Loss of mitochondrial functions associated with azole resistance in Candida glabrata results in enhanced virulence in mice.

Mitochondrial dysfunction is one of the possible mechanisms by which azole resistance can occur in Candida glabrata. Cells with mitochondrial DNA deficiency (so-called "petite mutants") upregulate ATP binding cassette (ABC) transporter genes and thus display increased resistance to azoles. Isolation of such C. glabrata mutants from patients receiving antifungal therapy or prophylaxis has been rarely reported. In this study, we characterized two sequential and related C. glabrata isolates recovered from the same patient undergoing azole therapy. The first isolate (BPY40) was azole susceptible (fluconazole MIC, 4 μg/ml), and the second (BPY41) was azole resistant (fluconazole MIC, >256 μg/ml). BPY41 exhibited mitochondrial dysfunction and upregulation of the ABC transporter genes C. glabrata CDR1 (CgCDR1), CgCDR2, and CgSNQ2. We next assessed whether mitochondrial dysfunction conferred a selective advantage during host infection by testing the virulence of BPY40 and BPY41 in mice. Surprisingly, even with in vitro growth deficiency compared to BPY40, BPY41 was more virulent (as judged by mortality and fungal tissue burden) than BPY40 in both systemic and vaginal murine infection models. The increased virulence of the petite mutant correlated with a drastic gain of fitness in mice compared to that of its parental isolate. To understand this unexpected feature, genome-wide changes in gene expression driven by the petite mutation were analyzed by use of microarrays during in vitro growth. Enrichment of specific biological processes (oxido-reductive metabolism and the stress response) was observed in BPY41, all of which was consistent with mitochondrial dysfunction. Finally, some genes involved in cell wall remodelling were upregulated in BPY41 compared to BPY40, which may partially explain the enhanced virulence of BPY41. In conclusion, this study shows for the first time that mitochondrial dysfunction selected in vivo under azole therapy, even if strongly affecting in vitro growth characteristics, can confer a selective advantage under host conditions, allowing the C. glabrata mutant to be more virulent than wild-type isolates.

Alkylpurine-DNA-N-glycosylase confers resistance to temozolomide in xenograft models of glioblastoma multiforme and is associated with poor survival in patients.

Glioblastoma multiforme (GBM) is the most common and lethal of all gliomas. The current standard of care includes surgery followed by concomitant radiation and chemotherapy with the DNA alkylating agent temozolomide (TMZ). O⁶-methylguanine-DNA methyltransferase (MGMT) repairs the most cytotoxic of lesions generated by TMZ, O⁶-methylguanine. Methylation of the MGMT promoter in GBM correlates with increased therapeutic sensitivity to alkylating agent therapy. However, several aspects of TMZ sensitivity are not explained by MGMT promoter methylation. Here, we investigated our hypothesis that the base excision repair enzyme alkylpurine-DNA-N-glycosylase (APNG), which repairs the cytotoxic lesions N³-methyladenine and N⁷-methylguanine, may contribute to TMZ resistance. Silencing of APNG in established and primary TMZ-resistant GBM cell lines endogenously expressing MGMT and APNG attenuated repair of TMZ-induced DNA damage and enhanced apoptosis. Reintroducing expression of APNG in TMZ-sensitive GBM lines conferred resistance to TMZ in vitro and in orthotopic xenograft mouse models. In addition, resistance was enhanced with coexpression of MGMT. Evaluation of APNG protein levels in several clinical datasets demonstrated that in patients, high nuclear APNG expression correlated with poorer overall survival compared with patients lacking APNG expression. Loss of APNG expression in a subset of patients was also associated with increased APNG promoter methylation. Collectively, our data demonstrate that APNG contributes to TMZ resistance in GBM and may be useful in the diagnosis and treatment of the disease.

