Acknowledgements
The authors wish to thank the nursing and clinical staff of the Department of Gynecology & Obstetrics of the Manzoni Hospital of Lecco (Prof. Antonio Pellegrino and Dr. Roberta Tironi) and of the Hospital of Desio and Vimercate (branch of Carate Brianza; Prof. Anna Locatelli and Mrs. Rossella Fumagalli). Many thanks to Lara Lanzoni, Giulia Melesi, Chiara Miotti, Giulia Mornati and Vittoria Trezzi, for their help in data collection. Finally, special thanks go to all infants and their parents participating in this study and to two anonymous reviewers for their thoughtful suggestions. The research was supported by Grant RC2013 from the Italian Ministry of Health, by Fondazione della Provincia di Lecco Onlus/Rotary Club (Rotary Club Lecco, Rotary Club Le Grigne, Rotary Club Manzoni) and the l’Oreal-UNESCO Italy for Women in Science Fellowship (CC).
Introduction
Adolescence has been characterized as a period of increased impulsivity and risky behavior that often leads to the initiation of health compromising behaviors such as substance use (Chambers et al., 2003). Indeed, national estimates indi
cate that rates of substance use during adolescence climb steadily from 20 to 70% between 12 and 17 years of age (Miech et al., 2015; Stagman et al., 2011). Of great concern is that substance use such as alcohol, cigarette, marijuana and other illicit drugs often increases the risk of dependency, unprotected intercourse, and interpersonal violence, leading to adverse health outcomes later in life (Verdejo-García et al., 2008). Developmental neuroscience research suggests that the increased risky behaviors including substance use in adolescence are due, in part, to imbalanced developmental trajectories of NU7441 manufacturer circuits between the subcortical limbic and prefrontal networks (Casey et al., 2008; see also Crone et al., 2016), and thus adolescents exhibit heightened emotional impulsivity and a lack of effective cognitive control (Hare et al., 2008), resulting in greater substance use behaviors (Casey and Jones, 2010). Therefore, investigating the neural underpinnings associated with substance use is critical to reduce risk of substance use and increase protective factors for adolescents.
A growing body of literature in developmental neuroscience provides considerable evidence that functional coupling between limbic and prefrontal systems plays an important role in emotion regulation and risk-taking behavior during adolescence, contributing to substance use (Cservenka et al., 2014; Fareri et al., 2015; Gabard-Durnam et al., 2014; Gee et al., 2013a,b, 2014; Hare et al., 2008; Porter et al., 2015; Qu et al., 2015; van Duijvenvoorde et al., 2016; Weissman et al., 2015). For example, functional MRI (fMRI) studies using task-based indexes of functional brain activation have demonstrated a developmental shift in functional connectivity between limbic and prefrontal circuitry, such that children show positive coupling, which switches to negative coupling by adulthood (Gee et al., 2013a,b, 2014; Hare et al., 2008). Thus, negative functional coupling between these circuitries is an index of maturation of neural networks of the brain, representing enhanced inhibitory projections from frontal to limbic regions. Consistently, a recent longitudinal fMRI study highlighted inverse connectivity between limbic and prefrontal systems by demonstrating that adolescents who show longitudinal declines in functional coupling between the ventral striatum (VS), a region in the limbic system, and medial prefrontal cortex (mPFC), a region in the prefrontal, executive system, exhibited greater decreases in risk-taking behavior over time (Qu et al., 2015). Tract-tracing studies in rodents further supports this view by showing that an inverse association between these two systems is derived from greater suppression of limbic activity via descending projections from the prefrontal region, which increasingly emerges over development (Bouwmeester et al., 2002a,b; Cressman et al., 2010; Cunningham et al., 2002). Resting-state fMRI (rs-fMRI) studies in humans have further demonstrated the importance of functional connectivity and links to adolescent behavior (Fareri et al., 2015; Gabard-Durnam et al., 2014; van Duijvenvoorde et al., 2016; Weissman et al., 2015). For example, higher positive functional connectivity between limbic and prefrontal circuits is associated with hyperresponsiveness and overvaluation of rewards (Fareri et al., 2015; van Duijvenvoorde et al., 2016), behaviors which contribute to higher frequency of substance-use behavior in adolescents (Casey and Jones, 2010). Indeed, a recent rs-fMRI study demonstrated that positive resting-state functional connectivity between the nucleus accumbens (NAcc) and prefrontal regions was related to earlier substance use (Weissman et al., 2015). Similarly, adolescents with a family history of alcoholism showed less negative connectivity between NAcc and other executive control regions including the inferior frontal gyrus and ventrolateral PFC (Cservenka et al., 2014). In sum, these findings suggest that inverse or segregated limbic-prefrontal connectivity is related to decreased risky-behaviors during adolescence
br Acknowledgments br Introduction Psoriatic skin lesions are
Acknowledgments
Introduction
Psoriatic skin lesions are characterized by histological evidence of inflammation, abnormal keratinocyte proliferation/terminal differentiation, and dermal angiogenesis. Although the etiology of psoriasis remains unknown, it is clear that an interaction among genetic susceptibility variants, the immune system, and environmental factors contribute to the development of the chronic inflammatory process.
