Human liver microbiota modeling strategy at the early onset of fibrosis.

BACKGROUND: Gut microbiota is involved in the development of liver diseases such as fibrosis. We and others identified that selected sets of gut bacterial DNA and bacteria translocate to tissues, notably the liver, to establish a non-infectious tissue microbiota composed of microbial DNA and a low frequency live bacteria. However, the precise set of bacterial DNA, and thereby the corresponding taxa associated with the early stages of fibrosis need to be identified. Furthermore, to overcome the impact of different group size and patient origins we adapted innovative statistical approaches. Liver samples with low liver fibrosis scores (F0, F1, F2), to study the early stages of the disease, were collected from Romania(n = 36), Austria(n = 10), Italy(n = 19), and Spain(n = 17). The 16S rRNA gene was sequenced. We considered the frequency, sparsity, unbalanced sample size between cohorts to identify taxonomic profiles and statistical differences. RESULTS: Multivariate analyses, including adapted spectral clustering with L1-penalty fair-discriminant strategies, and predicted metagenomics were used to identify that 50% of liver taxa associated with the early stage fibrosis were Enterobacteriaceae, Pseudomonadaceae, Xanthobacteriaceae and Burkholderiaceae. The Flavobacteriaceae and Xanthobacteriaceae discriminated between F0 and F1. Predicted metagenomics analysis identified that the preQ0 biosynthesis and the potential pathways involving glucoryranose and glycogen degradation were negatively associated with liver fibrosis F1-F2 vs F0. CONCLUSIONS: Without demonstrating causality, our results suggest first a role of bacterial translocation to the liver in the progression of fibrosis, notably at the earliest stages. Second, our statistical approach can identify microbial signatures and overcome issues regarding sample size differences, the impact of environment, and sets of analyses. TRIAL REGISTRATION: TirguMECCH ROLIVER Prospective Cohort for the Identification of Liver Microbiota, registration 4065/2014. Registered 01 01 2014.

Ambulatory blood pressure and drug treatment for orthostatic hypotension as predictors of mortality in patients with multiple system atrophy.

OBJECTIVES: Multiple system atrophy (MSA) is a rare fatal neurodegenerative disease characterized by parkinsonism, cerebellar ataxia and autonomic failure. This study was aimed at investigating possible associations between mortality, 24-h blood pressure (BP) level and variability, and drug treatments for orthostatic hypotension (OH) in MSA patients. METHODS: A total of 129 patients followed at the French Reference Center for MSA who underwent routine 24-h ambulatory BP monitoring were included. Unified MSA Rating Scale (UMSARS) scores, drug treatments and the occurrence and cause of death were recorded. RESULTS: Seventy patients died during follow-up (2.9 ± 1.8 years), mainly from terminal illness, pulmonary or sudden death. Multivariate Cox regression analysis, after adjustment for gender, disease duration and severity (UMSARS I+II score), showed that increased daytime systolic BP variability, OH severity and OH drug treatment were independently correlated with mortality. OH treatment was associated with the risk of cardiac causes and/or sudden death (p = 0.01). In a fully adjusted model, male gender [(female vs. male) hazard ratio (HR) 0.56, 95% CI 0.34-0.94, p = 0.03], UMSARS I+II score (HR 1.04, 95% CI 1.02-1.06, p < 0.01), systolic BP daytime variability (HR 3.66, 95% CI 1.46-9.17, p < 0.01) and OH treatment (HR: 2.13, 95% CI 1.15-3.94, p = 0.02) predicted mortality. CONCLUSIONS: Increased daytime BP variability and OH treatment were predictive of mortality in patients with MSA, independently from disease severity. Further studies are required to assess if these associations are explained by more severe autonomic dysfunction or if OH treatment exposes per se to a specific risk in this population.

Intracellular detection of singlet oxygen using fluorescent nanosensors.

Detection of singlet oxygen is of great importance for a range of therapeutic applications, particularly photodynamic therapy, plasma therapy and also during photo-endosomolytic activity. Here we present a novel method of intracellular detection of singlet oxygen using biocompatible polymeric nanosensors, encapsulating the organic fluorescent dye, Singlet Oxygen Sensor Green (SOSG) within its hydrophobic core. The singlet oxygen detection efficiency of the nanosensors was quantified experimentally by treating them with a plasma source and these results were further validated by using Monte Carlo simulations. The change in fluorescence intensity of the nanosensors serves as a metric to detect singlet oxygen in the local micro-environment inside mammalian cancer cells. We used these nanosensors for monitoring singlet oxygen inside endosomes and lysosomes of cancer cells, during cold plasma therapy, using a room-temperature Helium plasma jet.

