BackgroundAutism spectrum disorders (ASD) are a group of neurodevelopmental disorders with high heritability. Recent findings support a highly heterogeneous and complex genetic etiology including rare de novo and inherited mutations or chromosomal rearrangements as well as double or multiple hits.MethodsWe performed whole-exome sequencing (WES) and blood cell transcriptome by RNAseq in a subset of male patients with idiopathic ASD (n = 36) in order to identify causative genes, transcriptomic alterations, and susceptibility variants.ResultsWe detected likely monogenic causes in seven cases: five de novo (SCN2A, MED13L, KCNV1, CUL3, and PTEN) and two inherited X-linked variants (MAOA and CDKL5). Transcriptomic analyses allowed the identification of intronic causative mutations missed by the usual filtering of WES and revealed functional consequences of some rare mutations. These included aberrant transcripts (PTEN, POLR3C), deregulated expression in 1.7% of mutated genes (that is, SEMA6B, MECP2, ANK3, CREBBP), allele-specific expression (FUS, MTOR, TAF1C), and non-sense-mediated decay (RIT1, ALG9). The analysis of rare inherited variants showed enrichment in relevant pathways such as the PI3K-Akt signaling and the axon guidance.ConclusionsIntegrative analysis of WES and blood RNAseq data has proven to be an efficient strategy to identify likely monogenic forms of ASD (19% in our cohort), as well as additional rare inherited mutations that can contribute to ASD risk in a multifactorial manner. Blood transcriptomic data, besides validating 88% of expressed variants, allowed the identification of missed intronic mutations and revealed functional correlations of genetic variants, including changes in splicing, expression levels, and allelic expression.Electronic supplementary materialThe online version of this article (doi:10.1186/s13229-015-0017-0) contains supplementary material, which is available to authorized users.

Seventeen years after the sequencing of the human genome, the human proteome is still under revision. One in eight of the 22 210 coding genes listed by the Ensembl/GENCODE, RefSeq and UniProtKB reference databases are annotated differently across the three sets. We have carried out an in-depth investigation on the 2764 genes classified as coding by one or more sets of manual curators and not coding by others. Data from large-scale genetic variation analyses suggests that most are not under protein-like purifying selection and so are unlikely to code for functional proteins. A further 1470 genes annotated as coding in all three reference sets have characteristics that are typical of non-coding genes or pseudogenes. These potential non-coding genes also appear to be undergoing neutral evolution and have considerably less supporting transcript and protein evidence than other coding genes. We believe that the three reference databases currently overestimate the number of human coding genes by at least 2000, complicating and adding noise to large-scale biomedical experiments. Determining which potential non-coding genes do not code for proteins is a difficult but vitally important task since the human reference proteome is a fundamental pillar of most basic research and supports almost all large-scale biomedical projects.

Introns can be extraordinarily large and they account for the majority of the DNA sequence in human genes. However, little is known about their population patterns of structural variation and their functional implication. By combining the most extensive maps of CNVs in human populations, we have found that intronic losses are the most frequent copy number variants (CNVs) in protein-coding genes in human, with 12,986 intronic deletions, affecting 4,147 genes (including 1,154 essential genes and 1,638 disease-related genes). This intronic length variation results in dozens of genes showing extreme population variability in size, with 40 genes with 10 or more different sizes and up to 150 allelic sizes. Intronic losses are frequent in evolutionarily ancient genes that are highly conserved at the protein sequence level. This result contrasts with losses overlapping exons, which are observed less often than expected by chance and almost exclusively affect primate-specific genes. An integrated analysis of CNVs and RNA-seq data showed that intronic loss can be associated with significant differences in gene expression levels in the population (CNV-eQTLs). These intronic CNV-eQTLs regions are enriched for intronic enhancers and can be associated with expression differences of other genes showing long distance intron-promoter 3D interactions. Our data suggests that intronic structural variation of protein-coding genes makes an important contribution to the variability of gene expression and splicing in human populations.

This manuscript shows that ACN can be an excellent choice for the coupling of hindered amino acids as illustrated by the coupling of Fmoc-amino acids on free amino acids anchored on a BAL synthesis. Furthermore, ACN can be a good alternative for solid-phase peptide synthesis in the absence of DMF (washings, removal of Fmoc, and coupling).