4.5.4. Omega Ratio

The Omega ratio introduced by Keating and Shadwick (2002) is one of the most important performance measures by using the probability-weighted ratio of gains and losses for any threshold return target. Guo et al. (2017b) developed the properties to study the relationships between the Omega ratio and (a) the first-order SD; (b) the second-order SD for risk-averters; and (c) the second-order SD for risk-seekers. Chow et al. (2019b) further established the necessary conditions between the Omega ratio and SD for both risk-averters and risk-seekers, and demonstrated that the Omega ratio outperforms the Sharpe ratio in many cases.

#### 4.5.5. Economic Performance Measure

Homm and Pigorsch (2012) developed the economic performance measure (EPM), which is related to Behavioral Finance, because it is related to SD, which, in turn, is related Behavioral Finance. Thus, EPM is related to Behavioral Finance. To make the EPM become useful in the comparison of different assets, Niu et al. (2018) developed the theory of construction confidence intervals for EPMs, including one-sample and two-sample confidence intervals, and derived the asymptotic distributions for one-sample confidence interval and for two-sample confidence interval for samples that are independent. The testing approach developed by Niu et al. (2018) is robust for many dependent cases.

#### 4.5.6. Other Risk Measures and Performance Measures

Because variance gives the same weight in measuring downside risk as well as upside profit for any prospect, it is not a good measure to capture the downside risk. To circumvent the limitation, several risk measures and performance measures have been proposed, including Value-at-Risk (VaR, Jorion 2000; Guo et al. 2019b), conditional-VaR (C-VaR, Rockafellar and Uryasev 2000; Guo et al. 2019b), Kappa ratio (Kaplan and Knowles 2004), Farinelli and Tibiletti (FT, Farinelli and Tibiletti 2008), economic performance measure (Homm and Pigorsch 2012), and others. In addition, Ma and Wong (2010) proved that VaR is related to first-order SD and C-VaR is related to second-order SD. Niu et al. (2017) proved that the Kappa ratio is related to SD, and Guo et al. (2019a) proved that the F–T ratio is related to SD for both risk-averse and risk-seeking investors under some conditions.

#### 4.5.7. Applications of Risk Measures and Performance Measures

There are many applications of using risk measures and performance measures to test for market efficiency, and check whether there is any anomaly in the market. Examples include Sharpe (1966), Keating and Shadwick (2002), Kaplan and Knowles (2004), Broll et al. (2006, 2011, 2015), Wong et al. (2008, 2018a), Leung and Wong (2008a), Fong et al. (2008), Abid et al. (2009, 2013, 2014), Qiao et al. (2010, 2012, 2013), Lean et al. (2010a, 2010b, 2013, 2015), Homm and Pigorsch (2012), Chan et al. (2012, 2019a, 2019b), Bai et al. (2013), Qiao and Wong (2015), Hoang et al. (2015a, 2015b, 2018, 2019), Tsang et al. (2016), Guo et al. (2017b, 2019a), Mroua et al. (2017), Niu et al. (2017), Bouri et al. (2018), and Chow et al. (2019b), among others.

#### 4.5.8. Indi fference Curves

The indi fference curve, which was first developed by Tobin (1958), is one of the important areas in Behavioral Finance, because it reveals the behavior of both risk-averters and risk-seekers in the mean and variance diagram. Tobin (1958) proved that the indi fference curve is increasingly convex for risk-averters, decreasingly convex for risk-seekers, averse (seeking) investors, and is horizontal for risk-neutral investors when assets follow the normal distribution. Meyer (1987) extended the theory by relaxing the assumption of normality and including the location-scale (LS) family to the theory of indi fference curve.

Wong (2006, 2007) extended the theory to include the general conditions that were presented in Meyer (1987)Meyer Wong and Ma (2008) further extended the theory by introducing some general non-expected utility functions and the LS family with general n random seed sources, and established some important properties of the theory of indi fference curves. To date, the literature only discusses indi fference curves for risk-averters and risk-seekers. Broll et al. (2010) extended the theory by examining the behavior of indi fference curves for investors with S-shaped utility functions.

#### *4.6. Two-Moment Decision Models and Dynamic Models with Background Risk*

The two-moment decision model is related to Behavioral Finance because it can be used to measure the behaviors of both risk-averters and risk-seekers. Many works have been done in this area. For instance, Alghalith et al. (2017) showed that the change of the price of expected energy will a ffect the demand for both energy and non-risky inputs, but the uncertain energy price only a ffects uncertain energy price but not the demands for the non-risky inputs for any risk-averse firm.

Alghalith et al. (2017) showed that the variance of energy price a ffects the demands of both non-risky inputs and energy decrease when the variance is vulnerable, but does not a ffect the demands of the non-risky inputs when there is only uncertain in energy price for any risk-averse firm. Guo et al. (2018a) contributed to the MV model with multiple additive risks by establishing some properties on the marginal rate of substitution between mean and variance. They also illustrated the properties by using the MV model with multiple additive risks to study banks' risk-taking behaviors.

