Multiple Transcriptomic Data Research Articles

Role of glycosylation-related gene MGAT1 in pancreatic ductal adenocarcinoma.

pancreatic ductal adenocarcinoma (PDAC) is a malignant tumor with a very poor prognosis and a complex tumor microenvironment, which plays a key role in tumor progression and treatment resistance. Glycosylation plays an important role in processes such as cell signaling, immune response and protein stability. single-cell RNA sequencing data and spatial transcriptome data were obtained from GSE197177 and GSE224411, respectively, and RNA-seq data and survival information were obtained from UCSC Xena and TCGA. Multiple transcriptomic data were comprehensively analyzed to explore the role of glycosylation processes in tumor progression, and functional experiments were performed to assess the effects of MGAT1 overexpression on PDAC cell proliferation and migration. In PDAC tumor samples, the glycosylation level of macrophages was significantly higher than that of normal samples. MGAT1 was identified as a key glycosylation-related gene, and its high expression was associated with better patient prognosis. Overexpression of MGAT1 significantly inhibited the proliferation and migration of PDAC cells and affected intercellular interactions in the tumor microenvironment. MGAT1 plays an important role in PDAC by regulating glycosylation levels in macrophages, influencing tumor progression and improving prognosis.MGAT1 is a potential therapeutic target for PDAC and further studies are needed to develop targeted therapeutic strategies against MGAT1 to improve clinical outcomes.

Identification of the Major Facilitator Superfamily Efflux Pump KpsrMFS in Klebsiella pneumoniae That Is Down-Regulated in the Presence of Multi-Stress Factors.

Efflux pumps play important roles in bacterial detoxification and some of them are stress-response elements that are up-regulated when the host is treated with antibiotics. However, efflux pumps that are down-regulated by stimulations are rarely discovered. Herein, we analyzed multiple transcriptome data and discovered a special (Major Facilitator Superfamily) MFS efflux pump, KpsrMFS, from Klebsiella pneumoniae, which was down-regulated when treated with antibiotics or extra carbon sources. Interestingly, overexpression of kpsrmfs resulted in halted cell growth in normal conditions, while the viable cells were rarely affected. The function of KpsrMFS was further analyzed and this efflux pump was determined to be a proton-driven transporter that can reduce the intracellular tetracycline concentration. In normal conditions, the expression of kpsrmfs was at a low level, while artificial overexpression of it led to increased endogenous reactive oxygen species (ROS) production. Moreover, by comparing the functions of adjacent genes of kpsrmfs, we further discovered another four genes that can confer similar phenotypes, indicating a special regulon that regulates cell growth. Our work provides new insights into the roles of efflux pumps and suggests a possible regulon that may regulate cell growth and endogenous ROS levels.

Artificial intelligence learning landscape of triple-negative breast cancer uncovers new opportunities for enhancing outcomes and immunotherapy responses

Triple-negative breast cancer (TNBC) is a relatively aggressive breast cancer subtype due to tumor relapse, drug resistance, and multi-organ metastatic properties. Identifying reliable biomarkers to predict prognosis and precisely guide TNBC immunotherapy is still an unmet clinical need. To address this issue, we successfully constructed a novel 25 machine learning (ML) algorithms-based immune infiltrating cell (IIC) associated signature of TNBC (MLIIC), achieved by multiple transcriptome data of purified immune cells, TNBC cell lines, and TNBC entities. The TSI index was employed to determine IIC-RNAs that were accompanied by an expression pattern of upregulation in immune cells and downregulation in TNBC cells. LassoLR, Boruta, Xgboost, SVM, RF, and Pamr were utilized for further obtaining the optimal IIC-RNAs. Following univariate Cox regression analysis, LassoCox, CoxBoost, and RSF were utilized for the dimensionality reduction of IIC-RNAs from a prognostic perspective. RSF, Ranger, ObliqueRSF, Rpart, CoxPH, SurvivalSVM, CoxBoost, GlmBoost, SuperPC, StepwiseCox, Enet, LassoCox, CForest, Akritas, BlackBoost, PlsRcox, SurvReg, GBM, and CTree were used for determining the most potent MLIIC signature. Consequently, this MLIIC signature was correlated significantly with survival status validated by four independent TNBC cohorts. Also, the MLIIC signature had a superior predictive capability for TNBC prognosis, compared with 148 previously reported signatures. In addition, MLIIC signature scores developed by immunofluorescent staining of tissue arrays from TNBC patients showed a substantial prognostic value. In TNBC immunotherapy, the low MLIIC profile demonstrated significant immune-responsive efficacy in a dataset of multiple cancer types. MLIIC signature could also predict m6A epigenetic regulation which controls T cell homeostasis. Therefore, this well-established MLIIC signature is a robust predictive indicator for TNBC prognosis and the benefit of immunotherapy, thus providing an efficient tool for combating TNBC.

