Search

Introduction: Mesenchymal stem cells (MSCs) play a central role in mediating endogenous repair of cell and tissue damage. Biologic aging is a universal process that results in changes at the cellular and molecular levels. In the present study, the role of microRNA (miRNA) in age-induced molecular changes in MSCs derived from adipose tissue (ASCs) and bone marrow (BMSCs) from young and old human donors were investigated by using an unbiased genome-wide approach. Methods: Human ASCs and BMSCs from young and old donors were cultured, and total RNA was isolated. The miRNA fraction was enriched and used to determine the expression profile of miRNA in young and old donor MSCs. Based on miRNA expression, differences in donor MSCs were further investigated by using differentiation assays, Western blot, immunocytochemistry, and bioinformatics. Results: Biologic aging demonstrated reduced osteogenic and adipogenic potential in ASCs isolated from older donors, whereas cell size, complexity, and cell-surface markers remained intact with aging. Analysis of miRNA profiles revealed that small subsets of active miRNAs changed secondary to aging. Evaluation of miRNA showed significantly decreased levels of gene expression of inhibitory kappa B kinase (I\(\kappa\)B), interleukin-1\(\alpha\), inducible nitric oxide synthase (iNOS), mitogen-activated protein kinase/p38, ERK1/2, c-fos, and c-jun in MSCs from older donors by both bioinformatics and Western blot analysis. Nuclear factor kappa B (NF-\(\kappa\)B), myc, and interleukin-4 receptor mRNA levels were significantly elevated in aged cells from both the adipose and bone marrow depots. Immunocytochemistry showed nuclear localization in young donors, but a cytosolic predominance of phosphorylated NF-\(\kappa\)B in ASCs from older donors. Western blot demonstrated significantly elevated levels of NF-\(\kappa\)B subunits, p65 and p50, and AKT. Conclusions: These findings suggest that differential expression of miRNA is an integral component of biologic aging in MSCs.

Background: Colorectal cancer (CRC) is often diagnosed at a late stage with concomitant poor prognosis. The hypersensitive analytical technique of proteomics can detect molecular changes before the tumor is palpable. The surface-enhanced laser desorption/ionization-time of flight-mass spectra (SELDI-TOF-MS) is a newly-developed technique of evaluating protein separation in recent years. The protein chips have established the expression of tumor protein in the serum specimens and become the newly discovered markers for tumor diagnosis. The objective of this study was to find new markers of the diagnosis among groups of CRC, colorectal benign diseases (CBD) and healthy controls. The assay of SELDI-TOF-MS with analytical technique of protein-chip bioinformatics was used to detect the expression of protein mass peaks in the sera of patients or controls. One hundred serum samples, including 52 cases of colorectal cancer, 27 cases of colorectal benign disease, and 21 cases of healthy controls, were examined by SELDI-TOF-MS with WCX2 protein-chips. Results: The diagnostic models (I, II and III) were setup by analyzed the data and sieved markers using Ciphergen - Protein-Chip-Software 5.1. These models were combined with 3 protein mass peaks to discriminate CRC, CBD, and healthy controls. The accuracy, the sensitivity and the particularity of cross verification of these models are all highly over 80%. Conclusions: The SELDI-TOF-MS is a useful tool to help diagnose colorectal cancer, especially during the early stage. However, identification of the significantly differentiated proteins needs further study.

Background: Human immunity relies on the coordinated responses of many cellular subsets and functional states. Inter-individual variations in cellular composition and communication could thus potentially alter host protection. Here, we explore this hypothesis by applying single-cell RNA-sequencing to examine viral responses among the dendritic cells (DCs) of three elite controllers (ECs) of HIV-1 infection. Results: To overcome the potentially confounding effects of donor-to-donor variability, we present a generally applicable computational framework for identifying reproducible patterns in gene expression across donors who share a unifying classification. Applying it, we discover a highly functional antiviral DC state in ECs whose fractional abundance after in vitro exposure to HIV-1 correlates with higher CD4+ T cell counts and lower HIV-1 viral loads, and that effectively primes polyfunctional T cell responses in vitro. By integrating information from existing genomic databases into our reproducibility-based analysis, we identify and validate select immunomodulators that increase the fractional abundance of this state in primary peripheral blood mononuclear cells from healthy individuals in vitro. Conclusions: Overall, our results demonstrate how single-cell approaches can reveal previously unappreciated, yet important, immune behaviors and empower rational frameworks for modulating systems-level immune responses that may prove therapeutically and prophylactically useful. Electronic supplementary material The online version of this article (10.1186/s13059-017-1385-x) contains supplementary material, which is available to authorized users.

The ability to quantify cellular heterogeneity is a major advantage of single-cell technologies. However, statistical methods often treat cellular heterogeneity as a nuisance. We present a novel method to characterize differences in expression in the presence of distinct expression states within and among biological conditions. We demonstrate that this framework can detect differential expression patterns under a wide range of settings. Compared to existing approaches, this method has higher power to detect subtle differences in gene expression distributions that are more complex than a mean shift, and can characterize those differences. The freely available R package scDD implements the approach. Electronic supplementary material The online version of this article (doi:10.1186/s13059-016-1077-y) contains supplementary material, which is available to authorized users.