In addition, Alghalith et al. (2016) extended the theory of the stochastic factor model with an additive background risk and the dynamic model with either additive or multiplicative background risks by including a general utility function in the models in which the risks are correlated with the factors in the models.


# **The Systematics, Morphological, and Molecular Characterization of Economically Important Plant–Parasitic Nematodes A Themed Issue in Honor of Dr. Gary Bauchan**

Edited by Zafar Handoo and Mihail Kantor Printed Edition of the Special Issue Published in *Plants*

www.mdpi.com/journal/plants


**4.2 Water bodies and underground rivers**

There are two underground river systems on each side of the city of Tulum: the southern system: Sac Ox Belha and the northern system: Sac Actun (map 3). These underground water body systems are connected to the cenotes. These systems are very vulnerable to all kinds of contamination, by sanitation systems: most houses have no drainage, only septic tanks whose absorption wells can contaminate underground rivers. The partial plan of the State of Quintana Roo, (2010) locates urban growth on the southern system of underground rivers (see map 4). The karstic rock soil is characterized by its high permeability; therefore, the infiltration of contaminants is very likely (**Figures 4** and **5**).

#### **Figure 4.**

*Underground rivers: South system: Sac Ox Belha and North system: Sac Actun (Source: Urban Development Plan, 2006–2030).*

#### **Figure 5.**

*Growth area of the city of Tulum on the southern system of underground rivers (Source: from the Municipal Development Plan).*

The current urban center does not have drainage and the porosity of the soil means that all matter is filtered and reaches the underground body of water. Fecal matter has already been found in the caverns. Urban growth without sewage treatment plants would lead to environmental and health catastrophes.


**Abstract**

Pakistan is in the eye of global climate change and among the top five countries which are severely affected by it. Residents facing unprecedented heat waves temperature hikes and flash floods. The vulnerability of climate change varies from sector to sector relied on the adaptive capability and sensitivity of different regions. According to the researcher's knowledge, it is a pioneer study which will broadly disclose the vulnerabilities of climate change in different economic sectors as well as how to cope with it through domestic adaptation and mitigation strategies. Cereals consider a staple food which is most vulnerable to climate change, even in the presence of effective capital and advancement of mechanisation. The core objective is to capture the vulnerability of climate change on cereal production via the Cobb-Douglus Production Function on annual time series data over 1977-2016 with the help of the Liner ARDL-Bound Test Approach. This study encompasses temperature, precipitation and inputs indices as well as greenhouse gas emanations which make it prominent from prior studies. Temperature index and greenhouse gas emanations have been destructive despite the fact precipitation and input indices, cultivated area and rural population have constructive liaison with cereal production over a long period.

**Keywords:** vulnerability, climate change, adaptation, mitigation, Pakistan


**3.3 Comparison of marginal bone changes within each group at three different views**

In the coronal view, within the ultrasound group, there was statistically significant increase in the buccal and palatal bones' thickness (height and width) from day 0 to 3 months, day 0 and 6 months, and from 3 to 6 months as p value was less than 0.05, but it was more pronounced at the buccal bone plate, while in the control group, there was no statistically significant increase in bone thickness, and there was marginal bone loss at all aspects of 9\*dental implant.

In the sagittal view, within the ultrasound group, there was statistically significant increase in the mesial and distal bones' thickness (height and width) from day 0 to month 3, day 0 and month 6, and from month 3 to month 6 as p value was less than 0.05, while in the control group, there was no statistically significant increase in bone thickness, and there was marginal bone loss at all aspects of dental implant.

In the axial view, within the ultrasound group, there was statistically significant increase in the buccal, palatal, mesial, and distal bones' thickness (height and width) from day 0 to 3 months, day 0 and 6 months, and from 3 months to month 6 as p value was less than 0.05, while in the control group, there was no statistically significant increase in bone thickness.

### **3.4 Comparison of marginal bone changes between two groups at three different views**

In the coronal view, there was statistically significant increase in buccal and palatal bone width between two groups (ultrasound and control) at 3 and 6 months as p value was less than 0.05. Thus, this increase in bone plate width is contributed to ultrasound therapy. There was no statistically significant increase in buccal and palatal bone height at 3 months between ultrasound and control groups, but there was statistically significant increase in buccal and palatal bone height at 6 months. Thus, this increase in bone plate height is contributed by ultrasound therapy.

In the sagittal view, there was statistically significant increase in mesial and distal bone plates' width between two groups (ultrasound and control) at 3 and 6 months as p value was less than 0.05. Thus, this increase in bone plate thickness is contributed by ultrasound therapy. There was no statistically significant increase in mesial and distal bone height at 3 months between ultrasound and control groups, but there was statistically significant increase in mesial and distal bone height at 6 months. Thus, this increase in bone plate height is contributed by ultrasound therapy.