Identification of reference genes provides insights into the determinants of self-incompatibility in Camellia oleifera

Self-incompatibility (SI) is one of the main reasons leading to the low fruit set of Camellia oleifera and limiting the yield of Camellia oil. It is important to uncover the molecular mechanism of SI for guiding production. Accurate quantification of the expression level of SI determinants is essential, which requires qRT-PCR technology and appropriate reference genes. However, the screening of SI-related reference genes in C. oleifera has not been reported. In this study, multiple transcriptome data were innovatively integrated and candidate reference genes of SI were systematically and rapidly screened in C. oleifera. TIF3H1 was selected as the most stable gene expressed in pistillate tissues of C. oleifera under different pollination treatments based on the comprehensive evaluation using qRT-PCR and statistical algorithms of geNorm, NormFinder, BestKeeper, and RefFinder. The expression of SI-related genes of C. oleifera validated the stability of TIF3H1 as the reference gene. Using TIF3H1 as the reference gene, the expression of the potential SI determinants in compatible and incompatible pistils was accurately quantified, and the high expression of the homologous gene of pectin methylesterase (PME) in incompatible pistils suggested its positive effect on SI of C. oleifera. The identification of TIF3H1 as the reliable reference gene guarantees more accurate qRT-PCR quantification for the study of the SI of C. oleifera, laying a foundation for identifying determinants of SI and exploring its molecular mechanism.

Construction of a de novo assembly pipeline using multiple transcriptome data sets from Cypripedium macranthos (Orchidaceae).

The family Orchidaceae comprises the most species of any monocotyledonous family and has interesting characteristics such as seed germination induced by mycorrhizal fungi and flower morphology that co-adapted with pollinators. In orchid species, genomes have been decoded for only a few horticultural species, and there is little genetic information available. Generally, for species lacking sequenced genomes, gene sequences are predicted by de novo assembly of transcriptome data. Here, we devised a de novo assembly pipeline for transcriptome data from the wild orchid Cypripedium (lady slipper orchid) in Japan by mixing multiple data sets and integrating assemblies to create a more complete and less redundant contig set. Among the assemblies generated by combining various assemblers, Trinity and IDBA-Tran yielded good assembly with higher mapping rates and percentages of BLAST hit contigs and complete BUSCO. Using this contig set as a reference, we analyzed differential gene expression between protocorms grown aseptically or with mycorrhizal fungi to detect gene expressions required for mycorrhizal interaction. A pipeline proposed in this study can construct a highly reliable contig set with little redundancy even when multiple transcriptome data are mixed, and can provide a reference that is adaptable to DEG analysis and other downstream analysis in RNA-seq.

Integrated analysis of multiple transcriptomic data identifies ST8SIA6‑AS1 and LINC01093 as potential biomarkers in HBV‑associated liver cancer.

