Dataset Information

The role of the cilium in normal and abnormal cell cycles: emphasis on renal cystic pathologies.

ABSTRACT: The primary cilium protrudes from the cell surface and acts as a sensor for chemical and mechanical growth cues, with receptors for a number of growth factors (PDGF?, Hedgehog, Wnt, Notch) concentrated within the ciliary membrane. In normal tissues, the cilium assembles after cells exit mitosis and is resorbed as part of cell cycle re-entry. Although regulation of the cilium by cell cycle transitions has been appreciated for over 100 years, only recently have data emerged to indicate the cilium also exerts influence on the cell cycle. The resorption/protrusion cycle, regulated by proteins including Aurora-A, VHL, and GSK-3?, influences cell responsiveness to growth cues involving cilia-linked receptors; further, resorption liberates the ciliary basal body to differentiate into the centrosome, which performs discrete functions in S-, G2-, and M-phase. Besides these roles, the cilium provides a positional cue that regulates polarity of cell division, and thus directs cells towards fates of differentiation versus proliferation. In this review, we summarize the specific mechanisms mediating the cilia-cell cycle dialog. We then emphasize the examples of polycystic kidney disease (PKD), nephronopthisis (NPHP), and VHL-linked renal cysts as cases in which defects of ciliary function influence disease pathology, and may also condition response to treatment.

SUBMITTER: Pan J

PROVIDER: S-EPMC3657316 | biostudies-literature | 2013 Jun

REPOSITORIES: biostudies-literature

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The role of the cilium in normal and abnormal cell cycles: emphasis on renal cystic pathologies.

Pan Junmin J Seeger-Nukpezah Tamina T Golemis Erica A EA

Cellular and molecular life sciences : CMLS 20120711 11

The primary cilium protrudes from the cell surface and acts as a sensor for chemical and mechanical growth cues, with receptors for a number of growth factors (PDGFα, Hedgehog, Wnt, Notch) concentrated within the ciliary membrane. In normal tissues, the cilium assembles after cells exit mitosis and is resorbed as part of cell cycle re-entry. Although regulation of the cilium by cell cycle transitions has been appreciated for over 100 years, only recently have data emerged to indicate the cilium ...[more]

PMID: 22782110

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Project description:Renal pathology is essential for diagnosing and assessing the severity and prognosis of kidney diseases. Deep learning-based approaches have developed rapidly and have been applied in renal pathology. However, methods for the automated classification of normal and abnormal renal tubules remain scarce. Using a deep learning-based method, we aimed to classify normal and abnormal renal tubules, thereby assisting renal pathologists in the evaluation of renal biopsy specimens. Consequently, we developed a U-Net-based segmentation model using randomly selected regions obtained from 21 renal biopsy specimens. Further, we verified its performance in multiclass segmentation by calculating the Dice coefficients (DCs). We used 15 cases of tubulointerstitial nephritis to assess its applicability in aiding routine diagnoses conducted by renal pathologists and calculated the agreement ratio between diagnoses conducted by two renal pathologists and the time taken for evaluation. We also determined whether such diagnoses were improved when the output of segmentation was considered. The glomeruli and interstitium had the highest DCs, whereas the normal and abnormal renal tubules had intermediate DCs. Following the detailed evaluation of the tubulointerstitial compartments, the proximal, distal, atrophied, and degenerated tubules had intermediate DCs, whereas the arteries and inflamed tubules had low DCs. The annotation and output areas involving normal and abnormal tubules were strongly correlated in each class. The pathological concordance for the glomerular count, t, ct, and ci scores of the Banff classification of renal allograft pathology remained high with or without the segmented images. However, in terms of time consumption, the quantitative assessment of tubulitis, tubular atrophy, degenerated tubules, and the interstitium was improved significantly when renal pathologists considered the segmentation output. Deep learning algorithms can assist renal pathologists in the classification of normal and abnormal tubules in renal biopsy specimens, thereby facilitating the enhancement of renal pathology and ensuring appropriate clinical decisions.

Dataset Information

The role of the cilium in normal and abnormal cell cycles: emphasis on renal cystic pathologies.

Publications

The role of the cilium in normal and abnormal cell cycles: emphasis on renal cystic pathologies.

Similar Datasets

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