Unusual transformations in the biosynthesis of the antibiotic phosphinothricin tripeptide

Phosphinothricin tripeptide (PTT, phosphinothricylalanylalanine) is a natural-product antibiotic and potent herbicide that is produced by Streptomyces hygroscopicus ATCC 21705 (ref. 1) and Streptomyces viridochromogenes DSM 40736 (ref. 2). PTT has attracted widespread interest because of its commercial applications and unique phosphinic acid functional group. Despite intensive study since its discovery in 1972 (see ref. 3 for a comprehensive review), a number of steps early in the PTT biosynthetic pathway remain uncharacterized. Here we report a series of interdisciplinary experiments involving the construction of defined S. viridochromogenes mutants, chemical characterization of accumulated intermediates, and in vitro assay of selected enzymes to examine these critical steps in PTT biosynthesis. Our results indicate that early PTT biosynthesis involves a series of catalytic steps that to our knowledge has not been described so far, including a highly unusual reaction for carbon bond cleavage. In sum, we define a pathway for early PTT biosynthesis that is more complex than previously appreciated.     

High dimensional mediation analysis with latent variables

We propose a model for high dimensional mediation analysis that includes latent variables. We describe our model in the context of an epidemiologic study for incident breast cancer with one exposure and a large number of biomarkers (i.e., potential mediators). We assume that the exposure directly influences a group of latent, or unmeasured, factors which are associated with both the outcome and a subset of the biomarkers. The biomarkers associated with the latent factors linking the exposure to the outcome are considered “mediators.” We derive the likelihood for this model and develop an expectation‐maximization algorithm to maximize an L1‐penalized version of this likelihood to limit the number of factors and associated biomarkers. We show that the resulting estimates are consistent and that the estimates of the nonzero parameters have an asymptotically normal distribution. In simulations, procedures based on this new model can have significantly higher power for detecting the mediating biomarkers compared with the simpler approaches. We apply our method to a study that evaluates the relationship between body mass index, 481 metabolic measurements, and estrogen‐receptor positive breast cancer.     

β-Branched Amino Acids Stabilize Specific Conformations of Cyclic Hexapeptides

Cyclic peptides (CPs) are a promising class of molecules for drug development, particularly as inhibitors of protein-protein interactions. Predicting low-energy structures and global structural ensembles of individual CPs is critical for the design of bioactive molecules, but these are challenging to predict and difficult to verify experimentally. In our previous work, we used explicit-solvent molecular dynamics simulations with enhanced sampling methods to predict the global structural ensembles of cyclic hexapeptides containing different permutations of glycine, alanine, and valine. One peptide, cyclo-(VVGGVG) or P7, was predicted to be unusually well structured. In this work, we synthesized P7, along with a less well-structured control peptide, cyclo-(VVGVGG) or P6, and characterized their global structural ensembles in water using NMR spectroscopy. The NMR data revealed a structural ensemble similar to the prediction for P7 and showed that P6 was indeed much less well-structured than P7. We then simulated and experimentally characterized the global structural ensembles of several P7 analogs and discovered that β-branching at one critical position within P7 is important for overall structural stability. The simulations allowed deconvolution of thermodynamic factors that underlie this structural stabilization. Overall, the excellent correlation between simulation and experimental data indicates that our simulation platform will be a promising approach for designing well-structured CPs and also for understanding the complex interactions that control the conformations of constrained peptides and other macrocycles.     

Smell-induced gamma oscillations in human olfactory cortex are required for accurate perception of odor identity

Studies of neuronal oscillations have contributed substantial insight into the mechanisms of visual, auditory, and somatosensory perception. However, progress in such research in the human olfactory system has lagged behind. As a result, the electrophysiological properties of the human olfactory system are poorly understood, and, in particular, whether stimulus-driven high-frequency oscillations play a role in odor processing is unknown. Here, we used direct intracranial recordings from human piriform cortex during an odor identification task to show that 3 key oscillatory rhythms are an integral part of the human olfactory cortical response to smell: Odor induces theta, beta, and gamma rhythms in human piriform cortex. We further show that these rhythms have distinct relationships with perceptual behavior. Odor-elicited gamma oscillations occur only during trials in which the odor is accurately perceived, and features of gamma oscillations predict odor identification accuracy, suggesting that they are critical for odor identity perception in humans. We also found that the amplitude of high-frequency oscillations is organized by the phase of low-frequency signals shortly following sniff onset, only when odor is present. Our findings reinforce previous work on theta oscillations, suggest that gamma oscillations in human piriform cortex are important for perception of odor identity, and constitute a robust identification of the characteristic electrophysiological response to smell in the human brain. Future work will determine whether the distinct oscillations we identified reflect distinct perceptual features of odor stimuli.

