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Novel and disruptive biological strategies for resolving gut health challenges in monogastric food animal production

Use of feed antibiotics as growth promoters for control of pathogens associated with monogastric food animal morbidity and mortality has contributed to the development of antimicrobial resistance, which has now become a threat to public health on a global scale. Presently, a number of alternative feed additives have been developed and are divided into two major categories, including 1) the ones that are supposed to directly and indirectly control pathogenic bacterial proliferation; and 2) the other ones that are intended to up-regulate host gut mucosal trophic growth, whole body growth performance and active immunity. A thorough review of literature reports reveal that efficacy responses of current alternative feed additives in replacing feed antibiotics to improve performances and gut health are generally inconsistent dependent upon experimental conditions. Current alternative feed additives typically have no direct detoxification effects on endotoxin lipopolysaccharides (LPS) and this is likely the major reason that their effects are limited. It is now understood that pathogenic bacteria mediate their negative effects largely through LPS interactions with toll-like receptor 4, causing immune responses and infectious diseases. Therefore, disruptive biological strategies and a novel and new generation of feed additives need to be developed to replace feed antibiotic growth promoters and to directly and effectively detoxify the endotoxin LPS and improve gut health and performance in monogastric food animals.

Introduction
Evidence of the domestication of a handful of animal species, including pigs, can be traced back to early recorded human civilization (Diamond, 2002;Groenen et al., 2012). Animal production plays important roles in the continued human evolution in both physical and mental health as well as social and economic viability (Pond and Lei, 2001;Fan, 2013). Animal production, in providing meats, contributes greatly to social and economic activities and wellbeing (Pond and Lei, 2001;Fan et al., 2008). Current world food animal production is represented by compound feed production at 980 million dry tons valued at about $460 billion US per year with monogastric poultry and swine feeding accounting for 72% of the total production volume (Alltech, 2015). Feed cost typically accounts for about 70% of the operation expenses in intensive food animal production. China's compound feed production is at 183 million dry tons, accounting for about 18.7% of the world, market value, which is estimated at about $170 billion US per year (Alltech, 2015). Current intensive food animal production practices are faced with challenges and obstacles to evolve the industry to be socially, economically and environmentally sustainable with an emerging one being the antimicrobial resistance concern (Levy, 1998;Gorbach, 2001).
For the past several decades, antibiotics have been by far the most cost-effective way to maintain feed efficiency and health status in intensive animal production systems, including poultry and swine (Cromwell, 2001;Looft et al., 2012). However, dietary supplementation of sub-therapeutic levels of feed antibiotics in animal production has now been recognized as the main determinant of antimicrobial resistance (Davies et al., 1999;Forslund et al., 2013). Antibiotic-resistant commensal and pathogenic bacteria can be directly spread to humans via retail meat products (Johnson et al., 2005), transferred into the environment after manure Contents lists available at ScienceDirect journal homepage: www.www.keaipublishing.com/en/journals/aninu application (Chee-Sanford et al., 2009), and are further transmitted into ecosystems (Koike et al., 2007). Antimicrobial resistance that originates from use of feed antibiotics in livestock industries contributes to the development of multidrug resistance ''Superbugs'' that are jeopardizing effective treatment of infectious diseases in human patients (Gorbach, 2001), pets (Umber and Bender, 2009), and wild birds (Guenther et al., 2010), becoming a major threat to global public health (WHO, 2014).
On the other hand, use of sub-therapeutic levels of feed antibiotics neither eliminates food-borne pathogenic bacteria from food production animals (Casewell et al., 2003;Looft et al., 2012) nor detoxifies lipopolysaccharide (LPS) endotoxin (Koyama et al., 2002;Chen et al., 2010). These food animals' gut systems are a safe hideout and reservoir for the pathogenic bacteria (Johnson et al., 2005;Manges and Johnson, 2012). Thus, alternative strategies need to be developed to replace feed antibiotics and to effectively detoxify pathogenic endotoxins and minimize pathogenic bacterial populations while maintaining and enhancing efficiency and health status of agricultural production food animals including poultry and swine.

History of sub-therapeutic levels of antibiotics as growth promoters
Antibiotics are arguably the most successful form of chemotherapy discovered in the history of medicine (Wright, 2007). The initial use of antibiotics as growth promoters in food animal production dates back to the ''golden era'' of antibiotic discovery during 1940's to 1960's (Visek, 1978;Wright, 2007). The widespread use of sub-therapeutic levels of feed antibiotics primarily in monogastric food animal production has been practiced since the 1960's when the average cost of feed-grade antibiotics was dramatically reduced (Cromwell, 2001). Mode of actions associated with the use of sub-therapeutic levels of feed antibiotics for improving feed efficiency and health status in food animals is still not completely clear. The classic mechanisms of feed antibiotics for improving feed efficiency include the control of bacterial overgrowth, more essential luminal nutrients available for host growth, and stimulation of immunity (Visek, 1978;Cromwell, 2001;Gaskins et al., 2002). Feed antibiotics are recently shown to increase gut microbial genes relating to energy production and conversion (Looft et al., 2012). Thus, applications of subtherapeutic levels of feed antibiotics are still regarded as the most cost-effective way to improve feed utilization efficiency and health status in monogastric food animal nutrition. An effective alternative strategy needs to be developed in order to replace feed antibiotics as growth promoters in monogastric food animal production.
Use of sub-therapeutic levels of feed antibiotics induces significant changes in the gut microbiota by reducing bacterial diversity (McCracken et al., 2001;Looft et al., 2012). Studies have demonstrated that failure to establish and maintain a normal gut microbiota would promote innate immune activation and may impair immune homeostasis (Mulder et al., 2009. Thus, young animals fed sub-therapeutic levels of feed antibiotics are susceptible to innate immune activation. Furthermore, feed antibiotics do not eliminate commensal and pathogenic bacteria, and this is largely due to the fact that bacteria develop antibiotic resistance genes (Forslund et al., 2013). For example, feed antibiotics increase commensal and pathogenic Escherichia coli populations in weanling pigs' gut (Looft et al., 2012). Animals' gut is a main reservoir of pathogenic bacteria, which is the primary source of food-borne pathogens (Johnson et al., 2005;Manges and Johnson, 2012). Thus, applications of sub-therapeutic levels of feed antibiotics as growth promoters do not disinfect pathogenic bacteria in animal production. There is an urgent need to develop a fundamentally new approach to prevent dysbiosis in swine production and minimize pathogenic bacterial impact on meat safety and public health concerns.
Use of sub-therapeutic levels of feed antibiotics is known to attenuate but does not fundamentally resolve the traditional challenges of post-weaning growth lag or check and the weaningassociated pig morbidity and mortality, which hampers the swine industry (Varley and Wiseman, 2001). Scientifically, this is because feed antibiotics do not completely eliminate intestinal Gramnegative bacteria that produce and shed LPS endotoxin into the gut lumen (Rietschel et al., 1994;Erridge et al., 2002;Kulp and Kuehn, 2010). Biologically, feed antibiotics do not detoxify the LPS endotoxin. Furthermore, intestinal apical alkaline phosphatase, being recognized as the primary endogenous LPS detoxification enzyme, is less effective with its less physiological optimal pH of 10.5 (Fan et al., 1999), as well as its decreased maximal enzyme activity and enzyme affinity or enzyme efficiency in potentially detoxifying the LPS endotoxin in the weanling pig (Lackeyram et al., 2010), making weanling pigs susceptible to the development of endotoxemia.
It has now been demonstrated that LPS is effectively transported across the intestinal epithelial apical membrane via an endocytosis process in conjunction with long-chain fatty acid assembly into chylomicrons and makes its way into the systemic circulation (Ghoshal et al., 2009;Pendyala et al., 2012). Dr. Bruce Beutler was a recipient for the 2011 Nobel Prize in Physiology or Medicine for leading research in revealing that toll-like receptor 4 (TLR4) was the LPS receptor in the endotoxin-mediated inflammation via innate immunity (Poltorak et al., 1998). Luminal and systemic circulating LPS endotoxin triggers gut mucosal local (McCracken et al., 2001;Piéet al., 2004), and systemic inflammation (Klasing, 1988) via innate immunity mediated by TLR4, enhancing gut permeability (Salzman et al., 1994;Lallès et al., 2007), releasing pro-inflammatory cytokines into systemic circulation (Klasing, 1988), resulting in anorexia (Johnson, 1998;Buchanan and Johnson, 2007), and causing sickness behavior and diminishing welfare in animals (Kelley et al., 2003;Dantzer et al., 2008). Hence, intestinal luminal LPS and systemic endotoxemia are largely responsible for monogastric food animal morbidity and mortality. Applications of sub-therapeutic levels of feed antibiotics as growth promoters do not detoxify LPS endotoxin and associated pro-inflammatory cytokine effects in major food production monogastric animals such as poultry and swine.

