Category Archives: Science

Early Experiences and the Developing Brain

The developing brain is continuously absorbing sensory information and transforming this information into “fuel” for fine tuning the wiring and architecture of the brain circuit. The types of sensory information and early experiences the brain perceives during the malleable development period are critical for the evolution and structure of the brain circuit throughout life.

Part 1 from the “Three Core Concepts in Early Development” Series by the Center on the Developing Child at Harvard University.

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March 13, 2014 · 7:09 pm

Breast Cancer: New Driver Genes identified

Breast Cancer is the most common cancer type amongst women (but not to be mistaken, it can also affect men). Sadly, the cause of this disease has still to be pinned-down. The reason is the multiple-sided nature of the cancer i.e, each patient has a different cancer genetic profile. The genetic heterogeneity is not only among patients, but it is also, present at the very level of the tumor itself, i.e, researchers face the fact the the tumor is composed by cells of different genetic consistency, quality and mutational dictionary, thus forming a heterogeneous intra-tumor profile. Therefore this fact makes things even more complicated when it comes to understanding the genetic driving force behind tumor initiation, evolution and metastasis.

A recent publication by Stephens et al., (2012), undertook the advent of sequencing 21,416 protein coding genes, 1,664 microRNAs and copy numbers from breast cancer samples in order to understand the genetic roots and branches of  the cancer . From, the analysis, the authors concluded with 9 new candidate-cancer-driver genes: MAP3K1, MAP3K13, AKT2, NCOR1, SMARCD1, ARID1B, CDKN1B, CASP8 and TBX3.  In 6% of the cancers, a (somatic) mutation in  MAP3K1 (mitogen-activated protein kinase) was observed. Moreover, the authors state that this was observed predominantly in ER+ breast cancers. Moreover mutations were observed in MAP2K4 and MAP3KI3 and along with MAP3K1, these genes/proteins are implicated in the JUN kinase pathway and also in activation of the known tumor suppressor gene TP53. Along with MAP3K1, MAP3K13, AKT2 is another identified breast cancer-driver gene, which participates in the JUN kinase pathway.

A -complicated- illustration of JNK cascades and some of their effects on cellular physiology.

Genes NCORI, ARID1B and SMARCD1 , also included in the list of the newly recognized driver genes of this study, all participate in chromatin regulation.  CDKN1B is another identified target gene, which regulates cell cycle progression at phase G1 whereas, CASP8 is implicated in apoptosis. The last member on the 9 breast cancer driver-genes list is TBX3, which it self is a transcriptional factor that regulates morphogenesis of the forelimb in the anterior/posterior axis. Actually this gene participates in the normal development of the mammary tissue (Howard, B. & Ashworth), thus it could be considered a breast tissue specific-gene (?).

From the 40 driver mutations recognized, the authors state that a 58% was attributed to 7 known breast cancer genes: TP53, PIK3CA, ERBB2, MYC, FGFR1/ZNF703, GATA3 and CCND1.  This left a 42% (!) of driver mutations to be attributed in the relatively less frequently breast cancer associated/linked genes which includes the 9 new breast cancer driver-genes.

The importance of this study and others of its kind ( look for the specific edition of Nature Letters for similar studies) highlight the importance of deciphering the basic genetic dictionary and how it is read in cancer cells vs.in normal cells. Moreover, using whole-genome information, we can advance a step in understanding and predicting response to cancer treatment as Ellis et al., (2012) showed in the same edition of Nat.Letters.

References

Stephens et al., Nature Letters (2012) The landscape of cancer genes and mutational processes in breast cancer

Howard, B. & Ashworth, A. PLoS Genet. (2006) Signalling pathways implicated in early mammary gland morphogenesis and breast cancer. 

Ellis et al., Nature Letters (2012) Whole-genome analysis informs breast cancer response to aromatase inhibition.

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Filed under Cancer, Gene Expression, Science

Moments of Miraculous Insight: From the Lab Bench to Cancer Treatment, The Story of Velcade

This video is the proof that when a combination of knowledge is put in effect, science can make huge leaps, including improving cancer treatment and understanding the disease itself.

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On p300, enhancers and neurodevelopmental disorders.

