Computational identification of a systemic antibiotic for gram-negative bacteria.

Discovery of antibiotics acting against Gram-negative species is uniquely challenging due to their restrictive penetration barrier. BamA, which inserts proteins into the outer membrane, is an attractive target due to its surface location. Darobactins produced by Photorhabdus, a nematode gut microbiome symbiont, target BamA. We reasoned that a computational search for genes only distantly related to the darobactin operon may lead to novel compounds. Following this clue, we identified dynobactin A, a novel peptide antibiotic from Photorhabdus australis containing two unlinked rings. Dynobactin is structurally unrelated to darobactins, but also targets BamA. Based on a BamA-dynobactin co-crystal structure and a BAM-complex-dynobactin cryo-EM structure, we show that dynobactin binds to the BamA lateral gate, uniquely protruding into its ß-barrel lumen. Dynobactin showed efficacy in a mouse systemic Escherichia coli infection. This study demonstrates the utility of computational approaches to antibiotic discovery and suggests that dynobactin is a promising lead for drug development.

Brain Function in Gulf War Illness (GWI) and Associated Mental Health Comorbidities.

GWI has affected a substantial number of Gulf War (GW) veterans. The disease involves several organ systems among which the brain is most prominent. Neurological, cognitive and mood-related (NCM) symptoms frequently dominate and are at the root of chronic ill-health and disability in veterans suffering from GWI. In addition, such symptoms frequently co-occur with diagnosable mental health disorders, predominantly posttraumatic stress disorder (PTSD). Here we investigated the possibility that increased GWI severity leads, above a threshold, to a diagnosable mental health disorder (excluding psychosis). For this purpose, we used, in separate analyses, symptom severity scores and resting-state brain functional connectivity patterns, as determined by magnetoencephalography (MEG). Two-hundred-thirty GW-era veterans participated in this study. They completed diagnostic interviews to establish the presence of GWI and assess mental health status. This distinguished 3 groups: healthy controls (N = 41), veterans with GWI and no mental illness (GWI group, N = 91), and veterans with both GWI and mental health disorder (GWI+MH, N = 98). For each veteran, symptom severity scores in the 6 GWI domains (fatigue, pain, NCM, skin, gastrointestinal, respiratory) were available as well as 9 summary measures of the distribution of Synchronous Neural Interactions (SNI) derived from the MEG recordings. We tested the hypothesis that, in the presence of GWI, the appearance of a diagnosable mental health disorder may depend on GWI symptom severity. For that purpose, we performed a logistic regression on the GWI population, where the presence (or absence) of the MH disorder was the dependent variable and the age- and gender-adjusted GWI severity in the 6-symptom domains were the predictors. The outcome was the probability that a participant will have MH disorder or not. Similarly, we tested the hypothesis that the presence of the MH disorder can be predicted by the SNI distribution patterns by performing a second logistic regression as above but with the 9 SNI measures as predictors. We found GWI symptom severity differed significantly across groups (GWI+MH > GWI > Control). SNI distributions of the GWI group also differed significantly from the other groups in a systematic hemispheric pattern, such that the presence of GWI involved predominantly the left hemisphere, and presence of mental health disorders involved, in addition, the right hemisphere. Both logistic regressions yielded highly significant outcomes, demonstrating that both GWI symptom severity and SNI distribution measures can predict the presence of MH disorder in GWI. Remarkably, the prediction probabilities for MH presence derived from the symptom-based and SNI-based logistic regressions were positively and highly statistically significantly correlated. Taken together, both objective (neural) and subjective (symptoms) indices suggest that GWI is distinct from healthy controls and varies in severity in a continuum that leads, at the higher end, to a diagnosable MH disorder. The positive correlation between the GWI symptom-based and brain-based predicted classifications provides a key link between GWI symptom severity and synchronous neural interactions in the context of mental illness.

Diamide Inhibitors of the Bacillus subtilis N-Acetylglucosaminidase LytG That Exhibit Antibacterial Activity.

