Cross-species transcriptional profiles establish a functional portrait of embryonic stem cells

The NSL Chromatin-Modifying Complex Subunit KANSL2 Regulates Cancer Stem-like Properties in Glioblastoma That Contribute to Tumorigenesis

KANSL2 is an integral subunit of the nonspecific lethal (NSL) chromatin-modifying complex that contributes to epigenetic programs in embryonic stem cells. In this study, we report a role for KANSL2 in regulation of stemness in glioblastoma (GBM), which is characterized by heterogeneous tumor stem-like cells associated with therapy resistance and disease relapse. KANSL2 expression is upregulated in cancer cells, mainly at perivascular regions of tumors. RNAi-mediated silencing of KANSL2 in GBM cells impairs their tumorigenic capacity in mouse xenograft models. In clinical specimens, we found that expression levels of KANSL2 correlate with stemness markers in GBM stem-like cell populations. Mechanistic investigations showed that KANSL2 regulates cell self-renewal, which correlates with effects on expression of the stemness transcription factor POU5F1. RNAi-mediated silencing of POU5F1 reduced KANSL2 levels, linking these two genes to stemness control in GBM cells. Together, our findings indicate that KANSL2 acts to regulate the stem cell population in GBM, defining it as a candidate GBM biomarker for clinical use. Cancer Res; 76(18); 5383-94. ©2016 AACR.

Differences in the Early Development of Human and Mouse Embryonic Stem Cells

We performed a systematic analysis of gene expression features in early (10–21 days) development of human vs mouse embryonic cells (hESCs vs mESCs). Many development features were found to be conserved, and a majority of differentially regulated genes have similar expression change in both organisms. The similarity is especially evident, when gene expression profiles are clustered together and properties of clustered groups of genes are compared. First 10 days of mESC development match the features of hESC development within 21 days, in accordance with the differences in population doubling time in human and mouse ESCs. At the same time, several important differences are seen. There is a clear difference in initial expression change of transcription factors and stimulus responsive genes, which may be caused by the difference in experimental procedures. However, we also found that some biological processes develop differently; this can clearly be shown, for example, for neuron and sensory organ development. Some groups of genes show peaks of the expression levels during the development and these peaks cannot be claimed to happen at the same time points in the two organisms, as well as for the same groups of (orthologous) genes. We also detected a larger number of upregulated genes during development of mESCs as compared to hESCs. The differences were quantified by comparing promoters of related genes. Most of gene groups behave similarly and have similar transcription factor (TF) binding sites on their promoters. A few groups of genes have similar promoters, but are expressed differently in two species. Interestingly, there are groups of genes expressed similarly, although they have different promoters, which can be shown by comparing their TF binding sites. Namely, a large group of similarly expressed cell cycle-related genes is found to have discrepant TF binding properties in mouse vs human.

Conservation and divergence in Toll-like receptor 4-regulated gene expression in primary human versus mouse macrophages

Evolutionary change in gene expression is generally considered to be a major driver of phenotypic differences between species. We investigated innate immune diversification by analyzing interspecies differences in the transcriptional responses of primary human and mouse macrophages to the Toll-like receptor (TLR)-4 agonist lipopolysaccharide (LPS). By using a custom platform permitting cross-species interrogation coupled with deep sequencing of mRNA 5' ends, we identified extensive divergence in LPS-regulated orthologous gene expression between humans and mice (24% of orthologues were identified as "divergently regulated"). We further demonstrate concordant regulation of human-specific LPS target genes in primary pig macrophages. Divergently regulated orthologues were enriched for genes encoding cellular "inputs" such as cell surface receptors (e.g., TLR6, IL-7Rα) and functional "outputs" such as inflammatory cytokines/chemokines (e.g., CCL20, CXCL13). Conversely, intracellular signaling components linking inputs to outputs were typically concordantly regulated. Functional consequences of divergent gene regulation were confirmed by showing LPS pretreatment boosts subsequent TLR6 responses in mouse but not human macrophages, in keeping with mouse-specific TLR6 induction. Divergently regulated genes were associated with a large dynamic range of gene expression, and specific promoter architectural features (TATA box enrichment, CpG island depletion). Surprisingly, regulatory divergence was also associated with enhanced interspecies promoter conservation. Thus, the genes controlled by complex, highly conserved promoters that facilitate dynamic regulation are also the most susceptible to evolutionary change.

