Systems Biology Perspectives for Studying Neurodevelopmental Events DOI: http://dx.doi.org/10.5772/intechopen.85072

the adolescence where differential expressions among area reappear [8]. The spatial part of transcriptome analysis gave the proof of structure gene regulation in human brain. Especially, differences in gene expression profiling were demonstrated between brain substructures or sites with the presence of region-specific genes [9–11]. Hawrylycz et al. combined histological analysis with microarray in 900 neuroanatomic subdivisions from two human brains and observed that the spatial topography of the neocortex is reflected in its transcriptomic topography where closer cortical regions have similar gene expression [12]. However, symmetry bilaterally between two hemispheres was observed during development [8, 9, 11]. In addition, the gene expression variability exists also between layers of neocortex. The neocortex consists of six horizontal layers with subsets of neurons, the transcriptional analysis of the layers in prefrontal cortex showed human specific layer gene expression patterns [13]. A study realized by Miller et al. demonstrated differential gene expression between proliferative and postmitotic layers in mid gestation human fetal brain with the presence of a molecular gradient frontotemporal in cortical layers [14]. These observations supported the gene expression gradients along the anteroposterior axis of neocortex [15].

While informative, the transcriptome analysis over the whole brain or performed on specific regions is issued from the analysis of multiple cells possibly presenting heterogeneous cell types populations. The development in single-cell transcriptomics appears as a relevant alternative for gathering information about cell types. The single-cell whole transcriptomic analysis permitted to identify cellular heterogeneity in the brain and subtypes of neuronal cells with differential gene expression between fetal and adult neurons [16]. Single nuclear transcriptome in the adult cerebral cortex was used to see diversity in neuronal subtypes and neuroanatomical areas [17]. Habib et al. combined this technique of single nucleus RNA-Seq with pulse-labeling proliferative cells using the thymidine analog, the 5-ethynyl-2<sup>0</sup> -deoxyuridine (EdU), to identify hippocampal cellular types and track transcriptional trajectories single proliferating cells in the adult hippocampal neurogenic niche [18]. Similarly, a recent single-cell RNA-Seq study in the human fetal cortex and medial ganglionic eminence during prenatal neurogenesis demonstrated the presence of lineage specific trajectories dependent of transcription regulatory [19]. This study also demonstrated the modest transcriptional differences in cortical radial glia cascade which conducts robust typological differences in neurons. In the same context, Lake et al. combined single-cell sequencing with epigenome readouts in adult human brain cells to reveal chromatin/transcription factor regulatory events within distinct cell types [20]. Recently, Fan et al. also performed single-cell spatial transcriptome analysis in human brain mid gestation embryos, where they observed heterogeneity in each cortex region with no synchronization in cortex development and maturation [21].

The study of the transcriptional expression behavior during brain development is expected to enhance our understanding of pathological situations. Autism spectrum disorder (ASD), a heterogeneous pathology with prevalence of 1 in 59 children, is one of these examples. The pathogenesis of ASD is characterized by social impairments, disrupted communication skills and repetitive behaviors. Numerous genes were shown to be implicated in ASD and their gene co-expression and/or gene regulatory networks analyses are providing new insights on the impaired/ affected pathways on this disorder. In fact, several studies have tried to identify transcriptome alterations implicated in ASD using either DNA microarray hybridization assays or genome sequencing. By comparing autistic and control brain samples, upregulated genes implicated in immune function, while others repressed and involved in neurodevelopment or synaptogenesis were highlighted [22–24]. Another study described a dysregulation in mitochondrial oxidative

integrative computational strategies in use. Importantly, the concept of gene networks as an approach to describe the inter-relationship among the various implicated genes on the disease is discussed and illustrated by the major efforts

Timeline recapitulating major achievements in understanding of healthy or disease-affected human brain

development by the use of functional genomics approaches.

Neurodevelopment and Neurodevelopmental Disorder

performed over the last years in the field of neurodevelopment and related diseases (Figure 1). Finally, we discuss the arrival of new technological approaches for enhancing our capacity to interrogate the human nervous tissue, which in contrary to other tissues, remained till recently restricted to postmortem collected samples.

