AutoSOME: a clustering method for identifying gene expression modules without prior knowledge of cluster number

Decoding the universal human chromatin landscape through teratoma-based profiling

Teratoma formation is key for evaluating differentiation of human pluripotent stem cells into embryonic germ layers and serves as a model for understanding stem cell differentiation and developmental processes. Its potential for insights into epigenome and transcriptome profiling is significant. This study integrates the analysis of the epigenome and transcriptome of hESC-generated teratomas, comparing transcriptomes between hESCs and teratomas. It employs cell type-specific expression patterns from single-cell data to deconvolve RNA-Seq data and identify cell types within teratomas. Our results provide a catalog of activating and repressive histone modifications, while also elucidating distinctive features of chromatin states. Construction of an epigenetic signature matrix enabled the quantification of diverse cell populations in teratomas and enhanced the ability to unravel the epigenetic landscape in heterogeneous tissue contexts. This study also includes a single cell multiome atlas of expression (scRNA-Seq) and chromatin accessibility (scATAC-Seq) of human teratomas, further revealing the complexity of these tissues. A histology-based digital staining tool further complemented the annotation of cell types in teratomas, enhancing our understanding of their cellular composition. This research is a valuable resource for examining teratoma epigenomic and transcriptomic landscapes and serves as a model for epigenetic data comparison.

clusterMaker2: a major update to clusterMaker, a multi-algorithm clustering app for Cytoscape

BACKGROUND

Since the initial publication of clusterMaker, the need for tools to analyze large biological datasets has only increased. New datasets are significantly larger than a decade ago, and new experimental techniques such as single-cell transcriptomics continue to drive the need for clustering or classification techniques to focus on portions of datasets of interest. While many libraries and packages exist that implement various algorithms, there remains the need for clustering packages that are easy to use, integrated with visualization of the results, and integrated with other commonly used tools for biological data analysis. clusterMaker2 has added several new algorithms, including two entirely new categories of analyses: node ranking and dimensionality reduction. Furthermore, many of the new algorithms have been implemented using the Cytoscape jobs API, which provides a mechanism for executing remote jobs from within Cytoscape. Together, these advances facilitate meaningful analyses of modern biological datasets despite their ever-increasing size and complexity.

RESULTS

The use of clusterMaker2 is exemplified by reanalyzing the yeast heat shock expression experiment that was included in our original paper; however, here we explored this dataset in significantly more detail. Combining this dataset with the yeast protein-protein interaction network from STRING, we were able to perform a variety of analyses and visualizations from within clusterMaker2, including Leiden clustering to break the entire network into smaller clusters, hierarchical clustering to look at the overall expression dataset, dimensionality reduction using UMAP to find correlations between our hierarchical visualization and the UMAP plot, fuzzy clustering, and cluster ranking. Using these techniques, we were able to explore the highest-ranking cluster and determine that it represents a strong contender for proteins working together in response to heat shock. We found a series of clusters that, when re-explored as fuzzy clusters, provide a better presentation of mitochondrial processes.

CONCLUSIONS

clusterMaker2 represents a significant advance over the previously published version, and most importantly, provides an easy-to-use tool to perform clustering and to visualize clusters within the Cytoscape network context. The new algorithms should be welcome to the large population of Cytoscape users, particularly the new dimensionality reduction and fuzzy clustering techniques.

CustOmics: A versatile deep-learning based strategy for multi-omics integration

The availability of patient cohorts with several types of omics data opens new perspectives for exploring the disease's underlying biological processes and developing predictive models. It also comes with new challenges in computational biology in terms of integrating high-dimensional and heterogeneous data in a fashion that captures the interrelationships between multiple genes and their functions. Deep learning methods offer promising perspectives for integrating multi-omics data. In this paper, we review the existing integration strategies based on autoencoders and propose a new customizable one whose principle relies on a two-phase approach. In the first phase, we adapt the training to each data source independently before learning cross-modality interactions in the second phase. By taking into account each source's singularity, we show that this approach succeeds at taking advantage of all the sources more efficiently than other strategies. Moreover, by adapting our architecture to the computation of Shapley additive explanations, our model can provide interpretable results in a multi-source setting. Using multiple omics sources from different TCGA cohorts, we demonstrate the performance of the proposed method for cancer on test cases for several tasks, such as the classification of tumor types and breast cancer subtypes, as well as survival outcome prediction. We show through our experiments the great performances of our architecture on seven different datasets with various sizes and provide some interpretations of the results obtained. Our code is available on (https://github.com/HakimBenkirane/CustOmics).

Integration of Multi-Omics Data Using Probabilistic Graph Models and External Knowledge

Background:

High-throughput sequencing technologies have revolutionized the ability to perform systems-level biology and elucidate molecular mechanisms of disease through the comprehensive characterization of different layers of biological information. Integration of these heterogeneous layers can provide insight into the underlying biology but is challenged by modeling complex interactions.

Objective:

We introduce OBaNK: omics integration using Bayesian networks and external knowledge, an algorithm to model interactions between heterogeneous high-dimensional biological data to elucidate complex functional clusters and emergent relationships associated with an observed phenotype.

Method:

Using Bayesian network learning, we modeled the statistical dependencies and interactions between lipidomics, proteomics, and metabolomics data. The strength of a learned interaction between molecules was altered based on external knowledge.

Results :

Networks learned from synthetic datasets based on real pathways achieved an average area under the curve score of ~0.85, an improvement of ~0.23 from baseline methods. When applied to real multi-omics data collected during pregnancy, five distinct functional networks of heterogeneous biological data were identified, and the results were compared to other multi-omics integration approaches.

Conclusion:

OBaNK successfully improved the accuracy of learning interaction networks from data integrating external knowledge, identified heterogeneous functional networks from real data, and suggested potential novel interactions associated with the phenotype. These findings can guide future hypothesis generation. OBaNK source code is available at: https://github.com/bridgettripp/OBaNK.git, and a graphical user interface is available at: http://otulab.unl.edu/OBaNK.

Xeno- and Feeder-Free Differentiation of Human iPSCs to Trabecular Meshwork-Like Cells by Recombinant Cytokines

Purpose

Stem cell-based therapy has the potential to become one approach to regenerate the damaged trabecular meshwork (TM) in glaucoma. Co-culture of induced pluripotent stem cells (iPSCs) with human TM cells has been a successful approach to generate autologous TM resembling cells. However, the differentiated cells generated using this approach are still problematic for clinical usage. This study aimed to develop a clinically applicable strategy for generating TM-like cells from iPSCs.

Methods

Highly expressed receptors during iPSC differentiation were identified by AutoSOME, Gene Ontology, and reverse transcription polymerase chain reaction (RT-PCR) analysis. The recombinant cytokines that bind to these receptors were used to generate a new differentiation protocol. The resultant TM-like cells were characterized morphologically, immunohistochemically, and transcriptionally.

Results

We first determined two stages of iPSC differentiation and identified highly expressed receptors associated with the differentiation at each stage. The expression of these receptors was further confirmed by RT-PCR analysis. Exposure to the recombinant cytokines that bind to these receptors, including transforming growth factor beta 1, nerve growth factor beta, erythropoietin, prostaglandin F2 alpha, and epidermal growth factor, can efficiently differentiate iPSCs into TM-like cells, which express TM biomarkers and can form dexamethasone-inducible CLANs.

Conclusions

We successfully generated a xeno- and feeder-free differentiation protocol with recombinant cytokines to generate the TM progenitor and TM-like cells from human iPSCs.

