Patient-specific iPSCs carrying an SFTPC mutation reveal the intrinsic alveolar epithelial dysfunction at the inception of interstitial lung disease

Alveolar epithelial type 2 cell (AEC2) dysfunction is implicated in the pathogenesis of adult and pediatric interstitial lung disease (ILD), including idiopathic pulmonary fibrosis (IPF); however, identification of disease-initiating mechanisms has been impeded by inability to access primary AEC2s early on. Here, we present a human in vitro model permitting investigation of epithelial-intrinsic events culminating in AEC2 dysfunction, using patient-specific induced pluripotent stem cells (iPSCs) carrying an AEC2-exclusive disease-associated variant (SFTP

Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20 Pages, 1 Figur

A systematic analysis of host factors reveals a Med23-interferon-λ regulatory axis against herpes simplex virus type 1 replication

Herpes simplex virus type 1 (HSV-1) is a neurotropic virus causing vesicular oral or genital skin lesions, meningitis and other diseases particularly harmful in immunocompromised individuals. To comprehensively investigate the complex interaction between HSV-1 and its host we combined two genome-scale screens for host factors (HFs) involved in virus replication. A yeast two-hybrid screen for protein interactions and a RNA interference (RNAi) screen with a druggable genome small interfering RNA (siRNA) library confirmed existing and identified novel HFs which functionally influence HSV-1 infection. Bioinformatic analyses found the 358 HFs were enriched for several pathways and multi-protein complexes. Of particular interest was the identification of Med23 as a strongly anti-viral component of the largely pro-viral Mediator complex, which links specific transcription factors to RNA polymerase II. The anti-viral effect of Med23 on HSV-1 replication was confirmed in gain-of-function gene overexpression experiments, and this inhibitory effect was specific to HSV-1, as a range of other viruses including Vaccinia virus and Semliki Forest virus were unaffected by Med23 depletion. We found Med23 significantly upregulated expression of the type III interferon family (IFN-λ) at the mRNA and protein level by directly interacting with the transcription factor IRF7. The synergistic effect of Med23 and IRF7 on IFN-λ induction suggests this is the major transcription factor for IFN-λ expression. Genotypic analysis of patients suffering recurrent orofacial HSV-1 outbreaks, previously shown to be deficient in IFN-λ secretion, found a significant correlation with a single nucleotide polymorphism in the IFN-λ3 (IL28b) promoter strongly linked to Hepatitis C disease and treatment outcome. This paper describes a link between Med23 and IFN-λ, provides evidence for the crucial role of IFN-λ in HSV-1 immune control, and highlights the power of integrative genome-scale approaches to identify HFs critical for disease progression and outcome

False negative rates in Drosophila cell-based RNAi screens: a case study

<p>Abstract</p> <p>Background</p> <p>High-throughput screening using RNAi is a powerful gene discovery method but is often complicated by false positive and false negative results. Whereas false positive results associated with RNAi reagents has been a matter of extensive study, the issue of false negatives has received less attention.</p> <p>Results</p> <p>We performed a meta-analysis of several genome-wide, cell-based <it>Drosophila </it>RNAi screens, together with a more focused RNAi screen, and conclude that the rate of false negative results is at least 8%. Further, we demonstrate how knowledge of the cell transcriptome can be used to resolve ambiguous results and how the number of false negative results can be reduced by using multiple, independently-tested RNAi reagents per gene.</p> <p>Conclusions</p> <p>RNAi reagents that target the same gene do not always yield consistent results due to false positives and weak or ineffective reagents. False positive results can be partially minimized by filtering with transcriptome data. RNAi libraries with multiple reagents per gene also reduce false positive and false negative outcomes when inconsistent results are disambiguated carefully.</p

Directed -in vitro- evolution of Precambrian and extant Rubiscos

Rubisco is an ancient, catalytically conserved yet slow enzyme, which plays a central role in the biosphere’s carbon cycle. The design of Rubiscos to increase agricultural productivity has hitherto relied on the use of in vivo selection systems, precluding the exploration of biochemical traits that are not wired to cell survival. We present a directed -in vitro- evolution platform that extracts the enzyme from its biological context to provide a new avenue for Rubisco engineering. Precambrian and extant form II Rubiscos were subjected to an ensemble of directed evolution strategies aimed at improving thermostability. The most recent ancestor of proteobacteria -dating back 2.4 billion years- was uniquely tolerant to mutagenic loading. Adaptive evolution, focused evolution and genetic drift revealed a panel of thermostable mutants, some deviating from the characteristic trade-offs in CO2-fixing speed and specificity. Our findings provide a novel approach for identifying Rubisco variants with improved catalytic evolution potential.This work was supported by the REPSOL Research contracts Rubolution (RC020401120018), Rubolution 2.0 (RC 020401140042), the CSIC project PIE-201780E043 and the Australian Research Council grant CE140100015