The P300 or adenovirus E1A-associated cellular p300 transcriptional co-activator protein, is a transcriptional regulator (1, 2). It harbours an intrinsic acetyltransferase activity. Thus, by definition it affects gene transcription by inducing chromatin remodelling close to promoter sites and by providing chromatin accessibility to transcription factors and the transcriptional machinery (1). Moreover, P300 can interact with all four histone types of the nucleosome core i.e., H2A, H2B, H3 and H4 (2). Depending on the context of interactions with its co-regulators, gene transcription can either be upregulated  or downregulated, like in the case of p53 and ACTR respectively (3). By specifically interacting with the phosphorylated form of CREB, it also affects cAMP-gene regulation. Furthermore, the P300 protein has a critical role in embryonic development and neuraldevelopment. This is evident in humans, in the case of p300 mutations that cause loss of function and/or copy number alteration (reductions in copy number). These mutations cause an embryo to develop a condition called, broad thumb-hallux syndrome. Some of the phenotypic features of this syndrome, is craniofacial and limb formation abnormalities and mental retardation, highlighting the importance of p300 function during morphogenesis and neuraldevelopment in humans. The massive amount of genes under P300 control during these critical stages of embryonic development was revealed when Visel et al. (2009)(4), examined P300 binding sites by chromatin immunoprecipitation coupled to parallel massive sequencing (Chip-seq) in mouse embryos. Specifically the authors examined, P300 binding sites, in mouse embryos of embryonic age 11.5 (E11.5). This is an important stage for especially for neuraldevelopment, since in the mouse embryo, this is the stage were the neocortex epithelium initiates to expand by increasing proliferative divisions of radial glia cells (neural progenitors). Binding sites where examined in the forebrain (includes both the neocortex and the ventral telencephalon), the limbs and the midbrain. The sample size was more than 150 (!!!) embryos per tissue. In this case P300 binding was used to predict enhancer areas, since P300 was shown to associate in vitro with enhancer areas.Enhancers are DNA regions which enhance the transcription of a gene. Just for the forebrain, 2,543 P300 binding sites were identified by Chip-seq. Moreover, to examine the correspondence of binding sites to known genes, the authors performed tissue specific microarrays. In the forebrain’s case, they found that for the 885 genes which are overexpressed in the forebrain, 14 % of the identified P300 binding sites are within 101 kb from the promoter. Moreover, the enrichment for P300 binding sites was observed to increase according to the level of overexpression of forebrain-specific genes. The conclusion by the authors was that mapping P300 binding is a very accurate way to detect enhancers. In a relatively recent review, Williamson et al. (2011), provide information around enhancers and how knowing more about them might be useful to understand human disease. For example, quoting the authors and Noonan and McCallion (2010): “almost half of single nucleotide polymorphisms (SNPs) that show statistical associations with common/complex human disease and quantitative traits in genome-wide association studies (GWAS) are within noncoding regions and gene deserts and thus, potentially involve enhancers“. For the case of neurodevelopmental disorders, such as autism and schizophrenia, gaining more insights on how early imbalances in the brain structure come up though gene expression de-regulation, is critical. Achieving such progress, will help to understand how the disorder evolves and establishes in the brain. Also, this information, can hopefully lead us into finding ways to treat and perhaps prevent the disorder from evolving .

P300 Binding Sites in the mouse heart and (on the left ) conservation in rat, human, dog and elephant, by Hardison R.C., Nature Genetics (2010)

We are still a long way from home. Nevertheless, the rapid advancement of next generation sequencing technology (NGS), coupled with the parallel advancement in the computational methods created to explain sequencing and gene expression data, provide significant insights towards steps of progress.

References
1. Wikipedia>p300-CBP
2. GeneCards>p300
3. Li,Q. et al.(2002), Mol.Endo., 16(12),1819-2827.
4. Visel, A., et al.(2009), Nature 457(12), 854-858.
5. Williamson et al. (2011), Dev.Cell., 21(1), 17-19.
6. Noonan, J.P., and McCallion, A.S. (2010), Annu.Rev. Genomics Hum. Genet., 11, 1–23.
7. Hardison, R.C. (2010), Nature Genetics, 42, 734–735.

