Modulation of Host Immune Response during Leishmania infantum Natural Infection: A Whole-Transcriptome Analysis of the Popliteal Lymph Nodes in Dogs

Leishmania infantum, the etiological agent of canine leishmaniosis (CanL) in Europe, was responsible of the largest outbreak of human leishmaniosis in Spain. The parasite infects and survives within myeloid lineage cells, causing a potentially fatal disease if left untreated. The only treatment option relies on chemotherapy, although immunotherapy strategies are being considered as novel approaches to prevent progression of the disease. To this aim, a deeper characterization of the molecular mechanisms behind the immunopathogenesis of leishmaniosis is necessary. Thus, we evaluated, for the first time, the host immune response during L. infantum infection through transcriptome sequencing of the popliteal lymph nodes aspirates of dogs with CanL. Differential expression and weighted gene co-expression network analyses were performed, resulting in the identification of 5,461 differentially expressed genes (DEGs) and four key modules in sick dogs, compared to controls. As expected, defense response was the highest enriched biological process in the DEGs, with six genes related to immune response against pathogens (CHI3L1, SLPI, ACOD1, CCL5, MPO, BPI) included among the ten most expressed genes; and two of the key co-expression modules were associated with regulation of immune response, which also positively correlated with clinical stage and blood monocyte concentration. In particular, sick dogs displayed significant changes in the expression of Th1, Th2, Th17 and Tr1 cytokines (e. g. TNF-α, IFN-γ, IL-21, IL-17, IL-15), markers of T cell and NK cell exhaustion (e. g. LAG3, CD244, Blimp-1, JUN), and B cell, monocyte and macrophage disrupted functionality (e. g. CD40LG, MAPK4, IL-1R, NLRP3, BCMA). In addition, we found an overexpression of XBP1 and some other genes involved in endoplasmic reticulum stress and the IRE1 branch of the unfolded protein response, as well as one co-expression module associated with these processes, which could be induced by L. infantum to prevent host cell apoptosis and modulate inflammation-induced lymphangiogenesis at lymph nodes. Moreover, 21 lncRNAs were differentially expressed in sick dogs, and one key co-expression module was associated with chromatin organization, suggesting that epigenetic mechanisms could also contribute to dampening host immune response during natural L. infantum infection in the lymph nodes of dogs suffering from clinical leishmaniosis.

MicroRNA-362-3p Inhibits Glioma Growth by Targeting PAX3 and Regulating Wnt/Beta-Catenin Pathway

Glioma is the most common intracranial malignancy and has been recognized as one of the most invasive primary tumors. Although there have been many studies on its growth mechanism, the molecular mechanism for growth inhibition is still unclear. The aim of this study was to show that microRNA-362-3p inhibits glioma growth by targeting PAX3 and regulating Wnt/beta-catenin pathway. We collected platelets from 12 healthy controls and 8 patients with glioma from the GEO database for comparison. The ncRNA and transcription factors that regulate the module were predicted to reveal the mechanism of microRNA-362-3p through co-expression module analysis, enrichment analysis, and hypergeometric testing. Two functional modules were obtained by integrating potential pathogenic genes and co-expression analysis. GPATCH4 and MYOD1 genes were expressed differentially and had active regulatory roles in the dysfunction module; thus, they were identified as key genes for glioma growth. Next, we performed ncRNA pivot and transcription factors (TFs) especially about the pivot analysis. The results showed microRNA-362-3p gene interest that significantly regulated the dysfunction module. Therefore, we identified microRNA-362-3p as a dysfunctional molecule in the growth process of glioma. MicroRNA-362-3p could inhibit glioma growth by targeting PAX3 and regulating the Wnt/beta-catenin pathway. The inhibition of this pathway may be a new target for the treatment of glioma. This study improves our understanding of growth inhibition in glioma and provides reference values for the treatment of this disease.