Translational Relevance

The new approach minimizes the risks from contamination and also improves the differentiation efficiency and consistency, which are particularly crucial for clinical use of stem cells in glaucoma treatment.

Combining gene expression signature with clinical features for survival stratification of gastric cancer

The AJCC staging system is considered as the golden standard in clinical practice. However, it remains some pitfalls in assessing the prognosis of gastric cancer (GC) patients with similar clinicopathological characteristics. We aim to develop a new clinic and genetic risk score (CGRS) to improve the prognosis prediction of GC patients. We established genetic risk score (GRS) based on nine-gene signature including APOD, CCDC92, CYS1, GSDME, ST8SIA5, STARD3NL, TIMEM245, TSPYL5, and VAT1 based on the gene expression profiles of the training set from the Asian Cancer Research Group (ACRG) cohort by LASSO-Cox regression algorithms. CGRS was established by integrating GRS with clinical risk score (CRS) derived from Surveillance, Epidemiology, and End Results (SEER) database. GRS and CGRS dichotomized GC patients into high and low risk groups with significantly different prognosis in four independent cohorts with different data types, such as microarray, RNA sequencing and qRT-PCR (all HR > 1, all P < 0.001). Both GRS and CGRS were prognostic signatures independent of the AJCC staging system. Receiver operating characteristic (ROC) analysis showed that area under ROC curve of CGRS was larger than that of the AJCC staging system in most cohorts we studied. Nomogram and web tool (http://39.100.117.92/CGRS/) based on CGRS were developed for clinicians to conveniently assess GC prognosis in clinical practice. CGRS integrating genetic signature with clinical features shows strong robustness in predicting GC prognosis, and can be easily applied in clinical practice through the web application.

VICTOR: A visual analytics web application for comparing cluster sets

Clustering is the process of grouping different data objects based on similar properties. Clustering has applications in various case studies from several fields such as graph theory, image analysis, pattern recognition, statistics and others. Nowadays, there are numerous algorithms and tools able to generate clustering results. However, different algorithms or parameterizations may produce quite dissimilar cluster sets. In this way, the user is often forced to manually filter and compare these results in order to decide which of them generate the ideal clusters. To automate this process, in this study, we present VICTOR, the first fully interactive and dependency-free visual analytics web application which allows the visual comparison of the results of various clustering algorithms. VICTOR can handle multiple cluster set results simultaneously and compare them using ten different metrics. Clustering results can be filtered and compared to each other with the use of data tables or interactive heatmaps, bar plots, correlation networks, sankey and circos plots. We demonstrate VICTOR's functionality using three examples. In the first case, we compare five different network clustering algorithms on a Yeast protein-protein interaction dataset whereas in the second example, we test four different parameters of the MCL clustering algorithm on the same dataset. Finally, as a third example, we compare four different meta-analyses with hierarchically clustered differentially expressed genes found to be involved in myocardial infarction. VICTOR is available at http://victor.pavlopouloslab.info or http://bib.fleming.gr:3838/VICTOR.

Explore spatio-temporal PM2.5 features in northern Taiwan using machine learning techniques

The complex mixtures of local emission sources and regional transportations of air pollutants make accurate PM2.5 prediction a very challenging yet crucial task, especially under high pollution conditions. A symbolic representation of spatio-temporal PM2.5 features is the key to effective air pollution regulatory plans that notify the public to take necessary precautions against air pollution. The self-organizing map (SOM) can cluster high-dimensional datasets to form a meaningful topological map. This study implements the SOM to effectively extract and clearly distinguish the spatio-temporal features of long-term regional PM2.5 concentrations in a visible two-dimensional topological map. The spatial distribution of the configured topological map spans the long-term datasets of 25 monitoring stations in northern Taiwan using the Kriging method, and the temporal behavior of PM2.5 concentrations at various time scales (i.e., yearly, seasonal, and hourly) are explored in detail. Finally, we establish a machine learning model to predict PM2.5 concentrations for high pollution events. The analytical results indicate that: (1) high population density and heavy traffic load correspond to high PM2.5 concentrations; (2) the change of seasons brings obvious effects on PM2.5 concentration variation; and (3) the key input variables of the prediction model identified by the Gamma Test can improve model's reliability and accuracy for multi-step-ahead PM2.5 prediction. The results demonstrated that machine learning techniques can skillfully summarize and visibly present the clusted spatio-temporal PM2.5 features as well as improve air quality prediction accuracy.

Human-induced pluripotent stem cells for modelling metabolic perturbations and impaired bioenergetics underlying cardiomyopathies

Normal cardiac contractile and relaxation functions are critically dependent on a continuous energy supply. Accordingly, metabolic perturbations and impaired mitochondrial bioenergetics with subsequent disruption of ATP production underpin a wide variety of cardiac diseases, including diabetic cardiomyopathy, dilated cardiomyopathy, hypertrophic cardiomyopathy, anthracycline cardiomyopathy, peripartum cardiomyopathy, and mitochondrial cardiomyopathies. Crucially, there are no specific treatments for preventing the onset or progression of these cardiomyopathies to heart failure, one of the leading causes of death and disability worldwide. Therefore, new treatments are needed to target the metabolic disturbances and impaired mitochondrial bioenergetics underlying these cardiomyopathies in order to improve health outcomes in these patients. However, investigation of the underlying mechanisms and the identification of novel therapeutic targets have been hampered by the lack of appropriate animal disease models. Furthermore, interspecies variation precludes the use of animal models for studying certain disorders, whereas patient-derived primary cell lines have limited lifespan and availability. Fortunately, the discovery of human-induced pluripotent stem cells has provided a promising tool for modelling cardiomyopathies via human heart tissue in a dish. In this review article, we highlight the use of patient-derived iPSCs for studying the pathogenesis underlying cardiomyopathies associated with metabolic perturbations and impaired mitochondrial bioenergetics, as the ability of iPSCs for self-renewal and differentiation makes them an ideal platform for investigating disease pathogenesis in a controlled in vitro environment. Continuing progress will help elucidate novel mechanistic pathways, and discover novel therapies for preventing the onset and progression of heart failure, thereby advancing a new era of personalized therapeutics for improving health outcomes in patients with cardiomyopathy.

Examining the co-expression, transcriptome clustering and variation using fuzzy cluster network of testicular stem cells and pluripotent stem cells compared with other cell types

Stem cells are crucial in the field of tissue regeneration and developmental biology. Embryonic stem cells (ESCs) which are pluripotent in nature are derived from the inner cell mass of blastocyst. The gene expression profiles of ESCs and Induced pluripotent stem cells (iPSCs) were compared to identify the differences. Spermatogonial stem cells (SSCs) are also known as Germ-line stem cells (GSCs) present in testis is having the capability of producing the sperm in their whole lifetime. Therefore can be reprogrammed into pluripotent cells called male germline pluripotent cells (gPSCs). It is very difficult to interpret the larger genomic data sets which are available in public databases without high computational facilities. In order to identify the similar groups We studied the co-expression, clustering of the transcriptome and variation of the transcriptome of the GSCs, gPSCs, ESCs and other cell types using fuzzy clustering using AutoSOME. The series matrix file with GSE ID GSE11274 was retrieved and subjected to the various normalization methods, corresponding rows and columns were clustered using p values, ensemble runs, and different running modes. Transcriptome analysis using the proposed approach intuitively and consistently characterized the variation in cell-cell significantly. Collectively, our results suggest that the GSCs and the ESCs displayed differential gene expression profiles, and the GSCs possessed the potential to acquire pluripotency based on the high expression of epigenetic factors and transcription factors. These data may provide novel insights into the reprogramming mechanism of GSCs.

