Transcriptome network analysis links perinatal Staphylococcus epidermidis infection to microglia reprogramming in the immature hippocampus.

Staphylococcus epidermidis (S. epidermidis) is the most common nosocomial pathogen in preterm infants and associated with increased risk of cognitive delay, however, underlying mechanisms are unknown. We employed morphological, transcriptomic and physiological methods to extensively characterize microglia in the immature hippocampus following S. epidermidis infection. 3D morphological analysis revealed activation of microglia after S. epidermidis. Differential expression combined with network analysis identified NOD-receptor signaling and trans-endothelial leukocyte trafficking as major mechanisms in microglia. In support, active caspase-1 was increased in the hippocampus and using the LysM-eGFP knock-in transgenic mouse, we demonstrate infiltration of leukocytes to the brain together with disruption of the blood-brain barrier. Our findings identify activation of microglia inflammasome as a major mechanism underlying neuroinflammation following infection. The results demonstrate that neonatal S. epidermidis infection share analogies with S. aureus and neurological diseases, suggesting a previously unrecognized important role in neurodevelopmental disorders in preterm born children.

The proteome signature of cord blood plasma with high hematopoietic stem and progenitor cell count.

Hematopoietic stem and progenitor cells (HSPC) from umbilical cord blood (UCB) are used for transplantation to treat blood disorders. Methods to estimate the HSPC count in umbilical cord blood, and thereby identify high-value blood units, are time-consuming and costly. Recent studies indicate that the UCB plasma protein composition relates to the HSPC count. We compared the plasma proteome of UCB with high vs low HSPC cell count (>115 × 106 vs < 51 × 106 CD34+ cells l-1) by using a combination of global untargeted MS quantitative proteomics and targeted proximity extension assay (PEA) proteomics. For the MS platform, 96 proteins differed significantly between the CD34+ groups, and out of these, 44 proteins showed more than a two-fold difference. Seven pathways were enriched in high CD34+ samples, including pathways relating to platelets, coagulation, and lipid transport. For the PEA platform, 61 proteins were differentially abundant, and among these 7 proteins showed more than a two-fold difference between groups. In the PEA data, a high CD34+ cell count was associated with a protein hub with functions in platelet degranulation. We conclude that the HSPC count is related to the UCB plasma proteome, but that further studies are needed to discern if these findings reflect causal relationships.

Expression of S100A Alarmins in Cord Blood Monocytes Is Highly Associated With Chorioamnionitis and Fetal Inflammation in Preterm Infants.

Background: Preterm infants exposed to chorioamnionitis and with a fetal inflammatory response are at risk for neonatal morbidity and adverse outcome. Alarmins S100A8, S100A9, and S100A12 are expressed by myeloid cells and have been associated with inflammatory activation and monocyte modulation. Aim: To study S100A alarmin expression in cord blood monocytes from term healthy and preterm infants and relate results to clinical findings, inflammatory biomarkers and alarmin protein levels, as well as pathways identified by differentially regulated monocyte genes. Methods: Cord blood CD14+ monocytes were isolated from healthy term (n = 10) and preterm infants (<30 weeks gestational age, n = 33) by MACS technology. Monocyte RNA was sequenced and gene expression was analyzed by Principal Component Analysis and hierarchical clustering. Pathways were identified by Ingenuity Pathway Analysis. Inflammatory proteins were measured by Multiplex ELISA, and plasma S100A proteins by mass spectrometry. Histological chorioamnionitis (HCA) and fetal inflammatory response syndrome (FIRS) were diagnosed by placenta histological examination. Results: S100A8, S100A9, and S100A12 gene expression was significantly increased and with a wider range in preterm vs. term infants. High S100A8 and S100A9 gene expression (n = 17) within the preterm group was strongly associated with spontaneous onset of delivery, HCA, FIRS and elevated inflammatory proteins in cord blood, while low expression (n = 16) was associated with impaired fetal growth and physician-initiated delivery. S100A8 and S100A9 protein levels were significantly lower in preterm vs. term infants, but within the preterm group high S100A gene expression, spontaneous onset of labor, HCA and FIRS were associated with elevated protein levels. One thousand nine hundred genes were differentially expressed in preterm infants with high vs. low S100A alarmin expression. Analysis of 124 genes differentially expressed in S100A high as well as FIRS and HCA groups identified 18 common pathways and S100A alarmins represented major hubs in network analyses. Conclusion: High expression of S100A alarmins in cord blood monocytes identifies a distinct clinical risk group of preterm infants exposed to chorioamnionitis and with a fetal inflammatory response. Gene and pathway analyses suggest that high S100A alarmin expression also affects monocyte function. The connection with monocyte phenotype and inflammation-stimulated S100A expression in other cell types (e.g., neutrophils) warrants further investigation.