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Filed under Biology, Cell Cycle, Gene Expression, Microarray, Neurons, Neuroscience, Science, The Development Series

Extracting Biologically Meaningful Information from Gene Expression Data: Gene CoExpression Networks

Data generated from gene expression experiments hold a important amount of biological information (Eisen et al.1998). The end point of any analysis of this sort is to gain a thorough view and understanding in the “inner life” of a cell i.e. the ongoing biological processes in the cell. This can be considered as a bottom-up approach, whereby we can slowly build our way up from the transcript levels, to the cellular process and ultimately the understand biological process under question (of course by combining other appropriate methods in order to be able to extract causal relationships). A natural thought to do, is that genes that have similar expression patterns, within a dataset, may be participating in common biological processes or even be under the same regulatory mechanism(s) (Tavazoie et al., 1999). Clustering of genes with similar expression patterns is a useful approach to gain this sort of information and and also putatively extent the information of common regulatory control to extract participation of the genes in various pathways.  Paraphrasing/Quoting from Eisen et al., 1998: “Statistical organization (clustering) and graphical display  of a microarray dataset allows for researchers to assimilate and explore data in a biologically meaningful way.  … Also, similarity in the gene expression pattern may be the easiest way to make -at least provisional- attribution of function on a genomic scale”.

Along the same lines, Transcription factor (TF) binding sites are critical in our understanding of transcription and trascriptional regulation. A TF binding site lies close or in a promoter region, therefore it has the ability to regulate transcription by either recruiting the RNA-polymerase in the promoter, or by blocking its docking on the DNA. The actions of TFs are transcript specific i.e. the TFs has a range of genes whose transcription it modulates. Thephysical approachof constructing gene networks, seeks to determine the TFs and their respective DNA motifs to which they bind to regulate transcription. Another strategy, the “influence approach” of constructing gene networks, deals with gene expression data and describes the relationships between the transcript levels and how they interact to regulate each other’s transcription. The transcript interactions are described with a graph, in which the nodes represent transcripts and the edges represent a relationship between the connected transcripts, according to the graph-construction method followed. The graph can be constructed as a system of differential equation models, a bayesian network, a boolean network or as an association network. The latter approach creates a gene coexpression network by assigning edges to pairs of genes with high statistical similarity. Different similarity metrics have been used such as Euclidean distance, Pearson correlation coefficient, mutual information (e.g. ARACNE, CLR), partial correlation coefficient (graphical Gaussians models (GGMs)). Moreover to tackle with analysis of gene expression data from time-series experiments appropriate algorithms extract correlation relationships between transcript level changes at the different time points  (Schmit Raab Stephanopoulos Genome Res04; Arkin, Shen , Ross Science 1997).

Genomic strategies in our days are advancing with a speed-of-light and the amounts of data generated are massive. The aforementioned network approaches, borrowed by graph theory and statistics hold the promise to reveal critical biological information where the “data mining” ability of a bench researcher stops. This is especially important, but without being the only, for cancer research. For example, breast cancer is the leading cancer death cause in women. It self is of heterogeneous phenotype, both in terms of histological origin/initiation (e.g. can develop in the ducts or lobule of the breast) as also, in terms of heterogeneity in the mutational landscape of the cancer cells. The latter means that the tumor it self can be highly heterogeneous. Combining transcript level analysis by coexpression networks with the recent advancements in breast tumor whole-genome sequencing (see Gray and Druker Nature 2012), may prove critical in our understanding on cancer initiation and evolution.

For more information on coexpression network construction the interested reader is referred to Gardner and Faith PLReav 2005.

References

Tavazoie et al., Nature Genetics 1999 

Eisen et al .PNAS 1998

Gardner and Faith PLReav 2005

Schmit et al., Genome Res 2004

Arkin et al., Science 1997

Gray and Druker Nature 2012

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Filed under Biology, Coexpression, Gene Expression, Graph Theory, Microarray, Networks, Science, Similarity

Catchy Scientific Phrase For the Day “Oxytocin – The cuddle hormone”

Baby Cuddle

From youramazingbrain.org

I stumbled onto this blog while searching the effects of stress on the brain and fertility. From a first sight it looks interesting and has a variety of topics related to the brain and physiology-related brain matters, described in a way that even non-scientist persons can understand. Go on then, explore your amazing brain! 🙂

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Exercise More, Eat Less = More and Healthy Years

Greek Salad

A new study reveals that exercise increases autophagy (derived from Greek roots: auto, meaning ‘self’, and phagy, ‘to eat’.), a mechanism by which the body cleans up whatever is unwelcome and tagged as ‘bad’. A summary of the study main points is featured in The Economist. This study was performed on genetically modified mice, but taking a good look around you can easily see the differences between people that exercise and those who do not. In addition, many studies have been pin-pointing that low-fat diets and in general a healthy diet,  with the Mediterranean Diet being top on the list, promotes a better and longer life and increases the immune defense.

http://www.economist.com/node/21543129?fsrc=rss|sct

http://news.cell.com//story.php?title=exercise-and-longevity-worth-all-the-sweat

Diet and Exercise in Ancient Greece

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Glucocorticoids and Fetal Development

Figure 1. The word Gluco-corticoids is derived from the Greek word "γλυκός" for sweet and from the Latin word for cortex.

