Efficacy of a Non-Pharmacological Intervention in Seasonal Allergic Rhinitis Symptoms.

BACKGROUND: Patients suffering from allergic rhinitis seek for several therapeutic symptomatic options, including nonconventional treatments, to control their symptoms. OBJECTIVES: Through the present proof-of-concept study, we prospectively investigated the potential role of Puressentiel® nasal protection spray (SNPA) in patients suffering from cypress pollen allergic rhinitis. METHODS: In 15 adults, we performed two nasal provocation tests, with a cypress pollen extract, with a 15-day interval, with and without previous randomized administration of SNPA, and evaluated a nasal symptom score, the nasal inspiratory peak flow, and the concentration of inflammatory cytokines in the nasal lavage after the procedures. RESULTS: Comparing results in patients challenged with and without the SNPA spray before the nasal challenge, we found a 57% mean decrease in symptoms, and a 62% average difference in inspiratory peak flow, after the use of the spray. CONCLUSIONS: Puressentiel® SNPA is effective in reducing nasal symptoms, as assessed by nasal symptoms score and nasal inspiratory peak flow, and could be a valid natural non-pharmacological option in patients suffering from allergic rhinitis.

A2Sign: Agnostic Algorithms for Signatures-a universal method for identifying molecular signatures from transcriptomic datasets prior to cell-type deconvolution.

MOTIVATION: Molecular signatures are critical for inferring the proportions of cell types from bulk transcriptomics data. However, the identification of these signatures is based on a methodology that relies on prior biological knowledge of the cell types being studied. When working with less known biological material, a data-driven approach is required to uncover the underlying classes and generate ad hoc signatures from healthy or pathogenic tissue. RESULTS: We present a new approach, A2Sign: Agnostic Algorithms for Signatures, based on a non-negative tensor factorization (NTF) strategy that allows us to identify cell-type-specific molecular signatures, greatly reduce collinearities and also account for inter-individual variability. We propose a global framework that can be applied to uncover molecular signatures for cell-type deconvolution in arbitrary tissues using bulk transcriptome data. We also present two new molecular signatures for deconvolution of up to 16 immune cell types using microarray or RNA-seq data. AVAILABILITY AND IMPLEMENTATION: All steps of our analysis were implemented in annotated Python notebooks (https://github.com/paulfogel/A2SIGN). To perform NTF, we used the NMTF package, which can be downloaded using Python pip install. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Transcriptomic characterization of prurigo nodularis and the therapeutic response to nemolizumab.

BACKGROUND: Prurigo nodularis (PN) is a debilitating, difficult-to-treat, intensely pruritic, chronic inflammatory skin disease characterized by hyperkeratotic skin nodules. The pathogenesis of PN is not well understood but is believed to involve cross talk between sensory nerve fibers, immune cells, and the epidermis. It is centered around the neuroimmune cytokine IL-31, driving an intractable itch-scratch cycle. OBJECTIVE: We sought to provide a comprehensive view of the transcriptomic changes in PN skin and characterize the mechanism of action of the anti-IL-31 receptor inhibitor nemolizumab. METHOD: RNA sequencing of biopsy samples obtained from a cohort of patients treated with the anti-IL-31 receptor inhibitor nemolizumab and taken at baseline and week 12. Generation and integration of patient data with RNA-Seq data generated from reconstructed human epidermis stimulated with IL-31 and other proinflammatory cytokines. RESULTS: Our results demonstrate that nemolizumab effectively decreases IL-31 responses in PN skin, leading to effective suppression of downstream inflammatory responses including TH2/IL-13 and TH17/IL-17 responses. This is accompanied by decreased keratinocyte proliferation and normalization of epidermal differentiation and function. Furthermore, our results demonstrate how transcriptomic changes associated with nemolizumab treatment correlate with improvement in lesions, pruritus, stabilization of extracellular matrix remodeling, and processes associated with cutaneous nerve function. CONCLUSION: These data demonstrate a broad response to IL-31 receptor inhibition with nemolizumab and confirm the critical upstream role of IL-31 in PN pathogenesis.

Proteomic analysis of stratum corneum in Cutaneous T-Cell Lymphomas and psoriasis.