New Prognostic Biomarkers and Drug Targets for Skin Cutaneous Melanoma via Comprehensive Bioinformatic Analysis and Validation

Skin cutaneous melanoma (SKCM) is the most aggressive and fatal type of skin cancer. Its highly heterogeneous features make personalized treatments difficult, so there is an urgent need to identify markers for early diagnosis and therapy. Detailed profiles are useful for assessing malignancy potential and treatment in various cancers. In this study, we constructed a co-expression module using expression data for cutaneous melanoma. A weighted gene co-expression network analysis was used to discover a co-expression gene module for the pathogenesis of this disease, followed by a comprehensive bioinformatics analysis of selected hub genes. A connectivity map (CMap) was used to predict drugs for the treatment of SKCM based on hub genes, and immunohistochemical (IHC) staining was performed to validate the protein levels. After discovering a co-expression gene module for the pathogenesis of this disease, we combined GWAS validation and DEG analysis to identify 10 hub genes in the most relevant module. Survival curves indicated that eight hub genes were significantly and negatively associated with overall survival. A total of eight hub genes were positively correlated with SKCM tumor purity, and 10 hub genes were negatively correlated with the infiltration level of CD4+ T cells and B cells. Methylation levels of seven hub genes in stage 2 SKCM were significantly lower than those in stage 3. We also analyzed the isomer expression levels of 10 hub genes to explore the therapeutic target value of 10 hub genes in terms of alternative splicing (AS). All 10 hub genes had mutations in skin tissue. Furthermore, CMap analysis identified cefamandole, ursolic acid, podophyllotoxin, and Gly-His-Lys as four targeted therapy drugs that may be effective treatments for SKCM. Finally, IHC staining results showed that all 10 molecules were highly expressed in melanoma specimens compared to normal samples. These findings provide new insights into SKCM pathogenesis based on multi-omics profiles of key prognostic biomarkers and drug targets. GPR143 and SLC45A2 may serve as drug targets for immunotherapy and prognostic biomarkers for SKCM. This study identified four drugs with significant potential in treating SKCM patients.

TPSC: a module detection method based on topology potential and spectral clustering in weighted networks and its application in gene co-expression module discovery

Background Gene co-expression networks are widely studied in the biomedical field, with algorithms such as WGCNA and lmQCM having been developed to detect co-expressed modules. However, these algorithms have limitations such as insufficient granularity and unbalanced module size, which prevent full acquisition of knowledge from data mining. In addition, it is difficult to incorporate prior knowledge in current co-expression module detection algorithms. Results In this paper, we propose a novel module detection algorithm based on topology potential and spectral clustering algorithm to detect co-expressed modules in gene co-expression networks. By testing on TCGA data, our novel method can provide more complete coverage of genes, more balanced module size and finer granularity than current methods in detecting modules with significant overall survival difference. In addition, the proposed algorithm can identify modules by incorporating prior knowledge. Conclusion In summary, we developed a method to obtain as much as possible information from networks with increased input coverage and the ability to detect more size-balanced and granular modules. In addition, our method can integrate data from different sources. Our proposed method performs better than current methods with complete coverage of input genes and finer granularity. Moreover, this method is designed not only for gene co-expression networks but can also be applied to any general fully connected weighted network.

Molecular Manipulation of the MiR396/GRF Expression Module Alters the Salt Stress Response of Arabidopsis thaliana