The mechanisms of long-non-coding RNAs (lncRNAs) in hepatitis B virus (HBV) infection-associated liver cancer remain largely unclear. Therefore, the aim of the present study was to investigate the regulatory mechanisms of lncRNAs in this disease. HBV-liver cancer related transcriptome expression profile data (GSE121248 and GSE55092) from the Gene Expression Omnibus database and survival prognosis information from The Cancer Genome Atlas (TCGA) database were obtained for analysis. The limma package was used to identify the overlapped differentially expressed RNAs (DERs), including DElncRNAs and DEmRNAs, in the GSE121248 and GSE55092 datasets. The screened optimized lncRNA signatures were used to develop a nomogram model based on the GSE121248 dataset, which was validated using the GSE55092 and TCGA datasets. A competitive endogenous RNA (ceRNA) network was constructed based on the screened prognosis-associated lncRNA signatures from TCGA dataset. In addition, the levels of specific lncRNAs were evaluated in HBV-infected human liver cancer tissues and cells, and Cell Counting Kit-8, ELISA and Transwell assays were performed to evaluate the effects of the lncRNAs in HBV-expressing liver cancer cells. A total of 535 overlapped DERs, including 30 DElncRNAs and 505 DEmRNAs, were identified in the GSE121248 and GSE55092 datasets. An optimized DElncRNA signature comprising 10 lncRNAs was used to establish a nomogram. ST8SIA6-AS1 and LINC01093 were identified as lncRNAs associated with HBV-liver cancer prognosis in TCGA dataset, and were applied to construct a ceRNA network. Reverse transcription-quantitative PCR analysis showed that ST8SIA6-AS1 was upregulated and LINC01093 was downregulated in HBV-infected human liver cancer tissues and HBV-expressing liver cancer cells compared with non-HBV-infected controls. ST8SIA6-AS1 knockdown and LINC01093 overexpression independently reduced the number of copies of HBV DNA, the levels of hepatitis B surface antigen and hepatitis B e antigen, as well as cell proliferation, migration and invasion. In summary, the present study identified ST8SIA6-AS1 and LINC01093 as two potential biomarkers that may be effective therapeutic targets for HBV-associated liver cancer.

Analysis of transcriptomic data sets supports the role of IL-6 in NETosis and immunothrombosis in severe COVID-19

BackgroundThere is an urgent need to understand the key events driving pathogenesis of severe COVID-19 disease, so that precise treatment can be instituted. In this respect NETosis is gaining increased attention in the scientific community, as an important pathological process contributing to mortality. We sought to test if indeed there exists robust evidence of NETosis in multiple transcriptomic data sets from human subjects with severe COVID-19 disease. Gene set enrichment analysis was performed to test for up-regulation of gene set functional in NETosis in the blood of patients with COVID-19 illness.ResultsBlood gene expression functional in NETosis increased with severity of illness, showed negative correlation with blood oxygen saturation, and was validated in the lung of COVID-19 non-survivors. Temporal expression of IL-6 was compared between severe and moderate illness with COVID-19. Unsupervised clustering was performed to reveal co-expression of IL-6 with complement genes. In severe COVID-19 illness, there is transcriptional evidence of activation of NETosis, complement and coagulation cascade, and negative correlation between NETosis and respiratory function (oxygen saturation). An early spike in IL-6 is observed in severe COVID-19 illness that is correlated with complement activation.ConclusionsBased on the transcriptional dynamics of IL-6 expression and its downstream effect on complement activation, we constructed a model that links early spike in IL-6 level with persistent and self-perpetuating complement activation, NETosis, immunothrombosis and respiratory dysfunction. Our model supports the early initiation of anti-IL6 therapy in severe COVID-19 disease before the life-threatening complications of the disease can perpetuate themselves autonomously.

Identification of Key Gene Targets for Sensitizing Colorectal Cancer to Chemoradiation: an Integrative Network Analysis on Multiple Transcriptomics Data.