1. What are Glucocorticoids?

Glucocorticoids (from Greek “γλυκός” meaning ”sweet” and cortex) are a class of steroid hormones that are produced in the adrenal cortex. In human the main glucocorticoid (GC) is cortisol, whereas in rodents, the main glucocorticoid  is corticosterone. GC hormones are vital for the homeostatic regulation of the bodily functions, and their actions span a hierarchy of control levels. Accordingly, GCs act on the basic molecular level to control critical functions such the cell cycle, cellular metabolism, viability, synaptic plasticity and the immune response.  At the top of the hierarchical pyramid, GCs fine-tune the stress behavior and cognitive function. It is well-known and as every human has experienced, high levels of GCs caused by stressful situations impair memory. Also, GCs are very important for fetal and brain development.

2. The Glucocorticoid Receptor

GCs bind onto their receptor, the Glucocorticoid Receptor (GR), which is located in the cytoplasm of the cell. GR belongs to the superfamily of the ligand-activated nuclear transcription factors. Hence, in the absence of ligand, GR is located in the cellular cytoplasm in a complex with chaperone proteins such as HSP90, HSP60 and FKBP51 (1). Upon hormone binding, the receptor is shuttled in the nucleus (Fig.2).  There it recognizes and associates with partially pallidromic regulatory DNA areas in target genes, called Glucocorticoid Responsive Elements (i.e GREs) (2,3) . GR can either act as a transcriptional activator, by directly binding onto the promoter of the target gene. Alternatively, GR interacts with other co-factors and causes trans-repression of target genes (1, 4) .  Structure of the GR: GR shares the modular structure common among the steroid receptors. It consists of a variable N-terminal domain (NTD), a highly conserved DNA binding domain with two zinc finger motifs (DBD), a hinge region, and a C-terminal hormone binding domain (5).  The DBD is conserved throughout the members of the nuclear receptorsuperfamily and almostall vertebrate species (6). The hinge region participates in receptor–ligand binding.

Figure 2. The ligated GR can either activate or repress gene transcription. (by Holgate and Polosa, 2008) Figure 2. The ligated GR can either activate or repress gene transcription. (by Holgate and Polosa, 2008)

3. GCs and Adverse Effects on Fetal and Infant Brain Development

GC hormones are potent stimulators of organ maturation. Due to this property,  synthetic GC analogs are vastly used in prenatal medicine.   Specifically,  in pregnancies under the risk of pre-term delivery (10% of pregnancies in North America) .  GC administration is the preferred route followed, to accelerate fetal lung maturation (12,13). Moreover, other clinical conditions of the mother or the fetus make use of synthetic GC administration such as allergies, asthma, and when the fetus is suspected to suffer from  Congenital Adrenal Hyperplasia (CAH) (14). Synthetic GCs are marginally different from their endogenous equivalents and more potent agonists of the GR. Specifically, dexamethasone (DEX) one of the most widely used synthetic GC is a very potent GR agonist. DEX is less sensitive to degradation by 11β-hydroxysteroid dehydrogenase-2  (11β-HSD2) (8, 14); a key enzyme that transforms active GCs to inactive 11 keto-analogs in the fetoplacental area. Therefore, 11β-HSD2 reduces the exposure of the fetus to GCs.

Despite the benefits of DEX, accumulating data suggest a dark side of DEX.  Specifically, clinical follow-up studies have indicated that DEX can have detrimental effects on brain development. Specifically, in infants and school aged children having exposed to DEX around birth, cognitive and behavioral deficits such as reduced IQ, poor social interaction and difficulty in coping with stress have been documented. Moreover, imaging data have pointed to reduced cortical convolution and reduced head circumference of DEX exposed infants in respect to untreated controls. Even more significantly , animal studies on prenatal DEX treatment, have documented that brain development can be permanently impaired by DEX. For example, Uno et al.(1990 and 1994) demonstrated that the size of the  hippocampal structure is reduced by DEX treatment in utero in rhesus macaques (15, 16).

