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Timothy J O'Donnell

Publications and source records attributed to Timothy J O'Donnell.

2 recordsLinked to original sources

PhIP-Seq uncovers marked heterogeneity in acute rheumatic fever autoantibodies.

Acute rheumatic fever (ARF) and associated rheumatic heart disease are serious sequelae after infection with group A Streptococcus (Strep A). Autoantibodies are thought to contribute to pathogenesis, with deeper exploration of the autoantibody repertoire needed to improve mechanistic understanding and identify new biomarkers. Phage immunoprecipitation sequencing (PhIP-Seq) with the HuScan library (>250,000 overlapping 90-mer peptides spanning the human proteome) was utilized to analyze autoreactivity in sera from children with ARF, uncomplicated Strep A pharyngitis, and matched healthy controls. A global proteome-wide increase in autoantigen reactivity was observed in ARF, as was marked heterogeneity between patients. Public epitopes, common between individuals with ARF were rare, and comprised less than 1% of all enriched peptides. Differential analysis identified both unknown and previously identified ARF autoantigens, including PPP1R12B, a myosin phosphatase complex regulatory subunit expressed in cardiac muscle, and members of the collagen protein family, respectively. Pathway analysis found antigens from the disease-relevant processes encompassing sarcomere and heart morphogenesis were targeted. In sum, PhIP-Seq has substantially expanded the spectrum of autoantigens in ARF, and reveals the rarity of public epitopes in the disease. It provides further support for the role of epitope spreading in pathogenesis and has identified PPP1R12B as an enriched autoantigen.

Humans↗

Using mathematical models of language experimentally.

Understanding developmental and evolutionary aspects of the language faculty requires comparing adult languages users' abilities with those of non-verbal subjects, such as babies and non-human animals. Classically, comparative work in this area has relied on the rich theoretical frameworks developed by linguists in the generative grammar tradition. However, the great variety of generative theories and the fact that they are models of language specifically makes it difficult to know what to test in animals and children lacking the expressive abilities of normal, mature adults. We suggest that this problem can be mitigated by tapping equally rich, but more formal mathematical approaches to language.

Biological Evolution↗