A probabilistic approach to niche-based community models for spatial forecasts of assemblage properties and their uncertainties

Aim Conservation strategies are in need of predictions that capture spatial community composition and structure. Currently, the methods used to generate these predictions generally focus on deterministic processes and omit important stochastic processes and other unexplained variation in model outputs. Here we test a novel approach of community models that accounts for this variation and determine how well it reproduces observed properties of alpine butterfly communities. Location The western Swiss Alps. Methods We propose a new approach to process probabilistic predictions derived from stacked species distribution models (S-SDMs) in order to predict and assess the uncertainty in the predictions of community properties. We test the utility of our novel approach against a traditional threshold-based approach. We used mountain butterfly communities spanning a large elevation gradient as a case study and evaluated the ability of our approach to model species richness and phylogenetic diversity of communities. Results S-SDMs reproduced the observed decrease in phylogenetic diversity and species richness with elevation, syndromes of environmental filtering. The prediction accuracy of community properties vary along environmental gradient: variability in predictions of species richness was higher at low elevation, while it was lower for phylogenetic diversity. Our approach allowed mapping the variability in species richness and phylogenetic diversity projections. Main conclusion Using our probabilistic approach to process species distribution models outputs to reconstruct communities furnishes an improved picture of the range of possible assemblage realisations under similar environmental conditions given stochastic processes and help inform manager of the uncertainty in the modelling results

Integrative molecular bioinformatics study of human adrenocortical tumors : microRNA, tissue-specific target prediction, and pathway analysis.

MicroRNAs (miRs) are involved in the pathogenesis of several neoplasms; however, there are no data on their expression patterns and possible roles in adrenocortical tumors. Our objective was to study adrenocortical tumors by an integrative bioinformatics analysis involving miR and transcriptomics profiling, pathway analysis, and a novel, tissue-specific miR target prediction approach. Thirty-six tissue samples including normal adrenocortical tissues, benign adenomas, and adrenocortical carcinomas (ACC) were studied by simultaneous miR and mRNA profiling. A novel data-processing software was used to identify all predicted miR-mRNA interactions retrieved from PicTar, TargetScan, and miRBase. Tissue-specific target prediction was achieved by filtering out mRNAs with undetectable expression and searching for mRNA targets with inverse expression alterations as their regulatory miRs. Target sets and significant microarray data were subjected to Ingenuity Pathway Analysis. Six miRs with significantly different expression were found. miR-184 and miR-503 showed significantly higher, whereas miR-511 and miR-214 showed significantly lower expression in ACCs than in other groups. Expression of miR-210 was significantly lower in cortisol-secreting adenomas than in ACCs. By calculating the difference between dCT(miR-511) and dCT(miR-503) (delta cycle threshold), ACCs could be distinguished from benign adenomas with high sensitivity and specificity. Pathway analysis revealed the possible involvement of G2/M checkpoint damage in ACC pathogenesis. To our knowledge, this is the first report describing miR expression patterns and pathway analysis in sporadic adrenocortical tumors. miR biomarkers may be helpful for the diagnosis of adrenocortical malignancy. This tissue-specific target prediction approach may be used in other tumors too.

Accuracy of plant assemblage predictions from species distribution models varies along environmental gradients

Aim: Climatic niche modelling of species and community distributions implicitly assumes strong and constant climatic determinism across geographic space. This assumption had however never been tested so far. We tested it by assessing how stacked-species distribution models (S-SDMs) perform for predicting plant species assemblages along elevation. Location: Western Swiss Alps. Methods: Using robust presence-absence data, we first assessed the ability of topo-climatic S-SDMs to predict plant assemblages in a study area encompassing a 2800 m wide elevation gradient. We then assessed the relationships among several evaluation metrics and trait-based tests of community assembly rules. Results: The standard errors of individual SDMs decreased significantly towards higher elevations. Overall, the S-SDM overpredicted far more than they underpredicted richness and could not reproduce the humpback curve along elevation. Overprediction was greater at low and mid-range elevations in absolute values but greater at high elevations when standardised by the actual richness. Looking at species composition, the evaluation metrics accounting for both the presence and absence of species (overall prediction success and kappa) or focusing on correctly predicted absences (specificity) increased with increasing elevation, while the metrics focusing on correctly predicted presences (Jaccard index and sensitivity) decreased. The best overall evaluation - as driven by specificity - occurred at high elevation where species assemblages were shown to be under significant environmental filtering of small plants. In contrast, the decreased overall accuracy in the lowlands was associated with functional patterns representing any type of assembly rule (environmental filtering, limiting similarity or null assembly). Main Conclusions: Our study reveals interesting patterns of change in S-SDM errors with changes in assembly rules along elevation. Yet, significant levels of assemblage prediction errors occurred throughout the gradient, calling for further improvement of SDMs, e.g., by adding key environmental filters that act at fine scales and developing approaches to account for variations in the influence of predictors along environmental gradients.