2. Interrogating neurodevelopment events by functional genomics

The evolution of genomics analyses, notably due to the sequencing of the human genome, allowed to study neurodevelopment from a different perspective; i.e., by the interrogation of the role of the genetic context during neurodevelopment. In fact, while the implication of genes in this process was previously studied at the individual level with the use of in-situ hybridization and RT-PCR methods, the developments in DNA microarray and RNA-sequencing technologies provided a global perspective as witnessed by the various studies focused on the brain

transcriptome either from the whole organ or particular regions and across stages of development. Among them, the work, performed by Kang et al., for the establishment of transcriptomes from 57 postmortem human brains in 16 regions across the lifespan spanning developmental embryos through adulthood corresponds to one of the earliest most comprehensive studies. In fact, beyond the large amounts of data, they provided a spatiotemporal transcriptome regulation view enhanced by the establishment of gene co-expression networks recapitulating different stages of development. Importantly, this study highlighted that the majority of spatiotemporal differences happen before the birth with a shift of gene expression patterns around the birth in the neocortex. Principally in the fetal brain, genes with a role in cell proliferation, cell migration, and neuronal differentiation are expressed in contrast to the late fetal period and infancy, where genes coding to dendrite and

Further studies performed by Colantuoni et al. focused on the temporal dynamic of the transcriptome in prefrontal cortex in a large number of human brain samples demonstrated that genes expressed differently in prenatal brain fetal development are reversed during postnatal life [5] with the recruitment of new genes in the early developmental brain [6]. With the same idea, the pattern of spatial gene expression in brain was shown to follow a way determined by embryonic origin that can change during development [7]. In fact, Pletikos et al. defined three phases in neocortical development: the prenatal with highest differential gene expression, the preadolescent phase with increasing synchronization of areal transcriptome, and

synapse development are found [4].

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Figure 1.

phosphorylation and protein translation pathways without seeing changes in DNA methylation [25]. Consistent with this observation, the downregulation of genes involved in mitochondrial and synaptic function were also reported by using multiple genomics datasets like RNA-Seq and microarray studies previously published [26]. Interestingly, dysfunction in synaptic pathways was also described in another neurodevelopmental disease, namely schizophrenia [27–30]. This pathology affecting approximately 1% of the population is characterized by personality disturbances, hallucinations, delusions, and/or disorganizing behavior. High-throughput transcriptomic analysis revealed multiple deregulated genes in schizophrenia [29–32]. Several of them are implicated in neurodevelopmental pathways, neuronal communication, energy metabolism, and synaptic function [29, 30, 32]. Changes in DNA methylation related to the prenatal-postnatal life transition were also reported by comparing schizophrenia postmortem and unaffected control brain samples, strongly arguing for the implication of an epigenetic regulation in the disease's development [33–35].

data was developed [53]. Overall, the generation of these databases correspond to major efforts for the research community, providing centralized access to the large collections of data; thus, further efforts of data integration can be performed, for instance by the reconstruction of gene regulatory networks on the basis of previ-

3. Inferring molecular coregulatory events from the integration of

The development of mid/high throughput strategies for analyzing genome sequences, their variants, gene expression, or even the proteome composition, provided means to the scientific community to interrogate each of these layers of complexity in a variety of model systems and tissues and in addition to integrate them to reconstruct a regulatory view. As illustrated in the previous section, several studies described major functional genomic readouts focused on studying brain

While being comprehensive, in most cases they provide relevant list of players

Since then, further studies incorporated other types of data, like the use of RNA-Seq transcriptomic analysis to identify the differentially expressed genes between controls and individuals with schizophrenia, which are then associated to biological functions by Gene ontology analysis [55–57]. Furthermore, the development of computational solutions for enhancing data integration has being performed like in the case of NETBAG, which allows to integrate multiple types of genetic variations like single nucleotide variants (SNVs), rare copy number variants (CNVs), and genome-wide association studies (GWAS), to identify highly connected gene clusters, potentially related to functional roles. NETBAG was initially described in the

