Cell behaviors within a confined adhesive area fabricated using novel micropatterning methods

In the field of cell and tissue engineering, there is an increasing demand for techniques to spatially control the adhesion of cells to substrates of desired sizes and shapes. Here, we describe two novel methods for fabricating a substrate for adhesion of cells to a defined area. In the first method, the surface of the coverslip or plastic dish was coated with Lipidure, a non-adhesive coating material, and air plasma was applied through a mask with holes, to confer adhesiveness to the surface. In the second method, after the surface of the coverslip was coated with gold by sputtering and then with Lipidure; the Lipidure coat was locally removed using a novel scanning laser ablation method. These methods efficiently confined cells within the adhesive area and enabled us to follow individual cells for a longer duration, compared to the currently available commercial substrates. By following single cells within the confined area, we were able to observe several new aspects of cell behavior in terms of cell division, cell–cell collisions, and cell collision with the boundary between adhesive and non-adhesive areas.

Physical mechanisms of ESCRT-III–driven cell division

Living systems propagate by undergoing rounds of cell growth and division. Cell division is at heart a physical process that requires mechanical forces, usually exerted by assemblies of cytoskeletal polymers. Here we developed a physical model for the ESCRT-III–mediated division of archaeal cells, which despite their structural simplicity share machinery and evolutionary origins with eukaryotes. By comparing the dynamics of simulations with data collected from live cell imaging experiments, we propose that this branch of life uses a previously unidentified division mechanism. Active changes in the curvature of elastic cytoskeletal filaments can lead to filament perversions and supercoiling, to drive ring constriction and deform the overlying membrane. Abscission is then completed following filament disassembly. The model was also used to explore how different adenosine triphosphate (ATP)-driven processes that govern the way the structure of the filament is changed likely impact the robustness and symmetry of the resulting division. Comparisons between midcell constriction dynamics in simulations and experiments reveal a good agreement with the process when changes in curvature are implemented at random positions along the filament, supporting this as a possible mechanism of ESCRT-III–dependent division in this system. Beyond archaea, this study pinpoints a general mechanism of cytokinesis based on dynamic coupling between a coiling filament and the membrane.

Control-based continuation: a new approach to prototype synthetic gene networks

Control-Based Continuation (CBC) is a general and systematic method to carry out the bifurcation analysis of physical experiments. CBC does not rely on a mathematical model and thus overcomes the uncertainty introduced when identifying bifurcation curves indirectly through modelling and parameter estimation. We demonstrate, in silico, CBC applicability to biochemical processes by tracking the equilibrium curve of a toggle switch which includes additive process noise and exhibits bistability. We compare results obtained when CBC uses a model-free and model-based control strategy and show that both can track stable and unstable solutions, revealing bistability. We then demonstrate CBC in conditions more representative of a real experiment using an agent-based simulator describing cells growth and division, cell-to-cell variability, spatial distribution, and diffusion of chemicals. We further show how the identified curves can be used for parameter estimation and discuss how CBC can significantly accelerate the prototyping of synthetic gene regulatory networks.

A phosphorylation code regulates the multi-functional protein RETINOBLASTOMA-RELATED1 in Arabidopsis thaliana

The RETINOBLASTOMA-RELATED (RBR) proteins play a central role coordinating cell division, cell differentiation and cell survival within an environmental and developmental context. These roles reflect RBR ability to engage in multiple protein-protein interactions (PPIs), which are regulated by multi-site phosphorylation. However the functional outcomes of RBR phosphorylation in multicellular organisms remain largely unexplored. Here we test the hypothesis that phosphorylation allows diversification of RBR functions in multicellular context. Using a representative collection of transgenic loss- and gain of function point mutations in RBR phospho-sites, we analysed their complementation capacity in Arabidopsis thaliana root meristems. While the number of mutated residues often correlated to the phenotypic strength of RBR phospho-variants, phospho-sites contributed differentially to distinct phenotypes. For example, the pocket-domain has a greater influence on meristematic cell proliferation, whereas the C-terminal region associates to stem cell maintenance. We found combinatorial effects between the T406 phopspho-site with others in different protein domains. Moreover, a phospho-mimetic and a phospho-defective variant, both promoting cell death, indicate that RBR controls similar cell fate choices by distinct mechanisms. Thus, additivity and specificity of RBR phospho-sites fine tune RBR activity across its multiple roles. Interestingly, a mutation disrupting RBR interactions with the LXCXE motif suppresses dominant phospho-defective RBR phenotypes. By probing protein-protein interactions of RBR variants, we found that LXCXE-containing members of the DREAM complex constitute an important component of phosphorylation-regulated RBR function, but also that RBR participates in stress or environmental responses independently of its phosphorylation state. We conclude that developmental-related, but not stress- or environmental-related functions of RBR are defined and separable by a combinatorial phosphorylation code.

