Cystic fibrosis macrophage function and clinical outcomes after elexacaftor/tezacaftor/ivacaftor.

BACKGROUND: Abnormal macrophage function caused by dysfunctional cystic fibrosis transmembrane conductance regulator (CFTR) is a critical contributor to chronic airway infections and inflammation in people with cystic fibrosis (PWCF). Elexacaftor/tezacaftor/ivacaftor (ETI) is a new CFTR modulator therapy for PWCF. Host-pathogen and clinical responses to CFTR modulators are poorly described. We sought to determine how ETI impacts macrophage CFTR function, resulting effector functions and relationships to clinical outcome changes. METHODS: Clinical information and/or biospecimens were obtained at ETI initiation and 3, 6, 9 and 12 months post-ETI in 56 PWCF and compared with non-CF controls. Peripheral blood monocyte-derived macrophages (MDMs) were isolated and functional assays performed. RESULTS: ETI treatment was associated with increased CF MDM CFTR expression, function and localisation to the plasma membrane. CF MDM phagocytosis, intracellular killing of CF pathogens and efferocytosis of apoptotic neutrophils were partially restored by ETI, but inflammatory cytokine production remained unchanged. Clinical outcomes including increased forced expiratory volume in 1 s (+10%) and body mass index (+1.0 kg·m-2) showed fluctuations over time and were highly individualised. Significant correlations between post-ETI MDM CFTR function and sweat chloride levels were observed. However, MDM CFTR function correlated with clinical outcomes better than sweat chloride. CONCLUSION: ETI is associated with unique changes in innate immune function and clinical outcomes.

(R)-Roscovitine and CFTR modulators enhance killing of multi-drug resistant Burkholderia cenocepacia by cystic fibrosis macrophages.

Cystic fibrosis (CF) is characterized by chronic bacterial infections and heightened inflammation. Widespread ineffective antibiotic use has led to increased isolation of drug resistant bacterial strains from respiratory samples. (R)-roscovitine (Seliciclib) is a unique drug that has many benefits in CF studies. We sought to determine roscovitine's impact on macrophage function and killing of multi-drug resistant bacteria. Human blood monocytes were isolated from CF (F508del/F508del) and non-CF persons and derived into macrophages (MDMs). MDMs were infected with CF clinical isolates of B. cenocepacia and P. aeruginosa. MDMs were treated with (R)-roscovitine or its main hepatic metabolite (M3). Macrophage responses to infection and subsequent treatment were determined. (R)-roscovitine and M3 significantly increased killing of B. cenocepacia and P. aeruginosa in CF MDMs in a dose-dependent manner. (R)-roscovitine-mediated effects were partially dependent on CFTR and the TRPC6 channel. (R)-roscovitine-mediated killing of B. cenocepacia was enhanced by combination with the CFTR modulator tezacaftor/ivacaftor and/or the alternative CFTR modulator cysteamine. (R)-roscovitine also increased MDM CFTR function compared to tezacaftor/ivacaftor treatment alone. (R)-roscovitine increases CF macrophage-mediated killing of antibiotic-resistant bacteria. (R)-roscovitine also enhances other macrophage functions including CFTR-mediated ion efflux. Effects of (R)-roscovitine are greatest when combined with CFTR modulators or cysteamine, justifying further clinical testing of (R)-roscovitine or optimized derivatives.

Consequences of CRISPR-Cas9-Mediated CFTR Knockout in Human Macrophages.

