Generation of a non-small cell lung cancer transcriptome microarray

<p>Abstract</p> <p>Background</p> <p>Non-small cell lung cancer (NSCLC) is the leading cause of cancer mortality worldwide. At present no reliable biomarkers are available to guide the management of this condition. Microarray technology may allow appropriate biomarkers to be identified but present platforms are lacking disease focus and are thus likely to miss potentially vital information contained in patient tissue samples.</p> <p>Methods</p> <p>A combination of large-scale in-house sequencing, gene expression profiling and public sequence and gene expression data mining were used to characterise the transcriptome of NSCLC and the data used to generate a disease-focused microarray – the Lung Cancer DSA research tool.</p> <p>Results</p> <p>Built on the Affymetrix GeneChip platform, the Lung Cancer DSA research tool allows for interrogation of ~60,000 transcripts relevant to Lung Cancer, tens of thousands of which are unavailable on leading commercial microarrays.</p> <p>Conclusion</p> <p>We have developed the first high-density disease specific transcriptome microarray. We present the array design process and the results of experiments carried out to demonstrate the array's utility. This approach serves as a template for the development of other disease transcriptome microarrays, including non-neoplastic diseases.</p

Distributional fold change test – a statistical approach for detecting differential expression in microarray experiments

<p>Abstract</p> <p>Background</p> <p>Because of the large volume of data and the intrinsic variation of data intensity observed in microarray experiments, different statistical methods have been used to systematically extract biological information and to quantify the associated uncertainty. The simplest method to identify differentially expressed genes is to evaluate the ratio of average intensities in two different conditions and consider all genes that differ by more than an arbitrary cut-off value to be differentially expressed. This filtering approach is not a statistical test and there is no associated value that can indicate the level of confidence in the designation of genes as differentially expressed or not differentially expressed. At the same time the fold change by itself provide valuable information and it is important to find unambiguous ways of using this information in expression data treatment.</p> <p>Results</p> <p>A new method of finding differentially expressed genes, called distributional fold change (DFC) test is introduced. The method is based on an analysis of the intensity distribution of all microarray probe sets mapped to a three dimensional feature space composed of average expression level, average difference of gene expression and total variance. The proposed method allows one to rank each feature based on the signal-to-noise ratio and to ascertain for each feature the confidence level and power for being differentially expressed. The performance of the new method was evaluated using the total and partial area under receiver operating curves and tested on 11 data sets from Gene Omnibus Database with independently verified differentially expressed genes and compared with the t-test and shrinkage t-test. Overall the DFC test performed the best – on average it had higher sensitivity and partial AUC and its elevation was most prominent in the low range of differentially expressed features, typical for formalin-fixed paraffin-embedded sample sets.</p> <p>Conclusions</p> <p>The distributional fold change test is an effective method for finding and ranking differentially expressed probesets on microarrays. The application of this test is advantageous to data sets using formalin-fixed paraffin-embedded samples or other systems where degradation effects diminish the applicability of correlation adjusted methods to the whole feature set.</p

Age-Related Differences in Structure and Function of Nasal Epithelial Cultures From Healthy Children and Elderly People

The nasal epithelium represents the first line of defense against inhaled pathogens, allergens, and irritants and plays a key role in the pathogenesis of a spectrum of acute and chronic airways diseases. Despite age-dependent clinical phenotypes triggered by these noxious stimuli, little is known about how aging affects the structure and function of the airway epithelium that is crucial for lung homeostasis and host defense. The aim of this study was therefore to determine age-related differences in structural and functional properties of primary nasal epithelial cultures from healthy children and non-smoking elderly people. To achieve this goal, highly differentiated nasal epithelial cultures were established from nasal brushes at air-liquid interface and used to study epithelial cell type composition, mucin (MUC5AC and MUC5B) expression, and ion transport properties. Furthermore, we determined age-dependent molecular signatures using global proteomic analysis. We found lower numeric densities of ciliated cells and higher levels of MUC5AC expression in cultures from children vs. elderly people. Bioelectric studies showed no differences in basal ion transport properties, ENaC-mediated sodium absorption, or CFTR-mediated chloride transport, but detected decreased calcium-activated TMEM16A-mediated chloride secretory responses in cultures from children vs. elderly people. Proteome analysis identified distinct age-dependent molecular signatures associated with ciliation and mucin biosynthesis, as well as other pathways implicated in aging. Our data identified intrinsic, age-related differences in structure and function of the nasal epithelium and provide a basis for further studies on the role of these findings in age-dependent airways disease phenotypes observed with a spectrum of respiratory infections and other noxious stimuli

The human host response to monkeypox infection: a proteomic case series study

Abstract The rapid rise of monkeypox (MPX) cases outside previously endemic areas prompts for a better understanding of the disease. We studied the plasma proteome of a group of MPX patients with a similar infection history and clinical manifestation typical for the current outbreak. We report that MPX in this case series is associated with a strong plasma proteomic response among nutritional and acute phase response proteins. Moreover, we report a correlation between plasma proteins and disease severity. Contrasting the MPX host response with that of COVID‐19, we find a range of similarities, but also important differences. For instance, CFHR1 is induced in COVID‐19, but suppressed in MPX, reflecting the different roles of the complement system in the two infectious diseases. Of note, the spatial overlap in response proteins suggested that a COVID‐19 biomarker panel assay could be repurposed for MPX. Applying a targeted protein panel assay provided encouraging results and distinguished MPX cases from healthy controls. Hence, our results provide a first proteomic characterization of the MPX human host response and encourage further research on protein‐panel assays in emerging infectious diseases

Tryptophan metabolism is inversely regulated in the tumor and blood of patients with glioblastoma

Tryptophan (Trp)-catabolic enzymes (TCEs) produce metabolites that activate the aryl hydrocarbon receptor (AHR) and promote tumor progression and immunosuppression in glioblastoma. As therapies targeting TCEs or AHR become available, a better understanding of Trp metabolism is required. Methods: The combination of LC-MS/MS with chemical isobaric labeling enabled the simultaneous quantitative comparison of Trp and its amino group-bearing metabolites in multiple samples. We applied this method to the sera of a cohort of 43 recurrent glioblastoma patients and 43 age- and sex-matched healthy controls. Tumor volumes were measured in MRI data using an artificial neural network-based approach. MALDI MSI visualized Trp and its direct metabolite N-formylkynurenine (FK) in glioblastoma tissue. Analysis of scRNA-seq data was used to detect the presence of Trp metabolism and AHR activity in different cell types in glioblastoma. Results: Compared to healthy controls, glioblastoma patients showed decreased serum Trp levels. Surprisingly, the levels of Trp metabolites were also reduced. The decrease became smaller with more enzymatic steps between Trp and its metabolites, suggesting that Trp availability controls the levels of its systemic metabolites. High tumor volume associated with low systemic metabolite levels and low systemic kynurenine levels associated with worse overall survival. MALDI MSI demonstrated heterogeneity of Trp catabolism across glioblastoma tissues. Analysis of scRNA-seq data revealed that genes involved in Trp metabolism were expressed in almost all the cell types in glioblastoma and that most cell types, in particular macrophages and T cells, exhibited AHR activation. Moreover, high AHR activity associated with reduced overall survival in the glioblastoma TCGA dataset.Conclusion: The novel techniques we developed could support the identification of patients that may benefit from therapies targeting TCEs or AHR activatio