Circular Trajectory Reconstruction Uncovers Cell-Cycle Progression and Regulatory Dynamics from Single-Cell Hi-C Maps.
Ontology highlight
ABSTRACT:
SUBMITTER: Ye Y
PROVIDER: S-EPMC6891923 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Tissue regeneration is an orchestrated progression of cells from an immature state to a mature one, conventionally represented as distinctive cell subsets. A continuum of transitional cell states exists between these discrete stages. We combine the depth of single-cell mass cytometry and an algorithm developed to leverage this continuum by aligning single cells of a given lineage onto a unified trajectory that accurately predicts the developmental path de novo. Applied to human B cell lymphopoiesis, the algorithm (termed Wanderlust) constructed trajectories spanning from hematopoietic stem cells through to naive B cells. This trajectory revealed nascent fractions of B cell progenitors and aligned them with developmentally cued regulatory signaling including IL-7/STAT5 and cellular events such as immunoglobulin rearrangement, highlighting checkpoints across which regulatory signals are rewired paralleling changes in cellular state. This study provides a comprehensive analysis of human B lymphopoiesis, laying a foundation to apply this approach to other tissues and "corrupted" developmental processes including cancer.
| S-EPMC4045247 | biostudies-literature
Project description:BackgroundA transcriptional regulatory module (TRM) is a set of genes that is regulated by a common set of transcription factors (TFs). By organizing the genome into TRMs, a living cell can coordinate the activities of many genes and carry out complex functions. Therefore, identifying TRMs is helpful for understanding gene regulation.ResultsIntegrating gene expression and ChIP-chip data, we develop a method, called MOdule Finding Algorithm (MOFA), for reconstructing TRMs of the yeast cell cycle. MOFA identified 87 TRMs, which together contain 336 distinct genes regulated by 40 TFs. Using various kinds of data, we validated the biological relevance of the identified TRMs. Our analysis shows that different combinations of a fairly small number of TFs are responsible for regulating a large number of genes involved in different cell cycle phases and that there may exist crosstalk between the cell cycle and other cellular processes. MOFA is capable of finding many novel TF-target gene relationships and can determine whether a TF is an activator or/and a repressor. Finally, MOFA refines some clusters proposed by previous studies and provides a better understanding of how the complex expression program of the cell cycle is regulated.ConclusionMOFA was developed to reconstruct TRMs of the yeast cell cycle. Many of these TRMs are in agreement with previous studies. Further, MOFA inferred many interesting modules and novel TF combinations. We believe that computational analysis of multiple types of data will be a powerful approach to studying complex biological systems when more and more genomic resources such as genome-wide protein activity data and protein-protein interaction data become available.
| S-EPMC1637117 | biostudies-literature
Project description:Circular RNAs (circRNAs) are a class of noncoding RNAs produced by a noncanonical form of alternative splicing called back-splicing. To investigate a potential role of circRNAs in the p53 pathway, we analyzed RNA sequencing (RNA-seq) data from colorectal cancer cell lines (HCT116, RKO, and SW48) that were untreated or treated with a DNA-damaging agent. Surprisingly, unlike the strong p53-dependent induction of hundreds of p53-induced mRNAs upon DNA damage, only a few circRNAs were upregulated from p53-induced genes. circ-MDM2, an annotated circRNA from the MDM2 locus, was one of the handful of circRNAs that originated from a p53-induced gene. Given the central role of MDM2 in suppressing p53 protein levels and p53 activity, we investigated the function of circ-MDM2 Knocking down circ-MDM2 with small interfering RNAs (siRNAs) that targeted circ-MDM2 did not alter MDM2 mRNA or MDM2 protein levels but resulted in increased basal p53 levels and growth defects in vitro and in vivo Consistent with these results, transcriptome profiling showed increased expression of several direct p53 targets, reduced retinoblastoma protein (Rb) phosphorylation, and defects in G1-S progression upon silencing circ-MDM2 Our results on the initial characterization of circ-MDM2 identify a new player from the MDM2 locus that suppresses p53 levels and cell cycle progression.
