Coexpression analysis of large cancer datasets provides insight into the cellular phenotypes of the tumour microenvironment

Detection of key mRNAs in liver tissue of hepatocellular carcinoma patients based on machine learning and bioinformatics analysis

One methodology extensively used to develop biomarkers is the precise detection of highly responsive genes that can distinguish cancer samples from healthy samples. The purpose of this study was to screen for potential hepatocellular carcinoma (HCC) biomarkers based on non-fusion integrative multi-platform meta-analysis method. The gene expression profiles of liver tissue samples from two microarray platforms were initially analyzed using a meta-analysis based on an empirical Bayesian method to robust discover differentially expressed genes in HCC and non-tumor tissues. Then, using the bioinformatics technique of weighted correlation network analysis, the highly associated prioritized Differentially Expressed Genes (DEGs) were clustered. Co-expression network and topological analysis were utilized to identify sub-clusters and confirm candidate genes. Next, a diagnostic model was developed and validated using a machine learning algorithm. To construct a prognostic model, the Cox proportional hazard regression analysis was applied and validated. We identified three genes as specific biomarkers for the diagnosis of HCC based on accuracy and feasibility. The diagnostic model's area under the curve was 0.931 with confidence interval of 0.923-0.952.•Non-fusion integrative multi-platform meta-analysis method.•Classification methods and biomarkers recognition via machine learning method.•Biomarker validation models.

Association analysis of melanophilin (MLPH) gene expression and polymorphism with plumage color in quail

We explore the relationship between the melanophilin (MLPH) gene and quail plumage color and provide a reference for subsequent quail plumage color breeding. In this experiment, real-time quantitative PCR (RT-qPCR) technology was used to analyze the relative mRNA expression levels of Korean quail (maroon) and Beijing white quail embryos at different developmental stages. Two single-nucleotide polymorphisms (SNPs) in the MLPH gene were screened based on the RNA-sequencing (RNA-Seq) data of skin tissues of Korean quail and Beijing white quail during the embryonic stage. Kompetitive allele-specific PCR (KASP) technology was used for genotyping in the resource population, and correlation analysis was carried out with the plumage color traits of quail. Finally, bioinformatics was used to predict the effects of these two SNPs on the structure and function of the encoded protein. The results showed that the expression level of the MLPH gene during embryonic development of Beijing white quail was significantly higher than that of Korean quail ( P < 0.01 ). The frequency distribution of the three genotypes (CC, CA and AA) of the Beijing white quail at the c.1807C  >  A mutation site was significantly different from that of the Korean quail ( P < 0.01 ). The frequency distribution of the three genotypes (GG, GA and AA) of the Beijing white quail at the c.2129G  >  A mutation site was significantly different from that of the Korean quail ( P < 0.01 ). And there was a significant correlation between the c.1807C  >  A mutation site and the white plumage phenotype. Bioinformatics showed that SNP1 (c.1807C  >  A) was a neutral mutation and that SNP2 (c.2129G  >  A) was a deleterious mutation. The prediction of protein conservation showed that the mutation sites of coding proteins R603S and G710D caused by SNP1 (c.1807C  >  A) and SNP2 (c.2129G  >  A) were highly conserved.

Generation and network analysis of an RNA-seq transcriptional atlas for the rat

The laboratory rat is an important model for biomedical research. To generate a comprehensive rat transcriptomic atlas, we curated and downloaded 7700 rat RNA-seq datasets from public repositories, downsampled them to a common depth and quantified expression. Data from 585 rat tissues and cells, averaged from each BioProject, can be visualized and queried at http://biogps.org/ratatlas. Gene co-expression network (GCN) analysis revealed clusters of transcripts that were tissue or cell type restricted and contained transcription factors implicated in lineage determination. Other clusters were enriched for transcripts associated with biological processes. Many of these clusters overlap with previous data from analysis of other species, while some (e.g. expressed specifically in immune cells, retina/pineal gland, pituitary and germ cells) are unique to these data. GCN analysis on large subsets of the data related specifically to liver, nervous system, kidney, musculoskeletal system and cardiovascular system enabled deconvolution of cell type-specific signatures. The approach is extensible and the dataset can be used as a point of reference from which to analyse the transcriptomes of cell types and tissues that have not yet been sampled. Sets of strictly co-expressed transcripts provide a resource for critical interpretation of single-cell RNA-seq data.

Small-molecule CSF1R kinase inhibitors; review of patents 2015-present

INTRODUCTION

Colony stimulating factor 1 receptor (CSF-1R, also known as c-FMS kinase) is in the class III receptor tyrosine kinase family, along with c-Kit, Flt3 and PDGFRα. CSF-1/CSF-1R signaling promotes the differentiation and survival of myeloid progenitors into populations of monocytes, macrophages, dendritic cells and osteoclasts, as well as microglial cells and also recruits host macrophages to develop into tumor-associated macrophages (TAMs), which promote tumor progression and metastasis.

AREAS COVERED

In the last 5 years, and recently stimulated by the approval of pexidartinib (Turalio™, Daiichi Sankyo) in 2019 for the treatment of tenosynovial giant cell tumors, there has been a large increase in activity (both journal articles and patent applications) around small molecule inhibitors of CSF1R. Features of this work have been the surprising diversity of chemical classes shown to be potent and selective inhibitors, and the breadth of disease states (cancer, arthritis, and 'cytokine storm' syndromes) covered by CSF1R inhibitors. All these aspects are covered in the following sections.