In the axial view, there was statistically significant increase in buccal, palatal, mesial, and distal bone plates' width between two groups (ultrasound and control) at 3 and 6 months as p value was less than 0.05. Thus, this increase in bone plate thickness is contributed by ultrasound therapy.


diagnosis with 13 of 15 meeting a strict diagnosis on both "gold standard approaches," ADI-R and ADOS. Macrocephaly, defined as orbitofrontal circumference greater than two standard deviations of age and sex matched means, was exhibited in 12 of 15 patients (**Table 2**). Head circumference velocity data (n = 2) showed an initial orbital overgrowth within the first 2 months post birth and a continued trajectory of large head growth at or above the 97th percentile throughout early childhood (**Figure 1B**). The proportion of CHD8 mutation carriers with macrocephaly is significantly greater than that seen in the typical ASD population (p = 2.1 × 10−21). There were also many similarities in facial features among the group including: prominent forehead, wide set eyes, broad nose with full nasal tip, and pointed chin (**Figure 1A**). A majority also reported gastrointestinal (GI) problems, including recurrent constipation, and sleep problems, particularly with falling asleep (**Table 2**). Two patients described suffering from an inability to sleep for two-three straight days. Cognitive impairment was also pervasive, but intelligence spanned a wide range with some in normal range. Moreover, there is indication that additional symptoms may manifest as the children age. Three of the three female patients (including the translocation case) experienced precocious puberty (Talkowski et al., 2012; Bernier et al., 2014).

The implications of a CHD8 specific phenotype suggest ASD could be stratified into different subtypes based on genetic etiology. Identification of these subtypes would provide insight into ASD presentation and pathology in genetically homogenous populations and aid in the development and evaluation of treatment approaches (Bernier et al., 2014). Other high confidence ASD risk genes that exhibit recurrent loss of function mutations, such as ADNP and DYRK1A also appear to be associated with emerging subphenotypes (Helsmoortel et al., 2014; Bronicki et al., 2015; Ji et al., 2015). CHD8 appears to have a great deal of specificity for the ASD phenotype as almost all individuals identified from the screen met the criteria for ASD even though the cohort was not strictly defined for ASD individuals. This however may often not be the case as other strong ASD risk genes show more variable expressivity. For example, SYNGAP1 and CHD2 have been independently implicated in other neurological disorders such as epileptic encephalopathies (Carvill et al., 2013). Future studies examining how these similar genetic mutations span our current diagnostic and clinical boundaries may provide novel insights into the development of common neuropathologies.

### A CHROMATIN MODIFIER WITH DIVERSE FUNCTIONS

CHD8, previously called Duplin, was first identified in a screen for novel interactors within the canonical Wnt/β-catenin pathway using a yeast two-hybrid assay with a cDNA library generated from rat brain (Sakamoto et al., 2000). CHD8 is a member of the chromodomain-helicase-DNA binding protein family. The CHD family is characterized by a SNF-2 like ATPase and two chromo (chromatin organization modifier) domains (Marfella and Imbalzano, 2007). The first CHD gene, CHD1, was identified in a search for Ig promoter binding proteins (Delmas et al., 1993; Woodage et al., 1997) At least nine other members have been characterized to date with varying patterns of expression in timing and localization. The CHD family is a structurally diverse group comprised of three main subfamilies categorized by the presence of certain functional domains (Marfella and Imbalzano, 2007). CHD1 and CHD2 proteins contain DNA binding domains near the C-terminus that are thought to recognize AT rich motifs. CHD3 and CHD4 lack DNA binding domains and instead have PHD (plant homeo domain) Zinc finger like domains that may recognize methylated histones and potentially have repressive functions. CHD5-CHD9 also have DNA binding domains, along with additional functional domains located at the C-terminus. CHD8, as well as CHD7 and CHD9, contain a BRK domain, which is often seen in other SWI/SNF like proteins, and a DNA binding domain. Other SWI/SNF like complexes, such as BAF, regulate neural tube closure and neuron progenitor identity and differentiation (Ronan et al., 2013). Based on its ATP-dependent chromatin remodeling capabilities and SWI/SNF like domains, a potential role for CHD8 may lie in the regulation of neuronal progenitor cells.