Prediction of resting energy expenditure from fat-free mass and fat mass.

On the basis of literature values, the relationship between fat-free mass (FFM), fat mass (FM), and resting energy expenditure [REE (kJ/24 h)] was determined for 213 adults (86 males, 127 females). The objectives were to develop a mathematical model to predict REE based on body composition and to evaluate the contribution of FFM and FM to REE. The following regression equations were derived: 1) REE = 1265 + (93.3 x FFM) (r2 = 0.727, P < 0.001); 2) REE = 1114 + (90.4 x FFM) + (13.2 x FM) (R2 = 0.743, P < 0.001); and 3) REE = (108 x FFM) + (16.9 x FM) (R2 = 0.986, P < 0.001). FM explained only a small part of the variation remaining after FFM was accounted for. The models that include both FFM and FM are useful in examination of the changes in REE that occur with a change in both the FFM and FM. To account for more of the variability in REE, FFM will have to be divided into organ mass and skeletal muscle mass in future analyses.

Advanced geostatistical and machine-learning models for spatial data analysis of radioactively contaminated regions

Radioactive soil-contamination mapping and risk assessment is a vital issue for decision makers. Traditional approaches for mapping the spatial concentration of radionuclides employ various regression-based models, which usually provide a single-value prediction realization accompanied (in some cases) by estimation error. Such approaches do not provide the capability for rigorous uncertainty quantification or probabilistic mapping. Machine learning is a recent and fast-developing approach based on learning patterns and information from data. Artificial neural networks for prediction mapping have been especially powerful in combination with spatial statistics. A data-driven approach provides the opportunity to integrate additional relevant information about spatial phenomena into a prediction model for more accurate spatial estimates and associated uncertainty. Machine-learning algorithms can also be used for a wider spectrum of problems than before: classification, probability density estimation, and so forth. Stochastic simulations are used to model spatial variability and uncertainty. Unlike regression models, they provide multiple realizations of a particular spatial pattern that allow uncertainty and risk quantification. This paper reviews the most recent methods of spatial data analysis, prediction, and risk mapping, based on machine learning and stochastic simulations in comparison with more traditional regression models. The radioactive fallout from the Chernobyl Nuclear Power Plant accident is used to illustrate the application of the models for prediction and classification problems. This fallout is a unique case study that provides the challenging task of analyzing huge amounts of data ('hard' direct measurements, as well as supplementary information and expert estimates) and solving particular decision-oriented problems.

Atopic dermatitis-like disease and associated lethal myeloproliferative disorder arise from loss of Notch signaling in the murine skin.

BACKGROUND: The Notch pathway is essential for proper epidermal differentiation during embryonic skin development. Moreover, skin specific loss of Notch signaling in the embryo results in skin barrier defects accompanied by a B-lymphoproliferative disease. However, much less is known about the consequences of loss of Notch signaling after birth. METHODOLOGY AND PRINCIPAL FINDINGS: To study the function of Notch signaling in the skin of adult mice, we made use of a series of conditional gene targeted mice that allow inactivation of several components of the Notch signaling pathway specifically in the skin. We demonstrate that skin-specific inactivation of Notch1 and Notch2 simultaneously, or RBP-J, induces the development of a severe form of atopic dermatitis (AD), characterized by acanthosis, spongiosis and hyperkeratosis, as well as a massive dermal infiltration of eosinophils and mast cells. Likewise, patients suffering from AD, but not psoriasis or lichen planus, have a marked reduction of Notch receptor expression in the skin. Loss of Notch in keratinocytes induces the production of thymic stromal lymphopoietin (TSLP), a cytokine deeply implicated in the pathogenesis of AD. The AD-like associated inflammation is accompanied by a myeloproliferative disorder (MPD) characterized by an increase in immature myeloid populations in the bone marrow and spleen. Transplantation studies revealed that the MPD is cell non-autonomous and caused by dramatic microenvironmental alterations. Genetic studies demontrated that G-CSF mediates the MPD as well as changes in the bone marrow microenvironment leading to osteopenia. SIGNIFICANCE: Our data demonstrate a critical role for Notch in repressing TSLP production in keratinocytes, thereby maintaining integrity of the skin and the hematopoietic system.