Colorectal cancer (CRC) is a main cause of morbidity and mortality in the world. Chemoradioresistance is a major problem in CRC treatment. Identification of novel therapeutic targets in order to overcome treatment resistance in CRC is necessary. In this study, gene expression omnibus (GEO) database was searched to find microarray datasets. Data normalization/analyzing was performed using ExAtlas. The gene ontology (GO) and pathway enrichment analysis was performed using g:Profiler. Protein-protein interaction network (PPIN) was constructed by Search Tool for the Retrieval of Interacting Genes (STRING) and analyzed using Cytoscape. Survival analysis was done using Kaplan-Meier curve method. Forty-one eligible datasets were included in study. A total of 12,244 differentially expressed genes (DEGs) and 7337 unique DEGs were identified. Among them, 1187 DEGs were overlapped in ≥ 3 datasets. Fifty-five overlapped genes were considered as hub genes. Common hub genes in chemo/radiation/chemoradiation datasets were chosen as the essential candidate genes (n = 13). Forty-one hub gene and 7 essential candidate genes were contributed in the significant modules. The modules were mainly enriched in the signaling pathways of senescence, autophagy, NF-κB, HIF-1, stem cell pluripotency, notch, neovascularization, cell cycle, p53, chemokine, and PI3K-Akt. NGFR, FGF2, and PROM1 genes were significantly predictors of CRC patient's survival. Our study revealed three-gene signatures as potential therapeutic targets and also candidate molecular markers in CRC chemoradioresistance.

BnTIR: an online transcriptome platform for exploring RNA-seq libraries for oil crop Brassica napus.

With the increasing availability of massive transcriptome data in plants, it is possible to construct a comprehensive database with multiple transcriptome data and online imputation tools. Online databases with integrated, multifaceted functions for exploring published RNA-seq libraries or microarrays are available for several plant species (Darwish et al., 2013; Xia et al., 2017; Zhang et al., 2020), helping researchers take advantage of the vast collection of public datasets. Brassica napus (AACC, 2n = 38) is one of the most important oil crops, originating from a spontaneous hybridization between Brassica rapa (AA, 2n = 20) and Brassica oleracea (CC, 2n = 18) (Chalhoub et al., 2014). Recently, an online transcriptome database has been built in B. napus (Chao et al., 2020). This database includes transcriptomes from different labs with various genotypes and growth conditions, which affects the usability. A database with comprehensive transcriptomes from one B. napus genotype is greatly needed.

Arabidopsis Coexpression Tool: a tool for gene coexpression analysis in Arabidopsis thaliana

SummaryGene coexpression analysis refers to the discovery of sets of genes which exhibit similar expression patterns across multiple transcriptomic data sets, such as microarray experiment data of public repositories. Arabidopsis Coexpression Tool (ACT), a gene coexpression analysis web tool for Arabidopsis thaliana, identifies genes which are correlated to a driver gene. Primary microarray data from ATH1 Affymetrix platform were processed with Single-Channel Array Normalization algorithm and combined to produce a coexpression tree which contains ∼21,000 A. thaliana genes. ACT was developed to present subclades of coexpressed genes, as well as to perform gene set enrichment analysis, being unique in revealing enriched transcription factors targeting coexpressed genes. ACT offers a simple and user-friendly interface producing working hypotheses which can be experimentally verified for the discovery of gene partnership, pathway membership, and transcriptional regulation. ACT analyses have been successful in identifying not only genes with coordinated ubiquitous expressions but also genes with tissue-specific expressions.

Are Epithelial Ovarian Cancers of the Mesenchymal Subtype Actually Intraperitoneal Metastases to the Ovary?

