Probing the mechanobiological properties of human embryonic stem cells in cardiac differentiation by optical tweezers

Human embryonic stem cells (hESC) and hESC-derived cardiomyocytes (hESC-CM) hold great promise for the treatment of cardiovascular diseases. However the mechanobiological properties of hESC and hESC-CM remains elusive. In this paper, we examined the dynamic and static micromechanical properties of hESC and hESC-CM, by manipulating via optical tweezers at the single-cell level. Theoretical approaches were developed to model the dynamic and static mechanical responses of cells during optical stretching. Our experiments showed that the mechanical stiffness of differentiated hESC-CM increased after cardiac differentiation. Such stiffening could associate with increasingly organized myofibrillar assembly that underlines the functional characteristics of hESC-CM. In summary, our findings lay the ground work for using hESC-CMs as models to study mechanical and contractile defects in heart diseases.     

Progressive maturation in contracting cardiomyocytes derived from human embryonic stem cells: Qualitative effects on electrophysiological responses to drugs

The field of drug testing currently needs a new integrated assay system, as accurate as systems using native tissues, that will allow us to predict arrhythmia risks of candidate drugs and the relationship between genetic mutations and acquired electrophysiological phenotypes. This could be accomplished by combining the microelectrode array (MEA) system with cardiomyocytes (CMs) derived from human embryonic stem cells (hESC) and induced pluripotential stem cells. CMs have been successfully induced from both types, but their maturation process is not systematically controlled; this results in loss of beating potency and insufficient ion channel function. We generated a transgenic hESC line that facilitates maintenance of hESC-CM clusters every 2 weeks by expressing GFP driven by a cardiac-specific αMHC promoter, thereby producing a compact pacemaker lineage within a ventricular population over a year. Further analyses, including quantitative RT-PCR, patch-clamp, and MEA-mediated QT tests, demonstrated that replating culturing continuously enhanced gene expression, ionic current amplitudes, and resistance to K⁺ channel blockades in hESC-CMs. Moreover, temporal three-dimensional (3D) culturing accelerated maturation by restoring the global gene repressive status established in the adhesive status. Replating/3D culturing thus produces hESC-CMs that act as functional syncytia suitable for use in regenerative medicine and accurate drug tests.     

Ago2 Immunoprecipitation Identifies Predicted MicroRNAs in Human Embryonic Stem Cells and Neural Precursors

Background

MicroRNAs are required for maintenance of pluripotency as well as differentiation, but since more microRNAs have been computationally predicted in genome than have been found, there are likely to be undiscovered microRNAs expressed early in stem cell differentiation.

Methodology/Principal Findings

SOLiD ultra-deep sequencing identified >10⁷ unique small RNAs from human embryonic stem cells (hESC) and neural-restricted precursors that were fit to a model of microRNA biogenesis to computationally predict 818 new microRNA genes. These predicted genomic loci are associated with chromatin patterns of modified histones that are predictive of regulated gene expression. 146 of the predicted microRNAs were enriched in Ago2-containing complexes along with 609 known microRNAs, demonstrating association with a functional RISC complex. This Ago2 IP-selected subset was consistently expressed in four independent hESC lines and exhibited complex patterns of regulation over development similar to previously-known microRNAs, including pluripotency-specific expression in both hESC and iPS cells. More than 30% of the Ago2 IP-enriched predicted microRNAs are new members of existing families since they share seed sequences with known microRNAs.

Conclusions/Significance

Extending the classic definition of microRNAs, this large number of new microRNA genes, the majority of which are less conserved than their canonical counterparts, likely represent evolutionarily recent regulators of early differentiation. The enrichment in Ago2 containing complexes, the presence of chromatin marks indicative of regulated gene expression, and differential expression over development all support the identification of 146 new microRNAs active during early hESC differentiation.     

MarVis: a tool for clustering and visualization of metabolic biomarkers

Background

Gene set analysis based on Gene Ontology (GO) can be a promising method for the analysis of differential expression patterns. However, current studies that focus on individual GO terms have limited analytical power, because the complex structure of GO introduces strong dependencies among the terms, and some genes that are annotated to a GO term cannot be found by statistically significant enrichment.