Stratum corneum collected by tape stripping from 10 and 24 subjects with cutaneous T-cell lymphomas (CTCL) or psoriasis, respectively, were compared using quantitative label-free mass spectrometry analysis. A non-supervised statistical analysis (Posneg NMF) based on 352 differentially expressed proteins in both CTCL and psoriasis samples was able to separate the two disease groups and finally able to identify a set of 112 proteins that contributed most and significantly to the separation when compared to non-lesional samples. In addition, Luminex assay revealed that the increase in the amount of chemokines related to the inflammatory response, and immune cell infiltration and recruitment in lesional stratum corneum in CTCL, including CXCL8, CXCL9, CXCL10, CCL27, TNF and sICAM-1 was in agreement with published data on entire skin biopsies. Proteome analysis using quantitative methods including mass spectrometry and Luminex technology offered the possibility to investigate the relevant protein signature in CTCL and may be helpful to diagnose and investigate the efficacy of treatments in clinical investigations using non-invasive methods in future.

Preventing Acute Limb Ischemia during VA-ECMO-In Silico Analysis of Physical Parameters Associated with Lower Limb Perfusion.

BACKGROUND: Peripheral femoro-femoral veno-arterial extracorporeal membrane oxygenation is increasingly used in refractory cardiogenic shock. However, the obstruction of the femoral artery by the return cannula could lead to acute limb ischemia, a frequently encountered situation that is inconstantly prevented by the adjunction of a distal perfusion cannula (DPC). The aim of this study was to investigate the influence of three physical parameters on the perfusion of the cannulated lower limb. METHODS: Using patient-specific arterial models and computational fluid dynamic simulations, we studied four diameters of arterial cannula, three diameters of DPC, and two percentages of arterial section limitation. RESULTS: We found that adequate perfusion of the cannulated limb was achieved in only two out of the twenty-one configurations tested, specifically, when the arterial cannula had a diameter of 17 Fr, was considered to limit the section of the artery by 90%, and was associated with an 8 Fr or a 10 Fr DPC. Multivariable analysis revealed that the perfusion of the cannulated lower limb was correlated with the diameter of the DPC, but also with the diameter of the arterial cannula and the percentage of arterial section limitation. CONCLUSIONS: In most of the cases simulated here, the current system combining unsized arterial cannula and non-specific DPC was not sufficient to provide adequate perfusion of the cannulated lower limb, urging the need for innovative strategies to efficiently prevent acute limb ischemia during peripheral femoro-femoral veno-arterial extracorporeal membrane oxygenation.

Proteomic profiling of serum identifies a molecular signature that correlates with clinical outcomes in COPD.

OBJECTIVE: Novel biomarkers related to main clinical hallmarks of Chronic obstructive pulmonary disease (COPD), a heterogeneous disorder with pulmonary and extra-pulmonary manifestations, were investigated by profiling the serum levels of 1305 proteins using Slow Off-rate Modified Aptamers (SOMA)scan technology. METHODS: Serum samples were collected from 241 COPD subjects in the multicenter French Cohort of Bronchial obstruction and Asthma to measure the expression of 1305 proteins using SOMAscan proteomic platform. Clustering of the proteomics was applied to identify disease subtypes and their functional annotation and association with key clinical parameters were examined. Cluster findings were revalidated during a follow-up visit, and compared to those obtained in a group of 47 COPD patients included in the Melbourne Longitudinal COPD Cohort. RESULTS: Unsupervised clustering identified two clusters within COPD subjects at inclusion. Cluster 1 showed elevated levels of factors contributing to tissue injury, whereas Cluster 2 had higher expression of proteins associated with enhanced immunity and host defense, cell fate, remodeling and repair and altered metabolism/mitochondrial functions. Patients in Cluster 2 had a lower incidence of exacerbations, unscheduled medical visits and prevalence of emphysema and diabetes. These protein expression patterns were conserved during a follow-up second visit, and substanciated, by a large part, in a limited series of COPD patients. Further analyses identified a signature of 15 proteins that accurately differentiated the two COPD clusters at the 2 visits. CONCLUSIONS: This study provides insights into COPD heterogeneity and suggests that overexpression of factors involved in lung immunity/host defense, cell fate/repair/ remodelling and mitochondrial/metabolic activities contribute to better clinical outcomes. Hence, high throughput proteomic assay offers a powerful tool for identifying COPD endotypes and facilitating targeted therapies.

