PLAU inferred from a correlation network is critical for suppressor function of regulatory T cells

Nasal anti-CD3 monoclonal antibody ameliorates traumatic brain injury, enhances microglial phagocytosis and reduces neuroinflammation via IL-10-dependent Treg-microglia crosstalk

Neuroinflammation plays a crucial role in traumatic brain injury (TBI), contributing to both damage and recovery, yet no effective therapy exists to mitigate central nervous system (CNS) injury and promote recovery after TBI. In the present study, we found that nasal administration of an anti-CD3 monoclonal antibody ameliorated CNS damage and behavioral deficits in a mouse model of contusional TBI. Nasal anti-CD3 induced a population of interleukin (IL)-10-producing regulatory T cells (Treg cells) that migrated to the brain and closely contacted microglia. Treg cells directly reduced chronic microglia inflammation and regulated their phagocytic function in an IL-10-dependent manner. Blocking the IL-10 receptor globally or specifically on microglia in vivo abrogated the beneficial effects of nasal anti-CD3. However, the adoptive transfer of IL-10-producing Treg cells to TBI-injured mice restored these beneficial effects by enhancing microglial phagocytic capacity and reducing microglia-induced neuroinflammation. These findings suggest that nasal anti-CD3 represents a promising new therapeutic approach for treating TBI and potentially other forms of acute brain injury.

Characterizing the Tumor Microenvironment and Its Prognostic Impact in Breast Cancer

The tumor microenvironment (TME) is crucial in cancer development and therapeutic response. Immunotherapy is increasingly recognized as a critical component of cancer treatment. While immunotherapies have shown efficacy in various cancers, including breast cancer, patient responses vary widely. Some patients receive significant benefits, while others experience minimal or no improvement. This disparity underscores the complexity and diversity of the immune system. In this study, we investigated the immune landscape and cell-cell communication within the TME of breast cancer through integrated analysis of bulk and single-cell RNA sequencing data. We established profiles of tumor immune infiltration that span across a broad spectrum of adaptive and innate immune cells. Our clustering analysis of immune infiltration identified three distinct patient groups: high T cell abundance, moderate infiltration, and low infiltration. Patients with low immune infiltration exhibited the poorest survival rates, while those in the moderate infiltration group showed better outcomes than those with high T cell abundance. Moreover, the high cell abundance group was associated with a greater tumor burden and higher rates of TP53 mutations, whereas the moderate infiltration group was characterized by a lower tumor burden and elevated PIK3CA mutations. Analysis of an independent single-cell RNA-seq breast cancer dataset confirmed the presence of similar infiltration patterns. Further investigation into ligand-receptor interactions within the TME unveiled significant variations in cell-cell communication patterns among these groups. Notably, we found that the signaling pathways SPP1 and EGF were exclusively active in the low immune infiltration group, suggesting their involvement in immune suppression. This work comprehensively characterizes the composition and dynamic interplay in the breast cancer TME. Our findings reveal associations between the extent of immune infiltration and clinical outcomes, providing valuable prognostic information for patient stratification. The unique mutations and signaling pathways associated with different patient groups offer insights into the mechanisms underlying diverse tumor immune infiltration and the formation of an immunosuppressive tumor microenvironment.

Manipulating host secreted protein gene expression: an indirect approach by HPV11/16 E6/E7 to suppress PBMC cytokine secretion

Human papillomavirus (HPV) 11/16 E6/E7 proteins have been recognized to be pivotal in viral pathogenesis. This study sought to uncover the potential mechanisms of how HPV11/16 E6/E7-transfected keratinocytes inhibit cytokine secretion in peripheral blood mononuclear cells (PBMC). Upon co-culturing HPV11/16 E6/E7-transfected keratinocytes with PBMC in a non-contact manner, we observed a marked decrease in various cytokines secreted by PBMC. To determine if this suppression was mediated by specific common secreted factors, we conducted transcriptomic sequencing on these transfected cells. This analysis identified 53 common differentially secreted genes in all four HPV-transfected cells. Bioinformatics analysis demonstrated these genes were predominantly involved in immune regulation. Results from quantitative PCR (qPCR) and an extensive literature review suggested the downregulation of 12 genes (ACE2, BMP3, BPIFB1, CLU, CST6, CTF1, HMGB2, MMP12, PDGFA, RNASE7, SULF2, TGM2), and upregulation of 7 genes (CCL17, CCL22, FBLN1, PLAU, S100A7, S100A8, S100A9), may be crucial in modulating tumor immunity and combating pathogenic infections, with genes S100A8 and S100A9, and IL-17 signaling pathway being particularly noteworthy. Thus, HPV11/16 E6/E7 proteins may inhibit cytokine secretion of immune cells by altering the expression of host-secreted genes. Further exploration of these genes may yield new insights into the complex dynamics of HPV infection.

Transcriptomic profiling of thymic dysregulation and viral tropism after neonatal roseolovirus infection

Introduction

Herpesviruses, including the roseoloviruses, have been linked to autoimmune disease. The ubiquitous and chronic nature of these infections have made it difficult to establish a causal relationship between acute infection and subsequent development of autoimmunity. We have shown that murine roseolovirus (MRV), which is highly related to human roseoloviruses, induces thymic atrophy and disruption of central tolerance after neonatal infection. Moreover, neonatal MRV infection results in development of autoimmunity in adult mice, long after resolution of acute infection. This suggests that MRV induces durable immune dysregulation.

Methods

In the current studies, we utilized single-cell RNA sequencing (scRNAseq) to study the tropism of MRV in the thymus and determine cellular processes in the thymus that were disrupted by neonatal MRV infection. We then utilized tropism data to establish a cell culture system.

Results

Herein, we describe how MRV alters the thymic transcriptome during acute neonatal infection. We found that MRV infection resulted in major shifts in inflammatory, differentiation and cell cycle pathways in the infected thymus. We also observed shifts in the relative number of specific cell populations. Moreover, utilizing expression of late viral transcripts as a proxy of viral replication, we identified the cellular tropism of MRV in the thymus. This approach demonstrated that double negative, double positive, and CD4 single positive thymocytes, as well as medullary thymic epithelial cells were infected by MRV in vivo. Finally, by applying pseudotime analysis to viral transcripts, which we refer to as "pseudokinetics," we identified viral gene transcription patterns associated with specific cell types and infection status. We utilized this information to establish the first cell culture systems susceptible to MRV infection in vitro.

Conclusion

Our research provides the first complete picture of roseolovirus tropism in the thymus after neonatal infection. Additionally, we identified major transcriptomic alterations in cell populations in the thymus during acute neonatal MRV infection. These studies offer important insight into the early events that occur after neonatal MRV infection that disrupt central tolerance and promote autoimmune disease.

Inferring upstream regulatory genes of FOXP3 in human regulatory T cells from time-series transcriptomic data

The discovery of upstream regulatory genes of a gene of interest still remains challenging. Here we applied a scalable computational method to unbiasedly predict candidate regulatory genes of critical transcription factors by searching the whole genome. We illustrated our approach with a case study on the master regulator FOXP3 of human primary regulatory T cells (Tregs). While target genes of FOXP3 have been identified, its upstream regulatory machinery still remains elusive. Our methodology selected five top-ranked candidates that were tested via proof-of-concept experiments. Following knockdown, three out of five candidates showed significant effects on the mRNA expression of FOXP3 across multiple donors. This provides insights into the regulatory mechanisms modulating FOXP3 transcriptional expression in Tregs. Overall, at the genome level this represents a high level of accuracy in predicting upstream regulatory genes of key genes of interest.

PLAU and GREM1 are prognostic biomarkers for predicting immune response in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is a common malignant tumor. Identification of biomarkers and understanding their potential functions will facilitate the treatment and diagnosis in LUAD patients. The yellow module (cor = 0.31, P = 2e-6) was selected as the core module based on weighted gene co-expression network analysis (WGCNA) by integrating RNA-seq data and tumor stage. Two upregulated genes (PLAU and GREM1) in yellow module were identified to be biomarkers. Kaplan-Meier curve analysis displayed that high expression levels of them had a poor overall survival (OS). And, their high expression levels revealed higher tumor stage and relapse possibility in LUAD patients, and could be a prognostic parameter. Both biomarkers showed similar immune cell expression profiles in low- and high-expression groups. Strongly positive correlation between both biomarkers and biomarkers of tumor-infiltrating lymphocytes were also clarified in TCGA-LUAD cohort. Importantly, single gene GSEA showed that transcriptional mis-regulation in cancer and microRNAs in cancer were enriched in LUAD patients. Therefore, a miRNA-mRNA-transcription factors (TFs) co-expression regulatory networks was constructed for each biomarker, various miRNAs and TFs were related to PLAU and GREM1. Among which, 6 downstream TFs were overlapped genes for both biomarkers. Notably, 2 of these TFs (FOXF1 and TFAP2A) exhibited significantly abnormal expression levels. Among which, FOXF1 was downregulated and TFAP2A was upregulated in TCGA-LUAD cohort. Both TFs showed a significantly positive correlation with the expression level of PLAU. In conclusion, we identified 2 biomarkers related to immune response and achieved a good accuracy in predicting OS in patients with LUAD.