Independent losses of a xenobiotic receptor across teleost evolution.

Sensitivity to environmental stressors largely depend on the genetic complement of the organism. Recent sequencing and assembly of teleost fish genomes enable us to trace the evolution of defense genes in the largest and most diverse group of vertebrates. Through genomic searches and in-depth analysis of gene loci in 76 teleost genomes, we show here that the xenosensor pregnane X receptor (Pxr, Nr1i2) is absent in more than half of these species. Notably, out of the 27 genome assemblies that belong to the Gadiformes order, the pxr gene was only retained in the Merluccidae family (hakes) and Pelagic cod (Melanonus zugmayeri). As an important receptor for a wide range of drugs and environmental pollutants, vertebrate PXR regulate the transcription of a number of genes involved in the biotransformation of xenobiotics, including cytochrome P450 enzymes (CYP). In the absence of Pxr, we suggest that the aryl hydrocarbon receptor (Ahr) have evolved an extended regulatory role by governing the expression of certain Pxr target genes, such as cyp3a, in Atlantic cod (Gadus morhua). However, as several independent losses of pxr have occurred during teleost evolution, other lineages and species may have adapted alternative compensating mechanisms for controlling crucial cellular defense mechanisms.

ClusTrack: feature extraction and similarity measures for clustering of genome-wide data sets.

Clustering is a popular technique for explorative analysis of data, as it can reveal subgroupings and similarities between data in an unsupervised manner. While clustering is routinely applied to gene expression data, there is a lack of appropriate general methodology for clustering of sequence-level genomic and epigenomic data, e.g. ChIP-based data. We here introduce a general methodology for clustering data sets of coordinates relative to a genome assembly, i.e. genomic tracks. By defining appropriate feature extraction approaches and similarity measures, we allow biologically meaningful clustering to be performed for genomic tracks using standard clustering algorithms. An implementation of the methodology is provided through a tool, ClusTrack, which allows fine-tuned clustering analyses to be specified through a web-based interface. We apply our methods to the clustering of occupancy of the H3K4me1 histone modification in samples from a range of different cell types. The majority of samples form meaningful subclusters, confirming that the definitions of features and similarity capture biological, rather than technical, variation between the genomic tracks. Input data and results are available, and can be reproduced, through a Galaxy Pages document at http://hyperbrowser.uio.no/hb/u/hb-superuser/p/clustrack. The clustering functionality is available as a Galaxy tool, under the menu option "Specialized analyzis of tracks", and the submenu option "Cluster tracks based on genome level similarity", at the Genomic HyperBrowser server: http://hyperbrowser.uio.no/hb/.

The Genomic HyperBrowser: an analysis web server for genome-scale data.

The immense increase in availability of genomic scale datasets, such as those provided by the ENCODE and Roadmap Epigenomics projects, presents unprecedented opportunities for individual researchers to pose novel falsifiable biological questions. With this opportunity, however, researchers are faced with the challenge of how to best analyze and interpret their genome-scale datasets. A powerful way of representing genome-scale data is as feature-specific coordinates relative to reference genome assemblies, i.e. as genomic tracks. The Genomic HyperBrowser (http://hyperbrowser.uio.no) is an open-ended web server for the analysis of genomic track data. Through the provision of several highly customizable components for processing and statistical analysis of genomic tracks, the HyperBrowser opens for a range of genomic investigations, related to, e.g., gene regulation, disease association or epigenetic modifications of the genome.

Integrative analysis reveals relationships of genetic and epigenetic alterations in osteosarcoma.

BACKGROUND: Osteosarcomas are the most common non-haematological primary malignant tumours of bone, and all conventional osteosarcomas are high-grade tumours showing complex genomic aberrations. We have integrated genome-wide genetic and epigenetic profiles from the EuroBoNeT panel of 19 human osteosarcoma cell lines based on microarray technologies. PRINCIPAL FINDINGS: The cell lines showed complex patterns of DNA copy number changes, where genomic copy number gains were significantly associated with gene-rich regions and losses with gene-poor regions. By integrating the datasets, 350 genes were identified as having two types of aberrations (gain/over-expression, hypo-methylation/over-expression, loss/under-expression or hyper-methylation/under-expression) using a recurrence threshold of 6/19 (>30%) cell lines. The genes showed in general alterations in either DNA copy number or DNA methylation, both within individual samples and across the sample panel. These 350 genes are involved in embryonic skeletal system development and morphogenesis, as well as remodelling of extracellular matrix. The aberrations of three selected genes, CXCL5, DLX5 and RUNX2, were validated in five cell lines and five tumour samples using PCR techniques. Several genes were hyper-methylated and under-expressed compared to normal osteoblasts, and expression could be reactivated by demethylation using 5-Aza-2'-deoxycytidine treatment for four genes tested; AKAP12, CXCL5, EFEMP1 and IL11RA. Globally, there was as expected a significant positive association between gain and over-expression, loss and under-expression as well as hyper-methylation and under-expression, but gain was also associated with hyper-methylation and under-expression, suggesting that hyper-methylation may oppose the effects of increased copy number for detrimental genes. CONCLUSIONS: Integrative analysis of genome-wide genetic and epigenetic alterations identified dependencies and relationships between DNA copy number, DNA methylation and mRNA expression in osteosarcomas, contributing to better understanding of osteosarcoma biology.