Antimicrobial resistance and conventional alternative strategies
Antimicrobial resistance has now become a major threat to global public health (WHO, 2014). Antibiotic resistance genes existed before the ''antibiotic era'' (Aminov and Mackie, 2007). All genes encode proteins that show resistance to antimicrobials and are referred to as resistome (Wright, 2007). Development of antimicrobial resistance in swine production has been well documented to occur in two major distinctive types, including antibiotic-induced antibiotic resistance (e.g., Aminov and Mackie, 2007) and heavy metal-specific (i.e., Cu and Zn, in particular) antimicrobial resistance (Fard et al., 2011) due to feeding pharmacological levels of Cu (200 to 250 mg/kg) and Zn (2,000 to 3,000 mg/kg) as growth promoters (Fan, 2013).
To address the antibiotic resistance concerns, European Union banned sub-therapeutic levels of feed antibiotics during 1997 to 1999, which actually led more therapeutic antibiotics uses in food animal production and deteriorated antibiotic resistance to therapeutic antibiotics (Casewell et al., 2003). The Casewell et al. (2003) study support the view that a simple ban on subtherapeutic levels of feed antibiotics as growth promoters will not fundamentally eliminate the antibiotic resistance concerns (Looft et al., 2012). Therefore, a new effective approach to prevent dysbiosis and to detoxify LPS endotoxin needs to be developed in order to replace the sub-therapeutic levels of feed antibiotic and pharmacological levels of Cu and Zn uses in swine production and minimize the antimicrobial resistance threat to public health.
Development of alternative strategies in terms of nutritive and non-nutritive feed supplements and or additives to replace the sub-therapeutic levels of feed antibiotic in monogastric food animal production has been a hot topic of research for the past decade and this is reflected by a series of reviews of literature reports (Stein, 2002;Pettigrew, 2006;Lallès et al., 2007;Heo et al., 2013;Fan, 2013;Pluske, 2013). Current alternative strategies can be categorized into two groups, including the strategies that improve host animal growth and gut mucosal immunity and the strategies that directly or indirectly control pathogenic bacterial proliferation in the gut environment. Examples of the first category strategies include the use of rapidly digestible sugar supplements such as crystalline lactose and dry whey powder for optimal growth performances in weanling pigs (Mahan and Newton, 1993;Stein, 2002;Hayhoe, 2012). Several strategies have been developed to directly improve gut mucosal growth and functions, including using sweeteners (Shirazi-Beechey et al., 2011), gut trophic crystalline amino acids and short peptides (e.g., L-glutamine) (Wu et al., 1996;Wang et al., 2008;Fan, 2013), growth factors such as recombinant lactoferrin and epidermal growth factor (Fan, 2013), as well as positive immunomodulatory compounds such as βglucans (Hayhoe, 2012) and omega-3 polyunsaturated fatty acids (Fan, 2013). A great deal of research efforts have been devoted to develop various prebiotic, i.e., non-viscous soluble fibers, and probiotic supplements to promote healthy gut microbiota and improve host weanling pig health and growth (Pettigrew, 2006;Fan, 2014). Examples of the second category strategies include the formulation of low-crude protein diets by using crystalline free amino acids to reduce pathogenic bacterial proliferation (Le et al., 2005;Bauer et al., 2006;Gloaguen et al., 2014), dietary supplementation of organic acids (Kirchgessner and Roth, 1998;Partanen and Mroz, 1999;Fan, 2013) and dietary inclusion of pathogenspecific egg-white antibody proteins and spray-dried plasma proteins (Marquardt et al., 1999;Stein, 2002;Pettigrew, 2006;Fan, 2013). Although pharmacological levels of Cu and Zn are shown to be effective to promote pig growth, their dietary supplementations pose detrimental environmental concerns (Fan, 2013;Heo et al., 2013). More recent work has provided evidence to show that pharmacological levels of Cu and Zn used concomitantly induce certain types of antibiotic resistance (Cavaco et al., 2011).
The general key limitations associated with above reviewed alternative common strategies are that these strategies do not always and consistently prove to be effective under all tested conditions and nor do these strategies effectively disinfect pathogenic bacteria and or detoxify LPS endotoxin. It is essential to develop an effective strategy for detoxification of LPS endotoxin and resolve the gut health challenge facing the monogastric food animal production.

Metabolic endotoxemia and efficiency of nutrient utilization
Metabolic endotoxemia is defined as a two-to threefold increase in circulating endotoxin concentrations above normal detectable levels (Kaliannan et al., 2013). Normal blood circulating levels of endotoxin concentrations, when expressed as an endotoxin unit (EU), are 0.1 to 0.2 EU/mL plasma reported in rodents (Ghoshal et al., 2009;Kaliannan et al., 2013) and humans (Pendyala et al., 2012). Metabolic endotoxemia leads to low-grade systemic inflammation, as further manifested by increased serum levels of pro-inflammatory cytokines such as interleukin (IL)-1, IL-6 and tumor necrosis factor-alpha (TNF-a) and enhanced gut permeability, causing accelerated translocation of endotoxin and resulting in a vicious cycle of endotoxemia (Kaliannan et al., 2013). In human nutrition and health, high-fat Western diets induced metabolic endotoxemia and contributed to the development of metabolic syndrome (Ghoshal et al., 2009;Pendyala et al., 2012;Kaliannan et al., 2013). Paradoxically, metabolic endotoxemia is poorly defined in monogastric food animals for their nutrition and metabolic research especially under practical swine production conditions. Measurements of plasma concentrations of endotoxin are no longer a technical limitation and assay kits for this type of measurements are readily available from commercial sources.
Poor energy efficiency and low profit margin of swine production is known to be associated with very low efficiency of whole body total nitrogen, i.e., crude protein (CP), retention during the post-weaning pig growth and detrimental impacts on the environment in swine production (Fan et al., 2006). Whole body efficiency of nitrogen utilization in swine declines dramatically from the suckling (83%) to the finishing phase (51%), and this is largely because gastrointestinal endogenous nitrogen and catabolic nitrogen losses are dramatically augmented due to genetic programming and changes in diets and sanitary conditions (Fan et al., 2006). Efficiency of whole body nitrogen utilization is low during most of the post-weaning production growth in swine (Fan, 2013). Systemic inflammation has been well documented to affect efficiency of nutrient utilization in monogastric food animals (Klasing, 1988). Gut luminal endotoxin and inflammation is inevitably, in part, responsible for the increased gastrointestinal endogenous nitrogen loss (Fan et al., 2006), since endotoxemia leads to increases in visceral organ protein synthetic activities (Orellana et al., 2004). Systemic endotoxin is also demonstrated to dramatically increase muscle protein catabolism (Orellana et al., 2012). Majority of current literature reports regarding effects of endotoxin on swine nutrition and metabolism have been conducted with acute (Webel et al., 1997;De Ridder et al., 2012) or chronic intraperitoneal injection (i.p.) of LPS models (Orellana et al., 2002;Kim et al., 2012;Rakhshandeh and de Lange, 2012). Nevertheless, it can be concluded that the presence of endotoxin partially accounts for the poor efficiency of nutrient utilization, low profit margin and some of the major environmental sustainable issues facing monogastric food animal of poultry and swine production. Thus, it is essential to develop effective and direct strategies for mitigation of the detrimental impact of enteric pathogenic bacterial toxins such as LPS endotoxin that are challenging the gut health in intensive monogastric food animal production.

Novel and disruptive biological strategies to improve monogastric food animal gut health
Novel and disruptive strategies have been effectively developed and used in intensive monogastric food animal nutrition and production including the dietary supplementations of purified trace mineral and vitamin supplements (NRC, 1998(NRC, , 2012, the use crystalline limiting essential and non-essential amino acids in formulating low-protein diets (Gloaguen et al., 2014), and the application of phytase technologies for resolving the concern of poor efficiency of phosphorus utilization (Simons et al., 1990;Golovan et al., 2001;Cowieson et al., 2006). It is apparent from the above discussions that novel strategies need to be developed to directly and effectively disrupt the key step or component from pathogenic bacteria that causes immunological and infectious responses to the host. It is rather challenging to completely disinfect and sterilize pathogenic bacteria without affecting commensal microflora in the gut environment such as the current practices of dietary supplementations of feed antibiotics, organic acids, and pharmacological levels of copper and zinc. Three potentially disruptive biological strategies may be further developed to specifically target at the major pathogenic bacterial endotoxin LPS, including regulation of the bacterial membrane polysaccharide biosynthesis pathway, lipolysis of the LPS lipid A moiety by unique lipases & esterases, and dephosphorylation of the LPS diphosphoryl lipid A moiety by alkaline phosphatases. These three potential strategies are further elaborated below.
Both Gram-negative and Gram-positive bacterial cell surfaces anchor a large number of polysaccharides and these polysaccharides play important roles in a wide variety of biological processes such as adhesion to biotic and abiotic substrata, motility, biofilm formation, cell-cell communication and immune system interaction and activation (Islam and Lam, 2014). For example, polysaccharides represent a large core of the Gram-negative bacterial endotoxin LPS. Thus, understanding of regulation of the bacterial membrane polysaccharide biosynthesis pathway has been a major focus of molecular microbiology research to develop pharmacological strategies for combating bacterial immune activation and infections.
Pathogenic bacterial endotoxin LPS typically consist of two distinct structural features, including a hetropolysaccharide chain and a lipid A moiety. The lipid A moiety has been established as the main toxic determinant of LPS in mediating immune responses via TLR4 (Ahn et al., 2004). It has been well established that the presence of the three structural components in the lipid A moiety is essential for LPS to induce their toxic effects and these include both phosphate groups, the glucosamine disaccharide, and all the fatty acyl chains especially the 2'-lauroyl and 3'-myristoyl acyloxy residues (Ahn et al., 2004). Detoxification of bacterial LPS via deacylation and lipolysis of the lipid A moiety through leukocyte, liver and kidney acyloxyacyl hydrolase (AOAH), an endogenous lipase, was reported (Munford and Hunter, 1992;Shao et al., 2007). Ahn et al. (2004) demonstrated that some of the screened microbial lipases and esterases were considerably more efficacious than mammalian pancreatic and hepatic lipases and esterases in hydrolytically releasing laurate and myristate from LPS. These results have two important gut health implications. Firstly, the endogenous lipases and esterases in animals are likely limited in their efficacy in detoxification of bacterial LPS via deacylation and lipolysis of the lipid A moiety. Secondly, development of novel and efficacious lipases and esterases as exogenous enzyme supplements may be one of the alternative disruptive strategies for detoxification of enteric endotoxin LPS in protecting gut health of food animals.
Intestinal alkaline phosphatase (IAP) is one of the most abundant brush border membrane-bound proteins (McComb et al., 1979;Fan et al., 1999). Its classic physiological functions include intestinal absorption of lipids, regulation of duodenal secretions and pH, detoxification of bacterial endotoxins, and regulation of the transmucosal passage of bacteria (Lallès, 2010). The most important physiological function of alkaline phosphatases is their unique biochemical property of detoxification of the endotoxin LPS via dephosphorylation. McComb et al. (1979) were the first to propose the potential role of alkaline phosphatases in dephosphorylation of potential toxins. Poelstra et al. (1997a,b) were the first to demonstrate that alkaline phosphatases were responsible for detoxification of the endotoxin LPS via dephosphorylation in vivo studies with rats. Bentala et al. (2002) and Koyama et al. (2002) subsequently showed that the removal of phosphate from the lipid A moiety in LPS was the mechanism of endotoxin detoxification by alkaline phosphatases. Various physiological and dietary factors are shown to affect the expression and functionality of IAP in animals (Lallès, 2010). Inadequate supply of enteral nutrients resulted in abnormal expression and functionality of IAP, which was demonstrated in rodent models (Goldberg et al., 2008). Inflammation and pro-inflammatory cytokines inhibited intestinal alkaline phosphatase expression (Lallès, 2010). Intestinal alkaline phosphatases prevented metabolic endotoxemia, low-grade inflammation and metabolic syndrome in mice fed high-fat diets (Kaliannan et al., 2013). Our previous studies shown that early weaning in piglets dampened the maximal enzyme activity and the digestive capacity of the IAP by down-regulation of this gene at the transcriptional, translational and post-translational levels with considerable decreases in the apical IAP abundance and affinity in the proximal jejunum (Lackeyram et al., 2010). Therefore, development of novel and efficacious alkaline phosphatases as exogenous enzyme supplements may be another alternative disruptive strategy for detoxification of enteric endotoxin LPS in protecting gut health of food animals.

