Cell cycle-regulated transcription: effectively using a genomics toolbox

Single-cell Analysis Reveals Inter- and Intratumour Heterogeneity in Metastatic Breast Cancer

Metastasis is the leading cause of cancer-related deaths of breast cancer patients. Some cancer cells in a tumour go through successive steps, referred to as the metastatic cascade, and give rise to metastases at a distant site. We know that the plasticity and heterogeneity of cancer cells play critical roles in metastasis but the precise underlying molecular mechanisms remain elusive. Here we aimed to identify molecular mechanisms of metastasis during colonization, one of the most important yet poorly understood steps of the cascade. We performed single-cell RNA-Seq (scRNA-Seq) on tumours and matched lung macrometastases of patient-derived xenografts of breast cancer. After correcting for confounding factors such as the cell cycle and the percentage of detected genes (PDG), we identified cells in three states in both tumours and metastases. Gene-set enrichment analysis revealed biological processes specific to proliferation and invasion in two states. Our findings suggest that these states are a balance between epithelial-to-mesenchymal (EMT) and mesenchymal-to-epithelial transitions (MET) traits that results in so-called partial EMT phenotypes. Analysis of the top differentially expressed genes (DEGs) between these cell states revealed a common set of partial EMT transcription factors (TFs) controlling gene expression, including ZNF750, OVOL2, TP63, TFAP2C and HEY2. Our data suggest that the TFs related to EMT delineate different cell states in tumours and metastases. The results highlight the marked interpatient heterogeneity of breast cancer but identify common features of single cells from five models of metastatic breast cancer.

miRNA-17-5p Target Prediction and its Role in Senescence Mechanism through p21 Interference

BACKGROUND: Cellular senescence is known to be correlated with the cessation of cell cycle. The progression of cell cycle is promoted by activities of various proteins, including cyclin-dependent kinase (CDK) and cyclin proteins, which work synergistically. CDK-cyclin complexes are influenced by other proteins, such as retinoblastoma (Rb) and E2F proteins. In cell cycle, both Rb and E2F proteins could be affected by one of the CDK inhibitors, that is, p21. MicroRNA (miRNA) is well known for its role in biological processes, including cell cycle. However, the contribution of miRNA in cell cycle is still poorly understood. Some miRNAs play a role in pro-proliferation and anti-proliferation. AIM: This study was performed an in silico study analysis to reveal the relationship between miRNA-17-5p and p21 in the process of cellular senescence. METHODS: The extensive data mining was conducted to determine the miRNA that contributes to the process of anti-aging prevention and the desired target genes through the Human Protein Atlas and cancer database. miRNA target prediction was performed using DIANA-microT-CDS. Gene function of the miRNA-17-5p target was annotated using DAVID GO. RESULTS: The sequence of hsa-miRNA-17-5p (CAAAGUGCUUACAGUGCAGGUAG) has three attachment sites with binding types of 8 mer, 6 mer, and 8 mer at the transcription sites of 447–474, 485–513, and 1132–1154, respectively. The main profile of hsa-miRNA-17-5p showed that it bound to 3’-untranslated region and the coding region (exon). CONCLUSIONS: The miRNA-17-5p was involved in cellular senescence by influencing the process of cell proliferation in the cell cycle pathway.

Exosomes of Adipose-derived Stem Cells Conditioned Media Promotes Retinoblastoma and Forkhead-Box M1 Protein Expression