EXPERT OPINION

The field has developed rapidly from 2014 to the present, with many different chemotypes proving to be potent inhibitors. The range of potential utilities of CSF1R inhibitors has also expanded to include dementia, ulcerative colitis/Crohn's disease, rheumatoid arthritis inflammation, and fibrosis.

Network analysis of transcriptomic diversity amongst resident tissue macrophages and dendritic cells in the mouse mononuclear phagocyte system

The mononuclear phagocyte system (MPS) is a family of cells including progenitors, circulating blood monocytes, resident tissue macrophages, and dendritic cells (DCs) present in every tissue in the body. To test the relationships between markers and transcriptomic diversity in the MPS, we collected from National Center for Biotechnology Information Gene Expression Omnibus (NCBI-GEO) a total of 466 quality RNA sequencing (RNA-seq) data sets generated from mouse MPS cells isolated from bone marrow, blood, and multiple tissues. The primary data were randomly downsized to a depth of 10 million reads and requantified. The resulting data set was clustered using the network analysis tool BioLayout. A sample-to-sample matrix revealed that MPS populations could be separated based upon tissue of origin. Cells identified as classical DC subsets, cDC1s and cDC2s, and lacking Fcgr1 (encoding the protein CD64) were contained within the MPS cluster, no more distinct than other MPS cells. A gene-to-gene correlation matrix identified large generic coexpression clusters associated with MPS maturation and innate immune function. Smaller coexpression gene clusters, including the transcription factors that drive them, showed higher expression within defined isolated cells, including monocytes, macrophages, and DCs isolated from specific tissues. They include a cluster containing Lyve1 that implies a function in endothelial cell (EC) homeostasis, a cluster of transcripts enriched in intestinal macrophages, and a generic lymphoid tissue cDC cluster associated with Ccr7. However, transcripts encoding Adgre1, Itgax, Itgam, Clec9a, Cd163, Mertk, Mrc1, Retnla, and H2-a/e (encoding class II major histocompatibility complex [MHC] proteins) and many other proposed macrophage subset and DC lineage markers each had idiosyncratic expression profiles. Coexpression of immediate early genes (for example, Egr1, Fos, Dusp1) and inflammatory cytokines and chemokines (tumour necrosis factor [Tnf], Il1b, Ccl3/4) indicated that all tissue disaggregation and separation protocols activate MPS cells. Tissue-specific expression clusters indicated that all cell isolation procedures also co-purify other unrelated cell types that may interact with MPS cells in vivo. Comparative analysis of RNA-seq and single-cell RNA-seq (scRNA-seq) data from the same lung cell populations indicated that MPS heterogeneity implied by global cluster analysis may be even greater at a single-cell level. This analysis highlights the power of large data sets to identify the diversity of MPS cellular phenotypes and the limited predictive value of surface markers to define lineages, functions, or subpopulations.

The Adaptive and Innate Immune Cell Landscape of Uterine Leiomyosarcomas

Reactivation of the anti-tumor response has shown substantial progress in aggressive tumors such as melanoma and lung cancer. Data on less common histotypes are scanty. Immune checkpoint inhibitor therapy has been applied to few cases of uterine leiomyosarcomas, of which the immune cell composition was not examined in detail. We analyzed the inflammatory infiltrate of 21 such cases in high-dimensional, single cell phenotyping on routinely processed tissue. T-lymphoid cells displayed a composite phenotype common to all tumors, suggestive of antigen-exposure, acute and chronic exhaustion. To the contrary, myelomonocytic cells had case-specific individual combinations of phenotypes and subsets. We identified five distinct monocyte-macrophage cell types, some not described before, bearing immunosuppressive molecules (TIM3, B7H3, VISTA, PD1, PDL1). Detailed in situ analysis of routinely processed tissue yields comprehensive information about the immune status of sarcomas. The method employed provides equivalent information to extractive single-cell technology, with spatial contexture and a modest investment.

Assembly of a parts list of the human mitotic cell cycle machinery

The set of proteins required for mitotic division remains poorly characterized. Here, an extensive series of correlation analyses of human and mouse transcriptomics data were performed to identify genes strongly and reproducibly associated with cells undergoing S/G2-M phases of the cell cycle. In so doing, 701 cell cycle-associated genes were defined and while it was shown that many are only expressed during these phases, the expression of others is also driven by alternative promoters. Of this list, 496 genes have known cell cycle functions, whereas 205 were assigned as putative cell cycle genes, 53 of which are functionally uncharacterized. Among these, 27 were screened for subcellular localization revealing many to be nuclear localized and at least three to be novel centrosomal proteins. Furthermore, 10 others inhibited cell proliferation upon siRNA knockdown. This study presents the first comprehensive list of human cell cycle proteins, identifying many new candidate proteins.