Conclusions
Wide use of feed antibiotics as growth promoters in monogastric food animal production in the past six decades has contributed to the development of antimicrobial resistance, threatening to the world public health. Efficacy of currently available alternative feed additives in replacing feed antibiotics to maintain productivity and gut health is inconsistent dependent upon experimental conditions. Disruptive biological strategies such as novel lipases & esterases and alkaline phosphatases as exogenous feed enzymes need to be developed in replacement of feed antibiotic growth promoters to directly and effectively detoxify the endotoxin LPS and to maintain gut health and performances in monogastric food animals.


extfieldsofstudy: Biology Medicine

text:
An Interactive Tool for Animating Biology, and Its Use in Spatial and Temporal Modeling of a Cancerous Tumor and Its Microenvironment

The ability to visualize the ongoing events of a computational model of biology is critical, both in order to see the dynamics of the biological system in action and to enable interaction with the model from which one can observe the resulting behavior. To this end, we have built a new interactive animation tool, SimuLife, for visualizing reactive models of cellular biology. SimuLife is web-based, and is freely accessible at http://simulife.weizmann.ac.il/. We have used SimuLife to animate a model that describes the development of a cancerous tumor, based on the individual components of the system and its environment. This has helped in understanding the dynamics of the tumor and its surrounding blood vessels, and in verifying the behavior, fine-tuning the model accordingly, and learning in which way different factors affect the tumor.

Introduction
The modeling of biological systems by computerized models that support interactive executions (simulations) provides the opportunity to integrate a large amount of experimental data, and generate a comprehensive overview of the system as a whole. This includes the ability to observe the dynamics in operation, by an abstracted animation of the model, which is essential for a clear understanding of the biology represented in the model and is convenient as a basis for further analysis.
Systems biology, mathematical modeling and computational approaches can make important contributions to research and development in biology [1,2], and these are, in fact, becoming increasingly important in efforts to better understand complex biological behaviors. Extensive attempts to model and analyze biological systems or processes have been carried out [3,4], for the most part by traditional mathematical modeling [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19], using a top-down approach, whereby the known behavior of the system is built into the model. A different approach, which has been termed executable biology [20] focuses on the design of fully executable models that mimic complex biological phenomena, and is usually done bottom-up [21][22][23][24][25]. For a review see [2]. Another familiar example of biological system modeling includes the blue brain project to study the brain's architectural and functional principles [26]. There, the system's dynamics emerge from the model via reverse engineering using the NEURON software together with a biologically realistic model of neurons, based on exact mathematical representation.
Although computational models usually contain a lot of essential details, in most cases they cannot pass on to the user one of the most important aspects of the system being modeled, which is actually seeing it in operation [27]. Visualization is an effective way of representing the dynamics of a model. This should include at least the system's components, their interactions and the effect of changes to parameter values. The technique of reactive animation (RA), whereby the model of the reacting system is connected smoothly to an animation tool [28][29][30], has been used in the past to successfully model several nontrivial biological systems [22][23][24]. In [24] the dynamic architecture of a lymph node was modeled. RA aided in observing the behavior leading up to the unique meeting between the specific T and B cells, or how they can miss each other at times depending on the other factors present. In [23] the development of the mammalian pancreas was modeled. Here RA aided in observing the physical 3D formation of the pancreas as well as seeing which cells it is composed of at each stage. Also, changing the layout of the blood vessels revealed shapes that are different in nature from the genuine pancreatic structure. In [22] the differentiation of T cells in the thymus was modeled. RA revealed a previously unknown existence of competition among thymocytes for space and stimulation, which is essential for generating the normal structure and function of the thymus organ.
In this paper we describe SimuLife, a new RA-based interactive tool for animating computational models of cellular biology. Our main goal was to make the tool generic, so that it could serve a wide variety of types of biological systems. We also wanted SimuLife to help improve the experience of both developers and users, and to give a realistic look to the running model so that it would be accessible to biologists, and even laypeople. With this in mind, we have endowed the tool with a user-friendly interface that allows one to easily manipulate and experiment with the parameters, as well as with other aspects of the tool, such as the image types used, their colors, what to include in a view, etc.
The advantage of SimuLife, and one of the things that make the tool so attractive, is that it is accessible directly via the web and does not require any specific downloads or installations (http://simulife.weizmann.ac.il/). In addition its graphics is 3D allowing one to intuitively follow the morphological arrangement.
As mentioned, the main purpose of creating this tool was to constitute a first step towards a generic tool that would eventually be able to support a multitude of different kinds of biological models. Nevertheless, the process of building the tool was driven by our work on a complex computational model of a cancerous tumor and its microenvironment, which was done in parallel. In the present paper we illustrate the use of the tool and its capabilities on this particular model, and in future work we plan to adapt SimuLife to other types of systems too.
Cancer research is of great importance. It refers to many different and distinctive diseases, all of which stem from the same state of abnormal growth and regulation of cells, which proliferate in an uncontrolled way. Amongst the hallmarks of a cancerous cell, such as those stated above, it has been revealed that cells around the tumor constitute what is termed the tumor microenvironment [31]. The cancerous tumor cannot survive or progress on its own; it depends on the dynamic microenvironment in which it originates and the bi-directional interactions with this surrounding. The bi-directional cross-talk between the tumor and its environment takes place either by secretion of signals or by cell-cell interactions. This communication is very important and can act to enhance or block tumor formation.
Angiogenesis is the process of new blood vessels growing from pre-existing ones. It is a normal and vital process in embryo development and wound healing, but is also a fundamental step in the transition of tumors from a dormant state to a malignant one. Without blood vessels, tumors cannot grow beyond the size of 1mm 3 [32]; they need a large supply of oxygen and nutrients delivered to their cells. When the tumor is under hypoxia (lack of oxygen) [33] a series of events occurs, a crucial one being the secretion of VEGF from the tumor cells, leading to angiogenesis.
Using the SimuLife tool, we can visualize the ongoing events of the cancer model, including angiogenesis and its effect. This is of great importance, as it enables one to see the development and morphology of the tumor and its surroundings, understand the dynamics of the system based on its individual components, verify the behavior, fine-tune the model accordingly, and manipulate the input in order to visualize its effect on the resulting morphology. The latter includes changing parameters to see what happens to the tumor either without angiogenesis or when the level of factors affecting angiogenesis is changed.

Tool development
An interactive animation tool, SimuLife, for visualizing models of cellular biology, was built. SimuLife continuously receives inputs from the reactive model and draws and updates the graphics based on the changes. The main principles we used in designing and building Simu-Life were as follows; To make it fast, efficient, able to support thousands of objects, use realistic-looking images, show good performance, be as generic as possible, work both in real-time and offline using a pre-recorded file, present the animation in 3D, be interactive and with a user-friendly interface, and be web-based. We wanted to render it free from having to download or install specific tools, not require one to purchase a license or to have to use a propriety language.
SimuLife's GUI contains a main screen showing the actual animation, and side tabs with various options for the computational model itself and the animation images (Fig 1). It also provides statistics regarding the current time step and amount of objects, which are updated throughout the run (see demonstration in S1 Video or at http://youtu.be/xsOXtD7-LjE).
SimuLife has the following capabilities (see demonstration in S2 Video or at http://youtu. be/_U9rw1ACGhM): Examples referred to are taken from the cancer model.
• Represent cells using either simple spherical images or more realistic-looking ones (the latter is more costly computationally and can affect the performance) (Fig 2A).
• Represent molecules by density distribution, at each radius or by cubes.
• Change general or specific parameter values prior to or during a run (see demonstration in S3 Video or at http://youtu.be/bCkujp1E3m0).
• Click-and-select individual visible objects, or select hidden objects (such as those inside the tumor) by choosing from a list, and receive relevant information about them from the model (position, parent cell, etc.).
• Create or kill objects, either specific ones or at random (see demonstration in S4 Video or at http://youtu.be/Khgej9Cs0jo).
• Change the colors of the images (Fig 2B).
• Make certain objects invisible in order to focus on other objects (Fig 2C).
• Slice the animation at a given point along any of the 3D axes, in order to view a 2D cross section ( Fig 2D).
• Zoom in and out, spin the animation around the center, and navigate to any 3D position.
• Use a pre-recorded file of a run, in order to see the results faster and/or later.
• Play with the speed in which the file is read, and hence with the speed of the animation, and pause it at any given time. The advantages of such a tool are vast; whilst the computational model holds the information for each of the individual objects, SimuLife allows one to visualize the information of all the objects together at once. Although SimuLife was developed to eventually serve as a generic tool, one that will be able to connect to many different kinds of biological models, it is currently used for a specific biological modela Statecharts-based spatial and temporal model of a cancerous tumor and its microenvironment, developed using the Rhapsody tool from IBM (www.ibm.com/software/awdtools/rhapsody/). Our model focuses on a three dimensional tumor, going from a single cancerous cell through the formation of a primary avascular tumor, via the secretion of angiogenic factors and recruitment of nearby blood vessels, to a completely vascularized tumor. Tumor cells and endothelial cells (cells of the blood vessels) were modeled with regard to their size, position, state, proliferation, input from the surrounding area and output into the surrounding. Using the Statecharts language (see materials and methods section), a generic behavior was modeled for each kind of object, and during the executions of the model many instances of the objects were generated to represent each specific one taking on its explicit states accordingly. Many parameters were used in the model, such as cell size, area size, oxygen threshold, VEGF threshold, rate of proliferation, secretion rate, and more. The values of these parameters can easily be changed to see the effect on the system of any one parameter or of a combination thereof. Values of parameters used in the model do not have units of measurements, but are quantified relative to each other. In this way qualitative comparisons can be made with biological experiments. Nonetheless, many of the general parameters such as time, size, amounts, have approximate equivalent real values to them and thus allow for quantitative comparisons to real tumor growth dynamics. More details on the model and a set of parameters and their values can be found in (S1 Text). The result is a comprehensive, dynamic, reactive, 3D spatial and temporal computational model of a cancerous tumor growth and its microenvironment.
Analyzing the behavior of the model is a vital step for understanding the dynamics of the system, and for being able to compare it to existing biological data in order to check the model's correctness.
In the cancer model, we used SimuLife in order to understand the system better, compare to known biological data, verify that the model does not possess non-acceptable biological behaviors, fine-tune various parameters of the model, and test new and interesting possibilities. Some, of many examples of this include: a) Testing the system under conditions of no angiogenesis (i.e., no blood vessels that grow towards the tumor). Without the tumor's microenvironment, which includes angiogenic blood vessels, the tumor cannot survive, due to low oxygen/nutrient supply [33][34][35][36]. Therefore, as expected, this resulted in a primary tumor that stopped growing at some point, and then started to die off (Fig 3A). This could have been achieved by cell population curves of active cells, necrotic cells, endothelic cells and perhaps oxygen and VEGF curves. However, visualization shows the dynamics of all of the cells and molecules together, and allows one to get a sense of how it happenswhich cells are killed off first, the size the tumor reaches, etc. b) Testing the effects of high vs. low VEGF secretion. VEGF secretion by the tumor cells is what recruits the vessels towards the tumor in order to supply it with oxygen/nutrients [35]. The lower the VEGF level, or the longer it takes to reach the blood vessels, the longer it will take the endothelial cells to become activated to form the angiogenic network. Playing with this parameter reveals that low VEGF secretion results in far fewer blood vessels, whilst using high VEGF secretion values results in many blood vessels already being formed at the same time point (Fig 3B). Here too, visualization aids in seeing not only the amount of endothelial cells that are created over time but also the morphology that the blood vessels form at different time stages, as they follow the VEGF gradient to reach the tumor. In addition, it is possible to view the change in the VEGF gradient throughout space. c) Testing defective blood vessel growth. This is not taken from the literature but instead is an example showing the shape that the tumor takes on in different cases. Here the blood vessels were not allowed to branch into new ones, but only to become longer, so there is no exponential growth of vessels, hence less oxygen. This caused the tumor to grow towards the oxygen source, "hugging" the blood vessels and thus forming a branching shape and not a sphere-like shape (Fig 3C). This different growth pattern of the tumor was revealed with the aid of the SimuLife tool.

