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.
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 .
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.
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.
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. The “physical approach“of 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.
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
When we think of our brain what comes in mind? Well, typically, words as memory and neurons are some keywords associated with the word brain. Moreover, other keywords can be schizophrenia, depression, Alzheimer’s, Parkinson’s. All these characterize different functional aspects of the brain. But, lets take a more spherical look at this structure which holds the most valuable process in humans: cognition. The brain can be considered as the world, consisting of many countries and smaller communities. Countries will be brain regions, such as the prefrontal cortex, the parietal cortex or the hippocampus. Communities would then be smaller neuronal networks within the individual brain areas. All these functional units work together to produce an output, may it be a single thought or better an array of thoughts, or, a complex grasping movement. At the heart of this output, lie neuronal units. This post provides some introduction on the examination of the functional and architectural hierarchy of the brain, under the scope of Graph Theory. The information is taken by the excellent review of Bullmore and Sporns . I urge you to read this review for more details and references on the topic.
Graph Theory, which is the study of graphs, has been steadily gaining interest in brain research. Regardless of time and scale and species [Bullmore and Sporns, 2009], the brain has been shown to have the characteristics of a small-world network. Before going into the specifics of brain networks, lets go over some definitions.
What is a network? A network is characterized by a set of nodes (may it be anatomical brain areas, functional brain areas as measured by electroencephalography or neurons) and a set of edges that connect these nodes. The connection is determined on the basis of the actual experimental question e.g. an edge can represent anatomical connections between brain areas e.g. the corpus callosum between the two cerebral hemispheres (a very simple 2 node graph/network with one edge) or the individual 200–250 million (!!!) contralateral axonal projections of the corpus callosum ( more complex graph; the nodes in this case may be the individual neurons receiving these connections and their topology). Key attributes of a network are: Node Degree, the number of connections of the specific node; the Clustering Coefficient, which is the ratio of the number of connections of a node to the maximum possible number connections a node may have. Moreover other important network attributes are: Hubs, which are nodes with high degree or else, high centrality, which is simply the number of shortest paths of other nodes, that cross the node under question. Short path, denotes the minimum number of edges, one may cross between a specific pair of nodes.
What then is a Small-World Network? A small-world network is a type of graph/network which is characterized by high node degree, hence, most nodes are neighbors between them. Also, a small-world network is characterized by small short path lengths, thus each node can be reached from another within a small number of lengths. This type of graph has received a lot of attention, when studying social networks. A famous example is the Six-Degrees of Separation which refers to the theory that a person is approximately 6 steps (edges of connection) away from another person on Earth. The brain satisfies the criteria of a small-world network, and this holds true on various scales in time and space and along the evolutionary ladder.
Caenorhabdidis elegans is the first organism to have its nervous system described at a cellular level and shown to be a small-world network in terms of connectivity [Watts and Strogatz, 1998]. The organism has merely 302 neurons, a fact that made mapping of its connectome very easy in comparison to higher organisms such as human. In the human case, data produced by many methods of brain imaging have been used to build structural brain networks. For example, application of graph theory on data from Diffusion Tensor Imaging, revealed that a few areas such as the superior frontal cortex, superior parietal cortex, the precuneus and the insula have high correlation between them, in terms of centrality [Sporns and Kötter, 2004]. Moreover, functional brain networks have been constructed with the use of fMRI, MEG, EEG. Combination of data from the BOLD signal of fMRI which provides better spatial resolution and data from electrophysiological methods such as EEG, which provide better temporal resolution, and application of interpolation methods made it possible to generate more accurate networks of brain activation. Moreover, science has gone further down the line, and scientists are able to record neuronal network activity in a dish (in vitro) by MEA (multielectrode array). From these in vitro studies and animal studies, graph theory has shown that neuronal nodes with similar connection patterns, tend to have similar functions [White et al., 1986]. Graph theory has and is proving very useful in understanding the changes that take place in various neurological and neuropsychiatric syndromes. For example, network analysis of MEG and fMRI data from Alzheimer’s and schizophrenic patients showed that small-world organization is lost and this held true both on the functional and on the structural scale. Moreover network analysis, offers the ability to ”visualize” changes that happen in the molecular level, and allows better understanding of processes such as synaptic plasticity, which is a process fine-tuned on the scale of milliseconds, and it is considered (LTP), to be the molecular underpinning of memory formation. Comparison of the functional networks on the molecular scale, to functional networks collected from humans while performing a memory task , may help us understand the functional and even the structural changes taking place in the brain during memory formation and shed light in the pathophysiology of memory-related conditions.
