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Protein lipidation plays critical roles in regulating protein function and localization. However, the chemical diversity and specificity of fatty acyl group utilization have not been investigated using untargeted approaches, and it is unclear to what extent structures and biosynthetic origins ofS-acyl moieties differ fromN- andO-fatty acylation. Here, we show that fatty acylation patterns inCaenorhabditis elegansdiffer markedly between different amino acid residues. Hydroxylamine capture revealed predominant cysteineS-acylation with 15-methylhexadecanoic acid (isoC17:0), a monomethyl branched-chain fatty acid (mmBCFA) derived from endogenous leucine catabolism. In contrast, enzymatic protein hydrolysis showed that N-terminal glycine was acylated almost exclusively with straight-chain myristic acid, whereas lysine was acylated preferentially with two different mmBCFAs and serine was acylated promiscuously with a broad range of fatty acids, including eicosapentaenoic acid. Global profiling of fatty acylated proteins using a set of click chemistry–capable alkyne probes for branched- and straight-chain fatty acids uncovered 1,013S-acylated proteins and 510 hydroxylamine-resistantN- orO-acylated proteins. Subsets ofS-acylated proteins were labeled almost exclusively by either a branched-chain or a straight-chain probe, demonstrating acylation specificity at the protein level. Acylation specificity was confirmed for selected examples, including theS-acyltransferase DHHC-10. Last, homology searches for the identified acylated proteins revealed a high degree of conservation of acylation site patterns across metazoa. Our results show that protein fatty acylation patterns integrate distinct branches of lipid metabolism in a residue- and protein-specific manner, providing a basis for mechanistic studies at both the amino acid and protein levels. 

Epilepsy is one of the most common neurological diseases globally, affecting around 50 million people worldwide. Fortunately, up to 70 percent of people with epilepsy could live seizure-free if properly diagnosed and treated, and a reliable technique to monitor the onset of seizures could improve the quality of life of patients who are constantly facing the fear of random seizure attacks. The scalp-based EEG test, despite being the gold standard for diagnosing epilepsy, is costly, necessitates hospitalization, demands skilled professionals for operation, and is discomforting for users. In this paper, we propose EarSD, a novel lightweight, unobtrusive, and socially acceptable ear-worn system to detect epileptic seizure onsets by measuring the physiological signals from behind the user's ears. EarSD includes an integrated custom-built sensing, computing, and communication PCB to collect and amplify the signals of interest, remove the noises caused by motion artifacts and environmental impacts, and stream the data wirelessly to the computer or mobile phone nearby, where data are uploaded to the host computer for further processing. We conducted both in-lab and in-hospital experiments with epileptic seizure patients who were hospitalized for seizure studies. The preliminary results confirm that EarSD can detect seizures with up to 95.3 percent accuracy by just using classical machine learning algorithms.