Macrophage dysfunction is fundamentally related to altered immunity in cystic fibrosis (CF). How genetic deficits in the cystic fibrosis transmembrane conductance regulator (CFTR) lead to these defects remains unknown. Rapid advances in genomic editing such as the clustered regularly interspaced short palindromic repeats associated protein 9 (CRISPR/Cas9) system provide new tools for scientific study. We aimed to create a stable CFTR knockout (KO) in human macrophages in order to study how CFTR regulates macrophage function. Peripheral blood monocytes were isolated from non-CF healthy volunteers and differentiated into monocyte-derived macrophages (MDMs). MDMs were transfected with a CRISPR Cas9 CFTR KO plasmid. CFTR KO efficiency was verified and macrophage halide efflux, phagocytosis, oxidative burst, apoptosis, and cytokine functional assays were performed. CFTR KO in human MDMs was efficient and stable after puromycin selection. CFTR KO was confirmed by CFTR mRNA and protein expression. CFTR function was abolished in CFTR KO MDMs. CFTR KO recapitulated known defects in human CF MDM (CFTR class I/II variants) dysfunction including (1) increased apoptosis, (2) decreased phagocytosis, (3) reduced oxidative burst, and (4) increased bacterial load. Activation of the oxidative burst via nicotinamide adenine dinucleotide phosphate (NADPH) oxidase assembly was diminished in CFTR KO MDMs (decreased phosphorylated p47phox). Cytokine production was unchanged or decreased in response to infection in CFTR KO MDMs. In conclusion, we developed a primary human macrophage CFTR KO system. CFTR KO mimics most pathology observed in macrophages obtained from persons with CF, which suggests that many aspects of CF macrophage dysfunction are CFTR-dependent and not just reflective of the CF inflammatory milieu.

Author Correction: Baseline and Disease-Induced Transcriptional Profiles in Children with Sickle Cell Disease.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Baseline and Disease-Induced Transcriptional Profiles in Children with Sickle Cell Disease.

Acute chest syndrome (ACS) is a significant cause of morbidity and mortality in sickle cell disease (SCD), but preventive, diagnostic, and therapeutic options are limited. Further, ACS and acute vasoccclusive pain crises (VOC) have overlapping features, which causes diagnostic dilemmas. We explored changes in gene expression profiles among patients with SCD hospitalized for VOC and ACS episodes to better understand ACS disease pathogenesis. Whole blood RNA-Seq was performed for 20 samples from children with SCD at baseline and during a hospitalization for either an ACS (n = 10) or a VOC episode (n = 10). Respiratory viruses were identified from nasopharyngeal swabs. Functional gene analyses were performed using modular repertoires, IPA, Gene Ontology, and NetworkAnalyst 3.0. The VOC group had a numerically higher percentage of female, older, and hemoglobin SS participants compared to the ACS group. Viruses were detected in 50% of ACS cases and 20% of VOC cases. We identified 3004 transcripts that were differentially expressed during ACS episodes, and 1802 transcripts during VOC episodes. Top canonical pathways during ACS episodes were related to interferon signaling, neuro-inflammation, pattern recognition receptors, and macrophages. Top canonical pathways in patients with VOC included IL-10 signaling, iNOS signaling, IL-6 signaling, and B cell signaling. Several genes related to antimicrobial function were down-regulated during ACS compared to VOC. Gene enrichment nodal interactions demonstrated significantly altered pathways during ACS and VOC. A complex network of changes in innate and adaptive immune gene expression were identified during both ACS and VOC episodes. These results provide unique insights into changes during acute events in children with SCD.

Metabolomics profiling of tobacco exposure in children with cystic fibrosis.

BACKGROUND: Inflammation is integral to early disease progression in children with CF. The effect of modifiable environmental factors on infection and inflammation in persons with CF is poorly understood. Our prior studies determined that secondhand smoke exposure (SHSe) is highly prevalent in young children with CF. SHSe is associated with increased inflammation, heightened bacterial burden, and worsened clinical outcomes. However, the specific metabolite and signaling pathways that regulate responses to SHSe in CF are relatively unknown. METHODS: High-resolution metabolomics was performed on plasma samples from infants (n = 25) and children (n = 40) with CF compared to non-CF controls (n = 15). CF groups were stratified according to infant or child age and SHSe status. RESULTS: Global metabolomic profiles segregated by age and SHSe status. SHSe in CF was associated with changes in pathways related to steroid biosynthesis, fatty acid metabolism, cysteine metabolism, and oxidative stress. CF infants with SHSe demonstrated enrichment for altered metabolite localization to the small intestine, liver, and striatum. CF children with SHSe demonstrated metabolite enrichment for organs/tissues associated with oxidative stress including mitochondria, peroxisomes, and the endoplasmic reticulum. In a confirmatory analysis, SHSe was associated with changes in biomarkers of oxidative stress and cellular adhesion including MMP-9, MPO, and ICAM-1. CONCLUSIONS: SHSe in young children and infants with CF is associated with altered global metabolomics profiles and specific biochemical pathways, including enhanced oxidative stress. SHSe remains an important but understudied modifiable variable in early CF disease.

