CSL324, a granulocyte colony-stimulating factor receptor antagonist, blocks neutrophil migration markers that are upregulated in hidradenitis suppurativa.

BACKGROUND: Neutrophils have been shown to contribute to the pathophysiology of hidradenitis suppurativa (HS), a chronic, painful and debilitating inflammatory skin disease, yet their exact role remains to be fully defined. Granulocyte colony-stimulating factor (G-CSF), a major regulator of neutrophil development and survival, can be blocked by the novel, fully human anti-G-CSF receptor (G-CSFR) monoclonal antibody CSL324. OBJECTIVES: We investigated the activation and migration of neutrophils in HS and the impact of blocking G-CSFR with CSL324. METHODS: Biopsy and peripheral blood samples were taken from participants of two studies: 2018.206, a noninterventional research study of systemic and dermal neutrophils and inflammatory markers in patients with neutrophilic skin diseases, and CSL324_1001 (ACTRN12616000846426), a single-dose ascending and repeated dose, randomized, double-blind, placebo-controlled study to assess the safety, pharmacokinetics and pharmacodynamics of CSL324 in healthy adult subjects. Ex vivo experiments were performed, including neutrophil enumeration and immunophenotyping, migration, receptor occupancy and transcriptome analysis. RESULTS: The number of cells positive for the neutrophil markers myeloperoxidase (MPO) and neutrophil elastase (NE) was significantly higher in HS lesions compared with biopsies from healthy donors (HDs) (P < 0.0001 and P = 0.0223, respectively). In peripheral blood samples, mean neutrophil counts were significantly higher in patients with HS than in HDs (2.98 vs. 1.60 × 109 L-1, respectively; P = 8.8 × 10-4). Neutrophil migration pathways in peripheral blood were increased in patients with HS and their neutrophils demonstrated an increased migration phenotype, with higher mean CXCR1 on the surface of neutrophils in patients with HS (24453.20 vs. 20798.47 for HD; P = 0.03). G-CSF was a key driver of the transcriptomic changes in the peripheral blood of patients with HS and was elevated in serum from patients with HS compared with HDs (mean 6.61 vs. 3.84 pg mL-1, respectively; P = 0.013). Administration of CSL324 inhibited G-CSF-induced transcriptional changes in HDs, similar to those observed in the HS cohort, as highlighted by expression changes in genes related to neutrophil migratory capacity. CONCLUSIONS: Data suggest that neutrophils contribute to HS pathophysiology and that neutrophils are increased in lesions due to an increase in G-CSF-driven migration. CSL324 counteracted G-CSF-induced transcriptomic changes and blocked neutrophil migration by reducing cell-surface levels of chemokine receptors.

Blockade of the G-CSF Receptor Is Protective in a Mouse Model of Renal Ischemia-Reperfusion Injury.

Ischemia-reperfusion injury (IRI) is a complex inflammatory process that detrimentally affects the function of transplanted organs. Neutrophils are important contributors to the pathogenesis of renal IRI. Signaling by G-CSF, a regulator of neutrophil development, trafficking, and function, plays a key role in several neutrophil-associated inflammatory disease models. In this study, we investigated whether targeting neutrophils with a neutralizing mAb to G-CSFR would reduce inflammation and protect against injury in a mouse model of warm renal IRI. Mice were treated with anti-G-CSFR 24 h prior to 22-min unilateral renal ischemia. Renal function and histology, complement activation, and expression of kidney injury markers, and inflammatory mediators were assessed 24 h after reperfusion. Treatment with anti-G-CSFR protected against renal IRI in a dose-dependent manner, significantly reducing serum creatinine and urea, tubular injury, neutrophil and macrophage infiltration, and complement activation (plasma C5a) and deposition (tissue C9). Renal expression of several proinflammatory genes (CXCL1/KC, CXCL2/MIP-2, MCP-1/CCL2, CXCR2, IL-6, ICAM-1, P-selectin, and C5aR) was suppressed by anti-G-CSFR, as was the level of circulating P-selectin and ICAM-1. Neutrophils in anti-G-CSFR-treated mice displayed lower levels of the chemokine receptor CXCR2, consistent with a reduced ability to traffic to inflammatory sites. Furthermore, whole transcriptome analysis using RNA sequencing showed that gene expression changes in IRI kidneys after anti-G-CSFR treatment were indistinguishable from sham-operated kidneys without IRI. Hence, anti-G-CSFR treatment prevented the development of IRI in the kidneys. Our results suggest G-CSFR blockade as a promising therapeutic approach to attenuate renal IRI.