Toward a complete in silico, multi-layered embryonic stem cell regulatory network

Recent efforts in systematically profiling embryonic stem (ES) cells have yielded a wealth of high-throughput data. Complementarily, emerging databases and computational tools facilitate ES cell studies and further pave the way toward the in silico reconstruction of regulatory networks encompassing multiple molecular layers. Here, we briefly survey databases, algorithms, and software tools used to organize and analyze high-throughput experimental data collected to study mammalian cellular systems with a focus on ES cells. The vision of using heterogeneous data to reconstruct a complete multi-layered ES cell regulatory network is discussed. This review also provides an accompanying manually extracted dataset of different types of regulatory interactions from low-throughput experimental ES cell studies available at http://amp.pharm.mssm.edu/iscmid/literature.

Knowledge-guided gene ranking by coordinative component analysis

Background

In cancer, gene networks and pathways often exhibit dynamic behavior, particularly during the process of carcinogenesis. Thus, it is important to prioritize those genes that are strongly associated with the functionality of a network. Traditional statistical methods are often inept to identify biologically relevant member genes, motivating researchers to incorporate biological knowledge into gene ranking methods. However, current integration strategies are often heuristic and fail to incorporate fully the true interplay between biological knowledge and gene expression data.

Results

To improve knowledge-guided gene ranking, we propose a novel method called coordinative component analysis (COCA) in this paper. COCA explicitly captures those genes within a specific biological context that are likely to be expressed in a coordinative manner. Formulated as an optimization problem to maximize the coordinative effort, COCA is designed to first extract the coordinative components based on a partial guidance from knowledge genes and then rank the genes according to their participation strengths. An embedded bootstrapping procedure is implemented to improve statistical robustness of the solutions. COCA was initially tested on simulation data and then on published gene expression microarray data to demonstrate its improved performance as compared to traditional statistical methods. Finally, the COCA approach has been applied to stem cell data to identify biologically relevant genes in signaling pathways. As a result, the COCA approach uncovers novel pathway members that may shed light into the pathway deregulation in cancers.

Conclusion

We have developed a new integrative strategy to combine biological knowledge and microarray data for gene ranking. The method utilizes knowledge genes for a guidance to first extract coordinative components, and then rank the genes according to their contribution related to a network or pathway. The experimental results show that such a knowledge-guided strategy can provide context-specific gene ranking with an improved performance in pathway member identification.

Global Expression Patterns of Three Festuca Species Exposed to Different Doses of Glyphosate Using the Affymetrix GeneChip Wheat Genome Array

Glyphosate has been shown to act as an inhibitor of an aromatic amino acid biosynthetic pathway, while other pathways that may be affected by glyphosate are not known. Cross species hybridizations can provide a tool for elucidating biological pathways conserved among organisms. Comparative genome analyses have indicated a high level of colinearity among grass species and Festuca, on which we focus here, and showed rearrangements common to the Pooideae family. Based on sequence conservation among grass species, we selected the Affymetrix GeneChip Wheat Genome Array as a tool for the analysis of expression profiles of three Festuca (fescue) species with distinctly different tolerances to varying levels of glyphosate. Differences in transcript expression were recorded upon foliar glyphosate application at 1.58 mM and 6.32 mM, representing 5% and 20%, respectively, of the recommended rate. Differences highlighted categories of general metabolic processes, such as photosynthesis, protein synthesis, stress responses, and a larger number of transcripts responded to 20% glyphosate application. Differential expression of genes encoding proteins involved in the shikimic acid pathway could not be identified by cross hybridization. Microarray data were confirmed by RT-PCR and qRT-PCR analyses. This is the first report to analyze the potential of cross species hybridization in Fescue species and the data and analyses will help extend our knowledge on the cellular processes affected by glyphosate.