Pan-cancer analysis of PLAU indicates its potential prognostic value and correlation with neutrophil infiltration in BLCA

BACKGROUND

PLAU is known as a selected serine protease converting plasminogen to plasmin. The role of PLAU in the development of pan-cancer, especially bladder urothelial carcinoma (BLCA) remains unclear.

METHOD

A variety of online tools and cancer databases, including TCGA, GETx, HPA database, GSCALite, UALCAN, ESTIMATE, CIBERSORT, ssGSEA algorithms and SangerBox website, were applied to investigate the associations between PLAU expression and prognosis, genetic alterations, pathway activation, and tumor immunity in pan-cancer. Through cBioPortal and STITCH platforms, the oncogenic role of PLAU and related targeting medicines in BLCA were also explored. We verified the expression of PLAU in pan-cancer cells and its function in bladder cancer cell lines using wet-lab experiments.

RESULTS

PLAU expression levels were significantly higher in most cancer tissues. PLAU had a certain accuracy in the diagnosis of various types of cancers (90 % AUC > 0.700). In BLCA, PLAU has abundant methylated sites and showed statistical differences in clinical features. PLAU was involved in tumor immune infiltration, and especially positively correlated with neutrophil infiltration. High-expressed PLAU indicated poorer prognosis in the BLCA patients receiving Atezolizumab. A high mRNA and protein expression levels of PLAU were observed in pan-cancer cell lines, especially BLCA cells. Knockdown of PLAU inhibited the invasive, proliferative, and aggressive phenotypes of bladder cancer cells. Immunohistochemical staining validated PLAU's higher expression in BLCA tissues than in adjacent non-cancerous tissues. And overexpression of PLAU was associated with more advanced TNM stage, and high infiltrating depth.

CONCLUSION

Our study revealed that PLAU can serve as a potential therapeutic target and prognostic marker for various malignancies, especially BLCA.

Identifying local associations in biological time series: algorithms, statistical significance, and applications

Local associations refer to spatial-temporal correlations that emerge from the biological realm, such as time-dependent gene co-expression or seasonal interactions between microbes. One can reveal the intricate dynamics and inherent interactions of biological systems by examining the biological time series data for these associations. To accomplish this goal, local similarity analysis algorithms and statistical methods that facilitate the local alignment of time series and assess the significance of the resulting alignments have been developed. Although these algorithms were initially devised for gene expression analysis from microarrays, they have been adapted and accelerated for multi-omics next generation sequencing datasets, achieving high scientific impact. In this review, we present an overview of the historical developments and recent advances for local similarity analysis algorithms, their statistical properties, and real applications in analyzing biological time series data. The benchmark data and analysis scripts used in this review are freely available at http://github.com/labxscut/lsareview.

Identification of the novel FOXP3-dependent Treg cell transcription factor MEOX1 by high-dimensional analysis of human CD4+ T cells

CD4+ T cells play a central role in the adaptive immune response through their capacity to activate, support and control other immune cells. Although these cells have become the focus of intense research, a comprehensive understanding of the underlying regulatory networks that orchestrate CD4+ T cell function and activation is still incomplete. Here, we analyzed a large transcriptomic dataset consisting of 48 different human CD4+ T cell conditions. By performing reverse network engineering, we identified six common denominators of CD4+ T cell functionality (CREB1, E2F3, AHR, STAT1, NFAT5 and NFATC3). Moreover, we also analyzed condition-specific genes which led us to the identification of the transcription factor MEOX1 in Treg cells. Expression of MEOX1 was comparable to FOXP3 in Treg cells and can be upregulated by IL-2. Epigenetic analyses revealed a permissive epigenetic landscape for MEOX1 solely in Treg cells. Knockdown of MEOX1 in Treg cells revealed a profound impact on downstream gene expression programs and Treg cell suppressive capacity. These findings in the context of CD4+ T cells contribute to a better understanding of the transcriptional networks and biological mechanisms controlling CD4+ T cell functionality, which opens new avenues for future therapeutic strategies.

Metabolite and Proteomic Profiling of Serum Reveals the Differences in Molecular Immunity between Min and Large White Pig Breeds

Pig diseases seriously threaten the health of pigs and the benefits of pig production. Previous research has indicated that Chinese native pigs, such as the Min (M) pig, has a better disease resistance ability than Large White (LW) pigs. However, the molecular mechanism of this resistance is still unclear. In our study, we used serum untargeted metabolomics and proteomics, interrogated to characterize differences in the molecular immunities between six resistant and six susceptible pigs raised in the same environment. A total of 62 metabolites were identified as being significantly exhibited in M and LW pigs. Ensemble feature selection (EFS) machine learning methods were used to predict biomarkers of metabolites and proteins, and the top 30 were selected and retained. Weighted gene co-expression network analysis (WGCNA) confirmed that four key metabolites, PC (18:1 (11 Z)/20:0), PC (14:0/P-18: 0), PC (18:3 (6 Z, 9 Z, 12 Z)/16:0), and PC (16:1 (9 Z)/22:2 (13 Z, 16 Z)), were significantly associated with phenotypes, such as cytokines, and different pig breeds. Correlation network analysis showed that 15 proteins were significantly correlated with the expression of both cytokines and unsaturated fatty acid metabolites. Quantitative trait locus (QTL) co-location analysis results showed that 13 of 15 proteins co-localized with immune or polyunsaturated fatty acid (PUFA)-related QTL. Moreover, seven of them co-localized with both immune and PUFA QTLs, including proteasome 20S subunit beta 8 (PSMB8), mannose binding lectin 1 (MBL1), and interleukin-1 receptor accessory protein (IL1RAP). These proteins may play important roles in regulating the production or metabolism of unsaturated fatty acids and immune factors. Most of the proteins could be validated with parallel reaction monitoring, which suggests that these proteins may play an essential role in producing or regulating unsaturated fatty acids and immune factors to cope with the adaptive immunity of different pig breeds. Our study provides a basis for further clarifying the disease resistance mechanism of pigs.

Proteomics analysis uncovers plasminogen activator PLAU as a target of the STING pathway for suppression of cancer cell migration and invasion

The stimulator of interferon genes (STING) pathway is vital for immune defense against pathogen invasion and cancer. Although ample evidence substantiates that the STING signaling pathway plays an essential role in various cancers via cytokines, no comprehensive investigation of secretory proteins regulated by the STING pathway has been conducted hitherto. Herein, we identify 24 secretory proteins significantly regulated by the STING signaling pathway through quantitative proteomics. Mechanistic analyses reveal that STING activation inhibits the translation of urokinase-type plasminogen activator (PLAU) via the STING-PERK-eIF2α signaling axis. PLAU is highly expressed in a variety of cancers and promotes the migration and invasion of cancer cells. Notably, the activation of STING inhibits cancer cell migration and invasion by suppressing PLAU. Collectively, these results provide novel insights into the anticancer mechanism of the STING pathway, offering a theoretical basis for precision therapy for this patient population.

Identification of prognostic genes in the pancreatic adenocarcinoma immune microenvironment by integrated bioinformatics analysis

PURPOSE

Pancreatic adenocarcinoma (PAAD) is one of the most common causes of death among solid tumors, and its pathogenesis remains to be clarified. This study aims to elucidate the value of immune/stromal-related genes in the prognosis of PAAD through comprehensive bioinformatics analysis based on the immune microenvironment and validated in Chinese pancreatic cancer patients.

METHODS

Gene expression profiles of pancreatic cancer patients were obtained from TCGA database. Differentially expressed genes (DEGs) were identified based on the ESTIMATE algorithm. Gene co-expression networks were constructed using WGCNA. In the key module, survival analysis was used to reveal the prognostic value. Subsequently, we performed functional enrichment analysis to construct a protein-protein interaction (PPI) network. The relationship between tumor immune infiltration and hub genes was analyzed by TIMER and CIBERSORT. Finally, it was validated in the GEO database and in tissues of Chinese pancreatic cancer patients.

RESULTS

In the TCGA pancreatic cancer cohort, a low immune/stromal score was associated with a good prognosis. After bioinformatic analysis, 57 genes were identified to be significantly associated with pancreatic cancer prognosis. Among them, up-regulation of four genes (COL6A3, PLAU, MMP11 and MMP14) indicated poor prognosis and was associated with multiple immune cell infiltration. IHC results showed that PLAU protein levels from Chinese pancreatic cancer tissues were significantly higher than those from adjacent non-tumor tissues and were also associated with tumor TNM stage and lymph node metastasis.

CONCLUSION

In conclusion, this study demonstrates that PLAU may serve as a new diagnostic and therapeutic target, which is highly expressed in Chinese pancreatic cancer tissues and associated with lymph node metastasis.