The Unfolded Protein Response Mediator PERK Governs Myeloid Cell-Driven Immunosuppression in Tumors through Inhibition of STING Signaling.

The primary mechanisms supporting immunoregulatory polarization of myeloid cells upon infiltration into tumors remain largely unexplored. Elucidation of these signals could enable better strategies to restore protective anti-tumor immunity. Here, we investigated the role of the intrinsic activation of the PKR-like endoplasmic reticulum (ER) kinase (PERK) in the immunoinhibitory actions of tumor-associated myeloid-derived suppressor cells (tumor-MDSCs). PERK signaling increased in tumor-MDSCs, and its deletion transformed MDSCs into myeloid cells that activated CD8+ T cell-mediated immunity against cancer. Tumor-MDSCs lacking PERK exhibited disrupted NRF2-driven antioxidant capacity and impaired mitochondrial respiratory homeostasis. Moreover, reduced NRF2 signaling in PERK-deficient MDSCs elicited cytosolic mitochondrial DNA elevation and, consequently, STING-dependent expression of anti-tumor type I interferon. Reactivation of NRF2 signaling, conditional deletion of STING, or blockade of type I interferon receptor I restored the immunoinhibitory potential of PERK-ablated MDSCs. Our findings demonstrate the pivotal role of PERK in tumor-MDSC functionality and unveil strategies to reprogram immunosuppressive myelopoiesis in tumors to boost cancer immunotherapy.

Whole-blood transcriptomic responses to lumacaftor/ivacaftor therapy in cystic fibrosis.

BACKGROUND: Cystic fibrosis (CF) remains without a definitive cure. Novel therapeutics targeting the causative defect in the cystic fibrosis transmembrane conductance regulator (CFTR) gene are in clinical use. Lumacaftor/ivacaftor is a CFTR modulator approved for patients homozygous for the CFTR variant p.Phe508del, but there are wide variations in treatment responses preventing prediction of patient responses. We aimed to determine changes in gene expression related to treatment initiation and response. METHODS: Whole-blood transcriptomics was performed using RNA-Seq in 20 patients with CF pre- and 6 months post-lumacaftor/ivacaftor (drug) initiation and 20 non-CF healthy controls. Correlation of gene expression with clinical variables was performed by stratification via clinical responses. RESULTS: We identified 491 genes that were differentially expressed in CF patients (pre-drug) compared with non-CF controls and 36 genes when comparing pre-drug to post-drug profiles. Both pre- and post-drug CF profiles were associated with marked overexpression of inflammation-related genes and apoptosis genes, and significant under-expression of T cell and NK cell-related genes compared to non-CF. CF patients post-drug demonstrated normalized protein synthesis expression, and decreased expression of cell-death genes compared to pre-drug profiles, irrespective of clinical response. However, CF clinical responders demonstrated changes in eIF2 signaling, oxidative phosphorylation, IL-17 signaling, and mitochondrial function compared to non-responders. Top overexpressed genes (MMP9 and SOCS3) that decreased post-drug were validated by qRT-PCR. Functional assays demonstrated that CF monocytes normalized calcium (increases MMP9 expression) concentrations post-drug. CONCLUSIONS: Transcriptomics revealed differentially regulated pathways in CF patients at baseline compared to non-CF, and in clinical responders to lumacaftor/ivacaftor.

AR-13 reduces antibiotic-resistant bacterial burden in cystic fibrosis phagocytes and improves cystic fibrosis transmembrane conductance regulator function.