A targeted neuroglial reporter line generated by homologous recombination in human embryonic stem cells

In this study, we targeted Olig2, a basic helix-loop-helix transcription factor that plays an important role in motoneuron and oligodendrocyte development, in human embryonic stem cell (hESC) line BG01 by homologous recombination. One allele of Olig2 locus was replaced by a green fluorescent protein (GFP) cassette with a targeting efficiency of 5.7%. Targeted clone R-Olig2 (like the other clones) retained pluripotency, typical hESC morphology, and a normal parental karyotype 46,XY. Most importantly, GFP expression recapitulated endogenous Olig2 expression when R-Olig2 was induced by sonic hedgehog and retinoic acid, and GFP-positive cells could be purified by fluorescence-activated cell sorting. Consistent with previous reports on rodents, early GFP-expressing cells appeared biased to a neuronal fate, whereas late GFP-expressing cells appeared biased to an oligodendrocytic fate. This was corroborated by myoblast coculture, transplantation into the rat spinal cords, and whole genome expression profiling. The present work reports an hESC reporter line generated by homologous recombination targeting a neural lineage-specific gene, which can be differentiated and sorted to obtain pure neural progenitor populations.

Genomic reaction norms: using integrative biology to understand molecular mechanisms of phenotypic plasticity

Phenotypic plasticity is the development of different phenotypes from a single genotype, depending on the environment. Such plasticity is a pervasive feature of life, is observed for various traits and is often argued to be the result of natural selection. A thorough study of phenotypic plasticity should thus include an ecological and an evolutionary perspective. Recent advances in large-scale gene expression technology make it possible to also study plasticity from a molecular perspective, and the addition of these data will help answer long-standing questions about this widespread phenomenon. In this review, we present examples of integrative studies that illustrate the molecular and cellular mechanisms underlying plastic traits, and show how new techniques will grow in importance in the study of these plastic molecular processes. These techniques include: (i) heterologous hybridization to DNA microarrays; (ii) next generation sequencing technologies applied to transcriptomics; (iii) techniques for studying the function of noncoding small RNAs; and (iv) proteomic tools. We also present recent studies on genetic model systems that uncover how environmental cues triggering different plastic responses are sensed and integrated by the organism. Finally, we describe recent work on changes in gene expression in response to an environmental cue that persist after the cue is removed. Such long-term responses are made possible by epigenetic molecular mechanisms, including DNA methylation. The results of these current studies help us outline future avenues for the study of plasticity.

Human embryonic stem cells as a model for embryotoxicity screening

Reproductive toxicity encompasses harmful effects of various agents on all aspects and stages of the reproductive cycle, including infertility and the induction of adverse effects in the embryo/fetus. In developing a model for reproductive toxicity screening, it is important to define the stage of the human reproductive cycle that this specific model is going to recreate in vitro and to identify molecular targets that are critical for this stage of development. In this review, we focus our discussion on modeling pre-implantation embryotoxicity. The rationale for this is that despite advances on both clinical and biological levels, many unresolved infertility cases may be due to our lack of knowledge regarding environmental influences on this short, but critical stage of development. Data from in vitro fertilization practice suggest that the early-dividing embryo is very sensitive to numerous factors present in its microenvironment. In vivo, as the embryo travels down the oviduct, physical or chemical insults can directly damage the embryo and/or prevent implantation, and cause infertility. Multiple lines of evidence point to the differences between mouse and human pre-implantation development and between mouse and human embryonic stem cells (hESCs). In light of these data we present the case that hESCs and their derivatives are better suited as in vitro models for human pre-implantation development than their mouse counterparts. We then describe some of the most promising hESC-based systems that are used today to model certain aspects of development in the human pre-implantation embryo and that have the potential to be used for embryo toxicity screening tests in the near future. Described systems model two major events during differentiation of the human pre-implantation embryo: differentiation of the trophectoderm and segregation of the inner cell mass into epiblast and hypoblast. The first event is replicated in vitro by triggering either direct or indirect (through embryoid body stage) differentiation into trophectoderm. The second event can be modeled using the recently described system of high-throughput generation of embryoid bodies that recapitulate segregation of inner cell mass. We conclude by discussing the potential of these existing models in toxicology studies and the possibilities for their improvement in the future.

