Yin Yang 1 is associated with cancer stem cell transcription factors (SOX2, OCT4, BMI1) and clinical implication.

The transcription factor Yin Yang 1 (YY1) is frequently overexpressed in cancerous tissues compared to normal tissues and has regulatory roles in cell proliferation, cell viability, epithelial-mesenchymal transition, metastasis and drug/immune resistance. YY1 shares many properties with cancer stem cells (CSCs) that drive tumorigenesis, metastasis and drug resistance and are regulated by overexpression of certain transcription factors, including SOX2, OCT4 (POU5F1), BMI1 and NANOG. Based on these similarities, it was expected that YY1 expression would be associated with SOX2, OCT4, BMI1, and NANOG's expressions and activities. Data mining from the proteomic tissue-based datasets from the Human Protein Atlas were used for protein expression patterns of YY1 and the four CSC markers in 17 types of cancer, including both solid and hematological malignancies. A close association was revealed between the frequency of expressions of YY1 and SOX2 as well as SOX2 and OCT4 in all cancers analyzed. Two types of dynamics were identified based on the nature of their association, namely, inverse or direct, between YY1 and SOX2. These two dynamics define distinctive patterns of BMI1 and OCT4 expressions. The relationship between YY1 and SOX2 expressions as well as the expressions of BMI1 and OCT4 resulted in the classification of four groups of cancers with distinct molecular signatures: (1) Prostate, lung, cervical, endometrial, ovarian and glioma cancers (YY1(lo)SOX2(hi)BMI1(hi)OCT4(hi)) (2) Skin, testis and breast cancers (YY1(hi)SOX2(lo)BMI1(hi)OCT4(hi)) (3) Liver, stomach, renal, pancreatic and urothelial cancers (YY1(lo)SOX2(lo)BMI1(hi)OCT4(hi)) and (4) Colorectal cancer, lymphoma and melanoma (YY1(hi)SOX2(hi)BMI1(lo)OCT4(hi)). A regulatory loop is proposed consisting of the cross-talk between the NF-kB/PI3K/AKT pathways and the downstream inter-regulation of target gene products YY1, OCT4, SOX2 and BMI1

Unveiling combinatorial regulation through the combination of ChIP information and in silico cis-regulatory module detection

Computationally retrieving biologically relevant cis-regulatory modules (CRMs) is not straightforward. Because of the large number of candidates and the imperfection of the screening methods, many spurious CRMs are detected that are as high scoring as the biologically true ones. Using ChIP-information allows not only to reduce the regions in which the binding sites of the assayed transcription factor (TF) should be located, but also allows restricting the valid CRMs to those that contain the assayed TF (here referred to as applying CRM detection in a query-based mode). In this study, we show that exploiting ChIP-information in a query-based way makes in silico CRM detection a much more feasible endeavor. To be able to handle the large datasets, the query-based setting and other specificities proper to CRM detection on ChIP-Seq based data, we developed a novel powerful CRM detection method 'CPModule'. By applying it on a well-studied ChIP-Seq data set involved in self-renewal of mouse embryonic stem cells, we demonstrate how our tool can recover combinatorial regulation of five known TFs that are key in the self-renewal of mouse embryonic stem cells. Additionally, we make a number of new predictions on combinatorial regulation of these five key TFs with other TFs documented in TRANSFAC

An Algorithm for Cellular Reprogramming

The day we understand the time evolution of subcellular elements at a level of detail comparable to physical systems governed by Newton's laws of motion seems far away. Even so, quantitative approaches to cellular dynamics add to our understanding of cell biology, providing data-guided frameworks that allow us to develop better predictions about and methods for control over specific biological processes and system-wide cell behavior. In this paper we describe an approach to optimizing the use of transcription factors in the context of cellular reprogramming. We construct an approximate model for the natural evolution of a synchronized population of fibroblasts, based on data obtained by sampling the expression of some 22,083 genes at several times along the cell cycle. (These data are based on a colony of cells that have been cell cycle synchronized) In order to arrive at a model of moderate complexity, we cluster gene expression based on the division of the genome into topologically associating domains (TADs) and then model the dynamics of the expression levels of the TADs. Based on this dynamical model and known bioinformatics, we develop a methodology for identifying the transcription factors that are the most likely to be effective toward a specific cellular reprogramming task. The approach used is based on a device commonly used in optimal control. From this data-guided methodology, we identify a number of validated transcription factors used in reprogramming and/or natural differentiation. Our findings highlight the immense potential of dynamical models models, mathematics, and data guided methodologies for improving methods for control over biological processes

Major transcriptome re-organisation and abrupt changes in signalling, cell cycle and chromatin regulation at neural differentiation <em>in vivo</em>