Beyond correlating changes in gene expression with the identification of genetic variations, further efforts are required for stratifying information, like the use of gene co-expression strategies. This approach aims at aggregating genes on the grounds of their expression levels under the hypothesis that co-expressed genes are

(gene variants, differentially expressed genes, etc.) on the basis of statistical descriptors but forgets completely to address their potential relationship. Or, from a biological point of view, each of the players composing the system under study is expected to directly (or indirectly) influence the behavior of others. As a consequence, the current challenge is to evolve into an integrative view, focused on studying the various "deregulated events" as interconnected entities by the incorporation of multiple types of readouts and supported by computational solutions. From an historical perspective, the article of Walsh et al. released in Science in

2008, corresponds to one of the first major studies aiming at identifying neurodevelopmental programs involved in a disease context like schizophrenia [54]. In this study, the authors hypothesized that the collective contribution of each of the rare structural variants retrieved on neurological/neurodevelopmental syndromes accounts for these disorders, and in the specific case of schizophrenia, they have demonstrated a difference of at least 3-fold between controls and individuals with schizophrenia on the frequency of rare structural variants within coding regions. Furthermore, they have focused on structural mutations that disrupt genes, and evaluated their functions with the help of computational solutions querying for gene enrichment in one or more functionally defined pathways (PANTHER and Ingenuity Pathway Analysis). This strategy per se aims at establishing gene relationships on the basis of their annotation to a given program (or pathway), even

though in this case such relationships are inferred in-silico.

context of de novo CNVs in autism [58] and schizophrenia [59].

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ously generated transcriptomes.

DOI: http://dx.doi.org/10.5772/intechopen.85072

collected functional genomic readouts

Systems Biology Perspectives for Studying Neurodevelopmental Events

development in normal and disease settings.

In addition to the observed changes in gene expression, alternative RNA splicing has been described to occur at high frequency in human brain samples, corresponding to more than one-third of the human brain transcriptome [9, 36]. In addition, beyond the reported changes in protein coding gene expression [37], noncoding micro RNAs (miRNA) and/or long non-coding RNAs (lncRNA) were shown to have a role in neurodevelopment, participating in the reinforcement of brain complexity. Indeed, Ziats et al. described differential miRNAs expression in different parts of human brain along time of development with a principal shift that happens after the birth [38]. In the same idea, changes in lncRNA transcriptome during brain development [39], preferentially across fetal development with spatial regulation, were described [40]. LncRNAs also play a role in neuronal differentiation and neurogenesis, as suggested by studies highlighting a differential expression of lncRNAs during differentiation from human pluripotent stem cells [41, 42]. One example is the lncRNA rhabdomyosarcoma 2-associated transcript (RMST) which through its interaction with SOX2 regulates downstream genes implicated in neurogenesis [43]. The dysregulation of miRNA or lncRNA expression was also observed in autism [44–46], schizophrenia [47], and intellectual disability [48]. In this last case, lncRNAs were shown to be implicated in synaptic transmission, neurogenesis, or neurodevelopment.

Across these different transcriptome studies, a variety of databases hosting microarray and/or RNA-Seq data are currently available (for a comprehensive review, see [49]). Among them, we can cite the HB Atlas [4, 9], the BrainSpan Consortium [14], Brain Cloud [5], the Allen Brain map portal [12], the cortex single cells [19], or the single-cell portal [18]. In addition, several consortia, sometimes covering topics beyond the brain tissue, are at the basis of the establishment of major databases. Among others, we can cite the "Genotype Tissue Expression (GTex)" regrouping gene expression data issued from different tissues covering more than 600 donors [50]. Similarly, the "Encyclopedia of DNA Elements (ENCODE)" regroups large-scale datasets from various projects and combines multi-omics data from different species, variety of cell lines and tissues at different stages of development. A more specialized version of ENCODE, the "Psychiatric Encyclopedia of DNA Elements (PsychENCODE)," collects datasets concerning epigenetic modifications and non-coding RNA in healthy and disease-related human brains [51]. In the context of the data issued from brain samples, Huisman et al. developed the web portal "Brainscope" providing an interactive visualization of Allen Atlas adult brain transcriptome and across different stages of development [52]. Recently, a method to predict mRNA expression in whole brain using microarray data from Allen Brain Atlas with in-vivo positron emission tomography (PET) Systems Biology Perspectives for Studying Neurodevelopmental Events DOI: http://dx.doi.org/10.5772/intechopen.85072