Differential dependency network analysis to identify condition-specific topological changes in biological networks

MOTIVATION

Significant efforts have been made to acquire data under different conditions and to construct static networks that can explain various gene regulation mechanisms. However, gene regulatory networks are dynamic and condition-specific; under different conditions, networks exhibit different regulation patterns accompanied by different transcriptional network topologies. Thus, an investigation on the topological changes in transcriptional networks can facilitate the understanding of cell development or provide novel insights into the pathophysiology of certain diseases, and help identify the key genetic players that could serve as biomarkers or drug targets.

RESULTS

Here, we report a differential dependency network (DDN) analysis to detect statistically significant topological changes in the transcriptional networks between two biological conditions. We propose a local dependency model to represent the local structures of a network by a set of conditional probabilities. We develop an efficient learning algorithm to learn the local dependency model using the Lasso technique. A permutation test is subsequently performed to estimate the statistical significance of each learned local structure. In testing on a simulation dataset, the proposed algorithm accurately detected all the genes with network topological changes. The method was then applied to the estrogen-dependent T-47D estrogen receptor-positive (ER+) breast cancer cell line datasets and human and mouse embryonic stem cell datasets. In both experiments using real microarray datasets, the proposed method produced biologically meaningful results. We expect DDN to emerge as an important bioinformatics tool in transcriptional network analyses. While we focus specifically on transcriptional networks, the DDN method we introduce here is generally applicable to other biological networks with similar characteristics.

AVAILABILITY

The DDN MATLAB toolbox and experiment data are available at http://www.cbil.ece.vt.edu/software.htm.

Neural transplantation and stem cells

Recent results have raised important questions on our ability to amplify stem cell populations in sufficient numbers as to be useful for therapy. Several reports have indicated that human stem cell populations harvested from the adult have low or undetectable telomerase levels, age in culture, and may not be propagated indefinitely. Other groups have shown that stem cells age and as such, their properties will have changed depending on the age of the individual from which they are harvested, and the time for which they are propagated in culture. Other groups have shown that cells maintained in culture may undergo alterations as they are propagated, and that these alterations may alter the predicted behavior of stem cells. Yet others have shown that human cells differ from their counterparts in other species in significant ways and have identified important difficulties in assessing cells in a xeno environment. Clinical colleagues have identified issues of variability and difficulties in the long-term follow-up that is being requested. Researchers in the stem cell field focused on translational work need to develop a practical plan that takes into account such difficulties while developing manufacturing protocols, designing animal studies, or developing trial protocols. Such proactive planning will be critical in ensuring a successful transition from the bench to the clinic.

Evolutionarily conserved transcriptional co-expression guiding embryonic stem cell differentiation

Background

Understanding the molecular mechanisms controlling pluripotency in embryonic stem cells (ESCs) is of central importance towards realizing their potentials in medicine and science. Cross-species examination of transcriptional co-expression allows elucidation of fundamental and species-specific mechanisms regulating ESC self-renewal or differentiation.