Kinetically driven island morphology in growth on strained Cu(100).

We study the effects of strain on the monomer and dimer diffusion mechanisms and island morphology during the growth of Cu on a biaxially strained Cu(100) substrate. We find an approximately linear dependence of the activation barriers on strain. In particular, while hopping is favored for compressive and/or small (<2%) tensile strain, for greater than 2% tensile strain, the exchange mechanism is favored. We then present the results of temperature-accelerated dynamics simulations of submonolayer growth at 200 K. For the case of 2% compressive strain we find that, as in previous kinetic Monte Carlo simulations of Cu/Ni(100) growth, the competition between island growth and multi-atom relaxation ("pop-out") events leads to an island morphology with a mixture of open and closed steps. At slightly higher coverage, island coalescence then leads to elongated islands. However, annealing leads to a significant decrease in the number of open steps. In contrast, for the case of 8% tensile strain, only one large strongly anisotropic island is formed. Surprisingly, we find that despite the large strain, the island anisotropy is not due to energetics but is instead due to anisotropic attachment barriers that favor the exchange-mediated attachment of monomers to corners over close-packed step-edges. An explanation for the asymmetry in attachment barriers is provided. Our results provide a new general kinetic mechanism for the formation of anisotropic islands in the presence of isotropic diffusion and tensile strain.

Breast cancer and spironolactone: an observational postmarketing study.

INTRODUCTION: Recent studies have discussed the risk of breast cancer with antihypertensive drugs. For spironolactone, data are conflicting. The present paper investigates this potential signal in VigiBase®, the World Health Organization Global Individual Case Safety Report (ICSR) database. METHODS: In VigiBase®, we performed a case/non-case study using data registered from 1981 (spironolactone's marketing authorization) to December 31, 2017. Among women ≥ 50 years, we measured the risk of reporting "Breast malignant tumors" compared with all other adverse drug reactions (as a crude and adjusted (a) reporting odds ratio (ROR 95% CI)) for spironolactone compared with first, all other drugs and second, pseudo aldosterone antagonists (amiloride, triamterene). ROR were adjusted for age, year of report, continent of report, number of drug prescribed, and completeness score. Sensitivity analyses were performed after exclusion of drug competitors (i.e., drugs like estroprogestative therapy and progestogens that could mask a putative signal) and reports from health professionals. RESULTS: During the study period, 125 ICSRs reported spironolactone exposure and breast malignant cancer in women ≥ 50 years. We failed to find a positive association between spironolactone exposure and breast cancer in comparison with exposure to other drugs (aROR = 0.63 95% CI [0.52-0.75]) or pseudo aldosterone antagonists (amiloride, triamterene) (0.56 [0.44-0.72]). Similar trends were found after exclusion of drug competitors and/or reports from health professionals. CONCLUSION: This study did not find evidence for breast cancer associated with spironolactone.

Changes in blood microbiota profiles associated with liver fibrosis in obese patients: A pilot analysis.

The early detection of liver fibrosis among patients with nonalcoholic fatty liver disease (NAFLD) is an important clinical need. In view of the suggested role played by bacterial translocation in liver disease and obesity, we sought to investigate the relationship between blood microbiota and liver fibrosis (LF) in European cohorts of patients with severe obesity. We carried out a cross-sectional study of obese patients, well characterized with respect to the severity of the NAFLD, in the cohort FLORINASH. This cohort has been divided into a discovery cohort comprising 50 Spanish patients and then in a validation cohort of 71 Italian patients. Blood bacterial DNA was analyzed both quantitatively by 16S ribosomal DNA (rDNA) quantitative polymerase chain reaction and qualitatively by 16S rDNA targeted metagenomic sequencing and functional metagenome prediction. Spanish plasma bile acid contents were analyzed by liquid chromatography/mass spectrometry. The 16S rDNA concentration was significantly higher in patients of the discovery cohort with LF. By 16S sequencing, we found specific differences in the proportion of several bacterial taxa in both blood and feces that correlate with the presence of LF, thus defining a specific signature of the liver disease. Several secondary/primary bile acid ratios were also decreased with LF in the discovery cohort. We confirmed, in the validation cohort, the correlation between blood 16S rDNA concentration and LF, whereas we did not confirm the specific bacterial taxa signature, despite a similar trend in patients with more-severe fibrosis. CONCLUSION: Changes in blood microbiota are associated with LF in obese patients. Blood microbiota analysis provides potential biomarkers for the detection of LF in this population. (Hepatology 2016;64:2015-2027).