Background: Color polymorphism in the nacre of pteriomorphian bivalves is of great interest for the pearl culture industry. The nacreous layer of the Polynesian black-lipped pearl oyster Pinctada margaritifera exhibits a large array of color variation among individuals including reflections of blue, green, yellow and pink in all possible gradients. Although the heritability of nacre color variation patterns has been demonstrated by experimental crossing, little is known about the genes involved in these patterns. In this study, we identify a set of genes differentially expressed among extreme color phenotypes of P. margaritifera using a suppressive and subtractive hybridization (SSH) method comparing black phenotypes with full and half albino individuals. Results: Out of the 358 and 346 expressed sequence tags (ESTs) obtained by conducting two SSH libraries respectively, the expression patterns of 37 genes were tested with a real-time quantitative PCR (RT-qPCR) approach by pooling five individuals of each phenotype. The expression of 11 genes was subsequently estimated for each individual in order to detect inter-individual variation. Our results suggest that the color of the nacre is partially under the influence of genes involved in the biomineralization of the calcitic layer. A few genes involved in the formation of the aragonite tablets of the nacre layer and in the biosynthesis chain of melanin also showed differential expression patterns. Finally, high variability in gene expression levels were observed within the black phenotypes. Conclusions: Our results revealed that three main genetic processes were involved in color polymorphisms: the biomineralization of the nacreous and calcitic layers and the synthesis of pigments such as melanin, suggesting that color polymorphism takes place at different levels in the shell structure. The high variability of gene expression found within black phenotypes suggests that the present work should serve as a basis for future studies exploring more thoroughly the expression patterns of candidate genes within black phenotypes with different dominant iridescent colors. Electronic supplementary material The online version of this article (doi:10.1186/s12864-015-1776-x) contains supplementary material, which is available to authorized users.

Background: The accumulation of somatic mutations in genes and molecular pathways is a major factor in the evolution of oral squamous cell carcinoma (OSCC), which sparks studies to identify somatic mutations with clinical potentials. Recently, massively parallel sequencing technique has started to revolutionize biomedical studies, due to the rapid increase in its throughput and drop in cost. Hence sequencing of whole transcriptome (RNA-Seq) becomes a superior approach in cancer studies, which enables the detection of somatic mutations and accurate measurement of gene expression simultaneously. Methods: We used RNA-Seq data from tumor and matched normal samples to investigate somatic mutation spectrum in OSCC. Results: By applying a sophisticated bioinformatic pipeline, we interrogated two tumor samples and their matched normal tissues and identified 70,472 tumor somatic mutations in protein-coding regions. We further identified 515 significantly mutated genes (SMGs) and 156 tumor-specific disruptive genes (TDGs), with six genes in both sets, including ANKRA2, GTF2H5, STOML1, NUP37, PPP1R26, and TAF1L. Pathway analysis suggested that SMGs were enriched in cell adhesion pathways, which are frequently indicated in tumor development. We also found that SMGs tend to be differentially expressed between tumors and normal tissues, implying a regulatory role of accumulation of genetic aberrations in these genes. Conclusions: Our finding of known tumor genes proves of the utility of RNA-Seq in mutation screening, and functional analysis of genes detected here would help understand the molecular mechanism of OSCC.

Background: Obesity is a multi-factorial health problem in which genetic factors play an important role. Limited results have been obtained in single-gene studies using either genomic or transcriptomic data. RNA sequencing technology has shown its potential in gaining accurate knowledge about the transcriptome, and may reveal novel genes affecting complex diseases. Integration of genomic and transcriptomic variation (expression quantitative trait loci [eQTL] mapping) has identified causal variants that affect complex diseases. We integrated transcriptomic data from adipose tissue and genomic data from a porcine model to investigate the mechanisms involved in obesity using a systems genetics approach. Methods: Using a selective gene expression profiling approach, we selected 36 animals based on a previously created genomic Obesity Index for RNA sequencing of subcutaneous adipose tissue. Differential expression analysis was performed using the Obesity Index as a continuous variable in a linear model. eQTL mapping was then performed to integrate 60 K porcine SNP chip data with the RNA sequencing data. Results were restricted based on genome-wide significant single nucleotide polymorphisms, detected differentially expressed genes, and previously detected co-expressed gene modules. Further data integration was performed by detecting co-expression patterns among eQTLs and integration with protein data. Results: Differential expression analysis of RNA sequencing data revealed 458 differentially expressed genes. The eQTL mapping resulted in 987 cis-eQTLs and 73 trans-eQTLs (false discovery rate < 0.05), of which the cis-eQTLs were associated with metabolic pathways. We reduced the eQTL search space by focusing on differentially expressed and co-expressed genes and disease-associated single nucleotide polymorphisms to detect obesity-related genes and pathways. Building a co-expression network using eQTLs resulted in the detection of a module strongly associated with lipid pathways. Furthermore, we detected several obesity candidate genes, for example, ENPP1, CTSL, and ABHD12B. Conclusions: To our knowledge, this is the first study to perform an integrated genomics and transcriptomics (eQTL) study using, and modeling, genomic and subcutaneous adipose tissue RNA sequencing data on obesity in a porcine model. We detected several pathways and potential causal genes for obesity. Further validation and investigation may reveal their exact function and association with obesity. Electronic supplementary material The online version of this article (doi:10.1186/s13073-015-0229-0) contains supplementary material, which is available to authorized users.

