Autism spectrum disorders and autistic traits share genetics and biology

Autism spectrum disorders (ASDs) and autistic traits in the general population may share genetic susceptibility factors. In this study, we investigated such potential overlap based on common genetic variants. We developed and validated a self-report questionnaire of autistic traits in adults. We then conducted genome-wide association studies (GWASs) of six trait scores derived from the questionnaire through exploratory factor analysis in 1981 adults from the general population. Using the results from the Psychiatric Genomics Consortium GWAS of ASDs, we observed genetic sharing between ASDs and the autistic traits 'childhood behavior', 'rigidity' and 'attention to detail'. Gene-set analysis subsequently identified 'rigidity' to be significantly associated with a network of ASD gene-encoded proteins that regulates neurite outgrowth. Gene-wide association with the well-established ASD gene MET reached significance. Taken together, our findings provide evidence for an overlapping genetic and biological etiology underlying ASDs and autistic population traits, which suggests that genetic studies in the general population may yield novel ASD genes.

Autism spectrum disorders and autistic traits share genetics and biology

This corrects the article DOI: 10.1038/mp.2017.98.

Integrated molecular landscape of Parkinson's disease

Parkinson’s disease is caused by a complex interplay of genetic and environmental factors. Although a number of independent molecular pathways and processes have been associated with familial Parkinson’s disease, a common mechanism underlying especially sporadic Parkinson’s disease is still largely unknown. In order to gain further insight into the etiology of Parkinson’s disease, we here conducted genetic network and literature analyses to integrate the top-ranked findings from thirteen published genome-wide association studies of Parkinson’s disease (involving 13.094 cases and 47.148 controls) and other genes implicated in (familial) Parkinson’s disease, into a molecular interaction landscape. The molecular Parkinson’s disease landscape harbors four main biological processes—oxidative stress response, endosomal-lysosomal functioning, endoplasmic reticulum stress response, and immune response activation—that interact with each other and regulate dopaminergic neuron function and death, the pathological hallmark of Parkinson’s disease. Interestingly, lipids and lipoproteins are functionally involved in and influenced by all these processes, and affect dopaminergic neuron-specific signaling cascades. Furthermore, we validate the Parkinson’s disease -lipid relationship by genome-wide association studies data-based polygenic risk score analyses that indicate a shared genetic risk between lipid/lipoprotein traits and Parkinson’s disease. Taken together, our findings provide novel insights into the molecular pathways underlying the etiology of (sporadic) Parkinson’s disease and highlight a key role for lipids and lipoproteins in Parkinson’s disease pathogenesis, providing important clues for the development of disease-modifying treatments of Parkinson’s disease.

Lipids and lipoproteins play a central role in four key biological processes underlying Parkinson’s disease (PD). Using bioinformatics and other extensive analyses of previously published data, Geert Poelmans, Cornelius Klemann and colleagues in The Netherlands, Germany and the USA have mapped the interactions of proteins that are encoded by genes associated with both familial and sporadic forms of PD. They identify the oxidative stress response, lysosomal function, endoplasmic reticulum stress response and immune response activation as the main mechanisms leading to the death of dopaminergic neurons. Lipid signaling is implicated in all four of these processes and the authors find a link between the levels of particular lipids and lipoproteins and the risk of PD. These findings suggest that compounds that regulate lipid or lipoprotein levels offer a potential new treatment strategy for PD.

Separating burst from background spikes in multichannel neuronal recordings using return map analysis

We propose a preprocessing method to separate coherent neuronal network activity, referred to as “bursts”, from background spikes. High background activity in neuronal recordings reduces the effectiveness of currently available burst detection methods. For long-term, stationary recordings, burst and background spikes have a bimodal ISI distribution which makes it easy to select the threshold to separate burst and background spikes. Finite, nonstationary recordings lead to noisy ISIs for which the bimodality is not that clear. We introduce a preprocessing method to separate burst from background spikes to improve burst detection reliability because it efficiently uses both single and multichannel activity. The method is tested using a stochastic model constrained by data available in the literature and recordings from primary cortical neurons cultured on multielectrode arrays. The separation between burst and background spikes is obtained using the interspike interval return map. The cutoff threshold is the key parameter to separate the burst and background spikes. We compare two methods for selecting the threshold. The 2-step method, in which threshold selection is based on fixed heuristics. The iterative method, in which the optimal cutoff threshold is directly estimated from the data. The proposed preprocessing method significantly increases the reliability of several established burst detection algorithms, both for simulated and real recordings. The preprocessing method makes it possible to study the effects of diseases or pharmacological manipulations, because it can deal efficiently with nonstationarity in the data.