Human β-defensins (hBDs) are a family of small, secreted antimicrobial peptides. In addition to antibacterial and antiviral effects, β-defensins have been shown to be involved in the immunological reactions that protect the host from various pathogens. The expression of hBD-1 is generally constitutive, and the level of hBD-2 is thought to be induced by proinflammatory cytokines and bacteria. Histologically, hBDs are expressed by epithelial aromatase inhibitors of the skin, gut, respiratory tissue, and urogenital tissue. In addition to epithelial cells, the expression of hBD-1 and hBD-2 have also been found in human monocytes, macrophages, and dendritic cells (DCs).
HBDs are encoded by DEFB genes in three main gene clusters: two on chromosome 20 and one on chromosome 8p23.1. Of the eight β-defensin genes at 8p23.1, not including DEFB1 (encoding the protein hBD-1) but including DEFB4 (encoding the protein hBD-2), SPAG11, DEFB103 (encoding the protein hBD-3), DEFB104 (encoding the protein hBD-4), DEFB105, DEFB106, and DEFB107, are on a large repeat unit that varies in copy number. In humans, up to 12 copies of this repeat have been found, and three to five copies per diploid genome are more prevalent. HBD-2, hBD-3, and hBD-4 have been demonstrated to stimulate keratinocytes to release interleukin (IL) IL-8, IL-18, and IL-20, which are all proinflammatory cytokines that have an established role in the pathogenesis of psoriasis. Recently, Hollox et al found an association between higher copy number variations (CNVs) for DEFBs on chromosome 8p23.1 and risk of psoriasis in a Caucasian population. However, the relationship of DEFBs CNVs and psoriasis, until now, remains unclear in the Chinese population. Further, the role of the DEFB1 gene as a potential modifier in psoriasis has not previously been studied. Three singe nucleotide polymorphisms (SNPs) at positions c.-20G>A (rs11362), c.-44C>G (rs1800972), and c.-52G>A (rs1799946) in the 5\’-untranslated region of DEFB1 gene have been described to influence the hBD-1 expression or function. Recent studies in the Mexican, Egyptian, and Korean populations have identified an association between DEFB1 SNPs and the susceptibility of atopic dermatitis, which shares with psoriasis the similar phenotypes of dry, scaly skin and disturbed epidermal differentiation. We, therefore, consider it to be important to investigate the relevance of the SNPs of the DEFB1 gene and the CNVs of the DEB4 genes in patients with psoriasis among the Taiwanese population.
Methods
Results
Table 1 lists the characteristics of the cases and control participants. There was no significant difference between the two groups with regard to gender, age, hypertension, diabetes, smoking, and BMI. The distributions of the DEFB4 genomic copy number in the study population, shown in Figure 1, exhibited a range of 2–11 per genome, with a median number of five copies. The difference in overall DEFB4 genomic copy number distribution between psoriasis patients and control participants was insignificant, as determined with the Kolmogorov–Smirnov test (p = 0.657). The proportions of control individuals who carried a number of copies less than the median (< 5) or greater than median (≥ 5) were 42.9% and 57.1%, respectively. In the patients with psoriasis, the frequency of distribution of the subgroup (38.8% and 61.2%) did not significantly differ from that of the control group (p = 0.411; Figure 2). Individuals with a copy number ≥ 5 had an insignificantly higher risk of psoriasis than did individuals with a copy number < 5 (odds ratio = 1.18, 95% confidence interval = 0.79–1.77). The average number of copies per genome in psoriasis patients was slightly greater than that of the control participants (5.14 ± 2.01 vs. 5.02 ± 1.75), but the difference did not reach statistical significance (p = 0.520). In the logistic regression analysis with the DEFB4 CNVs as an integer variable, they were also not associated with psoriasis after adjusting for age, sex, smoking, diabetes, hypertension, and BMI (odds ratio = 1.03, 95% confidence interval = 0.89–1.19, p = 0.720). Additionally, comparison between the disease subgroup (i.e., early/late onset and severity of psoriasis) among psoriasis patients, revealed no significant difference in the frequencies of the carriers with a copy number ≥ 5 (data not shown).