Primary ovarian high-grade serous carcinoma (HGSC) has been classified into 4 molecular subtypes: Immunoreactive, Proliferative, Differentiated, and Mesenchymal (Mes), of which the Mes subtype (Mes-HGSC) is associated with the worst clinical outcomes. We propose that Mes-HGSC comprise clusters of cancer and associated stromal cells that detached from tumors in the upper abdomen/omentum and disseminated in the peritoneal cavity, including to the ovary. Using comparative analyses of multiple transcriptomic data sets, we provide the following evidence that the phenotype of Mes-HGSC matches the phenotype of tumors in the upper abdomen/omentum: (1) irrespective of the primary ovarian HGSC molecular subtype, matched upper abdominal/omental metastases were typically of the Mes subtype, (2) the Mes subtype was present at the ovarian site only in patients with concurrent upper abdominal/omental metastases and not in those with HGSC confined to the ovary, and (3) ovarian Mes-HGSC had an expression profile characteristic of stromal cells in the upper abdominal/omental metastases. We suggest that ovarian Mes-HGSC signifies advanced intraperitoneal tumor dissemination to the ovary rather than a subtype of primary ovarian HGSC. This is consistent with the presence of upper abdominal/omental disease, suboptimal debulking, and worst survival previously reported in patients with ovarian Mes-HGSC compared to other molecular subtypes.

Genome-wide identification, characterization analysis and expression profiling of auxin-responsive GH3 family genes in wheat (Triticum aestivum L.).

Auxin affects many aspects of plant growth and development by regulating the expression of auxin-responsive genes. As one of the three major auxin-responsive families the Gretchen Hagen3 (GH3) gene family maintains hormonal homeostasis by conjugating excess indole-3-acetic acid (IAA), salicylic acid (SA), and jasmonic acid (JA) to amino acids during hormone and stress-related signaling. Although some work has been carried out the functions of wheat GH3 (TaGH3) family genes in response to abiotic stresses (including salt stress and osmotic stress) are largely unknown. Access to the complete wheat genome sequence permits genome-wide studies on TaGH3s. We performed a systematic identification of the TaGH3 gene family at the genome level and detected 36 members on 14 wheat chromosomes. Many of the genes were segmentally duplicated and Ka/Ks and inter-species synthetic analyses indicated that polyploidization was the contributor to the increased number of TaGH3 members. Phylogenetic analyses revealed that TaGH3 proteins could divided into three major categories (TaGH3-I, TaGH3-II, and TaGH3-III). Diversified cis-elements in the promoters of TaGH3 genes were predicted as essential players in regulating TaGH3 expression patterns. Gene structure and motif analyses indicated that most TaGH3 genes have relatively conserved exon/intron arrangements and motif compositions. Analysis of multiple transcriptome data sets indicated that many TaGH3 genes are responsive to biological and abiotic stresses and possibly have important functions in stress response. qRT-PCR analysis revealed that TaGH3s were induced by salt and osmotic stresses. Customized annotation results revealed that TaGH3s were widely involved in phytohormone response, defense, growth and development, and metabolism. Overall, our work provides a comprehensive insight into the TaGH3 family members, and a basis for the further study of their biological functions in wheat.

Analysis of multiple transcriptome data to determine age-associated genes for the progression of Parkinson's disease

Transcriptome data analysis between ageing Parkinson's disease and healthy individuals were conducted. Network-based approach and correlation measure were employed to find the significant genes followed by pathway enrichment analysis. Significant differentially expressed genes (DEGs) were identified using ANOVA from microarray datasets specifically sorted for substantia nigra pars compacta region of the brain and a protein-protein interaction network (DEGs-PPIN) was developed using HIPPIE database. The network was subdivided into two sub-networks; where the sub-network for control set was developed based on the gene pair having high correlation in healthy ageing and low or negative correlation in diseased condition and vice-a-versa for patient sub-network. 74 highly correlated gene pairs were identified for control sub-network and 191 pairs for patient, followed by pathway enrichment analysis. Here, we have discussed the role of specific genes along with their pathways, which were not reported earlier, thereby opening the vista for characterizing novel targets for PD.