Results

We proposed a method for enriching clustered GO terms based on semantic similarity, namely cluster enrichment analysis based on GO (CeaGO), to extend the individual term analysis method. Using an Affymetrix HGU95aV2 chip dataset with simulated gene sets, we illustrated that CeaGO was sensitive enough to detect moderate expression changes. When compared to parent-based individual term analysis methods, the results showed that CeaGO may provide more accurate differentiation of gene expression results. When used with two acute leukemia (ALL and ALL/AML) microarray expression datasets, CeaGO correctly identified specifically enriched GO groups that were overlooked by other individual test methods.

Conclusion

By applying CeaGO to both simulated and real microarray data, we showed that this approach could enhance the interpretation of microarray experiments. CeaGO is currently available at http://chgc.sh.cn/en/software/CeaGO/.     

Microarray Meta-Analysis Identifies Acute Lung Injury Biomarkers in Donor Lungs That Predict Development of Primary Graft Failure in Recipients

Objectives

To perform a meta-analysis of gene expression microarray data from animal studies of lung injury, and to identify an injury-specific gene expression signature capable of predicting the development of lung injury in humans.

Methods

We performed a microarray meta-analysis using 77 microarray chips across six platforms, two species and different animal lung injury models exposed to lung injury with or/and without mechanical ventilation. Individual gene chips were classified and grouped based on the strategy used to induce lung injury. Effect size (change in gene expression) was calculated between non-injurious and injurious conditions comparing two main strategies to pool chips: (1) one-hit and (2) two-hit lung injury models. A random effects model was used to integrate individual effect sizes calculated from each experiment. Classification models were built using the gene expression signatures generated by the meta-analysis to predict the development of lung injury in human lung transplant recipients.

Results

Two injury-specific lists of differentially expressed genes generated from our meta-analysis of lung injury models were validated using external data sets and prospective data from animal models of ventilator-induced lung injury (VILI). Pathway analysis of gene sets revealed that both new and previously implicated VILI-related pathways are enriched with differentially regulated genes. Classification model based on gene expression signatures identified in animal models of lung injury predicted development of primary graft failure (PGF) in lung transplant recipients with larger than 80% accuracy based upon injury profiles from transplant donors. We also found that better classifier performance can be achieved by using meta-analysis to identify differentially-expressed genes than using single study-based differential analysis.

Conclusion

Taken together, our data suggests that microarray analysis of gene expression data allows for the detection of “injury" gene predictors that can classify lung injury samples and identify patients at risk for clinically relevant lung injury complications.     

Network-Based Meta-Analyses of Associations of Multiple Gene Expression Profiles with Bone Mineral Density Variations in Women

Background

Existing microarray studies of bone mineral density (BMD) have been critical for understanding the pathophysiology of osteoporosis, and have identified a number of candidate genes. However, these studies were limited by their relatively small sample sizes and were usually analyzed individually. Here, we propose a novel network-based meta-analysis approach that combines data across six microarray studies to identify functional modules from human protein-protein interaction (PPI) data, and highlight several differentially expressed genes (DEGs) and a functional module that may play an important role in BMD regulation in women.

Methods

Expression profiling studies were identified by searching PubMed, Gene Expression Omnibus (GEO) and ArrayExpress. Two meta-analysis methods were applied across different gene expression profiling studies. The first, a nonparametric Fisher’s method, combined p-values from individual experiments to identify genes with large effect sizes. The second method combined effect sizes from individual datasets into a meta-effect size to gain a higher precision of effect size estimation across all datasets. Genes with Q test’s p-values < 0.05 or I² values > 50% were assessed by a random effects model and the remainder by a fixed effects model. Using Fisher’s combined p-values, functional modules were identified through an integrated analysis of microarray data in the context of large protein–protein interaction (PPI) networks. Two previously published meta-analysis studies of genome-wide association (GWA) datasets were used to determine whether these module genes were genetically associated with BMD. Pathway enrichment analysis was performed with a hypergeometric test.

