Risk for Substance Use Disorders in young adulthood: Associations with developmental experiences of homelessness, foster care, and adverse childhood experiences.

BACKGROUND: Multiple developmental risk factors for Substance Use Disorders (SUDs) during young adulthood have been identified. In this investigation, we examined the impact of homelessness, foster care, and adverse childhood experiences (ACEs) prior to 12th grade on the development of three common SUDs during young adulthood-Alcohol Use Disorder (AUD), Tobacco Use Disorder (TUD) and Cannabis Use Disorder (CUD). Our hypothesis was that while both homelessness and ACEs are significant risk factors for young adult SUDs, foster care involvement might convey protection. METHODS: Using nationally representative data from the National Longitudinal Study of Adolescent to Adult Health, measures of ACEs were derived from the CDC-Kaiser ACE study, and DSM-V SUD diagnoses were derived from items originally based on DSM-IV. SUD diagnoses were binned into "mild", "moderate", and "severe" groupings. Survey-based logistic models were used to estimate risks of SUDs while controlling for demographics. RESULTS: The results suggest that the experience of homelessness prior to 12th grade in addition to ACEs were significantly associated with the development in young adulthood of the most severe forms of AUD and TUD and all severity levels of CUD. Foster care was not associated with either risk or protection from SUDs. CONCLUSIONS: The experience of homelessness during development may be viewed as another detrimental ACE that is a risk factor for the most common SUDs in young adulthood. Given the magnitude of the current epidemic of homelessness in the U.S., these results should raise substantial concern.

Phosphorylation-specific MS/MS scoring for rapid and accurate phosphoproteome analysis.

The promise of mass spectrometry as a tool for probing signal-transduction is predicated on reliable identification of post-translational modifications. Phosphorylations are key mediators of cellular signaling, yet are hard to detect, partly because of unusual fragmentation patterns of phosphopeptides. In addition to being accurate, MS/MS identification software must be robust and efficient to deal with increasingly large spectral data sets. Here, we present a new scoring function for the Inspect software for phosphorylated peptide tandem mass spectra for ion-trap instruments, without the need for manual validation. The scoring function was modeled by learning fragmentation patterns from 7677 validated phosphopeptide spectra. We compare our algorithm against SEQUEST and X!Tandem on testing and training data sets. At a 1% false positive rate, Inspect identified the greatest total number of phosphorylated spectra, 13% more than SEQUEST and 39% more than X!Tandem. Spectra identified by Inspect tended to score better in several spectral quality measures. Furthermore, Inspect runs much faster than either SEQUEST or X!Tandem, making desktop phosphoproteomics feasible. Finally, we used our new models to reanalyze a corpus of 423,000 LTQ spectra acquired for a phosphoproteome analysis of Saccharomyces cerevisiae DNA damage and repair pathways and discovered 43% more phosphopeptides than the previous study.