Introduction High nitrogen austenitic stainless steel is
Introduction
High nitrogen austenitic stainless steel is a nickel free high Cr–Mn–N steel having a wide scope in defence sector for manufacturing battle tanks by replacing the existing armour steel. Austenitic stainless steel (>0.4% N) are becoming an important engineering material with combination of strength, toughness and wear resistance [1]. Nitrogen has the following advantages: it is an effective solid solution strengthener than carbon and also enhances strengthening of grain size [2,3]. Austenitic steels can benefit from high nitrogen on several aspects: Nitrogen in a solid solution is a beneficial alloying element to increase the strength without significant loss of ductility and toughness. Nitrogen is a strong austenite stabilizer, thereby reducing the amount of nickel required for austenite stabilization. Nitrogen remarkably improves resistance to intergranular, pitting, crevice and stress corrosion cracking [4]. As these steels are used for structural purposes, welding is an important consideration to join the structural components. During welding, it is very essential to avoid nitrogen losses, which result in poor mechanical properties and corrosion resistance. In conventional fusion welding process, it leads to several problems like formation of nitrogen pores, solidification cracking in the weld zone, lowering the dissolved nitrogen for solute strengthening and precipitation of Cr-nitrides in the heat affected zone [5]. The nitride precipitation reduces seriously the mechanical and corrosion resistance. To alleviate the above problems, careful control of shielding gas, filler metal composition with low impurity levels (e.g., S, P) in addition to control on segregation of major alloying elements and minimizing the level of intermetallic precipitates in the weld metal [6]. Defects like porosity and solidification cracking may be overcome by using a suitable filler wire, which produces the required amount of delta ferrite in fusion welds. Based on service conditions, delta ferrite requirement in austenitic stainless steel welds is often specified to ensure that weld contains a desired ferrite level [7]. No commercial matching filler wires are available for welding high nitrogen austenitic stainless steel [3]. Electrode with near matching composition similar to guanabenz metal resulted in improving the corrosion resistance but decreases the mechanical properties [8]. Studies on microstructure and mechanical property correlations of nickel free high nitrogen steel welds are really scarce. In view of the above problems, the present work is aimed at studying the microstructural changes in high nitrogen steel welds made using shielded metal arc welding (SMAW), gas tungsten arc welding (GTAW), electron beam welding (EBW) and friction stir welding (FSW) processes, and to correlate microstructure with observed mechanical properties of the welds.
Experimental details
Nickel free high nitrogen austenitic stainless steel (HNS) plates of size (500 mm × 150 mm × 5 mm) in wrought form were used in the present study. Weld joint design for SMAW and GTAW processes is shown in Fig. 1 and the welds made with various processes were shown in Fig. 2. Electrode of near matching composition of Cr–Mn–N type is used for the shielded metal arc welding process. Gas tungsten arc welding was made with standard high strength nickel based (18Ni) MDN 250 filler as no suitable fillers are available. Autogenous welds were made using electron beam welding and friction stir welding was carried out using tungsten–molybdenum (W–Mo) tool. The composition of the base metal and filler wires are given in Table 1. After having several experiments, welding parameters were optimized and 
we have obtained a sound weld free from defects. Optimized welding parameters of all the welding processes are given in Tables 2–5. Microstructural studies were conducted at various zones of the welds using optical microscopy and scanning electron microscopy. Orientation image mapping studies were performed with electron backscatter diffraction (EBSD) method to observe the orientation of the grains and phase analysis maps in various zones of the welds. Tensile testing is carried out using a universal testing machine at room temperature and specimens were prepared as per ASTM-E8 standard. Microhardness values were recorded towards the longitudinal directions of the weld with a load of 0.5 kgf for 20 seconds as per ASTM E384-09 standards using Vickers hardness tester. Face bend testing of the material was conducted to observe the crack development to know the ductile nature of the weld as per ASTM E190-92 standards.
In line with the restoration of
In line with the restoration of this pagoda, the Southeast University organized a workshop that aims to enhance the communication between the Eastern and Western countries focusing on the restoration of bricks and monumental stone architectures. In cooperation with the Sapienza University of Rome and the IUAV University of Venice, the current project proposes a repair plan and gives suggestions and recommendations for the restoration program. We hope that the experience of our Western counterparts in the field of historical architectural restoration brompheniramine maleate as well as their technological expertise can help in the domestic efforts and actual projects on the area of architectural heritage protection.
