Multi-feature clustering of CTCF binding creates robustness for loop extrusion blocking and Topologically Associating Domain boundaries.

Topologically Associating Domains (TADs) separate vertebrate genomes into insulated regulatory neighborhoods that focus genome-associated processes. TADs are formed by Cohesin-mediated loop extrusion, with many TAD boundaries consisting of clustered binding sites of the CTCF insulator protein. Here we determine how this clustering of CTCF binding contributes to the blocking of loop extrusion and the insulation between TADs. We identify enrichment of three features of CTCF binding at strong TAD boundaries, consisting of strongly bound and closely spaced CTCF binding peaks, with a further enrichment of DNA-binding motifs within these peaks. Using multi-contact Nano-C analysis in cells with normal and perturbed CTCF binding, we establish that individual CTCF binding sites contribute to the blocking of loop extrusion, but in an incomplete manner. When clustered, individual CTCF binding sites thus create a stepwise insulation between neighboring TADs. Based on these results, we propose a model whereby multiple instances of temporal loop extrusion blocking create strong insulation between TADs.

Progerin impairs 3D genome organization and induces fragile telomeres by limiting the dNTP pools.

Chromatin organization within the nuclear volume is essential to regulate many aspects of its function and to safeguard its integrity. A key player in this spatial scattering of chromosomes is the nuclear envelope (NE). The NE tethers large chromatin domains through interaction with the nuclear lamina and other associated proteins. This organization is perturbed in cells from Hutchinson-Gilford progeria syndrome (HGPS), a genetic disorder characterized by premature aging features. Here, we show that HGPS-related lamina defects trigger an altered 3D telomere organization with increased contact sites between telomeres and the nuclear lamina, and an altered telomeric chromatin state. The genome-wide replication timing signature of these cells is perturbed, with a shift to earlier replication for regions that normally replicate late. As a consequence, we detected a higher density of replication forks traveling simultaneously on DNA fibers, which relies on limiting cellular dNTP pools to support processive DNA synthesis. Remarkably, increasing dNTP levels in HGPS cells rescued fragile telomeres, and improved the replicative capacity of the cells. Our work highlights a functional connection between NE dysfunction and telomere homeostasis in the context of premature aging.

CTCF modulates allele-specific sub-TAD organization and imprinted gene activity at the mouse Dlk1-Dio3 and Igf2-H19 domains.

BACKGROUND: Genomic imprinting is essential for mammalian development and provides a unique paradigm to explore intra-cellular differences in chromatin configuration. So far, the detailed allele-specific chromatin organization of imprinted gene domains has mostly been lacking. Here, we explored the chromatin structure of the two conserved imprinted domains controlled by paternal DNA methylation imprints-the Igf2-H19 and Dlk1-Dio3 domains-and assessed the involvement of the insulator protein CTCF in mouse cells. RESULTS: Both imprinted domains are located within overarching topologically associating domains (TADs) that are similar on both parental chromosomes. At each domain, a single differentially methylated region is bound by CTCF on the maternal chromosome only, in addition to multiple instances of bi-allelic CTCF binding. Combinations of allelic 4C-seq and DNA-FISH revealed that bi-allelic CTCF binding alone, on the paternal chromosome, correlates with a first level of sub-TAD structure. On the maternal chromosome, additional CTCF binding at the differentially methylated region adds a further layer of sub-TAD organization, which essentially hijacks the existing paternal-specific sub-TAD organization. Perturbation of maternal-specific CTCF binding site at the Dlk1-Dio3 locus, using genome editing, results in perturbed sub-TAD organization and bi-allelic Dlk1 activation during differentiation. CONCLUSIONS: Maternal allele-specific CTCF binding at the imprinted Igf2-H19 and the Dlk1-Dio3 domains adds an additional layer of sub-TAD organization, on top of an existing three-dimensional configuration and prior to imprinted activation of protein-coding genes. We speculate that this allele-specific sub-TAD organization provides an instructive or permissive context for imprinted gene activation during development.

Tracking global gene expression responses in T cell differentiation.