| S-EPMC7156218 | biostudies-literature
Project description:Most RNAs generated by the human genome have no protein-coding ability and are termed non-coding RNAs. Among these include circular RNAs, which include exonic circular RNAs (circRNA), mainly found in the cytoplasm, and intronic RNAs (ciRNA), predominantly detected in the nucleus. The biological functions of circular RNAs remain largely unknown, although ciRNAs have been reported to promote gene transcription, while circRNAs may function as microRNA sponges. We demonstrate that the circular RNA circ-Foxo3 was highly expressed in non-cancer cells and were associated with cell cycle progression. Silencing endogenous circ-Foxo3 promoted cell proliferation. Ectopic expression of circ-Foxo3 repressed cell cycle progression by binding to the cell cycle proteins cyclin-dependent kinase 2 (also known as cell division protein kinase 2 or CDK2) and cyclin-dependent kinase inhibitor 1 (or p21), resulting in the formation of a ternary complex. Normally, CDK2 interacts with cyclin A and cyclin E to facilitate cell cycle entry, while p21works to inhibit these interactions and arrest cell cycle progression. The formation of this circ-Foxo3-p21-CDK2 ternary complex arrested the function of CDK2 and blocked cell cycle progression.
| S-EPMC4824104 | biostudies-literature
Project description:BackgroundPrecise regulation of the cell cycle is crucial to the growth and development of all organisms. Understanding the regulatory mechanism of the cell cycle is crucial to unraveling many complicated diseases, most notably cancer. Multiple sources of biological data are available to study the dynamic interactions among many genes that are related to the cancer cell cycle. Integrating these informative and complementary data sources can help to infer a mutually consistent gene transcriptional regulatory network with strong similarity to the underlying gene regulatory relationships in cancer cells.Results and principal findingsWe propose an integrative framework that infers gene regulatory modules from the cell cycle of cancer cells by incorporating multiple sources of biological data, including gene expression profiles, gene ontology, and molecular interaction. Among 846 human genes with putative roles in cell cycle regulation, we identified 46 transcription factors and 39 gene ontology groups. We reconstructed regulatory modules to infer the underlying regulatory relationships. Four regulatory network motifs were identified from the interaction network. The relationship between each transcription factor and predicted target gene groups was examined by training a recurrent neural network whose topology mimics the network motif(s) to which the transcription factor was assigned. Inferred network motifs related to eight well-known cell cycle genes were confirmed by gene set enrichment analysis, binding site enrichment analysis, and comparison with previously published experimental results.ConclusionsWe established a robust method that can accurately infer underlying relationships between a given transcription factor and its downstream target genes by integrating different layers of biological data. Our method could also be beneficial to biologists for predicting the components of regulatory modules in which any candidate gene is involved. Such predictions can then be used to design a more streamlined experimental approach for biological validation. Understanding the dynamics of these modules will shed light on the processes that occur in cancer cells resulting from errors in cell cycle regulation.
| S-EPMC2858157 | biostudies-literature
Project description:Colorectal cancer (CRC) is one of the most common and fatal gastrointestinal malignancies, in which cancer stem cells (CSCs) were identified to enable tumor heterogeneity and initiate tumor formation. However, the process from CSCs to invasion cells is unconfirmed. Several bioinformatics methods, including clustering, pseudotime analysis, gene set variation analysis and gene ontology enrichment, were used to construct a path of gradual transformation of CSCs to invasive cells, called "stem-to-invasion path". A large amount of signaling interactions were collated to build the multilayer regulatory network. Kaplan-Meier curve and time-dependent ROC method were applied to reveal prognostic values. We validated the heterogeneity of cells in the tumor microenvironment and revealed the presence of malignant epithelial cells with high invasive potential within primary colonic carcinomas. Next, the "stem-to-invasion path" was identified through constructing a branching trajectory with cancer cells arranged in order. A multilayer regulatory network considered as the vital factor involved in acquiring invasion characteristics underlying the path was built to elucidate the interactions between tumor cell and tumor-associated microenvironment. Then we further identified a novel combinatorial biomarker that can be used to assess the prognosis for CRC patients, and validated its predictive robustness on the independent dataset. Our work provides new insights into the acquisition of invasive potential in primary tumor cells, as well as potential therapeutic targets for CRC invasiveness, which may be useful for the cancer research and clinical treatment.