Island nucleation and growth with anomalous diffusion in one-dimension.

Recently a general rate-equation (RE) theory of submonolayer island nucleation and growth was developed [J. G. Amar and M. Semaan, Phys. Rev. E 93, 062805 (2016)] which takes into account the critical island-size i, island fractal dimension df, substrate dimension d, and diffusion exponent µ, and good agreement with simulations was found for the case of irreversible growth corresponding to a critical island-size i=1 with d = 2. Here we present the results of simulations carried out in 1D (corresponding to d = 1) of island nucleation and growth with anomalous diffusion which were carried out for both the case of superdiffusion (µ>1) and subdiffusion (µ<1). Excellent agreement is found with the general RE theory for both irreversible growth (i=1) and reversible growth with i=2 for all 0≤µ≤2.

Comprehensive description of blood microbiome from healthy donors assessed by 16S targeted metagenomic sequencing.

BACKGROUND: Recent studies have revealed that the blood of healthy humans is not as sterile as previously supposed. The objective of this study was to provide a comprehensive description of the microbiome present in different fractions of the blood of healthy individuals. STUDY DESIGN AND METHODS: The study was conducted in 30 healthy blood donors to the French national blood collection center (Établissement Français du Sang). We have set up a 16S rDNA quantitative polymerase chain reaction assay as well as a 16S targeted metagenomics sequencing pipeline specifically designed to analyze the blood microbiome, which we have used on whole blood as well as on different blood fractions (buffy coat [BC], red blood cells [RBCs], and plasma). RESULTS: Most of the blood bacterial DNA is located in the BC (93.74%), and RBCs contain more bacterial DNA (6.23%) than the plasma (0.03%). The distribution of 16S DNA is different for each fraction and spreads over a relatively broad range among donors. At the phylum level, blood fractions contain bacterial DNA mostly from the Proteobacteria phylum (more than 80%) but also from Actinobacteria, Firmicutes, and Bacteroidetes. At deeper taxonomic levels, there are striking differences between the bacterial profiles of the different blood fractions. CONCLUSION: We demonstrate that a diversified microbiome exists in healthy blood. This microbiome has most likely an important physiologic role and could be implicated in certain transfusion-transmitted bacterial infections. In this regard, the amount of 16S bacterial DNA or the microbiome profile could be monitored to improve the safety of the blood supply.

Improved scaling of temperature-accelerated dynamics using localization.

While temperature-accelerated dynamics (TAD) is a powerful method for carrying out non-equilibrium simulations of systems over extended time scales, the computational cost of serial TAD increases approximately as N(3) where N is the number of atoms. In addition, although a parallel TAD method based on domain decomposition [Y. Shim et al., Phys. Rev. B 76, 205439 (2007)] has been shown to provide significantly improved scaling, the dynamics in such an approach is only approximate while the size of activated events is limited by the spatial decomposition size. Accordingly, it is of interest to develop methods to improve the scaling of serial TAD. As a first step in understanding the factors which determine the scaling behavior, we first present results for the overall scaling of serial TAD and its components, which were obtained from simulations of Ag/Ag(100) growth and Ag/Ag(100) annealing, and compare with theoretical predictions. We then discuss two methods based on localization which may be used to address two of the primary "bottlenecks" to the scaling of serial TAD with system size. By implementing both of these methods, we find that for intermediate system-sizes, the scaling is improved by almost a factor of N(1/2). Some additional possible methods to improve the scaling of TAD are also discussed.

Critical island-size, stability, and morphology of 2D colloidal Au nanoparticle islands.

