Animal cells within a tissue typically display a striking regularity in their size. To date, the molecular mechanisms that control this uniformity are still unknown. We have previously shown that ...size uniformity in animal cells is promoted, in part, by size-dependent regulation of G1 length. To identify the molecular mechanisms underlying this process, we performed a large-scale small molecule screen and found that the p38 MAPK pathway is involved in coordinating cell size and cell cycle progression. Small cells display higher p38 activity and spend more time in G1 than larger cells. Inhibition of p38 MAPK leads to loss of the compensatory G1 length extension in small cells, resulting in faster proliferation, smaller cell size and increased size heterogeneity. We propose a model wherein the p38 pathway responds to changes in cell size and regulates G1 exit accordingly, to increase cell size uniformity.
What programs the size of animal cells? Liu, Shixuan; Tan, Ceryl; Tyers, Mike ...
Frontiers in cell and developmental biology,
11/2022, Letnik:
10
Journal Article
Recenzirano
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The human body is programmed with definite quantities, magnitudes, and proportions. At the microscopic level, such definite sizes manifest in individual cells - different cell types are characterized ...by distinct cell sizes whereas cells of the same type are highly uniform in size. How do cells in a population maintain uniformity in cell size, and how are changes in target size programmed? A convergence of recent and historical studies suggest - just as a thermostat maintains room temperature - the size of proliferating animal cells is similarly maintained by homeostatic mechanisms. In this review, we first summarize old and new literature on the existence of cell size checkpoints, then discuss additional advances in the study of size homeostasis that involve feedback regulation of cellular growth rate. We further discuss recent progress on the molecules that underlie cell size checkpoints and mechanisms that specify target size setpoints. Lastly, we discuss a less-well explored teleological question: why does cell size matter and what is the functional importance of cell size control?
While molecules that promote the growth of animal cells have been identified, it remains unclear how such signals are orchestrated to determine a characteristic target size for different cell types. ...It is increasingly clear that cell size is determined by size checkpoints—mechanisms that restrict the cell cycle progression of cells that are smaller than their target size. Previously, we described a p38 MAPK-dependent cell size checkpoint mechanism whereby p38 is selectively activated and prevents cell cycle progression in cells that are smaller than a given target size. In this study, we show that the specific target size required for inactivation of p38 and transition through the cell cycle is determined by CDK4 activity. Our data suggest a model whereby p38 and CDK4 cooperate analogously to the function of a thermostat: while p38 senses irregularities in size, CDK4 corresponds to the thermostat dial that sets the target size.
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•Homeostatic mechanisms maintain cells at a given target size•p38 MAPK promotes growth of cells that are smaller than the critical target size•CDK4 determines the target size by regulating both the duration and rate of cell growth
Tan et al. study homeostatic mechanisms that maintain animal cells at their appropriate target size. They find that p38 is part of a sensing mechanism that identifies inappropriately sized cells, while CDK4 is analogous to a thermostat dial that sets the target size set point referenced by p38.
While molecules that promote the growth of animal cells have been identified, it remains unclear how such signals are orchestrated to determine and maintain a characteristic target size for the ...different cell types. More recent studies have established that cell size is determined by size checkpoints – mechanisms that restrict the cell cycle progression of cells that are smaller than their target size. However, what determines such a target size remains unclear. Through a chemical screen and subsequent validation studies, I have identified CDK4 activity to set the critical size referenced to by homeostatic mechanisms in proliferating animal cells (Chapter 2). In other words, CDK4 activity shifts the size threshold at which checkpoint mechanisms consider a cell to be ‘too small’. While these findings shed light on the molecular processes involved with target size determination, the cell size checkpoint remains limited in rectifying cells that are too large. To that end, I followed up on previous reports highlighting mechanisms of growth rate regulation (i.e., how fast cells accumulate mass) that function in parallel with size checkpoints to maintain cell size homeostasis. It has been shown that large cells slow down rates of growth; however, mechanistic studies on such a compensatory response are lacking. I have demonstrated that large cells inhibit growth not by slowing down protein production but rather by upregulating rates of protein degradation (Chapter 3). Together, these findings contribute insights into the mechanisms that control cell size in animal cells.
Metformin has known mechanistic benefits on COVID-19 infection due to its anti-inflammatory effects and its action on the ACE2 receptor. However, some physicians are reluctant to use it in hypoxemic ...patients due to potential lactic acidosis. The primary purpose of the study was to determine whether metformin use is associated with survival. We also wanted to determine whether there is a difference in outcomes in subcategories of metformin use, whether at home, in-hospital, or mixed home/in-hospital use.