Background: Analyses of Expressed Sequence Tags (ESTs) databases suggest that most human genes have multiple alternative splice variants. The alternative splicing of pre-mRNA is tightly regulated during development and in different tissue types. Changes in splicing patterns have been described in disease states. Recently, we used whole-transcriptome shotgun pryrosequencing to characterize 4 malignant pleural mesothelioma (MPM) tumors, 1 lung adenocarcinoma and 1 normal lung. We hypothesized that alternative splicing profiles might be detected in the sequencing data for the expressed genes in these samples. Methods: We developed a software pipeline to map the transcriptome read sequences of the 4 MPM samples and 1 normal lung sample onto known exon junction sequences in the comprehensive AceView database of expressed sequences and to count how many reads map to each junction. 13,274,187 transcriptome reads generated by the Roche/454 sequencing platform for 5 samples were compared with 151,486 exon junctions from the AceView database. The exon junction expression index (EJEI) was calculated for each exon junction in each sample to measure the differential expression of alternative splicing events. Top ten exon junctions with the largest EJEI difference between the 4 mesothelioma and the normal lung sample were then examined for differential expression using Quantitative Real Time PCR (qRT-PCR) in the 5 sequenced samples. Two of the differentially expressed exon junctions (ACTG2.aAug05 and CDK4.aAug05) were further examined with qRT-PCR in additional 18 MPM and 18 normal lung specimens. Results: We found 70,953 exon junctions covered by at least one sequence read in at least one of the 5 samples. All 10 identified most differentially expressed exon junctions were validated as present by RT-PCR, and 8 were differentially expressed exactly as predicted by the sequence analysis. The differential expression of the AceView exon junctions for the ACTG2 and CDK4 genes were also observed to be statistically significant in an additional 18 MPM and 18 normal lung samples examined using qRT-PCR. The differential expression of these two junctions was shown to successfully classify these mesothelioma and normal lung specimens with high sensitivity (89% and 78%, respectively).Conclusion Whole-transcriptome shotgun sequencing, combined with a downstream bioinformatics pipeline, provides powerful tools for the identification of differentially expressed exon junctions resulting from alternative splice variants. The alternatively spliced genes discovered in the study could serve as useful diagnostic markers as well as potential therapeutic targets for MPM.

Background: Microarray gene expression time-course experiments provide the opportunity to observe the evolution of transcriptional programs that cells use to respond to internal and external stimuli. Most commonly used methods for identifying differentially expressed genes treat each time point as independent and ignore important correlations, including those within samples and between sampling times. Therefore they do not make full use of the information intrinsic to the data, leading to a loss of power. Results: We present a flexible random-effects model that takes such correlations into account, improving our ability to detect genes that have sustained differential expression over more than one time point. By modeling the joint distribution of the samples that have been profiled across all time points, we gain sensitivity compared to a marginal analysis that examines each time point in isolation. We assign each gene a probability of differential expression using an empirical Bayes approach that reduces the effective number of parameters to be estimated. Conclusions: Based on results from theory, simulated data, and application to the genomic data presented here, we show that BETR has increased power to detect subtle differential expression in time-series data. The open-source R package betr is available through Bioconductor. BETR has also been incorporated in the freely-available, open-source MeV software tool available from http://www.tm4.org/mev.html.

Background: High density oligonucleotide tiling arrays are an effective and powerful platform for conducting unbiased genome-wide studies. The ab initio probe selection method employed in tiling arrays is unbiased, and thus ensures consistent sampling across coding and non-coding regions of the genome. These arrays are being increasingly used to study the associated processes of transcription, transcription factor binding, chromatin structure and their association. Studies of differential expression and/or regulation provide critical insight into the mechanics of transcription and regulation that occurs during the developmental program of a cell. The time-course experiment, which comprises an in-vivo system and the proposed analyses, is used to determine if annotated and un-annotated portions of genome manifest coordinated differential response to the induced developmental program. Results: We have proposed a novel approach, based on a piece-wise function – to analyze genome-wide differential response. This enables segmentation of the response based on protein-coding and non-coding regions; for genes the methodology also partitions differential response with a 5' versus 3' versus intra-genic bias. Conclusion: The algorithm built upon the framework of Significance Analysis of Microarrays, uses a generalized logic to define regions/patterns of coordinated differential change. By not adhering to the gene-centric paradigm, discordant differential expression patterns between exons and introns have been identified at a FDR of less than 12 percent. A co-localization of differential binding between RNA Polymerase II and tetra-acetylated histone has been quantified at a p-value < 0.003 it is most significant at the 5' end of genes, at a p-value < 10^{-13}. The prototype R code has been made available as supplementary material [see Additional file 1].

Background: Specific cellular states are often associated with distinct gene expression patterns. These states are plastic, changing during development, or in the transition from health to disease. One relatively simple extension of this concept is to recognize that we can classify different cell-types by their active gene regulatory networks and that, consequently, transitions between cellular states can be modeled by changes in these underlying regulatory networks. Results: Here we describe MONSTER, MOdeling Network State Transitions from Expression and Regulatory data, a regression-based method for inferring transcription factor drivers of cell state conditions at the gene regulatory network level. As a demonstration, we apply MONSTER to four different studies of chronic obstructive pulmonary disease to identify transcription factors that alter the network structure as the cell state progresses toward the disease-state. Conclusions: We demonstrate that MONSTER can find strong regulatory signals that persist across studies and tissues of the same disease and that are not detectable using conventional analysis methods based on differential expression. An R package implementing MONSTER is available at github.com/QuackenbushLab/MONSTER. Electronic supplementary material The online version of this article (doi:10.1186/s12918-017-0517-y) contains supplementary material, which is available to authorized users.