Studies of CHD8 protein in the context of human cellular function show that it is involved in the regulation of transcription factor activity. CHD8 is able to activate the transcription of genes driven by U6 promoters through interactions with the transcription factor hStaf (Yuan et al., 2007). CHD8 is also required for CTCF, a major transcriptional repressor, to function as an inhibitor and insulator (Ishihara et al., 2006). CHD8 can also regulate RNA synthesis as well due to interactions with RNA polymerase II (Rodríguez-Paredes et al., 2009). CHD8 also promotes cellular proliferation in multiple human-derived cell types. Cells with CHD8 knockdown were found to have highly reduced S-phase cell populations and increased populations of cells arresting in G1 (Rodríguez-Paredes et al., 2009). This effect was later found to be a result of CHD8 binding to Sphase-dependent promoters and recruiting E2F, a transcription factor which controls cell cycle regulation (Subtil-Rodríguez et al., 2014). CHD8's role in cell cycle regulation could be a contributing factor in the observed macrocephaly exhibited by individuals with CHD8 LoF mutations. While macrocephaly may suggest overproliferation, in mouse models deficient in PTEN, macrocephaly appears to be attributed to increased cell body or soma size. Similarly, if cells are unable to progress through the cell cycle due to CHD8 loss, they may continue to grow in size (Kwon et al., 2001; Luikart et al., 2011). However, both CHD8 upregulation and downregulation has been observed in cancers suggesting CHD8 cell cycle regulation is likely complex and further study will be needed to determine how it impacts neurodevelopment (Lawrence et al., 2014; Subtil-Rodríguez et al., 2014).

What is also still unclear is the mechanism by which CHD8 controls transcription factor activity. Many studies have demonstrated that CHD8 has affinity for histone H3 lysine 4 (H3K4me3), a marker for active transcription, and is present at active promoters (Yuan et al., 2007; Rodríguez-Paredes et al., 2009; Sugathan et al., 2014; Cotney et al., 2015). Additionally the loss of CHD8 protein was associated with CpG hypermethylation and histone hypoacetylation near CTCF binding sites. Though CHD8 did not affect the ability of CTCF to bind DNA, potentially the CTCF-CHD8 complex may interact with methyltransferases and histone acetyltransferases to influence the state of nearby chromatin (Ishihara et al., 2006). Thus, CHD8 could promote transcription factor binding by making chromatin more accessible or acting as a scaffold between H3K4me3 labeled histones and the transcription factor.

### CHD8 DISRUPTION IN ANIMAL MODELS MIMICS HUMAN PHENOTYPES

In the in vivo setting, CHD8 was first examined in the context of β-catenin signaling due to its nuclear localization and ability to repress β-catenin dependent transcription (Sakamoto et al., 2000). In zebrafish, β-catenin is necessary for axis formation. Dorsal injections of Chd8 mRNA, causing overexpression of the protein, resulted in loss of the head. CHD8 expression in the zebrafish is enriched in the brain and spinal cord at 1 day post fertilization, and becomes progressively restricted to head and gut after 3–4 days post fertilization. Expression appears strongest in the mid-gastrula stage suggesting an important role for CHD8 in embryonic development regulating Wnt/β-catenin activity (Sakamoto et al., 2000).

The association of macrocephaly with a reduction in Chd8 expression is also observed in zebrafish. An increase in the interorbital distance, a surrogate measurement for head circumference, is seen in fish treated with Chd8 targeting morpholinos (Bernier et al., 2014; Sugathan et al., 2014). The distance increases in a morpholino dose dependent fashion. The reduction in CHD8 also coincides with increases in chordin, required in forebrain development, otx2, early marker of midbrain/forebrain neural progenitor cells, and HuC/D, marker for newborn neurons, indicating elevated proliferation, particularly in the mid and forebrain (Bernier et al., 2014). The zebrafish studies also suggest a role for CHD8 in GI development. Many of the human patients with CHD8 mutations suffer from severe GI issues. Morpholino treated zebrafish have decreased gut motility as evidenced by delayed progression of fluorescent microspheres (Bernier et al., 2014). There is also an overall reduction in enteric neurons in the hindgut.

Similarly in mice, Chd8 expression is at its highest embryonically at E8.5–E12.5 but reduced by E16.5 and low in newborns. Therefore, function is likely restricted to earlymiddle stage embryonic development (Nishiyama et al., 2004). Expression of Chd8 is most prominent in the brain, face, branchial arches, limb buds, and tail. Complete knockout of Chd8 is embryonic lethal. Embryos are resorbed by E9.5 but still recoverable by E8.5. Growth begins to delay as soon as E5.5 and is completely arrested at E6.5 with pronounced apoptosis occurring at E7.5. Heterozygotes though did not have gross abnormalities and were fertile. Interestingly, Wnt/β-catenin targets are not increased suggesting that aberrant signaling is likely not the cause for lethality. Rather, Chd8 protein regulates p53 activity by recruiting histone H1, forming a p53-Chd8-H1 complex that represses p53 target gene expression (Nishiyama et al., 2009). Unrestrained p53 activity appears to be the cause for the massive apoptosis seen in embryos. Deletion of p53 in Chd8−/− mice delayed arrest to E10.5 at which time embryos died from a heart defect. Chd8−/− p53+/− mice did not recover as well. Taken together, these studies demonstrate a conserved role for CHD8 as a regulator of early formative developmental pathways.