Improvement of cardiovascular risk prediction: time to review current knowledge, debates, and fundamentals on how to assess test characteristics.

Cardiovascular risk assessment might be improved with the addition of emerging, new tests derived from atherosclerosis imaging, laboratory tests or functional tests. This article reviews relative risk, odds ratios, receiver-operating curves, posttest risk calculations based on likelihood ratios, the net reclassification improvement and integrated discrimination. This serves to determine whether a new test has an added clinical value on top of conventional risk testing and how this can be verified statistically. Two clinically meaningful examples serve to illustrate novel approaches. This work serves as a review and basic work for the development of new guidelines on cardiovascular risk prediction, taking into account emerging tests, to be proposed by members of the 'Taskforce on Vascular Risk Prediction' under the auspices of the Working Group 'Swiss Atherosclerosis' of the Swiss Society of Cardiology in the future.

Fatigue and weight loss predict survival on circadian chemotherapy for metastatic colorectal cancer.

BACKGROUND: Chemotherapy-induced neutropenia has been associated with prolonged survival selectively in patients on a conventional schedule (combined 5-fluorouracil, leucovorin, and oxaliplatin [FOLFOX2]) but not on a chronomodulated schedule of the same drugs administered at specific circadian times (chronoFLO4). The authors hypothesized that the early occurrence of chemotherapy-induced symptoms correlated with circadian disruption would selectively hinder the efficacy of chronotherapy. METHODS: Fatigue and weight loss (FWL) were considered to be associated with circadian disruption based on previous data. Patients with metastatic colorectal cancer (nâeuro0/00=âeuro0/00543) from an international phase 3 trial comparing FOLFOX2 with chronoFLO4 were categorized into 4 subgroups according to the occurrence of FWL or other clinically relevant toxicities during the initial 2 courses of chemotherapy. Multivariate Cox models were used to assess the role of toxicity on the time to progression (TTP) and overall survival (OS). RESULTS: The proportions of patients in the 4 subgroups were comparable in both treatment arms (Pâeuro0/00=âeuro0/00.77). No toxicity was associated with TTP or OS on FOLFOX2. The median OS on FOLFOX2 ranged from 16.4 (95% confidence limits [CL], 7.2-25.6 months) to 19.8 months (95% CL, 17.7-22.0 months) according to toxicity subgroup (Pâeuro0/00=âeuro0/00.45). Conversely, FWL, but no other toxicity, independently predicted for significantly shorter TTP (Pâeuro0/00<âeuro0/00.0001) and OS (Pâeuro0/00=âeuro0/00.001) on chronoFLO4. The median OS on chronoFLO4 was 13.8 months (95% CL, 10.4-17.2 months) or 21.1 months (95% CL, 19.0-23.1 months) according to presence or absence of chemotherapy-induced FWL, respectively. CONCLUSIONS: Early onset chemotherapy-induced FWL was an independent predictor of poor TTP and OS only on chronotherapy. Dynamic monitoring to detect early chemotherapy-induced circadian disruption could allow the optimization of rapid chronotherapy and concomitant improvements in safety and efficacy.