PARK7/DJ-1 promotes pyruvate dehydrogenase activity and maintains Treg homeostasis during ageing

Pyruvate dehydrogenase (PDH) is the gatekeeper enzyme of the tricarboxylic acid (TCA) cycle. Here we show that the deglycase DJ-1 (encoded by PARK7, a key familial Parkinson's disease gene) is a pacemaker regulating PDH activity in CD4⁺ regulatory T cells (T_reg cells). DJ-1 binds to PDHE1-β (PDHB), inhibiting phosphorylation of PDHE1-α (PDHA), thus promoting PDH activity and oxidative phosphorylation (OXPHOS). Park7 (Dj-1) deletion impairs T_reg survival starting in young mice and reduces T_reg homeostatic proliferation and cellularity only in aged mice. This leads to increased severity in aged mice during the remission of experimental autoimmune encephalomyelitis (EAE). Dj-1 deletion also compromises differentiation of inducible T_reg cells especially in aged mice, and the impairment occurs via regulation of PDHB. These findings provide unforeseen insight into the complicated regulatory machinery of the PDH complex. As T_reg homeostasis is dysregulated in many complex diseases, the DJ-1-PDHB axis represents a potential target to maintain or re-establish T_reg homeostasis.

Efficient Approximation of Statistical Significance in Local Trend Analysis of Dependent Time Series

Biological time series data plays an important role in exploring the dynamic changes of biological systems, while the determinate patterns of association between various biological factors can further deepen the understanding of biological system functions and the interactions between them. At present, local trend analysis (LTA) has been commonly conducted in many biological fields, where the biological time series data can be the sequence at either the level of gene expression or OTU abundance, etc., A local trend score can be obtained by taking the similarity degree of the upward, constant or downward trend of time series data as an indicator of the correlation between different biological factors. However, a major limitation facing local trend analysis is that the permutation test conducted to calculate its statistical significance requires a time-consuming process. Therefore, the problem attracting much attention from bioinformatics scientists is to develop a method of evaluating the statistical significance of local trend scores quickly and effectively. In this paper, a new approach is proposed to evaluate the efficient approximation of statistical significance in the local trend analysis of dependent time series, and the effectiveness of the new method is demonstrated through simulation and real data set analysis.

Prognostic Significance and Gene Co-Expression Network of PLAU and PLAUR in Gliomas

The urokinase-type plasminogen activator(PLAU) and its receptor PLAUR participate in a series of cell physiological activities on the extracellular surface. Abnormal expression of PLAU and PLAUR is associated with tumorigenesis. This study aims to evaluate the prognostic value of PLAU/PLAUR transcription expression in glioma and to explore how they affect the generation and progression of glioma. In this study, online databases are applied, such as Oncomine, GEPIA, CGGA, cBioPortal, and LinkedOmics. Overexpression of PLAU/PLAUR was found to be significantly associated with clinical variables including age, tumor type, WHO grade, histology, IDH-1 mutation, and 1p19q status. PLAU and PLAUR had a high correlation in transcriptional expression levels. High expression of PLAU and PLAUR predicted a poor prognosis in primary glioma and recurrent glioma patients, especially in lower grade gliomas. Cox regression analysis indicated that high expression of PLAU and PLAUR were independent prognostic factors for shorter overall survival in glioma patients. In gene co-expression network analysis PLAU and PLAUR and their co-expression genes were found to be involved in inflammatory activities and tumor-related signaling pathways. In conclusion, PLAU and PLAUR could be promising prognostic biomarkers and potential therapeutic targets of glioma patients.

Characterization of Immunoactive and Immunotolerant CD4+ T Cells in Breast Cancer by Measuring Activity of Signaling Pathways That Determine Immune Cell Function

Simple Summary

Immunotherapy enhances the immune response against cancer and is potentially curative. Unfortunately, few patients with breast cancer benefit from this therapy. It is not possible to predict which patients will benefit. A blood cell, called CD4+ T-cell, plays a role in the immune response and in resistance to immunotherapy. Its function is determined by activity of biochemical processes, called signal transduction pathways (STPs). We developed a new technology to measure activity of these STPs, which was used to investigate whether CD4+ T cells function abnormally in breast cancer patients. We show that in CD4+ T-cells from most of the investigated breast cancer patients a number of these STPs are overactive. The abnormal activity of a few notable STPs (Notch and TGFβ) suggests that CD4+ T-cells have changed into regulatory T-cells, which inhibit the immune response against cancer and have been associated with resistance to immunotherapy. We also provide evidence that this change in the CD4+ T- cells is caused by a factor produced by breast cancer cells. We conclude that this new technology can be used to measure STP activity in blood of patients with cancer and has the potential to better identify patients who will benefit from immunotherapy.

Abstract

Cancer immunotolerance may be reversed by checkpoint inhibitor immunotherapy; however, only a subset of patients responds to immunotherapy. The prediction of clinical response in the individual patient remains a challenge. CD4+ T cells play a role in activating adaptive immune responses against cancer, while the conversion to immunosuppression is mainly caused by CD4+ regulatory T cell (Treg) cells. Signal transduction pathways (STPs) control the main functions of immune cells. A novel previously described assay technology enables the quantitative measurement of activity of multiple STPs in individual cell and tissue samples. The activities of the TGFβ, NFκB, PI3K-FOXO, JAK-STAT1/2, JAK-STAT3, and Notch STPs were measured in CD4+ T cell subsets and used to investigate cellular mechanisms underlying breast cancer-induced immunotolerance. Methods: STP activity scores were measured on Affymetrix expression microarray data of the following: (1) resting and immune-activated CD4+ T cells; (2) CD4+ T-helper 1 (Th1) and T-helper 2 (Th2) cells; (3) CD4+ Treg cells; (4) immune-activated CD4+ T cells incubated with breast cancer tissue supernatants; and (5) CD4+ T cells from blood, lymph nodes, and cancer tissue of 10 primary breast cancer patients. Results: CD4+ T cell activation induced PI3K, NFκB, JAK-STAT1/2, and JAK-STAT3 STP activities. Th1, Th2, and Treg cells each showed a typical pathway activity profile. The incubation of activated CD4+ T cells with cancer supernatants reduced the PI3K, NFκB, and JAK-STAT3 pathway activities and increased the TGFβ pathway activity, characteristic of an immunotolerant state. Immunosuppressive Treg cells were characterized by high NFκB, JAK-STAT3, TGFβ, and Notch pathway activity scores. An immunotolerant pathway activity profile was identified in CD4+ T cells from tumor infiltrate and blood of a subset of primary breast cancer patients, which was most similar to the pathway activity profile in immunosuppressive Treg cells. Conclusion: Signaling pathway assays can be used to quantitatively measure the functional immune response state of lymphocyte subsets in vitro and in vivo. Clinical results suggest that, in primary breast cancer, the adaptive immune response of CD4+ T cells may be frequently replaced by immunosuppressive Treg cells, potentially causing resistance to checkpoint inhibition. In vitro study results suggest that this is mediated by soluble factors from cancer tissue. Signaling pathway activity analysis on TIL and/or blood samples may improve response prediction and monitoring response to checkpoint inhibitors and may provide new therapeutic targets (e.g., the Notch pathway) to reduce resistance to immunotherapy.

Tissue-resident macrophages provide a pro-tumorigenic niche to early NSCLC cells

Macrophages have a key role in shaping the tumour microenvironment (TME), tumour immunity and response to immunotherapy, which makes them an important target for cancer treatment^1,2. However, modulating macrophages has proved extremely difficult, as we still lack a complete understanding of the molecular and functional diversity of the tumour macrophage compartment. Macrophages arise from two distinct lineages. Tissue-resident macrophages self-renew locally, independent of adult haematopoiesis^3-5, whereas short-lived monocyte-derived macrophages arise from adult haematopoietic stem cells, and accumulate mostly in inflamed lesions¹. How these macrophage lineages contribute to the TME and cancer progression remains unclear. To explore the diversity of the macrophage compartment in human non-small cell lung carcinoma (NSCLC) lesions, here we performed single-cell RNA sequencing of tumour-associated leukocytes. We identified distinct populations of macrophages that were enriched in human and mouse lung tumours. Using lineage tracing, we discovered that these macrophage populations differ in origin and have a distinct temporal and spatial distribution in the TME. Tissue-resident macrophages accumulate close to tumour cells early during tumour formation to promote epithelial-mesenchymal transition and invasiveness in tumour cells, and they also induce a potent regulatory T cell response that protects tumour cells from adaptive immunity. Depletion of tissue-resident macrophages reduced the numbers and altered the phenotype of regulatory T cells, promoted the accumulation of CD8⁺ T cells and reduced tumour invasiveness and growth. During tumour growth, tissue-resident macrophages became redistributed at the periphery of the TME, which becomes dominated by monocyte-derived macrophages in both mouse and human NSCLC. This study identifies the contribution of tissue-resident macrophages to early lung cancer and establishes them as a target for the prevention and treatment of early lung cancer lesions.