Background: Chronic obstructive pulmonary disease (COPD) is currently the third leading cause of death and there is a huge unmet clinical need to identify disease biomarkers in peripheral blood. Compared to gene level differential expression approaches to identify gene signatures, network analyses provide a biologically intuitive approach which leverages the co-expression patterns in the transcriptome to identify modules of co-expressed genes. Methods: A weighted gene co-expression network analysis (WGCNA) was applied to peripheral blood transcriptome from 238 COPD subjects to discover co-expressed gene modules. We then determined the relationship between these modules and forced expiratory volume in 1 s (FEV1). In a second, independent cohort of 381 subjects, we determined the preservation of these modules and their relationship with FEV1. For those modules that were significantly related to FEV1, we determined the biological processes as well as the blood cell-specific gene expression that were over-represented using additional external datasets. Results: Using WGCNA, we identified 17 modules of co-expressed genes in the discovery cohort. Three of these modules were significantly correlated with FEV1 (FDR < 0.1). In the replication cohort, these modules were highly preserved and their FEV1 associations were reproducible (P < 0.05). Two of the three modules were negatively related to FEV1 and were enriched in IL8 and IL10 pathways and correlated with neutrophil-specific gene expression. The positively related module, on the other hand, was enriched in DNA transcription and translation and was strongly correlated to CD4+, CD8+ T cell-specific gene expression. Conclusions: Network based approaches are promising tools to identify potential biomarkers for COPD. Trial registration The ECLIPSE study was funded by GlaxoSmithKline, under ClinicalTrials.gov identifier NCT00292552 and GSK No. SCO104960 Electronic supplementary material The online version of this article (doi:10.1186/s12931-017-0558-1) contains supplementary material, which is available to authorized users.

Background: Allele-specific expression (ASE) is differential expression of each of the two chromosomal alleles of an autosomal gene. We assessed ASE patterns in the human left atrium (LA, n = 62) and paired samples from the left ventricle (LV, n = 76) before and after ischemia, and tested the utility of differential ASE to identify genes associated with postoperative atrial fibrillation (poAF) and myocardial ischemia. Methods: Following genotyping from whole blood and whole-genome sequencing of LA and LV samples, we called ASE using sequences overlapping heterozygous SNPs using rigorous quality control to minimize false ASE calling. ASE patterns were compared between cardiac chambers and with a validation cohort from cadaveric tissue. ASE patterns in the LA were compared between patients who had poAF and those who did not. Changes in ASE in the LV were compared between paired baseline and post-ischemia samples. Results: ASE was found for 3404 (5.1%) and 8642 (4.0%) of SNPs analyzed in the LA and LV, respectively. Out of 6157 SNPs with ASE analyzed in both chambers, 2078 had evidence of ASE in both LA and LV (p < 0.0001). The SNP with the greatest ASE difference in the LA of patients with and without postoperative atrial fibrillation was within the gelsolin (GSN) gene, previously associated with atrial fibrillation in mice. The genes with differential ASE in poAF were enriched for myocardial structure genes, indicating the importance of atrial remodeling in the pathophysiology of AF. The greatest change in ASE between paired post-ischemic and baseline samples of the LV was in the zinc finger and homeodomain protein 2 (ZHX2) gene, a modulator of plasma lipids. Genes with differential ASE in ischemia were enriched in the ubiquitin ligase complex pathway associated with the ischemia-reperfusion response. Conclusions: Our results establish a pattern of ASE in the human heart, with a high degree of shared ASE between cardiac chambers as well as chamber-specific ASE. Furthermore, ASE analysis can be used to identify novel genes associated with (poAF) and myocardial ischemia. Electronic supplementary material The online version of this article (doi:10.1186/s13073-016-0381-1) contains supplementary material, which is available to authorized users.

Background: Genome-wide libraries of yeast deletion strains have been used to screen for genes that drive phenotypes such as stress response. A surprising observation emerging from these studies is that the genes with the largest changes in mRNA expression during a state transition are not those that drive that transition. Here, we show that integrating gene expression data with context-independent protein interaction networks can help prioritize master regulators that drive biological phenotypes. Results: Genes essential for survival had previously been shown to exhibit high centrality in protein interaction networks. However, the set of genes that drive growth in any specific condition is highly context-dependent. We inferred regulatory networks from gene expression data and transcription factor binding motifs in Saccharomyces cerevisiae, and found that high-degree nodes in regulatory networks are enriched for transcription factors that drive the corresponding phenotypes. We then found that using a metric combining protein interaction and transcriptional networks improved the enrichment for drivers in many of the contexts we examined. We applied this principle to a dataset of gene expression in normal human fibroblasts expressing a panel of viral oncogenes. We integrated regulatory interactions inferred from this data with a database of yeast two-hybrid protein interactions and ranked 571 human transcription factors by their combined network score. The ranked list was significantly enriched in known cancer genes that could not be found by standard differential expression or enrichment analyses. Conclusions: There has been increasing recognition that network-based approaches can provide insight into critical cellular elements that help define phenotypic state. Our analysis suggests that no one network, based on a single data type, captures the full spectrum of interactions. Greater insight can instead be gained by exploring multiple independent networks and by choosing an appropriate metric on each network. Moreover we can improve our ability to rank phenotypic drivers by combining the information from individual networks. We propose that such integrative network analysis could be used to combine clinical gene expression data with interaction databases to prioritize patient- and disease-specific therapeutic targets. Electronic supplementary material The online version of this article (doi:10.1186/s12918-015-0228-1) contains supplementary material, which is available to authorized users.