Metabolic reprogramming and Notch activity distinguish between non-small cell lung cancer subtypes

BackgroundPrevious studies suggested that the metabolism is differently reprogrammed in the major subtypes of non-small cell lung cancer (NSCLC), squamous cell carcinomas (SCC) and adenocarcinomas (AdC). However, a comprehensive analysis of this differential metabolic reprogramming is lacking.MethodsPublicly available gene expression data from human lung cancer samples and cell lines were analysed. Stable isotope resolved metabolomics were performed on SCC and ADC tumours in human patients and in freshly resected tumour slices.ResultsAnalysis of multiple transcriptomics data from human samples identified a SCC-distinguishing enzyme gene signature. SCC tumours from patients infused with [U-13C]-glucose and SCC tissue slices incubated with stable isotope tracers demonstrated differential glucose and glutamine catabolism compared to AdCs or non-cancerous lung, confirming increased activity through pathways defined by the SCC metabolic gene signature. Furthermore, the upregulation of Notch target genes was a distinguishing feature of SCCs, which correlated with the metabolic signature. Notch and MYC-driven murine lung tumours recapitulated the SCC-distinguishing metabolic reprogramming. However, the differences between SCCs and AdCs disappear in established cell lines in 2D culture.ConclusionsOur data emphasise the importance of studying lung cancer metabolism in vivo. They also highlight potential targets for therapeutic intervention in SCC patients including differentially expressed enzymes that catalyse reactions in glycolysis, glutamine catabolism, serine, nucleotide and glutathione biosynthesis.

Pan-senescence transcriptome analysis identified RRAD as a marker and negative regulator of cellular senescence

Cellular senescence, an irreversible proliferative arrest, functions in tissue remodeling during development and is implicated in multiple aging-associated diseases. While senescent cells often manifest an array of senescence-associated phenotypes, such as cell cycle arrest, altered heterochromatin architecture, reprogrammed metabolism and senescence-associated secretory phenotype (SASP), the identification of senescence cells has been hindered by lack of specific and universal biomarkers. To systematically identify universal biomarkers of cellular senescence, we integrated multiple transcriptome data sets of senescent cells obtained through different in vitro manipulation modes as well as age-related gene expression data of human tissues. Our analysis showed that RRAD (Ras-related associated with diabetes) expression is up-regulated in all the manipulation modes and increases with age in human skin and adipose tissues. The elevated RRAD expression was then confirmed in senescent human fibroblasts that were induced by Ras, H2O2, ionizing radiation, hydroxyurea, etoposide and replicative passage, respectively. Further functional study suggests that RRAD up-regulation acts as a negative feedback mechanism to counter cellular senescence by reducing the level of reactive oxygen species. Finally, we found both p53 and NF-κB bind to RRAD genomic regions and modulate RRAD transcription. This study established RRAD to be a biomarker as well as a novel negative regulator of cellular senescence.

A regulation probability model-based meta-analysis of multiple transcriptomics data sets for cancer biomarker identification

BackgroundLarge-scale accumulation of omics data poses a pressing challenge of integrative analysis of multiple data sets in bioinformatics. An open question of such integrative analysis is how to pinpoint consistent but subtle gene activity patterns across studies. Study heterogeneity needs to be addressed carefully for this goal.ResultsThis paper proposes a regulation probability model-based meta-analysis, jGRP, for identifying differentially expressed genes (DEGs). The method integrates multiple transcriptomics data sets in a gene regulatory space instead of in a gene expression space, which makes it easy to capture and manage data heterogeneity across studies from different laboratories or platforms. Specifically, we transform gene expression profiles into a united gene regulation profile across studies by mathematically defining two gene regulation events between two conditions and estimating their occurring probabilities in a sample. Finally, a novel differential expression statistic is established based on the gene regulation profiles, realizing accurate and flexible identification of DEGs in gene regulation space. We evaluated the proposed method on simulation data and real-world cancer datasets and showed the effectiveness and efficiency of jGRP in identifying DEGs identification in the context of meta-analysis.ConclusionsData heterogeneity largely influences the performance of meta-analysis of DEGs identification. Existing different meta-analysis methods were revealed to exhibit very different degrees of sensitivity to study heterogeneity. The proposed method, jGRP, can be a standalone tool due to its united framework and controllable way to deal with study heterogeneity.