Working method
The established plan includes documentation, research, and decision-making, which depends 
on the aster integrity and accuracy of the documentation, including basic data collection, on-site geometry mapping, and track investigation recording. The research includes the historical analysis, as well as material and damage analyses and bricklaying (Fig. 1).
Operating instructions of the field sketch
Description of the drawings
The bricklaying and damage analyses were helpful in determining the current situation, and the exact analysis of the brick surface was then concluded (Figs. 5–13).
Restoration strategies and measures
Conclusions
br Conclusions br Introduction Genetically modified organisms GMOs also
Conclusions
Introduction
Genetically modified organisms (GMOs), also known as genetically engineered or transgenic organisms, for use as human foods or animal feeds are commonplace nowadays. On November 19, 2015, the Food and Drug Administration (FDA) of the United States approved the first-ever genetically modified animal for human consumption [1]. This approval not only marked a new milestone for GM foods but also re-evoked the public call for safety assessment and transparency of GM foods.
The advances in recombinant DNA technology in the 1980s have led to the creation of GM foods. FlavrSavr® tomato, the first GM food approved for human consumption in the world, entered the U.S. market in 1994 as fresh tomato and reached the United Kingdom in 1996 in the form of tomato paste [2]. Since then, the market of GM foods has grown rapidly and keeps expanding. Many food products currently on our shelves are derived from GM organisms or contain GM ingredients. Most GM foods are produced from plants, but GM microorganisms or GM animals have also been used to produce food products such as yogurt and cheese [3]. GM crops are now widely cultivated throughout the world. In 2014, approximately 18 million farmers in 28 countries planted GM crops in more than 181 million hectares, corresponding to 13% of the world\’s arable surface [4,5]. Globally, GM soybeans accounted for about 82% of the total soybean area, while GM cotton, maize and oilseed rape occupied 68%, 30% and 25% of the total crops area respectively [4].
Approximately 70%–90% of GM crops are used as feeds for food-producing animals [4]. Foods produced from these GM food-fed animals are not regulated or labeled as GM foods in most if not all countries [6]. Therefore, c-kit inhibitor may unknowingly consume GM ingredients even if they actively avoid GM foods.
As of October 2014, 65 countries have granted regulatory approvals to GM crops for use as foods, feeds or for environmental release, encompassing a total of 27 types of GM crop and 357 GM events [5]. Most countries permit the production and marketing of GM organisms except a few such as Russia, Norway, Netherland and Israel, but require an official approval in advance. Among those permissive countries, China, France, New Zealand and South Korea appear to be more restrictive as these countries so far have issued no or few permits to GM crops for commercial cultivation [6].
While for GM foods, production and marketing are generally permitted; many countries do not mandate a labeling of GM foods [6]. The European Union (EU) imposes stricter approval and labeling requirements for GM foods, demanding a risk assessment for all new GM foods and feeds before marketing and a compulsory labeling of GM foods. Following the EU law, many European countries such as the United Kingdom, Sweden, Norway and Germany now require foods containing or produced from GMOs, or containing ingredients produced from GMOs be clearly labeled. However, foods produced with GM technology such as recombinant enzymes, or from animals fed with GM foods are exempted [6].
Available scientific findings seem to be in favor of the safety of GM organisms. Remarkably, a few U.S. scientific organizations have concluded melanocytes there is no evidence showing GM organisms are risker than, or present unique safety risks than the conventional organisms [6]. However, the public has not built sufficient confidence in GM foods and generally desires that GM foods be adequately labeled. It is postulated that the lack of objective information about the risks and benefits of GM foods may account for the consumers’ skeptical attitude to GM foods [4]. For instance in the U.S., a survey interviewed 2002 adults and 3748 scientists from the American Association for the Advancement of Science (AAAS) revealed that 88% of the scientists think that GM foods are safe for eating compared with only 37% of the general public [7]. The public even thinks that scientists do not have a clear understanding of the health effects of GM foods [4].