Upon receiving antigens from the innate immune cells, CD4(+) T cells differentiate into distinct effector cells. To probe the global responses of distinct effector cells, we analyzed transcriptome-wide expressions of Th1, Th2, Treg and Th17 using Pearson correlation, entropy and principal component analyses, with Th0 as a control. Although the global response of Th0 was quite distinct from Th17, surprisingly, it was highly similar to Th1, Th2 and Treg. Moreover, 8 major temporal groups consisting of 5704 differentially expressed genes were revealed for both Th0 and Th17. Gene functional enrichment analysis showed immune responses and metabolic processes were mainly activated between Th0 and Th17, while genes related to cell cycle and replication were differentially regulated. Moreover, we found the upregulation of several novel genes for Th0 and Th17. Overall, we deduce that Th0 is globally similar to Th1, Th2 and Treg. Our results indicate that Th0 is a differentiated state and, therefore, may not be used as a control cell type.

The reduction of gene expression variability from single cells to populations follows simple statistical laws.

Recent studies on single cells and population transcriptomics have revealed striking differences in global gene expression distributions. Single cells display highly variable expressions between cells, while cell populations present deterministic global patterns. The mechanisms governing the reduction of transcriptome-wide variability over cell ensemble size, however, remain largely unknown. To investigate transcriptome-wide variability of single cells to different sizes of cell populations, we examined RNA-Seq datasets of 6 mammalian cell types. Our statistical analyses show, for each cell type, increasing cell ensemble size reduces scatter in transcriptome-wide expressions and noise (variance over square mean) values, with corresponding increases in Pearson and Spearman correlations. Next, accounting for technical variability by the removal of lowly expressed transcripts, we demonstrate that transcriptome-wide variability reduces, approximating the law of large numbers. Subsequent analyses reveal that the entire gene expressions of cell populations and only the highly expressed portion of single cells are Gaussian distributed, following the central limit theorem.

Transcriptome-wide variability in single embryonic development cells.

Molecular heterogeneity of individual molecules within single cells has been recently shown to be crucial for cell fate diversifications. However, on a global scale, the effect of molecular variability for embryonic developmental stages is largely underexplored. Here, to understand the origins of transcriptome-wide variability of oocytes to blastocysts in human and mouse, we examined RNA-Seq datasets. Evaluating Pearson correlation, Shannon entropy and noise patterns (η(2) vs. µ), our investigations reveal a phase transition from low to saturating levels of diversity and variability of transcriptome-wide expressions through the development stages. To probe the observed behaviour further, we utilised a stochastic transcriptional model to simulate the global gene expressions pattern for each development stage. From the model, we concur that transcriptome-wide regulation initially begins from 2-cell stage, and becomes strikingly variable from 8-cell stage due to amplification and quantal transcriptional activity.

Systems Biology Strategy Reveals PKCδ is Key for Sensitizing TRAIL-Resistant Human Fibrosarcoma.

Cancer cells are highly variable and largely resistant to therapeutic intervention. Recently, the use of the tumor necrosis factor related apoptosis-inducing ligand (TRAIL) induced treatment is gaining momentum due to TRAIL's ability to specifically target cancers with limited effect on normal cells. Nevertheless, several malignant cancer types still remain non-sensitive to TRAIL. Previously, we developed a dynamic computational model, based on perturbation-response differential equations approach, and predicted protein kinase C (PKC) as the most effective target, with over 95% capacity to kill human fibrosarcoma (HT1080) in TRAIL stimulation (1). Here, to validate the model prediction, which has significant implications for cancer treatment, we conducted experiments on two TRAIL-resistant cancer cell lines (HT1080 and HT29). Using PKC inhibitor bisindolylmaleimide I, we demonstrated that cell viability is significantly impaired with over 95% death of both cancer types, in consistency with our previous model. Next, we measured caspase-3, Poly (ADP-ribose) polymerase (PARP), p38, and JNK activations in HT1080, and confirmed cell death occurs through apoptosis with significant increment in caspase-3 and PARP activations. Finally, to identify a crucial PKC isoform, from 10 known members, we analyzed each isoform mRNA expressions in HT1080 cells and shortlisted the highest 4 for further siRNA knock-down (KD) experiments. From these KDs, PKCδ produced the most cancer cell death in conjunction with TRAIL. Overall, our approach combining model predictions with experimental validation holds promise for systems biology based cancer therapy.

A systems biology approach to suppress TNF-induced proinflammatory gene expressions.

