Empirical Bayesian Random Censoring Threshold Model Improves Detection of Differentially Abundant Proteins

A challenge in proteomics is that many observations are missing with the probability of missingness increasing as abundance decreases. Adjusting for this informative missingness is required to assess accurately which proteins are differentially abundant. We propose an empirical Bayesian random censoring threshold (EBRCT) model that takes the pattern of missingness in account in the identification of differential abundance. We compare our model with four alternatives, one that considers the missing values as missing completely at random (MCAR model), one with a fixed censoring threshold for each protein species (fixed censoring model) and two imputation models, <i>k</i>-nearest neighbors (IKNN) and singular value thresholding (SVTI). We demonstrate that the EBRCT model bests all alternative models when applied to the CPTAC study 6 benchmark data set. The model is applicable to any label-free peptide or protein quantification pipeline and is provided as an R script

Interaction Proteomics Reveals Brain Region-Specific AMPA Receptor Complexes

Fast excitatory synaptic transmission in the brain is mediated by glutamate acting on postsynaptic AMPA receptors. Recent studies have revealed a substantial number of AMPA receptor auxiliary proteins, which potentially contribute to the regulation of AMPA receptor trafficking, subcellular receptor localization, and receptor gating properties. Here we examined the AMPA receptor interactomes from cortex, hippocampus, and cerebellum by comprehensive interaction proteomics. The study reveals that AMPA receptor auxiliary proteins are engaged in distinct brain region-specific AMPA receptors subcomplexes, which might underlie brain region-specific differential regulation of AMPA receptor properties. Depending on the brain region, an interacting protein can be involved in an AMPA and a non-AMPA receptor complex

Interaction Proteomics Reveals Brain Region-Specific AMPA Receptor Complexes

Fast excitatory synaptic transmission in the brain is mediated by glutamate acting on postsynaptic AMPA receptors. Recent studies have revealed a substantial number of AMPA receptor auxiliary proteins, which potentially contribute to the regulation of AMPA receptor trafficking, subcellular receptor localization, and receptor gating properties. Here we examined the AMPA receptor interactomes from cortex, hippocampus, and cerebellum by comprehensive interaction proteomics. The study reveals that AMPA receptor auxiliary proteins are engaged in distinct brain region-specific AMPA receptors subcomplexes, which might underlie brain region-specific differential regulation of AMPA receptor properties. Depending on the brain region, an interacting protein can be involved in an AMPA and a non-AMPA receptor complex

Mapping genomic loci implicates genes and synaptic biology in schizophrenia

Schizophrenia has a heritability of 60-80%1, much of which is attributable to common risk alleles. Here, in a two-stage genome-wide association study of up to 76,755 individuals with schizophrenia and 243,649 control individuals, we report common variant associations at 287 distinct genomic loci. Associations were concentrated in genes that are expressed in excitatory and inhibitory neurons of the central nervous system, but not in other tissues or cell types. Using fine-mapping and functional genomic data, we identify 120 genes (106 protein-coding) that are likely to underpin associations at some of these loci, including 16 genes with credible causal non-synonymous or untranslated region variation. We also implicate fundamental processes related to neuronal function, including synaptic organization, differentiation and transmission. Fine-mapped candidates were enriched for genes associated with rare disruptive coding variants in people with schizophrenia, including the glutamate receptor subunit GRIN2A and transcription factor SP4, and were also enriched for genes implicated by such variants in neurodevelopmental disorders. We identify biological processes relevant to schizophrenia pathophysiology; show convergence of common and rare variant associations in schizophrenia and neurodevelopmental disorders; and provide a resource of prioritized genes and variants to advance mechanistic studies