Getting sweeter: new evidence for glucose transporters in specific cell types of the airway?

New technologies such as single-cell RNA sequencing (scRNAseq) has enabled identification of the mRNA transcripts expressed by individual cells. This review provides insight from recent scRNAseq studies on the expression of glucose transporters in the epithelial cells of the airway epithelium from trachea to alveolus. The number of studies analyzed was limited, not all reported the full range of glucose transporters and there were differences between cells freshly isolated from the airways and those grown in vitro. Furthermore, glucose transporter mRNA transcripts were expressed at lower levels than other epithelial marker genes. Nevertheless, these studies highlighted that there were differences in cellular expression of glucose transporters. GLUT1 was the most abundant of the broadly expressed transporters that included GLUT8, 10, and 13. GLUT9 transcripts were more common in basal cells and GLUT12 in ionocytes/ciliated cells. In addition to alveolar cells, SGLT1 transcripts were present in secretory cells. GLUT3 mRNA transcripts were expressed in a cell cluster that expressed monocarboxylate (MCT2) transporters. Such distributions likely underlie cell-specific metabolic requirements to support proliferation, ion transport, mucous secretion, environment sensing, and airway glucose homeostasis. These studies have also highlighted the role of glucose transporters in the movement of dehydroascorbic acid/vitamin C/myoinositol/urate, which are factors important to the innate immune properties of the airways. Discrepancies remain between detection of mRNAs, protein, and function of glucose transporters in the lungs. However, collation of the data from further scRNAseq studies may provide a better consensus and understanding, supported by qPCR, immunohistochemistry, and functional experiments

Effect of glucose on growth and co-culture of Staphylococcus aureus and Pseudomonas aeruginosa in artificial sputum medium

People with cystic fibrosis-related diabetes (CFRD) suffer from chronic infections with Staphylococcus aureus and/or Pseudomonas aeruginosa. In people with CFRD, the concentration of glucose in the airway surface liquid (ASL) was shown to be elevated from 0.4 to 4 mM. The effect of glucose on bacterial growth/interactions in ASL is not well understood and here we studied the relationship between these lung pathogens in artificial sputum medium (ASM), an environment similar to ASL in vivo. S. aureus exhibited more rapid adaptation to growth in ASM than P. aeruginosa. Supplementation of ASM with glucose significantly increased the growth of S. aureus (p < 0.01, n = 5) and P. aeruginosa (p < 0.001, n = 3). ASM conditioned by the presence of S. aureus promoted growth of P. aeruginosa with less lag time compared with non-conditioned ASM, or conditioned medium that had been heated to 121 °C. Stable co-culture of S. aureus and P. aeruginosa could be established in a 50:50 mix of ASM and S. aureus-conditioned supernatant. These data indicate that glucose, in a nutrient depleted environment, can promote the growth of S. aureus and P. aeruginosa. In addition, heat labile factors present in S. aureus pre-conditioned ASM promoted the growth of P. aeruginosa. We suggest that the use of ASM allows investigation of the effects of nutrients such as glucose on common lung pathogens. ASM could be further used to understand the relationship between S. aureus and P. aeruginosa in a co-culture scenario. Our model of stable co-culture could be extrapolated to include other common lung pathogens and could be used to better understand disease progression in vitro

The presence of cystic fibrosis-related diabetes modifies the sputum microbiome in cystic fibrosis disease.

Cystic fibrosis-related diabetes (CFRD) affects 40%-50% of adults with CF and is associated with a decline in respiratory health. The microbial flora of the lung is known to change with the development of CF disease, but how CFRD affects the microbiome has not been described. We analyzed the microbiome in sputa from 14 people with CF, 14 with CFRD, and two who were classed as pre-CFRD by extracting DNA and amplifying the variable V3-V4 region of the microbial 16S ribosomal RNA gene by PCR. Sequences were analyzed and sources were identified to genus level. We found that the α-diversity of the microbiome using Shannon's diversity index was increased in CFRD compared with CF. Bray Curtis dissimilarity analysis showed that there was separation of the microbiomes in CF and CFRD sputa. The most abundant phyla identified in the sputum samples were Firmicutes and Proteobacteria, Actinobacteriota and Bacteroidota, and the ratio of Firmicutes/Bacteroidota was reduced in CFRD compared with CF. Pseudomonas, Azhorizophilus, Porphyromonas, and Actinobacillus were more abundant in CFRD compared with CF, whereas Staphylococcus was less abundant. The relative abundance of these genera did not correlate with age; some correlated with a decline in FEV1/FVC but all correlated with hemoglobin A1C (HbA1c) indicating that development of CFRD mediates further changes to the respiratory microbiome in CF.NEW & NOTEWORTHY Cystic fibrosis-related diabetes (CFRD) is associated with a decline in respiratory health. We show for the first time that there was a change in the sputum microbiome of people with CFRD compared with CF that correlated with markers of raised blood glucose

A modified fluorescent sensor for reporting glucose concentration in the airway lumen.