Mode of Neonatal Delivery Influences the Nutrient Composition of Human Milk: Results From a Multicenter European Cohort of Lactating Women.

Background: The effect of the mode of neonatal delivery (cesarean or vaginal) on the nutrient composition of human milk (HM) has rarely been studied. Given the increasing prevalence of cesarean section (C-section) globally, understanding the impact of C-section vs. vaginal delivery on the nutrient composition of HM is fundamental when HM is the preferred source of infant food during the first 4 postnatal months. Objective: This study aimed to evaluate the association between mode of delivery and nutrient composition of HM in the first 4 months of life. Design: Milk samples were obtained from 317 healthy lactating mothers as part of an exploratory analyses within a multicenter European longitudinal cohort (ATLAS cohort) to study the HM composition, and its potential association with the mode of delivery. We employed traditional mixed models to study individual nutrient associations adjusted for mother's country, infant birth weight, parity, and gestational age, and complemented it, for the first time, with a multidimensional data analyses approach (non-negative tensor factorization, NTF) to examine holistically how patterns of multiple nutrients and changes over time are associated with the delivery mode. Results: Over the first 4 months, nutrient profiles in the milk of mothers who delivered vaginally (n = 237) showed significantly higher levels of palmitoleic acid (16:1n-7), stearic acid (18:0), oleic acid (18:1n-9), arachidic acid (20:0), alpha-linolenic acid (18:3n-3), eicosapentaenoic acid (20:5n-3), docosahexenoic acid (22:6n-3), erucic acid (22:1n-9), monounsaturated fatty acids (MUFA)%, calcium, and phosphorus, whereas the ratios of arachidonic acid/docosahexaenoic acid (ARA/DHA) and n-6/n-3, as well as polyunsaturated fatty acids (PUFA)% were higher in milk from women who had C-sections, in the unadjusted analyses (p < 0.05 for all), but did not retain significance when adjusted for confounders in the mixed models. Using a complementary multidimension data analyses approach (NTF), we show few similar patterns wherein a group of mothers with a high density of C-sections showed increased values for PUFA%, n-6/n-3, and ARA/DHA ratios, but decreased values of MUFA%, 20:1n-9, iodine, and fucosyl-sialyl-lacto-N-tetraose 2 during the first 4 months of lactation. Conclusion: Our data provide preliminary insights on differences in concentrations of several HM nutrients (predominantly fatty acids) among women who delivered via C-section. Although these effects tend to disappear after adjustment for confounders, given the similar patterns observed using two different data analytical approaches, these preliminary findings warrant further confirmation and additional insight on the biological and clinical effects related to such differences early in life.

Assessing Methods for Evaluating the Number of Components in Non-Negative Matrix Factorization.

Non-negative matrix factorization is a relatively new method of matrix decomposition which factors an m×n data matrix X into an m×k matrix W and a k×n matrix H, so that X≈W×H. Importantly, all values in X, W, and H are constrained to be non-negative. NMF can be used for dimensionality reduction, since the k columns of W can be considered components into which X has been decomposed. The question arises: how does one choose k? In this paper, we first assess methods for estimating k in the context of NMF in synthetic data. Second, we examine the effect of normalization on this estimate's accuracy in empirical data. In synthetic data with orthogonal underlying components, methods based on PCA and Brunet's Cophenetic Correlation Coefficient achieved the highest accuracy. When evaluated on a well-known real dataset, normalization had an unpredictable effect on the estimate. For any given normalization method, the methods for estimating k gave widely varying results. We conclude that when estimating k, it is best not to apply normalization. If underlying components are known to be orthogonal, then Velicer's MAP or Minka's Laplace-PCA method might be best. However, when orthogonality of the underlying components is unknown, none of the methods seemed preferable.

Inferential, robust non-negative matrix factorization analysis of microarray data.