Phenotypic impacts of CSF1R deficiencies in humans and model organisms

Mϕ proliferation, differentiation, and survival are controlled by signals from the Mϕ CSF receptor (CSF1R). Mono-allelic gain-of-function mutations in CSF1R in humans are associated with an autosomal-dominant leukodystrophy and bi-allelic loss-of-function mutations with recessive skeletal dysplasia, brain disorders, and developmental anomalies. Most of the phenotypes observed in these human disease states are also observed in mice and rats with loss-of-function mutations in Csf1r or in Csf1 encoding one of its two ligands. Studies in rodent models also highlight the importance of genetic background and likely epistatic interactions between Csf1r and other loci. The impacts of Csf1r mutations on the brain are usually attributed solely to direct impacts on microglial number and function. However, analysis of hypomorphic Csf1r mutants in mice and several other lines of evidence suggest that primary hydrocephalus and loss of the physiological functions of Mϕs in the periphery contribute to the development of brain pathology. In this review, we outline the evidence that CSF1R is expressed exclusively in mononuclear phagocytes and explore the mechanisms linking CSF1R mutations to pleiotropic impacts on postnatal growth and development.

Optimal pathways involved in the treatment of sevoflurane or propofol for patients undergoing coronary artery bypass graft surgery

The cardio-protection mechanisms of sevoflurane and propofol still remain unclear in patients undergoing coronary artery bypass grafting (CABG). We designed the present study to identify the optimal pathways through integrating differential co-expressed network (DCN)-based guilt by association (GBA) principle based on the expression data of E-GEOD-4386 downloaded from EMBL-EBI. Differentially expressed genes (DEGs) were firstly identified and then DCN and sub-DCN were established. The seed pathways were predicted through GBA principle using the area under the curve (AUC) for pathway categories, and the pathway terms with AUC >0.9 were defined as the seed pathways. KEGG pathway analysis was applied to the DEGs based on DAVIA to detect significant pathways. The final optimal pathways were identified based on the traditional pathway analysis and network-based pathway inference approach. There were 83 common, 99 sevoflurane-specific and 4 propofol-specific DEGs in the expression profile of artial samples. Finally, 8 and 4 pathway terms having the AUC >0.9 were identified and determined as the seed pathways in the propofol and sevoflurane group, respectively. TNF signaling pathway, NF-κB signaling pathway, as well as NOD-like receptor signaling pathway were the common optimal ones in these two groups. Only the pathway of cytokine-cytokine receptor interaction was unique to sevoflurane, and no pathway was specific to propofol. Our results suggested that sevoflurane and propofol might synergistically possess some cardio-protective properties in patients undergoing CABG.

Immune Cell Gene Signatures for Profiling the Microenvironment of Solid Tumors

The immune composition of the tumor microenvironment regulates processes including angiogenesis, metastasis, and the response to drugs or immunotherapy. To facilitate the characterization of the immune component of tumors from transcriptomics data, a number of immune cell transcriptome signatures have been reported that are made up of lists of marker genes indicative of the presence a given immune cell population. The majority of these gene signatures have been defined through analysis of isolated blood cells. However, blood cells do not reflect the differentiation or activation state of similar cells within tissues, including tumors, and consequently markers derived from blood cells do not necessarily transfer well to tissues. To address this issue, we generated a set of immune gene signatures derived directly from tissue transcriptomics data using a network-based deconvolution approach. We define markers for seven immune cell types, collectively named ImSig, and demonstrate how these markers can be used for the quantitative estimation of the immune cell content of tumor and nontumor tissue samples. The utility of ImSig is demonstrated through the stratification of melanoma patients into subgroups of prognostic significance and the identification of immune cells with the use of single-cell RNA-sequencing data derived from tumors. Use of ImSig is facilitated by an R package (imsig). Cancer Immunol Res; 6(11); 1388-400. ©2018 AACR.

Combination of novel and public RNA-seq datasets to generate an mRNA expression atlas for the domestic chicken

Background

The domestic chicken (Gallus gallus) is widely used as a model in developmental biology and is also an important livestock species. We describe a novel approach to data integration to generate an mRNA expression atlas for the chicken spanning major tissue types and developmental stages, using a diverse range of publicly-archived RNA-seq datasets and new data derived from immune cells and tissues.

Results

Randomly down-sampling RNA-seq datasets to a common depth and quantifying expression against a reference transcriptome using the mRNA quantitation tool Kallisto ensured that disparate datasets explored comparable transcriptomic space. The network analysis tool Graphia was used to extract clusters of co-expressed genes from the resulting expression atlas, many of which were tissue or cell-type restricted, contained transcription factors that have previously been implicated in their regulation, or were otherwise associated with biological processes, such as the cell cycle. The atlas provides a resource for the functional annotation of genes that currently have only a locus ID. We cross-referenced the RNA-seq atlas to a publicly available embryonic Cap Analysis of Gene Expression (CAGE) dataset to infer the developmental time course of organ systems, and to identify a signature of the expansion of tissue macrophage populations during development.

Conclusion

Expression profiles obtained from public RNA-seq datasets – despite being generated by different laboratories using different methodologies – can be made comparable to each other. This meta-analytic approach to RNA-seq can be extended with new datasets from novel tissues, and is applicable to any species.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-4972-7) contains supplementary material, which is available to authorized users.