JNMFMA: a joint non-negative matrix factorization meta-analysis of transcriptomics data.

Tremendous amount of omics data being accumulated poses a pressing challenge of meta-analyzing the heterogeneous data for mining new biological knowledge. Most existing methods deal with each gene independently, thus often resulting in high false positive rates in detecting differentially expressed genes (DEG). To our knowledge, no or little effort has been devoted to methods that consider dependence structures underlying transcriptomics data for DEG identification in meta-analysis context. This article proposes a new meta-analysis method for identification of DEGs based on joint non-negative matrix factorization (jNMFMA). We mathematically extend non-negative matrix factorization (NMF) to a joint version (jNMF), which is used to simultaneously decompose multiple transcriptomics data matrices into one common submatrix plus multiple individual submatrices. By the jNMF, the dependence structures underlying transcriptomics data can be interrogated and utilized, while the high-dimensional transcriptomics data are mapped into a low-dimensional space spanned by metagenes that represent hidden biological signals. jNMFMA finally identifies DEGs as genes that are associated with differentially expressed metagenes. The ability of extracting dependence structures makes jNMFMA more efficient and robust to identify DEGs in meta-analysis context. Furthermore, jNMFMA is also flexible to identify DEGs that are consistent among various types of omics data, e.g. gene expression and DNA methylation. Experimental results on both simulation data and real-world cancer data demonstrate the effectiveness of jNMFMA and its superior performance over other popular approaches. R code for jNMFMA is available for non-commercial use via http://micblab.iim.ac.cn/Download/. hqwang@ustc.edu Supplementary data are available at Bioinformatics online.

A Modular Cell-Type Focused Inflammatory Process Network Model for Non-Diseased Pulmonary Tissue

Exposure to environmental stressors such as cigarette smoke (CS) elicits a variety of biological responses in humans, including the induction of inflammatory responses. These responses are especially pronounced in the lung, where pulmonary cells sit at the interface between the body’s internal and external environments. We combined a literature survey with a computational analysis of multiple transcriptomic data sets to construct a computable causal network model (the Inflammatory Process Network (IPN)) of the main pulmonary inflammatory processes. The IPN model predicted decreased epithelial cell barrier defenses and increased mucus hypersecretion in human bronchial epithelial cells, and an attenuated pro-inflammatory (M1) profile in alveolar macrophages following exposure to CS, consistent with prior results. The IPN provides a comprehensive framework of experimentally supported pathways related to CS-induced pulmonary inflammation. The IPN is freely available to the scientific community as a resource with broad applicability to study the pathogenesis of pulmonary disease.

The Role of Progesterone in Oocyte Acquisition of Developmental Competence

It is generally accepted that progesterone (P4) is a key regulator of reproductive function in mammals. In cattle, the primary focus of P4's actions has been uterine receptivity and maintenance of pregnancy. Studies in mammalian laboratory species and ovarian derived cell lines also highlight their role in ovarian function. Extensive research in non-mammalian species has elucidated a critical role for P4 and both its nuclear and membrane-bound receptors in oocyte maturation and ovulation. Until recently, such a role in mammalian oocytes has been disputed. However, as oestrous synchronization regimes are constantly tweaked and revised to maximize pregnancy rates to artificial insemination in cattle, the importance of P4 priming of the dominant ingfollicle is once again tak centre stage. Sequencing of the bovine genome and the development of multiple transcriptomic data mining tools have facilitated an explosion in global transcriptome profiling of immature and matured oocytes and their surrounding cumulus cells. Many of the differentially regulated genes and their associated preferentially populated pathways appear to be P4 regulated in other tissues. Therefore, attention is once again turning to a potential role for P4 in ovulatory follicle development and oocyte maturation in cattle. The current review summarizes the most recent findings in these areas.