Intracranial recordings from human olfactory cortex reveal a characteristic spectrotemporal response to odors, including theta, beta and gamma oscillations, and show that high-frequency responses are critical for accurate perception of odors.     

NK cells and monocytes modulate primary HTLV-1 infection

We investigated the impact of monocytes, NK cells, and CD8⁺ T-cells in primary HTLV-1 infection by depleting cell subsets and exposing macaques to either HTLV-1 wild type (HTLV-1_WT) or to the HTLV-1_p12KO mutant unable to infect replete animals due to a single point mutation in orf-I that inhibits its expression. The orf-I encoded p8/p12 proteins counteract cytotoxic NK and CD8⁺ T-cells and favor viral DNA persistence in monocytes. Double NK and CD8⁺ T-cells or CD8 depletion alone accelerated seroconversion in all animals exposed to HTLV-1_WT. In contrast, HTLV-1_p12KO infectivity was fully restored only when NK cells were also depleted, demonstrating a critical role of NK cells in primary infection. Monocyte/macrophage depletion resulted in accelerated seroconversion in all animals exposed to HTLV-1_WT, but antibody titers to the virus were low and not sustained. Seroconversion did not occur in most animals exposed to HTLV-1_p12KO. In vitro experiments in human primary monocytes or THP-1 cells comparing HTLV-1_WT and HTLV-1_p12KO demonstrated that orf-I expression is associated with inhibition of inflammasome activation in primary cells, with increased CD47 “don’t-eat-me” signal surface expression in virus infected cells and decreased monocyte engulfment of infected cells. Collectively, our data demonstrate a critical role for innate NK cells in primary infection and suggest a dual role of monocytes in primary infection. On one hand, orf-I expression increases the chances of viral transmission by sparing infected cells from efferocytosis, and on the other may protect the engulfed infected cells by modulating inflammasome activation. These data also suggest that, once infection is established, the stoichiometry of orf-I expression may contribute to the chronic inflammation observed in HTLV-1 infection by modulating monocyte efferocytosis.     

Measuring the repertoire of age-related behavioral changes in Drosophila melanogaster

Aging affects almost all aspects of an organism—its morphology, its physiology, its behavior. Isolating which biological mechanisms are regulating these changes, however, has proven difficult, potentially due to our inability to characterize the full repertoire of an animal’s behavior across the lifespan. Using data from fruit flies (D. melanogaster) we measure the full repertoire of behaviors as a function of age. We observe a sexually dimorphic pattern of changes in the behavioral repertoire during aging. Although the stereotypy of the behaviors and the complexity of the repertoire overall remains relatively unchanged, we find evidence that the observed alterations in behavior can be explained by changing the fly’s overall energy budget, suggesting potential connections between metabolism, aging, and behavior.     

Characterising metabolomic signatures of lipid-modifying therapies through drug target mendelian randomisation

Large-scale molecular profiling and genotyping provide a unique opportunity to systematically compare the genetically predicted effects of therapeutic targets on the human metabolome. We firstly constructed genetic risk scores for 8 drug targets on the basis that they primarily modify low-density lipoprotein (LDL) cholesterol (HMGCR, PCKS9, and NPC1L1), high-density lipoprotein (HDL) cholesterol (CETP), or triglycerides (APOC3, ANGPTL3, ANGPTL4, and LPL). Conducting mendelian randomisation (MR) provided strong evidence of an effect of drug-based genetic scores on coronary artery disease (CAD) risk with the exception of ANGPTL3. We then systematically estimated the effects of each score on 249 metabolic traits derived using blood samples from an unprecedented sample size of up to 115,082 UK Biobank participants. Genetically predicted effects were generally consistent among drug targets, which were intended to modify the same lipoprotein lipid trait. For example, the linear fit for the MR estimates on all 249 metabolic traits for genetically predicted inhibition of LDL cholesterol lowering targets HMGCR and PCSK9 was r² = 0.91. In contrast, comparisons between drug classes that were designed to modify discrete lipoprotein traits typically had very different effects on metabolic signatures (for instance, HMGCR versus each of the 4 triglyceride targets all had r² < 0.02). Furthermore, we highlight this discrepancy for specific metabolic traits, for example, finding that LDL cholesterol lowering therapies typically had a weak effect on glycoprotein acetyls, a marker of inflammation, whereas triglyceride modifying therapies assessed provided evidence of a strong effect on lowering levels of this inflammatory biomarker. Our findings indicate that genetically predicted perturbations of these drug targets on the blood metabolome can drastically differ, despite largely consistent effects on risk of CAD, with potential implications for biomarkers in clinical development and measuring treatment response.