Materials and Methods
SimuLife is an interactive animation tool that interacts with the computational model, sending information to it and receiving information from it (both as XML files). This is in the spirit of the reactive animation technique [28][29][30]. SimuLife then draws and/or modifies the graphics based on the changes in the model. SimuLife is based on WebGL (Web Graphics Library), and a JavaScript API (THREE.js framework in our case) for rendering interactive 3D graphics within any compatible web browser without the use of plug-ins. The client side is Chrome and the communication with external engines is carried out via sockets (Fig 4). The bases for the realistic versions of the images (e.g., cells) were prepared by a professional animator and are in COLLADA, a format that can be used with our framework. SimuLife is open source and the scripts used to build it are available at https://github.com/simulife/simulife. Some of the challenges arising during the tools' development include: • Dealing with animating thousands of behavior-rich objects, while trying not to impair performance. One of the ways we did this was by grouping many objects together into a single object and ungrouping when necessary. Each object was treated individually and was referred to separately when needed, but when not in use, e.g., when inside the tumor or on a side not visible to the user, the objects were grouped in order to take up less computer memory so that the performance will not be harmed.
• When showing a slicing of the animation, with its 2D cross section, there was a need to make the sliced area appear to be flat, meaning that each of the images in the border had to be "cut". 3D objects are hollow and when slicing them there is a need to close the unclosed sliced 3D shape. In order to solve this we used ThreeBSP package, a CSG plugin for Three.js (https://github.com/sshirokov/ThreeBSP), which allows us to subtract two 3D objects from each other; the 3D area that is to be cut from the scene at each of the three axes was subtracted from the 3D cell, resulting in a closed sliced image.
• Presenting a large amount of objects (such as millions of molecules). We solved this by presenting the gradient distribution of the molecules instead of each of the individual molecules. This was done by displaying the total amount of molecules either at every sphere from the center of the tumor or at every cube throughout the space modeled.
• Rendering the connection between separate but adjacent objects to appear as a continuous object (such as presenting connecting endothelial cells as a blood vessel). This was done by joining the center of each object and displaying the connection by a tube-like image. The Statecharts modeling language Our computational cancer model was designed using the language of Statecharts as the centerpiece [37,38]. Statecharts makes it possible to describe the behavior of reactive systems in a discrete fashion, using an edifice of states and the transitions between them with a mix of hierarchy and concurrency. In this way, one can specify the behavior of the individual entities that take part in the cancer process and insert biological data, in order to capture and be able to execute the dynamic behavior and morphology of the system. Statecharts are executable on several appropriate tools, such as Rhapsody, available from IBM, which is the tool we used (www.ibm.com/software/awdtools/rhapsody/) (see Fig 5).

Discussion
In order to better understand biological models, a detailed and realistic visualization of the model can be very useful. For this purpose we developed the SimuLife tool, where an animation is dynamically constructed at real time according to the model, producing a different interactive visualization of the system's run each time.
As mentioned, although visualization of biological models has been used in the past [22][23][24], one of our goals was to make the tool generic, so that it could serve a wide variety of types of biological systems, especially those describing cells and using agent-based modeling. In principle, any model can be connected to SimuLife, as long as it can output messages to the animation via a socket. This will involve making changes to the interface and the images used according to the specific model. We also wanted SimuLife to help improve the experience of both developers and users, and to give a realistic look to the running model so that it would be attractive and useful to biologists, and even to laypeople.
In our research we used the language of Statecharts with the Rhapsody tool in order to create a comprehensive 3D model of a cancerous solid tumor, together with its microenvironment. SimuLife aided in tracking and validating the development and progression of the tumor and the vessels. It enabled seeing the tumor cells at their precise 3D locations, together with the blood vessels that consist of the individual endothelial cells, and their elongation towards the tumor, as well as distribution of molecules' density, and more. SimuLife allows to easily play with the animation, send commands back to the model during runtime and observe the immediate resulting output, as well as adjust many aspects of the animation itself to meet the user's requirements. It also enables viewing only certain elements of interest and getting real current quantitative data. Such a tool is important especially in the case where the spatial organization is of major interest. Using SimuLife for the cancer model enabled us to see how the model resembles the behavior of a solid tumor; a necrotic core developed in the inner part of the tumor [39] and the branching of the blood vessels occurred more often as they approached the tumor [40]. These and more are behaviors that emerged from the model and were revealed by the animation. We also came to the conclusion that the tumor has a turning point, where it either dies or recovers. This was revealed by varying the values of key parameters that affect amounts of VEGF and oxygen and noting the behavior of the tumor together with the rest of the system's components. These results will be discussed more thoroughly in a follow-up paper, in which we will describe in more detail the biological issues, and the relevant insights gained from the interactive visualization of the cancer model.
Other visualization tools that describe computational models of cancer exist [41,42]. However, they are fundamentally different from SimuLife in that SimuLife is based on reactive animation, where the animation platform is an entity totally separate from the model; whilst the aforementioned tools are the actual models, SimuLife is solely the animation driven by the model, built using another generic approach, Statecharts, and its underlying tool Rhapsody, and which is interlinked with SimuLife via RA. By separating these two facets, each can be constructed using state-of-the-art tools [29].
With more effort we aim to continue improving the SimuLife tool, making it more generic -able to easily connect to other biological models, other modeling engines, or other programming languages, as well as improving its performance. Finally we are continuing to work on the cancer model with the aid of the SimuLife in order to learn more about the tumor and perhaps gain new insights.


s2fieldsofstudy: Biology Psychology

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Microribonucleic acid dysregulations in children and adolescents with obsessive–compulsive disorder

Aim Obsessive–compulsive disorder (OCD) is a disorder characterized by the presence of obsessions and/or compulsions. Although disorder etiology and pathogenesis remains unknown, several theories about OCD development have been proposed, and many researchers believe that it is caused by both genetic and environmental factors. In the current study, our aim was to investigate miRNA levels in OCD. Methods In the current study, we evaluated miR18a-5p, miR22-3p, miR24-3p, miR106b-5p, miR107, miR125b-5p, and miR155a-5p levels in child and adolescent OCD patients. The research sample consisted of a group of 23 OCD patients and 40 healthy volunteer controls. Results There was no significant difference in age and sex between the two groups (P>0.05). The levels of miR22-3p, miR24-3p, miR106b-5p, miR125b-5p, and miR155a-5p were significantly increased in the OCD subjects (P≤0.05). There were no statistically significant differences in miR18a-5p or miR107 levels between groups (P≥0.05). Conclusion There could be a close relationship between levels of circulating miRNAs and OCD. If we could understand how the signaling pathways arranged by miRNAs impact on central nervous system development, function, and pathology, this understanding could improve our knowledge about OCD etiology and treatment.

