Meta-analysis of GWAS of over 16,000 individuals with autism spectrum disorder highlights a novel locus at 10q24.32 and a significant overlap with schizophrenia.

To access publisher's full text version of this article, please click on the hyperlink in Additional Links field or click on the hyperlink at the top of the page marked FilesOver the past decade genome-wide association studies (GWAS) have been applied to aid in the understanding of the biology of traits. The success of this approach is governed by the underlying effect sizes carried by the true risk variants and the corresponding statistical power to observe such effects given the study design and sample size under investigation. Previous ASD GWAS have identified genome-wide significant (GWS) risk loci; however, these studies were of only of low statistical power to identify GWS loci at the lower effect sizes (odds ratio (OR) <1.15).We conducted a large-scale coordinated international collaboration to combine independent genotyping data to improve the statistical power and aid in robust discovery of GWS loci. This study uses genome-wide genotyping data from a discovery sample (7387 ASD cases and 8567 controls) followed by meta-analysis of summary statistics from two replication sets (7783 ASD cases and 11359 controls; and 1369 ASD cases and 137308 controls).We observe a GWS locus at 10q24.32 that overlaps several genes including PITX3, which encodes a transcription factor identified as playing a role in neuronal differentiation and CUEDC2 previously reported to be associated with social skills in an independent population cohort. We also observe overlap with regions previously implicated in schizophrenia which was further supported by a strong genetic correlation between these disorders (Rg = 0.23; P = 9 × 10(-6)). We further combined these Psychiatric Genomics Consortium (PGC) ASD GWAS data with the recent PGC schizophrenia GWAS to identify additional regions which may be important in a common neurodevelopmental phenotype and identified 12 novel GWS loci. These include loci previously implicated in ASD such as FOXP1 at 3p13, ATP2B2 at 3p25.3, and a 'neurodevelopmental hub' on chromosome 8p11.23.This study is an important step in the ongoing endeavour to identify the loci which underpin the common variant signal in ASD. In addition to novel GWS loci, we have identified a significant genetic correlation with schizophrenia and association of ASD with several neurodevelopmental-related genes such as EXT1, ASTN2, MACROD2, and HDAC4.National Institutes of Mental Health (NIMH, USA) ACE Network Autism Genetic Resource Exchange (AGRE) is a program of Autism Speaks (USA) The Autism Genome Project (AGP) from Autism Speaks (USA) Canadian Institutes of Health Research (CIHR), Genome Canada Health Research Board (Ireland) Hilibrand Foundation (USA) Medical Research Council (UK) National Institutes of Health (USA) Ontario Genomics Institute University of Toronto McLaughlin Centre Simons Foundation Johns Hopkins Autism Consortium of Boston NLM Family foundation National Institute of Health grants National Health Medical Research Council Scottish Rite Spunk Fund, Inc. Rebecca and Solomon Baker Fund APEX Foundation National Alliance for Research in Schizophrenia and Affective Disorders (NARSAD) endowment fund of the Nancy Pritzker Laboratory (Stanford) Autism Society of America Janet M. Grace Pervasive Developmental Disorders Fund The Lundbeck Foundation universities and university hospitals of Aarhus and Copenhagen Stanley Foundation Centers for Disease Control and Prevention (CDC) Netherlands Scientific Organization Dutch Brain Foundation VU University Amsterdam Trinity Centre for High Performance Computing through Science Foundation Ireland Autism Genome Project (AGP) from Autism Speak

Polygenic transmission disequilibrium confirms that common and rare variation act additively to create risk for autism spectrum disorders

Autism spectrum disorder (ASD) risk is influenced by common polygenic and de novo variation. We aimed to clarify the influence of polygenic risk for ASD and to identify subgroups of ASD cases, including those with strongly acting de novo variants, in which polygenic risk is relevant. Using a novel approach called the polygenic transmission disequilibrium test and data from 6,454 families with a child with ASD, we show that polygenic risk for ASD, schizophrenia, and greater educational attainment is over-transmitted to children with ASD. These findings hold independent of proband IQ. We find that polygenic variation contributes additively to risk in ASD cases who carry a strongly acting de novo variant. Lastly, we show that elements of polygenic risk are independent and differ in their relationship with phenotype. These results confirm that the genetic influences on ASD are additive and suggest that they create risk through at least partially distinct etiologic pathways