Classification of gene signatures for their information value and functional redundancy

Gene signatures are more and more used to interpret results of omics data analyses but suffer from compositional (large overlap) and functional (correlated read-outs) redundancy. Moreover, many gene signatures rarely come out as significant in statistical tests. Based on pan-cancer data analysis, we construct a restricted set of 962 signatures defined as informative and demonstrate that they have a higher probability to appear enriched in comparative cancer studies. We show that the majority of informative signatures conserve their weights for the genes composing the signature (eigengenes) from one cancer type to another. We finally construct InfoSigMap, an interactive online map of these signatures and their cross-correlations. This map highlights the structure of compositional and functional redundancies between informative signatures, and it charts the territories of biological functions. InfoSigMap can be used to visualize the results of omics data analyses and suggests a rearrangement of existing gene sets.

An informative collection of gene signatures for transcriptomic data analysis is constructed. The number of transcriptomic signatures grows fast and their collections are highly redundant that hampers omics data analyses interpretation. A computational biology team from Institut Curie led by Andrei Zinovyev selected a collection of 962 gene signatures shown to be informative for cancer studies and reflecting mechanisms of cancer progression. The signatures were filtered from a large compendium without requiring any manual curation by experts through a large-scale unbiased analysis of pancancer data. They have much higher chance to obtain significant enrichment scores in a comparative trancriptomic study. The authors integrated the 962 signatures into InfoSigMap, a new data visualization resource for the interpretation of the results of omics data analyses, which facilitates getting an insight into the mechanisms driving cancer.

A high resolution atlas of gene expression in the domestic sheep (Ovis aries)

Sheep are a key source of meat, milk and fibre for the global livestock sector, and an important biomedical model. Global analysis of gene expression across multiple tissues has aided genome annotation and supported functional annotation of mammalian genes. We present a large-scale RNA-Seq dataset representing all the major organ systems from adult sheep and from several juvenile, neonatal and prenatal developmental time points. The Ovis aries reference genome (Oar v3.1) includes 27,504 genes (20,921 protein coding), of which 25,350 (19,921 protein coding) had detectable expression in at least one tissue in the sheep gene expression atlas dataset. Network-based cluster analysis of this dataset grouped genes according to their expression pattern. The principle of 'guilt by association' was used to infer the function of uncharacterised genes from their co-expression with genes of known function. We describe the overall transcriptional signatures present in the sheep gene expression atlas and assign those signatures, where possible, to specific cell populations or pathways. The findings are related to innate immunity by focusing on clusters with an immune signature, and to the advantages of cross-breeding by examining the patterns of genes exhibiting the greatest expression differences between purebred and crossbred animals. This high-resolution gene expression atlas for sheep is, to our knowledge, the largest transcriptomic dataset from any livestock species to date. It provides a resource to improve the annotation of the current reference genome for sheep, presenting a model transcriptome for ruminants and insight into gene, cell and tissue function at multiple developmental stages.

Transcriptional mechanisms that control expression of the macrophage colony-stimulating factor receptor locus

The proliferation, differentiation, and survival of cells of the macrophage lineage depends upon signals from the macrophage colony-stimulating factor (CSF) receptor (CSF1R). CSF1R is expressed by embryonic macrophages and induced early in adult hematopoiesis, upon commitment of multipotent progenitors to the myeloid lineage. Transcriptional activation of CSF1R requires interaction between members of the E26 transformation-specific family of transcription factors (Ets) (notably PU.1), C/EBP, RUNX, AP-1/ATF, interferon regulatory factor (IRF), STAT, KLF, REL, FUS/TLS (fused in sarcoma/ranslocated in liposarcoma) families, and conserved regulatory elements within the mouse and human CSF1R locus. One element, the Fms-intronic regulatory element (FIRE), within intron 2, is conserved functionally across all the amniotes. Lineage commitment in multipotent progenitors also requires down-regulation of specific transcription factors such as MYB, FLI1, basic leucine zipper transcriptional factor ATF-like (BATF3), GATA-1, and PAX5 that contribute to differentiation of alternative lineages and repress CSF1R transcription. Many of these transcription factors regulate each other, interact at the protein level, and are themselves downstream targets of CSF1R signaling. Control of CSF1R transcription involves feed–forward and feedback signaling in which CSF1R is both a target and a participant; and dysregulation of CSF1R expression and/or function is associated with numerous pathological conditions. In this review, we describe the regulatory network behind CSF1R expression during differentiation and development of cells of the mononuclear phagocyte system.

Differential co-expression analysis reveals a novel prognostic gene module in ovarian cancer

Ovarian cancer is one of the most significant disease among gynecological disorders that women suffered from over the centuries. However, disease-specific and effective biomarkers were still not available, since studies have focused on individual genes associated with ovarian cancer, ignoring the interactions and associations among the gene products. Here, ovarian cancer differential co-expression networks were reconstructed via meta-analysis of gene expression data and co-expressed gene modules were identified in epithelial cells from ovarian tumor and healthy ovarian surface epithelial samples to propose ovarian cancer associated genes and their interactions. We propose a novel, highly interconnected, differentially co-expressed, and co-regulated gene module in ovarian cancer consisting of 84 prognostic genes. Furthermore, the specificity of the module to ovarian cancer was shown through analyses of datasets in nine other cancers. These observations underscore the importance of transcriptome based systems biomarkers research in deciphering the elusive pathophysiology of ovarian cancer, and here, we present reciprocal interplay between candidate ovarian cancer genes and their transcriptional regulatory dynamics. The corresponding gene module might provide new insights on ovarian cancer prognosis and treatment strategies that continue to place a significant burden on global health.