### A CHD8-ASD DEVELOPMENTAL WINDOW

With the great functional diversity observed in the de novo, rare variant, and other candidate ASD risk genes, it seems difficult to reconcile their role in an overarching mechanism for ASD biology. However, a number of biologic network approaches suggest that there may be a converging biology at particular developmental windows and brain regions, which includes CHD8 as a central player. These approaches are leveraging new spatial and temporal genomics data from developing human and nonhuman primates, such as the BrainSpan Atlas, a freely available resource that includes gene expression (microarray and RNAseq) and in-situ hybridization data sets (Shen et al., 2012). As in other organisms, CHD8 expression seems to be most significant early in human development. While CHD8 is widely expressed in the adult brain, expression is highest at 9–16 post conception weeks (PCW) in both progenitor and post mitotic neocortical layers and then gradually declines (Bernier et al., 2014).

To discover time points of development and brain regions for which candidate and high-confidence ASD risk genes, such as CHD8, may converge, two recent concurrent studies made use of BrainSpan's gene expression data to develop coexpression networks or modules representing genes that share similar expression patterns during development (Parikshak et al., 2013; Willsey et al., 2013). Each of these networks or modules corresponded to different brain regions along progressing stages of development. Remarkably, while the groups took different approaches to develop these networks and assess the potential for enrichment of candidate ASD risk genes they arrived at similar results implicating the mid-fetal time periods as a possible critical window for ASD etiology.

Parikshak and colleagues used BrainSpan RNA-seq data from human neocortex 8 PCW to 12 months after birth to construct co-expression modules by Weighted Gene Coexpression Network Analysis (WGCNA), an unbiased, genomewide method for constructing networks based on pairwise correlations of gene expression (Langfelder and Horvath, 2008; Parikshak et al., 2013).

They identified 17 modules and mapped sets of candidate ASD risk genes onto proteins within the modules. These gene sets included: "asdM12" which was derived from a WGCNA expression module from ASD patient postmortem cerebral cortex and "SFARI ASD" which was a subset of 155 genes from the larger AutDB gene list filtered by gene evidence score (Voineagu et al., 2011; see **Table 3** for detailed gene list descriptions). De novo variants identified from 2012 whole-exome sequencing studies performed by O'Roak et al., Sanders et al., and Neale et al. were also mapped onto networks. They then assessed enrichment for candidate ASD risk gene within specific co-expression modules, cortical layers and cell types. The de novo mutation candidate ASD risk genes were more concentrated in modules designated M2 (p = 0.006) and M3 (p = 0.0011). M2 peaks at 12–22 PCW and M3 is highly upregulated until PCW 12. The overall set

#### TABLE 3 | Referenced Candidate ASD Risk Gene Lists.

of genes in these modules, including non-ASD risk genes, were highly enriched for chromatin modifiers, DNA binding proteins, and transcriptional regulators (p < 1 × 10−4 ). Modules M2 and M3 seemed specific to the superficial cortical layers L2–L4 and cells expressing glutamatergic neurons.

In contrast, the "SFARI ASD" and "asdM12" gene sets were enriched in modules M13 (SFARI ASD, p = 0.059, asdM12, p = 3 × 10−15), M16 (SFARI ASD, p = 0.0024, asdM12, p = 3.5 × 10−15), and M17 (SFARI ASD, p = 0.033, asdM12, p = 1.0 × 10−7 ). The overall set of genes in these modules had a concentration of synaptic proteins (p < 1 × 10−4 ) and peaks late in fetal development, starting at 16 PCW and into birth. An enrichment for fragile-X mental retardation protein (FMRP) targets was seen in the M2 module, suggesting a common mechanism between ASD and fragile-X syndrome, in line with the enrichment of FMRP targets first observed by Iossifov et al. (2012). On the other hand, 401 genes implicated in monogenic forms of ID compiled from four publications, showed little enrichment for any one module suggesting the mid-fetal time period may be specific to ASD.

Willsey and colleagues took an alternative approach by focusing on nine high-confidence ASD risk genes, defined as having two or more LoF mutations, and determining their coexpression networks (Willsey et al., 2013; **Table 3**). These nine genes were used as seeds to generate networks composed of the top positively correlated genes. Gene expression data was also derived from BrainSpan, but based on the Affymetrix GeneChip Human Exon 1.0 ST Array rather than RNA-seq. In addition, all brain regions were considered and 15 periods of development, 5.7 PCW–82 years were examined. Developmental periods were condensed into three time windows resulting in 52 networks based on time and brain region with transcriptional similarity as determined by hierarchical clustering. To see if additional candidate ASD risk genes were enriched in these networks, a list of 122 candidate ASD genes (termed "Willsey set") with at least one LoF mutation identified from whole-exome sequencing studies was generated and mapped onto the networks (**Table 3**). Permutation testing of enriched networks was used to correct for gene size and GC content. They also looked for enrichment of this gene set within specific cortical layers and cell types. Immunostaining and in-situ hybridization was performed on frontal cortex sections to determine protein expression for five of the high-confidence ASD genes. Similar to Parikshak et al., they found enrichment in networks exhibiting peak expression between 10–19 (p = 0.003) and 13–24 (p = 0.05) PCW (midfetal time period). These networks were specific to the prefrontal and primary motor-somatosensory cortex, but in contrast to Parikshak et al., the networks were specific to the deep layers, L5- 6. Immunostaining revealed that CHD8 and other ASD genes like SCN2A, DYRK1A, and TBR1 were highly expressed in cortical projection neurons, particularly glutamatergic cell types.