Methodology/Principal Findings

We examined transcriptional co-expression of ESCs from pathways to global networks under the framework of human-mouse comparisons. Using generalized singular value decomposition and comparative partition around medoids algorithms, evolutionarily conserved and divergent transcriptional co-expression regulating pluripotency were identified from ESC-critical pathways including ACTIVIN/NODAL, ATK/PTEN, BMP, CELL CYCLE, JAK/STAT, PI3K, TGFβ and WNT. A set of transcription factors, including FOX, GATA, MYB, NANOG, OCT, PAX, SOX and STAT, and the FGF response element were identified that represent key regulators underlying the transcriptional co-expression. By transcriptional intervention conducted in silico, dynamic behavior of pathways was examined, which demonstrate how much and in which specific ways each gene or gene combination effects the behavior transition of a pathway in response to ESC differentiation or pluripotency induction. The global co-expression networks of ESCs were dominated by highly connected hub genes such as IGF2, JARID2, LCK, MYCN, NASP, OCT4, ORC1L, PHC1 and RUVBL1, which are possibly critical in determining the fate of ESCs.

Conclusions/Significance

Through these studies, evolutionary conservation at genomic, transcriptomic, and network levels is shown to be an effective predictor of molecular factors and mechanisms controlling ESC development. Various hypotheses regarding mechanisms controlling ESC development were generated, which could be further validated by in vitro experiments. Our findings shed light on the systems-level understanding of how ESC differentiation or pluripotency arises from the connectivity or networks of genes, and provide a “road-map” for further experimental investigation.

GeneChip analysis of human embryonic stem cell differentiation into hemangioblasts: an in silico dissection of mixed phenotypes

Transcriptional profiling of human embryonic stem cells differentiating into blast cells reveals that erythroblasts are the predominant cell type in the blast cell population. In silico comparisons with publicly available data sets revealed the presence of endothelia, cardiomyocytes and hematopoietic lineages.

Background

Microarrays are being used to understand human embryonic stem cell (hESC) differentiation. Most differentiation protocols use a multi-stage approach that induces commitment along a particular lineage. Therefore, each stage represents a more mature and less heterogeneous phenotype. Thus, characterizing the heterogeneous progenitor populations upon differentiation are of increasing importance. Here we describe a novel method of data analysis using a recently developed differentiation protocol involving the formation of functional hemangioblasts from hESCs. Blast cells are multipotent and can differentiate into multiple lineages of hematopoeitic cells (erythroid, granulocyte and macrophage), endothelial and smooth muscle cells.

Results

Large-scale transcriptional analysis was performed at distinct time points of hESC differentiation (undifferentiated hESCs, embryoid bodies, and blast cells, the last of which generates both hematopoietic and endothelial progenies). Identifying genes enriched in blast cells relative to hESCs revealed a genetic signature indicative of erythroblasts, suggesting that erythroblasts are the predominant cell type in the blast cell population. Because of the heterogeneity of blast cells, numerous comparisons were made to publicly available data sets in silico, some of which blast cells are capable of differentiating into, to assess and characterize the blast cell population. Biologically relevant comparisons masked particular genetic signatures within the heterogeneous population and identified genetic signatures indicating the presence of endothelia, cardiomyocytes, and hematopoietic lineages in the blast cell population.

Conclusion

The significance of this microarray study is in its ability to assess and identify cellular populations within a heterogeneous population through biologically relevant in silico comparisons of publicly available data sets. In conclusion, multiple in silico comparisons were necessary to characterize tissue-specific genetic signatures within a heterogeneous hemangioblast population.

Avian model for B-cell immunology--new genomes and phylotranscriptomics

The purpose of this review is to discuss the use of chicken and other model organisms in the study of B-cell development and function as well as to highlight the opportunities afforded by the expanded genome-sequencing efforts. A brief introduction on chicken B-cell biology is followed by discussion of somatic cell reverse genetic approaches using the DT40 cell line. The unique advantages of the DT40 system are emphasized with discussion on B-cell receptor signalling research as well as on DNA repair and mechanisms of immunoglobulin diversification. An attempt is made to compare and contrast the results from chicken with mouse knockouts on the one hand and RNAi with human cell lines on the other. Chicken is also emerging strongly as a platform for gene expression analysis, and avian studies are compared with mammalian studies. Multi-species gene co-expression analysis, which could also be termed phylotranscriptomics, aims to use the evolutionary distance between organisms to its advantage. This approach, still in its infancy, is also reviewed and its applicability to the chicken is discussed.