Results

Six gene expression datasets were identified, which included a total of 249 (129 high BMD and 120 low BMD) female subjects. Using a network-based meta-analysis, a consensus module containing 58 genes (nodes) and 83 edges was detected. Pathway enrichment analysis of the 58 module genes revealed that these genes were enriched in several important KEGG pathways including Osteoclast differentiation, B cell receptor signaling pathway, MAPK signaling pathway, Chemokine signaling pathway and Insulin signaling pathway. The importance of module genes was replicated by demonstrating that most module genes were genetically associated with BMD in the GWAS data sets. Meta-analyses were performed at the individual gene level by combining p-values and effect sizes. Five candidate genes (ESR1, MAP3K3, PYGM, RAC1 and SYK) were identified based on gene expression meta-analysis, and their associations with BMD were also replicated by two BMD meta-analysis studies.

Conclusions

In summary, our network-based meta-analysis not only identified important differentially expressed genes but also discovered biologically meaningful functional modules for BMD determination. Our study may provide novel therapeutic targets for osteoporosis in women.     

Equivalence of conventionally-derived and parthenote-derived human embryonic stem cells

Background

As human embryonic stem cell (hESC) lines can be derived via multiple means, it is important to determine particular characteristics of individual lines that may dictate the applications to which they are best suited. The objective of this work was to determine points of equivalence and differences between conventionally-derived hESC and parthenote-derived hESC lines (phESC) in the undifferentiated state and during neural differentiation.

Methodology/Principal Findings

hESC and phESC were exposed to the same expansion conditions and subsequent neural and retinal pigmented epithelium (RPE) differentiation protocols. Growth rates and gross morphology were recorded during expansion. RTPCR for developmentally relevant genes and global DNA methylation profiling were used to compare gene expression and epigenetic characteristics. Parthenote lines proliferated more slowly than conventional hESC lines and yielded lower quantities of less mature differentiated cells in a neural progenitor cell (NPC) differentiation protocol. However, the cell lines performed similarly in a RPE differentiation protocol. The DNA methylation analysis showed similar general profiles, but the two cell types differed in methylation of imprinted genes. There were no major differences in gene expression between the lines before differentiation, but when differentiated into NPCs, the two cell types differed in expression of extracellular matrix (ECM) genes.

Conclusions/Significance

These data show that hESC and phESC are similar in the undifferentiated state, and both cell types are capable of differentiation along neural lineages. The differences between the cell types, in proliferation and extent of differentiation, may be linked, in part, to the observed differences in ECM synthesis and methylation of imprinted genes.     

Gene Set Enrichment Analysis (GSEA) of Toxoplasma gondii expression datasets links cell cycle progression and the bradyzoite developmental program

Background

Large amounts of microarray expression data have been generated for the Apicomplexan parasite Toxoplasma gondii in an effort to identify genes critical for virulence or developmental transitions. However, researchers’ ability to analyze this data is limited by the large number of unannotated genes, including many that appear to be conserved hypothetical proteins restricted to Apicomplexa. Further, differential expression of individual genes is not always informative and often relies on investigators to draw big-picture inferences without the benefit of context. We hypothesized that customization of gene set enrichment analysis (GSEA) to T. gondii would enable us to rigorously test whether groups of genes serving a common biological function are co-regulated during the developmental transition to the latent bradyzoite form.

Results

Using publicly available T. gondii expression microarray data, we created Toxoplasma gene sets related to bradyzoite differentiation, oocyst sporulation, and the cell cycle. We supplemented these with lists of genes derived from community annotation efforts that identified contents of the parasite-specific organelles, rhoptries, micronemes, dense granules, and the apicoplast. Finally, we created gene sets based on metabolic pathways annotated in the KEGG database and Gene Ontology terms associated with gene annotations available at http://www.toxodb.org. These gene sets were used to perform GSEA analysis using two sets of published T. gondii expression data that characterized T. gondii stress response and differentiation to the latent bradyzoite form.

Conclusions

GSEA provides evidence that cell cycle regulation and bradyzoite differentiation are coupled. Δgcn5A mutants unable to induce bradyzoite-associated genes in response to alkaline stress have different patterns of cell cycle and bradyzoite gene expression from stressed wild-type parasites. Extracellular tachyzoites resemble a transitional state that differs in gene expression from both replicating intracellular tachyzoites and in vitro bradyzoites by expressing genes that are enriched in bradyzoites as well as genes that are associated with the G1 phase of the cell cycle. The gene sets we have created are readily modified to reflect ongoing research and will aid researchers’ ability to use a knowledge-based approach to data analysis facilitating the development of new insights into the intricate biology of Toxoplasma gondii.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-515) contains supplementary material, which is available to authorized users.     