Soil stabilization with lime for the construction of forest roads

The mechanical performance of soil stabilization using lime to improve forest roads was assessed. This study was conducted with lateritic soil (LVAd30) using lime content of 2% in the municipality of Niquelândia, Goiás state, Brazil. Geotechnical tests of soil characterization, compaction, and mechanical strength were performed applying different compaction efforts and curing periods. The results showed that lime content significantly changed the mechanical performance of natural soil, increasing its mechanical strength and load-carrying capacity. Compaction effort and curing time provided different responses in the unconfined compressive strength (UCS) and California Bearing Ratio (CBR) tests. The best UCS value (786.59 kPa) for the soil-lime mixture was achieved with modified compaction effort and curing time of 28 days. In the CBR test, soil-lime mixtures compacted at intermediate and modified efforts and cured for 28 days were considered for application as subbase material of flexible road pavements, being a promising alternative for use in layers of forest roads

Whole-genome sequencing resolves a polyclonal outbreak by extended-spectrum beta-lactam and carbapenem-resistant Klebsiella pneumoniae in a Portuguese tertiary-care hospital.

Klebsiella pneumoniae has emerged as an important nosocomial pathogen, with whole-genome sequencing (WGS) significantly improving our ability to characterize associated outbreaks. Our study sought to perform a genome-wide analysis of multiclonal K. pneumoniae isolates (n=39; 23 patients) producing extended spectrum beta-lactamases and/or carbapenemases sourced between 2011 and 2016 in a Portuguese tertiary-care hospital. All isolates showed resistance to third-generation cephalosporins and six isolates (five patients) were also carbapenem resistant. Genome-wide-based phylogenetic analysis revealed a topology representing ongoing dissemination of three main sequence-type (ST) clades (ST15, ST147 and ST307) and transmission across different wards, compatible with missing links that can take the form of undetected colonized patients. Two carbapenemase-coding genes were detected: blaKPC-3, located on a Tn4401d transposon, and blaGES-5 on a novel class 3 integron. Additionally, four genes coding for ESBLs (blaBEL-1, blaCTX-M-8, blaCTX-M-15 and blaCTX-M-32) were also detected. ESBL horizontal dissemination across five clades is highlighted by the similar genetic environments of blaCTX-M-15 gene upstream of ISEcp1 on a Tn3-like transposon. Overall, this study provides a high-resolution genome-wide perspective on the epidemiology of ESBL and carbapenemase-producing K. pneumoniae in a healthcare setting while contributing for the adoption of appropriate intervention and prevention strategies

The Impact of Oxygen on Metabolic Evolution: A Chemoinformatic Investigation

The appearance of planetary oxygen likely transformed the chemical and biochemical makeup of life and probably triggered episodes of organismal diversification. Here we use chemoinformatic methods to explore the impact of the rise of oxygen on metabolic evolution. We undertake a comprehensive comparative analysis of structures, chemical properties and chemical reactions of anaerobic and aerobic metabolites. The results indicate that aerobic metabolism has expanded the structural and chemical space of metabolites considerably, including the appearance of 130 novel molecular scaffolds. The molecular functions of these metabolites are mainly associated with derived aspects of cellular life, such as signal transfer, defense against biotic factors, and protection of organisms from oxidation. Moreover, aerobic metabolites are more hydrophobic and rigid than anaerobic compounds, suggesting they are better fit to modulate membrane functions and to serve as transmembrane signaling factors. Since higher organisms depend largely on sophisticated membrane-enabled functions and intercellular signaling systems, the metabolic developments brought about by oxygen benefit the diversity of cellular makeup and the complexity of cellular organization as well. These findings enhance our understanding of the molecular link between oxygen and evolution. They also show the significance of chemoinformatics in addressing basic biological questions