BACKGROUND: Tumor necrosis factor (TNF) is a widely studied cytokine (ligand) that induces proinflammatory signaling and regulates myriad cellular processes. In major illnesses, such as rheumatoid arthritis and certain cancers, the expression of TNF is elevated. Despite much progress in the field, the targeted regulation of TNF response for therapeutic benefits remains suboptimal. Here, to effectively regulate the proinflammatory response induced by TNF, a systems biology approach was adopted. RESULTS: We developed a computational model to investigate the temporal activations of MAP kinase (p38), nuclear factor (NF)-κB, and the kinetics of 3 groups of genes, defined by early, intermediate and late phases, in murine embryonic fibroblast (MEF) and 3T3 cells. To identify a crucial target that suppresses, and not abolishes, proinflammatory genes, the model was tested in several in silico knock out (KO) conditions. Among the candidate molecules tested, in silico RIP1 KO effectively regulated all groups of proinflammatory genes (early, middle and late). To validate this result, we experimentally inhibited TNF signaling in MEF and 3T3 cells with RIP1 inhibitor, Necrostatin-1 (Nec-1), and investigated 10 genes (Il6, Nfkbia, Jun, Tnfaip3, Ccl7, Vcam1, Cxcl10, Mmp3, Mmp13, Enpp2) belonging to the 3 major groups of upregulated genes. As predicted by the model, all measured genes were significantly impaired. CONCLUSIONS: Our results demonstrate that Nec-1 modulates TNF-induced proinflammatory response, and may potentially be used as a therapeutic target for inflammatory diseases such as rheumatoid arthritis and osteoarthritis.

Is central dogma a global property of cellular information flow?

The central dogma of molecular biology has come under scrutiny in recent years. Here, we reviewed high-throughput mRNA and protein expression data of Escherichia coli, Saccharomyces cerevisiae, and several mammalian cells. At both single cell and population scales, the statistical comparisons between the entire transcriptomes and proteomes show clear correlation structures. In contrast, the pair-wise correlations of single transcripts to proteins show nullity. These data suggest that the organizing structure guiding cellular processes is observed at omics-wide scale, and not at single molecule level. The central dogma, thus, globally emerges as an average integrated flow of cellular information.

Enhancing apoptosis in TRAIL-resistant cancer cells using fundamental response rules.

The tumor necrosis factor related apoptosis-inducing ligand (TRAIL) induces apoptosis in malignant cells, while leaving other cells mostly unharmed. However, several carcinomas remain resistant to TRAIL. To investigate the resistance mechanisms in TRAIL-stimulated human fibrosarcoma (HT1080) cells, we developed a computational model to analyze the temporal activation profiles of cell survival (IκB, JNK, p38) and apoptotic (caspase-8 and -3) molecules in wildtype and several (FADD, RIP1, TRAF2 and caspase-8) knock-down conditions. Based on perturbation-response approach utilizing the law of information (signaling flux) conservation, we derived response rules for population-level average cell response. From this approach, i) a FADD-independent pathway to activate p38 and JNK, ii) a crosstalk between RIP1 and p38, and iii) a crosstalk between p62 and JNK are predicted. Notably, subsequent simulations suggest that targeting a novel molecule at p62/sequestosome-1 junction will optimize apoptosis through signaling flux redistribution. This study offers a valuable prospective to sensitive TRAIL-based therapy.

Collective dynamics of specific gene ensembles crucial for neutrophil differentiation: the existence of genome vehicles revealed.

