A hierarchical coherent‐gene‐group model for brain development

Abstract

We have described a strategy to analyze the data available on brain genes expression, using the concept of coherent-gene groups controlled by transcription factors (TFs). A hierarchical model of gene-expression patterns during brain development was established that identified the genes assumed to behave as functionally coding.Analysis of the concerned signaling pathways and processes showed distinct temporal gene-expression patterns in relation with neurogenesis/synaptogenesis. We identified the hierarchical tree of TF networks that determined the patterns of genes expressed during brain development. Some 'master TFs' at the top level of the hierarchy regulated the expression of gene groups. Enhanced/decreased activity of a few master TFs may explain paradoxes raised by the genetic determination of autism-spectrum disorders and schizophrenia. Our analysis showed gene-TF networks, common or related, to these disorders that exhibited two maxima of expression, one in the prenatal and the other at early postnatal period of development, consistent with the view that these disorders originate in the prenatal period, develop in the postnatal period, and reach the ultimate neural and behavioral phenotype with different sets of genes regulating each of these periods. We proposed a strategy for drug design based upon the temporal patterns of expression of the concerned TFs. Ligands targeting specific TFs can be designed to specifically affect the pathological evolution of the mutated gene(s) in genetically predisposed patients when administered at relevant stages of brain development.Keywords: Autism-spectrum disorder, brain development, coherent-gene groups, cortex, hierarchical coherent-gene group model, hierarchical transcription-factor network, informational entropy, neurogenesis, neurotransmission, schizophrenia, self-organizing maps, signaling pathway, synaptic epigenesis, synaptogenesis Several difficulties impede our understanding of the relationships between the organization of the genome and the neural phenotype of the brain: (1) The human genome has only 22 000 structural genes and (2) the differences in sequences of mouse, rat, monkey, chimpanzee and human genomes are so small that genome evolution, as opposed to brain evolution, appears strikingly non-linear (Changeux 1983(Changeux , 2004Ebersberger et al. 2002;Jiang Z et al. 2007;Konopka et al. 2012;Liu et al. 2012). Plausible explanations for such non-linear evolution include (1) the combinatorial co-expression of genetic determinants creating networks of interacting genes accounting for normal brain development (Johnson et al. 2011;Kang et al. 2011) and its pathologies (Ben-David and Shifman 2012;Torkamani et al. 2010), (2) the length of postnatal development (13-15 years in Homo sapiens), accompanied by major increase of brain weight (about fivefold before adulthood) (Lagercrantz et al. 2010), (3) the selective stabilization and elimination (pruning) of synapses under the control of evoked or spontaneous activity elicited by interactions with the physical...

Transcription factor motifs associated with anterior insula gene-expression underlying mood disorder phenotypes

“…If replicated, the identified TF motifs associated with DEGs in mood disorder diagnoses and overall disease morbidity will likely reveal a vulnerability in the general transcriptional pathway mechanism in mood disorder disease states.The identified DEG-profile associated TF motifs are known to regulate expression of tolllike receptor signaling genes, cellular homeostatic control genes, and embryonic and cellular including neurodevelopmental gene networks found to be differentially expressed in BD vs. controls, MDD vs. controls, and in high Axis-I comorbid mood disorder individuals vs. low Axis-I comorbidity mood disorder individuals. We found putative hierarchical TF regulatory involvement in the gene expression landscapes associated with BD and MDD diagnoses, such that enrichments of master transcription factors were predominantly associated with the gene expression landscape in elevated mood disorder morbidity, thereby echoing previous work(Tsigelny et al 2013; Lee & Young 2013; Changeaux 2017;Lambert et al 2018; Chen C et al 2019). It is important to note that a number of the TFs we have associated with anterior insula gene expression in bipolar disorder vs. controls (i.e., MIB2, Esrrb, MZF1, and ZNF148) and in major depressives vs. controls (i.e., PCP1, HNF1B, and Esrrb) are novel in that they have not been previously been identified in any genetic screens.…”

Mesenchymal Stem Cell Transplantation Promotes Neurogenesis and Ameliorates Autism Related Behaviors in BTBR Mice

“…Post mortem studies have indicated that ASD patients display pathologic neuroanatomical changes pointing to defects in neurogenesis (Wegiel et al, 2010). Moreover, genetic studies conducted on human tissue have highlighted the role of neurogenesis as a key biological process involved in ASD pathophysiology (Hussman et al, 2011;Kumar et al, 2011;Tsigelny, Kouznetsova, Baitaluk, & Changeux, 2013). In that context, BTBR mice provide a useful model as recent studies describe extensive neuroanatomical differences from C57BL/6 mice (a commonly used control strain) (Ellegood, Babineau, Henkelman, Lerch, & Crawley, 2013) and alterations in connective tissue/vasculature and associated extracellular matrix in a neurogenic niche of these mice (Mercier, Cho Kwon, & Kodama, 2011), alongside with histopathological findings indicating reduced hippocampal neurogenesis (Stephenson et al, 2011).…”

The Q-Exponential Decay of Subjective Probability for Future Reward: A Psychophysical Time Approach

“…Future studies in neuroeconomics and neurofinance [3,14] should examine neural basis of this psychological tendency. Furthermore, recent advances in high throughput genomic data in neurobiology by Changeux's group can help us to analyze brain's organization by utilizing Tsallis' statistics [19]. Future neuroeconomic studies can also utilize this type of research strategies by utilizing q-generalized statistics.…”