Transcriptional Regulation and Macrophage Differentiation

Monocytes and macrophages are professional phagocytes that occupy specific niches in every tissue of the body. Their survival, proliferation, and differentiation are controlled by signals from the macrophage colony-stimulating factor receptor (CSF-1R) and its two ligands, CSF-1 and interleukin-34. In this review, we address the developmental and transcriptional relationships between hematopoietic progenitor cells, blood monocytes, and tissue macrophages as well as the distinctions from dendritic cells. A huge repertoire of receptors allows monocytes, tissue-resident macrophages, or pathology-associated macrophages to adapt to specific microenvironments. These processes create a broad spectrum of macrophages with different functions and individual effector capacities. The production of large transcriptomic data sets in mouse, human, and other species provides new insights into the mechanisms that underlie macrophage functional plasticity.

Tumor Metabolic Features Identified by 18F-FDG PET Correlate with Gene Networks of Immune Cell Microenvironment in Head and Neck Cancer

The importance of ¹⁸F-FDG PET in imaging head and neck squamous cell carcinoma (HNSCC) has grown in recent decades. Because PET has prognostic values, and provides functional and molecular information in HNSCC, the genetic and biologic backgrounds associated with PET parameters are of great interest. Here, as a systems biology approach, we aimed to investigate gene networks associated with tumor metabolism and their biologic function using RNA sequence and ¹⁸F-FDG PET data. Methods: Using RNA sequence data of HNSCC downloaded from The Cancer Genome Atlas data portal, we constructed a gene coexpression network. PET parameters including lesion-to-blood-pool ratio, metabolic tumor volume, and tumor lesion glycolysis were calculated. The Pearson correlation test was performed between module eigengene-the first principal component of modules' expression profile-and the PET parameters. The significantly correlated module was functionally annotated with gene ontology terms, and its hub genes were identified. Survival analysis of the significantly correlated module was performed. Results: We identified 9 coexpression network modules from the preprocessed RNA sequence data. A network module was significantly correlated with total lesion glycolysis as well as maximum and mean ¹⁸F-FDG uptake. The expression profiles of hub genes of the network were inversely correlated with ¹⁸F-FDG uptake. The significantly annotated gene ontology terms of the module were associated with immune cell activation and aggregation. The module demonstrated significant association with overall survival, and the group with higher module eigengene showed better survival than the other groups with statistical significance (P = 0.022). Conclusion: We showed that a gene network that accounts for immune cell microenvironment was associated with ¹⁸F-FDG uptake as well as prognosis in HNSCC. Our result supports the idea that competition for glucose between cancer cell and immune cell plays an important role in cancer progression associated with hypermetabolic features. In the future, PET parameters could be used as a surrogate marker of HNSCC for estimating molecular status of immune cell microenvironment.

In vitro gene expression and mRNA translocation from transformed walnut (Juglans regia) rootstocks expressing DsRED fluorescent protein to wild-type scions

An in vitro grafting method was developed for examining gene translocation from rootstock to scion in walnut. Results showed the DsRED gene itself was not translocated but expressed mRNA was. Grafting is widely used in plants, especially in fruit and nut crops. Selected rootstocks can control scion growth and physiological traits, including shortening of the juvenile phase and controlling tree size. Rootstocks also can provide improved soil adaptation and pathogen resistance. Development of genetically modified (GM) fruit crops has progressed recently, but commercial cultivation is still limited due to the time required for evaluation and issues with deregulation. In this study, we evaluated the stability of DsRED marker gene expression in in vitro walnut shoots and examined translocation of the gene and its mRNA from transformed rootstock to wild-type scion. Results show that DsRED was expressed uniformly in transformed tissue-cultured shoots. When used as in vitro rootstocks, these had good graft affinity with wild-type control scion. PCR and qRT-PCR analysis showed that the DsRED gene was not transported from rootstock to scion, but the transcribed mRNA was translocated. This result provides further evidence of gene signal transport from rootstock to scion in fruit and nut crops.

Prediction of optimal gene functions for osteosarcoma using network-based- guilt by association method based on gene oncology and microarray profile

In the current study, we planned to predict the optimal gene functions for osteosarcoma (OS) by integrating network-based method with guilt by association (GBA) principle (called as network-based gene function inference approach) based on gene oncology (GO) data and gene expression profile. To begin with, differentially expressed genes (DEGs) were extracted using linear models for microarray data (LIMMA) package. Then, construction of differential co-expression network (DCN) relying on DEGs was implemented, and sub-DCN was identified using Spearman correlation coefficient (SCC). Subsequently, GO annotations for OS were collected according to known confirmed database and DEGs. Ultimately, gene functions were predicted by means of GBA principle based on the area under the curve (AUC) for GO terms, and we determined GO terms with AUC >0.7 as the optimal gene functions for OS. Totally, 123 DEGs and 137 GO terms were obtained for further analysis. A DCN was constructed, which included 123 DEGs and 7503 interactions. A total of 105 GO terms were identified when the threshold was set as AUC >0.5, which had a good classification performance. Among these 105 GO terms, 2 functions had the AUC >0.7 and were determined as the optimal gene functions including angiogenesis (AUC =0.767) and regulation of immune system process (AUC =0.710). These gene functions appear to have potential for early detection and clinical treatment of OS in the future.