DNA Binding to Gold Nanoparticles through the Prism of Molecular Selection: Sequence-Affinity Relation

Native DNA strongly adsorbs to citrate-coated gold nanoparticles (AuNPs). The resulting composites (DNA/AuNPs) are valuable materials in many fields, especially in biomedicine. For this reason, the process of adsorption is a focus for intensive research. In this work, DNA adsorption to gold nanoparticles was studied using a molecular selection procedure followed by high-throughput DNA sequencing. The chemically synthesized DNA library containing a central N₂₆ randomized fragment was sieved through four cycles of adsorption to AuNPs in a tree-like selection-amplification scheme (SELEX (Selective Evolution of Ligands by EXponential enrichment)). The frequencies of occurrence of specific oligomeric DNA motifs, k-mers ( k = 1-6), in the initial and selected pools were calculated. Distribution of secondary structures in the pools was analyzed. A large set of diverse A, T, and G enriched k-mers undergo a pronounced positive selection, and these sequences demonstrate faster and strong binding to the AuNPs. For facile binding, such structural motifs should be located in the loop regions of weak intramolecular complexes-hairpins with imperfect stem, or other portion of the structure, which is unpaired under selection conditions. Our data also show that, under the conditions employed in this study, cytosine is significantly depleted during the selection process, although guanine remains unchanged. These regularities were confirmed in a series of binding experiments with a set of synthetic DNA oligonucleotides. The detailed analysis of DNA binding to AuNPs shows that the sequence specificity of this interaction is low due to its nature, although the presence and the number of specific structural motifs in DNA affect both the rate of formation and the strength of the formed noncovalent associates with AuNPs.

Contribution of H3K4 demethylase KDM5B to nucleosome organization in embryonic stem cells revealed by micrococcal nuclease sequencing

BACKGROUND

Positioning of nucleosomes along DNA is an integral regulator of chromatin accessibility and gene expression in diverse cell types. However, the precise nature of how histone demethylases including the histone 3 lysine 4 (H3K4) demethylase, KDM5B, impacts nucleosome positioning around transcriptional start sites (TSS) of active genes is poorly understood.

RESULTS

Here, we report that KDM5B is a critical regulator of nucleosome positioning in embryonic stem (ES) cells. Micrococcal nuclease sequencing (MNase-Seq) revealed increased enrichment of nucleosomes around TSS regions and DNase I hypersensitive sites in KDM5B-depleted ES cells. Moreover, depletion of KDM5B resulted in a widespread redistribution and disorganization of nucleosomes in a sequence-dependent manner. Dysregulated nucleosome phasing was also evident in KDM5B-depleted ES cells, including asynchronous nucleosome spacing surrounding TSS regions, where nucleosome variance was positively correlated with the degree of asynchronous phasing. The redistribution of nucleosomes around TSS regions in KDM5B-depleted ES cells is correlated with dysregulated gene expression, and altered H3K4me3 and RNA polymerase II occupancy. In addition, we found that DNA shape features varied significantly at regions with shifted nucleosomes.

CONCLUSION

Altogether, our data support a role for KDM5B in regulating nucleosome positioning in ES cells.

Intrinsic-overlapping co-expression module detection with application to Alzheimer's Disease

Genes interact with each other and may cause perturbation in the molecular pathways leading to complex diseases. Often, instead of any single gene, a subset of genes interact, forming a network, to share common biological functions. Such a subnetwork is called a functional module or motif. Identifying such modules and central key genes in them, that may be responsible for a disease, may help design patient-specific drugs. In this study, we consider the neurodegenerative Alzheimer's Disease (AD) and identify potentially responsible genes from functional motif analysis. We start from the hypothesis that central genes in genetic modules are more relevant to a disease that is under investigation and identify hub genes from the modules as potential marker genes. Motifs or modules are often non-exclusive or overlapping in nature. Moreover, they sometimes show intrinsic or hierarchical distributions with overlapping functional roles. To the best of our knowledge, no prior work handles both the situations in an integrated way. We propose a non-exclusive clustering approach, CluViaN (Clustering Via Network) that can detect intrinsic as well as overlapping modules from gene co-expression networks constructed using microarray expression profiles. We compare our method with existing methods to evaluate the quality of modules extracted. CluViaN reports the presence of intrinsic and overlapping motifs in different species not reported by any other research. We further apply our method to extract significant AD specific modules using CluViaN and rank them based the number of genes from a module involved in the disease pathways. Finally, top central genes are identified by topological analysis of the modules. We use two different AD phenotype data for experimentation. We observe that central genes, namely PSEN1, APP, NDUFB2, NDUFA1, UQCR10, PPP3R1 and a few more, play significant roles in the AD. Interestingly, our experiments also find a hub gene, PML, which has recently been reported to play a role in plasticity, circadian rhythms and the response to proteins which can cause neurodegenerative disorders. MUC4, another hub gene that we find experimentally is yet to be investigated for its potential role in AD. A software implementation of CluViaN in Java is available for download at https://sites.google.com/site/swarupnehu/publications/resources/CluViaN Software.rar.

Culture of haploid blastocysts in FGF4 favors the derivation of epiblast stem cells with a primed epigenetic and transcriptional landscape

Pluripotent stem cells within the inner cell mass and epiblast of mammalian embryos have the capacity to form all lineages in the adult organism, while multipotent trophoblast stem (TS) cells derived from the trophectoderm are capable of differentiating into fetal lineages of the placenta. While mouse embryonic stem (ES) cells and epiblast stem cells (EpiSCs) exhibit distinct expression patterns and utilize distinct external signaling pathways for self-renewal, because mouse EpiSCs resemble human ES cells they are a useful model to investigate mechanisms of human ES cell self-renewal and differentiation. Recent studies have shown that haploid embryos and ES cells can be generated from chemically-activated unfertilized mouse oocytes. However, it is unclear whether EpiSCs or TS cells can be derived from haploid embryos. Here, we describe the derivation of EpiSCs from haploid blastocyst-stage embryos using culture conditions that promote TS cell self-renewal. Maternal (parthenogenetic/gynogenetic) EpiSCs (maEpiSCs) functionally and morphologically resemble conventional EpiSCs. Established maEpiSCs and conventional EpiSCs are diploid and exhibit a normal number of chromosomes. Moreover, global expression analyses and epigenomic profiling revealed that maEpiSCs and conventional EpiSCs exhibit similarly primed transcriptional programs and epigenetic profiles, respectively. Altogether, our results describe a useful experimental model to generate EpiSCs from haploid embryos, provide insight into self-renewal mechanisms of EpiSCs, and suggest that FGF4 is not sufficient to derive TS cells from haploid blastocyst-stage embryos.

Revealing Mytilus galloprovincialis transcriptomic profiles during ontogeny

Mediterranean mussels are a worldwide spread bivalve species with extraordinary biological success. One of the reasons of this success could be the reproduction strategy of bivalves, characterized by the presence of trochophore larvae. Larval development in bivalves has been a topic of raising interest in the scientific community but it deserves much more attention. The principal objective of this work was to study the transcriptomic profile of the ontogeny of Mytilus galloprovincialis analyzing the gene expression in different developmental stages, from oocytes to juveniles. For this purpose, after conducting a 454 sequencing of the transcriptomes of mussel hemocytes, adult tissues and larvae, a new DNA microarray was designed and developed. The studied developmental stages: unfertilized oocytes, veliger, pediveliger, settled larvae and juveniles, showed very different transcriptomic profiles and clustered in groups defining their characteristic gene expression along ontogeny. Our results show that oocytes present a distinct and characteristic transcriptome. After metamorphosis, both settled larvae and juveniles showed a very similar transcriptome, with no enriched GO terms found between these two stages. This suggests: 1.- the progressive loss of RNA of maternal origin through larval development and 2.- the stabilization of the gene expression after settlement. On the other hand during metamorphosis a specific profile of differentially expressed genes was found. These genes were related to processes such as differentiation and biosynthesis. Processes related to the immune response were strongly down regulated. These suggest a development commitment at the expense of other non-essential functions, which are temporary set aside. Immune genes such as antimicrobial peptides suffer a decreased expression during metamorphosis. In fact, we found that the oocytes which express a higher quantity of genes such as myticins are more likely to reach success of the offspring, compared to oocytes poor in such mRNAs, whose progeny died before reaching metamorphosis.