A systems biology-based classifier for hepatocellular carcinoma diagnosis

Aim

The diagnosis of hepatocellular carcinoma (HCC) in the early stage is crucial to the application of curative treatments which are the only hope for increasing the life expectancy of patients. Recently, several large-scale studies have shed light on this problem through analysis of gene expression profiles to identify markers correlated with HCC progression. However, those marker sets shared few genes in common and were poorly validated using independent data. Therefore, we developed a systems biology based classifier by combining the differential gene expression with topological features of human protein interaction networks to enhance the ability of HCC diagnosis.

Methods and Results

In the Oncomine platform, genes differentially expressed in HCC tissues relative to their corresponding normal tissues were filtered by a corrected Q value cut-off and Concept filters. The identified genes that are common to different microarray datasets were chosen as the candidate markers. Then, their networks were analyzed by GeneGO Meta-Core software and the hub genes were chosen. After that, an HCC diagnostic classifier was constructed by Partial Least Squares modeling based on the microarray gene expression data of the hub genes. Validations of diagnostic performance showed that this classifier had high predictive accuracy (85.88∼92.71%) and area under ROC curve (approximating 1.0), and that the network topological features integrated into this classifier contribute greatly to improving the predictive performance. Furthermore, it has been demonstrated that this modeling strategy is not only applicable to HCC, but also to other cancers.

Conclusion

Our analysis suggests that the systems biology-based classifier that combines the differential gene expression and topological features of human protein interaction network may enhance the diagnostic performance of HCC classifier.     

Intrinsic properties and external factors determine the differentiation bias of human embryonic stem cell lines

A major goal of human embryonic stem cell (hESC) research is to regulate differentiation through external means to generate specific cell types with high purity for regenerative medicine applications. Although all hESC lines express pluripotency‐associated genes, their differentiation ability to various lineages differs considerably. We have compared spontaneous differentiation propensity of the two hESC lines, RelicellhES1 and BG01. Spontaneous differentiation of hESC lines grown in different media conditions was followed by differentiation using two methods. Kinetic data generated by real‐time gene expression studies for differentiated cell types were analyzed, and confirmed at protein levels. Both cell lines showed upregulation of genes associated with the 3 germ layers, although stark contrast was evident in the magnitude of upregulation of lineage specific genes. A distinct difference was also found in the rate at which the pluripoteny factors, Oct‐4 and Nanog, were downregulated during differentiation. Once differentiation was initiated, both Oct‐4 and Nanog gene expression was drastically reduced in RelicellhES1, whereas a gradual decrease was observed in BG01. A clear trend is seen in RelicellhES1 to differentiate into neuroectodermal and mesenchymal lineages, whereas BG01 cells are more prone to mesoderm and endoderm development. In addition, suspension versus plated methods of cell culture significantly influenced the outcome of differentiation of certain types of cells. Results obtained by spontaneous differentiation of hESCs were also amplified by induced differentiation. Thus, differential rates of downregulation of pluripotency markers along with culture conditions seem to play an important role in determining the developmental bias of human ES cell lines.     

Evaluation of normalization methods for microarray data

Background

Microarray technology allows the monitoring of expression levels for thousands of genes simultaneously. This novel technique helps us to understand gene regulation as well as gene by gene interactions more systematically. In the microarray experiment, however, many undesirable systematic variations are observed. Even in replicated experiment, some variations are commonly observed. Normalization is the process of removing some sources of variation which affect the measured gene expression levels. Although a number of normalization methods have been proposed, it has been difficult to decide which methods perform best. Normalization plays an important role in the earlier stage of microarray data analysis. The subsequent analysis results are highly dependent on normalization.

Results

In this paper, we use the variability among the replicated slides to compare performance of normalization methods. We also compare normalization methods with regard to bias and mean square error using simulated data.

Conclusions

Our results show that intensity-dependent normalization often performs better than global normalization methods, and that linear and nonlinear normalization methods perform similarly. These conclusions are based on analysis of 36 cDNA microarrays of 3,840 genes obtained in an experiment to search for changes in gene expression profiles during neuronal differentiation of cortical stem cells. Simulation studies confirm our findings.