Derivation of marker gene signatures from human skin and their use in the interpretation of the transcriptional changes associated with dermatological disorders

Numerous studies have explored the altered transcriptional landscape associated with skin diseases to understand the nature of these disorders. However, data interpretation represents a significant challenge due to a lack of good maker sets for many of the specialized cell types that make up this tissue, whose composition may fundamentally alter during disease. Here we have sought to derive expression signatures that define the various cell types and structures that make up human skin, and demonstrate how they can be used to aid the interpretation of transcriptomic data derived from this organ. Two large normal skin transcriptomic datasets were identified, one RNA-seq (n = 578), the other microarray (n = 165), quality controlled and subjected separately to network-based analyses to identify clusters of robustly co-expressed genes. The biological significance of these clusters was then assigned using a combination of bioinformatics analyses, literature, and expert review. After cross comparison between analyses, 20 gene signatures were defined. These included expression signatures for hair follicles, glands (sebaceous, sweat, apocrine), keratinocytes, melanocytes, endothelia, muscle, adipocytes, immune cells, and a number of pathway systems. Collectively, we have named this resource SkinSig. SkinSig was then used in the analysis of transcriptomic datasets for 18 skin conditions, providing in-context interpretation of these data. For instance, conventional analysis has shown there to be a decrease in keratinization and fatty metabolism with age; we more accurately define these changes to be due to loss of hair follicles and sebaceous glands. SkinSig also highlighted the over-/under-representation of various cell types in skin diseases, reflecting an influx in immune cells in inflammatory disorders and a relative reduction in other cell types. Overall, our analyses demonstrate the value of this new resource in defining the functional profile of skin cell types and appendages, and in improving the interpretation of disease data. © 2016 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.

Modulation of macrophage antitumor potential by apoptotic lymphoma cells

In aggressive non-Hodgkin's lymphoma (NHL), constitutive apoptosis of a proportion of the tumor cell population can promote net tumor growth. This is associated with the accumulation of tumor-associated macrophages (TAMs) that clear apoptotic cells and exhibit pro-oncogenic transcriptional activation profiles characteristic of reparatory, anti-inflammatory and angiogenic programs. Here we consider further the activation status of these TAMs. We compare their transcriptomic profile with that of a range of other macrophage types from various tissues noting especially their expression of classically activated (IFN-γ and LPS) gene clusters – typically antitumor – in addition to their previously described protumor phenotype. To understand the impact of apoptotic cells on the macrophage activation state, we cocultured apoptotic lymphoma cells with classically activated macrophages (M_(IFN-γ/LPS), also known as M1, macrophages). Although untreated and M_(IFN-γ/LPS) macrophages were able to bind apoptotic lymphoma cells equally well, M_(IFN-γ/LPS) macrophages displayed enhanced ability to phagocytose them. We found that direct exposure of M_(IFN-γ/LPS) macrophages to apoptotic lymphoma cells caused switching towards a protumor activation state (often referred to as M2-like) with concomitant inhibition of antitumor activity that was a characteristic feature of M_(IFN-γ/LPS) macrophages. Indeed, M_(IFN-γ/LPS) macrophages exposed to apoptotic lymphoma cells displayed increased lymphoma growth-promoting activities. Antilymphoma activity by M_(IFN-γ/LPS) macrophages was mediated, in part, by galectin-3, a pleiotropic glycoprotein involved in apoptotic cell clearance that is strongly expressed by lymphoma TAMs but not lymphoma cells. Intriguingly, aggressive lymphoma growth was markedly impaired in mice deficient in galectin-3, suggesting either that host galectin-3-mediated antilymphoma activity is required to sustain net tumor growth or that additional functions of galectin-3 drive key oncogenic mechanisms in NHL. These findings have important implications for anticancer therapeutic approaches aimed at polarizing macrophages towards an antitumor state and identify galectin-3 as a potentially important novel target in aggressive NHL.

Monocyte/Macrophage: NK Cell Cooperation-Old Tools for New Functions

Monocyte/macrophage and natural killer (NK) cells are partners from a phylogenetic standpoint of innate immune system development and its evolutionary progressive interaction with adaptive immunity. The equally conservative ways of development and differentiation of both invertebrate hemocytes and vertebrate macrophages are reviewed. Evolutionary conserved molecules occurring in macrophage receptors and effectors have been inherited by vertebrates after their common ancestor with invertebrates. Cytolytic functions of mammalian NK cells, which are rooted in immune cells of invertebrates, although certain NK cell receptors (NKRs) are mammalian new events, are characterized. Broad heterogeneity of macrophage and NK cell phenotypes that depends on surrounding microenvironment conditions and expression profiles of specific receptors and activation mechanisms of both cell types are discussed. The particular tissue specificity of macrophages and NK cells, as well as their plasticity and mechanisms of their polarization to different functional subtypes have been underlined. The chapter summarized studies revealing the specific molecular mechanisms and regulation of NK cells and macrophages that enable their highly specific cross-cooperation. Attention is given to the evolving role of human monocyte/macrophage and NK cell interaction in pathogenesis of hypersensitivity reaction-based disorders, including autoimmunity, as well as in cancer surveillance and progression.