Cellular Microenvironment Stiffness Regulates Eicosanoid Production and Signaling Pathways.

Pathological changes in the biomechanical environment are implicated in the progression of idiopathic pulmonary fibrosis (IPF). Stiffened matrix augments fibroblast proliferation and differentiation and activates TGF-ß1 (transforming growth factor-ß1). Stiffened matrix impairs the synthesis of the antifibrogenic lipid mediator prostaglandin E2 (PGE2) and reduces the expression of the rate-limiting prostanoid biosynthetic enzyme cyclooxygenase-2 (COX-2). We now show that prostaglandin E synthase (PTGES), the final enzyme in the PGE2 biosynthetic pathway, is expressed at lower levels in the lungs of patients with IPF. We also show substantial induction of COX-2, PTGES, prostaglandin E receptor 4 (EP4), and cytosolic phospholipase A2 (cPLA2) expression in human lung fibroblasts cultured in soft collagen hydrogels or in spheroids compared with conventional culture on stiff plastic culture plates. Induction of COX-2, cPLA2, and PTGES expression in spheroid cultures was moderately inhibited by the p38 mitogen-activated protein kinase inhibitor SB203580. The induction of prostanoid biosynthetic enzyme expression was accompanied by an increase in PGE2 levels only in non-IPF-derived fibroblast spheroids. Our study reveals an extensive dysregulation of prostanoid biosynthesis and signaling pathways in IPF-derived fibroblasts, which are only partially abrogated by culture in soft microenvironments.

Combining multiple tools outperforms individual methods in gene set enrichment analyses.

Motivation: Gene set enrichment (GSE) analysis allows researchers to efficiently extract biological insight from long lists of differentially expressed genes by interrogating them at a systems level. In recent years, there has been a proliferation of GSE analysis methods and hence it has become increasingly difficult for researchers to select an optimal GSE tool based on their particular dataset. Moreover, the majority of GSE analysis methods do not allow researchers to simultaneously compare gene set level results between multiple experimental conditions. Results: The ensemble of genes set enrichment analyses (EGSEA) is a method developed for RNA-sequencing data that combines results from twelve algorithms and calculates collective gene set scores to improve the biological relevance of the highest ranked gene sets. EGSEA's gene set database contains around 25 000 gene sets from sixteen collections. It has multiple visualization capabilities that allow researchers to view gene sets at various levels of granularity. EGSEA has been tested on simulated data and on a number of human and mouse datasets and, based on biologists' feedback, consistently outperforms the individual tools that have been combined. Our evaluation demonstrates the superiority of the ensemble approach for GSE analysis, and its utility to effectively and efficiently extrapolate biological functions and potential involvement in disease processes from lists of differentially regulated genes. Availability and Implementation: EGSEA is available as an R package at http://www.bioconductor.org/packages/EGSEA/ . The gene sets collections are available in the R package EGSEAdata from http://www.bioconductor.org/packages/EGSEAdata/ . Contacts: monther.alhamdoosh@csl.com.au mritchie@wehi.edu.au. Supplementary information: Supplementary data are available at Bioinformatics online.

Whole blood transcriptomics reveals granulocyte colony-stimulating factor as a mediator of cardiopulmonary bypass-induced systemic inflammatory response syndrome.

