Dancing Styles of Collective Cell Migration: Image-Based Computational Analysis of JRAB/MICAL-L2

Comparison of particle image velocimetry and the underlying agents dynamics in collectively moving self propelled particles

Collective migration of cells is a fundamental behavior in biology. For the quantitative understanding of collective cell migration, live-cell imaging techniques have been used using e.g., phase contrast or fluorescence images. Particle tracking velocimetry (PTV) is a common recipe to quantify cell motility with those image data. However, the precise tracking of cells is not always feasible. Particle image velocimetry (PIV) is an alternative to PTV, corresponding to Eulerian picture of fluid dynamics, which derives the average velocity vector of an aggregate of cells. However, the accuracy of PIV in capturing the underlying cell motility and what values of the parameters should be chosen is not necessarily well characterized, especially for cells that do not adhere to a viscous flow. Here, we investigate the accuracy of PIV by generating images of simulated cells by the Vicsek model using trajectory data of agents at different noise levels. It was found, using an alignment score, that the direction of the PIV vectors coincides with the direction of nearby agents with appropriate choices of PIV parameters. PIV is found to accurately measure the underlying motion of individual agents for a wide range of noise level, and its condition is addressed.

A machine learning based approach for quantitative evaluation of cell migration in Transwell assays based on deformation characteristics

Many pathological and physiological processes, including embryonic development, immune response and cancer metastasis, involve studies on cell migration, and especially detection methods, for which it is difficult to satisfy the requirements for rapid and quantitative evaluation and analysis. In view of the shortcomings in simultaneously quantifying the number of migrated cells and non-migrated cells using Transwell assays, we propose a novelty approach for the evaluation of cell migration by distinguishing whether the cells have migrated based on the regularity of the cell morphology changes. Traditionally, the status of living cells and dead cells are detected and analyzed by machine learning using some common morphological characteristics, e.g., area and perimeter of the cells. However, the accuracy of detecting whether cells have migrated or not using these common characteristics is not high, and the characteristics are not appropriate for our studies. Therefore, from the point of view of mechanism analysis for the migration behavior, we examined the regularity of different morphology changes of migrated cells and non-migrated cells, and thus discovered the distinguishable morphological characteristics. Then, two deformation characteristics, deformation index and taper index are proposed. Then, a machine learning based algorithm that can identify migrated cells according to the proposed deformation characteristics was devised. In addition, images of migrated cells and non-migrated cells were obtained from the Transwell assays. This algorithm was trained, and was able to successfully identify migrated cells with an accuracy of 84% using the proposed morphological characteristics. This method greatly improves the identification accuracy when compared with the identification of traditional characteristics of which the accuracy was about 54.7%. This machine learning based method might be employed as a potential tool for cell counting and evaluation of cell migration with the aim of reducing time and improving automation compared with the traditional method. This method is effective, rapid, and incorporate advances in artificial intelligence which could be used for adapting the current evaluation methods.

High MICAL-L2 expression and its role in the prognosis of colon adenocarcinoma

BACKGROUND

MICAL-like protein 2 (MICAL-L2), a member of the molecules interacting with CasL (MICAL) family of proteins, is strongly associated with the malignancy of multiple types of cancer. However, the role of MICAL-L2 in colon adenocarcinoma (COAD) has not been well characterized.

METHODS

In this study, we analyzed the role of MICAL-L2 in COAD using datasets available from public databases. The mRNA and protein expression of MICAL-L2 was investigated using TCGA, UALCAN, and independent immunohistochemical assays. Overall survival (OS) and disease-specific survival (DSS) of COAD patients were assessed based on the MICAL-L2 expression level using the Kaplan-Meier method. Univariate and multivariate analysis was employed to determine whether MICAL-L2 could serve as an independent prognostic indicator of OS. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) were further utilized to explore the possible cellular mechanism underlying the role of MICAL-L2 in COAD. In addition, the correlation between MICAL-L2 expression and immune cell infiltration levels was investigated via single-sample gene set enrichment analysis (ssGSEA).