Gene Network Rewiring to Study Melanoma Stage Progression and Elements Essential for Driving Melanoma

Metastatic melanoma patients have a poor prognosis, mainly attributable to the underlying heterogeneity in melanoma driver genes and altered gene expression profiles. These characteristics of melanoma also make the development of drugs and identification of novel drug targets for metastatic melanoma a daunting task. Systems biology offers an alternative approach to re-explore the genes or gene sets that display dysregulated behaviour without being differentially expressed. In this study, we have performed systems biology studies to enhance our knowledge about the conserved property of disease genes or gene sets among mutually exclusive datasets representing melanoma progression. We meta-analysed 642 microarray samples to generate melanoma reconstructed networks representing four different stages of melanoma progression to extract genes with altered molecular circuitry wiring as compared to a normal cellular state. Intriguingly, a majority of the melanoma network-rewired genes are not differentially expressed and the disease genes involved in melanoma progression consistently modulate its activity by rewiring network connections. We found that the shortlisted disease genes in the study show strong and abnormal network connectivity, which enhances with the disease progression. Moreover, the deviated network properties of the disease gene sets allow ranking/prioritization of different enriched, dysregulated and conserved pathway terms in metastatic melanoma, in agreement with previous findings. Our analysis also reveals presence of distinct network hubs in different stages of metastasizing tumor for the same set of pathways in the statistically conserved gene sets. The study results are also presented as a freely available database at http://bioinfo.icgeb.res.in/m3db/. The web-based database resource consists of results from the analysis presented here, integrated with cytoscape web and user-friendly tools for visualization, retrieval and further analysis.

CXCR4 3'UTR functions as a ceRNA in promoting metastasis, proliferation and survival of MCF-7 cells by regulating miR-146a activity

CXCR4 is the most common chemokine receptor expressed on tumor cells, and it is closely correlated with cancer cell stemness. This study was carried out to explore whether CXCR4 could function as a competitive endogenous RNA to promote metastasis, proliferation and survival in MCF-7 breast cancer cells. We validated that CXCR4, together with TRAF6 and EGFR, was directly targeted by miR-146a in MCF-7 cells. Overexpression of CXCR4 3'UTR inhibited the activity of miR-146a, thus elevating the expression of CXCR4, TRAF6 and EGFR. These oncoproteins further activated NF-κB pathway and promoted the proliferation, migration, invasion and anti-apoptotic activity of MCF-7 cells. Collectively, our study provided new insights into the function of CXCR4 in breast cancer: it promotes tumor progression as both a protein-coding gene and a non-coding RNA, complicating the mechanism by which oncogenes promote tumor progression.

The Many Alternative Faces of Macrophage Activation

Monocytes and macrophages provide the first line of defense against pathogens. They also initiate acquired immunity by processing and presenting antigens and provide the downstream effector functions. Analysis of large gene expression datasets from multiple cells and tissues reveals sets of genes that are co-regulated with the transcription factors that regulate them. In macrophages, the gene clusters include lineage-specific genes, interferon-responsive genes, early inflammatory genes, and genes required for endocytosis and lysosome function. Macrophages enter tissues and alter their function to deal with a wide range of challenges related to development and organogenesis, tissue injury, malignancy, sterile, or pathogenic inflammatory stimuli. These stimuli alter the gene expression to produce "activated macrophages" that are better equipped to eliminate the cause of their influx and to restore homeostasis. Activation or polarization states of macrophages have been classified as "classical" and "alternative" or M1 and M2. These proposed states of cells are not supported by large-scale transcriptomic data, including macrophage-associated signatures from large cancer tissue datasets, where the supposed markers do not correlate with other. Individual macrophage cells differ markedly from each other, and change their functions in response to doses and combinations of agonists and time. The most studied macrophage activation response is the transcriptional cascade initiated by the TLR4 agonist lipopolysaccharide. This response is reviewed herein. The network topology is conserved across species, but genes within the transcriptional network evolve rapidly and differ between mouse and human. There is also considerable divergence in the sets of target genes between mouse strains, between individuals, and in other species such as pigs. The deluge of complex information related to macrophage activation can be accessed with new analytical tools and new databases that provide access for the non-expert.

Transcriptomic analysis of mononuclear phagocyte differentiation and activation

Monocytes and macrophages differentiate from progenitor cells under the influence of colony-stimulating factors. Genome-scale data have enabled the identification of the sets of genes that are associated with specific functions and the mechanisms by which thousands of genes are regulated in response to pathogen challenge. In large datasets, it is possible to identify large sets of genes that are coregulated with the transcription factors that regulate them. They include macrophage-specific genes, interferon-responsive genes, early inflammatory genes, and those associated with endocytosis. Such analyses can also extract macrophage-associated signatures from large cancer tissue datasets. However, cluster analysis provides no support for a signature that distinguishes macrophages from antigen-presenting dendritic cells, nor the classification of macrophage activation states as classical versus alternative, or M1 versus M2. Although there has been a focus on a small subset of lineage-enriched transcription factors, such as PU.1, more than half of the transcription factors in the genome can be expressed in macrophage lineage cells under some state of activation, and they interact in a complex network. The network architecture is conserved across species, but many of the target genes evolve rapidly and differ between mouse and human. The data and publication deluge related to macrophage biology require the development of new analytical tools and ways of presenting information in an accessible form.