BACKGROUND: There are no effective treatments for Burkholderia cenocepacia in patients with cystic fibrosis (CF) due to bacterial multi-drug resistance and defective host killing. We demonstrated that decreased bacterial killing in CF is caused by reduced macrophage autophagy due to defective cystic fibrosis transmembrane conductance regulator (CFTR) function. AR-12 is a small molecule autophagy inducer that kills intracellular pathogens such as Francisella. We evaluated the efficacy of AR-12 and a new analogue AR-13 in reducing bacterial burden in CF phagocytes. METHODS: Human CF and non-CF peripheral blood monocyte-derived macrophages, neutrophils, and nasal epithelial cells were exposed to CF bacterial strains in conjunction with treatment with antibiotics and/or AR compounds. RESULTS: AR-13 and not AR-12 had growth inhibition on B. cenocepacia and methicillin-resistantStaphylococcus aureus (MRSA) in media alone. There was a 99% reduction in MRSA in CF macrophages, 71% reduction in Pseudomonas aeruginosa in CF neutrophils, and 70% reduction in non-CF neutrophils using AR-13. Conversely, there was no reduction in B. cenocepacia in infected CF and non-CF macrophages using AR-13 alone, but AR-13 and antibiotics synergistically reduced B. cenocepacia in CF macrophages. AR-13 improved autophagy in CF macrophages and CF patient-derived epithelial cells, and increased CFTR protein expression and channel function in CF epithelial cells. CONCLUSIONS: The novel AR-12 analogue AR-13, in combination with antibiotics, reduced antibiotic-resistant bacterial burden in CF phagocytes, which correlated with increased autophagy and CFTR expression. AR-13 is a novel therapeutic for patients infected with B. cenocepacia and other resistant organisms that lack effective therapies.

Cystic fibrosis transmembrane conductance regulator (CFTR) modulators have differential effects on cystic fibrosis macrophage function.

Despite the addition of cystic fibrosis transmembrane conductance regulator (CFTR) modulators to the cystic fibrosis (CF) treatment regimen, patients with CF continue to suffer from chronic bacterial infections that lead to progressive respiratory morbidity. Host immunity, and macrophage dysfunction specifically, has an integral role in the inability of patients with CF to clear bacterial infections. We sought to characterize macrophage responses to CFTR modulator treatment as we hypothesized that there would be differential effects based on patient genotype. Human CF and non-CF peripheral blood monocyte-derived macrophages (MDMs) were analyzed for CFTR expression, apoptosis, polarization, phagocytosis, bacterial killing, and cytokine production via microscopy, flow cytometry, and ELISA-based assays. Compared to non-CF MDMs, CF MDMs display decreased CFTR expression, increased apoptosis, and decreased phagocytosis. CFTR expression increased and apoptosis decreased in response to ivacaftor or lumacaftor/ivacaftor therapy, and phagocytosis improved with ivacaftor alone. Ivacaftor restored CF macrophage polarization responses to non-CF levels and reduced Pseudomonas aeruginosa bacterial burden, but did not reduce other bacterial loads. Macrophage inflammatory cytokine production decreased in response to ivacaftor alone. In summary, ivacaftor and lumacaftor/ivacaftor have differential impacts on macrophage function with minimal changes observed in CF patients treated with lumacaftor/ivacaftor. Overall improvements in macrophage function in ivacaftor-treated CF patients result in modestly improved macrophage-mediated bacterial killing.

Highly accurate model for prediction of lung nodule malignancy with CT scans.

Computed tomography (CT) examinations are commonly used to predict lung nodule malignancy in patients, which are shown to improve noninvasive early diagnosis of lung cancer. It remains challenging for computational approaches to achieve performance comparable to experienced radiologists. Here we present NoduleX, a systematic approach to predict lung nodule malignancy from CT data, based on deep learning convolutional neural networks (CNN). For training and validation, we analyze >1000 lung nodules in images from the LIDC/IDRI cohort. All nodules were identified and classified by four experienced thoracic radiologists who participated in the LIDC project. NoduleX achieves high accuracy for nodule malignancy classification, with an AUC of ~0.99. This is commensurate with the analysis of the dataset by experienced radiologists. Our approach, NoduleX, provides an effective framework for highly accurate nodule malignancy prediction with the model trained on a large patient population. Our results are replicable with software available at http://bioinformatics.astate.edu/NoduleX .

Metabolomic responses to lumacaftor/ivacaftor in cystic fibrosis.