In summary, both groups found a convergence of candidate ASD genes with de novo mutations within the mid-fetal stage of development, 10–24 PCW. The resulting networks appeared specific for glutamaterigic cortical projection neurons, but differed in their implication of specific cortical layers. The cortical projection neurons form synaptic connections early in development and may then be highly sensitive to changes in gene regulation (Willsey et al., 2013). Therefore, de novo mutations in genes like CHD8 may largely impact the midfetal stage of development particularly prefrontal and motorsensory cortex development.

### CHD8 DISRUPTION ALTERS MANY TARGETS INCLUDING OTHER CANDIDATE ASD RISK GENES

The heterozygous LoF CHD8 mutations seen in ASD likely result in insufficient levels of CHD8 protein and the disregulation of CHD8 targets. The regulatory landscape and widespread reach of CHD8 regulation has been examined in three recent studies using genomic approaches and a number of different neural cellular models that attempt to mimic early development (**Figure 2A**). Sugathan and colleagues performed knockdown of CHD8 in induced pluripotent stem cell (iPSC) derived neuron progenitor cells (NPCs) using lentiviral delivery of six independent shRNA achieving a range of 38–69% reduction in CHD8 mRNA expression (Sugathan et al., 2014). They then assessed changes in gene expression using RNA-seq in these knockdown lines and also performed chromatin immunoprecipitation-sequencing (ChIP-seq) in control NPCs to identify CHD8 binding sites. By contrast, Cotney and colleagues performed ChIP-seq to locate CHD8 binding sites (under non-perturbed conditions) in three different systems: human midfetal cortical tissue 16– 19 PCW, H9 derived human neural stem cells (hNSCs), and embryonic mouse cortex (Cotney et al., 2015). Using primary tissue allowed for representation of mid-fetal stages critical in CHD8-ASD development and to evaluate potential differences between CHD8 binding sites identified in in vivo tissue and in vitro cells. They also performed CHD8 knockdown in hNSCs using two lentiviral shRNA, followed with RNA-seq, to assess changes in gene expression. Finally in the third study, Wilkinson and colleagues performed knockdown of CHD8, achieving about a 50% reduction, using siRNA in human SK-N-SH neural progenitor cells followed by RNA-seq (Wilkinson et al., 2015). These studies reveal a complex role for CHD8 in ASD development as it appears to regulate expression of additional candidate ASD risk genes by both indirect and direct means.

Sugathan and colleagues found that knockdown of CHD8 lead to differential expression of some 1756 genes (at nominal P < 0.05; 369 genes at q < 0.05 Benhamini-Hochberg), most of which were upregulated (n = 1140, p < 0.05; 286, q < 0.05; Sugathan et al., 2014). The set of down regulated genes (n = 616) were enriched for roles in neuronal development. Intriguingly, this same set of downregulated genes was also enriched for candidate ASD risk genes from a large set of 628 genes from AutDB (p = 3.25 × 10−8 ; **Table 3**). Though no enrichment for any candidate ASD risk gene sets were observed in the set of up-regulated genes, cancer associated genes, defined by The Cancer Genome Atlas, were enriched in this group. Loss of CHD8 function has been observed in a broad range of tumor types and other chromatin regulators have been implicated in cancer development (Lawrence et al., 2014). This suggests CHD8 may have other important roles extending past development and may be a point of commonality between some cancers and ASD.

In the ChIP-seq data, 7324 reproducible binding sites were identified (Benjamini-Hochberg q < 0.05). CHD8 preferentially bound to regions that in ES cell-derived neuroprogenitor lines from the Roadmap Epigenomics are marked with histone H3 trimethyl Lysine4 (H3K4me3), an indicator of active TSSs (Bernstein et al., 2010). These TSSs were strongly enriched in the CHD8 set, 83%, relative to the genome background, 1%. A small number of binding sites, 4%, overlap enhancer marked sites (H3K4me1), representing a two-fold enrichment. Sequence motifs at CHD8 binding sites were enriched for CTCF and YY1 transcription factor motifs. Genes with CHD8 sites were enriched for p53, hedgehog, and cell cycle pathways.