This post only provides a brief introduction on the subject of Graph Analysis and brain function and structure. Nevertheless, it is clear from the little described here, that Graph Analysis along with other mathematical and statistical methods, may provide the key to understand better this eloquently-build structure called brain.
Bullmore, E. & Sporns, O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat.Rev.Neurosc.10, 186-198 (2009)
Watts, D. J. & Strogatz, S. H. Collective dynamics of “small-world” networks. Nature 393, 440–442 (1998).
Sporns, O. & Kötter, R. Motifs in brain networks. PLoSBiol. 2, 1910–1918 (2004)
White, J. G., Southgate, E., Thomson, J. N. & Brenner, S. The structure of the nervous system of the nematode Caenorhabditis elegans. Philos. Trans. R. Soc. Lond. B Biol. Sci. 314, 1–340 (1986).
This beautiful song is an abridged version of the”Hymn to Love”, Epistole 1 Corinthians:13 of Apostol Paul, taken from the New Testament. Agapi, is the greek word for love, and as desribed in the verses below, agapi is the purest form of love and defeats all : “ Thus then shall linger only faith, hope and love but the greatest of these is love – Nini de meni, pistis, elpis, agapi, ta tria tafta, mizon de touton i agapi”.
The song’s language is Ancient Greek and the music is composed by Zbigniew Preisner.
Ean tes gloses ton anthropon lalo ke ton angelon
agapin de mi eho, gegona halkos ihon i cymbalon alalazon.
Ke ean eho profitian ke ido ta mystiria panta
oste ori methistanin,agapin de mi eho
I agapi makrothymi, christevete,
I agapi ou zili, i agapi ou perperevete, ou fysioute.
Panta stegi, panta pistevi, panta elpizi, panta ypomeni,
i agapi oudepote ekpipti
ite de profitie katargithisonte
ite glose pafsonte
ite gnosis katargithisete
Nini de meni, pistis, elpis, agapi,
ta tria tafta,
mizon de touton i agapi.
Though I speak with the tongues of angels
if have not love, my worlds would resound with but a tinkling cymbal.
And though I have the gift of prophecy and understand all mysteries
and though I have all faith so that I could remove mountains
if I have not love I am nothing
Love is patient, full of goodness,
love tolerates all things, aspires to all things
Love never dies
while the prophecies shall be done away
tongues shall be silenced
knowledge shall fade
Thus then shall linger only
faith, hope and love
but the greatest of these is love.
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.
The information below is taken from Danglot et al. review on hippocampal interneurons, and it can serve as short introduction on the subject.
Interneuron hereby signifies a local circuit neuron (unless specified differently), which synthesizes and releases γ-aminobutyric acid (GABA). This type of cell is critical for hippocampal function since it keeps the excitatory glutamatergic component under a yin-yan balance. Accordingly, if the GABA-ergic component is reduced, epileptiform activity develops in the hippocampus, whereas enhancing the GABA-ergic tone impairs hippocampal function. Various types of interneurons are characterized from their axonal projection pattern/target, for example oriens-lacunosum-moleculare cells target the distal apical dendrites of principal cells in the stratum lacunosum-moleculare.