From heterogeneous morphogenetic fields to homogeneous regions as a step towards understanding complex tissue dynamics

ABSTRACT Within developing tissues, cell proliferation, cell motility and other cell behaviors vary spatially, and this variability gives a complexity to the morphogenesis. Recently, novel formalisms have been developed to quantify tissue deformation and underlying cellular processes. A major challenge for the study of morphogenesis now is to objectively define tissue sub-regions exhibiting different dynamics. Here, we propose a method to automatically divide a tissue into regions where the local deformation rate is homogeneous. This was achieved by several steps including image segmentation, clustering and region boundary smoothing. We illustrate the use of the pipeline using a large dataset obtained during the metamorphosis of the Drosophila pupal notum. We also adapt it to determine regions in which the time evolution of the local deformation rate is homogeneous. Finally, we generalize its use to find homogeneous regions for cellular processes such as cell division, cell rearrangement, or cell size and shape changes. We also illustrate it on wing blade morphogenesis. This pipeline will contribute substantially to the analysis of complex tissue shaping, and the biochemical and biomechanical regulations driving tissue morphogenesis.

Screening and predicted value of potential biomarkers for breast cancer using bioinformatics analysis

Breast cancer is the most common cancer and the leading cause of cancer-related deaths in women. Increasing molecular targets have been discovered for breast cancer prognosis and therapy. However, there is still an urgent need to identify new biomarkers. Therefore, we evaluated biomarkers that may aid the diagnosis and treatment of breast cancer. We searched three mRNA microarray datasets (GSE134359, GSE31448 and GSE42568) and identified differentially expressed genes (DEGs) by comparing tumor and non-tumor tissues using GEO2R. Functional and pathway enrichment analyses of the DEGs were performed using the DAVID database. The protein–protein interaction (PPI) network was plotted with STRING and visualized using Cytoscape. Module analysis of the PPI network was done using MCODE. The associations between the identified genes and overall survival (OS) were analyzed using an online Kaplan–Meier tool. The redundancy analysis was conducted by DepMap. Finally, we verified the screened HUB gene at the protein level. A total of 268 DEGs were identified, which were mostly enriched in cell division, cell proliferation, and signal transduction. The PPI network comprised 236 nodes and 2132 edges. Two significant modules were identified in the PPI network. Elevated expression of the genes Discs large-associated protein 5 (DLGAP5), aurora kinase A (AURKA), ubiquitin-conjugating enzyme E2 C (UBE2C), ribonucleotide reductase regulatory subunit M2(RRM2), kinesin family member 23(KIF23), kinesin family member 11(KIF11), non-structural maintenance of chromosome condensin 1 complex subunit G (NCAPG), ZW10 interactor (ZWINT), and denticleless E3 ubiquitin protein ligase homolog(DTL) are associated with poor OS of breast cancer patients. The enriched functions and pathways included cell cycle, oocyte meiosis and the p53 signaling pathway. The DEGs in breast cancer have the potential to become useful targets for the diagnosis and treatment of breast cancer.

ARHGAP11A Is a Prognostic Biomarker and Correlated With Immune Infiltrates in Gastric Cancer

Background: ARHGAP11A, belongs to RhoGAPs family, is vital for cell motility. However, the role of ARHGAP11A in gastric cancer is obscure.Methods: The expression level of ARHGAP11A was analyzed by Oncomine database. The correlation of ARHGAP11A expression with immune infiltrates and associated gene markers was clarified by Tumor IMmune Estimation Resource and Gene Expression Profiling Interactive Analysis database. The correlation between ARHGAP11A expression and the patient prognosis was identified by Kaplan-Meier plotter and PrognoScan. Genetic changes of ARHGAP11A were analyzed by cBioPortal. The protein-protein interaction network and gene functional enrichment analysis were constructed and performed by GeneMANIA and Metascape.Results: We found that the expression levels of ARHGAP11A were elevated in various cancers including gastric cancer when compared with normal tissues. High expression of ARHGAP11A was significantly correlated with a better prognosis in gastric cancer. We revealed that the expression of ARHGAP11A was negatively associated with infiltration levels of CD8+ T cells, CD4+ T cells, macrophages and dendritic cells. In addition, ARHGAP11A expression was significantly correlated with gene markers of these immune cells. Lastly, gene functional enrichment analysis indicated that ARHGAP11A involved in regulating lymphocyte activation, cell division, cell killing, myeloid leukocyte differentiation and leukocyte apoptosis.Conclusion: Our findings demonstrated that ARHGAP11A was a valuable prognostic biomarker in gastric cancer. Further work is needed to validate its role and underlying mechanisms in regulating immune infiltrates.