Reflections on Partial Least Squares Path Modeling

The purpose of the present article is to take stock of a recent exchange in Organizational Research Methods between critics (Rönkkö & Evermann, 2013) and proponents (Henseler et al., 2014) of partial least squares path modeling (PLS-PM). The two target articles were centered around six principal issues, namely whether PLS-PM: (1) can be truly characterized as a technique for structural equation modeling (SEM); (2) is able to correct for measurement error; (3) can be used to validate measurement models; (4) accommodates small sample sizes; (5) is able to provide null hypothesis tests for path coefficients; and (6) can be employed in an exploratory, model-building fashion. We summarize and elaborate further on the key arguments underlying the exchange, drawing from the broader methodological and statistical literature in order to offer additional thoughts concerning the utility of PLS-PM and ways in which the technique might be improved. We conclude with recommendations as to whether and how PLS-PM serves as a viable contender to SEM approaches for estimating and evaluating theoretical models.

Prediction of free imatinib concentrations based on total plasma concentrations in patients with gastrointestinal stromal tumours.

AIM: Total imatinib concentrations are currently measured for the therapeutic drug monitoring of imatinib, whereas only free drug equilibrates with cells for pharmacological action. Due to technical and cost limitations, routine measurement of free concentrations is generally not performed. In this study, free and total imatinib concentrations were measured to establish a model allowing the confident prediction of imatinib free concentrations based on total concentrations and plasma proteins measurements. METHODS: One hundred and fifty total and free plasma concentrations of imatinib were measured in 49 patients with gastrointestinal stromal tumours. A population pharmacokinetic model was built up to characterize mean total and free concentrations with inter-patient and intrapatient variability, while taking into account α1 -acid glycoprotein (AGP) and human serum albumin (HSA) concentrations, in addition to other demographic and environmental covariates. RESULTS: A one compartment model with first order absorption was used to characterize total and free imatinib concentrations. Only AGP influenced imatinib total clearance. Imatinib free concentrations were best predicted using a non-linear binding model to AGP, with a dissociation constant Kd of 319 ng ml(-1) , assuming a 1:1 molar binding ratio. The addition of HSA in the equation did not improve the prediction of imatinib unbound concentrations. CONCLUSION: Although free concentration monitoring is probably more appropriate than total concentrations, it requires an additional ultrafiltration step and sensitive analytical technology, not always available in clinical laboratories. The model proposed might represent a convenient approach to estimate imatinib free concentrations. However, therapeutic ranges for free imatinib concentrations remain to be established before it enters into routine practice.

Prediction of cross-recognition of peptide-HLA A2 by Melan-A-specific cytotoxic T lymphocytes using three-dimensional quantitative structure-activity relationships.

The cross-recognition of peptides by cytotoxic T lymphocytes is a key element in immunology and in particular in peptide based immunotherapy. Here we develop three-dimensional (3D) quantitative structure-activity relationships (QSARs) to predict cross-recognition by Melan-A-specific cytotoxic T lymphocytes of peptides bound to HLA A*0201 (hereafter referred to as HLA A2). First, we predict the structure of a set of self- and pathogen-derived peptides bound to HLA A2 using a previously developed ab initio structure prediction approach [Fagerberg et al., J. Mol. Biol., 521-46 (2006)]. Second, shape and electrostatic energy calculations are performed on a 3D grid to produce similarity matrices which are combined with a genetic neural network method [So et al., J. Med. Chem., 4347-59 (1997)] to generate 3D-QSAR models. The models are extensively validated using several different approaches. During the model generation, the leave-one-out cross-validated correlation coefficient (q (2)) is used as the fitness criterion and all obtained models are evaluated based on their q (2) values. Moreover, the best model obtained for a partitioned data set is evaluated by its correlation coefficient (r = 0.92 for the external test set). The physical relevance of all models is tested using a functional dependence analysis and the robustness of the models obtained for the entire data set is confirmed using y-randomization. Finally, the validated models are tested for their utility in the setting of rational peptide design: their ability to discriminate between peptides that only contain side chain substitutions in a single secondary anchor position is evaluated. In addition, the predicted cross-recognition of the mono-substituted peptides is confirmed experimentally in chromium-release assays. These results underline the utility of 3D-QSARs in peptide mimetic design and suggest that the properties of the unbound epitope are sufficient to capture most of the information to determine the cross-recognition.