Identification of a Competing Endogenous RNA Network Related to Immune Signature in Lung Adenocarcinoma

Background

The establishment of immunotherapy has led to a new era in oncotherapy. But the signature of immune-related genes (IRGs) in LUAD remains to be elucidated. Here we use integrated analysis to identify IRGs roles in immune signature and detect their relationship with competing endogenous RNA (ceRNA) networks in LUAD progression.

Methods

By analyzing the RNA-seq data from different platforms, we recognized the differentially expressed genes (DEGs) of each platform and screened out the top 20 hub IRGs related to immune responses. Then, we applied the CIBERSORT algorithm to explore the landscape of tumor-infiltrating immune cells (TILs) in LUAD and their connection with hub genes. Next, we predicted and validated the upstream miRNAs and lncRNAs according to their expression and prognostic roles. Finally, we constructed and validated an immune-related ceRNA network by co-expression analysis.

Results

A total of 71 IRGs were identified among 248 DEGs, which play key roles in immune responses. CIBERSORT analysis showed that six hub genes were closely related to TILs, such as SPP1 and naive B cells (R = −0.17), TEK and resting mast cells (R = 0.37). Stepwise prediction and validation from mRNA to lncRNA, including 6 hub genes, 5 miRNAs, and 9 lncRNAs, were applied to construct a ceRNA network. Ultimately, we confirmed the TMPO-AS1/miR-126-5p/SPP1 and CARD8-AS1/miR-21-5p/TEK as immune-related ceRNA networks in LUAD progression.

Conclusion

We elucidated two immune-related ceRNA networks in LUAD progression, which can be considered as immunotherapy targets for this disease.

Integrated analysis and identification of nine-gene signature associated to oral squamous cell carcinoma pathogenesis

Oral squamous cell carcinoma (OSCC) is one of the leading cancers with poor disease survival rate. Herein, we explored molecular basis, in silico identification and in vitro verification of genes associated with OSCC. Five gene expression series including, GSE30784, GSE13601, GSE9844, GSE23558 and GSE37991 were screened for differentially expressed genes (DEGs). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were enriched by cluster Profiler. Further, protein-protein interaction network was analysed and hub genes were verified. A total of 6476 (up-regulated: 2848; down-regulated: 3628) DEGs were identified among OSCC patients and healthy controls. Gene Ontology analysis indicated DEGs enrichment in cellular motility, invasion and adhesion processes. KEGG analysis revealed enrichment of PI3K-Akt signalling, focal adhesion and regulation of actin cytoskeleton pathways. Subsequently, nine DEGs including APP, EHMT1, ACACB, PCNA, PLAU, FST, HMGA2, LAMC2 and SPP1 were correlated with TCGA expression data along with significant association towards patient's survival, recognized as hub genes. This dysregulated mRNA signature of genes was validated in two OSCC cell lines with an anti-cancer agent, fisetin. Fisetin inhibited the expression of APP, EHMT1, PCNA, PLAU, FST, HMGA2, LAMC2, SPP1 and upregulated the expression of ACACB gene which were associated with growth inhibition of both the OSCC cell lines. The regulatory effect of fisetin supported crucial role of nine hub genes identified in OSCC. This study signified that hub genes and pathways might influence the aggressiveness of OSCC. Thus, the proposed hub genes could be potential diagnostic biomarker and drug targets for OSCC.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s13205-021-02737-4.

Identification of VIMP as a gene inhibiting cytokine production in human CD4+ effector T cells

Many players regulating the CD4+ T cell-mediated inflammatory response have already been identified. However, the critical nodes that constitute the regulatory and signaling networks underlying CD4 T cell responses are still missing. Using a correlation-network-guided approach, here we identified VIMP (VCP-interacting membrane protein), one of the 25 genes encoding selenoproteins in humans, as a gene regulating the effector functions of human CD4 T cells, especially production of several cytokines including IL2 and CSF2. We identified VIMP as an endogenous inhibitor of cytokine production in CD4 effector T cells via both the E2F5 transcription regulatory pathway and the Ca2+/NFATC2 signaling pathway. Our work not only indicates that VIMP might be a promising therapeutic target for various inflammation-associated diseases but also shows that our network-guided approach can significantly aid in predicting new functions of the genes of interest.

Identification of an Immune-Related Signature for Predicting Prognosis in Patients With Pancreatic Ductal Adenocarcinoma

PURPOSE

Pancreatic ductal adenocarcinoma (PDAC) is one of the highest fatality rate cancers with poor survival rates. The tumor microenvironment (TME) is vital for tumor immune responses, leading to resistance to chemotherapy and poor prognosis of PDAC patients. This study aimed to provide a comprehensive evaluation of the immune genes and microenvironment in PDAC that might help in predicting prognosis and guiding clinical treatments.

METHODS

We developed a prognosis-associated immune signature (i.e., PAIS) based on immune-associated genes to predict the overall survival of patients with PDAC. The clinical significance and immune landscapes of the signature were comprehensively analyzed.

RESULTS

Owing to gene expression profiles from TCGA database, functional enrichment analysis revealed a significant difference in the immune response between PDAC and normal pancreas. Using transcriptome data analysis of a training set, we identified an immune signature represented by 5 genes (ESR2, IDO1, IL20RB, PPP3CA, and PLAU) related to the overall survival of patients with PDAC, significantly. This training set was well-validated in a test set. Our results indicated a clear association between a high-risk score and a very poor prognosis. Stratification analysis and multivariate Cox regression analysis revealed that PAIS was an important prognostic factor. We also found that the risk score was positively correlated with the inflammatory response, antigen-presenting process, and expression level of some immunosuppressive checkpoint molecules (e.g., CD73, PD-L1, CD80, and B7-H3). These results suggested that high-risk patients had a suppressed immune response. However, they could respond better to chemotherapy. In addition, PAIS was positively correlated with the infiltration of M2 macrophages in PDAC.

CONCLUSIONS

This study highlighted the relationship between the immune response and prognosis in PDAC and developed a clinically feasible signature that might serve as a powerful prognostic tool and help further optimize the cancer therapy paradigm.

Identification and validation of a prognostic signature and combination drug therapy for immunotherapy of head and neck squamous cell carcinoma

Immunotherapy has become a promising therapeutic option for Head and neck squamous cell carcinoma (HNSC). However, only a small percentage of patients could benefit from it, and the overall prognosis was far from satisfactory. In this study, by comprehensively computational analyses of hundreds of HNSC samples, a prognostic signature composed of 13 immune-related genes (IRGs) was constructed. The results of the analyses in multiple datasets indicated that our signature had high predictive accuracy and could serve as an independent prognostic predictor. Based on this signature and multiple clinical variables, we also established a prognostic nomogram to quantitatively predict the survival risk of individual patients. Moreover, this signature could accurately predict survival, reflect the immune microenvironment, and predict immunotherapy efficacy among HNSC patients. Two potential drugs (doxorubicin and daunorubicin) were also identified via Connectivity Map and molecular docking, which could be used for HNSC combination therapy. Taken together, we developed and validated a robust IRG-based prognostic signature to monitor the prognosis of HNSC, which could provide a solid foundation for individualized cancer immunotherapy.

Identification of prognostic aging-related genes associated with immunosuppression and inflammation in head and neck squamous cell carcinoma

Aging is regarded as a dominant risk factor for cancer. Additionally, inflammation and asthenic immune surveillance with aging may facilitate tumor formation and development. However, few studies have comprehensively analyzed the relationship between aging-related genes (AGs) and the prognosis, inflammation and tumor immunity of head and neck squamous cell carcinoma (HNSCC). Here, we initially screened 41 differentially expressed AGs from The Cancer Genome Atlas (TCGA) database. In the training set, a prognosis risk model with seven AGs (APP, CDKN2A, EGFR, HSPD1, IL2RG, PLAU and VEGFA) was constructed and validated in the TCGA test set and the GEO set (P < 0.05). Using univariate and multivariate Cox regression analyses, we confirmed that risk score was an independent prognostic factor of HNSCC patients. In addition, a high risk score was significantly correlated with immunosuppression, and high expression of PLAU, APP and EGFR was the main factor. Furthermore, we confirmed that a high risk score was significantly associated with levels of proinflammatory factors (IL-1α, IL-1β, IL-6 and IL-8) in HNSCC samples. Thus, this risk model may serve as a prognostic signature and provide clues for individualized immunotherapy for HNSCC patients.