| S-EPMC9075720 | biostudies-literature
Project description:Ewing sarcoma (ES) is a malignant pediatric bone and soft tissue tumor. Patients with metastatic ES have a dismal outcome which has not been improved in decades. The major challenge in the treatment of metastatic ES is the lack of specific targets and rational combinatorial therapy. We recently found that protein phosphatase 1 regulatory subunit 1A (PPP1R1A) is specifically highly expressed in ES and promotes tumor growth and metastasis in ES. In the current investigation, we show that PPP1R1A regulates ES cell cycle progression in G1/S phase by down-regulating cell cycle inhibitors p21Cip1 and p27Kip1, which leads to retinoblastoma (Rb) protein hyperphosphorylation. In addition, we show that PPP1R1A promotes normal transcription of histone genes during cell cycle progression. Importantly, we demonstrate a synergistic/additive effect of the combinatorial therapy of PPP1R1A and insulin-like growth factor 1 receptor (IGF-1R) inhibition on decreasing ES cell proliferation and migration in vitro and limiting xenograft tumor growth and metastasis in vivo. Taken together, our findings suggest a role of PPP1R1A as an ES specific cell cycle modulator and that simultaneous targeting of PPP1R1A and IGF-1R pathways is a promising specific and effective strategy to treat both primary and metastatic ES.
| S-EPMC7233808 | biostudies-literature
Project description:To estimate k-space trajectory errors in non-Cartesian acquisitions and reconstruct distortion-free images, without trajectory measurements or gradient calibrations.The Trajectory Auto-Corrected image Reconstruction method jointly estimates k-space trajectory errors and images, based on SENSE and SPIRiT parallel imaging reconstruction. The underlying idea is that parallel imaging and oversampling in the center of k-space provides data redundancy that can be exploited to simultaneously reconstruct images and correct trajectory errors. Trajectory errors are represented as weighted sums of trajectory-dependent error basis functions, the coefficients of which are estimated using gradient-based optimization.Trajectory Auto-Corrected image Reconstruction was applied to reconstruct images and errors in golden angle radial, center-out radial, and spiral in vivo 7 Tesla brain acquisitions in five subjects. Compared to reconstructions using nominal trajectories, Trajectory auto-corrected image reconstructions contained considerably less blurring and streaking and were of similar quality to images reconstructed using measured k-space trajectories in the center-out radial and spiral cases. Reconstruction cost function reductions and improvements in normalized image gradient squared were also similar to those for images reconstructed using measured trajectories.Trajectory Auto-Corrected image Reconstruction enables non-Cartesian image reconstructions free from trajectory errors without the need for separate gradient calibrations or trajectory measurements. Magn Reson Med 76:757-768, 2016. © 2015 Wiley Periodicals, Inc.
| S-EPMC4788989 | biostudies-literature
Project description:Virtually all biomedical applications of positron emission tomography (PET) use images to represent the distribution of a radiotracer. However, PET is increasingly used in cell tracking applications, for which the "imaging" paradigm may not be optimal. Here, we investigate an alternative approach, which consists in reconstructing the time-varying position of individual radiolabeled cells directly from PET measurements. As a proof of concept, we formulate a new algorithm for reconstructing the trajectory of one single moving cell directly from list-mode PET data. We model the trajectory as a 3-D B-spline function of the temporal variable and use nonlinear optimization to minimize the mean-square distance between the trajectory and the recorded list-mode coincidence events. Using Monte Carlo simulations (GATE), we show that this new algorithm can track a single source moving within a small-animal PET system with 3 mm accuracy provided that the activity of the cell [Bq] is greater than four times its velocity [mm/s]. The algorithm outperforms conventional ML-EM as well as the "minimum distance" method used for positron emission particle tracking (PEPT). The new method was also successfully validated using experimentally acquired PET data. In conclusion, we demonstrated the feasibility of a new method for tracking a single moving cell directly from PET list-mode data, at the whole-body level, for physiologically relevant activities and velocities.
| S-EPMC4392854 | biostudies-literature
Project description:We show that phospholipid anabolism does not occur uniformly during the metazoan cell cycle. Transition to S-phase is required for optimal mobilization of lipid precursors, synthesis of specific phospholipid species and endoplasmic reticulum (ER) homeostasis. Average changes observed in whole-cell phospholipid composition, and total ER lipid content, upon stimulation of cell growth can be explained by the cell cycle distribution of the population. TORC1 promotes phospholipid anabolism by slowing S/G2 progression. The cell cycle stage-specific nature of lipid biogenesis is dependent on p53. We propose that coupling lipid metabolism to cell cycle progression is a means by which cells have evolved to coordinate proliferation with cell and organelle growth.
| S-EPMC4593667 | biostudies-literature