phosphorylation and protein translation pathways without seeing changes in DNA methylation [25]. Consistent with this observation, the downregulation of genes involved in mitochondrial and synaptic function were also reported by using multiple genomics datasets like RNA-Seq and microarray studies previously published [26]. Interestingly, dysfunction in synaptic pathways was also described in another neurodevelopmental disease, namely schizophrenia [27–30]. This pathology affecting approximately 1% of the population is characterized by personality disturbances, hallucinations, delusions, and/or disorganizing behavior. High-throughput transcriptomic analysis revealed multiple deregulated genes in schizophrenia [29–32]. Several of them are implicated in neurodevelopmental pathways, neuronal communication, energy metabolism, and synaptic function [29, 30, 32]. Changes in DNA methylation related to the prenatal-postnatal life transition were also reported by comparing schizophrenia postmortem and unaffected control brain samples, strongly arguing for the implication of an epigenetic regulation in the

In addition to the observed changes in gene expression, alternative RNA splicing

corresponding to more than one-third of the human brain transcriptome [9, 36]. In addition, beyond the reported changes in protein coding gene expression [37], noncoding micro RNAs (miRNA) and/or long non-coding RNAs (lncRNA) were shown to have a role in neurodevelopment, participating in the reinforcement of brain complexity. Indeed, Ziats et al. described differential miRNAs expression in different parts of human brain along time of development with a principal shift that happens after the birth [38]. In the same idea, changes in lncRNA transcriptome during brain development [39], preferentially across fetal development with spatial regulation, were described [40]. LncRNAs also play a role in neuronal differentiation and neurogenesis, as suggested by studies highlighting a differential expression of lncRNAs during differentiation from human pluripotent stem cells [41, 42]. One example is the lncRNA rhabdomyosarcoma 2-associated transcript (RMST) which through its interaction with SOX2 regulates downstream genes implicated in neurogenesis [43]. The dysregulation of miRNA or lncRNA expression was also observed in autism [44–46], schizophrenia [47], and intellectual disability [48]. In this last case, lncRNAs were shown to be implicated in synaptic transmission,

Across these different transcriptome studies, a variety of databases hosting microarray and/or RNA-Seq data are currently available (for a comprehensive review, see [49]). Among them, we can cite the HB Atlas [4, 9], the BrainSpan Consortium [14], Brain Cloud [5], the Allen Brain map portal [12], the cortex single cells [19], or the single-cell portal [18]. In addition, several consortia, sometimes covering topics beyond the brain tissue, are at the basis of the establishment of major databases. Among others, we can cite the "Genotype Tissue Expression (GTex)" regrouping gene expression data issued from different tissues covering more than 600 donors [50]. Similarly, the "Encyclopedia of DNA Elements (ENCODE)" regroups large-scale datasets from various projects and combines multi-omics data from different species, variety of cell lines and tissues at different stages of development. A more specialized version of ENCODE, the "Psychiatric Encyclopedia of DNA Elements (PsychENCODE)," collects datasets concerning epigenetic modifications and non-coding RNA in healthy and disease-related human brains [51]. In the context of the data issued from brain samples, Huisman et al. developed the web portal "Brainscope" providing an interactive visualization of Allen Atlas adult brain transcriptome and across different stages of development [52]. Recently, a method to predict mRNA expression in whole brain using microarray data from Allen Brain Atlas with in-vivo positron emission tomography (PET)

has been described to occur at high frequency in human brain samples,

disease's development [33–35].

Neurodevelopment and Neurodevelopmental Disorder

neurogenesis, or neurodevelopment.

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data was developed [53]. Overall, the generation of these databases correspond to major efforts for the research community, providing centralized access to the large collections of data; thus, further efforts of data integration can be performed, for instance by the reconstruction of gene regulatory networks on the basis of previously generated transcriptomes.