Identification of osteosarcoma driver genes by integrative analysis of copy number and gene expression data.

High-grade osteosarcoma is a tumor with a complex genomic profile, occurring primarily in adolescents with a second peak at middle age. The extensive genomic alterations obscure the identification of genes driving tumorigenesis during osteosarcoma development. To identify such driver genes, we integrated DNA copy number profiles (Affymetrix SNP 6.0) of 32 diagnostic biopsies with 84 expression profiles (Illumina Human-6 v2.0) of high-grade osteosarcoma as compared with its putative progenitor cells, i.e., mesenchymal stem cells (n = 12) or osteoblasts (n = 3). In addition, we performed paired analyses between copy number and expression profiles of a subset of 29 patients for which both DNA and mRNA profiles were available. Integrative analyses were performed in Nexus Copy Number software and statistical language R. Paired analyses were performed on all probes detecting significantly differentially expressed genes in corresponding LIMMA analyses. For both nonpaired and paired analyses, copy number aberration frequency was set to >35%. Nonpaired and paired integrative analyses resulted in 45 and 101 genes, respectively, which were present in both analyses using different control sets. Paired analyses detected >90% of all genes found with the corresponding nonpaired analyses. Remarkably, approximately twice as many genes as found in the corresponding nonpaired analyses were detected. Affected genes were intersected with differentially expressed genes in osteosarcoma cell lines, resulting in 31 new osteosarcoma driver genes. Cell division related genes, such as MCM4 and LATS2, were overrepresented and genomic instability was predictive for metastasis-free survival, suggesting that deregulation of the cell cycle is a driver of osteosarcomagenesis.

The differential disease regulome.

BACKGROUND: Transcription factors in disease-relevant pathways represent potential drug targets, by impacting a distinct set of pathways that may be modulated through gene regulation. The influence of transcription factors is typically studied on a per disease basis, and no current resources provide a global overview of the relations between transcription factors and disease. Furthermore, existing pipelines for related large-scale analysis are tailored for particular sources of input data, and there is a need for generic methodology for integrating complementary sources of genomic information. RESULTS: We here present a large-scale analysis of multiple diseases versus multiple transcription factors, with a global map of over-and under-representation of 446 transcription factors in 1010 diseases. This map, referred to as the differential disease regulome, provides a first global statistical overview of the complex interrelationships between diseases, genes and controlling elements. The map is visualized using the Google map engine, due to its very large size, and provides a range of detailed information in a dynamic presentation format.The analysis is achieved through a novel methodology that performs a pairwise, genome-wide comparison on the cartesian product of two distinct sets of annotation tracks, e.g. all combinations of one disease and one TF.The methodology was also used to extend with maps using alternative data sets related to transcription and disease, as well as data sets related to Gene Ontology classification and histone modifications. We provide a web-based interface that allows users to generate other custom maps, which could be based on precisely specified subsets of transcription factors and diseases, or, in general, on any categorical genome annotation tracks as they are improved or become available. CONCLUSION: We have created a first resource that provides a global overview of the complex relations between transcription factors and disease. As the accuracy of the disease regulome depends mainly on the quality of the input data, forthcoming ChIP-seq based binding data for many TFs will provide improved maps. We further believe our approach to genome analysis could allow an advance from the current typical situation of one-time integrative efforts to reproducible and upgradable integrative analysis. The differential disease regulome and its associated methodology is available at http://hyperbrowser.uio.no.

The Genomic HyperBrowser: inferential genomics at the sequence level.

The immense increase in the generation of genomic scale data poses an unmet analytical challenge, due to a lack of established methodology with the required flexibility and power. We propose a first principled approach to statistical analysis of sequence-level genomic information. We provide a growing collection of generic biological investigations that query pairwise relations between tracks, represented as mathematical objects, along the genome. The Genomic HyperBrowser implements the approach and is available at http://hyperbrowser.uio.no.