Bipartite graphs in systems biology and medicine: a survey of methods and applications

The latest advances in high-throughput techniques during the past decade allowed the systems biology field to expand significantly. Today, the focus of biologists has shifted from the study of individual biological components to the study of complex biological systems and their dynamics at a larger scale. Through the discovery of novel bioentity relationships, researchers reveal new information about biological functions and processes. Graphs are widely used to represent bioentities such as proteins, genes, small molecules, ligands, and others such as nodes and their connections as edges within a network. In this review, special focus is given to the usability of bipartite graphs and their impact on the field of network biology and medicine. Furthermore, their topological properties and how these can be applied to certain biological case studies are discussed. Finally, available methodologies and software are presented, and useful insights on how bipartite graphs can shape the path toward the solution of challenging biological problems are provided.

Analysis of transcriptional activity by the Myt1 and Myt1l transcription factors

Myt1 and Myt1l (Myelin transcription factor 1, and Myt1-like) are members of a small family of closely related zinc finger transcription factors, characterized by two clusters of C2HC zinc fingers. Both are widely expressed during early embryogenesis, but are largely restricted to expression within the brain in the adult. Myt1l, as part of a three transcription factor mix, can reprogram fibroblasts to neurons and plays a role in maintaining neuronal identity. Previous analyses have indicated roles in both transcriptional activation and repression and suggested that Myt1 and Myt1l may have opposing functions in gene expression. We show that when targeted to DNA via multiple copies of the consensus Myt1/Myt1l binding site Myt1 represses transcription, whereas Myt1l activates. By targeting via a heterologous DNA binding domain we mapped an activation function in Myt1l to an amino-terminal region that is poorly conserved in Myt1. However, genome wide analyses of the effects of Myt1 and Myt1l expression in a glioblastoma cell line suggest that the two proteins have largely similar effects on endogenous gene expression. Transcriptional repression is likely mediated by binding to DNA via the known consensus site, whereas this site is not associated with the transcriptional start sites of genes with higher expression in the presence of Myt1 or Myt1l. This work suggests that these two proteins function similarly, despite differences observed in analyses based on synthetic reporter constructs.

Review of Statistical Learning Methods in Integrated Omics Studies (An Integrated Information Science)

Integrated omics is becoming a new channel for investigating the complex molecular system in modern biological science and sets a foundation for systematic learning for precision medicine. The statistical/machine learning methods that have emerged in the past decade for integrated omics are not only innovative but also multidisciplinary with integrated knowledge in biology, medicine, statistics, machine learning, and artificial intelligence. Here, we review the nontrivial classes of learning methods from the statistical aspects and streamline these learning methods within the statistical learning framework. The intriguing findings from the review are that the methods used are generalizable to other disciplines with complex systematic structure, and the integrated omics is part of an integrated information science which has collated and integrated different types of information for inferences and decision making. We review the statistical learning methods of exploratory and supervised learning from 42 publications. We also discuss the strengths and limitations of the extended principal component analysis, cluster analysis, network analysis, and regression methods. Statistical techniques such as penalization for sparsity induction when there are fewer observations than the number of features and using Bayesian approach when there are prior knowledge to be integrated are also included in the commentary. For the completeness of the review, a table of currently available software and packages from 23 publications for omics are summarized in the appendix.

Network Analysis of Gene Expression

Studies have pointed out that the expression of genes are highly regulated, which result in a cascade of distinct patterns of coexpression forming a network. Identifying and understanding such patterns is crucial in deciphering molecular mechanisms that underlie the pathophysiology of diseases. With the advance of high throughput assay of messenger RNA (mRNA) and high performance computing, reconstructing such network from molecular data such as gene expression is now possible. This chapter discusses an overview of methods of constructing such networks, practical considerations, and an example.

OCT4 supports extended LIF-independent self-renewal and maintenance of transcriptional and epigenetic networks in embryonic stem cells

Embryonic stem (ES) cell pluripotency is governed by OCT4-centric transcriptional networks. Conventional ES cells can be derived and maintained in vitro with media containing the cytokine leukemia inhibitory factor (LIF), which propagates the pluripotent state by activating STAT3 signaling, and simultaneous inhibition of glycogen synthase kinase-3 (GSK3) and MAP kinase/ERK kinase signaling. However, it is unclear whether overexpression of OCT4 is sufficient to overcome LIF-dependence. Here, we show that inducible expression of OCT4 (iOCT4) supports long-term LIF-independent self-renewal of ES cells cultured in media containing fetal bovine serum (FBS) and a glycogen synthase kinase-3 (GSK3) inhibitor, and in serum-free media. Global expression analysis revealed that LIF-independent iOCT4 ES cells and control ES cells exhibit similar transcriptional programs relative to epiblast stem cells (EpiSCs) and differentiated cells. Epigenomic profiling also demonstrated similar patterns of histone modifications between LIF-independent iOCT4 and control ES cells. Moreover, LIF-independent iOCT4 ES cells retain the capacity to differentiate in vitro and in vivo upon downregulation of OCT4 expression. These findings indicate that OCT4 expression is sufficient to sustain intrinsic signaling in a LIF-independent manner to promote ES cell pluripotency and self-renewal.

Using induced pluripotent stem cells to explore genetic and epigenetic variation associated with Alzheimer's disease

It is thought that both genetic and epigenetic variation play a role in Alzheimer's disease initiation and progression. With the advent of somatic cell reprogramming into induced pluripotent stem cells it is now possible to generate patient-derived cells that are able to more accurately model and recapitulate disease. Furthermore, by combining this with recent advances in (epi)genome editing technologies, it is possible to begin to examine the functional consequence of previously nominated genetic variants and infer epigenetic causality from recently identified epigenetic variants. In this review, we explore the role of genetic and epigenetic variation in Alzheimer's disease and how the functional relevance of nominated loci can be investigated using induced pluripotent stem cells and (epi)genome editing techniques.

Feathermoss and epiphytic Nostoc cooperate differently: expanding the spectrum of plant-cyanobacteria symbiosis

Dinitrogen (N₂)-fixation by cyanobacteria in symbiosis with feathermosses is the primary pathway of biological nitrogen (N) input into boreal forests. Despite its significance, little is known about the cyanobacterial gene repertoire and regulatory rewiring needed for the establishment and maintenance of the symbiosis. To determine gene acquisitions and regulatory changes allowing cyanobacteria to form and maintain this symbiosis, we compared genomically closely related symbiotic-competent and -incompetent Nostoc strains using a proteogenomics approach and an experimental set up allowing for controlled chemical and physical contact between partners. Thirty-two gene families were found only in the genomes of symbiotic strains, including some never before associated with cyanobacterial symbiosis. We identified conserved orthologs that were differentially expressed in symbiotic strains, including protein families involved in chemotaxis and motility, NO regulation, sulfate/phosphate transport, and glycosyl-modifying and oxidative stress-mediating exoenzymes. The physical moss–cyanobacteria epiphytic symbiosis is distinct from other cyanobacteria–plant symbioses, with Nostoc retaining motility, and lacking modulation of N₂-fixation, photosynthesis, GS-GOGAT cycle and heterocyst formation. The results expand our knowledge base of plant–cyanobacterial symbioses, provide a model of information and material exchange in this ecologically significant symbiosis, and suggest new currencies, namely nitric oxide and aliphatic sulfonates, may be involved in establishing and maintaining the cyanobacteria–feathermoss symbiosis.