Background: The maturation of the brain involves the coordinated expression of thousands of genes, proteins and regulatory elements over time. In sensory pathways, gene expression profiles are modified by age and sensory experience in a manner that differs between brain regions and cell types. In the auditory system of altricial animals, neuronal activity increases markedly after the opening of the ear canals, initiating events that culminate in the maturation of auditory circuitry in the brain. This window provides a unique opportunity to study how gene expression patterns are modified by the onset of sensory experience through maturity. As a tool for capturing these features, next-generation sequencing of total RNA (RNAseq) has tremendous utility, because the entire transcriptome can be screened to index expression of any gene. To date, whole transcriptome profiles have not been generated for any central auditory structure in any species at any age. In the present study, RNAseq was used to profile two regions of the mouse auditory forebrain (A1, primary auditory cortex; MG, medial geniculate) at key stages of postnatal development (P7, P14, P21, adult) before and after the onset of hearing (~P12). Hierarchical clustering, differential expression, and functional geneset enrichment analyses (GSEA) were used to profile the expression patterns of all genes. Selected genesets related to neurotransmission, developmental plasticity, critical periods and brain structure were highlighted. An accessible repository of the entire dataset was also constructed that permits extraction and screening of all data from the global through single-gene levels. To our knowledge, this is the first whole transcriptome sequencing study of the forebrain of any mammalian sensory system. Although the data are most relevant for the auditory system, they are generally applicable to forebrain structures in the visual and somatosensory systems, as well. Results: The main findings were: (1) Global gene expression patterns were tightly clustered by postnatal age and brain region; (2) comparing A1 and MG, the total numbers of differentially expressed genes were comparable from P7 to P21, then dropped to nearly half by adulthood; (3) comparing successive age groups, the greatest numbers of differentially expressed genes were found between P7 and P14 in both regions, followed by a steady decline in numbers with age; (4) maturational trajectories in expression levels varied at the single gene level (increasing, decreasing, static, other); (5) between regions, the profiles of single genes were often asymmetric; (6) GSEA revealed that genesets related to neural activity and plasticity were typically upregulated from P7 to adult, while those related to structure tended to be downregulated; (7) GSEA and pathways analysis of selected functional networks were not predictive of expression patterns in the auditory forebrain for all genes, reflecting regional specificity at the single gene level. Conclusions: Gene expression in the auditory forebrain during postnatal development is in constant flux and becomes increasingly stable with age. Maturational changes are evident at the global through single gene levels. Transcriptome profiles in A1 and MG are distinct at all ages, and differ from other brain regions. The database generated by this study provides a rich foundation for the identification of novel developmental biomarkers, functional gene pathways, and targeted studies of postnatal maturation in the auditory forebrain. Electronic supplementary material The online version of this article (doi:10.1186/s12864-015-1709-8) contains supplementary material, which is available to authorized users.

Background: Gestational diabetes mellitus (GDM) affects approximately 10 % of pregnancies in the United States and increases the risk of adverse health outcomes in the offspring. These adult disease propensities may be set by anatomical and molecular alterations in the placenta associated with GDM. Results: To assess the mechanistic aspects of fetal programming, we measured genome-wide methylation (Infinium HumanMethylation450 BeadChips) and expression (Affymetrix transcriptome microarrays) in placental tissue of 41 GDM cases and 41 matched pregnancies without maternal complications from the Harvard Epigenetic Birth Cohort. Specific transcriptional and epigenetic perturbations associated with GDM status included alterations in the major histocompatibility complex (MHC) region, which were validated in an independent cohort, the Rhode Island Child Health Study. Gene ontology enrichment among gene regulation influenced by GDM revealed an over-representation of immune response pathways among differential expression, reflecting these coordinated changes in the MHC region. This differential methylation and expression may be capturing shifts in cellular composition, reflecting physiological changes in the placenta associated with GDM. Conclusions: Our study represents the largest investigation of transcriptomic and methylomic differences associated with GDM, providing comprehensive insight into how GDM shapes the intrauterine environment, which may have implications for fetal (re)programming. Electronic supplementary material The online version of this article (doi:10.1186/s13148-015-0116-y) contains supplementary material, which is available to authorized users.