Pyramids and other cells
Pioneer work by Bayer and Altman in the rat and of Soriano et al in mice, back in the late 70s and 80s, gave insight in the neurogenetic events taking place in the putative hippocampus. It was recognized that the hippocampal neuroepithelium consists of three distinct components, each giving birth to different neuronal types. Accordingly, the Ammonic neuroepithelium, gives rise to the pyramidal cells and large neurons in the stratum oriens and radiatum, the Dentate neuroepithelium generates granule cells and stratum moleculare large neurons. Lastly, the Fimbrial glioepithelium generates the fimbria glial cells. There is a specific timeline in the generation of each cell type , which differs among the hippocampal areas, as also within the same area pending on the cell type. Therefore, pyramidal cells of CA3 show a peak in neurogenesis on embryonic day 17 (E17) in the rat, whereas for CA1 pyramids peak neurogenesis is seen on E19. In mice, CA3 pyramids are generated between E14-E15 and CA1 between E15-E16 (Soriano et al., 1986, 1989a,b). Generally there is a succession of steps before the pyramids are established in the pyramidal layer: one day folowing their genesis, they migrate in the intermediate plate , a temporary lamina, and the next day they migrate towards the hippocampal plate, taking around four days for CA1 pyramids to reach their lamina and even longer for CA3.Hence, in rat, the CA1 pyramidal layer is obvious around E20 and CA3 on E22. The Dentate Gyrus can be distinguished around E21 since around 85% of the granules are generated postnatally.
Both in rats and mice GABA-ergic interneurons are generated prenatally, around E13-E18 in rat and E11-E17 for mice. Again there are regional differences in the birth time of the interneurons even within one hippocampal area. Hence, like cortical neurons there is a inside-out gradient of interneuron settling in the pyramidal layer, early generated neurons populate the deep positions whereas younger interneurons pass by them to occupy higher positions. Moreover, interneurons of the stratum oriens and stratum radiatum (plexiform layers or dendritic layers) are formed before the ones of the stratum pyramidale. In contrast to most CA1 and CA3 interneurons generated between E12-E13, DG interneurons arise later, between E13-E14.
Hippocampal Interneuron Matrix (or matrices? )
Early tracing studies by Altman and Bayer suggested that interneurons may originate from the roof of the telencephalon. There is still dichotomy as to where the GABA-ergic interneurons originate, but according to Danglot et al., a considerable number of interneurons arise from the subpallial telencephalon (ventral telencephalon) , migrate tangenially ( in contrast to cortical neurons that migrate radially) and populate the hippocampus, striatum and neocortex. The Medial and Caudal Ganglionic Eminences supply the hippocampus with interneurons (MGE: supplies only CA areas and CGE: supplies both CA and DG). These GABA-ergic interneurons are positive for Dlx2 (Dlx1/2 are homeobox genes expressed in the subpallium and have a role in the induction of GABA-ergic interneuron fate). Dlx2 positive cells can be seen on E15.5 in stratum radiatum and on E16.5 in stratum oriens (Pleasure et al. 2000). Due to this early placement in the hippocampus, its has been postulated that interneurons serve as “lighthouses” for incoming pyramidal cells and hippocampal afferents.
Danglot, L., Triller, A. & Marty, S. The development of hippocampal interneurons in rodents. Hippocampus 16, 1032-1060 (2006).
Soriano, E., Cobas, A. & Fairen, A. Asynchronism in the neurogenesis of GABAergic and non-GABAergic neurons in the mouse hippocampus. Brain Res 395, 88-92 (1986).
Soriano, E., Cobas, A. & Fairen, A. Neurogenesis of glutamic acid decarboxylase immunoreactive cells in the hippocampus of the mouse. II: Area dentata. J Comp Neurol 281, 603-611 (1989).
Soriano, E., Cobas, A. & Fairen, A. Neurogenesis of glutamic acid decarboxylase immunoreactive cells in the hippocampus of the mouse. I: Regio superior and regio inferior. J Comp Neurol 281, 586-602 (1989).
Pleasure, S.J., et al. Cell migration from the ganglionic eminences is required for the development of hippocampal GABAergic interneurons. Neuron 28, 727-740 (2000).
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.
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).
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.
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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.