Objectives: Systemic inflammatory response syndrome (SIRS) is a frequent complication of cardiopulmonary bypass (CPB). SIRS is associated with significant morbidity and mortality, but its pathogenesis remains incompletely understood, and as a result, biomarkers are lacking and treatment remains expectant and supportive. This study aimed to understand the pathophysiological mechanisms driving SIRS induced by CPB and identify novel therapeutic targets that might reduce systemic inflammation and improve patient outcomes. Methods: Twenty-one patients undergoing cardiac surgery and CPB were recruited, and blood was sampled before, during and after surgery. SIRS was defined using the American College of Chest Physicians/Society of Critical Care Medicine criteria. We performed immune cell profiling and whole blood transcriptomics and measured individual mediators in plasma/serum to characterise SIRS induced by CPB. Results: Nineteen patients fulfilled criteria for SIRS, with a mean duration of 2.7 days. Neutrophil numbers rose rapidly with CPB and remained elevated for at least 48 h afterwards. Transcriptional signatures associated with neutrophil activation and degranulation were enriched during CPB. We identified a network of cytokines governing these transcriptional changes, including granulocyte colony-stimulating factor (G-CSF), a regulator of neutrophil production and function. Conclusions: We identified neutrophils and G-CSF as major regulators of CPB-induced systemic inflammation. Short-term targeting of G-CSF could provide a novel therapeutic strategy to limit neutrophil-mediated inflammation and tissue damage in SIRS induced by CPB.

Modeling group heteroscedasticity in single-cell RNA-seq pseudo-bulk data.

Group heteroscedasticity is commonly observed in pseudo-bulk single-cell RNA-seq datasets and its presence can hamper the detection of differentially expressed genes. Since most bulk RNA-seq methods assume equal group variances, we introduce two new approaches that account for heteroscedastic groups, namely voomByGroup and voomWithQualityWeights using a blocked design (voomQWB). Compared to current gold-standard methods that do not account for group heteroscedasticity, we show results from simulations and various experiments that demonstrate the superior performance of voomByGroup and voomQWB in terms of error control and power when group variances in pseudo-bulk single-cell RNA-seq data are unequal.

Improving the prediction of disulfide bonds in Eukaryotes with machine learning methods and protein subcellular localization.

MOTIVATION: Disulfide bonds stabilize protein structures and play relevant roles in their functions. Their formation requires an oxidizing environment and their stability is consequently depending on the redox ambient potential, which may differ according to the subcellular compartment. Several methods are available to predict cysteine-bonding state and connectivity patterns. However, none of them takes into consideration the relevance of protein subcellular localization. RESULTS: Here we develop DISLOCATE, a two-step method based on machine learning models for predicting both the bonding state and the connectivity patterns of cysteine residues in a protein chain. We find that the inclusion of protein subcellular localization improves the performance of these predictive steps by 3 and 2 percentage points, respectively. When compared with previously developed methods for predicting disulfide bonds from sequence, DISLOCATE improves the overall performance by more than 10 percentage points. AVAILABILITY: The method and the dataset are available at the Web page http://www.biocomp.unibo.it/savojard/Dislocate.html. GRHCRF code is available at http://www.biocomp.unibo.it/savojard/biocrf.html. CONTACT: piero.fariselli@unibo.it.

Matrix metalloproteinase-7 is increased in lung bases but not apices in idiopathic pulmonary fibrosis.

Introduction: Idiopathic pulmonary fibrosis (IPF) is a progressively fibrotic lung condition with poor prognosis. Matrix metalloproteinase-7 (MMP7) is a protein secreted by epithelial cells in IPF lungs. It is not known if MMP7 expression correlates with fibrotic changes in lung tissue. Methods: Tissue samples from lung apices and bases were obtained from 20 IPF patients and 14 non-diseased control (NDC) donors. In formalin-fixed paraffin-embedded sections, histological assessment of fibrosis was performed; overall MMP7 positivity was assessed by immunohistochemistry and MMP7+ cells were quantified using multiplex immunohistochemistry. Protein expression of MMP7 in whole lung lysates was quantified by Western blotting. Bulk tissue transcriptomic profiles of 101 samples were analysed using RNA sequencing technologies. Results: Lung tissue from IPF bases was more fibrotic than in apices. MMP7 protein is elevated in IPF lung base tissue. In IPF whole lung lysates, MMP7 protein levels are increased compared to NDC donors and was increased in IPF lung bases compared to apices. MMP7 protein levels correlated with MMP7 gene expression levels in lung tissue. MMP7 transcript levels were increased in IPF base compared to NDC base lung tissue and increased in IPF base tissue compared to IPF apex tissue. Conclusions: Our cross-sectional study suggests that lung epithelial MMP7 expression increases as the tissue becomes more fibrotic and identifies a potentially nonepithelial or immune-cell source. Mechanisms of disease progression in IPF are still unclear, and our study suggests aberrant MMP7 production may be a histological starting point of lung tissue fibrosis.