Background: We recently identified two robust ovarian cancer subtypes, defined by the expression of genes involved in angiogenesis, with significant differences in clinical outcome. To identify potential regulatory mechanisms that distinguish the subtypes we applied PANDA, a method that uses an integrative approach to model information flow in gene regulatory networks. Results: We find distinct differences between networks that are active in the angiogenic and non-angiogenic subtypes, largely defined by a set of key transcription factors that, although previously reported to play a role in angiogenesis, are not strongly differentially-expressed between the subtypes. Our network analysis indicates that these factors are involved in the activation (or repression) of different genes in the two subtypes, resulting in differential expression of their network targets. Mechanisms mediating differences between subtypes include a previously unrecognized pro-angiogenic role for increased genome-wide DNA methylation and complex patterns of combinatorial regulation. Conclusions: The models we develop require a shift in our interpretation of the driving factors in biological networks away from the genes themselves and toward their interactions. The observed regulatory changes between subtypes suggest therapeutic interventions that may help in the treatment of ovarian cancer. Electronic supplementary material The online version of this article (doi:10.1186/s12859-015-0551-y) contains supplementary material, which is available to authorized users.

Background: During the latter half of the natural 48-h intraerythrocytic life cycle of human Plasmodium falciparum infection, parasites sequester deep in endothelium of tissues, away from the spleen and inaccessible to peripheral blood. These late-stage parasites may cause tissue damage and likely contribute to clinical disease, and a more complete understanding of their biology is needed. Because these life cycle stages are not easily sampled due to deep tissue sequestration, measuring in vivo gene expression of parasites in the trophozoite and schizont stages has been a challenge. Methods: We developed a custom nCounter® gene expression platform and used this platform to measure malaria parasite gene expression profiles in vitro and in vivo. We also used imputation to generate global transcriptional profiles and assessed differential gene expression between parasites growing in vitro and those recovered from malaria-infected patient tissues collected at autopsy. Results: We demonstrate, for the first time, global transcriptional expression profiles from in vivo malaria parasites sequestered in human tissues. We found that parasite physiology can be correlated with in vitro data from an existing life cycle data set, and that parasites in sequestered tissues show an expected schizont-like transcriptional profile, which is conserved across tissues from the same patient. Imputation based on 60 landmark genes generated global transcriptional profiles that were highly correlated with genome-wide expression patterns from the same samples measured by microarray. Finally, differential expression revealed a limited set of in vivo upregulated transcripts, which may indicate unique parasite genes involved in human clinical infections. Conclusions: Our study highlights the utility of a custom nCounter® P. falciparum probe set, validation of imputation within Plasmodium species, and documentation of in vivo schizont-stage expression patterns from human tissues. Electronic supplementary material The online version of this article (doi:10.1186/s13073-014-0110-6) contains supplementary material, which is available to authorized users.

In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.

Background: Mito-nuclear gene interactions regulate energy conversion, and are fundamental to eukaryotes. Generally, mito-nuclear coadaptation would be most efficient if the interacting nuclear genes were X-linked, because this maximizes the probability of favorable mito-nuclear allelic combinations co-transmitting across generations. Thus, under a coadaptation (CA) hypothesis, nuclear genes essential for mitochondrial function might be under selection to relocate to the X-chromosome. However, maternal inheritance predisposes the mitochondrial DNA (mtDNA) to accumulate variation that, while male-harming, is benign to females. Numerous nuclear genes were recently reported in Drosophila melanogaster, which exhibit male-specific patterns of differential expression when placed alongside different mtDNA haplotypes, suggesting that nuclear genes are sensitive to an underlying male-specific mitochondrial mutation load. These genes are thus candidates for involvement in mito-nuclear interactions driven by sexual conflict (SC), and selection might have moved them off the X-chromosome to facilitate an optimal evolutionary counter-response, through males, to the presence of male-harming mtDNA mutations. Furthermore, the presence of male-harming mtDNA mutations could exert selection for modifiers on the Y-chromosome, thus placing these mito-sensitive nuclear genes at the center of an evolutionary tug-of-war between mitochondrion and Y-chromosome. We test these hypotheses by examining the chromosomal distributions of three distinct sets of mitochondrial-interacting nuclear genes in D. melanogaster; the first is a list of genes with mitochondrial annotations by Gene Ontologies, the second is a list comprising the core evolutionary-conserved mitochondrial proteome, and the third is a list of genes involved in male-specific responses to maternally-inherited mitochondrial variation and which might be putative targets of Y-chromosomal regulation. Results: Genes with mitochondrial annotations and genes representing the mitochondrial proteome do not exhibit statistically-significant biases in chromosomal representation. However, genes exhibiting sex-specific sensitivity to mtDNA are under-represented on the X-chromosome, over-represented among genes known to be sensitive to Y-chromosomal variation, and among genes previously associated with male fitness, but under-represented among genes associated with direct sexual antagonism. Conclusions: Our results are consistent with the SC hypothesis, suggesting that mitochondrial mutational pressure selects for gene movement off-the-X, hence enabling mito-nuclear coadaptation to proceed along trajectories that result in optimized fitness in both sexes.