Extensive rewiring of epithelial-stromal co-expression networks in breast cancer

BACKGROUND

Epithelial-stromal crosstalk plays a critical role in invasive breast cancer pathogenesis; however, little is known on a systems level about how epithelial-stromal interactions evolve during carcinogenesis.

RESULTS

We develop a framework for building genome-wide epithelial-stromal co-expression networks composed of pairwise co-expression relationships between mRNA levels of genes expressed in the epithelium and stroma across a population of patients. We apply this method to laser capture micro-dissection expression profiling datasets in the setting of breast carcinogenesis. Our analysis shows that epithelial-stromal co-expression networks undergo extensive rewiring during carcinogenesis, with the emergence of distinct network hubs in normal breast, and estrogen receptor-positive and estrogen receptor-negative invasive breast cancer, and the emergence of distinct patterns of functional network enrichment. In contrast to normal breast, the strongest epithelial-stromal co-expression relationships in invasive breast cancer mostly represent self-loops, in which the same gene is co-expressed in epithelial and stromal regions. We validate this observation using an independent laser capture micro-dissection dataset and confirm that self-loop interactions are significantly increased in cancer by performing computational image analysis of epithelial and stromal protein expression using images from the Human Protein Atlas.

CONCLUSIONS

Epithelial-stromal co-expression network analysis represents a new approach for systems-level analyses of spatially localized transcriptomic data. The analysis provides new biological insights into the rewiring of epithelial-stromal co-expression networks and the emergence of epithelial-stromal co-expression self-loops in breast cancer. The approach may facilitate the development of new diagnostics and therapeutics targeting epithelial-stromal interactions in cancer.

A null model for Pearson coexpression networks

Gene coexpression networks inferred by correlation from high-throughput profiling such as microarray data represent simple but effective structures for discovering and interpreting linear gene relationships. In recent years, several approaches have been proposed to tackle the problem of deciding when the resulting correlation values are statistically significant. This is most crucial when the number of samples is small, yielding a non-negligible chance that even high correlation values are due to random effects. Here we introduce a novel hard thresholding solution based on the assumption that a coexpression network inferred by randomly generated data is expected to be empty. The threshold is theoretically derived by means of an analytic approach and, as a deterministic independent null model, it depends only on the dimensions of the starting data matrix, with assumptions on the skewness of the data distribution compatible with the structure of gene expression levels data. We show, on synthetic and array datasets, that the proposed threshold is effective in eliminating all false positive links, with an offsetting cost in terms of false negative detected edges.

A transcriptional perspective on human macrophage biology

Macrophages are a major cell type in tissue homeostasis and contribute to both pathology and resolution in all acute and chronic inflammatory diseases ranging from infections, cancer, obesity, atherosclerosis, autoimmune disorders to neurodegenerative diseases such as Alzheimer's disease. The cellular and functional diversity of macrophages depends upon tightly regulated transcription. The innate immune system is under profound evolutionary selection. There is increasing recognition that human macrophage biology differs very significantly from that of commonly studied animal models, which therefore can have a limited predictive value. Here we report on the newest findings on transcriptional control of macrophage activation, and how we envision integrating studies on transcriptional and epigenetic regulation, and more classical approaches in murine models. Moreover, we provide new insights into how we can learn about transcriptional regulation in the human system from larger efforts such as the FANTOM (Functional Annotation of the Mammalian Genome) consortium.

Transcriptional coexpression network reveals the involvement of varying stem cell features with different dysregulations in different gastric cancer subtypes

Despite the advancements in the cancer therapeutics, gastric cancer ranks as the second most common cancers with high global mortality rate. Integrative functional genomic investigation is a powerful approach to understand the major dysregulations and to identify the potential targets toward the development of targeted therapeutics for various cancers. Intestinal and diffuse type gastric tumors remain the major subtypes and the molecular determinants and drivers of these distinct subtypes remain unidentified. In this investigation, by exploring the network of gene coexpression association in gastric tumors, mRNA expressions of 20,318 genes across 200 gastric tumors were categorized into 21 modules. The genes and the hub genes of the modules show gastric cancer subtype specific expression. The expression patterns of the modules were correlated with intestinal and diffuse subtypes as well as with the differentiation status of gastric tumors. Among these, G1 module has been identified as a major driving force of diffuse type gastric tumors with the features of (i) enriched mesenchymal, mesenchymal stem cell like, and mesenchymal derived multiple lineages, (ii) elevated OCT1 mediated transcription, (iii) involvement of Notch activation, and (iv) reduced polycomb mediated epigenetic repression. G13 module has been identified as key factor in intestinal type gastric tumors and found to have the characteristic features of (i) involvement of embryonic stem cell like properties, (ii) Wnt, MYC and E2F mediated transcription programs, and (iii) involvement of polycomb mediated repression. Thus the differential transcription programs, differential epigenetic regulation and varying stem cell features involved in two major subtypes of gastric cancer were delineated by exploring the gene coexpression network. The identified subtype specific dysregulations could be optimally employed in developing subtype specific therapeutic targeting strategies for gastric cancer.