Importantly, the observed impairments in the brain architecture could be the result of changes in different processes. Hence, the formation of the brain can be disrupted by altered migration of neuronal cells,or by impairments in the proliferation of Neural Progenitor Cells from which neurons originate. Accordingly, a series of in vitro studies demonstrated that exposure to GCs inhibits neural progenitor and stem cell proliferation and changes the balance between proliferation and differentiation (17, 18). The anti- proliferative effects of GCs are not limited in embryonic NPCs as increased GCs due to stress, or DEX exposure cause the same effects in adult NPCs of the hippocampus. Moreover, GCs are potent inhibitors of the proliferation of tumor cell lines such as medulloblastoma, neuroblastoma and osteosarcoma cells (17, 18).

4. Conclusion

In the light of the clinical and experimental observations on GC’s  dynamics in the developing brain and NPCs, it  is very important to understand the mechanisms by which they can impair brain development. Results from this line of research could be applied for improving the current GC-based treatments and the mode of DEX use in perinatal medicine.

References

1. Lu, N.Z., et al. International Union of Pharmacology. LXV. The pharmacology and classification of the nuclear receptor superfamily: glucocorticoid, mineralocorticoid, progesterone, and androgen receptors. Pharmacol Rev 58, 782-797 (2006).

2. Strahle, U., Schmid, W. & Schutz, G. Synergistic action of the glucocorticoid receptor with transcription factors. EMBO J 7, 3389-3395 (1988).

3. Strahle, U., Klock, G. & Schutz, G. A DNA sequence of 15 base pairs is sufficient to mediate both glucocorticoid and progesterone induction of gene expression. Proc Natl Acad Sci U S A 84, 7871-7875 (1987).

4. Zanchi, N.E., et al. Glucocorticoids: Extensive physiological actions modulated through multiple mechanisms of gene regulation. Journal of Cellular Physiology 224, 311-315.

5. McMaster, A. & Ray, D.W. Modelling the glucocorticoid receptor and producing therapeutic agents with anti-inflammatory effects but reduced side-effects. Experimental Physiology 92, 299-309 (2007).

6. Stolte, E.H., van Kemenade, B.M., Savelkoul, H.F. & Flik, G. Evolution of glucocorticoid receptors with different glucocorticoid sensitivity. J Endocrinol 190, 17-28 (2006).

7. Cole, T.J., et al. Molecular genetic analysis of glucocorticoid signaling during mouse development. Steroids 60, 93-96 (1995).

8. Speirs, H.J., Seckl, J.R. & Brown, R.W. Ontogeny of glucocorticoid receptor and 11beta-hydroxysteroid dehydrogenase type-1 gene expression identifies potential critical periods of glucocorticoid susceptibility during development. J Endocrinol 181, 105-116 (2004).

9. Noorlander, C.W., De Graan, P.N., Middeldorp, J., Van Beers, J.J. & Visser, G.H. Ontogeny of hippocampal corticosteroid receptors: effects of antenatal glucocorticoids in human and mouse. J Comp Neurol 499, 924-932 (2006).

10. Pryce, C.R. Postnatal ontogeny of expression of the corticosteroid receptor genes in mammalian brains: inter-species and intra-species differences. Brain Res Rev 57, 596-605 (2008).

11. Patel, P.D., et al. Glucocorticoid and mineralocorticoid receptor mRNA expression in squirrel monkey brain. J Psychiatr Res 34, 383-392 (2000).

12. Effect of corticosteroids for fetal maturation on perinatal outcomes. NIH Consensus Development Panel on the Effect of Corticosteroids for Fetal Maturation on Perinatal Outcomes. JAMA 273, 413-418 (1995).

13. Effect of corticosteroids for fetal maturation on perinatal outcomes. NIH Consens Statement 12, 1-24 (1994).

14. Tegethoff, M., Pryce, C. & Meinlschmidt, G. Effects of intrauterine exposure to synthetic glucocorticoids on fetal, newborn, and infant hypothalamic-pituitary-adrenal axis function in humans: a systematic review. Endocr Rev 30, 753-789 (2009).

15.Uno H, Lohmiller L, Thieme C, Kemnitz JW, Engle MJ, Roecker EB et al. Brain damage induced by prenatal exposure to dexamethasone in fetal rhesus macaques. I. Hippocampus. Brain Res Dev Brain Res 1990 May 1; 53(2): 157-167.

16.Uno H, Eisele S, Sakai A, Shelton S, Baker E, DeJesus O et al. Neurotoxicity of glucocorticoids in the primate brain. Horm Behav 1994 Dec; 28(4): 336-348.