Background: High-grade osteosarcoma is a primary malignant bone tumor mostly occurring in adolescents and young adults, with a second peak at middle age. Overall survival is approximately 60%, and has not significantly increased since the introduction of neoadjuvant chemotherapy in the 1970s. The genomic profile of high-grade osteosarcoma is complex and heterogeneous. Integration of different types of genome-wide data may be advantageous in extracting relevant information from the large number of aberrations detected in this tumor. Methods: We analyzed genome-wide gene expression data of osteosarcoma cell lines and integrated these data with a kinome screen. Data were analyzed in statistical language R, using LIMMA for detection of differential expression/phosphorylation. We subsequently used Ingenuity Pathways Analysis to determine deregulated pathways in both data types. Results: Gene set enrichment indicated that pathways important in genomic stability are highly deregulated in these tumors, with many genes showing upregulation, which could be used as a prognostic marker, and with kinases phosphorylating peptides in these pathways. Akt and AMPK signaling were identified as active and inactive, respectively. As these pathways have an opposite role on mTORC1 signaling, we set out to inhibit Akt kinases with the allosteric Akt inhibitor MK-2206. This resulted in inhibition of proliferation of osteosarcoma cell lines U-2 OS and HOS, but not of 143B, which harbors a KRAS oncogenic transformation. Conclusions: We identified both overexpression and hyperphosphorylation in pathways playing a role in genomic stability. Kinome profiling identified active Akt signaling, which could inhibit proliferation in 2/3 osteosarcoma cell lines. Inhibition of PI3K/Akt/mTORC1 signaling may be effective in osteosarcoma, but further studies are required to determine whether this pathway is active in a substantial subgroup of this heterogeneous tumor.

Background Flowering is a critical transition in plant development, the timing of which can have considerable fitness consequences. Until recently, research into the genetic control of flowering time and its associated developmental changes was focused on core eudicots (for example, Arabidopsis) or monocots (for example, Oryza). Here we examine the flowering response of Aquilegia formosa, a member of the eudicot order Ranunculales that is emerging as an important model for the investigation of plant ecology and evolution. Results We have determined that A. formosa has a strong vernalization requirement but little or no photoperiod response, making it a day neutral (DN) plant. Consistent with this, the Aquilegia homolog of FLOWERING LOCUS T (AqFT) is expressed in both long and short days but surprisingly, the locus is expressed before the transition to flowering. In situ hybridizations with homologs of several Arabidopsis Floral Pathway Integrators (FPIs) do not suggest conserved functions relative to Arabidopsis, the potential exceptions being AqLFY and AqAGL24.2. Conclusions In Aquilegia, vernalization is critical to flowering but this signal is not strictly required for the transcriptional activation of AqFT. The expression patterns of AqLFY and AqAGL24.2 suggest a hypothesis for the development of Aquilegia's determinate inflorescence whereby their differential expression controls the progression of each meristem from inflorescence to floral identity. Interestingly, none of the Aquilegia expression patterns are consistent with a function in floral repression which, combined with the lack of a FLC homolog, means that new candidate genes must be identified for the control of vernalization response in Aquilegia., Organismic and Evolutionary Biology

Background: Copy number variants (CNVs), defined as losses and gains of segments of genomic DNA, are a major source of genomic variation. Results: In this study, we identified over 2,000 human CNVs that overlap with orthologous chimpanzee or orthologous macaque CNVs. Of these, 170 CNVs overlap with both chimpanzee and macaque CNVs, and these were collapsed into 34 hotspot regions of CNV formation. Many of these hotspot regions of CNV formation are functionally relevant, with a bias toward genes involved in immune function, some of which were previously shown to evolve under balancing selection in humans. The genes in these primate CNV formation hotspots have significant differential expression levels between species and show evidence for positive selection, indicating that they have evolved under species-specific, directional selection. Conclusions: These hotspots of primate CNV formation provide a novel perspective on divergence and selective pressures acting on these genomic regions.

Background: The evolution of gene expression is a challenging problem in evolutionary biology, for which accurate, well-calibrated measurements and methods are crucial. Results: We quantified gene expression with whole-transcriptome sequencing in four diploid, prototrophic strains of Saccharomyces species grown under the same condition to investigate the evolution of gene expression. We found that variation in expression is gene-dependent with large variations in each gene's expression between replicates of the same species. This confounds the identification of genes differentially expressed across species. To address this, we developed a statistical approach to establish significance bounds for inter-species differential expression in RNA-Seq data based on the variance measured across biological replicates. This metric estimates the combined effects of technical and environmental variance, as well as Poisson sampling noise by isolating each component. Despite a paucity of large expression changes, we found a strong correlation between the variance of gene expression change and species divergence (\(R^2\) = 0.90). Conclusion: We provide an improved methodology for measuring gene expression changes in evolutionary diverged species using RNA Seq, where experimental artifacts can mimic evolutionary effects.

Background: Mesothelioma is an aggressive neoplasm with few effective treatments, one being cytoreductive surgery. We previously described a test, based on differential expression levels of four genes, to predict clinical outcome in prospectively consented mesothelioma patients after surgery. In this study, we determined whether any of these four genes could be linked to a cancer relevant phenotype. Methods: We conducted a high-throughput RNA inhibition screen to knockdown gene expression levels of the four genes comprising the test (ARHGDIA, COBLL1, PKM2, TM4SF1) in both a human lung-derived normal and a tumor cell line using three different small inhibitory RNA molecules per gene. Successful knockdown was confirmed using quantitative RT-PCR. Detection of statistically significant changes in apoptosis and mitosis was performed using immunological assays and quantified using video-assisted microscopy at a single time-point. Changes in nuclear shape, size, and numbers were used to provide additional support of initial findings. Each experiment was conducted in triplicate. Specificity was assured by requiring that at least 2 different siRNAs produced the observed change in each cell line/time-point/gene/assay combination. Results: Knockdown of ARHGDIA, COBLL1, and TM4SF1 resulted in 2- to 4-fold increased levels of apoptosis in normal cells (ARHGDIA only) and tumor cells (all three genes). No statistically significant changes were observed in apoptosis after knockdown of PKM2 or for mitosis after knockdown of any gene. Conclusions: We provide evidence that ARHGDIA, COBLL1, and TM4SF1 are negative regulators of apoptosis in cultured tumor cells. These genes, and their related intracellular signaling pathways, may represent potential therapeutic targets in mesothelioma.