A UTF1-based selection system for stable homogeneously pluripotent human embryonic stem cell cultures

Undifferentiated transcription factor 1 (UTF1) was identified first in mouse embryonic stem cells and is also expressed in human embryonic and adult stem cells. UTF1 transcription ceases at the onset of differentiation, which clearly distinguishes it from less sensitive pluripotency markers, such as Oct4 or Nanog. We present here two transgenic hESC lines, named ZUN. Each line harbors one copy of the UTF1 promoter/enhancer driving a resistance gene and yielded highly homogeneous cultures under selection pressure, with a larger proportion of Oct4 and Sox2 positive cells. While ZUN cultures, like parental HUES8 cultures, retained the capacity to differentiate into tissues of all three germ layers using a SICD mouse teratoma model, they surprisingly exhibited an increased refractoriness to various differentiation cues in vitro. Together with its small size of only 2.4 kb for the entire cassette, these features render our selection system a powerful novel tool for many stem cell applications and human somatic cell reprogramming strategies.

Deciphering modular and dynamic behaviors of transcriptional networks

The coordinated and dynamic modulation or interaction of genes or proteins acts as an important mechanism used by a cell in functional regulation. Recent studies have shown that many transcriptional networks exhibit a scale-free topology and hierarchical modular architecture. It has also been shown that transcriptional networks or pathways are dynamic and behave only in certain ways and controlled manners in response to disease development, changing cellular conditions, and different environmental factors. Moreover, evolutionarily conserved and divergent transcriptional modules underline fundamental and species-specific molecular mechanisms controlling disease development or cellular phenotypes. Various computational algorithms have been developed to explore transcriptional networks and modules from gene expression data. In silico studies have also been made to mimic the dynamic behavior of regulatory networks, analyzing how disease or cellular phenotypes arise from the connectivity or networks of genes and their products. Here, we review the recent development in computational biology research on deciphering modular and dynamic behaviors of transcriptional networks, highlighting important findings. We also demonstrate how these computational algorithms can be applied in systems biology studies as on disease, stem cells, and drug discovery.

Concise review: trends in stem cell proteomics

Gene expression analyses of stem cells (SCs) will help to uncover or further define signaling pathways and molecular mechanisms involved in the maintenance of self-renewal, pluripotency, and/or multipotency. In recent years, proteomic approaches have produced a wealth of data identifying proteins and mechanisms involved in SC proliferation and differentiation. Although many proteomics techniques have been developed and improved in peptide and protein separation, as well as mass spectrometry, several important issues, including sample heterogeneity, post-translational modifications, protein-protein interaction, and high-throughput quantification of hydrophobic and low-abundance proteins, still remain to be addressed and require further technical optimization. This review summarizes the methodologies used and the information gathered with proteome analyses of SCs, and it discusses biological and technical challenges for proteomic study of SCs. Disclosure of potential conflicts of interest is found at the end of this article.

Human stem cells, chromatin, and tissue engineering: Boosting relevancy in developmental toxicity testing

Risk assessment derives its confidence from toxicology research that is based on relevancy to human health. This article focuses on two highly topical areas of current scientific research, stem cells and chromatin biology, which present new avenues for preclinical and clinical applications, and the frontier role of tissue engineering and regeneration. Appreciating the utility and necessity of chromatin and human somatic stem cells as research tools and looking toward tissue engineering may close the uncertainty gaps between animal and human cross-species toxicology evaluations. The focus will be on developmental toxicology applications, but appropriate extrapolation to any other areas of toxicology can be made. We further provide background on basic biology of these three areas and examples of how early life exposure to known and potential environmental toxicants induce malformations, childhood and adult-onset diseases, through aberrant chromatin modification of critical gene expressions (acute lymphocyte leukemia, heavy-metal nickel and cadmium-associated defects, and reproductive tract malformations and carcinomas induced by the synthetic estrogen, diethylstilbestrol).