Multiway modeling and analysis in stem cell systems biology

<p>Abstract</p> <p>Background</p> <p>Systems biology refers to multidisciplinary approaches designed to uncover emergent properties of biological systems. Stem cells are an attractive target for this analysis, due to their broad therapeutic potential. A central theme of systems biology is the use of computational modeling to reconstruct complex systems from a wealth of reductionist, molecular data (e.g., gene/protein expression, signal transduction activity, metabolic activity, etc.). A number of deterministic, probabilistic, and statistical learning models are used to understand sophisticated cellular behaviors such as protein expression during cellular differentiation and the activity of signaling networks. However, many of these models are bimodal i.e., they only consider row-column relationships. In contrast, multiway modeling techniques (also known as tensor models) can analyze multimodal data, which capture much more information about complex behaviors such as cell differentiation. In particular, tensors can be very powerful tools for modeling the dynamic activity of biological networks over time. Here, we review the application of systems biology to stem cells and illustrate application of tensor analysis to model collagen-induced osteogenic differentiation of human mesenchymal stem cells.</p> <p>Results</p> <p>We applied Tucker1, Tucker3, and Parallel Factor Analysis (PARAFAC) models to identify protein/gene expression patterns during extracellular matrix-induced osteogenic differentiation of human mesenchymal stem cells. In one case, we organized our data into a tensor of type protein/gene locus link × gene ontology category × osteogenic stimulant, and found that our cells expressed two distinct, stimulus-dependent sets of functionally related genes as they underwent osteogenic differentiation. In a second case, we organized DNA microarray data in a three-way tensor of gene IDs × osteogenic stimulus × replicates, and found that application of tensile strain to a collagen I substrate accelerated the osteogenic differentiation induced by a static collagen I substrate.</p> <p>Conclusion</p> <p>Our results suggest gene- and protein-level models whereby stem cells undergo transdifferentiation to osteoblasts, and lay the foundation for mechanistic, hypothesis-driven studies. Our analysis methods are applicable to a wide range of stem cell differentiation models.</p

Roles of octopaminergic and dopaminergic neurons in appetitive and aversive memory recall in an insect

<p>Abstract</p> <p>Background</p> <p>In insect classical conditioning, octopamine (the invertebrate counterpart of noradrenaline) or dopamine has been suggested to mediate reinforcing properties of appetitive or aversive unconditioned stimulus, respectively. However, the roles of octopaminergic and dopaminergic neurons in memory recall have remained unclear.</p> <p>Results</p> <p>We studied the roles of octopaminergic and dopaminergic neurons in appetitive and aversive memory recall in olfactory and visual conditioning in crickets. We found that pharmacological blockade of octopamine and dopamine receptors impaired aversive memory recall and appetitive memory recall, respectively, thereby suggesting that activation of octopaminergic and dopaminergic neurons and the resulting release of octopamine and dopamine are needed for appetitive and aversive memory recall, respectively. On the basis of this finding, we propose a new model in which it is assumed that two types of synaptic connections are formed by conditioning and are activated during memory recall, one type being connections from neurons representing conditioned stimulus to neurons inducing conditioned response and the other being connections from neurons representing conditioned stimulus to octopaminergic or dopaminergic neurons representing appetitive or aversive unconditioned stimulus, respectively. The former is called 'stimulus-response connection' and the latter is called 'stimulus-stimulus connection' by theorists studying classical conditioning in higher vertebrates. Our model predicts that pharmacological blockade of octopamine or dopamine receptors during the first stage of second-order conditioning does not impair second-order conditioning, because it impairs the formation of the stimulus-response connection but not the stimulus-stimulus connection. The results of our study with a cross-modal second-order conditioning were in full accordance with this prediction.</p> <p>Conclusion</p> <p>We suggest that insect classical conditioning involves the formation of two kinds of memory traces, which match to stimulus-stimulus connection and stimulus-response connection. This is the first study to suggest that classical conditioning in insects involves, as does classical conditioning in higher vertebrates, the formation of stimulus-stimulus connection and its activation for memory recall, which are often called cognitive processes.</p