Targeting the Human ßc Receptor Inhibits Contact Dermatitis in a Transgenic Mouse Model.

Allergic contact dermatitis (ACD) is a prevalent and poorly controlled inflammatory disease caused by skin infiltration of T cells and granulocytes. The beta common (ßc) cytokines GM-CSF, IL-3, and IL-5 are powerful regulators of granulocyte function that signal through their common receptor subunit ßc, a property that has made ßc an attractive target to simultaneously inhibit these cytokines. However, the species specificity of ßc has precluded testing of inhibitors of human ßc in mouse models. To overcome this problem, we developed a human ßc receptor transgenic mouse strain with a hematopoietic cell‒specific expression of human ßc instead of mouse ßc. Human ßc receptor transgenic cells responded to mouse GM-CSF and IL-5 but not to IL-3 in vitro and developed tissue pathology and cellular inflammation comparable with those in wild-type mice in a model of ACD. Similarly, Il3-/- mice developed ACD pathology comparable with that of wild-type mice. Importantly, the blocking anti-human ßc antibody CSL311 strongly suppressed ear pinna thickening and histopathological changes typical of ACD and reduced accumulation of neutrophils, mast cells, and eosinophils in the skin. These results show that GM-CSF and IL-5 but not IL-3 are major mediators of ACD and define the human ßc receptor transgenic mouse as a unique platform to test the inhibitors of ßc in vivo.

A guide to creating design matrices for gene expression experiments.

Differential expression analysis of genomic data types, such as RNA-sequencing experiments, use linear models to determine the size and direction of the changes in gene expression. For RNA-sequencing, there are several established software packages for this purpose accompanied with analysis pipelines that are well described. However, there are two crucial steps in the analysis process that can be a stumbling block for many -- the set up an appropriate model via design matrices and the set up of comparisons of interest via contrast matrices. These steps are particularly troublesome because an extensive catalogue for design and contrast matrices does not currently exist. One would usually search for example case studies across different platforms and mix and match the advice from those sources to suit the dataset they have at hand. This article guides the reader through the basics of how to set up design and contrast matrices. We take a practical approach by providing code and graphical representation of each case study, starting with simpler examples (e.g. models with a single explanatory variable) and move onto more complex ones (e.g. interaction models, mixed effects models, higher order time series and cyclical models). Although our work has been written specifically with a limma-style pipeline in mind, most of it is also applicable to other software packages for differential expression analysis, and the ideas covered can be adapted to data analysis of other high-throughput technologies. Where appropriate, we explain the interpretation and differences between models to aid readers in their own model choices. Unnecessary jargon and theory is omitted where possible so that our work is accessible to a wide audience of readers, from beginners to those with experience in genomics data analysis.

Machine learning applied to whole-blood RNA-sequencing data uncovers distinct subsets of patients with systemic lupus erythematosus.

OBJECTIVES: Systemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease that is difficult to treat. There is currently no optimal stratification of patients with SLE, and thus, responses to available treatments are unpredictable. Here, we developed a new stratification scheme for patients with SLE, based on the computational analysis of patients' whole-blood transcriptomes. METHODS: We applied machine learning approaches to RNA-sequencing (RNA-seq) data sets to stratify patients with SLE into four distinct clusters based on their gene expression profiles. A meta-analysis on three recently published whole-blood RNA-seq data sets was carried out, and an additional similar data set of 30 patients with SLE and 29 healthy donors was incorporated in this study; a total of 161 patients with SLE and 57 healthy donors were analysed. RESULTS: Examination of SLE clusters, as opposed to unstratified SLE patients, revealed underappreciated differences in the pattern of expression of disease-related genes relative to clinical presentation. Moreover, gene signatures correlated with flare activity were successfully identified. CONCLUSION: Given that SLE disease heterogeneity is a key challenge hindering the design of optimal clinical trials and the adequate management of patients, our approach opens a new possible avenue addressing this limitation via a greater understanding of SLE heterogeneity in humans. Stratification of patients based on gene expression signatures may be a valuable strategy allowing the identification of separate molecular mechanisms underpinning disease in SLE. Further, this approach may have a use in understanding the variability in responsiveness to therapeutics, thereby improving the design of clinical trials and advancing personalised therapy.