MOTIVATION: Modern methods such as microarrays, proteomics and metabolomics often produce datasets where there are many more predictor variables than observations. Research in these areas is often exploratory; even so, there is interest in statistical methods that accurately point to effects that are likely to replicate. Correlations among predictors are used to improve the statistical analysis. We exploit two ideas: non-negative matrix factorization methods that create ordered sets of predictors; and statistical testing within ordered sets which is done sequentially, removing the need for correction for multiple testing within the set. RESULTS: Simulations and theory point to increased statistical power. Computational algorithms are described in detail. The analysis and biological interpretation of a real dataset are given. In addition to the increased power, the benefit of our method is that the organized gene lists are likely to lead better understanding of the biology. AVAILABILITY: An SAS JMP executable script is available from http://www.niss.org/irMF

Photodynamic therapy corrects abnormal cancer-associated gene expression observed in actinic keratosis lesions and induces a remodeling effect in photodamaged skin.

BACKGROUND: Actinic keratoses (AK) are proliferations of neoplastic keratinocytes in the epidermis resulting from cumulative exposure to ultraviolet radiation (UVR), which are liable to transform into squamous cell carcinoma (SCC). Organ Transplant Recipients (OTR) have an increased risk of developing SCC as a consequence of long-term immunosuppressive therapy. The aim of this study was to determine the molecular signature of AKs from OTR prior to treatment with methyl aminolevulinate-photodynamic therapy (MAL-PDT), and to assess what impact the treatment has on promoting remodeling of the photo-damaged skin. METHODS: Seven patients were enrolled on a clinical trial to assess the effect of MAL-PDT with biopsies taken at screening prior to the first treatment session (week 1), and six weeks after completion of final treatment (week 18). Whole-genome gene expression analysis was carried out on skin biopsies isolated from an AK lesion, an area surrounding the lesion, and a non-sun exposed region of the body. Quantitative PCR was utilized to confirm the differential expression of key genes. RESULTS: MAL-PDT treatment corrected abnormal proliferation-related gene profiles, corrected aberrantly expressed cancer-associated genes and induced expression of dermal extracellular matrix genes in photo-exposed skin. CONCLUSION: The efficacy of the MAL-PDT on AK lesions was confirmed at whole-genome gene expression level. A transcriptional signature of remodeling, identified through assessing the effect of MAL-PDT on photodamaged skin, supports the use of MAL-PDT for treating photodamaged skin and field cancerized areas.

Metabolomics based predictive classifier for early detection of pancreatic ductal adenocarcinoma.

The availability of robust classification algorithms for the identification of high risk individuals with resectable disease is critical to improving early detection strategies and ultimately increasing survival rates in PC. We leveraged high quality biospecimens with extensive clinical annotations from patients that received treatment at the Medstar-Georgetown University hospital. We used a high resolution mass spectrometry based global tissue profiling approach in conjunction with multivariate analysis for developing a classification algorithm that would predict early stage PC with high accuracy. The candidate biomarkers were annotated using tandem mass spectrometry. We delineated a six metabolite panel that could discriminate early stage PDAC from benign pancreatic disease with >95% accuracy of classification (Specificity = 0.85, Sensitivity = 0.9). Subsequently, we used multiple reaction monitoring mass spectrometry for evaluation of this panel in plasma samples obtained from the same patients. The pattern of expression of these metabolites in plasma was found to be discordant as compared to that in tissue. Taken together, our results show the value of using a metabolomics approach for developing highly predictive panels for classification of early stage PDAC. Future investigations will likely lead to the development of validated biomarker panels with potential for clinical translation in conjunction with CA-19-9 and/or other biomarkers.

High urinary ferritin reflects myoglobin iron evacuation in DMD patients.

Duchenne muscular dystrophy (DMD) is an X-linked disease caused by mutations in the dystrophin gene leading to the absence of the normal dystrophin protein. The efforts of many laboratories brought new treatments of DMD to the reality, but ongoing and forthcoming clinical trials suffer from absence of valuable biomarkers permitting to follow the outcome of the treatment day by day and to adjust the treatment if needed. In the present study the levels of 128 urinary proteins including growth factors, cytokines and chemokines were compared in urine of DMD patients and age related control subjects by antibody array approach. Surprisingly, statistically significant difference was observed only for urinary ferritin whose level was 50 times higher in young DMD patients. To explain the observed high urinary ferritin content we analysed the levels of iron, iron containing proteins and proteins involved in regulation of iron metabolism in serum and urine of DMD patients and their age-matched healthy controls. Obtained data strongly suggest that elevated level of urinary ferritin is functionally linked to the renal management of myoglobin iron derived from leaky muscles of DMD patients. This first observation of the high level of ferritin in urine of DMD patients permits to consider this protein as a new urinary biomarker in muscular dystrophies and sheds light on the mechanisms of iron metabolism and kidney functioning in DMD.