Cell fate decision remarkably generates specific cell differentiation path among the multiple possibilities that can arise through the complex interplay of high-dimensional genome activities. The coordinated action of thousands of genes to switch cell fate decision has indicated the existence of stable attractors guiding the process. However, origins of the intracellular mechanisms that create "cellular attractor" still remain unknown. Here, we examined the collective behavior of genome-wide expressions for neutrophil differentiation through two different stimuli, dimethyl sulfoxide (DMSO) and all-trans-retinoic acid (atRA). To overcome the difficulties of dealing with single gene expression noises, we grouped genes into ensembles and analyzed their expression dynamics in correlation space defined by Pearson correlation and mutual information. The standard deviation of correlation distributions of gene ensembles reduces when the ensemble size is increased following the inverse square root law, for both ensembles chosen randomly from whole genome and ranked according to expression variances across time. Choosing the ensemble size of 200 genes, we show the two probability distributions of correlations of randomly selected genes for atRA and DMSO responses overlapped after 48 hours, defining the neutrophil attractor. Next, tracking the ranked ensembles' trajectories, we noticed that only certain, not all, fall into the attractor in a fractal-like manner. The removal of these genome elements from the whole genomes, for both atRA and DMSO responses, destroys the attractor providing evidence for the existence of specific genome elements (named "genome vehicle") responsible for the neutrophil attractor. Notably, within the genome vehicles, genes with low or moderate expression changes, which are often considered noisy and insignificant, are essential components for the creation of the neutrophil attractor. Further investigations along with our findings might provide a comprehensive mechanistic view of cell fate decision.

Emergent genome-wide control in wildtype and genetically mutated lipopolysaccarides-stimulated macrophages.

Large-scale gene expression studies have mainly focused on highly expressed and 'discriminatory' genes to decipher key regulatory processes. Biological responses are consequence of the concerted action of gene regulatory network, thus, limiting our attention to genes having the most significant variations is insufficient for a thorough understanding of emergent whole genome response. Here we comprehensively analyzed the temporal oligonucleotide microarray data of lipopolysaccharide (LPS) stimulated macrophages in 4 genotypes; wildtype, Myeloid Differentiation factor 88 (MyD88) knockout (KO), TIR-domain-containing adapter-inducing interferon-beta (TRIF) KO and MyD88/TRIF double KO (DKO). Pearson correlations computed on the whole genome expression between different genotypes are extremely high (>0.98), indicating a strong co-regulation of the entire expression network. Further correlation analyses reveal genome-wide response is biphasic, i) acute-stochastic mode consisting of small number of sharply induced immune-related genes and ii) collective mode consisting of majority of weakly induced genes of diverse cellular processes which collectively adjust their expression level. Notably, temporal correlations of a small number of randomly selected genes from collective mode show scalability. Furthermore, in collective mode, the transition from large scatter in expression distributions for single ORFs to smooth linear lines emerges as an organizing principle when grouping of 50 ORFs and above. With this emergent behavior, the role of MyD88, TRIF and novel MyD88, TRIF-independent processes for gene induction can be linearly superposed to decipher quantitative whole genome differential control of transcriptional and mRNA decay machineries. Our work demonstrates genome-wide co-regulated responses subsequent to specific innate immune stimulus which have been largely neglected.

Sequential logic model deciphers dynamic transcriptional control of gene expressions.

BACKGROUND: Cellular signaling involves a sequence of events from ligand binding to membrane receptors through transcription factors activation and the induction of mRNA expression. The transcriptional-regulatory system plays a pivotal role in the control of gene expression. A novel computational approach to the study of gene regulation circuits is presented here. METHODOLOGY: Based on the concept of finite state machine, which provides a discrete view of gene regulation, a novel sequential logic model (SLM) is developed to decipher control mechanisms of dynamic transcriptional regulation of gene expressions. The SLM technique is also used to systematically analyze the dynamic function of transcriptional inputs, the dependency and cooperativity, such as synergy effect, among the binding sites with respect to when, how much and how fast the gene of interest is expressed. PRINCIPAL FINDINGS: SLM is verified by a set of well studied expression data on endo16 of Strongylocentrotus purpuratus (sea urchin) during the embryonic midgut development. A dynamic regulatory mechanism for endo16 expression controlled by three binding sites, UI, R and Otx is identified and demonstrated to be consistent with experimental findings. Furthermore, we show that during transition from specification to differentiation in wild type endo16 expression profile, SLM reveals three binary activities are not sufficient to explain the transcriptional regulation of endo16 expression and additional activities of binding sites are required. Further analyses suggest detailed mechanism of R switch activity where indirect dependency occurs in between UI activity and R switch during specification to differentiation stage. CONCLUSIONS/SIGNIFICANCE: The sequential logic formalism allows for a simplification of regulation network dynamics going from a continuous to a discrete representation of gene activation in time. In effect our SLM is non-parametric and model-independent, yet providing rich biological insight. The demonstration of the efficacy of this approach in endo16 is a promising step for further application of the proposed method.