CD is also known as choline transporter like
CD92 is also known as choline transporter-like protein 1 (CTL1) and its main function is to transport choline, which is then incorporated into phosphatidylcholine (PC), across the cell membrane (Michel and Bakovic, 2009; Nakamura et al., 2010). In vivo, PC is found in the lipid fraction in the calcification front during both intramembranous and endochondral bone formation and the addition of PC to bone graft material has been shown to increase the osteoinductivity of the material and ALP activity in the surrounding tissue (Han et al., 2003). Albeit speculative, it is therefore possible that the strong upregulation of CD92 in osteogenically differentiated cells, demonstrated in the present study, is related to the increased 
synthesis of PC during osteogenic differentiation. Furthermore, the gene expression of CD92 is upregulated in response to dexamethasone in the context of choline uptake in human lung adenocarcinoma (Nakamura et al., 2010). This is in line with the present results, as dexamethasone was the glucocorticoid steroid used in this study to induce differentiation. The present results extend previous observations, demonstrating the upregulation of CD92 in osteogenically differentiated hBMSCs at protein level. A significant increase in CD92 expression was also seen in adipogenically differentiated hBMSCs compared with undifferentiated hBMSCs, a difference that was most pronounced during the initial stages of differentiation. There appears to be a lack of knowledge in the literature regarding the expression of CD92 in relation to both osteogenically and adipogenically differentiated enolase and further studies of the mechanism via which CD92 affects the differentiation process are needed.
In addition to the identified membrane-bound proteins, the subcellular fractionation further isolated proteins that were not annotated as membrane bound. The intracellular protein, CRYaB, is a small heat shock protein belonging to the alpha family, which is composed of two gene products, alpha-A (acidic) and alpha-B (basic) proteins. Alpha-A is associated with the vertebrate eye lens, while alpha-B is expressed in many tissues and organs. The gene expression of CRYaB has been shown to be significantly regulated in the early stages of the chondrogenic differentiation of the ATDC-5 chondroprogenitor cell 
line (Chen et al., 2005). Further, Lambrecht et al. (2009) have reported a reduction in CRYaB expression in dedifferentiating chondrocytes, thus indicating that CRYaB perhaps plays a role in the chondrogenic differentiation process. However, the present results indicate an increase in the protein expression of CRYaB during the osteogenic differentiation of hBMSCs. The data indicate an osteogenic lineage specificity of this marker, since the present results did not reveal any upregulation of CRYaB during early adipogenic and chondrogenic differentiation. The elevated expression of CRYaB has previously been associated with many neurological diseases (Fort and Lampi, 2011; Klemenz et al., 1991). However, relatively little is known about the role of CRYaB in osteogenic differentiation. Furushima et al. (2002) previously reported that CRYaB gene expression is associated with bone metabolism, which has also been demonstrated in a gene microarray study of hBMSCs during differentiation into osteoblasts (Kulterer et al., 2007). Apart from this, no specific role for CRYaB in osteogenic differentiation is known. The role and the underlying mechanisms of this protein in the osteogenic differentiation process therefore require further studies.
Acknowledgments
The support of the Swedish Research Council (VR grants K2009-52X-09495-22-3 and 621-2011-6037), the BIOMATCELL VINN Excellence Center of Biomaterials and Cell Therapy and the Västra Götaland Region, the Inga-Britt and Arne Lundberg Research Foundation and Handlanden Hjalmar Svensson Forskningsfond is gratefully acknowledged. The authors are grateful to Katarina Junevik for assistance during flow cytometry.
For ESCs on the other hand we and others
For ESCs, on the other hand, we and others recently established that single factors, such as neurogenic differentiation factor 1 (NEUROD1) or neurogenin 2 (NGN2), alone are sufficient to rapidly induce the neuronal fate (Thoma et al., 2012; Zhang et al., 2013). In fibroblasts, however, we had originally observed that ASCL1 can induce neuronal Amyloid β-Peptide (1-42) only with very immature features, suggesting that single factors may initiate, but cannot complete, the reprogramming process (Vierbuchen et al., 2010). This raised interesting questions about the capacity and relative contribution of reprogramming factors toward neurogenesis from different cellular lineages. Our recent studies suggested a clear hierarchical role of the reprogramming factors, as ASCL1 alone, of the three BAM factors, immediately and directly accessed the majority of its cognate target sites in the fibroblast chromatin as a pioneer factor (Wapinski et al., 2013). BRN2 and MYT1L, on the other hand, bind to ectopic sites in a tight cell-context-specific manner and appear to be mainly required at later reprogramming stages. This suggests that ASCL1 might be the central driver of iN cell reprogramming, but it remained unclear whether ASCL1 is sufficient to induce generation of mature iN cells without further assistance from BRN2 and MYT1L.