BACKGROUND: Cystic fibrosis (CF) is a life-limiting disease caused by a defect in the cystic fibrosis transmembrane conductance regulator (CFTR) gene. Lumacaftor/Ivacaftor is a novel CFTR modulator approved for patients that are homozygous for Phe508del CFTR, but its clinical effectiveness varies amongst patients, making it difficult to determine clinical responders. Therefore, identifying biochemical biomarkers associated with drug response are clinically important for follow-up studies. METHODS: Serum metabolomics was performed on twenty patients with CF pre- and 6-month post-Lumacaftor/Ivacaftor response via Ultrahigh Performance Liquid Chromatography-Tandem Mass Spectroscopy (UPLC-MS/MS). Correlation with clinical variables was performed. RESULTS: Metabolomics analysis demonstrated 188 differentially regulated metabolites between patients pre- and post-Lumacaftor/Ivacaftor initiation, with a predominance of lipid and amino acid alterations. The top 30 metabolites were able to differentiate pre- and post-Lumacaftor/Ivacaftor status in greater than 90% of patients via a random-forest confusion matrix. Alterations in bile acids, phospholipids, and bacteria-associated metabolites were the predominant changes associated with drug response. Importantly, changes in metabolic patterns were associated with clinical responders. CONCLUSIONS: Selected key lipid and amino acid metabolic pathways were significantly affected by Lumacaftor/Ivacaftor initiation and similar pathways were affected in clinical responders. Targeted metabolomics may provide useful and relevant biomarkers of CFTR modulator responses.

Cysteamine-mediated clearance of antibiotic-resistant pathogens in human cystic fibrosis macrophages.

Members of the Burkholderia cepacia complex are virulent, multi-drug resistant pathogens that survive and replicate intracellularly in patients with cystic fibrosis (CF). We have discovered that B. cenocepacia cannot be cleared from CF macrophages due to defective autophagy, causing continued systemic inflammation and infection. Defective autophagy in CF is mediated through constitutive reactive oxygen species (ROS) activation of transglutaminase-2 (TG2), which causes the sequestration (accumulation) of essential autophagy initiating proteins. Cysteamine is a TG2 inhibitor and proteostasis regulator with the potential to restore autophagy. Therefore, we sought to examine the impact of cysteamine on CF macrophage autophagy and bacterial killing. Human peripheral blood monocyte-derived macrophages (MDMs) and alveolar macrophages were isolated from CF and non-CF donors. Macrophages were infected with clinical isolates of relevant CF pathogens. Cysteamine caused direct bacterial growth killing of live B. cenocepacia, B. multivorans, P. aeruginosa and MRSA in the absence of cells. Additionally, B. cenocepacia, B. multivorans, and P. aeruginosa invasion were significantly decreased in CF MDMs treated with cysteamine. Finally, cysteamine decreased TG2, p62, and beclin-1 accumulation in CF, leading to increased Burkholderia uptake into autophagosomes, increased macrophage CFTR expression, and decreased ROS and IL-1ß production. Cysteamine has direct anti-bacterial growth killing and improves human CF macrophage autophagy resulting in increased macrophage-mediated bacterial clearance, decreased inflammation, and reduced constitutive ROS production. Thus, cysteamine may be an effective adjunct to antibiotic regimens in CF.

SparRec: An effective matrix completion framework of missing data imputation for GWAS.

Genome-wide association studies present computational challenges for missing data imputation, while the advances of genotype technologies are generating datasets of large sample sizes with sample sets genotyped on multiple SNP chips. We present a new framework SparRec (Sparse Recovery) for imputation, with the following properties: (1) The optimization models of SparRec, based on low-rank and low number of co-clusters of matrices, are different from current statistics methods. While our low-rank matrix completion (LRMC) model is similar to Mendel-Impute, our matrix co-clustering factorization (MCCF) model is completely new. (2) SparRec, as other matrix completion methods, is flexible to be applied to missing data imputation for large meta-analysis with different cohorts genotyped on different sets of SNPs, even when there is no reference panel. This kind of meta-analysis is very challenging for current statistics based methods. (3) SparRec has consistent performance and achieves high recovery accuracy even when the missing data rate is as high as 90%. Compared with Mendel-Impute, our low-rank based method achieves similar accuracy and efficiency, while the co-clustering based method has advantages in running time. The testing results show that SparRec has significant advantages and competitive performance over other state-of-the-art existing statistics methods including Beagle and fastPhase.

SPARCoC: a new framework for molecular pattern discovery and cancer gene identification.

