Gene expression signatures defining fundamental biological processes in pluripotent, early, and late differentiated embryonic stem cells

Investigating the molecular mechanisms controlling the in vivo developmental program postembryogenesis is challenging and time consuming. However, the developmental program can be partly recapitulated in vitro by the use of cultured embryonic stem cells (ESCs). Similar to the totipotent cells of the inner cell mass, gene expression and morphological changes in cultured ESCs occur hierarchically during their differentiation, with epiblast cells developing first, followed by germ layers and finally somatic cells. Combination of high throughput -omics technologies with murine ESCs offers an alternative approach for studying developmental processes toward organ-specific cell phenotypes. We have made an attempt to understand differentiation networks controlling embryogenesis in vivo using a time kinetic, by identifying molecules defining fundamental biological processes in the pluripotent state as well as in early and the late differentiation stages of ESCs. Our microarray data of the differentiation of the ESCs clearly demonstrate that the most critical early differentiation processes occur at days 2 and 3 of differentiation. Besides monitoring well-annotated markers pertinent to both self-renewal and potency (capacity to differentiate to different cell lineage), we have identified candidate molecules for relevant signaling pathways. These molecules can be further investigated in gain and loss-of-function studies to elucidate their role for pluripotency and differentiation. As an example, siRNA knockdown of MageB16, a gene highly expressed in the pluripotent state, has proven its influence in inducing differentiation when its function is repressed

Genomic variation and strain-specific functional adaptation in the human gut microbiome during early life

The human gut microbiome matures towards the adult composition during the first years of life and is implicated in early immune development. Here, we investigate the effects of microbial genomic diversity on gut microbiome development using integrated early childhood data sets collected in the DIABIMMUNE study in Finland, Estonia and Russian Karelia. We show that gut microbial diversity is associated with household location and linear growth of children. Single nucleotide polymorphism- and metagenomic assembly-based strain tracking revealed large and highly dynamic microbial pangenomes, especially in the genus Bacteroides, in which we identified evidence of variability deriving from Bacteroides-targeting bacteriophages. Our analyses revealed functional consequences of strain diversity; only 10% of Finnish infants harboured Bifidobacterium longum subsp. infantis, a subspecies specialized in human milk metabolism, whereas Russian infants commonly maintained a probiotic Bifidobacterium bifidum strain in infancy. Groups of bacteria contributing to diverse, characterized metabolic pathways converged to highly subject-specific configurations over the first two years of life. This longitudinal study extends the current view of early gut microbial community assembly based on strain-level genomic variation.Peer reviewe

Genome-wide promoter analysis of histone modifications in human monocyte-derived antigen presenting cells

<p>Abstract</p> <p>Background</p> <p>Monocyte-derived macrophages and dendritic cells (DCs) are important in inflammatory processes and are often used for immunotherapeutic approaches. Blood monocytes can be differentiated into macrophages and DCs, which is accompanied with transcriptional changes in many genes, including chemokines and cell surface markers.</p> <p>Results</p> <p>To study the chromatin modifications associated with this differentiation, we performed a genome wide analysis of histone H3 trimethylation on lysine 4 (H3K4me3) and 27 (H3K27me3) as well as acetylation of H3 lysines (AcH3) in promoter regions. We report that both H3K4me3 and AcH3 marks significantly correlate with transcriptionally active genes whereas H3K27me3 mark is associated with inactive gene promoters. During differentiation, the H3K4me3 levels decreased on monocyte-specific CD14, CCR2 and CX3CR1 but increased on DC-specific TM7SF4/DC-STAMP, TREM2 and CD209/DC-SIGN genes. Genes associated with phagocytosis and antigen presentation were marked by H3K4me3 modifications. We also report that H3K4me3 levels on clustered chemokine and surface marker genes often correlate with transcriptional activity.</p> <p>Conclusion</p> <p>Our results provide a basis for further functional correlations between gene expression and histone modifications in monocyte-derived macrophages and DCs.</p

Exome sequencing of a colorectal cancer family reveals shared mutation pattern and predisposition circuitry along tumor pathways