Results
Discussion
We previously found that the combined expression of three transcription factors (BAM) is required to induce fully functional iN cells from fibroblasts (Vierbuchen et al., 2010). Under the same conditions, single factors could only generate cells that had some neuronal characteristics but lacked critical others such as morphological and functional properties. Based on this observation, we had assumed that single factors can only initiate a partial reprogramming toward iN cells and additional factors are required to complete the reprogramming process. In this study, we challenged this hypothesis and demonstrate instead that cells reprogrammed with the single factor ASCL1 are in fact fully reprogrammed to a neuronal lineage but are simply less mature compared to cells reprogrammed with all three factors at early time points. Later in the reprogramming process, the single-factor iN cells can reach maturation levels almost equivalent to three-factor cells. This conclusion has important implications on how we view the molecular mechanism of iN cell reprogramming. Obviously, ASCL1 is the single most important driver of reprogramming, and success or failure of reprogramming of a given cell type will critically depend on the efficient engagement of ASCL1 with the proper chromatin targets. We recently identified an intriguing trivalent chromatin state (consisting of high H3K4 monomethylation, high H3K27 acetylation, and low H3K9 trimethylation levels) associated with ASCL1 targets in MEFs and potentially important for the correct targeting of ASCL1 to its proper sites (Wapinski et al., 2013). Now, with the knowledge that ASCL1 alone is sufficient to generate mature iN cells, these ASCL1-specific chromatin findings are even more relevant than originally assumed. In particular, for future attempts to generate iN cells from thus-far reprogramming-resistant cells such as keratinocytes or blood cells, the efforts should focus on targeting ASCL1 to its proper chromatin sites. The other two transcription factors, BRN2 and MYT1L, are not less important, but their predominant role appears to be to enhance neuronal maturation and less to contribute to the cell lineage conversion mechanism. These stud
ies would predict that Pou-domain-containing and MYT-domain-containing transcription factors also act as maturation factors during normal neural development. Furthermore, ASCL1 can activate endogenous Myt1l and Brn2 expression, which supports the notion that these two transcription factors are responsible for neuronal maturation also in ASCL1-induced iN cells.
Molecular ESC analyses and the discovery of
Molecular ESC analyses and the discovery of iPSC reprogramming attributed pluripotency and self-renewal functions to the transcriptional regulators OCT4, SOX2, NANOG, and others (Ivanova et al., 2006; Loh et al., 2006; Takahashi and Yamanaka, 2006). Since then, numerous PGRN versions have been proposed (Festuccia et al., 2013; Yang et al., 2010), and the list of factors continues to grow. However, the hierarchy of the PGRN, the order of regulatory links, and the principles of PRGN function remain largely elusive.
In our studies (see flowchart in Figure S1), hundreds of single mESCs grown either under serum + LIF or serum-free 2i + LIF conditions have been collected, and their expression signatures with respect to 46 pluripotency genes were retrieved using high-throughput microfluidic single-cell qRT-PCR (White et al., 2011). Clustering individual ap5 cost based on their gene expression profiles revealed the presence of two major cell subpopulations in cells grown under the serum + LIF 
condition. In contrast, under 2i conditions, the two populations collapsed into one, which is in agreement with recent data suggesting a reduction in gene expression heterogeneity in 2i versus LIF alone (Marks et al., 2012). Comparison of our single-cell data with published single-cell data (Kumar et al., 2014; Tang et al., 2010) established that one subpopulation detected under the LIF condition has a gene expression signature similar to that of the inner cell mass (ICM) (Boroviak et al., 2014), whereas the other subpopulation resembles more mature epiblast cells from the embryo. Detection of subpopulations became possible here because of the large number of analyzed cells (96 cells on each chip, seven chips in total).
We integrated the single-cell data obtained in this study with the data available for knockdowns of major pluripotency transcription factors (Feng et al., 2009; Ivanova et al., 2006; Loh et al., 2006; Lu et al., 2009; Martello et al., 2012). PGRNs reconstructed based on the integrated data revealed network motifs such as incoherent feedforward loops (iFFL) (Goentoro et al., 2009; Milo et al., 2002; Papatsenko and Levine, 2011), linking OCT4 and NANOG with their target genes and suggesting an antagonistic interaction between OCT4 and NANOG. Certain genes alternatively regulated by OCT4 and NANOG (Sall4 and Zscan10) appear to feed back to Oct4 and Sox2. We discuss how these loops may stabilize OCT4 concentrations required for self-renewal.
