or misfolded proteins are engulfed by autophagosomes and then merged with lysosomes to be degraded. Thus, canonical autophagy is an important constituent part of the cellular “quality control.” Besides, accumulating evidence indicates that ULK1 exerts autophagy-independent effects in a cell-specific manner. For example, ULK1 facilitates neurite elongation through the regulation of endoplasmic reticulum (ER)–Golgi trafficking in neurons, stimulates phosphopentose pathway to help NADPH (nicotinamide adenine dinucleotide phosphate hydrogen) production, and acts as a duplex regulator in type I IFN (type I interferon) induced innate immune response. Considering the importance and diversity of ULK1 in various biological processes, this review aims to present a comprehensive overview of autophagy and non-autophagy related functions of ULK1 in a variety of human physiological, pathological, and disease processes.     

<i>In Silico</i> analysis and linking of metabolism-related genes with the immune landscape in head and neck squamous cell carcinoma

Metabolic reprogramming and immunologic suppression are two critical characteristics promoting the progression of head and neck squamous cell carcinoma (HNSCC). The integrative analysis of all the metabolism-related genes (MRGs) in HNSCC is lacking and the interaction between the metabolism and the immune characteristics also requires more exploration to uncover the potential mechanisms. Therefore, this study was designed to establish a prognostic signature based on all the MRGs in HNSCC. Genes of HNSCC samples were available from the TCGA and GEO databases while the MRGs were retrieved from a previous study. Ultimately 4 prognostic MRGs were selected to construct a model possessing robust prognostic value and accuracy in TCGA cohorts. The favorable reproducibility of this model was confirmed in validation cohorts from GEO databases. The risk score calculated by this model was an independent prognostic factor that further classified these HNSCC patients into high-/low-risk groups. GSEA analyses and somatic mutations indicated the low-risk group could activate several anti-tumor pathways and possessed lower TP53 mutation. The results of ESTIMATE, single-sample GSEA, CIBERSORT, and some immune-related molecules analyses suggested the low-risk group exhibited lower metabolic activities and higher immune characteristics. The Spearman correlation test implied most metabolic pathways with tumor-promoting function were negatively correlated with the immune activity, indicating a plausible approach of combining the anti-metabolism and the immunotherapy drugs in the high-risk group to enhance therapeutic effects than applied separately. In conclusion, this prognostic signature linking MRGs with the immune landscape could promote the individualized treatment for HNSCC patients.     

CD34⁺ CD38^- subpopulation without CD123 and CD44 is responsible for LSC and correlated with imbalance of immune cell subsets in AML

Acute myeloid leukemia (AML) is regarded as a stem cell disease. However, no one unique marker is expressed on leukemia stem cells (LSC) but not on leukemic blasts nor normal hematopoietic stem cells (HSC). CD34⁺ CD38- with or without CD123 or CD44 subpopulations are immunophenotypically defined as putative LSC fractions in AML. Nevertheless, markers that can be effectively and simply held responsible for the intrinsical heterogeneity of LSC is still unclear. In the present study, we examined the frequency of three different LSC subtypes (CD34⁺ CD38^-, CD34⁺ CD38^- CD123⁺ , CD34⁺ CD38^- CD44⁺ ) in AML at diagnosis. We then validated their prognostic significance on the relevance of spectral features for diagnostic stratification, immune status, induction therapy response, treatment effect maintenance, and long-term survival. In our findings, high proportions of the above three different LSC subtypes were all significantly characterized with low complete remission (CR) rate, high relapse/refractory rate, poor overall survival (OS), frequent FLT3-ITD mutation, the high level of regulatory T cells (Treg) and monocytic myeloid-derived suppressor cells (M-MDSC). However, there was no significant statistical difference in all kinds of other clinical performance among the three different LSC groups. It was demonstrated that CD34⁺ CD38^- subpopulation without CD123 and CD44 might be held responsible for LSC and correlated with an imbalance of immune cell subsets in AML.     

An integrated bioinformatics analysis and experimental study identified key biomarkers CD300A or CXCL1, pathways and immune infiltration in diabetic nephropathy mice