We previously demonstrated that microRNA396 (miR396) abundance is altered in 15-day-old Arabidopsis thaliana (Arabidopsis) whole seedlings following their exposure to a 7-day salt stress treatment regime. We, therefore, used a molecular modification approach to generate two new Arabidopsis transformant populations with reduced (MIM396 plants) and elevated (MIR396 plants) miR396 abundance. The exposure of 8-day-old wild-type Arabidopsis whole seedlings and a representative plant line of the MIM396 and MIR396 transformant populations to a 7-day salt stress treatment regime revealed unique phenotypic and physiological responses to the imposed stress by unmodified wild-type Arabidopsis plants and the MIM396 and MIR396 transformat lines. A quantitative reverse transcriptase polymerase chain reaction (RT-qPCR) approach was, therefore, applied to demonstrate that the plant line specific responses to salt stress likely stemmed from the unique molecular profile of each of the GROWTH REGULATING FACTOR (GRF) transcription factor gene family members which form posttranscriptional targets of miR396-directed expression regulation. RT-qPCR additionally revealed that, in 15-day-old Arabidopsis whole seedlings, the three previously identified putative target genes of miR396 belonging to the NEUTRAL/ALKALINE NONLYSOSOMAL CERAMIDASE-LIKE (NCER) gene family, including NCER1, NCER2, and NCER3, do not form targets of miR396-directed expression regulation at the posttranscriptional level. Taken together, the phenotypic and molecular analyses presented here demonstrate that alteration of the miR396/GRF expression module is central to the molecular response of Arabidopsis to salt stress.

Exploring Biomarkers Related to Autophagy in Alzheimer's Disease Based on Pathway Crosstalk Analysis

The pathological mechanism of Alzheimer's disease (AD) involves multiple pathways, and the crosstalk between autophagy and other pathways plays an increasingly prominent role in AD. However, current methods are primarily based on single-gene analysis or a single signal pathway to find therapeutic targets for AD, which are somewhat limited. The aim of our study is to identify autophagy-related biomarkers in AD based on the crosstalk between autophagy and other pathways. The pathway analysis method (PAGI) was applied to find the feature mRNAs involved in the crosstalk between autophagy and many other AD-related pathways. Then, the weighted gene co-expression network analysis (WGCNA) was used to construct a co-expression module of feature mRNAs and differential lncRNAs. Finally, clinical information was used to screen the biomarkers related to the prognosis of AD in the co-expressed gene modules. The experiment finally identified 8 mRNAs and 2 lncRNAs (TLN1, ARRB1, FZD4, AKT1, JMJD7-PLA2G4B, STAT5A, SMAD7, ZNF274; AC113349.1, AC015878.2) as biomarkers of AD, and they all interact directly or indirectly with autophagy. In summary, we provide an effective method for extracting autophagy-related biomarkers based on pathway crosstalk in AD. This method enriches the therapeutic targets of AD and provides new insights into the molecular mechanism of autophagy in AD.

Genome-Wide Analysis of Glycoside Hydrolase Family 35 Genes and Their Potential Roles in Cell Wall Development in Medicago truncatula

Plant β-galactosidases (BGAL) function in various cell wall biogeneses and modifications, and they belong to the glycoside hydrolase family. However, the roles of BGAL family members in Medicago truncatula cell wall remodeling remain unclear. In this study, a total of 25 MtBGAL members of the glycoside hydrolase gene family 35 were identified, and they were clustered into nine sub-families. Many cis-acting elements possibly related to MeJA and abscisic acid responses were identified in the promoter region of the MtBGAL genes. Transcript analyses showed that these MtBGAL genes exhibited distinct expression patterns in various tissues and developing stem internodes. Furthermore, a stem-specific expression module associated with cell wall metabolic pathways was identified by weighted correlation network analysis (WGCNA). In particular, MtBGAL1 and MtBGAL23 within the stem-specific expression module were highly expressed in mature stems. In addition, several genes involved in lignin, cellulose, hemicellulose and pectin pathways were co-expressed with MtBGAL1 and MtBGAL23. It was also found that MtBGAL1 and MtBGAL23 were localized to the cell wall at the subcellular level, indicating their roles in the modification of cell wall metabolites in Medicago. As a whole, these results will be useful for further functional characterization and utilization of BGAL genes in cell wall modifications aiming to improve the quality of legume forage crops.

