Biological changes in C57BL/6 mice following 3 weeks of inhalation exposure to cigarette smoke or e-vapor aerosols.

We compared early biological changes in mice after inhalation exposures to cigarette smoke or e-vapor aerosols (MarkTen® cartridge with Carrier, Test-1, or Test-2 formulations; 4% nicotine). Female C57BL/6 mice were exposed to 3R4F cigarette smoke or e-vapor aerosols by nose-only inhalation for up to 4 hours/day, 5 days/week, for 3 weeks. The 3R4F and e-vapor exposures were set to match the target nose port aerosol nicotine concentration (∼41 µg/L). Only the 3R4F group showed postexposure clinical signs such as tremors and lethargy. At necropsy, the 3R4F group had significant increases in lung weight and changes in bronchoalveolar lavage parameters, as well as microscopic findings in the respiratory tract. The e-vapor groups had minimal microscopic changes, including squamous metaplasia in laryngeal epiglottis, and histiocytic infiltrates in the lung (Test-2 group only). The 3R4F group had a higher incidence and severity of microscopic findings compared to any e-vapor group. Transcriptomic analysis also showed that the 3R4F group had the highest number of differentially expressed genes compared to Sham Control. Among e-vapor groups, Test-2 group had more differentially expressed genes but the magnitude of gene expression-based network perturbations in all e-vapor groups was ∼94% less than the 3R4F group. On proteome analysis in the lung, differentially regulated proteins were detected in the 3R4F group only. In conclusion, 3-weeks of 3R4F exposure induced molecular and microscopic changes associated with smoking-related diseases in the respiratory tract, while e-vapor exposures showed substantially reduced biological activities.

Image dataset of healthy and infected fig leaves with Ficus leaf worm.

This work presents an extensive dataset comprising images meticulously obtained from diverse geographic locations within Iraq, depicting both healthy and infected fig leaves affected by Ficus leafworm. This particular pest poses a significant threat to economic interests, as its infestations often lead to the defoliation of trees, resulting in reduced fruit production. The dataset comprises two distinct classes: infected and healthy, with the acquisition of images executed with precision during the fruiting season, employing state-of-the-art high-resolution equipment, as detailed in the specifications table. In total, the dataset encompasses a substantial 2,321 images, with 1,350 representing infected leaves and 971 depicting healthy ones. The images were acquired through a random sampling approach, ensuring a harmonious blend of balance and diversity across data emanating from distinct fig trees. The proposed dataset carries substantial potential for impact and utility, featuring essential attributes such as the binary classification of infected and healthy leaves. The presented dataset holds the potential to be a valuable resource for the pest control industry within the domains of agriculture and food production.

Human bronchial epithelial cells exposed in vitro to cigarette smoke at the air-liquid interface resemble bronchial epithelium from human smokers.

Organotypic culture of human primary bronchial epithelial cells is a useful in vitro system to study normal biological processes and lung disease mechanisms, to develop new therapies, and to assess the biological perturbations induced by environmental pollutants. Herein, we investigate whether the perturbations induced by cigarette smoke (CS) and observed in the epithelium of smokers' airways are reproducible in this in vitro system (AIR-100 tissue), which has been shown to recapitulate most of the characteristics of the human bronchial epithelium. Human AIR-100 tissues were exposed to mainstream CS for 7, 14, 21, or 28 min at the air-liquid interface, and we investigated various biological endpoints [e.g., gene expression and microRNA profiles, matrix metalloproteinase 1 (MMP-1) release] at multiple postexposure time points (0.5, 2, 4, 24, 48 h). By performing a Gene Set Enrichment Analysis, we observed a significant enrichment of human smokers' bronchial epithelium gene signatures derived from different public transcriptomics datasets in CS-exposed AIR-100 tissue. Comparison of in vitro microRNA profiles with microRNA data from healthy smokers highlighted various highly translatable microRNAs associated with inflammation or with cell cycle processes that are known to be perturbed by CS in lung tissue. We also found a dose-dependent increase of MMP-1 release by AIR-100 tissue 48 h after CS exposure in agreement with the known effect of CS on this collagenase expression in smokers' tissues. In conclusion, a similar biological perturbation than the one observed in vivo in smokers' airway epithelium could be induced after a single CS exposure of a human organotypic bronchial epithelium-like tissue culture.

The BEL information extraction workflow (BELIEF): evaluation in the BioCreative V BEL and IAT track.