A molecular signature for anastasis, recovery from the brink of apoptotic cell death

Cells can survive executioner caspase activation following transient apoptotic stimuli, a process called anastasis. Using whole-transcriptome RNA sequencing, Sun et al. show that anastasis is an active, two-stage program and characterize the cell behaviors and molecular signature involved in the process.

During apoptosis, executioner caspase activity has been considered a point of no return. However, recent studies show that cells can survive caspase activation following transient apoptotic stimuli, a process called anastasis. To identify a molecular signature, we performed whole-transcriptome RNA sequencing of untreated, apoptotic, and recovering HeLa cells. We found that anastasis is an active, two-stage program. During the early stage, cells transition from growth-arrested to growing. In the late stage, HeLa cells change from proliferating to migratory. Recovering cells also exhibited prolonged elevation of proangiogenic factors. Strikingly, some early-recovery mRNAs, including Snail, were elevated first during apoptosis, implying that dying cells poise to recover, even while under apoptotic stress. Snail was also required for recovery. This study reveals similarities in the anastasis genes, pathways, and cell behaviors to those activated in wound healing and identifies a repertoire of potential targets for therapeutic manipulation.

Empirical Comparison of Visualization Tools for Larger-Scale Network Analysis

Gene expression, signal transduction, protein/chemical interactions, biomedical literature cooccurrences, and other concepts are often captured in biological network representations where nodes represent a certain bioentity and edges the connections between them. While many tools to manipulate, visualize, and interactively explore such networks already exist, only few of them can scale up and follow today's indisputable information growth. In this review, we shortly list a catalog of available network visualization tools and, from a user-experience point of view, we identify four candidate tools suitable for larger-scale network analysis, visualization, and exploration. We comment on their strengths and their weaknesses and empirically discuss their scalability, user friendliness, and postvisualization capabilities.

SMYD5 regulates H4K20me3-marked heterochromatin to safeguard ES cell self-renewal and prevent spurious differentiation

Background

Epigenetic regulation of chromatin states is thought to control the self-renewal and differentiation of embryonic stem (ES) cells. However, the roles of repressive histone modifications such as trimethylated histone 4 lysine 20 (H4K20me3) in pluripotency and development are largely unknown.

Results

Here, we show that the histone lysine methyltransferase SMYD5 mediates H4K20me3 at heterochromatin regions. Depletion of SMYD5 leads to compromised self-renewal, including dysregulated expression of OCT4 targets, and perturbed differentiation. SMYD5-bound regions are enriched with repetitive DNA elements. Knockdown of SMYD5 results in a global decrease of H4K20me3 levels, a redistribution of heterochromatin constituents including H3K9me3/2, G9a, and HP1α, and de-repression of endogenous retroelements. A loss of SMYD5-dependent silencing of heterochromatin nearby genic regions leads to upregulated expression of lineage-specific genes, thus contributing to the decreased self-renewal and perturbed differentiation of SMYD5-depleted ES cells.

Conclusions

Altogether, these findings implicate a role for SMYD5 in regulating ES cell self-renewal and H4K20me3-marked heterochromatin.

Electronic supplementary material

The online version of this article (doi:10.1186/s13072-017-0115-7) contains supplementary material, which is available to authorized users.

Tgif1 and Tgif2 Repress Expression of the RabGAP Evi5l

Mouse embryos conditionally lacking Tgif1 and Tgif2 have holoprosencephaly and defects in left-right asymmetry. To identify pathways affected by loss of Tgif function during embryogenesis, we performed transcriptome profiling on whole mouse embryos. Among the genes with altered expression in embryos lacking Tgifs were a number with links to cilium function. One of these, Evi5l, encodes a RabGAP that is known to block the formation of cilia when overexpressed. Evi5l expression is increased in Tgif1; Tgif2-null embryos and in double-null mouse embryo fibroblasts (MEFs). Knockdown of Tgifs in a human retinal pigment epithelial cell line also increased EVI5L expression. We show that TGIF1 binds to a conserved consensus TGIF site 5' of the human and mouse Evi5l genes and represses Evi5l expression. In primary MEFs lacking both Tgifs, the number of cells with primary cilia was significantly decreased, and we observed a reduction in the transcriptional response to Shh pathway activation. Reducing Evi5l expression in MEFs lacking Tgifs resulted in a partial restoration of cilium numbers and in the transcriptional response to activation of the Shh pathway. In summary, this work shows that Tgifs regulate ciliogenesis and suggests that Evi5l mediates at least part of this effect.

Phosphorylation of αB-crystallin supports reactive astrogliosis in demyelination

The small heat shock protein αB-crystallin (CRYAB) has been implicated in multiple sclerosis (MS) pathogenesis. Earlier studies have indicated that CRYAB inhibits inflammation and attenuates clinical disease when administered in the experimental autoimmune encephalomyelitis model of MS. In this study, we evaluated the role of CRYAB in primary demyelinating events. Using the cuprizone model of demyelination, a noninflammatory model that allows the analysis of glial responses in MS, we show that endogenous CRYAB expression is associated with increased severity of demyelination. Moreover, we demonstrate a strong correlation between the expression of CRYAB and the extent of reactive astrogliosis in demyelinating areas and in in vitro assays. In addition, we reveal that CRYAB is differentially phosphorylated in astrocytes in active demyelinating MS lesions, as well as in cuprizone-induced lesions, and that this phosphorylation is required for the reactive astrocyte response associated with demyelination. Furthermore, taking a proteomics approach to identify proteins that are bound by the phosphorylated forms of CRYAB in primary cultured astrocytes, we show that there is clear differential binding of protein targets due to the specific phosphorylation of CRYAB. Subsequent Ingenuity Pathway Analysis of these targets reveals implications for intracellular pathways and biological processes that could be affected by these modifications. Together, these findings demonstrate that astrocytes play a pivotal role in demyelination, making them a potential target for therapeutic intervention, and that phosphorylation of CRYAB is a key factor supporting the pathogenic response of astrocytes to oligodendrocyte injury.

Systems Analysis of the Complement-Induced Priming Phase of Liver Regeneration

Liver regeneration is a well-orchestrated process in the liver that allows mature hepatocytes to reenter the cell cycle to proliferate and replace lost or damaged cells. This process is often impaired in fatty or diseased livers, leading to cirrhosis and other deleterious phenotypes. Prior research has established the role of the complement system and its effector proteins in the progression of liver regeneration; however, a detailed mechanistic understanding of the involvement of complement in regeneration is yet to be established. In this study, we have examined the role of the complement system during the priming phase of liver regeneration through a systems level analysis using a combination of transcriptomic and metabolomic measurements. More specifically, we have performed partial hepatectomy on mice with genetic deficiency in C3, the major component of the complement cascade, and collected their livers at various time points. Based on our analysis, we show that the C3 cascade activates c-fos and promotes the TNF-α signaling pathway, which then activates acute-phase genes such as serum amyloid proteins and orosomucoids. The complement activation also regulates the efflux and the metabolism of cholesterol, an important metabolite for cell cycle and proliferation. Based on our systems level analysis, we provide an integrated model for the complement-induced priming phase of liver regeneration.