Introduction
Obsessive-compulsive disorder (OCD) is a disorder characterized by the presence of obsessions and/or compulsions. Obsessions are recurrent intrusive thoughts, urges, or images that cause anxiety or distress, while compulsions are repetitive behaviors that are aimed at preventing or reducing anxiety or distress. 1 OCD may be chronic or have a periodic course, and significantly affects social and daily functions of patients. Although it was thought to be mostly evident in adulthood, studies in children and adolescents have shown a prevalence of OCD of 1%-4%. 2,3 Publications reporting onset of OCD before the age of 6 years and treatment notifications are available. 4,5 The coexistence of OCD and other psychiatric disorders is high. OCD is mostly associated with depression, tic disorder, and other anxiety disorders. More than 80% of patients diagnosed with OCD report that related symptoms began before the age of 18 years. 6 In terms of symptoms, OCD is heterogenous in its clinical appearance. 7 Phenotypic differences suggest that genetic, environmental, psychological, social, and biochemical factors are involved in the etiology. 8,9 Genetic factors have been thought of as important in the development of OCD. 10 It is estimated that OCD symptom heritability is between 45% and 65% in children, and between 27% and 47% in adults. 11 Molecular genetic studies of OCD have usually focused on the monoamine pathway genes. 12 Kandemir et al These studies have implicated genes such as the catechol O-methyltransferase (COMT ) gene, the serotonin 2β receptor (5HT2B) gene, 13 the serotonin transporter gene, 14 the serotonin 2α receptor (5HT2A) gene, 15 and the seven repeat of the dopamine D4 receptor (DRD4) gene. 16 Several molecular genetic studies have aimed to determine the contributions of candidate genes to the pathophysiology of OCD. A study indicated that there was association of OCD hoarding type and a common single nucleotide polymorphism in the neurotrophin-3 receptor gene (NTRK3), which is regulated by micro-ribonucleic acids (miRNAs). 17 miRNAs are evolutionally conserved small noncoding RNAs that regulate approximately 30% of human protein coding gene expression at the post-transcriptional level, and they play important roles in a wide variety of biological functions. 18 miRNAs control gene expression by inhibiting translation or facilitating degradation of their target messenger RNAs (mRNAs). Computational predictions indicate that thousands of genes could be targeted by miRNAs in mammals. 19 miRNAs are important for maintaining homeostasis at the neuromuscular junction. They also play an important role in synaptic plasticity in the central nervous system (CNS), and are involved in memory and mental retardation. 20 The ability of miRNAs to affect the activity of all biological pathways may underlie some of the difficulties associated with linking psychiatric disorders to specific causative genes. 21 The involvement of multiple signaling pathways in psychiatric disease complicates both the investigation of the underlying biological causes and efforts to identify effective therapies. Focusing on the roles of miRNAs in psychiatric diseases may lead not only to an explanation of the dysregulation of multiple pathways but also to novel therapies that can target entire gene networks. Perkins et al examined the expression of 16 miRNAs in the prefrontal cortex in subjects with schizophrenia or schizoaffective disorder and found decreased expression of 15 miRNAs in patients with schizophrenia. 22 However, although miRNAs have been shown to be particularly abundant in the brain, their role in the development and activity of the nervous system remains largely unknown. In the present study, we evaluated miR18a-5p, miR22-3p, miR24-3p, miR106b-5p, miR107, miR125b-5p, and miR155a-5p levels in children and adolescents with OCD. In selecting miRNAs for the current study, previous literature pointing out the potential underlying neurobiology (enzyme, carrier molecule, receptor, etc) of the disease was reviewed, and potential most disease related miRNAs from the miRNA database (http:// mirbase.org/) were chosen. Table 1 shows the features of the studied miRNAs.

Methods
The research sample consisted a group of 23 patients from Harran University Faculty of Medicine Research Hospital, Child and Adolescent Psychiatry Clinic, who were referred to the clinic for the first time and were diagnosed with OCD, and 40 healthy volunteer controls. All patients were diagnosed with OCD by a child psychiatrist according to the Diagnostic and Statistical Manual of Mental Disorders, 4th Edition, Text Revision (DSM-IV-TR) 23 diagnostic criteria, and they were treatment-naive. Patients with a history of cardiovascular disorders, epilepsy, diabetes mellitus, psychotic disorders, pervasive developmental disorders, Believed to be related to minimal brain change in OcD altered in traumatic brain injury and neurodegenerative diseases 28 mir125b-5p Believed to be related to hypoxia in OcD altered in high altitude sickness 29 mir155a-5p Believed to be related to inflammation which is a potential neurobiological mechanism in OcD

Altered in inflammation 30
Abbreviations: BDNF, brain-derived neurotrophic factor; OCD, obsessive-compulsive disorder. or severe head injury were excluded. The healthy controls were recruited from a healthy child outpatient unit. After complete description of the study to the subjects, written informed consent was obtained from the parents, and assent was obtained from the children and adolescents. All of the study procedures were performed in accordance with the Declaration of Helsinki. The ethics committee of the Harran University Medical School approved the trial. Also, a semi-structured form was used to detect several sociodemographic and clinical variables of the patients. The final patient and control groups displayed similar distributions in age and sex. The current study was funded by the Harran University Board of Scientific Research Projects (funding number 12010). The current study was a cross-sectional study, and blood samples were taken once from both controls and OCD patients. Total RNA was extracted from peripheral whole blood using Tri Reagent ® (Sigma-Aldrich Co, St Louis, MO, USA). Reverse transcriptase (RT) reactions contained 5 µL of extracted total RNA, 50 nM stem-loop RT primer, 1× RT buffer, 0.25 mM each of deoxynucleotide triphosphates, 50 units of modified moloney murine leukemia virus RT (Thermo Fisher Scientific, Waltham, MA, USA), 25 units of RiboLock™ RNase inhibitor (Thermo Fisher Scientific), and nuclease-free water to a total reaction volume of 15 µL. The reaction was performed on an automated thermal cycler (Veriti, Applied Biosystems, Foster, CA, USA), Real-time polymerase chain reaction (RT-PCR) conditions were sustained for 30 minutes at 16°C, 30 minutes at 42°C, 5 minutes at 85°C, and then samples were held at 4°C. Quantitativecomparative computed tomography (ΔΔCT) RT-PCR was performed in an ABI Prism ® 7500 RT-PCR system (Applied Biosystems), using SDS version 2.0.6 software (Applied Biosystems). The specific primers and fluorogenic ZNA™ probes for the miRNAs were designed using Primer Express 3.0 software (Applied Biosystems), and these primers and probes are listed in Table 2. The stem-loop hsa-miR26b-5p was used as control according to the Applied Biosystems application note cms_044972. 31 The mixed RNAs generated from the control group were used as a single reference sample. Primers and probes were purchased from Metabion International AG (Basel, Switzerland). The 25 µL PCR mixture included 3 µL RT-PCR product, 12.5 µL of 2× Taq-Man ® Universal PCR Master Mix (Applied Biosystems), 900 nmol of each primer (Primer F and Universal Primer R), and 200 nmol TaqMan ® probe. The reactions were incubated in a 96-well plate of pre-incubation conditions at 50°C for 2 minutes and at 95°C for 10 minutes, followed by 40 cycles at 95°C for 15 seconds at 60°C for 90 seconds. All reactions were run in triplicate. Relative expression of miRNA was calculated using the 2-ΔΔCT method. The 2-ΔΔCT method allows the calculation of the relative expression, which is the most common way to express this type of result. Higher ΔΔCT values indicate lower expression rates.
The data were processed and analyzed using the statistical package SPSS 11.5 for Windows. Normality of the 2-ΔΔCT values was checked by using the Shapiro-Wilk test. In the case of non-normal distribution, 2-ΔΔCT values were expressed as medians, first quartiles (25th percentile), and third quartiles (75th percentile), and the comparison between groups was performed using the Mann-Whitney U-test. There are issues with using the Mann-Whitney U-test, especially in the context of gene expression data, as outliers may affect the results, and we are usually quite safe using the Mann-Whitney U-test. This test rank transforms and smoothes the data by reducing the effect of outliers. A box-plot graph was used to represent data distribution of miR18a-5p, miR22-3p, miR24-3p, miR106b-5p, miR107, miR125b-5p, and miR155a-5p variables according to the groups. Significant differences (two-tailed P-values) of 0.05 were regarded as significant.

Results
This study included 23 OCD subjects with a mean age of 10.60±2.06 years (range, 7-16 years) and 40 control subjects with a mean age of 11.25±2.93 years (range, 7-17 years). No statistically significant differences were detected in age and sex between the two groups (P0.05) (see Table 3).
Zip nucleic acid (ZNA) probes provide broad flexibility in assay design and represent an effective alternative to minor groove binder (MGB)-and locked nucleic acid (LNA)-containing oligonucleotides. 34 Recent studies also reported an increase in the hypothalamicpituitary-adrenal axis activity in OCD patients. 35,36 Muiños-Gimeno et al reported that miR22 was associated with panic disorder and that it regulated several candidate genes and related pathways involved in anxiety. 25 In the Muiños-Gimeno et al study, miR22 was the miRNA with the highest number of functional targets, including the neurotrophic factor brain-derived neurotrophic factor and monoamine oxidase A genes, which are also potential targets in the neurobiology of OCD. 37,38 A study performed in Turkey indicated that of OCD patients, 9.5% have co-morbid panic disorder. 39 Moreau et al reported dysregulation of miR22 in schizophrenia and bipolar disorders. 40 In our previous study, we found significantly decreased miR18a-5p, miR22-3p, miR24-3p, miR106b-5p, and miR107 levels and increased miRNA155a-5p levels in attention deficit hyperactivity disorder (ADHD) patients. 41 In the current study, we found increased miR155a-5p levels in patients with OCD compared with controls. A previous study reported miR155 dysregulation in patients with depression. 42 Additionally, increased miR155 levels were reported in patients treated with lithium 43 and increased miR155a-5p levels in ADHD patients. 41 Rodriguez suggested that miR155 plays a key role in the homeostasis and function of the immune system. 30 It was demonstrated that a wide spectrum of miR155-regulated genes, including cytokines, chemokines, and transcription factors. 30 miR155 was indicated to be induced in lipopolysaccharide-stimulated human monocytes. 44 There has been mounting evidence that immune mechanisms may be directly implicated in the pathophysiology of some subtypes of OCD, such as pediatric autoimmune neuropsychiatric disorders associated with streptococcal infections. Clinical observation of the elevated frequency of obsessivecompulsive symptoms in patients with rheumatic fever, a post-streptococcal autoimmune disease, provoked the study of immune parameters in OCD. 45 Konuk et al found that proinflammatory cytokine levels increased in OCD. 46 miR155 might therefore play role in inflammation in OCD.
Dysregulation of miR106b and miR24 levels has been reported in some psychiatric disorders including schizophrenia, bipolar disorder, and autism. 22,40,[47][48][49] Researchers found that miR24 levels were increased in acute pancreatitis. 50 It may be that miR24 levels are increased secondary to inflammation. Sarachana et al found that miR106b and miR107 levels were associated with autism. 51 In the present study, we observed increased miR125b levels in OCD subjects. Eipper-Mains et al reported an association of miR125 levels and addiction disorders in mice. 52 Additionally, miR125-5p levels have been studied in autism, Huntington's disease, and Alzheimer's disease. 51,53-55 miR125-5p levels were also increased in hypoxia, 29 which might be released during cellular damage. Understanding the signaling pathways affected by miRNAs in CNS development, function, and pathology could lead to improved therapies for treating heterogenic diseases.
Target genes for each miRNA and their potential neurobiological mechanisms are listed in Tables 1 and 2. We think that DNA damage, oxidative stress, hypoxia, and inflammationrelated molecular mechanisms may be dysregulated in OCD. A study has revealed that aberrant expression of circulating miRNAs suggests potential diagnostic biomarkers of disease. 56 There are 17 studies of neuropsychiatric disorders such as schizophrenia, bipolar disorder, and Alzheimer's  dementia showing peripheral miRNA changes, reflecting the CNS pathology in those diseases. 57 On the other hand, since psychiatric disorders including OCD are syndromes that not only affect the brain but also most of the body, peripheral changes are expected as well. For example, in OCD peripheral oxidative stress, biomarkers are shown to be altered, as in most of the other neuropsychiatric disorders; 58 however, no blood test for OCD has yet been developed.