Topical application of human-derived Ig isotypes for the control of acute respiratory infection evaluated in a human CD89-expressing mouse model.

Recurrent and persistent airway infections remain prevalent in patients with primary immunodeficiency (PID), despite restoration of serum immunoglobulin levels by intravenous or subcutaneous plasma-derived IgG. We investigated the effectiveness of different human Ig isotype preparations to protect mice against influenza when delivered directly to the respiratory mucosa. Four polyvalent Ig preparations from pooled plasma were compared: IgG, monomeric IgA (mIgA), polymeric IgA-containing IgM (IgAM) and IgAM associated with the secretory component (SIgAM). To evaluate these preparations, a transgenic mouse expressing human FcαRI/CD89 within the myeloid lineage was created. CD89 was expressed on all myeloid cells in the lung and blood except eosinophils, reflecting human CD89 expression. Intranasal administration of IgA-containing preparations was less effective than IgG in reducing pulmonary viral titres after infection of mice with A/California/7/09 (Cal7) or the antigenically distant A/Puerto Rico/8/34 (PR8) viruses. However, IgA reduced weight loss and inflammatory mediator expression. Both IgG and IgA protected mice from a lethal dose of PR8 virus and for mIgA, this effect was partially CD89 dependent. Our data support the beneficial effect of topically applied Ig purified from pooled human plasma for controlling circulating and non-circulating influenza virus infections. This may be important for reducing morbidity in PID patients.

Easy and efficient ensemble gene set testing with EGSEA.

Gene set enrichment analysis is a popular approach for prioritising the biological processes perturbed in genomic datasets. The Bioconductor project hosts over 80 software packages capable of gene set analysis. Most of these packages search for enriched signatures amongst differentially regulated genes to reveal higher level biological themes that may be missed when focusing only on evidence from individual genes. With so many different methods on offer, choosing the best algorithm and visualization approach can be challenging. The EGSEA package solves this problem by combining results from up to 12 prominent gene set testing algorithms to obtain a consensus ranking of biologically relevant results.This workflow demonstrates how EGSEA can extend limma-based differential expression analyses for RNA-seq and microarray data using experiments that profile 3 distinct cell populations important for studying the origins of breast cancer. Following data normalization and set-up of an appropriate linear model for differential expression analysis, EGSEA builds gene signature specific indexes that link a wide range of mouse or human gene set collections obtained from MSigDB, GeneSetDB and KEGG to the gene expression data being investigated. EGSEA is then configured and the ensemble enrichment analysis run, returning an object that can be queried using several S4 methods for ranking gene sets and visualizing results via heatmaps, KEGG pathway views, GO graphs, scatter plots and bar plots. Finally, an HTML report that combines these displays can fast-track the sharing of results with collaborators, and thus expedite downstream biological validation. EGSEA is simple to use and can be easily integrated with existing gene expression analysis pipelines for both human and mouse data.

Four simple recommendations to encourage best practices in research software.

Scientific research relies on computer software, yet software is not always developed following practices that ensure its quality and sustainability. This manuscript does not aim to propose new software development best practices, but rather to provide simple recommendations that encourage the adoption of existing best practices. Software development best practices promote better quality software, and better quality software improves the reproducibility and reusability of research. These recommendations are designed around Open Source values, and provide practical suggestions that contribute to making research software and its source code more discoverable, reusable and transparent. This manuscript is aimed at developers, but also at organisations, projects, journals and funders that can increase the quality and sustainability of research software by encouraging the adoption of these recommendations.

RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR.

The ability to easily and efficiently analyse RNA-sequencing data is a key strength of the Bioconductor project. Starting with counts summarised at the gene-level, a typical analysis involves pre-processing, exploratory data analysis, differential expression testing and pathway analysis with the results obtained informing future experiments and validation studies. In this workflow article, we analyse RNA-sequencing data from the mouse mammary gland, demonstrating use of the popular edgeR package to import, organise, filter and normalise the data, followed by the limma package with its voom method, linear modelling and empirical Bayes moderation to assess differential expression and perform gene set testing. This pipeline is further enhanced by the Glimma package which enables interactive exploration of the results so that individual samples and genes can be examined by the user. The complete analysis offered by these three packages highlights the ease with which researchers can turn the raw counts from an RNA-sequencing experiment into biological insights using Bioconductor.