Systematic characterization of lncRNAs' cell-to-cell expression heterogeneity in glioblastoma cells

Glioblastoma (GBM) is the most common malignant adult brain tumor generally associated with high level of cellular heterogeneity and a dismal prognosis. Long noncoding RNAs (lncRNAs) are emerging as novel mediators of tumorigenesis. Recently developed single-cell RNA-seq provides an unprecedented way for analysis of the cell-to-cell variability in lncRNA expression profiles. Here we comprehensively examined the expression patterns of 2,003 lncRNAs in 380 cells from five primary GBMs and two glioblastoma stem-like cell (GSC) lines. Employing the self-organizing maps, we displayed the landscape of the lncRNA expression dynamics for individual cells. Further analyses revealed heterogeneous nature of lncRNA in abundance and splicing patterns. Moreover, lncRNA expression variation is also ubiquitously present in the established GSC lines composed of seemingly identical cells. Through comparative analysis of GSC and corresponding differentiated cell cultures, we defined a stemness signature by the set of 31 differentially expressed lncRNAs, which can disclose stemness gradients in five tumors. Additionally, based on known classifier lncRNAs for molecular subtypes, each tumor was found to comprise individual cells representing four subtypes. Our systematic characterization of lncRNA expression heterogeneity lays the foundation for future efforts to further understand the function of lncRNA, develop valuable biomarkers, and enhance knowledge of GBM biology.

Cross-Clustering: A Partial Clustering Algorithm with Automatic Estimation of the Number of Clusters

Four of the most common limitations of the many available clustering methods are: i) the lack of a proper strategy to deal with outliers; ii) the need for a good a priori estimate of the number of clusters to obtain reasonable results; iii) the lack of a method able to detect when partitioning of a specific data set is not appropriate; and iv) the dependence of the result on the initialization. Here we propose Cross-clustering (CC), a partial clustering algorithm that overcomes these four limitations by combining the principles of two well established hierarchical clustering algorithms: Ward’s minimum variance and Complete-linkage. We validated CC by comparing it with a number of existing clustering methods, including Ward’s and Complete-linkage. We show on both simulated and real datasets, that CC performs better than the other methods in terms of: the identification of the correct number of clusters, the identification of outliers, and the determination of real cluster memberships. We used CC to cluster samples in order to identify disease subtypes, and on gene profiles, in order to determine groups of genes with the same behavior. Results obtained on a non-biological dataset show that the method is general enough to be successfully used in such diverse applications. The algorithm has been implemented in the statistical language R and is freely available from the CRAN contributed packages repository.

Trends in aptamer selection methods and applications

Aptamers are target specific ssDNA, RNA or peptide sequences generated by an in vitro selection and amplification method called SELEX (Systematic Evolution of Ligands by EXponential Enrichment), which involves repetitive cycles of binding, recovery and amplification steps. Aptamers have the ability to bind with a variety of targets such as drugs, proteins, heavy metals, and pathogens with high specificity and selectivity. Aptamers are similar to monoclonal antibodies regarding their binding affinities, but they offer a number of advantages over the existing antibody-based detection methods, which make the aptamers promising diagnostic and therapeutic tools for future biomedical and analytical applications. The aim of this review article is to provide an overview of the recent advancements in aptamer screening methods along with a concise description of the major application areas of aptamers including biomarker discovery, diagnostics, imaging and nanotechnology.

Integrating Tumor and Stromal Gene Expression Signatures With Clinical Indices for Survival Stratification of Early-Stage Non-Small Cell Lung Cancer

BACKGROUND

Accurate survival stratification in early-stage non-small cell lung cancer (NSCLC) could inform the use of adjuvant therapy. We developed a clinically implementable mortality risk score incorporating distinct tumor microenvironmental gene expression signatures and clinical variables.

METHODS

Gene expression profiles from 1106 nonsquamous NSCLCs were used for generation and internal validation of a nine-gene molecular prognostic index (MPI). A quantitative polymerase chain reaction (qPCR) assay was developed and validated on an independent cohort of formalin-fixed paraffin-embedded (FFPE) tissues (n = 98). A prognostic score using clinical variables was generated using Surveillance, Epidemiology, and End Results data and combined with the MPI. All statistical tests for survival were two-sided.

RESULTS

The MPI stratified stage I patients into prognostic categories in three microarray and one FFPE qPCR validation cohorts (HR = 2.99, 95% CI = 1.55 to 5.76, P < .001 in stage IA patients of the largest microarray validation cohort; HR = 3.95, 95% CI = 1.24 to 12.64, P = .01 in stage IA of the qPCR cohort). Prognostic genes were expressed in distinct tumor cell subpopulations, and genes implicated in proliferation and stem cells portended poor outcomes, while genes involved in normal lung differentiation and immune infiltration were associated with superior survival. Integrating the MPI with clinical variables conferred greatest prognostic power (HR = 3.43, 95% CI = 2.18 to 5.39, P < .001 in stage I patients of the largest microarray cohort; HR = 3.99, 95% CI = 1.67 to 9.56, P < .001 in stage I patients of the qPCR cohort). Finally, the MPI was prognostic irrespective of somatic alterations in EGFR, KRAS, TP53, and ALK.

CONCLUSION

The MPI incorporates genes expressed in the tumor and its microenvironment and can be implemented clinically using qPCR assays on FFPE tissues. A composite model integrating the MPI with clinical variables provides the most accurate risk stratification.

The prognostic landscape of genes and infiltrating immune cells across human cancers

Molecular profiles of tumors and tumor-associated cells hold great promise as biomarkers of clinical outcomes. However, existing data sets are fragmented and difficult to analyze systematically. Here we present a pan-cancer resource and meta-analysis of expression signatures from ∼18,000 human tumors with overall survival outcomes across 39 malignancies. By using this resource, we identified a forkhead box MI (FOXM1) regulatory network as a major predictor of adverse outcomes, and we found that expression of favorably prognostic genes, including KLRB1 (encoding CD161), largely reflect tumor-associated leukocytes. By applying CIBERSORT, a computational approach for inferring leukocyte representation in bulk tumor transcriptomes, we identified complex associations between 22 distinct leukocyte subsets and cancer survival. For example, tumor-associated neutrophil and plasma cell signatures emerged as significant but opposite predictors of survival for diverse solid tumors, including breast and lung adenocarcinomas. This resource and associated analytical tools (http://precog.stanford.edu) may help delineate prognostic genes and leukocyte subsets within and across cancers, shed light on the impact of tumor heterogeneity on cancer outcomes, and facilitate the discovery of biomarkers and therapeutic targets.

Self-Organizing Feature Maps Identify Proteins Critical to Learning in a Mouse Model of Down Syndrome

Down syndrome (DS) is a chromosomal abnormality (trisomy of human chromosome 21) associated with intellectual disability and affecting approximately one in 1000 live births worldwide. The overexpression of genes encoded by the extra copy of a normal chromosome in DS is believed to be sufficient to perturb normal pathways and normal responses to stimulation, causing learning and memory deficits. In this work, we have designed a strategy based on the unsupervised clustering method, Self Organizing Maps (SOM), to identify biologically important differences in protein levels in mice exposed to context fear conditioning (CFC). We analyzed expression levels of 77 proteins obtained from normal genotype control mice and from their trisomic littermates (Ts65Dn) both with and without treatment with the drug memantine. Control mice learn successfully while the trisomic mice fail, unless they are first treated with the drug, which rescues their learning ability. The SOM approach identified reduced subsets of proteins predicted to make the most critical contributions to normal learning, to failed learning and rescued learning, and provides a visual representation of the data that allows the user to extract patterns that may underlie novel biological responses to the different kinds of learning and the response to memantine. Results suggest that the application of SOM to new experimental data sets of complex protein profiles can be used to identify common critical protein responses, which in turn may aid in identifying potentially more effective drug targets.