Here, we exploit the spatial separation of temporal events of neural differentiation in the elongating chick body axis to provide the first analysis of transcriptome change in progressively more differentiated neural cell populations in vivo. Microarray data, validated against direct RNA sequencing, identified: (1) a gene cohort characteristic of the multi-potent stem zone epiblast, which contains neuro-mesodermal progenitors that progressively generate the spinal cord; (2) a major transcriptome reorganisation as cells then adopt a neural fate; and (3) increasing diversity as neural patterning and neuron production begin. Focussing on the transition from multi-potent to neural state cells, we capture changes in major signalling pathways, uncover novel Wnt and Notch signalling dynamics, and implicate new pathways (mevalonate pathway/steroid biogenesis and TGF beta). This analysis further predicts changes in cellular processes, cell cycle, RNA-processing and protein turnover as cells acquire neural fate. We show that these changes are conserved across species and provide biological evidence for reduced proteasome efficiency and a novel lengthening of S phase. This latter step may provide time for epigenetic events to mediate large-scale transcriptome re-organisation; consistent with this, we uncover simultaneous downregulation of major chromatin modifiers as the neural programme is established. We further demonstrate that transcription of one such gene, HDAC1, is dependent on FGF signalling, making a novel link between signals that control neural differentiation and transcription of a core regulator of chromatin organisation. Our work implicates new signalling pathways and dynamics, cellular processes and epigenetic modifiers in neural differentiation in vivo, identifying multiple new potential cellular and molecular mechanisms that direct differentiation

scAI: an unsupervised approach for the integrative analysis of parallel single-cell transcriptomic and epigenomic profiles.

Simultaneous measurements of transcriptomic and epigenomic profiles in the same individual cells provide an unprecedented opportunity to understand cell fates. However, effective approaches for the integrative analysis of such data are lacking. Here, we present a single-cell aggregation and integration (scAI) method to deconvolute cellular heterogeneity from parallel transcriptomic and epigenomic profiles. Through iterative learning, scAI aggregates sparse epigenomic signals in similar cells learned in an unsupervised manner, allowing coherent fusion with transcriptomic measurements. Simulation studies and applications to three real datasets demonstrate its capability of dissecting cellular heterogeneity within both transcriptomic and epigenomic layers and understanding transcriptional regulatory mechanisms

Regulating Retinoic Acid Availability during Development and Regeneration: The Role of the CYP26 Enzymes.

This review focuses on the role of the Cytochrome p450 subfamily 26 (CYP26) retinoic acid (RA) degrading enzymes during development and regeneration. Cyp26 enzymes, along with retinoic acid synthesising enzymes, are absolutely required for RA homeostasis in these processes by regulating availability of RA for receptor binding and signalling. Cyp26 enzymes are necessary to generate RA gradients and to protect specific tissues from RA signalling. Disruption of RA homeostasis leads to a wide variety of embryonic defects affecting many tissues. Here, the function of CYP26 enzymes is discussed in the context of the RA signalling pathway, enzymatic structure and biochemistry, human genetic disease, and function in development and regeneration as elucidated from animal model studies

Functional Identification and Characterization of cis-Regulatory Elements

Transcription is regulated through interactions between regulatory proteins, such as transcription factors (TFs), and DNA sequence. It is known that TFs act combinatorially in some cases to regulate transcription, but in which situations and to what degree is unclear. I first studied the contribution of TF binding sites to expression in mouse embryonic stem (ES) cells by using synthetic cis-regulatory elements (CREs). The synthetic CREs were comprised of combinations of binding sites for the pluripotency TFs Oct4, Sox2, Klf4, and Esrrb. A statistical thermodynamic model explained 72% of the variation in expression driven by these CREs. The high predictive power of this model depended on five TF interaction parameters, including favorable heterotypic interactions between Oct4 and Sox2, Klf4 and Sox2, and Klf4 and Esrrb. The model also included two unfavorable homotypic interaction parameters. These homotypic parameters help to explain the fact that synthetic CREs with mixtures of binding sites for various TFs drive much higher expression than multiple binding sites for the same TF. I then found that the expression of these synthetic CREs largely changes as ES cells differentiate down the neural lineage. However, CREs with no repeat binding sites drove similar levels of expression, suggesting that heterotypic interactions may be similar in the two conditions. In a separate set of experiments I interrogated the determinants of expression driven by genomic sequences previously segmented into classes based on chromatin features. A set of these sequences was assayed in K562 cells. As expected, we found that Enhancers and Weak Enhancers drove expression over background, while Repressed elements and Enhancers from another cell type did not. Unexpectedly, we found that Weak Enhancers drove higher expression than Enhancers, possibly based on their lower H3K36me3 and H3K27ac, which we found to be weakly associated with lower expression. Using a logistic regression model, we showed that matches to TF binding motifs were best able to predict active sequences, but chromatin features contributed significantly as well. These results demonstrate that interactions between certain combinations of pluripotency TFs, but not all combinations, are important to transcriptional regulation. Furthermore, chromatin modifications can still contribute to predictions of expression even after accounting for binding site motifs. Better understanding of the process of cis-regulation will allow us to predict which sequences can drive expression and how perturbations affect this expression