Azole resistance by loss of function of the sterol Δ⁵,⁶-desaturase gene (ERG3) in Candida albicans does not necessarily decrease virulence.

The inactivation of ERG3, a gene encoding sterol Δ⁵,⁶-desaturase (essential for ergosterol biosynthesis), is a known mechanism of in vitro resistance to azole antifungal drugs in the human pathogen Candida albicans. ERG3 inactivation typically results in loss of filamentation and attenuated virulence in animal models of disseminated candidiasis. In this work, we identified a C. albicans clinical isolate (VSY2) with high-level resistance to azole drugs in vitro and an absence of ergosterol but normal filamentation. Sequencing of ERG3 in VSY2 revealed a double base deletion leading to a premature stop codon and thus a nonfunctional enzyme. The reversion of the double base deletion in the mutant allele (erg3-1) restored ergosterol biosynthesis and full fluconazole susceptibility in VSY2, confirming that ERG3 inactivation was the mechanism of azole resistance. Additionally, the replacement of both ERG3 alleles by erg3-1 in the wild-type strain SC5314 led to the absence of ergosterol and to fluconazole resistance without affecting filamentation. In a mouse model of disseminated candidiasis, the clinical ERG3 mutant VSY2 produced kidney fungal burdens and mouse survival comparable to those obtained with the wild-type control. Interestingly, while VSY2 was resistant to fluconazole both in vitro and in vivo, the ERG3-derived mutant of SC5314 was resistant only in vitro and was less virulent than the wild type. This suggests that VSY2 compensated for the in vivo fitness defect of ERG3 inactivation by a still unknown mechanism(s). Taken together, our results provide evidence that contrary to previous reports inactivation of ERG3 does not necessarily affect filamentation and virulence.

Are niche-based species distribution models transferable in space?

Aim To assess the geographical transferability of niche-based species distribution models fitted with two modelling techniques. Location Two distinct geographical study areas in Switzerland and Austria, in the subalpine and alpine belts. Methods Generalized linear and generalized additive models (GLM and GAM) with a binomial probability distribution and a logit link were fitted for 54 plant species, based on topoclimatic predictor variables. These models were then evaluated quantitatively and used for spatially explicit predictions within (internal evaluation and prediction) and between (external evaluation and prediction) the two regions. Comparisons of evaluations and spatial predictions between regions and models were conducted in order to test if species and methods meet the criteria of full transferability. By full transferability, we mean that: (1) the internal evaluation of models fitted in region A and B must be similar; (2) a model fitted in region A must at least retain a comparable external evaluation when projected into region B, and vice-versa; and (3) internal and external spatial predictions have to match within both regions. Results The measures of model fit are, on average, 24% higher for GAMs than for GLMs in both regions. However, the differences between internal and external evaluations (AUC coefficient) are also higher for GAMs than for GLMs (a difference of 30% for models fitted in Switzerland and 54% for models fitted in Austria). Transferability, as measured with the AUC evaluation, fails for 68% of the species in Switzerland and 55% in Austria for GLMs (respectively for 67% and 53% of the species for GAMs). For both GAMs and GLMs, the agreement between internal and external predictions is rather weak on average (Kulczynski's coefficient in the range 0.3-0.4), but varies widely among individual species. The dominant pattern is an asymmetrical transferability between the two study regions (a mean decrease of 20% for the AUC coefficient when the models are transferred from Switzerland and 13% when they are transferred from Austria). Main conclusions The large inter-specific variability observed among the 54 study species underlines the need to consider more than a few species to test properly the transferability of species distribution models. The pronounced asymmetry in transferability between the two study regions may be due to peculiarities of these regions, such as differences in the ranges of environmental predictors or the varied impact of land-use history, or to species-specific reasons like differential phenotypic plasticity, existence of ecotypes or varied dependence on biotic interactions that are not properly incorporated into niche-based models. The lower variation between internal and external evaluation of GLMs compared to GAMs further suggests that overfitting may reduce transferability. Overall, a limited geographical transferability calls for caution when projecting niche-based models for assessing the fate of species in future environments.