Conclusion
Our present study had several limitations, including the sample size, cross-sectional design, and the limited number of miRNA types. Moreover, the lack of previous studies in the literature regarding OCD and miRNAs prevents the comparison of our findings with previous studies. Despite these limitations, we believe our findings provide important groundwork for future studies.
In conclusion, understanding the dysregulation of circulating miRNA levels may improve our knowledge about OCD etiology and treatment. Our results should be regarded as preliminary until they are replicated.


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Controlling the Regional Identity of hPSC-Derived Neurons to Uncover Neuronal Subtype Specificity of Neurological Disease Phenotypes

Summary The CNS contains many diverse neuronal subtypes, and most neurological diseases target specific subtypes. However, the mechanism of neuronal subtype specificity of disease phenotypes remains elusive. Although in vitro disease models employing human pluripotent stem cells (PSCs) have great potential to clarify the association of neuronal subtypes with disease, it is currently difficult to compare various PSC-derived subtypes. This is due to the limited number of subtypes whose induction is established, and different cultivation protocols for each subtype. Here, we report a culture system to control the regional identity of PSC-derived neurons along the anteroposterior (A-P) and dorsoventral (D-V) axes. This system was successfully used to obtain various neuronal subtypes based on the same protocol. Furthermore, we reproduced subtype-specific phenotypes of amyotrophic lateral sclerosis (ALS) and Alzheimer’s disease (AD) by comparing the obtained subtypes. Therefore, our culture system provides new opportunities for modeling neurological diseases with PSCs.

INTRODUCTION
Modeling human diseases with pluripotent stem cells (PSCs), including embryonic stem cells (ESCs) and induced PSCs (iPSCs), has remarkable potential to generate new insights into understanding disease pathogenesis and to open up new avenues for effective therapies. In particular, modeling neurological diseases is of great interest given that it is difficult to obtain patient-derived neural cells or tissues because of the limited accessibility to the brain. Indeed, ESCs and iPSCs derived from patients have been used to study several neurological diseases, including amyotrophic lateral sclerosis (ALS; Dimos et al., 2008;Egawa et al., 2012), Alzheimer's disease (AD; Israel et al., 2012;Kondo et al., 2013;Yagi et al., 2011), Parkinson's disease (Devine et al., 2011;Imaizumi et al., 2012;Nguyen et al., 2011), schizophrenia (Brennand et al., 2011;Bundo et al., 2014;Hook et al., 2014), epilepsy (Higurashi et al., 2013;Jiao et al., 2013;Liu et al., 2013), and Rett syndrome (Andoh-Noda et al., 2015;Marchetto et al., 2010). Because most neurological diseases affect one or more specific lesion area(s), PSCs were differentiated into corresponding neuronal subtypes in such studies (Imaizumi and Okano, 2014;Marchetto and Gage, 2012;Mattis and Svendsen, 2011;Okano and Yamanaka, 2014). However, these approaches cannot procure the mechanism of subtype specificity of disease phenotypes; that is, why some neuronal subtypes are selectively damaged whereas others evade pathogenesis.
To clarify the mechanism of subtype specificity, it is necessary to compare phenotypes between disease-suscep-tible and -insusceptible neuronal subtypes. Nonetheless, this approach has not been developed to date for two reasons. First, only a few neuronal subtypes can be successfully induced from PSCs. Second, each previously reported protocol to induce specific subtypes varies in its cultivation procedures, giving rise to variability between protocols in efficiency, maturity, and culture-afflicted stress and preventing a direct comparison across different neuronal subtypes.
Here we addressed these problems by focusing on control of the regional identity of PSC-derived neural progenitors and neurons. The developing neural tube is subdivided into distinct regions along the anteroposterior (A-P) and dorsoventral (D-V) axes, and each region produces a specific subtype of neurons (Kiecker and Lumsden, 2012). Namely, neuronal subtype specification in the neural tube is determined in a region-specific manner. If the regional identity of PSC-derived neurons could be regulated at will based on the same protocol, then any desired subtypes could be induced with the same efficiency and under the same culture conditions. A protocol permitting such regulation would enable the reproduction of disease phenotypes in any given brain region and also the comparison of phenotypes between different neuronal subtypes.
In this study, we report a robust method to control the regional identity of PSC-derived neural progenitors and neurons based on a single protocol. To do so, Wnt, retinoic acid (RA), and sonic hedgehog (Shh) signaling were modulated to manipulate the A-P and D-V identities of neural progenitors. Regional identity was maintained throughout neural differentiation, generating a variety of neuronal subtypes, including cortical projection neurons, cortical interneurons, midbrain dopaminergic neurons, hindbrain serotonergic neurons, spinal cord sensory interneurons, and spinal cord motor neurons. Finally, we compared these neuronal subtypes and detected ventral spinal cord-specific neurite swellings in ALS iPSCs and forebrain-specific accumulation of phosphorylated tau (p-tau) in AD iPSCs. Therefore, this culture system could be a useful tool to approach the mechanism of subtype specificity of neurological disease phenotypes.

RESULTS
Strategy for Defining the Regional Identity of PSC-Derived Neural Progenitors Because Wnt, RA, and Shh signaling are involved in the formation of the A-P and D-V axes during neural development (Briscoe and Ericson, 2001;Marshall et al., 1992;Nordström et al., 2002), we explored the possibility that these patterning signaling molecules might have analogous effects in neural cells differentiated from PSCs. We hypothesized that the A-P identity (ranging from the telencephalon to the spinal cord) can be controlled by regulating Wnt and RA signaling and that the D-V identity can be controlled similarly by regulating Shh signaling ( Figure 1A). To evaluate this hypothesis, we induced neural progenitors derived from ESCs (KhES-1; Suemori et al., 2006) by using the neurosphere culture system (modified from Chaddah et al., 2012) and treated the cells with modulators of these patterning signaling pathways during neurosphere formation ( Figure 1B). The modulators included the porcupine inhibitor IWP-2 (a Wnt antagonist; Chen et al., 2009), the glycogen synthase kinase (GSK) 3b inhibitor CHIR99021 (a Wnt agonist, referred to hereafter as CHIR; Ring et al., 2003), RA, Shh protein, and the Smo receptor agonist purmorphamine (a Shh agonist; Sinha and Chen, 2006). Although the diameter of the neurospheres was increased by treatment with CHIR and RA, each of the experimental conditions was able to generate neuro-spheres (Figures S1A and S1B). Moreover, the patterning factors did not influence the number of neurospheres generated ( Figure S1C), and the neural progenitor marker NESTIN was expressed similarly under all conditions (Figure S1D). These results indicate that neural induction is unaffected by Wnt, RA, or Shh signaling.

Modulation of the A-P Identity of PSC-Derived Neural Progenitors by Wnt and RA Signaling
We next examined the expression of A-P markers (Figure 2A) in ESC-derived neurospheres by qRT-PCR (Figure 2B). IWP-2-treated neurospheres expressed high levels of the forebrain markers FOXG1 and SIX3 (Oliver et al., 1995;Xuan et al., 1995). The expression levels of the forebrain/midbrain markers OTX1 and OTX2 (Simeone et al., 1992) were highest in untreated neurospheres. The low expression level of OTX1 in IWP-2treated neurospheres agrees with the observation that this gene is not expressed in the anterior-most region of the forebrain (Shimamura et al., 1997). EN1, which is expressed in the midbrain and the anterior hindbrain (Hanks et al., 1995), was highly expressed in neurospheres treated with CHIR at concentrations of 0.5 and 1 mM. HOXB4 and HOXC4, markers of the posterior hindbrain and the spinal cord (Hunt et al., 1991), were expressed primarily in cultures exposed to 3 mM CHIR, and their expression levels were enhanced further by the addition of RA. These results indicate that the posteriorization of neural progenitors is driven by activation of the Wnt and RA signaling pathways.
The regulation of marker expression correlating with the A-P axis was also confirmed by immunocytochemical analysis for the FOXG1, OTX1, and HOXB4 proteins (Figure 2C). Furthermore, we confirmed the robustness of our protocol by using the 201B7 and 253G1 iPSC lines (Nakagawa et al., 2008;Takahashi et al., 2007). In both of these iPSC lines, the expression levels of the A-P markers in the neurospheres were similar to those in ESC-derived neurospheres ( Figure S2). These results suggest that the Figure 1. Strategy for Defining the Regional Identity of PSC-Derived Neural Progenitors (A) Schematic of the strategy employed for the manipulation of regional identity. Wnt signaling was modulated by using the porcupine inhibitor IWP-2 and the GSK3b inhibitor CHIR. Tel, telencephalon; Di, diencephalon; Mid, midbrain; Hind, hindbrain; S.C., spinal cord. (B) Overview of the culture protocol. Patterning factors (IWP-2, CHIR, RA, Shh, and purmorphamine) were added during neurosphere formation.
A-P identity of PSC-derived neural progenitors can be controlled by IWP-2, CHIR, and RA during neurosphere formation.