The critical island-size, stability, and morphology of 2D colloidal Au nanoparticle islands formed during drop-drying are studied using an empirical potential which takes into account core-core, ligand-ligand, and ligand-solvent interactions. Good agreement with experiment is obtained for the dependence of the critical island-size on nanoparticle diameter. Our results for the critical length-scale for smoothing via edge-diffusion are also consistent with the limited facet size and island-relaxation observed in experiments. In addition, the relatively high rate of monomer diffusion on an island as well as the low barrier for interlayer diffusion are consistent with experimental observations that second-layer growth does not occur until after the first layer is complete.

Adsorption and diffusion of colloidal Au nanoparticles at a liquid-vapor interface.

Motivated by recent drop-drying experiments of Au nanoparticle (NP) island self-assembly, we investigate the structure, diffusion, and binding of dodecanethiol-coated Au NPs adsorbed at the toluene-vapor interface using molecular dynamics (MD) simulations as well as analytical calculations. For a 6 nm core diameter NP our results indicate the existence of significant intermixing between the ligands and the solvent. As a result, the NP lies primarily below the interface with only a portion of the ligands sticking out, while the toluene-vapor interface is significantly higher in the region above the NP core than away from the NP. These results are consistent with a competition between the negative free energy of mixing of toluene and the dodecanethiol ligands, which tends to keep the NP below the interface, and the effects of surface tension which keeps the NP near the interface. Consistent with this result, we find that the coefficient for nanoparticle diffusion along the interface is close to the Stokes-Einstein prediction for three-dimensional bulk diffusion. An analysis of the ligand arrangement surrounding the NP also indicates that there is relatively little asymmetry in the ligand-coating. We then consider the effects of van der Waals interactions on the adsorption energy. In particular, we derive an analytical expression for the van der Waals interaction energy between a coated nanoparticle and the surrounding solvent along with a closed-form expression for the van der Waals corrections to the binding energy at the interface due to the long-range core-solvent interaction. Using these results along with the results of our MD simulations, we then estimate the van der Waals corrections to the adsorption energy for dodecanethiol-coated Au nanoparticles at the toluene-vapor interface as well as for decanethiol-coated nanoparticles at the water-vapor interface. In both cases, we find that the long-range core-solvent interaction may significantly reduce the binding energy. Based on these results, we conclude that in many cases, the core-solvent van der Waals interaction is likely to have a significant effect on the binding energy and interface position of Au NPs. Our results also indicate that the competition between the van der Waals interaction and the short-range attraction to the interface leads to the existence of well-defined activation barriers for nanoparticle adsorption from the solvent as well as for interfacial desorption.

Island density scaling in reversible submonolayer growth with attachment barriers.

Island nucleation and growth play an important role in thin-film growth. One quantity of particular interest is the exponent χ, which describes the dependence of the saturation island density N_{sat}∼(D_{h}/F)^{-χ} on the ratio D_{h}/F of the monomer hopping rate D_{h} to the deposition rate F. While standard rate equation (RE) theory predicts that χ=i/(i+2) (where i is the critical island size), more recently it has been predicted that in the presence of a strong barrier to the attachment of monomers to islands, a significantly larger value χ=2i/(i+3) may be observed. While this prediction has recently been tested using kinetic Monte Carlo simulations for the case of irreversible growth corresponding to i=1, it has not been tested for the case of reversible island growth corresponding to i>1. Here we present a mean-field self-consistent RE method which we have used to study the dependence of the effective value of χ on D_{h}/F and barrier-strength for i=1,2,3, and 6. Both the no-nucleation-barrier case in which there exists a barrier for monomers to attach to islands larger than the critical island size (but not to smaller islands) and the nucleation-barrier case in which there is a barrier for monomers to attach to islands of all sizes are studied. In all cases, we find that the existence of attachment barriers significantly increases the effective value of χ for a given barrier strength. In addition, for i=1 we find good agreement between our extrapolated asymptotic value of χ and the theoretical strong-barrier prediction both with and without a nucleation barrier. In contrast, for i>1 the value of χ is significantly larger in the presence of a nucleation barrier than in its absence. In particular, while an asymptotic analysis of our results for i>1 also leads to excellent agreement with the strong barrier prediction in the presence of a nucleation barrier, in the absence of a nucleation barrier the asymptotic values are significantly lower.

Metagenome and metabolism: the tissue microbiota hypothesis.