Restoration of mesenchymal retinal pigmented epithelial cells by TGFβ pathway inhibitors: implications for age-related macular degeneration

BACKGROUND

Age-related macular degeneration (AMD) is a leading cause of blindness. Most vision loss occurs following the transition from a disease of deposit formation and inflammation to a disease of neovascular fibrosis and/or cell death. Here, we investigate how repeated wound stimulus leads to seminal changes in gene expression and the onset of a perpetual state of stimulus-independent wound response in retinal pigmented epithelial (RPE) cells, a cell-type central to the etiology of AMD.

METHODS

Transcriptome wide expression profiles of human fetal RPE cell cultures as a function of passage and time post-plating were determined using Agilent 44 K whole genome microarrays and RNA-Seq. Using a systems level analysis, differentially expressed genes and pathways of interest were identified and their role in the establishment of a persistent mesenchymal state was assessed using pharmacological-based experiments.

RESULTS

Using a human fetal RPE cell culture model that considers monolayer disruption and subconfluent culture as a proxy for wound stimulus, we show that prolonged wound stimulus leads to terminal acquisition of a mesenchymal phenotype post-confluence and altered expression of more than 40 % of the transcriptome. In contrast, at subconfluence fewer than 5 % of expressed transcripts have two-fold or greater expression differences after repeated passage. Protein-protein and pathway interaction analysis of the genes with passage-dependent expression levels in subconfluent cultures reveals a 158-node interactome comprised of two interconnected modules with functions pertaining to wound response and cell division. Among the wound response genes are the TGFβ pathway activators: TGFB1, TGFB2, INHBA, INHBB, GDF6, CTGF, and THBS1. Significantly, inhibition of TGFBR1/ACVR1B mediated signaling using receptor kinase inhibitors both forestalls and largely reverses the passage-dependent loss of epithelial potential; thus extending the effective lifespan by at least four passages. Moreover, a disproportionate number of RPE wound response genes have altered expression in neovascular and geographic AMD, including key members of the TGFβ pathway.

CONCLUSIONS

In RPE cells the switch to a persistent mesenchymal state following prolonged wound stimulus is driven by lasting activation of the TGFβ pathway. Targeted inhibition of TGFβ signaling may be an effective approach towards retarding AMD progression and producing RPE cells in quantity for research and cell-based therapies.

Transcriptomics of critical period of visual cortical plasticity in mice

In contrast to the prenatal development of the cerebral cortex, when cell production, migration, and layer formation dominate, development after birth involves more subtle processes, such as activity-dependent plasticity that includes refinement of synaptic connectivity by its stabilization and elimination. In the present study, we use RNA-seq with high spatial resolution to examine differential gene expression across layers 2/3, 4, 5, and 6 of the mouse visual cortex before the onset of the critical period of plasticity [postnatal day 5 (P5)], at its peak (P26), and at the mature stage (P180) and compare it with the prefrontal association area. We find that, although genes involved in early developmental events such as cell division, neuronal migration, and axon guidance are still prominent at P5, their expression largely terminates by P26, when synaptic plasticity and associated signaling pathways become enriched. Unexpectedly, the gene expression profile was similar in both areas at this age, suggesting that activity-dependent plasticity between visual and association cortices are subject to the same genetic constraints. Although gene expression changes follow similar paths until P26, we have identified 30 regionally enriched genes that are prominent during the critical period. At P180, we identified several hundred differentially expressed gene isoforms despite subsiding levels of gene expression differences. This result indicates that, once genetic developmental programs cease, the remaining morphogenetic processes may depend on posttranslational events.

Skin fibrosis. Identification and isolation of a dermal lineage with intrinsic fibrogenic potential

Dermal fibroblasts represent a heterogeneous population of cells with diverse features that remain largely undefined. We reveal the presence of at least two fibroblast lineages in murine dorsal skin. Lineage tracing and transplantation assays demonstrate that a single fibroblast lineage is responsible for the bulk of connective tissue deposition during embryonic development, cutaneous wound healing, radiation fibrosis, and cancer stroma formation. Lineage-specific cell ablation leads to diminished connective tissue deposition in wounds and reduces melanoma growth. Using flow cytometry, we identify CD26/DPP4 as a surface marker that allows isolation of this lineage. Small molecule-based inhibition of CD26/DPP4 enzymatic activity during wound healing results in diminished cutaneous scarring. Identification and isolation of these lineages hold promise for translational medicine aimed at in vivo modulation of fibrogenic behavior.

Transcriptomics profiling of human SGBS adipogenesis

Obesity is an ever-growing epidemic where tissue homeostasis is influenced by the differentiation of adipocytes that function in lipid metabolism, endocrine and inflammatory processes. While this differentiation process has been well-characterized in mice, limited data is available from human cells. Applying microarray expression profiling in the human SGBS pre-adipocyte cell line, we identified genes with differential expression during differentiation in combination with constraint-based modeling of metabolic pathway activity. Here we describe the experimental design and quality controls in detail for the gene expression and related results published by Galhardo et al. in Nucleic Acids Research 2014 associated with the data uploaded to NCBI Gene Expression Omnibus (GSE41352).

Abnormalities in human pluripotent cells due to reprogramming mechanisms

Human pluripotent stem cells hold potential for regenerative medicine, but available cell types have significant limitations. Although embryonic stem cells (ES cells) from in vitro fertilized embryos (IVF ES cells) represent the 'gold standard', they are allogeneic to patients. Autologous induced pluripotent stem cells (iPS cells) are prone to epigenetic and transcriptional aberrations. To determine whether such abnormalities are intrinsic to somatic cell reprogramming or secondary to the reprogramming method, genetically matched sets of human IVF ES cells, iPS cells and nuclear transfer ES cells (NT ES cells) derived by somatic cell nuclear transfer (SCNT) were subjected to genome-wide analyses. Both NT ES cells and iPS cells derived from the same somatic cells contained comparable numbers of de novo copy number variations. In contrast, DNA methylation and transcriptome profiles of NT ES cells corresponded closely to those of IVF ES cells, whereas iPS cells differed and retained residual DNA methylation patterns typical of parental somatic cells. Thus, human somatic cells can be faithfully reprogrammed to pluripotency by SCNT and are therefore ideal for cell replacement therapies.

Transcriptomic changes during regeneration of the central nervous system in an echinoderm

BACKGROUND

Echinoderms are emerging as important models in regenerative biology. Significant amount of data are available on cellular mechanisms of post-traumatic repair in these animals, whereas studies of gene expression are rare. In this study, we employ high-throughput sequencing to analyze the transcriptome of the normal and regenerating radial nerve cord (a homolog of the chordate neural tube), in the sea cucumber Holothuria glaberrima.

