Genomic analysis of macrophage gene signatures during idiopathic pulmonary fibrosis development

Idiopathic Pulmonary Fibrosis (IPF) is a chronic, progressive, irreversible lung disease. After diagnosis, the interstitial condition commonly presents 3-5 years of life expectancy if untreated. Despite the limited capacity of recapitulating IPF, animal models have been useful for identifying related pathways relevant for drug discovery and diagnostic tools development. Using these techniques, several immune-related mechanisms have been implicated to IPF. For instance, subpopulations of macrophages and monocytes-derived cells are recognized as centrally active in pulmonary immunological processes. One of the most used technologies is high-throughput gene expression analysis, which has been available for almost two decades now. The “omics” revolution has presented major impacts on macrophage and pulmonary fibrosis research. The present study aims to investigate macrophage dynamics within the context of IPF at the transcriptomic level. Using publicly available gene-expression data, we applied modern data science approaches to (1) understand longitudinal profiles within IPF models; (2) investigate correlation between macrophage genomic dynamics and IPF development; and (3) apply longitudinal profiles uncovered through multivariate data analysis to the development of new sets of predictors able to classify IPF and control samples accordingly. Principal Component Analysis and Hierarchical Clustering showed that our pipeline was able to construct a complex set of biomarker candidates that together outperformed gene expression alone in separating treatment groups in an IPF animal model dataset. We further assessed the predictive performance of our candidates on publicly available gene expression data from IPF patients. Once again, the constructed biomarker candidates were significantly differentiated between IPF and control samples. The data presented in this work strongly suggest that longitudinal data analysis holds major unappreciated potentials for translational medicine research

Scanning electron microscopy and machine learning reveal heterogeneity in capsular morphotypes of the human pathogen Cryptococcus spp.

Phenotypic heterogeneity is an important trait for the development and survival of many microorganisms including the yeast Cryptococcus spp., a deadly pathogen spread worldwide. Here, we have applied scanning electron microscopy (SEM) to defne four Cryptococcus spp. capsule morphotypes, namely Regular, Spiky, Bald, and Phantom. These morphotypes were persistently observed in varying proportions among yeast isolates. To assess the distribution of such morphotypes we implemented an automated pipeline capable of (1) identifying potentially cell-associated objects in the SEM-derived images; (2) computing object-level features; and (3) classifying these objects into their corresponding classes. The machine learning approach used a Random Forest (RF) classifer whose overall accuracy reached 85% on the test dataset, with per-class specifcity above 90%, and sensitivity between 66 and 94%. Additionally, the RF model indicates that structural and texture features, e.g., object area, eccentricity, and contrast, are most relevant for classifcation. The RF results agree with the observed variation in these features, consistently also with visual inspection of SEM images. Finally, our work introduces morphological variants of Cryptococcus spp. capsule. These can be promptly identifed and characterized using computational models so that future work may unveil morphological associations with yeast virulence

Evaluating omics-based tests with Bayesian Decision Curve Analysis

Omics-based tests (OBTs) combine high-dimensional omics features into clinical prediction models that predict diagnosis, prognosis, or treatment effects. Past incidences of premature implementa- tion of OBTs into clinical trials have demonstrated the need for increased rigour in their clinical evaluation. However, their performance assessment is often limited to classification metrics such as sensitivity and specificity, with little regard for formal analysis of clinical decision-making. Decision curve analysis (DCA) complements classification metrics by combining classical assessment of pre- dictive performance with the consequences of using a test or model to guide clinical decisions. In DCA, the best clinical decision strategy, such as diagnosing or treating based on an OBT, is the one that maximizes the concept of net benefit: the net number of true positives (or negatives) provided by a given clinical decision strategy. Before reaching real patients, we must be sufficiently confi- dent that new OBTs actually provide superior clinical decision strategies, as compared to default, standard-of-care strategies. Trained on hundreds to thousands of features, OBTs are particularly prone to chance results. In this context, the present work develops parametric Bayesian approaches to DCA that allow uncertainty quantification around four fundamental concerns when evaluating OBT-guided clinical decision strategies: (i) which strategies are clinically useful, (ii) what is the best available decision strategy, (iii) direct pairwise comparisons between strategies, and (iv) what is the consequence of the current level of uncertainty. We evaluate the methods using simulation studies and present a comprehensive case study. We also provide an application to a recently- developed OBT for multi-cancer early detection. Software implementation of the method is freely available in the bayesDCA R package. Ultimately, the Bayesian DCA workflow may help clinicians and health policymakers make better-informed decisions when choosing and implementing clinical decision strategies based on OBTs.Science, Faculty ofGraduat

Genomic analysis of macrophage gene signatures during idiopathic pulmonary fibrosis development