Human Tregs at the materno-fetal interface show site-specific adaptation reminiscent of tumor Tregs

Tregs are crucial for maintaining maternal immunotolerance against the semiallogeneic fetus. We investigated the elusive transcriptional profile and functional adaptation of human uterine Tregs (uTregs) during pregnancy. Uterine biopsies, from placental bed (materno-fetal interface) and incision site (control) and blood were obtained from women with uncomplicated pregnancies undergoing cesarean section. Tregs and CD4⁺ non-Tregs were isolated for transcriptomic profiling by Cel-Seq2. Results were validated on protein and single cell levels by flow cytometry. Placental bed uTregs showed elevated expression of Treg signature markers, including FOXP3, CTLA-4, and TIGIT. Their transcriptional profile was indicative of late-stage effector Treg differentiation and chronic activation, with increased expression of immune checkpoints GITR, TNFR2, OX-40, and 4-1BB; genes associated with suppressive capacity (HAVCR2, IL10, LAYN, and PDCD1); and transcription factors MAF, PRDM1, BATF, and VDR. uTregs mirrored non-Treg Th1 polarization and tissue residency. The particular transcriptional signature of placental bed uTregs overlapped strongly with that of tumor-infiltrating Tregs and was remarkably pronounced at the placental bed compared with uterine control site. In conclusion, human uTregs acquire a differentiated effector Treg profile similar to tumor-infiltrating Tregs, specifically at the materno-fetal interface. This introduces the concept of site-specific transcriptional adaptation of Tregs within 1 organ.

Human regulatory T cells at the maternal-fetal interface show uterine site-specific functional adaptation with late-stage effector differentiation, chronic activation, Th1 polarization, and tumor-infiltrating, Treg-like features.

Transcriptional Repressor HIC1 Contributes to Suppressive Function of Human Induced Regulatory T Cells

Regulatory T (Treg) cells are critical in regulating the immune response. In vitro induced Treg (iTreg) cells have significant potential in clinical medicine. However, applying iTreg cells as therapeutics is complicated by the poor stability of human iTreg cells and their variable suppressive activity. Therefore, it is important to understand the molecular mechanisms of human iTreg cell specification. We identified hypermethylated in cancer 1 (HIC1) as a transcription factor upregulated early during the differentiation of human iTreg cells. Although FOXP3 expression was unaffected, HIC1 deficiency led to a considerable loss of suppression by iTreg cells with a concomitant increase in the expression of effector T cell associated genes. SNPs linked to several immune-mediated disorders were enriched around HIC1 binding sites, and in vitro binding assays indicated that these SNPs may alter the binding of HIC1. Our results suggest that HIC1 is an important contributor to iTreg cell development and function.

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Hypermethylated in cancer 1 (HIC1) is upregulated in iTreg cells

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HIC1-deficient iTreg cells express FOXP3 but have reduced suppressive ability

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Autoimmune-disease-associated SNPs are enriched within HIC1 binding loci

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HIC1 is an important regulator of iTreg development and function

Warburg Effects in Cancer and Normal Proliferating Cells: Two Tales of the Same Name

It has been observed that both cancer tissue cells and normal proliferating cells (NPCs) have the Warburg effect. Our goal here is to demonstrate that they do this for different reasons. To accomplish this, we have analyzed the transcriptomic data of over 7000 cancer and control tissues of 14 cancer types in TCGA and data of five NPC types in GEO. Our analyses reveal that NPCs accumulate large quantities of ATPs produced by the respiration process before starting the Warburg effect, to raise the intracellular pH from ∼6.8 to ∼7.2 and to prepare for cell division energetically. Once cell cycle starts, the cells start to rely on glycolysis for ATP generation followed by ATP hydrolysis and lactic acid release, to maintain the elevated intracellular pH as needed by cell division since together the three processes are pH neutral. The cells go back to the normal respiration-based ATP production once the cell division phase ends. In comparison, cancer cells have reached their intracellular pH at ∼7.4 from top down as multiple acid-loading transporters are up-regulated and most acid-extruding ones except for lactic acid exporters are repressed. Cancer cells use continuous glycolysis for ATP production as way to acidify the intracellular space since the lactic acid secretion is decoupled from glycolysis-based ATP generation and is pH balanced by increased expressions of acid-loading transporters. Co-expression analyses suggest that lactic acid secretion is regulated by external, non-pH related signals. Overall, our data strongly suggest that the two cell types have the Warburg effect for very different reasons.

Identification of key candidate genes and pathways in oral squamous cell carcinoma by integrated Bioinformatics analysis

Oral squamous cell carcinoma (OSCC) is one of the most common types of malignant head and neck tumor, which poses a serious threat to human health. In recent years, the incidence of OSCC has been increasing, while the prognosis has not significantly improved. Elucidation of the molecular mechanisms underlying the development of OSCC may provide novel therapeutic strategies. In the present study, the gene expression profiles from 4 datasets, including 244 OSCC and 95 normal oral mucosa samples, were subjected to statistical and Bioinformatics analysis. A total of 34 differentially expressed genes (DEGs) were identified, among which 14 were upregulated and 20 were downregulated in OSCC compared with normal oral mucosa tissues. Gene Ontology enrichment analysis indicated that the DEGs were mainly involved in regulation of the immune response, cell adhesion and cell proliferative processes. The Kyoto Encyclopedia of Genes and Genomes pathway analysis revealed that the DEGs were mainly associated with the phosphoinositide-3 kinase Akt and Toll-like receptor signaling pathway. The key candidate DEGs were identified from the complex protein-protein interaction network, and secreted phosphoprotein 1 (SPP1), integrin subunit α 3 and plasminogen activator, urokinase (PLAU) were confirmed to be significantly associated with the survival rate. Cell Counting Kit-8 and Transwell assays demonstrated that SPP1 and PLAU regulate cell proliferation, migration and invasion. The candidate genes/pathways identified in the present study may include promising diagnostic biomarkers or therapeutic targets for OSCC.

Characterization of whole blood transcriptome and early-life fecal microbiota in high and low responder pigs before, and after vaccination for Mycoplasma hyopneumoniae

We investigated gene expression patterns in whole blood and fecal microbiota profile as potential predictors of immune response to vaccination, using healthy M. hyopneumoniae infection free piglets (n = 120). Eighty piglets received a dose of prophylactic antibiotics during the first two days of life, whereas the remaining 40 did not. Blood samples for RNA-Seq analysis were collected on experimental Day 0 (D0; 28 days of age) just prior to vaccination, D2, and D6 post-vaccination. A booster vaccine was given at D24. Fecal samples for microbial 16SrRNA sequencing were collected at 7 days of age, and at D0 and D35 post-vaccination. Pigs were ranked based on the levels of M. hyopneumoniae-specific antibodies in serum samples collected at D35, and groups of 'high' (HR) and 'low' (LR) responder pigs (n = 15 each) were selected. Prophylactic antibiotics did not influence antibody titer levels and differential expression analysis did not reveal differences between HR and LR at any time-point (FDR > 0.05); however, based on functional annotation with Ingenuity Pathway Analysis, D2 post-vaccination, HR pigs were enriched for biological terms relating to increased activation of immune cells. In contrast, the immune activation decreased in HR, 6 days post-vaccination. No significant differences were observed prior to vaccination (D0). Two days post-vaccination, multivariate analysis revealed that ADAM8, PROSER3, B4GALNT1, MAP7D1, SPP1, HTRA4, and ENO3 genes were the most promising potential biomarkers. At D0, OTUs annotated to Prevotella, CF21, Bacteroidales and S24-7 were more abundant in HR, whereas Fibrobacter, Paraprevotella, Anaerovibrio, [Prevotella], YRC22, and Helicobacter positively correlated with the antibody titer as well as MYL1, SPP1, and ENO3 genes. Our study integrates gene differential expression and gut microbiota to predict vaccine response in pigs. The results indicate that post-vaccination gene-expression and early-life gut microbiota profile could potentially predict vaccine response in pigs, and inform a direction for future research.

The responses of lungs and adjacent lymph nodes in responding to Yersinia pestis infection: A transcriptomic study using a non-human primate model

Initiation of treatment during the pre-symptomatic phase of Yersinia pestis (Y. pestis) infection is particularly critical. The rapid proliferation of Y. pestis typically couples with the manifestation of common flu-like early symptoms that often misguides the medical intervention. Our study used African green monkeys (AGM) that did not exhibit clear clinical symptoms for nearly two days after intranasal challenge with Y. pestis and succumbed within a day after showing the first signs of clinical symptoms. The lung, and mediastinal and submandibular lymph nodes (LN) accumulated significant Y. pestis colonization immediately after the intranasal challenge. Hence, organ-specific molecular investigations are deemed to be the key to elucidating mechanisms of the initial host response. Our previous study focused on the whole blood of AGM, and we found early perturbations in the ubiquitin-microtubule-mediated host defense. Altered expression of the genes present in ubiquitin and microtubule networks indicated an early suppression of these networks in the submandibular lymph nodes. In concert, the upstream toll-like receptor signaling and downstream NFκB signaling were inhibited at the multi-omics level. The inflammatory response was suppressed in the lungs, submandibular lymph nodes and mediastinal lymph nodes. We posited a causal chain of molecular mechanisms that indicated Y. pestis was probably able to impair host-mediated proteolysis activities and evade autophagosome capture by dysregulating both ubiquitin and microtubule networks in submandibular lymph nodes. Targeting these networks in a submandibular LN-specific and time-resolved fashion could be essential for development of the next generation therapeutics for pneumonic plague.