RESULTS

Our de novo assembly yielded 70,173 contigs, of which 24,324 showed significant similarity to known protein-coding sequences. Expression profiling revealed large-scale changes in gene expression (4,023 and 3,257 up-regulated and down-regulated transcripts, respectively) associated with regeneration. Functional analysis of sets of differentially expressed genes suggested that among the most extensively over-represented pathways were those involved in the extracellular matrix (ECM) remodeling and ECM-cell interactions, indicating a key role of the ECM in regeneration. We also searched the sea cucumber transcriptome for homologs of factors known to be involved in acquisition and/or control of pluripotency. We identified eleven genes that were expressed both in the normal and regenerating tissues. Of these, only Myc was present at significantly higher levels in regeneration, whereas the expression of Bmi-1 was significantly reduced. We also sought to get insight into which transcription factors may operate at the top of the regulatory hierarchy to control gene expression in regeneration. Our analysis yielded eleven putative transcription factors, which constitute good candidates for further functional studies. The identified candidate transcription factors included not only known regeneration-related genes, but also factors not previously implicated as regulators of post-traumatic tissue regrowth. Functional annotation also suggested that one of the possible adaptations contributing to fast and efficient neural regeneration in echinoderms may be related to suppression of excitotoxicity.

CONCLUSIONS

Our transcriptomic analysis corroborates existing data on cellular mechanisms implicated in regeneration in sea cucumbers. More importantly, however, it also illuminates new aspects of echinoderm regeneration, which have been scarcely studied or overlooked altogether. The most significant outcome of the present work is that it lays out a roadmap for future studies of regulatory mechanisms by providing a list of key candidate genes for functional analysis.

KDM5B focuses H3K4 methylation near promoters and enhancers during embryonic stem cell self-renewal and differentiation

Background

Pluripotency of embryonic stem (ES) cells is controlled in part by chromatin-modifying factors that regulate histone H3 lysine 4 (H3K4) methylation. However, it remains unclear how H3K4 demethylation contributes to ES cell function.

Results

Here, we show that KDM5B, which demethylates lysine 4 of histone H3, co-localizes with H3K4me3 near promoters and enhancers of active genes in ES cells; its depletion leads to spreading of H3K4 methylation into gene bodies and enhancer shores, indicating that KDM5B functions to focus H3K4 methylation at promoters and enhancers. Spreading of H3K4 methylation to gene bodies and enhancer shores is linked to defects in gene expression programs and enhancer activity, respectively, during self-renewal and differentiation of KDM5B-depleted ES cells. KDM5B critically regulates H3K4 methylation at bivalent genes during differentiation in the absence of LIF or Oct4. We also show that KDM5B and LSD1, another H3K4 demethylase, co-regulate H3K4 methylation at active promoters but they retain distinct roles in demethylating gene body regions and bivalent genes.

Conclusions

Our results provide global and functional insight into the role of KDM5B in regulating H3K4 methylation marks near promoters, gene bodies, and enhancers in ES cells and during differentiation.

Integrated analysis of transcript-level regulation of metabolism reveals disease-relevant nodes of the human metabolic network

Metabolic diseases and comorbidities represent an ever-growing epidemic where multiple cell types impact tissue homeostasis. Here, the link between the metabolic and gene regulatory networks was studied through experimental and computational analysis. Integrating gene regulation data with a human metabolic network prompted the establishment of an open-sourced web portal, IDARE (Integrated Data Nodes of Regulation), for visualizing various gene-related data in context of metabolic pathways. Motivated by increasing availability of deep sequencing studies, we obtained ChIP-seq data from widely studied human umbilical vein endothelial cells. Interestingly, we found that association of metabolic genes with multiple transcription factors (TFs) enriched disease-associated genes. To demonstrate further extensions enabled by examining these networks together, constraint-based modeling was applied to data from human preadipocyte differentiation. In parallel, data on gene expression, genome-wide ChIP-seq profiles for peroxisome proliferator-activated receptor (PPAR) γ, CCAAT/enhancer binding protein (CEBP) α, liver X receptor (LXR) and H3K4me3 and microRNA target identification for miR-27a, miR-29a and miR-222 were collected. Disease-relevant key nodes, including mitochondrial glycerol-3-phosphate acyltransferase (GPAM), were exposed from metabolic pathways predicted to change activity by focusing on association with multiple regulators. In both cell types, our analysis reveals the convergence of microRNAs and TFs within the branched chain amino acid (BCAA) metabolic pathway, possibly providing an explanation for its downregulation in obese and diabetic conditions.

Efficient selection of biomineralizing DNA aptamers using deep sequencing and population clustering

DNA-based information systems drive the combinatorial optimization processes of natural evolution, including the evolution of biominerals. Advances in high-throughput DNA sequencing expand the power of DNA as a potential information platform for combinatorial engineering, but many applications remain to be developed due in part to the challenge of handling large amounts of sequence data. Here we employ high-throughput sequencing and a recently developed clustering method (AutoSOME) to identify single-stranded DNA sequence families that bind specifically to ZnO semiconductor mineral surfaces. These sequences were enriched from a diverse DNA library after a single round of screening, whereas previous screening approaches typically require 5-15 rounds of enrichment for effective sequence identification. The consensus sequence of the largest cluster was poly d(T)30. This consensus sequence exhibited clear aptamer behavior and was shown to promote the synthesis of crystalline ZnO from aqueous solution at near-neutral pH. This activity is significant, as the crystalline form of this wide-bandgap semiconductor is not typically amenable to solution synthesis in this pH range. High-resolution TEM revealed that this DNA synthesis route yields ZnO nanoparticles with an amorphous-crystalline core-shell structure, suggesting that the mechanism of mineralization involves nanoscale coacervation around the DNA template. We thus demonstrate that our new method, termed Single round Enrichment of Ligands by deep Sequencing (SEL-Seq), can facilitate biomimetic synthesis of technological nanomaterials by accelerating combinatorial selection of biomolecular-mineral interactions. Moreover, by enabling direct characterization of sequence family demographics, we anticipate that SEL-Seq will enhance aptamer discovery in applications employing additional rounds of screening.

Identifying stem cell gene expression patterns and phenotypic networks with AutoSOME

Stem cells have the unique property of differentiation and self-renewal and play critical roles in normal development, tissue repair, and disease. To promote systems-wide analysis of cells and tissues, we developed AutoSOME, a machine-learning method for identifying coordinated gene expression patterns and correlated cellular phenotypes in whole-transcriptome data, without prior knowledge of cluster number or structure. Here, we present a facile primer demonstrating the use of AutoSOME for identification and characterization of stem cell gene expression signatures and for visualization of transcriptome networks using Cytoscape. This protocol should serve as a general foundation for gene expression cluster analysis of stem cells, with applications for studying pluripotency, multi-lineage potential, and neoplastic disease.

Integrating and mining the chromatin landscape of cell-type specificity using self-organizing maps

We tested whether self-organizing maps (SOMs) could be used to effectively integrate, visualize, and mine diverse genomics data types, including complex chromatin signatures. A fine-grained SOM was trained on 72 ChIP-seq histone modifications and DNase-seq data sets from six biologically diverse cell lines studied by The ENCODE Project Consortium. We mined the resulting SOM to identify chromatin signatures related to sequence-specific transcription factor occupancy, sequence motif enrichment, and biological functions. To highlight clusters enriched for specific functions such as transcriptional promoters or enhancers, we overlaid onto the map additional data sets not used during training, such as ChIP-seq, RNA-seq, CAGE, and information on cis-acting regulatory modules from the literature. We used the SOM to parse known transcriptional enhancers according to the cell-type-specific chromatin signature, and we further corroborated this pattern on the map by EP300 (also known as p300) occupancy. New candidate cell-type-specific enhancers were identified for multiple ENCODE cell types in this way, along with new candidates for ubiquitous enhancer activity. An interactive web interface was developed to allow users to visualize and custom-mine the ENCODE SOM. We conclude that large SOMs trained on chromatin data from multiple cell types provide a powerful way to identify complex relationships in genomic data at user-selected levels of granularity.