The molecular basis of cancer and cancer multiple phenotypes are not yet fully understood. Next Generation Sequencing promises new insight into the role of genetic interactions in shaping the complexity of cancer. Aiming to outline the differences in mutation patterns between familial colorectal cancer cases and controls we analyzed whole exomes of cancer tissues and control samples from an extended colorectal cancer pedigree, providing one of the first data sets of exome sequencing of cancer in an African population against a background of large effective size typically with excess of variants. Tumors showed hMSH2 loss of function SNV consistent with Lynch syndrome. Sets of genes harboring insertions-deletions in tumor tissues revealed, however, significant GO enrichment, a feature that was not seen in control samples, suggesting that ordered insertions-deletions are central to tumorigenesis in this type of cancer. Network analysis identified multiple hub genes of centrality. ELAVL1/HuR showed remarkable centrality, interacting specially with genes harboring non-synonymous SNVs thus reinforcing the proposition of targeted mutagenesis in cancer pathways. A likely explanation to such mutation pattern is DNA/RNA editing, suggested here by nucleotide transition-to-transversion ratio that significantly departed from expected values (p-value 5e-6). NFKB1 also showed significant centrality along with ELAVL1, raising the suspicion of viral etiology given the known interaction between oncogenic viruses and these proteins

Shared activity patterns arising at genetic susceptibility loci reveal underlying genomic and cellular architecture of human disease

<div><p>Genetic variants underlying complex traits, including disease susceptibility, are enriched within the transcriptional regulatory elements, promoters and enhancers. There is emerging evidence that regulatory elements associated with particular traits or diseases share similar patterns of transcriptional activity. Accordingly, shared transcriptional activity (coexpression) may help prioritise loci associated with a given trait, and help to identify underlying biological processes. Using cap analysis of gene expression (CAGE) profiles of promoter- and enhancer-derived RNAs across 1824 human samples, we have analysed coexpression of RNAs originating from trait-associated regulatory regions using a novel quantitative method (network density analysis; NDA). For most traits studied, phenotype-associated variants in regulatory regions were linked to tightly-coexpressed networks that are likely to share important functional characteristics. Coexpression provides a new signal, independent of phenotype association, to enable fine mapping of causative variants. The NDA coexpression approach identifies new genetic variants associated with specific traits, including an association between the regulation of the OCT1 cation transporter and genetic variants underlying circulating cholesterol levels. NDA strongly implicates particular cell types and tissues in disease pathogenesis. For example, distinct groupings of disease-associated regulatory regions implicate two distinct biological processes in the pathogenesis of ulcerative colitis; a further two separate processes are implicated in Crohn’s disease. Thus, our functional analysis of genetic predisposition to disease defines new distinct disease endotypes. We predict that patients with a preponderance of susceptibility variants in each group are likely to respond differently to pharmacological therapy. Together, these findings enable a deeper biological understanding of the causal basis of complex traits.</p></div

MyoMiner: explore gene co-expression in normal and pathological muscle

International audienceBackground: High-throughput transcriptomics measures mRNA levels for thousands of genes in a biological sample. Most gene expression studies aim to identify genes that are differentially expressed between different biological conditions, such as between healthy and diseased states. However, these data can also be used to identify genes that are co-expressed within a biological condition. Gene co-expression is used in a guilt-by-association approach to prioritize candidate genes that could be involved in disease, and to gain insights into the functions of genes, protein relations, and signaling pathways. Most existing gene co-expression databases are generic, amalgamating data for a given organism regardless of tissue-type.Methods: To study muscle-specific gene co-expression in both normal and pathological states, publicly available gene expression data were acquired for 2376 mouse and 2228 human striated muscle samples, and separated into 142 categories based on species (human or mouse), tissue origin, age, gender, anatomic part, and experimental condition. Co-expression values were calculated for each category to create the MyoMiner database.Results: Within each category, users can select a gene of interest, and the MyoMiner web interface will return all correlated genes. For each co-expressed gene pair, adjusted p-value and confidence intervals are provided as measures of expression correlation strength. A standardized expression-level scatterplot is available for every gene pair r-value. MyoMiner has two extra functions: (a) a network interface for creating a 2-shell correlation network, based either on the most highly correlated genes or from a list of genes provided by the user with the option to include linked genes from the database and (b) a comparison tool from which the users can test whether any two correlation coefficients from different conditions are significantly different.Conclusions: These co-expression analyses will help investigators to delineate the tissue-, cell-, and pathology-specific elements of muscle protein interactions, cell signaling and gene regulation. Changes in co-expression between pathologic and healthy tissue may suggest new disease mechanisms and help define novel therapeutic targets. Thus, MyoMiner is a powerful muscle-specific database for the discovery of genes that are associated with related functions based on their co-expression. MyoMiner is freely available at https://www.sys-myo.com/myominer