Background: Human peripheral blood monocytes (Mo) consist of subsets distinguished by expression of CD16 (FCγRIII) and chemokine receptors. Classical CD16- Mo express CCR2 and migrate in response to CCL2, while a minor CD16+ Mo subset expresses CD16 and CX3CR1 and migrates into tissues expressing CX3CL1. CD16+ Mo produce pro-inflammatory cytokines and are expanded in certain inflammatory conditions including sepsis and HIV infection. Results: To gain insight into the developmental relationship and functions of CD16+ and CD16- Mo, we examined transcriptional profiles of these Mo subsets in peripheral blood from healthy individuals. Of 16,328 expressed genes, 2,759 genes were differentially expressed and 228 and 250 were >2-fold upregulated and downregulated, respectively, in CD16+ compared to CD16- Mo. CD16+ Mo were distinguished by upregulation of transcripts for dendritic cell (DC) (SIGLEC10, CD43, RARA) and macrophage (MΦ) (CSF1R/CD115, MafB, CD97, C3aR) markers together with transcripts relevant for DC-T cell interaction (CXCL16, ICAM-2, LFA-1), cell activation (LTB, TNFRSF8, LST1, IFITM1-3, HMOX1, SOD-1, WARS, MGLL), and negative regulation of the cell cycle (CDKN1C, MTSS1), whereas CD16- Mo were distinguished by upregulation of transcripts for myeloid (CD14, MNDA, TREM1, CD1d, C1qR/CD93) and granulocyte markers (FPR1, GCSFR/CD114, S100A8-9/12). Differential expression of CSF1R, CSF3R, C1QR1, C3AR1, CD1d, CD43, CXCL16, and CX3CR1 was confirmed by flow cytometry. Furthermore, increased expression of RARA and KLF2 transcripts in CD16+ Mo coincided with absence of cell surface cutaneous lymphocyte associated antigen (CLA) expression, indicating potential imprinting for non-skin homing. Conclusion: These results suggest that CD16+ and CD16- Mo originate from a common myeloid precursor, with CD16+ Mo having a more MΦ – and DC-like transcription program suggesting a more advanced stage of differentiation. Distinct transcriptional programs, together with their recruitment into tissues via different mechanisms, also suggest that CD16+ and CD16- Mo give rise to functionally distinct DC and MΦ in vivo.

Background: High-throughput systems for gene expression profiling have been developed and have matured rapidly through the past decade. Broadly, these can be divided into two categories: hybridization-based and sequencing-based approaches. With data from different technologies being accumulated, concerns and challenges are raised about the level of agreement across technologies. As part of an ongoing large-scale cross-platform data comparison framework, we report here a comparison based on identical samples between one-dye DNA microarray platforms and MPSS (Massively Parallel Signature Sequencing). Results: The DNA microarray platforms generally provided highly correlated data, while moderate correlations between microarrays and MPSS were obtained. Disagreements between the two types of technologies can be attributed to limitations inherent to both technologies. The variation found between pooled biological replicates underlines the importance of exercising caution in identification of differential expression, especially for the purposes of biomarker discovery. Conclusion: Based on different principles, hybridization-based and sequencing-based technologies should be considered complementary to each other, rather than competitive alternatives for measuring gene expression, and currently, both are important tools for transcriptome profiling.

Background: Biologists often conduct multiple but different cDNA microarray studies that all target the same biological system or pathway. Within each study, replicate slides within repeated identical experiments are often produced. Pooling information across studies can help more accurately identify true target genes. Here, we introduce a method to integrate multiple independent studies efficiently. Results: We introduce a Bayesian hierarchical model to pool cDNA microarray data across multiple independent studies to identify highly expressed genes. Each study has multiple sources of variation, i.e. replicate slides within repeated identical experiments. Our model produces the gene-specific posterior probability of differential expression, which provides a direct method for ranking genes, and provides Bayesian estimates of false discovery rates (FDR). In simulations combining two and five independent studies, with fixed FDR levels, we observed large increases in the number of discovered genes in pooled versus individual analyses. When the number of output genes is fixed (e.g., top 100), the pooled model found appreciably more truly differentially expressed genes than the individual studies. We were also able to identify more differentially expressed genes from pooling two independent studies in Bacillus subtilis than from each individual data set. Finally, we observed that in our simulation studies our Bayesian FDR estimates tracked the true FDRs very well. Conclusion: Our method provides a cohesive framework for combining multiple but not identical microarray studies with several sources of replication, with data produced from the same platform. We assume that each study contains only two conditions: an experimental and a control sample. We demonstrated our model's suitability for a small number of studies that have been either pre-scaled or have no outliers., Statistics