Idiopathic Pulmonary Fibrosis (IPF) is a chronic, progressive, irreversible lung disease. After diagnosis, the interstitial condition commonly presents 3-5 years of life expectancy if untreated. Despite the limited capacity of recapitulating IPF, animal models have been useful for identifying related pathways relevant for drug discovery and diagnostic tools development. Using these techniques, several immune-related mechanisms have been implicated to IPF. For instance, subpopulations of macrophages and monocytes-derived cells are recognized as centrally active in pulmonary immunological processes. One of the most used technologies is high-throughput gene expression analysis, which has been available for almost two decades now. The “omics” revolution has presented major impacts on macrophage and pulmonary fibrosis research. The present study aims to investigate macrophage dynamics within the context of IPF at the transcriptomic level. Using publicly available gene-expression data, we applied modern data science approaches to (1) understand longitudinal profiles within IPF models; (2) investigate correlation between macrophage genomic dynamics and IPF development; and (3) apply longitudinal profiles uncovered through multivariate data analysis to the development of new sets of predictors able to classify IPF and control samples accordingly. Principal Component Analysis and Hierarchical Clustering showed that our pipeline was able to construct a complex set of biomarker candidates that together outperformed gene expression alone in separating treatment groups in an IPF animal model dataset. We further assessed the predictive performance of our candidates on publicly available gene expression data from IPF patients. Once again, the constructed biomarker candidates were significantly differentiated between IPF and control samples. The data presented in this work strongly suggest that longitudinal data analysis holds major unappreciated potentials for translational medicine research

Phage on tap–a quick and efficient protocol for the preparation of bacteriophage laboratory stocks

A major limitation with traditional phage preparations is the variability in titer, salts, and bacterial contaminants between successive propagations. Here we introduce the Phage On Tap (PoT) protocol for the quick and efficient preparation of homogenous bacteriophage (phage) stocks. This method produces homogenous, laboratory-scale, high titer (up to 1010–11 PFU·ml−1), endotoxin reduced phage banks that can be used to eliminate the variability between phage propagations and improve the molecular characterizations of phage. The method consists of five major parts, including phage propagation, phage clean up by 0.22 μm filtering and chloroform treatment, phage concentration by ultrafiltration, endotoxin removal, and the preparation and storage of phage banks for continuous laboratory use. From a starting liquid lysate of > 100 mL, the PoT protocol generated a clean, homogenous, laboratory phage bank with a phage recovery efficiency of 85% within just two days. In contrast, the traditional method took upwards of five days to produce a high titer, but lower volume phage stock with a recovery efficiency of only 4%. Phage banks can be further purified for the removal of bacterial endotoxins, reducing endotoxin concentrations by over 3,000-fold while maintaining phage titer. The PoT protocol focused on T-like phages, but is broadly applicable to a variety of phages that can be propagated to sufficient titer, producing homogenous, high titer phage banks that are applicable for molecular and cellular assays

Swab pooling: A new method for large-scale RT-qPCR screening of SARS-CoV-2 avoiding sample dilution.

To minimize sample dilution effect on SARS-CoV-2 pool testing, we assessed analytical and diagnostic performance of a new methodology, namely swab pooling. In this method, swabs are pooled at the time of collection, as opposed to pooling of equal volumes from individually collected samples. Paired analysis of pooled and individual samples from 613 patients revealed 94 positive individuals. Having individual testing as reference, no false-positives or false-negatives were observed for swab pooling. In additional 18,922 patients screened with swab pooling (1,344 pools), mean Cq differences between individual and pool samples ranged from 0.1 (Cr.I. -0.98 to 1.17) to 2.09 (Cr.I. 1.24 to 2.94). Overall, 19,535 asymptomatic patients were screened using 4,400 RT-qPCR assays. This corresponds to an increase of 4.4 times in laboratory capacity and a reduction of 77% in required tests. Therefore, swab pooling represents a major alternative for reliable and large-scale screening of SARS-CoV-2 in low prevalence populations

One year cross-sectional study in adult and neonatal intensive care units reveals the bacterial and antimicrobial resistance genes profiles in patients and hospital surfaces.

Several studies have shown the ubiquitous presence of bacteria in hospital surfaces, staff, and patients. Frequently, these bacteria are related to HAI (healthcare-associated infections) and carry antimicrobial resistance (AMR). These HAI-related bacteria contribute to a major public health issue by increasing patient morbidity and mortality during or after hospital stay. Bacterial high-throughput amplicon gene sequencing along with identification of AMR genes, as well as whole genome sequencing (WGS), are biotechnological tools that allow multiple-sample screening for a diversity of bacteria. In this paper, we used these methods to perform a one-year cross sectional profiling of bacteria and AMR genes in adult and neonatal intensive care units (ICU and NICU) in a Brazilian public, tertiary hospital. Our results showed high abundances of HAI-related bacteria such as S. epidermidis, S. aureus, K. pneumoniae, A. baumannii complex, E. coli, E. faecalis, and P. aeruginosa in patients and hospital surfaces. Most abundant AMR genes detected throughout ICU and NICU were mecA, blaCTX-M-1 group, blaSHV-like, and blaKPC-like. We found that NICU environment and patients were more widely contaminated with pathogenic bacteria than ICU. Patient samples, despite the higher bacterial load, have lower bacterial diversity than environmental samples in both units. Finally, we also identified contamination hotspots in the hospital environment showing constant frequencies of bacterial and AMR contamination throughout the year. Whole genome sequencing (WGS), 16S rRNA oligotypes, and AMR identification allowed a high-resolution characterization of the hospital microbiome profile