Combining the differentially expressed (DE) set of genes with CHD8 binding data, Sugathan and colleagues further explored a number of enrichments using functional classifications, candidate ASD risk gene sets, and other disease gene sets. Overall, only 9.2% of genes with CHD8 binding sites were DE in the nominal P gene set (29.7% of all the DE genes). Candidate ASD risk genes from AutDB were enriched in the set of nominally downregulated genes, but not targeted by CHD8 (p = 1.09 × 10−9 ). This set of candidate ASD risk genes was enriched for pathways involved in neurodevelopment such as axon guidance and neurotransmitter regulation. Enrichment of the more restricted set of AutDB genes from Parikshak et al., "SFARI ASD" was also observed in the downregulated, non-CHD8 bound genes (p = 2.26 × 10−2 ). However, genes from Willsey et al., "Willsey set" were enriched as CHD8 bound genes (p = 4.34 × 10−3 ), but were not associated with any changes in gene expression. CHD8 bound genes in the "Willsey set" were enriched for chromatin and transcription. Though these data sets are not independent, they do have different gene representations, with the "Willsey set" more biased toward rare de novo variants. CHD8 binding sites with or without differential expression were explored in the modules described in Parikshak et al. The downregulated genes with no CHD8 binding site were enriched in modules M13, M16, and M17 which correspond to mid-late fetal develop whereas CHD8 bound genes were enriched in modules M2 and M3 corresponding to early-midfetal development. CHD8 bound genes were also highly enriched in TGCA cancer associated genes and also showed no specific enrichment in either up or downregulated genes. No other disease gene sets from the 184 lists for complex disease and traits available from the National Human Genome Research Institute were as significantly enriched as the ASD or cancer gene sets for CHD8 targets or genes affected by CHD8 knockdown. These data suggest both direct and indirect roles of CHD8 in gene regulation, particularly in the development of ASD and cancer.

Cotney et al. found 9414 reproducible CHD8 binding sites in hNSCs and 4428 in human midfetal brain. Between these two human sets, 2777 sites were overlapping. Chromatin state of hNSC CHD8 binding sites was determined from data generated on hNSCs from their own lab and the ENCODE/Roadmap Epigenomics (Bernstein et al., 2010). Similarly to Sugathan and colleagues, they found that CHD8 had strong affinity for TSSs labeled with H3K4me3 or H3K27ac and 99% of promoters bound by CHD8, 8056, were active (Cotney et al., 2015). Additionally, they observed that a small fraction of CHD8 binding sites, 1028, were indicative of enhancer functions. Moreover, CHD8 binding negatively correlated with H3K27me3, indicative of repression. Enriched sequence motifs for transcription factors included CTCF, E2F, YY1, and Sp/Kruppel-like family.

genes. (C) List of the shared candidate ASD risk genes from the "Willsey Set" bound by CHD8 between both studies and conserved between human and mouse.

Genes that had CHD8 binding sites identified in both midfetal brain and hNSCs were highly enriched for candidate ASD risk genes from two gene sets as defined by and Liu et al., "Liu set" (p < 0.0001) and the "Willsey set" (p < 0.0001; Willsey et al., 2013; Liu et al., 2014), while genes with binding sites only in hNSCs were not enriched. Candidate ASD risk genes with CHD8 binding sites were enriched for chromatin regulation and modification. CHD8 targets supported by both human datasets were largely evolutionarily conserved. Among mouse (E17.5 cortex), human midfetal brain, and hNSC, 1910 CHD8 binding sites were shared and also enriched for candidate ASD risk genes from both aforementioned gene sets (p < 0.0001), further suggesting a conserved mechanism for CHD8 regulation in development. Of the candidate ASD risk genes from the "Willsey set" found to contain CHD8 binding sites, 25 were shared between Cotney et al. and Sugathan et al. and were also conserved in mouse (**Figures 2B,C**). Enrichment of CHD8 bound proteins was also observed in networks reconstructed from the Willsey et al. 10–19 and 13–24 PCW networks using genes with active promoters (Willsey et al., 2013).

In their knockdown of CHD8 in hNSC, genes with conserved CHD8 binding sites had the greatest fraction of DE genes (56%) as compared to genes with binding sites only found in hNSC (46%) or hNSC and human brain (54%). Cell cycle, Hippo, and p53 pathways were among the pathways most affected. Candidate ASD risk genes actually exhibited the most significant dysregulation compared to any other set of CHD8 targeted genes (∼60% for both gene sets). Additionally, those candidate ASD risk genes most DE, tended to be downregulated. While Sugathan et al. did find that candidate ASD risk genes from the Willsey set were enriched as CHD8 targets, they were not significantly DE in either direction. In contrast, Cotney et al.'s data suggest that CHD8 directly activates this same set of candidate ASD risk genes.