Gene expression analysis method integration and co-expression module detection applied to rare glucide metabolism disorders using ExpHunterSuite

High-throughput gene expression analysis is widely used. However, analysis is not straightforward. Multiple approaches should be applied and methods to combine their results implemented and investigated. We present methodology for the comprehensive analysis of expression data, including co-expression module detection and result integration via data-fusion, threshold based methods, and a Naïve Bayes classifier trained on simulated data. Application to rare-disease model datasets confirms existing knowledge related to immune cell infiltration and suggest novel hypotheses including the role of calcium channels. Application to simulated and spike-in experiments shows that combining multiple methods using consensus and classifiers leads to optimal results. ExpHunter Suite is implemented as an R/Bioconductor package available from https://bioconductor.org/packages/ExpHunterSuite. It can be applied to model and non-model organisms and can be run modularly in R; it can also be run from the command line, allowing scalability with large datasets. Code and reports for the studies are available from https://github.com/fmjabato/ExpHunterSuiteExamples.

Gene co-expression reveals the modularity and integration of C4 and CAM in Portulaca

C4 and Crassulacean acid metabolism (CAM) have been considered as largely independent photosynthetic adaptations in spite of sharing key biochemical modules. Portulaca is a geographically widespread clade of over 100 annual and perennial angiosperm species that primarily use C4 photosynthesis, but facultatively exhibit CAM when drought stressed, a photosynthetic system known as C4+CAM. It has been hypothesized that C4+CAM is rare because of pleiotropic constraints, but these have not been deeply explored. We generated a chromosome-level genome assembly of P. amilis and sampled mRNA from P. amilis and P. oleracea during CAM induction. Gene co-expression network analyses identified C4 and CAM gene modules shared and unique to both Portulaca species. A conserved CAM module linked phosphoenolpyruvate carboxylase (PEPC) to starch turnover during the day-night transition and was enriched in circadian clock regulatory motifs in the P. amilis genome. Preservation of this co-expression module regardless of water status suggests that Portulaca constitutively operate a weak CAM cycle that is transcriptionally and post-transcriptionally upregulated during drought. C4 and CAM mostly used mutually exclusive paralogs for primary carbon fixation and, although it is likely that nocturnal CAM malate stores are shuttled into diurnal C4 decarboxylation pathways, we find some evidence that metabolite cycling may occur at low levels. C4 likely evolved in Portulaca through co-option of redundant paralogs and integration of the diurnal portion of CAM. Thus, the ancestral CAM system did not strongly constrain C4 evolution because photosynthetic gene networks are not co-regulated for both daytime and nighttime functions.

Analysis of Co-Expression Module of Liver Metastasis Genes in Colorectal Cancer and Mining of Potential High-Expression Biomarkers

AimsThe Hub genes highly related to the disease were found from the gene co-expression module, and the potential high expression genes were analyzed to predict the liver metastasis of colorectal cancer, so as to provide reference for subsequent targeted therapy.MethodsIn this study, we used the public data set of GEO database (GSE50760) to analyze the gene co-expression of liver metastasis of colon cancer, primary colon cancer and normal colon tissue (54 cases) and 50 cases of clinical cases. The functional annotations based on GO database are enriched, and the functional annotations of five gene modules are obtained through the enrichment of biological processes. Then the data mining is carried out to find the sub-networks with high adjacency in the gene co-expression network. At the same time, these sub-networks are annotated to find oncogenes related to liver metastasis of colorectal cancer.ResultsThis experiment found that KRAS, APC, FBXW7, PIK3CA, TP53 were highly correlated with liver metastasis of colorectal cancer. Finally, two protein genes STAT1 and MAPK1 were found by MCODE, which may be highly correlated with liver metastasis of colorectal cancer. Two new genes with high expression proteins found in this experiment have potential cancer, which has not been reflected in previous studies.ConclusionAccording to clinical data, KRAS, APC, FBXW7, PIK3CA, TP53 are related to colorectal cancer liver metastasis, and the analysis of the data set shows that STAT1 and MAPK1 are not only related to colorectal cancer liver metastasis oncogene but also related to clinically obtained genes.