It is challenging to cluster cancer patients of a certain histopathological type into molecular subtypes of clinical importance and identify gene signatures directly relevant to the subtypes. Current clustering approaches have inherent limitations, which prevent them from gauging the subtle heterogeneity of the molecular subtypes. In this paper we present a new framework: SPARCoC (Sparse-CoClust), which is based on a novel Common-background and Sparse-foreground Decomposition (CSD) model and the Maximum Block Improvement (MBI) co-clustering technique. SPARCoC has clear advantages compared with widely-used alternative approaches: hierarchical clustering (Hclust) and nonnegative matrix factorization (NMF). We apply SPARCoC to the study of lung adenocarcinoma (ADCA), an extremely heterogeneous histological type, and a significant challenge for molecular subtyping. For testing and verification, we use high quality gene expression profiling data of lung ADCA patients, and identify prognostic gene signatures which could cluster patients into subgroups that are significantly different in their overall survival (with p-values < 0.05). Our results are only based on gene expression profiling data analysis, without incorporating any other feature selection or clinical information; we are able to replicate our findings with completely independent datasets. SPARCoC is broadly applicable to large-scale genomic data to empower pattern discovery and cancer gene identification.

Big data - a 21st century science Maginot Line? No-boundary thinking: shifting from the big data paradigm.

Whether your interests lie in scientific arenas, the corporate world, or in government, you have certainly heard the praises of big data: Big data will give you new insights, allow you to become more efficient, and/or will solve your problems. While big data has had some outstanding successes, many are now beginning to see that it is not the Silver Bullet that it has been touted to be. Here our main concern is the overall impact of big data; the current manifestation of big data is constructing a Maginot Line in science in the 21st century. Big data is not "lots of data" as a phenomena anymore; The big data paradigm is putting the spirit of the Maginot Line into lots of data. Big data overall is disconnecting researchers and science challenges. We propose No-Boundary Thinking (NBT), applying no-boundary thinking in problem defining to address science challenges.

PARP activity in peripheral blood lymphocytes as a predictive biomarker for PARP inhibition in tumor tissues - A population pharmacokinetic/pharmacodynamic analysis of rucaparib.

PURPOSE: Rucaparib is a potent Poly (ADP-ribose) Polymerase (PARP) inhibitor currently under clinical development. The objectives of this analysis were to establish population PK and PK/PD models for rucaparib, and to evaluate the predictability of PARP activity in PBL for PARP activity in tumor tissues. EXPERIMENTAL DESIGN: Rucaparib concentrations and PARP activity in human PBLs and tumor issues were obtained from 32 patients with solid tumors in a Phase 1 First-in-Patient study. Simulations were conducted to evaluate different dosing regimens. RESULTS: A 3-compartment PK model best described the PK of rucaparib. An Emax model best described the exposure and PARP inhibition relationship. The maximum PARP inhibition (Imax) achieved in PBLs and in tumors were 90.9% and 90.0% of the baseline PARP activity, and the IC50 values were 1.05 ng/mL and 1.10 ng/mL, respectively. PAR polymer baseline value was found to be a covariate of Emin. CONCLUSION: Population PK and PK/PD models have been established to describe population PK of rucaparib and the relationship between rucaparib plasma concentration and PARP inhibition in both PBLs and tumor issues. Results from this trial indicated that PARP inhibition in PBLs can be used as a substitute for PARP inhibition in melanoma tumor tissues.

eMBI: Boosting Gene Expression-based Clustering for Cancer Subtypes.

Identifying clinically relevant subtypes of a cancer using gene expression data is a challenging and important problem in medicine, and is a necessary premise to provide specific and efficient treatments for patients of different subtypes. Matrix factorization provides a solution by finding checker-board patterns in the matrices of gene expression data. In the context of gene expression profiles of cancer patients, these checkerboard patterns correspond to genes that are up- or down-regulated in patients with particular cancer subtypes. Recently, a new matrix factorization framework for biclustering called Maximum Block Improvement (MBI) is proposed; however, it still suffers several problems when applied to cancer gene expression data analysis. In this study, we developed many effective strategies to improve MBI and designed a new program called enhanced MBI (eMBI), which is more effective and efficient to identify cancer subtypes. Our tests on several gene expression profiling datasets of cancer patients consistently indicate that eMBI achieves significant improvements in comparison with MBI, in terms of cancer subtype prediction accuracy, robustness, and running time. In addition, the performance of eMBI is much better than another widely used matrix factorization method called nonnegative matrix factorization (NMF) and the method of hierarchical clustering, which is often the first choice of clinical analysts in practice.