Transcriptional response to hypoxic stress in melanoma and prognostic potential of GBE1 and BNIP3.

Gradients of hypoxia occur in most solid tumors and cells found in hypoxic regions are associated with the most aggressive and therapy-resistant fractions of the tumor. Despite the ubiquity and importance of hypoxia responses, little is known about the variation in the global transcriptional response to hypoxia in melanoma. Using microarray technology, whole genome gene expression profiling was first performed on established melanoma cell lines. From gene set enrichment analyses, we derived a robust 35 probes signature (hypomel for HYPOxia MELanoma) associated with hypoxia-response pathways, including 26 genes up regulated, and 9 genes down regulated. The microarray data were validated by RT-qPCR for the 35 transcripts. We then validated the signature in hypoxic zones from 8 patient specimens using laser microdissection or macrodissection of Formalin fixed-paraffin-embedded (FFPE) material, followed with RT-qPCR. Moreover, a similar hypoxia-associated gene expression profile was observed using NanoString technology to analyze RNAs from FFPE melanoma tissues of a cohort of 19 patients treated with anti-PD1. Analysis of NanoString data from validation sets using Non-Negative Matrix Factorization (NMF) analysis (26 genes up regulated in hypoxia) and dual clustering (samples and genes) further revealed that the increased level of BNIP3 (Bcl-2 adenovirus E1B 19 kDa-interacting protein 3)/GBE1 (glycogen branching enzyme1) differential pair correlates with the lack of response of melanoma patients to anti-PD1 (pembrolizumab) immunotherapy. These studies suggest that through elevated glycogenic flux and induction of autophagy, hypoxia is a critical molecular program that could be considered as a prognostic factor for melanoma.

Analysis and prediction of combinatorial chemistry synthesis and screening data.

The goal of combinatorial chemistry is to simultaneously synthesize sets of compounds possessing properties that are then distinguished through screening. As the size of a compound set increases, data analysis becomes more challenging. Analysis of Variance (ANOVA) is an accepted statistical method that offers a straightforward solution to this problem. Two steps encountered by combinatorial scientists appear well suited to ANOVA: the prediction of synthetic outcomes (purity and yield) of set members and the analysis of screening data to identify combinations of reagent inputs that result in molecules with a desired property. To illustrate, a subset of a combinatorial array, referred to as a reaction rehearsal set, is evaluated to create a model predictive of the individual synthetic outcomes of the full matrix. In a second exercise, the biochemical screening data obtained from a combinatorial library is analyzed to identify reagent interactions that result in molecules possessing the sought activity.

Applications of a Novel Clustering Approach Using Non-Negative Matrix Factorization to Environmental Research in Public Health.

Often data can be represented as a matrix, e.g., observations as rows and variables as columns, or as a doubly classified contingency table. Researchers may be interested in clustering the observations, the variables, or both. If the data is non-negative, then Non-negative Matrix Factorization (NMF) can be used to perform the clustering. By its nature, NMF-based clustering is focused on the large values. If the data is normalized by subtracting the row/column means, it becomes of mixed signs and the original NMF cannot be used. Our idea is to split and then concatenate the positive and negative parts of the matrix, after taking the absolute value of the negative elements. NMF applied to the concatenated data, which we call PosNegNMF, offers the advantages of the original NMF approach, while giving equal weight to large and small values. We use two public health datasets to illustrate the new method and compare it with alternative clustering methods, such as K-means and clustering methods based on the Singular Value Decomposition (SVD) or Principal Component Analysis (PCA). With the exception of situations where a reasonably accurate factorization can be achieved using the first SVD component, we recommend that the epidemiologists and environmental scientists use the new method to obtain clusters with improved quality and interpretability.