Inflammatory macrophage-associated 3-gene signature predicts subclinical allograft injury and graft survival

Late allograft failure is characterized by cumulative subclinical insults manifesting over many years. Although immunomodulatory therapies targeting host T cells have improved short-term survival rates, rates of chronic allograft loss remain high. We hypothesized that other immune cell types may drive subclinical injury, ultimately leading to graft failure. We collected whole-genome transcriptome profiles from 15 independent cohorts composed of 1,697 biopsy samples to assess the association of an inflammatory macrophage polarization-specific gene signature with subclinical injury. We applied penalized regression to a subset of the data sets and identified a 3-gene inflammatory macrophage-derived signature. We validated discriminatory power of the 3-gene signature in 3 independent renal transplant data sets with mean AUC of 0.91. In a longitudinal cohort, the 3-gene signature strongly correlated with extent of injury and accurately predicted progression of subclinical injury 18 months before clinical manifestation. The 3-gene signature also stratified patients at high risk of graft failure as soon as 15 days after biopsy. We found that the 3-gene signature also distinguished acute rejection (AR) accurately in 3 heart transplant data sets but not in lung transplant. Overall, we identified a parsimonious signature capable of diagnosing AR, recognizing subclinical injury, and risk-stratifying renal transplant patients. Our results strongly suggest that inflammatory macrophages may be a viable therapeutic target to improve long-term outcomes for organ transplantation patients.

Identification of key genes implicated in the suppressive function of human FOXP3+CD25+CD4+ regulatory T cells through the analysis of time‑series data

Human forkhead box P3 (FOXP3)⁺ cluster of differentiation (CD)25⁺CD4⁺ regulatory T cells (Tregs) are a type of T cell that express CD4, CD25 and FOXP3, which are critical for maintaining immune homeostasis. The present study aimed to determine the mechanisms underlying Treg function. The GSE11292 dataset was downloaded from the Gene Expression Omnibus, which included data from Treg cells at 19 time points (0–360 min) with an equal interval of 20 min, and corresponding repeated samples. However, data for Treg cells at time point 120 min were missing. Using the Mfuzz package, the key genes were identified by clustering analysis. Subsequently, regulatory networks and protein-protein interaction (PPI) networks were constructed and merged into integrated networks using Cytoscape software. Using Database for Annotation, Visualization and Integrated Discover software, enrichment analyses were performed for the genes involved in the PPI networks. Cluster 1 (including 292 genes), cluster 2 (including 111 genes), cluster 3 (including 194 genes) and cluster 4 (including 103 genes) were obtained from the clustering analysis. GAPDH (degree, 40) in cluster 1, Janus kinase 2 (JAK2) (degree, 10) and signal transducer and activator of transcription 5A (STAT5A) (degree, 9) in cluster 3, and tumor necrosis factor (TNF) (degree, 26) and interleukin 2 (IL2) (degree, 22) in cluster 4 had higher degrees in the PPI networks. In addition, it was indicated that several genes may have a role in Treg function by targeting other genes [e.g. microRNA (miR)-146b-3p→TNF, miR-146b-5p→TNF, miR-142-5p→TNF and tripartite motif containing 28 (TRIM28)→GAPDH]. Enrichment analyses indicated that IL2 and TNF were enriched in the immune response and T cell receptor signaling pathway. In conclusion, GAPDH targeted by TRIM28, TNF targeted by miR-146b-3p, miR-146b-5p and miR-142-5p, in addition to JAK2, IL2, and STAT5A may serve important roles in Treg function.

Immune signature profiling identified predictive and prognostic factors for esophageal squamous cell carcinoma

Understanding interactions between tumor and the host immune system holds great promise to uncover biomarkers for targeted therapies and clinical outcomes. However, systematical analysis of immune signatures in esophageal squamous cell carcinoma (ESCC) remains largely unstudied. In this study, immune signatures containing 708 immune related genes were curated from mRNA microarray data with tumor and paired normal tissues from 119 ESCC patients. Differential expression and survival analysis were performed with validations from Human Protein Atlas and an independent cohort of 110 ESCC patients by immunohistochemistry staining. We identified a total of 186 significantly dysregulated genes in ESCC, including downregulated genes SPINK5, IL1RN and upregulated genes SPP1 and PLAU, which were further confirmed in Human Protein Atlas data. Moreover, nine immune related genes (ABL1, ATF2, ATG5, C6, CD38, HMGB1, ICOSLG, IL12RB2 and PLAU) were significantly associated with patients' overall survival, among which, prognostic model was built including three independent factors ABL1, CD38 and ICOSLG. Validation by immunohistochemistry staining suggested that combination with tumor infiltrated CD4+ and CD8+ T lymphocytes would yield higher performance in distinguishing cases as high or low risk of unfavorable prognosis. In summary, we profiled the immune status in ESCC and established predictive and prognostic factors for ESCC, which could reflect immune disorders within tumor microenvironments and independently distinguish patients with a high risk of reduced survival, providing novel predictive and therapeutic targets for ESCC patients in the future.

Gypenosides induce cell death and alter gene expression in human oral cancer HSC-3 cells

Gypenosides (Gyp), the primary components of Gynostemma pentaphyllum Makino, have long been used as a Chinese herbal medicine. In the present study, the effects of Gyp on cell viability, the cell cycle, cell apoptosis, DNA damage and chromatin condensation were investigated in vitro using human oral cancer HSC-3 cells. The results of the present study indicated that Gyp induces cell death, G2/M phase arrest and apoptosis in HSC-3 cells in a dose-dependent manner. It was also demonstrated that Gyp decreased the depolarization of mitochondrial membrane potential in a time-dependent manner. A cDNA microarray assay was performed and the results indicated that a number of genes were upregulated following Gyp treatment. The greatest increase was a 75.42-fold increase in the expression of GTP binding protein in skeletal muscle. Levels of the following proteins were also increased by Gyp: Serpine peptidase inhibitor, clade E, member 1 by 20.25-fold; ras homolog family member B by 18.04-fold, kelch repeat and BTB domain containing 8 by 15.22-fold; interleukin 11 by 14.96-fold; activating transcription factor 3 by 14.49-fold; cytochrome P450, family 1 by 14.44-fold; ADP-ribosylation factor-like 14 by 13.88-fold; transfer RNA selenocysteine 2 by 13.23-fold; and syntaxin 11 by 13.08-fold. However, the following genes were downregulated by GYP: Six-transmembrane epithelial antigen of prostate family member 4, 14.19-fold; γ-aminobutyric acid A receptor by 14.58-fold; transcriptional-regulating factor 1 by 14.69-fold; serpin peptidase inhibitor, clade B, member 13 by 14.71-fold; apolipoprotein L 1 by 14.85-fold; follistatin by 15.22-fold; uncharacterized LOC100506718; fibronectin leucine rich transmembrane protein 2 by 15.61-fold; microRNA 205 by 16.38-fold; neuregulin 1 by 19.69-fold; and G protein-coupled receptor 110 by 22.05-fold. These changes in gene expression illustrate the effects of Gyp at the genetic level and identify potential targets for oral cancer therapy.

Network-Guided Key Gene Discovery for a Given Cellular Process

Identification of key genes for a given physiological or pathological process is an essential but still very challenging task for the entire biomedical research community. Statistics-based approaches, such as genome-wide association study (GWAS)- or quantitative trait locus (QTL)-related analysis have already made enormous contributions to identifying key genes associated with a given disease or phenotype, the success of which is however very much dependent on a huge number of samples. Recent advances in network biology, especially network inference directly from genome-scale data and the following-up network analysis, opens up new avenues to predict key genes driving a given biological process or cellular function. Here we review and compare the current approaches in predicting key genes, which have no chances to stand out by classic differential expression analysis, from gene-regulatory, protein-protein interaction, or gene expression correlation networks. We elaborate these network-based approaches mainly in the context of immunology and infection, and urge more usage of correlation network-based predictions. Such network-based key gene discovery approaches driven by information-enriched 'omics' data should be very useful for systematic key gene discoveries for any given biochemical process or cellular function, and also valuable for novel drug target discovery and novel diagnostic, prognostic and therapeutic-efficiency marker prediction for a specific disease or disorder.