A novel meta-heuristic optimization algorithm based on cell division: Cell Division Optimizer

A novel meta-heuristic algorithm named as the Cell Division Optimizer (CDO) is proposed. The proposed algorithm is inspired by the reproduction methods at the cellular level, which is formulated by the well-known cell division process known as mitosis and meiosis. In the proposed model Meiosis and Mitosis govern the exploration and exploitation aspects of the optimization algorithm, respectively. In the proposed method, the solutions are updated in two phases to achieve the global optimum solution. The proposed algorithm can be easily adopted to solve the combinatorial optimization method. To evaluate the proposed method, 50 well-known benchmark test functions and also 2 classical engineering optimization problems including 1 mechanical engineering problem and 1 electrical engineering problem are employed. The results of the proposed method are compared with the latest versions of state-of-the-art algorithms like Particle Swarm Optimization, Cuckoo Search, Grey Wolf Optimizer, FruitFly Optimization, Whale Optimizer, Water-Wave Optimizer and recently proposed variants of top-performing algorithms like SHADE (success history-based adaptive differential evolution) and CMAES (Covariance matrix adaptation evolution strategy). Moreover, the convergence speed of the proposed algorithm is better than the considered competitive methods in most cases.

Transcriptome and Metabolome Comparison of Smooth and Rough Citrus limon L. Peels Grown on Same Trees and Harvested in Different Seasons

Background: Farmers harvest two batches fruits of Lemons (Citrus limon L. Burm. f.) i.e., spring flowering fruit and autumn flowering fruit in dry-hot valley in Yunnan, China. Regular lemons harvested in autumn have smooth skin. However, lemons harvested in spring have rough skin, which makes them less attractive to customers. Furthermore, the rough skin causes a reduction in commodity value and economical losses to farmers. This is a preliminary study that investigates the key transcriptomic and metabolomic differences in peels of lemon fruits (variety Yuning no. 1) harvested 30, 60, 90, 120, and 150 days after flowering from the same trees in different seasons.Results: We identified 5,792, 4,001, 3,148, and 5,287 differentially expressed genes (DEGs) between smooth peel (C) and rough peel (D) 60, 90, 120, and 150 days after flowering, respectively. A total of 1,193 metabolites differentially accumulated (DAM) between D and C. The DEGs and DAMs were enriched in the mitogen-activated protein kinase (MAPK) and plant hormone signaling, terpenoid biosynthesis, flavonoid, and phenylalanine biosynthesis, and ribosome pathways. Predominantly, in the early stages, phytohormonal regulation and signaling were the main driving force for changes in peel surface. Changes in the expression of genes associated with asymmetric cell division were also an important observation. The biosynthesis of terpenoids was possibly reduced in rough peels, while the exclusive expression of cell wall synthesis-related genes could be a possible reason for the thick peel of the rough-skinned lemons. Additionally, cell division, cell number, hypocotyl growth, accumulation of fatty acids, lignans and coumarins- related gene expression, and metabolite accumulation changes were major observations.Conclusion: The rough peels fruit (autumn flowering fruit) and smooth peels fruit (spring flowering fruit) matured on the same trees are possibly due to the differential regulation of asymmetric cell division, cell number regulation, and randomization of hypocotyl growth related genes and the accumulation of terpenoids, flavonoids, fatty acids, lignans, and coumarins. The preliminary results of this study are important for increasing the understanding of peel roughness in lemon and other citrus species.

A flow cytometric journey into cell cycle analysis

Cell cycle involves a series of changes that lead to cell growth and division. Cell cycle analysis is crucial to understand cellular responses to changing environmental conditions. Since its inception, flow cytometry has been particularly useful for cell cycle analysis at single cell level due to its speed and precision. Previously, flow cytometric cell cycle analysis relied solely on the measurement of cellular DNA content. Later, methods were developed for multiparametric analysis. This review explains the journey of flow cytometry to understand different molecular and cellular events underlying cell cycle using various protocols. Recent advances in the field that overcome the shortcomings of traditional flow cytometry and expand its scope for cell cycle studies are also discussed.