Over the last decade, the research community has revealed the role of a new organ: the intestinal microbiota. It is considered as a symbiont that is part of our organism since, at birth, it educates the immune system and contributes to the development of the intestinal vasculature and most probably the nervous system. With the advent of new generation sequencing techniques, a catalogue of genes that belong to this microbiome has been established that lists more than 5 million non-redundant genes called the metagenome. Using germ free mice colonized with the microbiota from different origins, it has been formally demonstrated that the intestinal microbiota causes the onset of metabolic diseases. Further to the role of point mutations in our genome, the microbiota can explain the on-going worldwide pandemic of obesity and diabetes, its dissemination and family inheritance, as well as the diversity of the associated metabolic phenotypes. More recently, the discovery of bacterial DNA within host tissues, such as the liver, the adipose tissue and the blood, which establishes a tissue microbiota, introduces new opportunities to identify targets and predictive biomarkers based on the host to microbiota interaction, as well as to define new strategies for pharmacological, immunomodulatory vaccines and nutritional applications.

Localized saddle-point search and application to temperature-accelerated dynamics.

We present a method for speeding up temperature-accelerated dynamics (TAD) simulations by carrying out a localized saddle-point (LSAD) search. In this method, instead of using the entire system to determine the energy barriers of activated processes, the calculation is localized by only including a small chunk of atoms around the atoms directly involved in the transition. Using this method, we have obtained N-independent scaling for the computational cost of the saddle-point search as a function of system size N. The error arising from localization is analyzed using a variety of model systems, including a variety of activated processes on Ag(100) and Cu(100) surfaces, as well as multiatom moves in Cu radiation damage and metal heteroepitaxial growth. Our results show significantly improved performance of TAD with the LSAD method, for the case of Ag/Ag(100) annealing and Cu/Cu(100) growth, while maintaining a negligibly small error in energy barriers.

[Intestinal microbiota and novel therapeutic perspectives for the treatment of metabolic diseases]. / Le microbiote intestinal à l'origine de nouvelles perspectives thérapeutiques pour les maladies métaboliques ?

A new organ has emerged over the course of the last century: the intestinal microbiota. It is characterized by numerous functions provided by several billions of bacteria inhabiting and living in harmony in the lumen and in the mucosal layer of the intestinal epithelium. More than 4 million genes composed by more than 1 500 species interact with each other, with the host and the environment to set up a mutualistic ecological group. A nutritional stress will modify the terms of the symbiosis between the host and the microbiota for the control of energy homeostasis. It is now thought that the pandemic of diabetes and obesity, not being due to the sole variations of our genome, would be due to changes in our metagenome: our intestinal bacteria. This organ which genomic varies on an everyday basis is inherited from our mother and the closed environment at birth. The corresponding diversity, the rapid evolution of gene expression, its influence on metabolism, as well as the very recent discovery of the existence of an tissue microbiota within the host, open new therapeutic pharmacological and nutritional opportunities as well as the identification of very accurate biomarkers constituting a personalized metagenomic identity card. Hence, individualized medicine foresees its origin within the metagenome.

Metabolic adaptation to a high-fat diet is associated with a change in the gut microbiota.

OBJECTIVE: The gut microbiota, which is considered a causal factor in metabolic diseases as shown best in animals, is under the dual influence of the host genome and nutritional environment. This study investigated whether the gut microbiota per se, aside from changes in genetic background and diet, could sign different metabolic phenotypes in mice. METHODS: The unique animal model of metabolic adaptation was used, whereby C57Bl/6 male mice fed a high-fat carbohydrate-free diet (HFD) became either diabetic (HFD diabetic, HFD-D) or resisted diabetes (HFD diabetes-resistant, HFD-DR). Pyrosequencing of the gut microbiota was carried out to profile the gut microbial community of different metabolic phenotypes. Inflammation, gut permeability, features of white adipose tissue, liver and skeletal muscle were studied. Furthermore, to modify the gut microbiota directly, an additional group of mice was given a gluco-oligosaccharide (GOS)-supplemented HFD (HFD+GOS). RESULTS: Despite the mice having the same genetic background and nutritional status, a gut microbial profile specific to each metabolic phenotype was identified. The HFD-D gut microbial profile was associated with increased gut permeability linked to increased endotoxaemia and to a dramatic increase in cell number in the stroma vascular fraction from visceral white adipose tissue. Most of the physiological characteristics of the HFD-fed mice were modulated when gut microbiota was intentionally modified by GOS dietary fibres. CONCLUSIONS: The gut microbiota is a signature of the metabolic phenotypes independent of differences in host genetic background and diet.