Results
Discussion
Experimental Procedures
Author contributions
Acknowledgments
Introduction
Myotonic dystrophy type 1 (DM1) is an autosomal dominant muscular dystrophy that affects a wide range of body systems (DM1 [OMIM: 160900]). It results from a trinucleotide CTG repeat expansion (50–4,000 copies) in the 3′ UTR of the dystrophia myotonica protein kinase gene (DMPK) (Aslanidis et al., 1992; Brook et al., 1992). The CTG repeat region, which resides within a CpG island (CGI), commonly results in hypermethylation and the spread of heterochromatin when expanded (Cho et al., 2005; Filippova et al., 2001; Steinbach et al., 1998). Hypermethylation is largely age- and tissue-specific and does not necessarily correlate with expansion size in somatic cells of patients (López Castel et al., 2011; Spits et al., 2010). In addition, when the CTG repeats expand, they commonly result in a reduction in the expression of a neighboring gene, SIX5 (Klesert et al., 1997, 2000; Korade-Mirnics et al., 1999; Sarkar et al., 2000, 2004; Thornton et al., 1997). The contribution of hypermethylation to disease pathogenesis is still not fully understood, nor is the precise mechanism by which CTG expansion leads to SIX5 reduction in cis. Using a wide range of DM1-affected human embryonic stem cell (hESC) lines, we aimed to uncover the mechanistic relationship between CTG expansion, aberrant methylation, and reduced expression of SIX5 in DM1.
A variety of studies have compared the expression profiles
A variety of studies have compared the expression profiles, pluripotentiality, and genetic and epigenetic stability of hESC and iPSC including lines generated using different strategies, distinct parental somatic cell types, or reprogramming methods (Bock et al., 2011; International Stem Cell Initiative et al., 2007; Müller et al., 2011; Rouhani et al., 2014; Schlaeger et al., 2015). However, these have been limited to a few variables, have multiple methods or laboratories collecting and processing samples, and typically employ a single genomics platform. “Multi-omics” analyses have proved to be essential in deciphering complex gene regulatory programs, as demonstrated by analyses of iPSC reprogramming transitional states (Clancy et al., 2014; Lee et al., 2014; Tonge et al., 2014).
Using integrative analyses across genomic analysis platforms, we present comparative results on phenotype, genetics, epigenetics, and gene regulation for a diverse panel of iPSC and hESC. Standardized methods and strict control of reagents during cell culture, sample collection, and assay performance were used to evaluate the innate potential and limitations of these Flavopiridol hydrochloride with fewer confounding factors. Our use o
f this uniform analytical methodology allowed us to discover candidate regulators of the fate of reprogrammed cells. To maximize the utility of this resource, we developed an interactive open data portal for access to the raw data, metadata, results, and protocols from these experiments for further analysis (https://www.synapse.org/PCBC).
Results
Discussion
The large-scale profiling of dozens of iPSC and previously characterized hESC represents an important analytical reference for the stem cell research community. Evaluating these lines using the same post-reprogramming culture conditions and profiling technologies has allowed us to carefully examine many possible variables. The creation of metadata standards and associated ontologies was essential to make informed comparisons and identify confounders in our study. All metadata, raw genomic files, protocols, processed results, and analyses are provided in Synapse (Omberg et al., 2013).
Our studies identified 23 iPSC lines with adverse characteristics such as contamination, karyotypic abnormalities, flow cytometry, or culture morphology consistent with differentiation. Surprisingly, teratomas generated from 45 of 46 lines, including three with characteristics of differentiation, were pluripotent as they contained cells from all three embryonic germ layers. Notably, three pluripotent teratomas also contained undifferentiated cells identified by histological or immunostaining analyses, although independent tumors from the same lines were fully differentiated and did not. Given that the teratoma assay is commonly used to confirm PSC pluripotency and quality (Muller et al., 2010), these results suggest that teratoma analysis should be considered within the context of other analyses and results to determine the quality of the PSC line and not as a stand-alone quality measure.
Evaluation of deleterious CNV provided strong evidence that the same genetic abnormalities can occur in distinct iPSC lines and that such abnormalities can arise during the reprogramming process. As described in other studies, we were unable to exclude the possibility that there was heterogeneity in the starting cell population (Ma et al., 2014). CNV that were coincident with differential expression frequently resulted in the deletion of known tumor suppressors or duplication of cell growth/oncogenic factors. Such genetic abnormalities could result in clonal selection advantages that are undesirable for clinical applications (Cunningham et al., 2012).