This study aimed to determine an association between metformin use and mortality among patients with type 2 diabetes mellitus hospitalized for COVID-19 infection.
This was a cross-sectional analysis of data acquired from the COVID-19 database of two tertiary hospitals in Cebu from March 1, 2020, to September 30, 2020. Hospitalized adult Filipino patients with type 2 diabetes mellitus who tested positive for COVID-19 via RT-PCR were included and categorized as either metformin users or metformin non-users.
We included 355 patients with type 2 diabetes mellitus in the study, 186 (52.4%) were metformin users. They were further categorized into home metformin users (n=109, 30.7%), in-hospital metformin users (n=40, 11.3%), and mixed home/in-hospital metformin users (n=37, 10.4%). Metformin use was associated with a lower risk for mortality compared to non-users (
=0.001; OR=0.424). In-hospital and mixed home/in-hospital metformin users were associated with lower mortality odds than non-users (
=0.002; OR=0.103 and
=0.005; OR 0.173, respectively). The lower risk for mortality was noted in metformin, regardless of dosage, from 500 mg to 2 g daily (
=0.002). Daily dose between ≥1000 mg to <2000 mg was associated with the greatest benefit on mortality (
≤0.001; OR=0.252). The survival distributions between metformin users and non-users were statistically different, showing inequality in survival (χ2=5.67,
=0.017).
Metformin was associated with a lower risk for mortality in persons with type 2 diabetes mellitus hospitalized for COVID-19 disease compared to non-users. Use of metformin in-hospital, and mixed home/in-hospital metformin use, was also associated with decreased risk for mortality. The greatest benefit seen was in those taking a daily dose of ≥1000 mg to <2000 mg.
Identifying the chemical regulators of biological pathways is a time-consuming bottleneck in developing therapeutics and research compounds. Typically, thousands to millions of candidate small ...molecules are tested in target-based biochemical screens or phenotypic cell-based screens, both expensive experiments customized to each disease. Here, our uncustomized, virtual, profile-based screening approach instead identifies compounds that match to pathways based on the phenotypic information in public cell image data, created using the Cell Painting assay. Our straightforward correlation-based computational strategy retrospectively uncovered the expected, known small-molecule regulators for 32% of positive-control gene queries. In prospective, discovery mode, we efficiently identified new compounds related to three query genes and validated them in subsequent gene-relevant assays, including compounds that phenocopy or pheno-oppose YAP1 overexpression and kill a Yap1-dependent sarcoma cell line. This image-profile-based approach could replace many customized labor- and resource-intensive screens and accelerate the discovery of biologically and therapeutically useful compounds.
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•Compounds impacting particular genes’ function are highly sought•Most chemicals and overexpressed genes impact cell morphology in the Cell Painting assay•Matching these image profiles can find chemicals that impact a particular gene’s function•This virtual screen using public data found new chemical regulators of several pathways
If a chemical compound and a gene overexpression yield the same cell morphology in the microscopy-based assay Cell Painting, then they are likely to impact the same functions. This principle is exploited to retrieve useful compounds for particular query genes in public Cell Painting datasets.
To determine the beliefs and attitudes towards diabetes of rural health care providers in Aklan, Philippines using the Diabetes Attitude Scale 3 (DAS-3) and to determine factors associated with it.
...This is a cross-sectional analytic survey. A total of 339 health care providers were given self-administered DAS-3 questionnaires. Additional data gathered included their age, highest educational attainment, position, municipality class, diabetes as a co-morbidity, attendance to diabetes classes, and family history of diabetes.
Rural health care providers showed an overall mean positive attitude score of 3.5 using the DAS-3 questionnaire. In decreasing order, mean scores of participants according to subscale is as follows: "Need for Special Training in Education" (4.13) >"Autonomy of diabetes for patients" (3.70) >"Psychosocial Impact of Diabetes" (3.60) >"Value of Tight Glucose Control" (3.14) and "Seriousness of Type 2 Diabetes" (3.09). Physicians have the highest mean scores consistently in all subscales compared to other health care providers. Among the different factors considered, educational attainment (
=0.005) and work position (
=<0.001) were found out to affect attitude score of health care providers.
This study has shown that the majority of the rural health care providers believe in the need for special training of healthcare providers, psychosocial impact of diabetes and patient autonomy in diabetes self-care. However, the majority still do not strongly believe in the seriousness of diabetes and the benefits of tight sugar control. Educational attainment and work position are the consistent factors that impact diabetes-related attitude; therefore, the need to strengthen continuous medical education among health care providers.
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