A comprehensive time-course-based multicohort analysis of sepsis and sterile inflammation reveals a robust diagnostic gene set

Although several dozen studies of gene expression in sepsis have been published, distinguishing sepsis from a sterile systemic inflammatory response syndrome (SIRS) is still largely up to clinical suspicion. We hypothesized that a multicohort analysis of the publicly available sepsis gene expression data sets would yield a robust set of genes for distinguishing patients with sepsis from patients with sterile inflammation. A comprehensive search for gene expression data sets in sepsis identified 27 data sets matching our inclusion criteria. Five data sets (n = 663 samples) compared patients with sterile inflammation (SIRS/trauma) to time-matched patients with infections. We applied our multicohort analysis framework that uses both effect sizes and P values in a leave-one-data set-out fashion to these data sets. We identified 11 genes that were differentially expressed (false discovery rate ≤1%, inter-data set heterogeneity P > 0.01, summary effect size >1.5-fold) across all discovery cohorts with excellent diagnostic power [mean area under the receiver operating characteristic curve (AUC), 0.87; range, 0.7 to 0.98]. We then validated these 11 genes in 15 independent cohorts comparing (i) time-matched infected versus noninfected trauma patients (4 cohorts), (ii) ICU/trauma patients with infections over the clinical time course (3 cohorts), and (iii) healthy subjects versus sepsis patients (8 cohorts). In the discovery Glue Grant cohort, SIRS plus the 11-gene set improved prediction of infection (compared to SIRS alone) with a continuous net reclassification index of 0.90. Overall, multicohort analysis of time-matched cohorts yielded 11 genes that robustly distinguish sterile inflammation from infectious inflammation.

Gene Regulatory Network Inference of Immunoresponsive Gene 1 (IRG1) Identifies Interferon Regulatory Factor 1 (IRF1) as Its Transcriptional Regulator in Mammalian Macrophages

Immunoresponsive gene 1 (IRG1) is one of the highest induced genes in macrophages under pro-inflammatory conditions. Its function has been recently described: it codes for immune-responsive gene 1 protein/cis-aconitic acid decarboxylase (IRG1/CAD), an enzyme catalysing the production of itaconic acid from cis-aconitic acid, a tricarboxylic acid (TCA) cycle intermediate. Itaconic acid possesses specific antimicrobial properties inhibiting isocitrate lyase, the first enzyme of the glyoxylate shunt, an anaplerotic pathway that bypasses the TCA cycle and enables bacteria to survive on limited carbon conditions. To elucidate the mechanisms underlying itaconic acid production through IRG1 induction in macrophages, we examined the transcriptional regulation of IRG1. To this end, we studied IRG1 expression in human immune cells under different inflammatory stimuli, such as TNFα and IFNγ, in addition to lipopolysaccharides. Under these conditions, as previously shown in mouse macrophages, IRG1/CAD accumulates in mitochondria. Furthermore, using literature information and transcription factor prediction models, we re-constructed raw gene regulatory networks (GRNs) for IRG1 in mouse and human macrophages. We further implemented a contextualization algorithm that relies on genome-wide gene expression data to infer putative cell type-specific gene regulatory interactions in mouse and human macrophages, which allowed us to predict potential transcriptional regulators of IRG1. Among the computationally identified regulators, siRNA-mediated gene silencing of interferon regulatory factor 1 (IRF1) in macrophages significantly decreased the expression of IRG1/CAD at the gene and protein level, which correlated with a reduced production of itaconic acid. Using a synergistic approach of both computational and experimental methods, we here shed more light on the transcriptional machinery of IRG1 expression and could pave the way to therapeutic approaches targeting itaconic acid levels.

Statistical significance approximation in local trend analysis of high-throughput time-series data using the theory of Markov chains

BACKGROUND

Local trend (i.e. shape) analysis of time series data reveals co-changing patterns in dynamics of biological systems. However, slow permutation procedures to evaluate the statistical significance of local trend scores have limited its applications to high-throughput time series data analysis, e.g., data from the next generation sequencing technology based studies.

RESULTS

By extending the theories for the tail probability of the range of sum of Markovian random variables, we propose formulae for approximating the statistical significance of local trend scores. Using simulations and real data, we show that the approximate p-value is close to that obtained using a large number of permutations (starting at time points >20 with no delay and >30 with delay of at most three time steps) in that the non-zero decimals of the p-values obtained by the approximation and the permutations are mostly the same when the approximate p-value is less than 0.05. In addition, the approximate p-value is slightly larger than that based on permutations making hypothesis testing based on the approximate p-value conservative. The approximation enables efficient calculation of p-values for pairwise local trend analysis, making large scale all-versus-all comparisons possible. We also propose a hybrid approach by integrating the approximation and permutations to obtain accurate p-values for significantly associated pairs. We further demonstrate its use with the analysis of the Polymouth Marine Laboratory (PML) microbial community time series from high-throughput sequencing data and found interesting organism co-occurrence dynamic patterns.

AVAILABILITY

The software tool is integrated into the eLSA software package that now provides accelerated local trend and similarity analysis pipelines for time series data. The package is freely available from the eLSA website: http://bitbucket.org/charade/elsa.

The Human Blood Metabolome-Transcriptome Interface

Biological systems consist of multiple organizational levels all densely interacting with each other to ensure function and flexibility of the system. Simultaneous analysis of cross-sectional multi-omics data from large population studies is a powerful tool to comprehensively characterize the underlying molecular mechanisms on a physiological scale. In this study, we systematically analyzed the relationship between fasting serum metabolomics and whole blood transcriptomics data from 712 individuals of the German KORA F4 cohort. Correlation-based analysis identified 1,109 significant associations between 522 transcripts and 114 metabolites summarized in an integrated network, the 'human blood metabolome-transcriptome interface' (BMTI). Bidirectional causality analysis using Mendelian randomization did not yield any statistically significant causal associations between transcripts and metabolites. A knowledge-based interpretation and integration with a genome-scale human metabolic reconstruction revealed systematic signatures of signaling, transport and metabolic processes, i.e. metabolic reactions mainly belonging to lipid, energy and amino acid metabolism. Moreover, the construction of a network based on functional categories illustrated the cross-talk between the biological layers at a pathway level. Using a transcription factor binding site enrichment analysis, this pathway cross-talk was further confirmed at a regulatory level. Finally, we demonstrated how the constructed networks can be used to gain novel insights into molecular mechanisms associated to intermediate clinical traits. Overall, our results demonstrate the utility of a multi-omics integrative approach to understand the molecular mechanisms underlying both normal physiology and disease.

The suppressive function of human CD8(+) iTregs is inhibited by IL-1β and TNFα

CD8(+) Tregs display an immunoregulatory activity and may play an essential role in the immunopathology of several diseases. Therefore, their therapeutic potential is exquisite and further studies on their differentiation and function are essential. The aim of this study was to evaluate the role of the innate immune system in CD8(+) iTreg differentiation and function. Naive human CD8(+) CD25(-) CD45RA(+) T cells were cultured in Treg-inducing conditions with or without IL-1β, TNFα or monocyte-derived dendritic cells (DCs). The differentiation of CD8(+) CD127(-) CD25(hi) FoxP3(hi) -induced Tregs (CD8(+) iTregs) is dependent on TGF-β1 and IL-2, which had synergistic effect upon their differentiation. CD8(+) iTregs were also induced in a coculture with allogeneic mature DCs (mDCs). The CD8(+) iTregs suppressive function was confirmed, which was diminished in the presence of IL-1β and TNFα. The IL-1β-prevented suppressive function was associated with reduced secretion of IL-10 and IFNγ, whereas the presence of TNFα did not affect their secretion. Furthermore, the presence of TNFα reduced IL-10 and TGF-β1 secretion by CD8(+) iTregs, whereas only IL-10 secretion was decreased by IL-1β. Together, these results suggest that IL-1β and TNFα prevent IL-2- and TGF-β1-driven CD8(+) iTregs suppressive function in human T cells. Such pro-inflammatory innate immune response possibly mediates its negative tolerogenic effect through reduced IFNγ-, IL-10- and TGF-β1-driven mechanism.

Essential O2-responsive genes of Pseudomonas aeruginosa and their network revealed by integrating dynamic data from inverted conditions

Identification of the gene network through which Pseudomonas aeruginosa PAO1 (PA) adapts to altered oxygen-availability environments is essential for a better understanding of stress responses and pathogenicity of PA. We performed high-time-resolution (HTR) transcriptome analyses of PA in a continuous cultivation system during the transition from high oxygen tension to low oxygen tension (HLOT) and the reversed transition from low to high oxygen tension (LHOT). From those genes responsive to both transient conditions, we identified 85 essential oxygen-availability responsive genes (EORGs), including the expected ones (arcDABC) encoding enzymes for arginine fermentation. We then constructed the regulatory network for the EORGs of PA by integrating information from binding motif searching, literature and HTR data. Notably, our results show that only the sub-networks controlled by the well-known oxygen-responsive transcription factors show a very high consistency between the inferred network and literature knowledge, e.g. 87.5% and 83.3% of the obtained sub-network controlled by the anaerobic regulator (ANR) and a quorum sensing regulator RhIR, respectively. These results not only reveal stringent EORGs of PA and their transcription regulatory network, but also highlight that achieving a high accuracy of the inferred regulatory network might be feasible only for the apparently affected regulators under the given conditions but not for all the expressed regulators on a genome scale.

