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.     

Long-Term Carriage of Medicopsis romeroi,an Agent of Black-Grain Mycetoma,Presenting as Phaeohyphomycosis in a Renal Transplant Patient

Mycopathologia - Medicopsis species are rare fungal pathogens that frequently resist common antifungal therapies and are difficult to identify morphologically as conidia are produced in pycnidia, a...     

Context‐dependent biotic interactions control plant abundance across altitudinal environmental gradients

Many biotic interactions influence community structure, yet most distribution models for plants have focused on plant competition or used only abiotic variables to predict plant abundance. Furthermore, biotic interactions are commonly context‐dependent across abiotic gradients. For example, plant–plant interactions can grade from competition to facilitation over temperature gradients. We used a hierarchical Bayesian framework to predict the abundances of 12 plant species across a mountain landscape and test hypotheses on the context‐dependency of biotic interactions over abiotic gradients. We combined field‐based estimates of six biotic interactions (foliar herbivory and pathogen damage, fungal root colonization, fossorial mammal disturbance, plant cover and plant diversity) with abiotic data on climate and soil depth, nutrients and moisture. All biotic interactions were significantly context‐dependent along temperature gradients. Results supported the stress gradient hypothesis: as abiotic stress increased, the strength or direction of the relationship between biotic variables and plant abundance generally switched from negative (suggesting suppressed plant abundance) to positive (suggesting facilitation/mutualism). For half of the species, plant cover was the best predictor of abundance, suggesting that the prior focus on plant–plant interactions is well‐justified. Explicitly incorporating the context‐dependency of biotic interactions generated novel hypotheses about drivers of plant abundance across abiotic gradients and may improve the accuracy of niche models.     

Neuronal ceroid lipofuscinosis related ER membrane protein CLN8 regulates PP2A activity and ceramide levels

The neuronal ceroid lipofuscinoses (NCLs) are a group of inherited neurodegenerative lysosomal storage disorders. CLN8 deficiency causes a subtype of NCL, referred to as CLN8 disease. CLN8 is an ER resident protein with unknown function; however, a role in ceramide metabolism has been suggested. In this report, we identified PP2A and its biological inhibitor I2PP2A as interacting proteins of CLN8. PP2A is one of the major serine/threonine phosphatases in cells and governs a wide range of signaling pathways by dephosphorylating critical signaling molecules. We showed that the phosphorylation levels of several substrates of PP2A, namely Akt, S6 kinase, and GSK3β, were decreased in CLN8 disease patient fibroblasts. This reduction can be reversed by inhibiting PP2A phosphatase activity with cantharidin , suggesting a higher PP2A activity in CLN8-deficient cells. Since ceramides are known to bind and influence the activity of PP2A and I2PP2A, we further examined whether ceramide levels in the CLN8-deficient cells were changed. Interestingly, the ceramide levels were reduced by 60% in CLN8 disease patient cells compared to controls. Furthermore, we observed that the conversion of ER-localized NBD-C6-ceramide to glucosylceramide and sphingomyelin in the Golgi apparatus was not affected in CLN8-deficient cells, indicating transport of ceramides from ER to the Golgi apparatus was normal. A model of how CLN8 along with ceramides affects I2PP2A and PP2A binding and activities is proposed.     

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.