The DNA-methylation, gene-expression, miRNA, and splicing differences observed in these studies represent intriguing differences in PSC that could result in differences in pluripotentiality, cell growth, or potential tumorigenicity in vivo. The existence of consistent patterns between DNA methylation and mRNA or miRNA expression provides an additional layer of confidence in these observations. While methylation profiles were highly and consistently different among iPSC and hESC, fewer differences were observed for mRNA and miRNA. Although many DNA-methylation probes were identified that were highly distinct between iPSC and hESC, including a number of probes anti-correlated with gene expression (FRG1B, CCL28, CR1L, PEG3), none could perfectly distinguish between these cell types.
br Experimental Procedures br Author Contributions br Acknowledgments
Experimental Procedures
Author Contributions
Acknowledgments
This work is supported by national Portuguese funding through FCT, Fundação para a Ciência e a Tecnologia, project UID/BIM/04773/2013 CBMR, projects PEst-OE/EQB/LA0023/2013 and PTDC/SAU-ENB/111702/2009 to J.B., U01HL100408, NIH/NHLBI to I.D., and SFRH/BPD/74807/2010 to G.M.O. I.P.L. thanks the Biomedical Sciences PhD program and FCT for funding (SFRH/BD/62054/2009). J.M.A.S. is a PhD student of the ProRegeM – PhD Program in Mechanisms of Disease and Regenerative Medicine of the University of Algarve 
and New University of Lisbon financed by the FCT. We thank Shoumo Bhattacharya (University of Oxford) for CITED2 expressing vectors, Austin Smith (University of Cambridge) for E14/T ck1 inhibitor and pPyCAGIP vector, Gergana Dobreva (Max Planck Institute for Heart and Lung Research) for Mef2c-luc reporter, Claudia Florindo, head of the Microscopy Facility (University of Algarve), and José A. Belo (New University of Lisbon) for reagents.
Introduction
G-protein-coupled receptors (GPCRs), such as PAR1 (Protease Activated Receptor 1, also referred to as CF2R, F2R, TR, and HTR), are transmembrane receptors that transmit extracellular signals into cells by coupling to specific heterotrimeric guanine nucleotide binding proteins (G proteins) and thus mediate an array of responses (Rosenbaum et al., 2009; Vassart and Costagliola, 2011). G-protein-activated pathways constitute the largest class of therapeutic targets (Ding et al., 2015; Thompson et al., 2005). The function ascribed to GPCRs is the result of agonist binding to the receptor, resulting in activation of specific G proteins such as stimulatory Gαs and inhibitory Gαi subunits, which selectively activate or inactivate effector pathways to mediate the desired responses (Kobilka, 2007; Wess, 1997). However, little is known about the role of GPCRs in mediating the differentiation of stem cells to terminally differentiated cells (Callihan et al., 2011; Kobayashi et al., 2010). To date, work has centered on pathways in adult stem cells such as signals emanating from specialized GPCRs (Frizzled proteins) of the WNT pathway and chemokine receptors such as CXCR4 expressed in stem cells (Holland et al., 2013; Van Camp et al., 2014). The role of GPCR signaling in mediating the differentiation of pluripotent embryonic stem cells (ESCs) into differentiated cells has not been widely explored.
ESCs are critical for regenerative therapies because unlike adult stem cells they expand indefinitely and are ideal for generating mature cells to replace injured tissue. Studies showed that the transcriptional programs underlying ESC differentiation mirror those during embryonic development (James et al., 2005; Shiraki et al., 2014). One example is the differentiation of ESCs into regenerative vascular endothelial cells (ECs), which requires upregulation of the developmental transcription factors such as ER71 (Kohler et al., 2013) and which serves as a window for investigation of signaling pathways mediating vascular regeneration in ischemic tissue. Here we used a GPCR gene expression screen to identify GPCRs expressed in mouse ESCs (mESCs) undergoing differentiation to ECs. We observed that PAR1 was highly upregulated, and further that it was required for EC differentiation. PAR1 functions as a scaffold for the transforming growth factor β (TGF-β) receptor TGFβRII, which thereby dampens SMAD signaling and activates the transition of ESCs to ECs capable of forming new blood vessels.
Results
Discussion
Studies in Par1 mouse embryos showed that PAR1 is a key regulator of vascular development; that is, ∼50% of Par1 mice died in utero because of defective vasculogenesis (Griffin et al., 2001). PAR1 utilizes multiple heterotrimeric G proteins, Gαi, Gαq, and Gα12/13, to transmit intracellular signals (Coughlin, 2000;
Soh et al., 2010). Only EC-specific Gα13 embryos died at embryonic days 9.5–11.5 with a phenotype resembling the Par1 mice (Ruppel et al., 2005). Furthermore, embryos re-expressing Gα13 in ECs did not differ from their Gα13 littermates and also showed intracranial bleeding (Ruppel et al., 2005), pointing to a key function of PAR1 independent of its associated canonical heterotrimeric G-protein signaling.