In line with the other studies, Wilkinson et al. found CHD8 reduction had a global impact on gene expression. Interestingly, the top DE genes in this study were noncoding RNAs, the majority of which were upregulated. Noncoding RNAs have only recently been appreciated as having a potential role in neurodevelopment and may be yet another layer in transcriptional control. Enrichment of candidate ASD risk genes Parikshak et al. "SFARI ASD" was only seen in the set of downregulated genes, similar to Cotney et al. as were processes involved in regulation of neuron projection, differentiation and neurogenesis (Parikshak et al., 2013; Wilkinson et al., 2015).

In summation, CHD8 appears to act as a master regulator in the foundational pathways of the developing brain, particularly those that may also be implicated in ASD development. Yet the mechanism for regulation remains unclear. Many of the candidate ASD risk genes are direct targets of CHD8 protein and are also involved in chromatin regulation (**Figure 3A**). Cotney et al. and Wilkinson et al. indicate that CHD8 directly activates expression of these genes but Sugathan et al. suggest that the presence or absence of CHD8 may not influence expression, despite the genes having a binding site. Other candidate ASD risk genes lack a CHD8 binding site, but still appear to be affected by CHD8 expression indicating an indirect mechanism of regulation. How CHD8 may be able to influence gene activation or transcription factor activity without directly binding to DNA is not clear, but may involve binding to other co-regulators or chromatin marks (**Figure 3B**). Continued study will be needed to fully elucidate the mechanism for CHD8 regulation in ASD.

### CONCLUSIONS AND FUTURE DIRECTIONS

From exome sequencing studies, a number of high-confident ASD risk genes have been identified with CHD8 emerging as particularly strong. Both human case studies and animal models suggest a common phenotype for CHD8 mutation including a significant association with ASD diagnosis and behavior, macrocephaly, and dysfunction in the enteric nervous system. Though ASD is highly heterogeneous, CHD8 systems biology and functional genomics studies give some hint as to the complex nature of the disorder. The CHD8 protein and those of many of the other candidate genes serve as master regulators, influencing the expression of a large network of genes and pathways that control neuron formation, proliferation, and differentiation. The timing of proper neural development is a highly regulated phenomenon, requiring a concerted effort by many different genes. While it's clear that CHD8 controls expression of genes involved in these developmental processes, the manner in which CHD8 regulates them is still unclear. Initial studies suggest that CHD8 may act as a direct repressor by modifying chromatin to make it less accessible. However, the loss of function studies point toward a role for CHD8 as an activator in the emerging ASD gene network. One of the consistent findings from these and previous studies is that CHD8 is associated with actively transcribed genes and has affinity for H3K4me3 labeled promoters suggesting an influential role in global gene expression. CHD8 may function to make promoters more accessible through chromatin remodeling, promote transcription factor binding, or indirectly enhance expression through as of yet, unknown mechanisms that may involve interactions with H3K4me3 itself. The mechanism of CHD8 regulation is likely to be gene dependent as well.

It's also still uncertain which CHD8 targets may be relevant for ASD. Though the studies indicate a number of candidate ASD risk genes contain CHD8 binding sites, further functional studies will be required to demonstrate if these genes interact in a physiologic setting. Additionally, although enrichment for candidate ASD risk genes may be seen in specific neuron types, it is still unclear how the alterations in these neurons ultimately relate to behavioral phenotypes. It will be important to determine how CHD8 affects neural circuitry and connect these affects to autistic associated behaviors. Lastly, development of co-expression modules have proved useful in identifying relevant ASD developmental time periods and interactions between candidate ASD risk genes, including CHD8. However, developing methods that integrate multiple types of data such

as co-expression and protein-protein interactions may improve identification of ASD and other neurodevelopmental disorder specific networks, as proposed recently (Hormozdiari et al., 2015). Through the combination of rapidly advancing genetics and the development of relevant neural models, ASD biology is slowly beginning to resolve, and providing the potential for individualized approaches to therapy.

### AUTHOR CONTRIBUTIONS

RAB: writing, editing, and figure conception, MBP: writing and figure design, BJO: writing, editing, and figure conception.

### REFERENCES

### ACKNOWLEDGMENTS

BJO currently holds and receives support from the Sloan Research Fellowship in Neurosciences (Alfred P. Sloan Foundation), Klingenstein-Simons Fellowship in Neurosciences (Esther A. & Joseph Klingenstein Fund & the Simons Foundation), and the NARSAD-Atherton Foundation Young Investigator Award (Brain and Behavior Research Foundation). We would like to thank Dr. Raphael Bernier for his input on the CHD8 mutation phenotype data. We would also like to thank the contributions of families and participants in the described studies. Without them, none of this work would be possible.

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Brian J. O'Roak is an inventor on patent PCT/US2009/30620: mutations in contactin associated protein 2 are associated with increased risk for idiopathic autism.

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