Diabetic nephropathy (DN) is a common microvascular complication that easily leads to end-stage renal disease. It is important to explore the key biomarkers and molecular mechanisms relevant to diabetic nephropathy (DN). We used highthroughput RNA sequencing to obtain the genes related to DN glomerular tissues and healthy glomerular tissues of mice. Then we used LIMMA to analyze differentially expressed genes (DEGs) between DN and non-diabetic glomerular samples. And we performed KEGG, gene ontology functional (GO) enrichment, and gene set enrichment analysis to reveal the signaling pathway of the disease. The CIBERSORT algorithm based on support vector machine was used to determine the immune infiltration score. Random forest algorithm and Cytoscape obtained hub genes. Finally, we applied co-staining, immunohistochemical staining, RT-qPCR and western blotting to validate the protein and mRNA expression of both hub genes. We obtained 913 DEGs mainly related to inflammatory factors and immunity. GSEA results showed that differential genes were mainly enriched in IL-17 signaling pathway, lipid and atherosclerosis, rheumatoid arthritis, TNF signaling pathway, neutrophil extracellular trap formation, Staphylococcus aureus infection and other pathways. The intersection of the random forest algorithm and Cytoscape revealed both hub genes of CD300A and CXCL1. Experiments have shown that the both key genes of CD300A and CXCL1 shown increased expression in glomerular podocytes, and are related to the inflammation of diabetic nephropathy. And immunohistochemical staining and RT-qPCR further confirmed that the protein and mRNA expression level of CD300A or CXCL1 in glomeruli tissue in DN mice were increased. The expression levels of CD300A and CXCL1 increased significantly under HG (high glucose) stimulation, further confirming that diabetes can lead to increased levels of CD300A and CXCL1 at the cellular level. Through bioinformatics analysis, machine learning algorithms, and experimental research, CD300A and CXCL1 are confirmed as both potential biomarkers in diabetic nephropathy. And we further revealed the main pathways of differential genes and the differentially distributed immune infiltrating cells in diabetic nephropathy.     

Effect of hybridizing Biogeography-Based Optimization (BBO) technique with Artificial Immune Algorithm (AIA) and Ant Colony Optimization (ACO)

Hybridization in optimization methods plays a very vital role to make it effective and efficient. Different optimization methods have different search tendency and it is always required to experiment the effect of hybridizing different search tendency of the optimization algorithm with each other. This paper presents the effect of hybridizing Biogeography-Based Optimization (BBO) technique with Artificial Immune Algorithm (AIA) and Ant Colony Optimization (ACO) in two different ways. So, four different variants of hybrid BBO, viz. two variants of hybrid BBO with AIA and two with ACO, are developed and experimented in this paper. All the considered optimization techniques have altogether a different search tendency. The proposed hybrid method is tested on many benchmark problems and real life problems. Friedman test and Holm–Sidak test are performed to have the statistical validity of the results. Results show that proposed hybridization of BBO with ACO and AIA is effective over a wide range of problems. Moreover, the proposed hybridization is also effective over other proposed hybridization of BBO and different variants of BBO available in the literature.     

Biological-based genetic algorithms for optimized disaster response resource allocation

An effective disaster response requires rapid coordination of existing resources, which can be considered a resource optimization problem. Genetic algorithms (GAs) have been proven effective for solving optimization problems in various fields. However, GAs essentially use generation succession to search for optimal solutions. Therefore, their use of reproduction, crossover, and mutation operations may exclude optimal chromosomes during generation succession and prevent full use of previous search experience. Meanwhile, premature convergence caused by inadequate diversity of chromosome populations limits the search to a local optimum. Genetic algorithms also incur high computational costs. The biological-based GAs (BGAs) proposed in this study address these problems by including mechanisms for elite reserve areas, nonlinear fitness value conversion, and migration. This study performed experimental simulations to compare BGAs with immune algorithms (IAs) and GAs in terms of effectiveness for allocating disaster refuge site staff and for planning relief supply distribution. The simulation results show that, compared to other methods, BGAs can compute optimal solutions faster. Therefore, they provide a more useful reference when performing the decision-making needed to solve disaster response resource optimization problems.     

Hybrid Taguchi-Immune Algorithm for the thermal unit commitment

In this paper, a Hybrid Taguchi-Immune Algorithm (HTIA) is presented to deal with the unit commitment problem. HTIA integrates the Taguchi method and the Traditional Immune Algorithm (TIA), providing a powerful global exploration capability. The Taguchi method (TM) is incorporated in the crossover operations in order to select the better gene for achieving crossover consequently, enhancing the TIA. It has been widely used in experimental designs for problems with multiple parameters. The effectiveness and efficiency of HTIA are demonstrated by presenting several cases, and the results are compared with previous publications. Our results show that the proposed method is feasible, robust, and more effective than many other previously developed computation algorithms.     

基于免疫算法的文本特征选择方法

文本的特征选择可以被看成是组合优化问题,而遗传算法是求解此类问题的较好方法。在遗传算法的基础上,提出了一种新的算法-免疫算法,用于文本的特征选择,通过免疫算子的构造,减轻了遗传算法后期的波动现象,提高了收敛速度。