Effect of Cellular Quiescence on the Success of Targeted CML Therapy

Similar to tissue stem cells, primitive tumor cells in chronic myelogenous leukemia have been observed to undergo quiescence; that is, the cells can temporarily stop dividing. Using mathematical models, we investigate the effect of cellular quiescence on the outcome of therapy with targeted small molecule inhibitors.According to the models, the initiation of treatment can result in different patterns of tumor cell decline: a biphasic decline, a one-phase decline, and a reverse biphasic decline. A biphasic decline involves a fast initial phase (which roughly corresponds to the eradication of cycling cells by the drug), followed by a second and slower phase of exponential decline (corresponding to awakening and death of quiescent cells), which helps explain clinical data. We define the time when the switch to the second phase occurs, and identify parameters that determine whether therapy can drive the tumor extinct in a reasonable period of time or not. We further ask how cellular quiescence affects the evolution of drug resistance. We find that it has no effect on the probability that resistant mutants exist before therapy if treatment occurs with a single drug, but that quiescence increases the probability of having resistant mutants if patients are treated with a combination of two or more drugs with different targets. Interestingly, while quiescence prolongs the time until therapy reduces the number of cells to low levels or extinction, the therapy phase is irrelevant for the evolution of drug resistant mutants. If treatment fails as a result of resistance, the mutants will have evolved during the tumor growth phase, before the start of therapy. Thus, prevention of resistance is not promoted by reducing the quiescent cell population during therapy (e.g., by a combination of cell activation and drug-mediated killing).The mathematical models provide insights into the effect of quiescence on the basic kinetics of the response to targeted treatment of CML. They identify determinants of success in the absence of drug resistant mutants, and elucidate how quiescence influences the emergence of drug resistant mutants

The copper centers of tyramine β-monooxygenase and its catalytic-site methionine variants: an X-ray absorption study

Tyramine β-monooxygenase (TBM) is a member of a family of copper monooxygenases containing two noncoupled copper centers, and includes peptidylglycine monooxygenase and dopamine β-monooxygenase. In its Cu(II) form, TBM is coordinated by two to three His residues and one to two non-His O/N ligands consistent with a [CuM(His)2(OH2)2–CuH(His)3(OH2)] formulation. Reduction to the Cu(I) state causes a change in the X-ray absorption spectroscopy (XAS) spectrum, consistent with a change to a [CuM(His)2S(Met)–CuH(His)3] environment. Lowering the pH to 4.0 results in a large increase in the intensity of the Cu(I)–S extended X-ray absorption fine structure (EXAFS) component, suggesting a tighter Cu–S bond or the coordination of an additional sulfur donor. The XAS spectra of three variants, where the CuM Met471 residue had been mutated to His, Cys, and Asp, were examined. Significant differences from the wild-type enzyme are evident in the spectra of the reduced mutants. Although the side chains of His, Cys, and Asp are expected to substitute for Met at the CuM site, the data showed identical spectra for all three reduced variants, with no evidence for coordination of residue 471. Rather, the K-edge data suggested a modest decrease in coordination number, whereas the EXAFS indicated an average of two His residues at each Cu(I) center. These data highlight the unique role of the Met residue at the CuM center, and pose interesting questions as to why replacement by the cuprophilic thiolate ligand leads to detectable activity whereas replacement by imidazole generates inactive TBM

Multiple Wnt/ß-Catenin Responsive Enhancers Align with the MYC Promoter through Long-Range Chromatin Loops

Inappropriate activation of c-Myc (MYC) gene expression by the Wnt/ß-catenin signaling pathway is required for colorectal carcinogenesis. The elevated MYC levels in colon cancer cells are attributed in part to ß-catenin/TCF4 transcription complexes that are assembled at proximal Wnt/ß-catenin responsive enhancers (WREs). Recent studies suggest that additional WREs that control MYC expression reside far upstream of the MYC transcription start site. Here, I report the characterization of five novel WREs that localize to a region over 400 kb upstream from MYC. These WREs harbor nucleosomes with post-translational histone modifications that demarcate enhancer and gene promoter regions. Using quantitative chromatin conformation capture, I show that the distal WREs are aligned with the MYC promoter through large chromatin loops. The chromatin loops are not restricted to colon cancer cells, but are also found in kidney epithelial and lung fibroblast cell lines that lack de-regulated Wnt signaling and nuclear ß-catenin/TCF4 complexes. While each chromatin loop is detected in quiescent cells, the positioning of three of the five distal enhancers with the MYC promoter is induced by serum mitogens. These findings suggest that the architecture of the MYC promoter is comprised of distal elements that are juxtaposed through large chromatin loops and that ß-catenin/TCF4 complexes utilize this conformation to activate MYC expression in colon cancer cells