Identification of molecular sub-networks associated with cell survival in a chronically SIVmac-infected human CD4+ T cell line

Background

The deciphering of cellular networks to determine susceptibility to infection by HIV or the related simian immunodeficiency virus (SIV) is a major challenge in infection biology.

Results

Here, we have compared gene expression profiles of a human CD4⁺ T cell line at 24 h after infection with a cell line of the same origin permanently releasing SIV_mac. A new knowledge-based-network approach (Inter-Chain-Finder, ICF) has been used to identify sub-networks associated with cell survival of a chronically SIV-infected T cell line. Notably, the method can identify not only differentially expressed key hub genes but also non-differentially expressed, critical, ‘hidden’ regulators. Six out of the 13 predicted major hidden key regulators were among the landscape of proteins known to interact with HIV. Several sub-networks were dysregulated upon chronic infection with SIV. Most prominently, factors reported to be engaged in early stages of acute viral infection were affected, e.g. entry, integration and provirus transcription and other cellular responses such as apoptosis and proliferation were modulated. For experimental validation of the gene expression analyses and computational predictions, individual pathways/sub-networks and significantly altered key regulators were investigated further. We showed that the expression of caveolin-1 (Cav-1), the top hub in the affected protein-protein interaction network, was significantly upregulated in chronically SIV-infected CD4⁺ T cells. Cav-1 is the main determinant of caveolae and a central component of several signal transduction pathways. Furthermore, CD4 downregulation and modulation of the expression of alternate and co-receptors as well as pathways associated with viral integration into the genome were also observed in these cells. Putatively, these modifications interfere with re-infection and the early replication cycle and inhibit cell death provoked by syncytia formation and bystander apoptosis.

Conclusions

Thus, by using the novel approach for network analysis, ICF, we predict that in the T cell line chronically infected with SIV, cellular processes that are known to be crucial for early phases of HIV/SIV replication are altered and cellular responses that result in cell death are modulated. These modifications presumably contribute to cell survival despite chronic infection.

Electronic supplementary material

The online version of this article (doi:10.1186/1743-422X-11-152) contains supplementary material, which is available to authorized users.

Democratizing systems immunology with modular transcriptional repertoire analyses

Individual elements that constitute the immune system have been characterized over the few past decades, mostly through reductionist approaches. The introduction of large-scale profiling platforms has more recently facilitated the assessment of these elements on a global scale. However, the analysis and the interpretation of such large-scale datasets remains a challenge and a barrier for the wider adoption of systems approaches in immunological and clinical studies. In this Innovation article, we describe an analytical strategy that relies on the a priori determination of co-dependent gene sets for a given biological system. Such modular transcriptional repertoires can in turn be used to simplify the analysis and the interpretation of large-scale datasets, and to design targeted immune fingerprinting assays and web applications that will further facilitate the dissemination of systems approaches in immunology.

Construction and verification of the transcriptional regulatory response network of Streptococcus mutans upon treatment with the biofilm inhibitor carolacton

Background

Carolacton is a newly identified secondary metabolite causing altered cell morphology and death of Streptococcus mutans biofilm cells. To unravel key regulators mediating these effects, the transcriptional regulatory response network of S. mutans biofilms upon carolacton treatment was constructed and analyzed. A systems biological approach integrating time-resolved transcriptomic data, reverse engineering, transcription factor binding sites, and experimental validation was carried out.

Results

The co-expression response network constructed from transcriptomic data using the reverse engineering algorithm called the Trend Correlation method consisted of 8284 gene pairs. The regulatory response network inferred by superimposing transcription factor binding site information into the co-expression network comprised 329 putative transcriptional regulatory interactions and could be classified into 27 sub-networks each co-regulated by a transcription factor. These sub-networks were significantly enriched with genes sharing common functions. The regulatory response network displayed global hierarchy and network motifs as observed in model organisms. The sub-networks modulated by the pyrimidine biosynthesis regulator PyrR, the glutamine synthetase repressor GlnR, the cysteine metabolism regulator CysR, global regulators CcpA and CodY and the two component system response regulators VicR and MbrC among others could putatively be related to the physiological effect of carolacton. The predicted interactions from the regulatory network between MbrC, known to be involved in cell envelope stress response, and the murMN-SMU_718c genes encoding peptidoglycan biosynthetic enzymes were experimentally confirmed using Electro Mobility Shift Assays. Furthermore, gene deletion mutants of five predicted key regulators from the response networks were constructed and their sensitivities towards carolacton were investigated. Deletion of cysR, the node having the highest connectivity among the regulators chosen from the regulatory network, resulted in a mutant which was insensitive to carolacton thus demonstrating not only the essentiality of cysR for the response of S. mutans biofilms to carolacton but also the relevance of the predicted network.

Conclusion

The network approach used in this study revealed important regulators and interactions as part of the response mechanisms of S. mutans biofilm cells to carolacton. It also opens a door for further studies into novel drug targets against streptococci.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-362) contains supplementary material, which is available to authorized users.

Urokinase-type plasminogen activator deficiency promotes neoplasmatogenesis in the colon of mice

Urokinase-type plasminogen activator (uPA) participates in cancer-related biologic processes, such as wound healing and inflammation. The present study aimed to investigate the effect of uPA deficiency on the long-term outcome of early life episodes of dextran sodium sulfate (DSS)-induced colitis in mice. Wild-type (WT) and uPA-deficient (uPA(-/-)) BALB/c mice were treated with DSS or remained untreated. Mice were necropsied either 1 week or 7 months after DSS treatment. Colon samples were analyzed by histopathology, immunohistochemistry, ELISA, and real-time polymerase chain reaction. At 7 months, with no colitis evident, half of the uPA(-/-) mice had large colonic polypoid adenomas, whereas WT mice did not. One week after DSS treatment, there were typical DSS-induced colitis lesions in both WT and uPA(-/-) mice. The affected colon of uPA(-/-) mice, however, had features of delayed ulcer re-epithelialization and dysplastic lesions of higher grade developing on the basis of a significantly altered mucosal inflammatory milieu. The later was characterized by more neutrophils and macrophages, less regulatory T cells (Treg), significantly upregulated cytokines, including interleukin-6 (IL-6), IL-17, tumor necrosis factor-α, and IL-10, and lower levels of active transforming growth factor-β1 (TGF-β1) compared to WT mice. Dysfunctional Treg, more robust protumorigenic inflammatory events, and an inherited inability to produce adequate amounts of extracellular active TGF-β1 due to uPA deficiency are interlinked as probable explanations for the inflammatory-induced neoplasmatogenesis in the colon of uPA(-/-) mice.

RMaNI: Regulatory Module Network Inference framework

Background

Cell survival and development are orchestrated by complex interlocking programs of gene activation and repression. Understanding how this gene regulatory network (GRN) functions in normal states, and is altered in cancers subtypes, offers fundamental insight into oncogenesis and disease progression, and holds great promise for guiding clinical decisions. Inferring a GRN from empirical microarray gene expression data is a challenging task in cancer systems biology. In recent years, module-based approaches for GRN inference have been proposed to address this challenge. Despite the demonstrated success of module-based approaches in uncovering biologically meaningful regulatory interactions, their application remains limited a single condition, without supporting the comparison of multiple disease subtypes/conditions. Also, their use remains unnecessarily restricted to computational biologists, as accurate inference of modules and their regulators requires integration of diverse tools and heterogeneous data sources, which in turn requires scripting skills, data infrastructure and powerful computational facilities. New analytical frameworks are required to make module-based GRN inference approach more generally useful to the research community.

Results

We present the RMaNI (Regulatory Module Network Inference) framework, which supports cancer subtype-specific or condition specific GRN inference and differential network analysis. It combines both transcriptomic as well as genomic data sources, and integrates heterogeneous knowledge resources and a set of complementary bioinformatic methods for automated inference of modules, their condition specific regulators and facilitates downstream network analyses and data visualization. To demonstrate its utility, we applied RMaNI to a hepatocellular microarray data containing normal and three disease conditions. We demonstrate that how RMaNI can be employed to understand the genetic architecture underlying three disease conditions. RMaNI is freely available at http://inspect.braembl.org.au/bi/inspect/rmani

Conclusion

RMaNI makes available a workflow with comprehensive set of tools that would otherwise be challenging for non-expert users to install and apply. The framework presented in this paper is flexible and can be easily extended to analyse any dataset with multiple disease conditions.

Supervised, semi-supervised and unsupervised inference of gene regulatory networks

Inference of gene regulatory network from expression data is a challenging task. Many methods have been developed to this purpose but a comprehensive evaluation that covers unsupervised, semi-supervised and supervised methods, and provides guidelines for their practical application, is lacking.

We performed an extensive evaluation of inference methods on simulated and experimental expression data. The results reveal low prediction accuracies for unsupervised techniques with the notable exception of the Z-SCORE method on knockout data. In all other cases, the supervised approach achieved the highest accuracies and even in a semi-supervised setting with small numbers of only positive samples, outperformed the unsupervised techniques.