We have modified the periplasmic Escherichia coli glucose/galactose binding protein (GBP) and labelled with environmentally sensitive fluorophores to further explore its potential as a sensor for the evaluation of glucose concentration in airway surface liquid (ASL). We identified E149C/A213R GBP labelled with N,N'-Dimethyl-N-(iodoacetyl)-N'-(7-nitrobenz-2-oxa-1,3-diazol-4-yl)ethylenediamine (IANBD, emission wavelength maximum 536nm) with a Kd for D-glucose of 1.02mM and a fluorescence dynamic range of 5.8. This sensor was specific for D-glucose and exhibited fluorescence stability in experiments for several hours. The use of E149C/A213R GBP-IANBD in the ASL of airway cells grown at air-liquid-interface (ALI) detected an increase in glucose concentration 10 minutes after raising basolateral glucose from 5 to 15mM. This sensor also reported a greater change in ASL glucose concentration in response to increased basolateral glucose in H441 airway cells compared to human bronchial epithelial cells (HBEC) and there was less variability with HBEC data than that of H441 indicating that HBEC more effectively regulate glucose movement into the ASL. The sensor detected glucose in bronchoalveolar lavage fluid (BALf) from diabetic db/db mice but not normoglycaemic wildtype mice, indicating limited sensitivity of the sensor at glucose concentrations <50μM. Using nasal inhalation of the sensor and spectral unmixing to generate images, E149C/A213R GBP-IANBD fluorescence was detected in luminal regions of cryosections of the murine distal lung that was greater in db/db than wildtype mice. In conclusion, this sensor provides a useful tool for further development to measure luminal glucose concentration in models of lung/airway to explore how this may change in disease

Towards an interoperable ecosystem of AI and LT platforms : a roadmap for the implementation of different levels of interoperability

With regard to the wider area of AI/LT platform interoperability, we concentrate on two core aspects: (1) cross-platform search and discovery of resources and services; (2) composition of cross-platform service workflows. We devise five different levels (of increasing complexity) of platform interoperability that we suggest to implement in a wider federation of AI/LT platforms. We illustrate the approach using the five emerging AI/LT platforms AI4EU, ELG, Lynx, QURATOR and SPEAKER

Carotid artery stenting outcomes in high-risk patients receiving best medical therapy : Results from a single high-volume interventional cardiology practice

Publisher Copyright: © 2016Background Carotid artery stenting (CAS) is now being widely used in the treatment of carotid artery stenosis. Recent clinical studies have demonstrated low adverse event rates after CAS. This study evaluates the 30-day and 1-year results in patients treated with CAS and receiving intensive medical therapy in a high-volume percutaneous coronary intervention center. Methods A total of 184 patients underwent CAS between January 2011 and December 2013. In addition to antiplatelet therapy, patients received intensive antihypertensive treatment, high intensity statin and heart rate normalization therapy. Patients were stratified according to age and symptomatic status. Results Most of the patients (86.4%) had at least one high surgical risk criteria. The procedural success rate was 98.4%. The 30-day and 1-year incidence of stroke were 4.1% and 4.5%, respectively. At 30 days the combined rate of stroke/cardiovascular (CV) death/myocardial infarction (MI) was 5.8% and 10.9% in 1 year. The 30-day incidence of stroke/CV death in asymptomatic and symptomatic patients was 5.4% and 4.2%, respectively. Age ≥80 years increased the risk of stroke/CV death/MI at 1 year (OR 4.41; 95% CI 1.06–18.36; P = 0.04). Conclusions The study demonstrated acceptable clinical outcome results in patients with high medical comorbidities treated with CAS and intensive medical therapy. Adverse event rate in symptomatic patients did not exceed the guideline recommended range while in asymptomatic patients it was increased.publishersversionPeer reviewe

Data Science in Healthcare: Benefits, Challenges and Opportunities

The advent of digital medical data has brought an exponential increase in information available for each patient, allowing for novel knowledge generation methods to emerge. Tapping into this data brings clinical research and clinical practice closer together, as data generated in ordinary clinical practice can be used towards rapid-learning healthcare systems, continuously improving and personalizing healthcare. In this context, the recent use of Data Science technologies for healthcare is providing mutual benefits to both patients and medical professionals, improving prevention and treatment for several kinds of diseases. However, the adoption and usage of Data Science solutions for healthcare still require social capacity, knowledge and higher acceptance. The goal of this chapter is to provide an overview of needs, opportunities, recommendations and challenges of using (Big) Data Science technologies in the healthcare sector. This contribution is based on a recent whitepaper (http://www.bdva.eu/sites/default/files/Big%20Data%20Technologies%20in%20Healthcare.pdf) provided by the Big Data Value Association (BDVA) (http://www.bdva.eu/), the private counterpart to the EC to implement the BDV PPP (Big Data Value PPP) programme, which focuses on the challenges and impact that (Big) Data Science may have on the entire healthcare chain

Cohort profile. the ESC-EORP chronic ischemic cardiovascular disease long-term (CICD LT) registry

The European Society of cardiology (ESC) EURObservational Research Programme (EORP) Chronic Ischemic Cardiovascular Disease registry Long Term (CICD) aims to study the clinical profile, treatment modalities and outcomes of patients diagnosed with CICD in a contemporary environment in order to assess whether these patients at high cardiovascular risk are treated according to ESC guidelines on prevention or on stable coronary disease and to determine mid and long term outcomes and their determinants in this population