Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder with a strong genetic basis. Yet, only a small fraction of potentially causal genes—about 65 genes out of an estimated several hundred—are known with strong genetic evidence from sequencing studies. We developed a complementary machine-learning approach based on a human brain-specific gene network to present a genome-wide prediction of autism risk genes, including hundreds of candidates for which there is minimal or no prior genetic evidence. Our approach was validated in a large independent case–control sequencing study. Leveraging these genomewide predictions and the brain-specific network, we demonstrated that the large set of ASD genes converges on a smaller number of key pathways and developmental stages of the brain. Finally, we identified likely pathogenic genes within frequent autism-associated copy-number variants and proposed genes and pathways that are likely mediators of ASD across multiple copy-number variants. All predictions and functional insights are available at http://asd.princeton.edu.

The biology and behavior of adults differ substantially from those of developing animals, and cell-specific information is critical for deciphering the biology of multicellular animals. Thus, adult tissue-specific transcriptomic data are critical for understanding molecular mechanisms that control their phenotypes. We used adult cell-specific isolation to identify the transcriptomes of C. elegans’ four major tissues (or “tissue-ome”), identifying ubiquitously expressed and tissue-specific “enriched” genes. These data newly reveal the hypodermis’ metabolic character, suggest potential worm-human tissue orthologies, and identify tissue-specific changes in the Insulin/IGF-1 signaling pathway. Tissue-specific alternative splicing analysis identified a large set of collagen isoforms. Finally, we developed a machine learning-based prediction tool for 76 sub-tissue cell types, which we used to predict cellular expression differences in IIS/FOXO signaling, stage-specific TGF-β activity, and basal vs. memory-induced CREB transcription. Together, these data provide a rich resource for understanding the biology governing multicellular adult animals.

We present SEEK (http://seek.princeton.edu), a query-based search engine across very large transcriptomic data collections, including thousands of human data sets from almost 50 microarray and next-generation sequencing platforms. SEEK uses a novel query-level cross-validation-based algorithm to automatically prioritize data sets relevant to the query and a robust search approach to identify query-coregulated genes, pathways, and processes. SEEK provides cross-platform handling, multi-gene query search, iterative metadata-based search refinement, and extensive visualization-based analysis options.

Highlights d Ribosomal profiling of AD vulnerable/resistant neurons in 5-, 12-, 24-month old mice d Using human neuron-type functional networks and GWASs to model vulnerability d Identification of axon plasticity genes linking Aß, aging, tau in vulnerable neurons d PTB, regulator of tau exon 10 splicing, might contribute to selective vulnerability

Highlights d 2-and 3-antigen AND or NOT logic gates improve tumor discrimination of CAR T cells d All transmembrane antigen combination pairs and triples are computationally screened d Combinatorial antigens that outperform current clinical CAR T cells are predicted d Adding antigens improves precision at the cost of recall; 2-3 is optimal