Control of the D-V Identity of PSC-Derived Neural Progenitors by Shh Signaling
In addition to the A-P identity, we next attempted to control the D-V identity by modulating Shh signaling. We hypothesized that neural progenitors derived from PSCs are dorsalized in the absence of Shh and that the activation of Shh signaling ventralizes neural progenitors with no influence on the A-P identity. To address this hypothesis, we assessed the expression levels of D-V makers in neurospheres by qRT-PCR analysis with the addition of Shh protein and purmorphamine (termed the ''+Shh'' group) (Figures 3A and 3B). We used these two Shh activators simultaneously because a previous report has demonstrated that co-treatment with Shh protein and purmorphamine was greatly superior to high concentrations of either of the two alone (Maroof et al., 2013). Other neurospheres received no exogenous ventralizing factors (termed the ''ÀShh'' group).
The D-V markers PAX6, PAX7, NKX2.1, and NKX2.2 are differentially expressed in vivo according to A-P and D-V positions ( Figure 3A). PAX6 is expressed dorsally in the forebrain, and the expression pattern also covers both the dorsal and the ventral portions of the posterior hindbrain and the spinal cord (Ericson et al., 1997;Osumi et al., 1997;Takahashi and Osumi, 2002;Walther and Gruss, 1991). In contrast, the midbrain and the anterior hindbrain lack PAX6 expression (with the exception of the rhombic lip) (Engelkamp et al., 1999;Schwarz et al., 1999;Swanson et al., 2005;Yamasaki et al., 2001). PAX7 is expressed throughout the dorsal part of the neural tube except for the anterior forebrain, where this gene is expressed only in the small dorsal-most region (Matsunaga et al., 2001). NKX2.1 marks the ventral forebrain (Qiu et al., 1998), whereas NKX2.2 is expressed ventrally throughout the A-P axis . Consistent with the in vivo expression patterns of these genes, PAX6 was highly (C) Immunocytochemical analysis of neurospheres for A-P markers. The frequency of neurospheres containing immunopositive cells is shown as the percentage of total neurospheres (n = 3 independent experiments, mean ± SEM). A-P patterning was also confirmed at the protein level. Scale bar, 50 mm). See also Table S1. expressed in neurospheres with forebrain characteristics (i.e., IWP-2-treated and untreated neurospheres) in the ÀShh group as well as those with the characteristics of the posterior hindbrain and the spinal cord (i.e., CHIR 3 +RA-treated neurospheres). Only the latter maintained PAX6 expression after Shh activation. Meanwhile, PAX7 was upregulated in the ÀShh group, except under the IWP-2-treated condition, whereas PAX7 expression was low under all conditions in the +Shh group. NKX2.1 was expressed primarily in neurospheres with forebrain characteristics (IWP-2-treated and untreated) in the +Shh group. The expression of NKX2.2 was generally high in the +Shh group. The relatively low expression level of NKX2.2 in neurospheres with posterior characteristics in the +Shh group agrees with the fact that NKX2.2 expression is initially only seen in the anterior part of the neural tube and then gradually extends to the spinal cord .
The control of the D-V axis shown by qRT-PCR analysis was also confirmed by immunocytochemical analysis for the PAX6, NKX2.1, and NKX2.2 proteins ( Figures  3C and 3D). Moreover, D-V regulation was reproduced readily in the iPSC lines ( Figure S2). Our results indicate that the D-V identity of PSC-derived neural progenitors can be controlled without perturbing the A-P identity.

Recapitulation of Telencephalic D-V Patterning
To further confirm the D-V control, we examined the expression of telencephalon-specific D-V markers (Figure 4A). EMX1/2 and DLX2 are confined to the dorsal and the ventral telencephalon, respectively (with the exception of small diencephalic regions) (Porteus et al., 1991;Simeone et al., 1992). Although the proneural genes NGN2 and ASCL1 are expressed in various regions of the neural tube, a D-V bias in their expression is clearly observed in the telencephalon (Fode et al., 2000). EMX1/2 and NGN2 were significantly upregulated in IWP-2-treated neurospheres without Shh activation. In contrast, the expression of DLX2 and ASCL1 was increased by Shh activation. These data provide additional evidence for the D-V control of PSC-derived neural progenitors.

Differentiation of Neural Progenitors into Specific
Neuronal Subtypes in Accordance with Regional Identity To investigate whether the regional identity of neural progenitors was retained upon differentiation into neurons, neurospheres were subjected to a neural differentiation protocol ( Figure 1B). Our protocol predominantly produced neurons rather than astrocytes or oligodendrocytes ( Figure S3A). We examined the expression of synapse markers and the intracellular calcium level during electrical (B) qRT-PCR analysis of neurospheres for D-V marker expression (n = 3 independent experiments, mean ± SEM). Neurospheres not treated with Shh showed a dorsal identity, whereas Shh treatment ventralized the neurospheres. (C and D) Immunocytochemical analysis of neurospheres for PAX6/NKX2.1 (C) and PAX6/NKX2.2 (D). The frequency of neurospheres containing immunopositive cells is shown as the percentage of total neurospheres (n = 3 independent experiments, mean ± SEM). D-V patterning was also confirmed at the protein level. Scale bars, 50 mm. See also Table S1.
field stimulation ( Figures S3B-S3D). These results indicate that neurons derived by our protocol were functional regardless of the experimental conditions.
We determined the A-P identity of neurosphere-differentiated neurons ( Figure 5A). Neurons generated from IWP-2treated neurospheres expressed high levels of the forebrain cortical neuronal marker TBR1 (Bulfone et al., 1995). GATA3, expressed from the diencephalon to the hindbrain (George et al., 1994), was upregulated at low CHIR concentrations (0À1 mM). LBX1, which marks dorsal neurons in the hindbrain and the spinal cord (Jagla et al., 1995), was highly expressed in neurons differentiated from CHIR 3 +RA-treated neurospheres. These results imply that neural progenitors retain their A-P identity in differentiated neuronal cultures.
We next examined the D-V identity of differentiated neurons ( Figures 5B-5F). In the telencephalon, TBR1+ cortical neurons are born dorsally, whereas the ventral area produces cortical interneurons, which is marked by LHX6 and LHX8 expression (Marín and Rubenstein, 2001). As expected, the expression level of TBR1 was elevated in neurons generated from IWP-2-treated neurospheres and decreased by activation of Shh signaling (Figures 5B and  5E). In contrast, LHX6 and LHX8 were significantly upregulated by Shh activation ( Figure 5B). Dopaminergic and serotonergic neurons are selectively derived from the ventral portion of the midbrain and the hindbrain, respectively (Goridis and Rohrer, 2002). Although the gene expression of the dopamine-synthesizing enzyme TH did not change significantly, that of LMX1A and FOXA2, master transcriptional regulators of dopaminergic neuronal differentiation (Andersson et al., 2006;Ferri et al., 2007), was upregulated markedly by Shh activation under the 0.5 mM CHIR-treated condition ( Figure 5C; Figure S4A). We also detected high expression of the serotonergic neuronal marker PET1 (Hendricks et al., 1999) concomitantly with a small population of cells that were immunopositive for serotonin ( Figure 5C; Figure S4C). Furthermore, VMAT2 and AADC, markers for both dopaminergic and serotonergic neurons (Flames and Hobert, 2011), were upregulated ( Figure S4A). LHX9, which marks d1 neurons in the dorsal spinal cord (Liem et al., 1997), was highly expressed in neurons generated from CHIR 3 +RA-treated neurospheres, whereas the expression of HB9, which marks motor neurons in the ventral spinal cord (Arber et al., 1999), was elevated by ventralization via Shh activation ( Figures 5D and 5F). CHT, an additional spinal cord motor neuron marker that is essential for the uptake of choline into motor neurons (Okuda et al., 2000), was also upregulated ( Figure S4B).
Finally, we tested whether neural crest-derived peripheral neurons were induced. The peripheral neuron marker Peripherin (Escurat et al., 1990) was not detected under almost all conditions. However, approximately 20% of neurons were immunopositive for this marker under the CHIR 3 +RAtreated and CHIR 3 +RA+Shh-treated conditions. Under the CHIR 3 +RA-treated condition, BRN3A, an additional peripheral neuron marker (Fedtsova and Turner, 1995), was also expressed in a portion of Peripherin+ neurons ( Figure S4D). Given that neural crest cells originate from the dorsal-most region of the neural tube (Knecht and Bronner-Fraser, 2002), these results are consistent with their dorsal identity. Under the CHIR 3 +RA+Shh-treated condition, however, Peripherin+ neurons expressed the motor neuron marker SMI-32 (Carriedo et al., 1996). This result is consistent with the fact that Peripherin is expressed not only in peripheral neurons but also in spinal cord motor neurons (Escurat et al., 1990). In summary, both the A-P and D-V identities acquired in the neural progenitor stage were maintained throughout neuronal differentiation in culture, and neural progenitors were able to differentiate into specific neuronal subtypes in accordance with their regional identities.