Evaluation of high-resolution microarray platforms for genomic profiling of bone tumours.

BACKGROUND: Several high-density oligonucleotide microarray platforms are available for genome-wide single nucleotide polymorphism (SNP) detection and microarray-based comparative genomic hybridisation (array CGH), which may be used to detect copy number aberrations in human tumours. As part of the EuroBoNeT network of excellence for research on bone tumours (eurobonet.eu), we have evaluated four different commercial high-resolution microarray platforms in order to identify the most appropriate technology for mapping DNA copy number aberrations in such tumours. FINDINGS: DNA from two different cytogenetically well-characterized bone sarcoma cell lines, representing a simple and a complex karyotype, respectively, was tested in duplicate on four high-resolution microarray platforms; Affymetrix Genome-Wide Human SNP Array 6.0, Agilent Human Genome CGH 244A, Illumina HumanExon510s-duo and Nimblegen HG18 CGH 385 k WG tiling v1.0. The data was analysed using the platform-specific analysis software, as well as a platform-independent analysis algorithm. DNA copy number was measured at six specific chromosomes or chromosomal regions, and compared with the expected ratio based on available cytogenetic information. All platforms performed well in terms of reproducibility and were able to delimit and score small amplifications and deletions at similar resolution, but Agilent microarrays showed better linearity and dynamic range. The platform-specific analysis software provided with each platform identified in general correct copy numbers, whereas using a platform-independent analysis algorithm, correct copy numbers were determined mainly for Agilent and Affymetrix microarrays. CONCLUSIONS: All platforms performed reasonably well, but Agilent microarrays showed better dynamic range, and like Affymetrix microarrays performed well with the platform-independent analysis software, implying more robust data. Bone tumours like osteosarcomas are heterogeneous tumours with complex karyotypes that may be difficult to interpret, and it is of importance to be able to well separate the copy number levels and detect copy number changes in subpopulations. Taking all this into consideration, the Agilent and Affymetrix microarray platforms were found to be a better choice for mapping DNA copy numbers in bone tumours, the latter having the advantage of also providing heterozygosity information.

Incorporating prior biological information in linkage studies increases power and limits multiple testing.

We used the Genetic Analysis Workshop 15 Problem 1 data set to search for expression phenotype quantitative trait loci in a highly selected group of genes with a supposedly correlated role in the development of the enteric nervous system. Our strategy was to reduce the level of multiple testing by analyzing at the genome-wide level a limited number of genes considered to be the most promising enteric nervous system candidates on the basis of mouse expression data, and then extend the analysis to a larger number of traits only for a small number of candidate linked regions. Such a study design allowed us to identify a "master regulator" locus for several genes involved in the enteric nervous system, located in 9q31. In particular, one of four traits included in the genome-wide analysis and 2 of 57 from the follow-up single-chromosome analysis showed LOD scores above 2 around position 109 on chromosome 9 by univariate variance-component linkage analysis. Bivariate linkage analysis further supported the presence of a common regulatory locus, with a maximum multipoint LOD score of 5.17 and five additional LOD scores > 3 in the same region. This region is particularly interesting because a susceptibility locus for Hirschsprung disease, a disease characterized by enteric malformation, was previously mapped to 9q31. The proposed strategy of limiting the genome-wide analysis to a small number of well characterized candidate expression phenotypes and following up the most promising results in a larger number of correlated traits may prove successful for other groups of genes involved in a common pathway.

Univariate and bivariate variance component linkage analysis of a whole-genome scan for loci contributing to bone mineral density.

Osteoporosis is a common condition characterized by reduced skeletal strength and increased susceptibility to fracture. The single major risk factor for osteoporosis is low bone mineral density (BMD) and strong evidence exists that genetic factors are in part responsible for an individual's BMD. A cohort of 40 multiplex Caucasian families selected through a proband with osteoporosis was genotyped for microsatellite markers spaced at an average of 10 cM, and linkage to femoral neck (FN), lumbar spine (LS) and trochanter (TR) BMD was analyzed using univariate and bivariate variance component linkage analysis. Maximum univariate multipoint lod-scores were 2.87 on chromosome 1p36 for FN BMD, 1.89 on 6q27 for TR BMD, and 2.15 on 7p15 for LS BMD. Results of bivariate linkage analysis were highly correlated with those of the univariate analysis, although generally less significant, suggesting the possibility that some of these susceptibility loci may exert pleiotropic effects on multiple skeletal sites.