Development and validation of machine learning algorithms to predict posthypertensive origin in left ventricular hypertrophy.

BACKGROUND: Left ventricular hypertrophy is often associated with hypertension, which is not necessarily the cause of hypertrophy. Non-hypertension-related aetiologies often have a strong impact on patient management, and therefore require a thorough and careful workup. When considering all left ventricular hypertrophies, even the mild ones, the number of patients who need a workup increases drastically. This raises the need for a tool to evaluate the pretest probability of the origin of left ventricular hypertrophy. AIM: To predict the hypertensive origin of left ventricular hypertrophy using machine learning on first-line clinical, laboratory and echocardiographic variables. METHODS: We used a retrospective single-centre population of 591 patients with left ventricular hypertrophy, starting at 12mm maximal left ventricular wall thickness. After splitting data in a training and testing set, we trained three different algorithms: decision tree; random forest; and support vector machine. Model performances were validated on the testing set. RESULTS: All models exhibited good areas under receiver operating characteristic curves: 0.82 (95% confidence interval: 0.77-0.88) for the decision tree; 0.90 (95% confidence interval 0.85-0.94) for the random forest; and 0.90 (95% confidence interval: 0.85-0.94) for the support vector machine. After threshold selection, the last model had the best balance between its specificity of 0.96 (95% confidence interval: 0.91-0.99) and its sensitivity of 0.31 (95% confidence interval: 0.17-0.44). All algorithms relied on similar most influential predictor variables. Online calculators were developed and made publicly available. CONCLUSIONS: Machine learning models were able to determine the hypertensive origin of left ventricular hypertrophy with good performances. Implementation in clinical practice could reduce the number of aetiological workups needed in patients presenting with left ventricular hypertrophy.

Shape transitions in strained Cu islands on Ni(100): kinetics versus energetics.

We examine the ramified islands observed in submonolayer Cu/Ni(100) growth. Our results indicate that the strain-energy contribution to the dependence of island energy on shape is surprisingly weak. In contrast, our accelerated dynamics simulations indicate that unexpected concerted popout processes occurring at step edges may be responsible. Kinetic Monte Carlo (KMC) simulations which include these processes produce island shapes which are very similar to those observed in experiment. These results suggest that the shape transition is of kinetic origin but is strongly mediated by strain.

Genetic biomarkers of life-threatening pheochromocytoma-induced cardiomyopathy.

The release of excessive amounts of catecholamine by pheochromocytoma-paragangliomas (PPGL) can lead to life-threatening catecholamine-induced cardiomyopathy (CIC). Single-nucleotide polymorphisms of the beta1 and alpha-2c adrenergic receptors alter myocyte receptor function and enhanced norepinephrine release. We tested the hypothesis that such genetic variations may impact the risk of developing CIC in the context of PPGL. Thirty-one patients with PPGL, including nine with a history of CIC, were analyzed for alpha-2-adrenergic receptors: ADRA2C, beta-1 and beta-2 adrenergic receptors: ADRB1 and ADRB2 genotyping. CIC was defined either by a history of heart failure or cardiogenic shock associated with dilated or Takotsubo cardiomyopathy. Subjects were genotyped for ADRA2C (rs61767072 for del322_325), ADRB1 (rs1801252 for Ser49Gly and rs1801253 for Arg389Gly) and ADRB2 (rs1042713 for Arg16Gly and rs1042714 for Gln27Glu). Single-locus analysis revealed that variant in ADRA2C (alpha 2CDel322-325) was more common among patients with CIC than among controls (allele frequency, 0.44 vs 0.05; P< 0.001). The lack of alpha 2CDel322-325 polymorphism has a negative predictive value of 95% for the onset of CIC. In a replication cohort including 26 patients with PPGL whom eight have developed a CIC, the association between Alpha 2CDel322-325 and CIC was confirmed (allele frequency, 0.33 vs 0.; P= 0.0001). In conclusion, Alpha 2CDel322-325 through the identification of patients at low risk of developing CIC can help physicians to better determine the most appropriate therapeutic approach, notably in patients at high risk of surgical complications.