RESULTS

Data from TCGA, HPA, and UALCAN datasets indicated that MICAL-L2 expression was significantly higher in COAD tissue than in adjacent normal tissues, and this was confirmed by immunohistochemical assays. Kaplan-Meier survival analysis revealed that patients with MICAL-L2 had shorter OS and DSS. Furthermore, multivariate Cox analysis indicated that MICAL-L2 was an independent risk factor for OS in COAD patients. ROC analysis confirmed the diagnostic value of MICAL-L2, and a prognostic nomogram involving age, M stage, and MICAL-L2 expression was constructed for OS. Functional enrichment analyses revealed that transport-related activity was closely associated with the role of MICAL-L2 in COAD. Regarding immune infiltration levels, MICAL-L2 was found to be positively associated with CD56^bright NK cells.

CONCLUSIONS

Our results suggested that MICAL-L2 is a promising biomarker for determining prognosis and correlated with immune infiltration levels in COAD.

MICAL-L2 Is Essential for c-Myc Deubiquitination and Stability in Non-small Cell Lung Cancer Cells

Objectives: MICAL-L2, a member of the molecules interacting with the CasL (MICAL) family, was reported to be highly expressed in several types of cancers, however, the roles of MICAL-L2 in NSCLC pathogenesis remain to be explored. This study is designed to clarify the mechanisms by which MICAL-L2 participates in NSCLC cell proliferation.

Materials and Methods: The expression levels of MICAL-L2 in human lung cancer samples were assessed by immunohistochemical staining. Cells were transfected with siRNA or plasmids to regulate MICAL-L2 expression. Cell proliferation was measured by EdU staining and CCK-8 assays. MICAL-L2 and phosphorylated/total c-Myc expression were examined by Western blotting analysis. Interaction between MICAL-L2 and c-Myc was assessed by immunofluorescence staining, Western blotting and co-immunoprecipitation assays. Western blotting, polyubiquitylation detection and protein stability assays were used to assess whether MICAL-L2 exerts its oncogenic effect via c-Myc.

Results: We found that MICAL-L2 was highly expressed in human NSCLC. While overexpressing MICAL-L2 increased NSCLC cell proliferation, MICAL-L2 depletion decreased the proliferation of NSCLC cells, an effect that was linked to cell cycle arrest. MICAL-L2 physically interacted with the c-Myc protein and functioned to maintain nuclear c-Myc levels and prolonged its half-life. Knockdown of MICAL-L2 expression led to decreased c-Myc protein stability through accelerating polyubiquitylation of c-Myc and gave rise to c-Myc degradation. We further found that MICAL-L2 deubiquitinated c-Myc and blocked its degradation, presumably by inhibiting c-Myc phosphorylation at threonine residue 58.

Conclusions: These results indicate that MICAL-L2 is a key regulator of c-Myc deubiquitination and stability in the nucleus, and this activity may be involved in promoting NSCLC cell proliferation.

Actin Cytoskeletal Reorganization Function of JRAB/MICAL-L2 Is Fine-tuned by Intramolecular Interaction between First LIM Zinc Finger and C-terminal Coiled-coil Domains

JRAB/MICAL-L2 is an effector protein of Rab13, a member of the Rab family of small GTPase. JRAB/MICAL-L2 consists of a calponin homology domain, a LIM domain, and a coiled-coil domain. JRAB/MICAL-L2 engages in intramolecular interaction between the N-terminal LIM domain and the C-terminal coiled-coil domain, and changes its conformation from closed to open under the effect of Rab13. Open-form JRAB/MICAL-L2 induces the formation of peripheral ruffles via an interaction between its calponin homology domain and filamin. Here, we report that the LIM domain, independent of the C-terminus, is also necessary for the function of open-form JRAB/MICAL-L2. In mechanistic terms, two zinc finger domains within the LIM domain bind the first and second molecules of actin at the minus end, potentially inhibiting the depolymerization of actin filaments (F-actin). The first zinc finger domain also contributes to the intramolecular interaction of JRAB/MICAL-L2. Moreover, the residues of the first zinc finger domain that are responsible for the intramolecular interaction are also involved in the association with F-actin. Together, our findings show that the function of open-form JRAB/MICAL-L2 mediated by the LIM domain is fine-tuned by the intramolecular interaction between the first zinc finger domain and the C-terminal domain.