17.Glick RD, Medary I, Aronson DC, Scotto KW, Swendeman SL, La Quaglia MP. The effects of serum depletion and dexamethasone on growth and differentiation of human neuroblastoma cell lines. J Pediatr Surg 2000 Mar; 35(3): 465-472.

18. Sundberg M, Savola S, Hienola A, Korhonen L, Lindholm D. Glucocorticoid hormones decrease proliferation of embryonic neural stem cells through ubiquitin-mediated degradation of cyclin D1. J Neurosci 2006 May 17; 26(20): 5402-5410.

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Filed under Biology, Cell Cycle, Glucocorticoid Receptor, Neuroscience, Science, The Development Series

Cell Cycle Regulators Related to the Glucocorticoid Receptor

Eukaryotic cell-cycle, with the relative duration of each phase

G1/S-specific cyclin-D1 (CD1) : The protein encoded by this gene belongs to the highly conserved cyclin family, whose members are characterized by a dramatic periodicity in protein abundance throughout the cell cycle. Cyclins function as regulators of CDK kinases. Different cyclins exhibit distinct expression and degradation patterns which contribute to the temporal coordination of each mitotic event. CD1 forms a complex with and functions as a regulatory subunit of CDK4 or CDK6, whose activity is required for cell cycle G1/S transition. This protein has been shown to interact with tumor suppressor protein Rb which also regulates the expression of CD1.

P21: p21 is a potent cyclin-dependent kinase inhibitor (CKI). The p21 (WAF1) protein binds to and inhibits the activity of cyclinCDK2 or –CDK4 complexes, and thus functions as a regulator of cell cycle progression at G1. The expression of this gene is tightly controlled by the tumor suppressor protein p53, through which this protein mediates the p53-dependent cell cycle G1 phase arrest in response to a variety of stress stimuli. This was a major discovery in the early 1990’s that revealed how cells stop dividing after being exposed to damaging agents such as radiation. In addition to growth arrest, p21 can mediate cellular senescence and one of the ways it was discovered was as a senescent cell-derived inhibitor. The p21(WAF1) protein can also interact with proliferating cell nuclear antigen (PCNA), a DNA polymerase accessory factor, and plays a regulatory role in S phase DNA replication and DNA damage repair. This protein was reported to be specifically cleaved by CASP3-like caspases, which thus leads to a dramatic activation of CDK2, and may be instrumental in the execution of apoptosis following caspase activation. However p21 may inhibit apoptosis and does not induce cell death on its own [4].

P27 or Cyclin-dependent kinase inhibitor 1B belongs to the Cip/Kip family of cyclin dependent kinase (Cdk) inhibitor proteins. The p27 protein binds to and prevents the activation of cyclin ECDK2 or cyclin DCDK4 complexes, and thus controls the cell cycle progression at G1. It is often referred to as a cell cycle inhibitor protein because its major function is to stop or slow down the cell division cycle.  p27Kip1 binds to cyclin D either alone, or when complexed to its catalytic subunit CDK4. In doing so p27Kip1 inhibits the catalytic activity of Cdk4, which means that it prevents Cdk4 from adding phosphate residues to its principal substrate, the retinoblastoma (pRb) protein. Increased levels of the p27Kip1 protein typically cause cells to arrest in the G1 phase of the cell cycle. Likewise, p27Kip1 is able to bind other Cdk proteins when complexed to cyclin subunits such as Cyclin E/Cdk2 and Cyclin A/Cdk2.

Retinoblastoma protein (pRb): pRb prevents the cell from replicating damaged DNA by preventing its progression along the cell cycle through G1 (first gap phase) into S (synthesis phase).[7] pRb binds and inhibits transcription factors of the E2F family, which are composed of dimers of an E2F protein and a DP protein.[8] The transcription activating complexes of E2 promoter-binding–protein-dimerization partners (E2F-DP) can push a cell into S phase.[9][10][11][12][13] As long as E2F-DP is inactivated, the cell remains stalled in the G1 phase. When pRb is bound to E2F, the complex acts as a growth suppressor and prevents progression through the cell cycle [3]. The pRb-E2F/DP complex also attracts a histone deacetylase (HDAC) protein to the chromatin, reducing transcription of S phase promoting factors, further suppressing DNA synthesis.

Source: Wikipedia (links are highlighted)

This list definitely does not cover the whole range of cell-cycle modulators, which have been shown or linked with GR . Albeit, it does include some critical molecules, linked to the antiproliferative effects of glucocorticoids and I intent to gradually expand the list as I go.

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