Reproduction of Subtype-Specific Disease Phenotypes In Vitro
In most neurological diseases, specific neuronal subtypes are selectively impaired. For example, spinal cord motor neurons are selectively damaged in ALS (Bruijn et al., 2004), and forebrain cortical neurons are preferentially affected in AD (Braak and Braak, 1991;Morrison and Hof, 1997;Thal et al., 2002). Although this subtype spec-ificity probably plays an important role in pathology, its mechanism remains to be elucidated. Particularly in PSC-based disease models, most previous reports have focused only on the disease-susceptible neuronal subtype, Figure 5. Generation of Region-Specific Neuronal Subtypes (A) qRT-PCR analysis of neurospherederived neurons for TBR1, GATA3, and LBX1 expression (n = 4À5 independent experiments, mean ± SEM). A-P patterning was maintained throughout neuronal differentiation in vitro. (BÀD) qRT-PCR analysis of neurospherederived neurons for the expression of neuronal subtype markers expressed in the forebrain (B), the mid/hindbrain (C), and the spinal cord (D) (n = 5À7 independent experiments; mean ± SEM; *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001; Student's t test). Neurospheres differentiated into specific neuronal subtypes in accordance with their regional identity. (E and F) Immunocytochemical analysis of neurosphere-derived neurons for TBR1 (E) and HB9 (F). The frequency of immunopositive cells is shown as the percentage of MAP2+ neurons (n = 3 independent experiments, mean ± SEM). Scale bars, 50 mm. See also Table S1. and other aspects of subtype specificity have yet to be explored.
Our first aim was to test whether disease subtype specificity can be recapitulated in vitro by using PSCs. We derived neurons from disease-specific iPSC lines via our protocol described above. Here we used ALS and AD iPSC lines carrying TARDBP M337V and PSEN1 A246E /PSEN2 N141I mutations, respectively (Egawa et al., 2012;Yagi et al., 2011), and the control of regional identity was confirmed in neurospheres derived from each iPSC line ( Figure S5).
In the ALS iPSC-derived neurons, neurite swellings were observed preferentially in neurons with the characteristics of the ventral spinal cord generated from CHIR 3 +RA+Shhtreated neurospheres (Figures 6A and 6B). In contrast, neurons with other regional identities rarely exhibited swellings. Because neurite swellings (or ''spheroids'') are a feature of neuronal cell dysfunction in ALS (Delisle and Carpenter, 1984), these observations suggest that ventral spinal cord-specific phenotypes of ALS were recapitulated successfully in our system. Next, we tried to demonstrate motor neuron specificity among the ventral spinal cord subtypes by labeling motor neurons with the fluorescence protein (Venus) reporter under control of the HB9 promoter. However, the cells showed fluorescence labeling of only the soma and proximal neurites, probably because of weak intracellular diffusion of the Venus protein. Consequently, we failed to identify neurite swellings distal to the soma in HB9-Venus-positive neurons (data not shown). Therefore, currently, we cannot eliminate the possibility that subtypes other than motor neurons in the ventral spinal cord were also affected by neurite swellings. Indeed, several recent reports indicate that spinal cord interneurons are implicated in ALS pathology (reviewed in Turner and Kiernan, 2012).
In AD iPSC-derived neurons, we found spot-like accumulations of p-tau ( Figures 6C and 6D). The number of p-tau+ spots increased in neurons with forebrain characteristics generated from IWP-2-treated neurospheres compared (E) Quantification of p-tau+ spots (n = 3 independent experiments, mean ± SEM, *p < 0.05, ANOVA with Tukey's test). Accumulation of p-tau was increased in AD iPSC-derived neurons with forebrain characteristics.
with the CHIR 3 +RA condition corresponding to the spinal cord ( Figure 6E; Figure S6A). To eliminate the possibility that the addition of CHIR during the neurosphere stage led to inhibition of GSK3b-mediated tau phosphorylation in the differentiation stage, we next measured GSK3b activity in differentiated neurons by quantifying nuclear b-catenin intensity. We confirmed that b-catenin intensity remained relatively constant under all conditions and that residual CHIR had no effect on tau phosphorylation in the differentiation stage ( Figure S6B). These results indicate that a characteristic feature of AD pathology, i.e., p-tau accumulation, was reproduced in a region-specific manner. Taken together, our findings show that the subtype specificity of neurological disease phenotypes can be modeled in vitro by defining the regional identity of PSC-derived neural progenitors and neurons.

DISCUSSION
In this study, we established a culture system for controlling the regional identity of PSC-derived neural progenitors and neurons based on an identical protocol. Simple treatment with three factors, IWP-2, CHIR, and RA, effectively patterned the A-P identity, ranging from the telencephalon to the spinal cord. In addition, Shh activation converted the D-V identity from dorsal into ventral without perturbing the A-P identity. Furthermore, subtype-specific ALS and AD phenotypes were reproduced in vitro by using the described protocol.
Our findings are consistent with previous reports demonstrating that the graded signaling activities of Wnt/RA and Shh establish the A-P and D-V axes in vivo. Also noteworthy is that our study shows that this model for early neural patterning, which is proposed mostly from work on Xenopus, chicks, and mice, can be applied to human development. Because in vivo experiments of the human embryo are ethically challenging and technically difficult, our culture system now provides novel opportunities to uncover the mechanism of neural patterning in humans.
A remaining issue regarding our present culture system is the ability to recapitulate the environment after regional specification. In some neuronal subtypes, additional signals are needed for further specification, maturation, and maintenance. When cells migrate from their birthplace or if they project to distant targets, they often receive such signals from cells with different regional identity. Such additional signals cannot be recapitulated in our present culture system. In the case of midbrain dopaminergic neurons, for example, transforming growth factor b (TGF-b) expressed in their projection targets is necessary for their maintenance (Poulsen et al., 1994). Dopami-nergic neurons in our culture did not express late-stage markers such as PITX3 and DAT, probably because of the lack of such additional signals (data not shown). In addition to treatment with signaling modulators, co-culturing with cells of the destination of migration or cells of the projection targets would be a promising approach to overcome this issue. Specific brain regions are preferentially damaged in most neurological diseases, whereas regions other than the lesioned area remain relatively unaffected. For this reason, the differentiation of patient-derived iPSCs into specific neuronal subtypes representing the lesion site is a valuable approach for modeling human neurological diseases (Imaizumi and Okano, 2014;Okano and Yamanaka, 2014). The current investigation reproduced p-tau accumulation as an AD phenotype by endowing neurons with forebrain characteristics. On the other hand, a previous report using the same iPSC clones did not detect such a phenotype, probably because of the induction of inappropriate subtypes (Yagi et al., 2011). These results highlight the importance of correct neuronal subtype specification in reproducing neurological disease phenotypes with PSCs.
Some investigators have reported the directed differentiation of PSCs into a limited number of neuronal subtypes, including midbrain dopaminergic neurons and spinal cord motor neurons (Kriks et al., 2011;Li et al., 2005). Given that each differentiation protocol is overly optimized for a particular subtype, the protocol in question cannot be readily applied to additional neuronal subtypes. Nevertheless, our culture system theoretically enables the differentiation of PSCs into all neuronal subtypes, making it possible to efficiently generate subtypes whose induction from PSCs has been difficult.
Also, as emphasized above, our culture system is based on the same protocol to obtain neuronal subtypes with equivalent efficiency and under the same general culture conditions (with the exception of the modulation of different signaling cascades). Therefore, our method allows comparative analysis of a variety of neuronal subtypes, whereas earlier investigations on disease modeling have focused only on selectively affected subtypes. Moreover, other investigators have reported that the modulation of Wnt signaling can direct the regional identity of PSC-derived neurons in a manner similar to that observed in our culture system. Nevertheless, a parallel analysis of disease phenotypes between neurons with different regional identities was not performed in these studies (Kirkeby et al., 2012;Moya et al., 2014). To fully understand the mechanism of disease pathology and progression, one must investigate not only the manner in which specific neuronal subtypes are affected but also how other subtypes remain unaffected. In the case of genetic diseases, neural subtype specificity of disease phenotypes is not necessarily accounted for by the expression pattern of the responsible genes. For example, some mutations in TARDBP, which is globally expressed across the entire brain, cause motor neuron-selective degeneration (Kabashi et al., 2008(Kabashi et al., , 2010Sephton et al., 2010). Similarly, although PSEN1 and PSEN2 show widespread expression in the brain, their mutations predominantly affect forebrain neurons Sherrington et al., 1995). However, the mechanism of neuronal subtype specificity of these disease phenotypes is unclear. Our culture system may permit investigations toward filling in the gaps in this knowledge base.
We do not yet know the mechanism of neuronal subtype specificity of various neurological disease phenotypes. Identification of factors enforcing pathogenesis in disease-susceptible subtypes or evading pathogenesis in insusceptible subtypes will help to clarify this. Regardless, our culture system will undoubtedly allow new insights into modeling neurological diseases with PSCs.
hESCs were used in accordance with the guidelines regarding the utilization of hESCs with approval from the Ministry of Education, Culture, Sports, Science, and Technology (MEXT) of Japan and the Keio University School of Medicine Ethics Committee. All experimental procedures for iPSCs derived from patients were approved by the Keio University School of Medicine Ethics Committee (approval no. 20080016).
Defining the day on which neurosphere culture was started as day 0, the following additives were included in the neurosphere culture: 2 mM IWP-2 (Sigma), 0.5-3 mM CHIR99021, and 1 mM RA (Sigma) from days 0-12 and 100 ng/ml Shh-C24II (R&D Systems) and 1 mM purmorphamine (Calbiochem) from days 2-12. On day 12, neurospheres were replated en bloc on dishes coated with poly-ornithine and laminin and cultured under conditions of 5% CO 2 . The medium was changed to MHM supplemented with 13 B27 and 1 mM DAPT (Sigma). DAPT was excluded in the AD phenotype experiments.

qRT-PCR
Total RNA was isolated with the RNeasy mini kit (QIAGEN) with DNase I treatment, and cDNA was prepared by using a ReverTraAce qPCR RT kit (Toyobo). The qRT-PCR analysis was performed with SYBR premix Ex TaqII (Takara Bio) on a ViiA 7 real-time PCR system (Applied Biosystems). Values were normalized to ACTB. Reactions were carried out in duplicate, and data were analyzed by using the comparative (DDCt) method. Relative expression levels are presented as geometric means ± geometric SEM. The primer sets used in these experiments are listed in Table S2.

Immunocytochemistry
Cells were fixed with 4% paraformaldehyde for 15 min at room temperature and then washed three times with PBS. After incubating with blocking buffer (PBS containing 5% normal fetal bovine serum and 0.3% Triton X-100) for 1 hr at room temperature, the cells were incubated overnight at 4 C with primary antibodies diluted with blocking buffer. We used blocking buffer without Triton X-100 for O4 staining and buffer with 0.03% Triton X-100 for Synaptotagmin and VAMP2 staining. Details of the primary antibodies and the dilution conditions are listed in Table  S3. The cells were again washed three times with PBS and incubated with secondary antibodies conjugated with Alexa Fluor 488, Alexa Fluor 555, or Alexa Fluor 647 (Life Technologies) and Hoechst33342 (Dojindo Laboratories) for 1 hr at room temperature. After washing three times with PBS and once with distilled water, samples were mounted on slides and examined by using a LSM-710 confocal laser-scanning microscope (Carl Zeiss). For anti-NKX2.2 antibody staining, biotinylated secondary antibodies (Jackson ImmunoResearch Laboratories) were used after exposure to 1% H 2 O 2 for 30 min at room temperature to inactivate endogenous peroxidase. The signals were then enhanced by using the Vectastain ABC kit (Vector Laboratories), followed by the TSA fluorescence system (PerkinElmer).

Supplemental Information includes Supplemental Experimental
Procedures, six figures, and three tables and can be found with this article online at http://dx.doi.org/10.1016/j.stemcr. 2015.10.005.