N-Acetylglucosaminidases (GlcNAcases) play an important role in the remodeling and recycling of bacterial peptidoglycan by degrading the polysaccharide backbone. Genetic deletions of autolysins can impair cell division and growth, suggesting an opportunity for using small molecule autolysin inhibitors both as tools for studying the chemical biology of autolysins and also as antibacterial agents. We report here the synthesis and evaluation of a panel of diamides that inhibit the growth of Bacillus subtilis. Two compounds, fgkc (21) and fgka (5), were found to be potent inhibitors (MIC 3.8 ± 1.0 and 21.3 ± 0.1 µM, respectively). These compounds inhibit the B. subtilis family 73 glycosyl hydrolase LytG, an exo GlcNAcase. Phenotypic analysis of fgkc (21)-treated cells demonstrates a propensity for cells to form linked chains, suggesting impaired cell growth and division.

A Magnetoencephalographic (MEG) Study of Gulf War Illness (GWI).

BACKGROUND: Gulf War Illness (GWI) has affected many Gulf War veterans. It involves several organs, most notably the brain. Neurological-cognitive-mood-related symptoms frequently dominate and are at the root of chronic ill-health and disability in GWI. Here we investigated the neural mechanisms underlying brain dysfunction in GWI in the absence of mental health disorders. METHODS: Eighty-six veterans completed diagnostic interviews to establish the presence of GWI and assess mental health status. Participants diagnosed with GWI met both Center for Disease Control and Kansas criteria. We studied 46 healthy controls and 40 veterans with GWI without mental illness. They all underwent a resting-state magnetoencephalographic (MEG) scan to assess brain communication based on synchronous neural interactions (SNI; Georgopoulos et al., 2007). FINDINGS: We found substantial differences in SNI between control and GWI groups centered on the cerebellum and frontal cortex. In addition, using the maxima and minima of SNI per sensor as predictors, we successfully classified 94.2% of the 86 participants (95% sensitivity, 93.5% specificity). INTERPRETATION: These findings document distinct differences in brain function between control and GWI in the absence of mental health comorbidities, differences that are excellent predictors of GWI. FUNDING: U.S. Department of Veterans Affairs and University of Minnesota.

Automated structural classification of lipids by machine learning.

MOTIVATION: Modern lipidomics is largely dependent upon structural ontologies because of the great diversity exhibited in the lipidome, but no automated lipid classification exists to facilitate this partitioning. The size of the putative lipidome far exceeds the number currently classified, despite a decade of work. Automated classification would benefit ongoing classification efforts by decreasing the time needed and increasing the accuracy of classification while providing classifications for mass spectral identification algorithms. RESULTS: We introduce a tool that automates classification into the LIPID MAPS ontology of known lipids with >95% accuracy and novel lipids with 63% accuracy. The classification is based upon simple chemical characteristics and modern machine learning algorithms. The decision trees produced are intelligible and can be used to clarify implicit assumptions about the current LIPID MAPS classification scheme. These characteristics and decision trees are made available to facilitate alternative implementations. We also discovered many hundreds of lipids that are currently misclassified in the LIPID MAPS database, strongly underscoring the need for automated classification. AVAILABILITY AND IMPLEMENTATION: Source code and chemical characteristic lists as SMARTS search strings are available under an open-source license at https://www.github.com/princelab/lipid_classifier.

Risk and protective factors associated with symptoms of post-traumatic stress, depression, and alcohol misuse in OEF/OIF veterans.

Military personnel returning from the conflicts in Iraq and Afghanistan commonly experience mental health problems and efforts are underway to determine risk and protective factors associated with postdeployment mental health concerns. This study examined the contribution of trait neuroticism, predeployment life events, combat experience, perceptions of threat, and postdeployment social support on mental health symptoms at 6 months, 12 months, and 24 months postdeployment. Two hundred seventy-one veterans completed self-report measures. Hierarchical regression analyses demonstrated that neuroticism predicted post-traumatic stress and depressive symptoms at all 3 time points; perceived threat predicted post-traumatic stress symptoms at time 1 and time 2 and depressive symptoms at time 2. Social support was a strong negative predictor of post-traumatic stress and depressive symptoms. Alcohol misuse was not significantly predicted by any of the variables. The present study highlights the role of perceived threat and trait neuroticism on postdeployment mental health symptoms and indicates social support is a robust protective factor. Efforts aimed at increasing sustained postdeployment social support may help defend against significant mental health problems among veterans.