BACKGROUND: In the senescence process, the retinoblastoma (Rb) protein binds to E2F in hypophosphorylated conditions, preventing the cell to enter the S-phase in the cell cycle. Human Forkhead Box M1 (FOXM1) protein, key regulator G1/S and G2/M phases, decreases in the senescence process. Many studies have been carried out to reverse this system, one of which used exosomes of adipose-derived stem c ells conditioned media (ADSC-CM). These exosomes contain a variety of specific proteins which have pro-proliferation properties, however, little is known on the role of these exosomes toward the change of phosphorylated Rb and FOXM1. AIM: This study aims to find out the involvement of exosomes of ADSC-CM on these two proteins on senescence human dermal fibroblasts (HDFs). METHODS: In vitro experiment was undergone randomization sample and non-blinded pre-/post-test control group. The primary culture of senescent HDFs was transfected with exosomes of ADSC-CM; then, its effect on migration and senescence reversal was observed through analyzing Sa-β-gal, Rb, and FOXM1 protein expression. RESULTS: The expression of Sa-β-gal was higher in the control group. Our result demonstrated the exosome of ADSC-CM significantly induced the expression of Rb and FOXM1 protein in senescent HDFs (p < 0.05). CONCLUSION: It proved that exosomes of ADSC-CM could shift the senescent fibroblast into metabolically active cells.

Identification of marker genes in Alzheimer's disease using a machine-learning model

Alzheimer's Disease (AD) is one of the most common causes of dementia, mostly affecting the elderly population. Currently, there is no proper diagnostic tool or method available for the detection of AD. The present study used two distinct data sets of AD genes, which could be potential biomarkers in the diagnosis. The differentially expressed genes (DEGs) curated from both datasets were used for machine learning classification, tissue expression annotation and co-expression analysis. Further, CNPY3, GPR84, HIST1H2AB, HIST1H2AE, IFNAR1, LMO3, MYO18A, N4BP2L1, PML, SLC4A4, ST8SIA4, TLE1 and N4BP2L1 were identified as highly significant DEGs and exhibited co-expression with other query genes. Moreover, a tissue expression study found that these genes are also expressed in the brain tissue. In addition to the earlier studies for marker gene identification, we have considered a different set of machine learning classifiers to improve the accuracy rate from the analysis. Amongst all the six classification algorithms, J48 emerged as the best classifier, which could be used for differentiating healthy and diseased samples. SMO/SVM and Logit Boost further followed J48 to achieve the classification accuracy.

Cdk1 activity acts as a quantitative platform for coordinating cell cycle progression with periodic transcription

Cell proliferation is regulated by cyclin-dependent kinases (Cdks) and requires the periodic expression of particular gene clusters in different cell cycle phases. However, the interplay between the networks that generate these transcriptional oscillations and the core cell cycle machinery remains largely unexplored. In this work, we use a synthetic regulable Cdk1 module to demonstrate that periodic expression is governed by quantitative changes in Cdk1 activity, with different clusters directly responding to specific activity levels. We further establish that cell cycle events neither participate in nor interfere with the Cdk1-driven transcriptional program, provided that cells are exposed to the appropriate Cdk1 activities. These findings contrast with current models that propose self-sustained and Cdk1-independent transcriptional oscillations. Our work therefore supports a model in which Cdk1 activity serves as a quantitative platform for coordinating cell cycle transitions with the expression of critical genes to bring about proper cell cycle progression.

Cell proliferation is regulated by cyclin-dependent kinases (Cdks) and relies on periodic gene cluster expression according to cell cycle phases. Here the authors use a synthetic regulatable Cdk1 module to demonstrate that periodic expression is governed by quantitative changes in Cdk1 activity.

Cycling Transcriptional Networks Optimize Energy Utilization on a Genome Scale

Genes expressing circadian RNA rhythms are enriched for metabolic pathways, but the adaptive significance of cyclic gene expression remains unclear. We estimated the genome-wide synthetic and degradative cost of transcription and translation in three organisms and found that the cost of cycling genes is strikingly higher compared to non-cycling genes. Cycling genes are expressed at high levels and constitute the most costly proteins to synthesize in the genome. We demonstrate that metabolic cycling is accelerated in yeast grown under higher nutrient flux and the number of cycling genes increases ∼40%, which are achieved by increasing the amplitude and not the mean level of gene expression. These results suggest that rhythmic gene expression optimizes the metabolic cost of global gene expression and that highly expressed genes have been selected to be downregulated in a cyclic manner for energy conservation.