Quantitative Analysis of Mechanisms That Govern Red Blood Cell Age Structure and Dynamics during Anaemia

Mathematical modelling has proven an important tool in elucidating and quantifying mechanisms that govern the age structure and population dynamics of red blood cells (RBCs). Here we synthesise ideas from previous experimental data and the mathematical modelling literature with new data in order to test hypotheses and generate new predictions about these mechanisms. The result is a set of competing hypotheses about three intrinsic mechanisms: the feedback from circulating RBC concentration to production rate of immature RBCs (reticulocytes) in bone marrow, the release of reticulocytes from bone marrow into the circulation, and their subsequent ageing and clearance. In addition we examine two mechanisms specific to our experimental system: the effect of phenylhydrazine (PHZ) and blood sampling on RBC dynamics. We performed a set of experiments to quantify the dynamics of reticulocyte proportion, RBC concentration, and erythropoietin concentration in PHZ-induced anaemic mice. By quantifying experimental error we are able to fit and assess each hypothesis against our data and recover parameter estimates using Markov chain Monte Carlo based Bayesian inference. We find that, under normal conditions, about 3% of reticulocytes are released early from bone marrow and upon maturation all cells are released immediately. In the circulation, RBCs undergo random clearance but have a maximum lifespan of about 50 days. Under anaemic conditions reticulocyte production rate is linearly correlated with the difference between normal and anaemic RBC concentrations, and their release rate is exponentially correlated with the same. PHZ appears to age rather than kill RBCs, and younger RBCs are affected more than older RBCs. Blood sampling caused short aperiodic spikes in the proportion of reticulocytes which appear to have a different developmental pathway than normal reticulocytes. We also provide evidence of large diurnal oscillations in serum erythropoietin levels during anaemia

Genomic and Proteomic Analysis of the Impact of Mitotic Quiescence on the Engraftment of Human CD34+ Cells

It is well established that in adults, long-term repopulating hematopoietic stem cells (HSC) are mitotically quiescent cells that reside in specialized bone marrow (BM) niches that maintain the dormancy of HSC. Our laboratory demonstrated that the engraftment potential of human HSC (CD34+ cells) from BM and mobilized peripheral blood (MPB) is restricted to cells in the G0 phase of cell cycle but that in the case of umbilical cord blood (UCB) -derived CD34+ cells, cell cycle status is not a determining factor in the ability of these cells to engraft and sustain hematopoiesis. We used this distinct in vivo behavior of CD34+ cells from these tissues to identify genes associated with the engraftment potential of human HSC. CD34+ cells from BM, MPB, and UCB were fractionated into G0 and G1 phases of cell cycle and subjected in parallel to microarray and proteomic analyses. A total of 484 target genes were identified to be associated with engraftment potential of HSC. System biology modeling indicated that the top four signaling pathways associated with these genes are Integrin signaling, p53 signaling, cytotoxic T lymphocyte-mediated apoptosis, and Myc mediated apoptosis signaling. Our data suggest that a continuum of functions of hematopoietic cells directly associated with cell cycle progression may play a major role in governing the engraftment potential of stem cells. While proteomic analysis identified a total of 646 proteins in analyzed samples, a very limited overlap between genomic and proteomic data was observed. These data provide a new insight into the genetic control of engraftment of human HSC from distinct tissues and suggest that mitotic quiescence may not be the requisite characteristic of engrafting stem cells, but instead may be the physiologic status conducive to the expression of genetic elements favoring engraftment

Optimizing Combination Therapies with Existing and Future CML Drugs

Small-molecule inhibitors imatinib, dasatinib and nilotinib have been developed to treat Chromic Myeloid Leukemia (CML). The existence of a triple-cross-resistant mutation, T315I, has been a challenging problem, which can be overcome by finding new inhibitors. Many new compounds active against T315I mutants are now at different stages of development. In this paper we develop an algorithm which can weigh different combination treatment protocols according to their cross-resistance properties, and find the protocols with the highest probability of treatment success. This algorithm also takes into account drug toxicity by minimizing the number of drugs used, and their concentration. Although our methodology is based on a stochastic model of CML microevolution, the algorithm itself does not require measurements of any parameters (such as mutation rates, or division/death rates of cells), and can be used by medical professionals without a mathematical background. For illustration, we apply this algorithm to the mutation data obtained in [1], [2]