The confirmation rate of primary hits: a predictive model.

HTS data from primary screening are usually analyzed by setting a cutoff for activity, in order to minimize both false-negative and false-positive rates. An alternative approach, based on a calculated probability of being active, is presented here. Given the predicted confirmation rate derived from this probability, the number of primary positives selected for follow-up can be optimized to maximize the number of true positives without picking too many false positives. Typical cutoff-determining methods are more serendipitous in their nature and not easily optimized in an effort to optimize screening efforts. An additional advantage of calculating a probability of being active for each compound screened is that orthogonal mixtures can be deconvoluted without presetting a deconvolution threshold. An important consequence of using the probability of being active with orthogonal mixtures is that individual compound screening results can be recorded irrespective of whether the assays were performed on single compounds or on cocktails.

Distinctive serum miRNA profile in mouse models of striated muscular pathologies.

Biomarkers are critically important for disease diagnosis and monitoring. In particular, close monitoring of disease evolution is eminently required for the evaluation of therapeutic treatments. Classical monitoring methods in muscular dystrophies are largely based on histological and molecular analyses of muscle biopsies. Such biopsies are invasive and therefore difficult to obtain. The serum protein creatine kinase is a useful biomarker, which is however not specific for a given pathology and correlates poorly with the severity or course of the muscular pathology. The aim of the present study was the systematic evaluation of serum microRNAs (miRNAs) as biomarkers in striated muscle pathologies. Mouse models for five striated muscle pathologies were investigated: Duchenne muscular dystrophy (DMD), limb-girdle muscular dystrophy type 2D (LGMD2D), limb-girdle muscular dystrophy type 2C (LGMD2C), Emery-Dreifuss muscular dystrophy (EDMD) and hypertrophic cardiomyopathy (HCM). Two-step RT-qPCR methodology was elaborated, using two different RT-qPCR miRNA quantification technologies. We identified miRNA modulation in the serum of all the five mouse models. The most highly dysregulated serum miRNAs were found to be commonly upregulated in DMD, LGMD2D and LGMD2C mouse models, which all exhibit massive destruction of striated muscle tissues. Some of these miRNAs were down rather than upregulated in the EDMD mice, a model without massive myofiber destruction. The dysregulated miRNAs identified in the HCM model were different, with the exception of one dysregulated miRNA common to all pathologies. Importantly, a specific and distinctive circulating miRNA profile was identified for each studied pathological mouse model. The differential expression of a few dysregulated miRNAs in the DMD mice was further evaluated in DMD patients, providing new candidates of circulating miRNA biomarkers for DMD.

The Global Error Assessment (GEA) model for the selection of differentially expressed genes in microarray data.

MOTIVATION: Microarray technology has become a powerful research tool in many fields of study; however, the cost of microarrays often results in the use of a low number of replicates (k). Under circumstances where k is low, it becomes difficult to perform standard statistical tests to extract the most biologically significant experimental results. Other more advanced statistical tests have been developed; however, their use and interpretation often remain difficult to implement in routine biological research. The present work outlines a method that achieves sufficient statistical power for selecting differentially expressed genes under conditions of low k, while remaining as an intuitive and computationally efficient procedure. RESULTS: The present study describes a Global Error Assessment (GEA) methodology to select differentially expressed genes in microarray datasets, and was developed using an in vitro experiment that compared control and interferon-gamma treated skin cells. In this experiment, up to nine replicates were used to confidently estimate error, thereby enabling methods of different statistical power to be compared. Gene expression results of a similar absolute expression are binned, so as to enable a highly accurate local estimate of the mean squared error within conditions. The model then relates variability of gene expression in each bin to absolute expression levels and uses this in a test derived from the classical ANOVA. The GEA selection method is compared with both the classical and permutational ANOVA tests, and demonstrates an increased stability, robustness and confidence in gene selection. A subset of the selected genes were validated by real-time reverse transcription-polymerase chain reaction (RT-PCR). All these results suggest that GEA methodology is (i) suitable for selection of differentially expressed genes in microarray data, (ii) intuitive and computationally efficient and (iii) especially advantageous under conditions of low k. AVAILABILITY: The GEA code for R software is freely available upon request to authors.