Network-based approaches have become extremely important in systems biology to achieve a better understanding of biological mechanisms. For network representation, the Biological Expression Language (BEL) is well designed to collate findings from the scientific literature into biological network models. To facilitate encoding and biocuration of such findings in BEL, a BEL Information Extraction Workflow (BELIEF) was developed. BELIEF provides a web-based curation interface, the BELIEF Dashboard, that incorporates text mining techniques to support the biocurator in the generation of BEL networks. The underlying UIMA-based text mining pipeline (BELIEF Pipeline) uses several named entity recognition processes and relationship extraction methods to detect concepts and BEL relationships in literature. The BELIEF Dashboard allows easy curation of the automatically generated BEL statements and their context annotations. Resulting BEL statements and their context annotations can be syntactically and semantically verified to ensure consistency in the BEL network. In summary, the workflow supports experts in different stages of systems biology network building. Based on the BioCreative V BEL track evaluation, we show that the BELIEF Pipeline automatically extracts relationships with an F-score of 36.4% and fully correct statements can be obtained with an F-score of 30.8%. Participation in the BioCreative V Interactive task (IAT) track with BELIEF revealed a systems usability scale (SUS) of 67. Considering the complexity of the task for new users-learning BEL, working with a completely new interface, and performing complex curation-a score so close to the overall SUS average highlights the usability of BELIEF.Database URL: BELIEF is available at http://www.scaiview.com/belief/.

Training and evaluation corpora for the extraction of causal relationships encoded in biological expression language (BEL).

Success in extracting biological relationships is mainly dependent on the complexity of the task as well as the availability of high-quality training data. Here, we describe the new corpora in the systems biology modeling language BEL for training and testing biological relationship extraction systems that we prepared for the BioCreative V BEL track. BEL was designed to capture relationships not only between proteins or chemicals, but also complex events such as biological processes or disease states. A BEL nanopub is the smallest unit of information and represents a biological relationship with its provenance. In BEL relationships (called BEL statements), the entities are normalized to defined namespaces mainly derived from public repositories, such as sequence databases, MeSH or publicly available ontologies. In the BEL nanopubs, the BEL statements are associated with citation information and supportive evidence such as a text excerpt. To enable the training of extraction tools, we prepared BEL resources and made them available to the community. We selected a subset of these resources focusing on a reduced set of namespaces, namely, human and mouse genes, ChEBI chemicals, MeSH diseases and GO biological processes, as well as relationship types 'increases' and 'decreases'. The published training corpus contains 11 000 BEL statements from over 6000 supportive text excerpts. For method evaluation, we selected and re-annotated two smaller subcorpora containing 100 text excerpts. For this re-annotation, the inter-annotator agreement was measured by the BEL track evaluation environment and resulted in a maximal F-score of 91.18% for full statement agreement. In addition, for a set of 100 BEL statements, we do not only provide the gold standard expert annotations, but also text excerpts pre-selected by two automated systems. Those text excerpts were evaluated and manually annotated as true or false supportive in the course of the BioCreative V BEL track task.Database URL: http://wiki.openbel.org/display/BIOC/Datasets.

Comprehensive systems biology analysis of a 7-month cigarette smoke inhalation study in C57BL/6 mice.

Smoking of combustible cigarettes has a major impact on human health. Using a systems toxicology approach in a model of chronic obstructive pulmonary disease (C57BL/6 mice), we assessed the health consequences in mice of an aerosol derived from a prototype modified risk tobacco product (pMRTP) as compared to conventional cigarettes. We investigated physiological and histological endpoints in parallel with transcriptomics, lipidomics, and proteomics profiles in mice exposed to a reference cigarette (3R4F) smoke or a pMRTP aerosol for up to 7 months. We also included a cessation group and a switching-to-pMRTP group (after 2 months of 3R4F exposure) in addition to the control (fresh air-exposed) group, to understand the potential risk reduction of switching to pMRTP compared with continuous 3R4F exposure and cessation. The present manuscript describes the study design, setup, and implementation, as well as the generation, processing, and quality control analysis of the toxicology and 'omics' datasets that are accessible in public repositories for further analyses.

Statistical analysis of predominantly transient protein-protein interfaces.

A non-redundant set of 170 protein-protein interfaces of known structure was statistically analyzed for residue and secondary-structure compositions, pairing preferences and side-chain-backbone interaction frequencies. By focussing mainly on transient protein-protein interfaces, the results underline previous findings for protein-protein interfaces but also show some new interesting aspects of transient interfaces. The residue compositions at interfaces found in this study correlate well with the results of other studies. On average, contacts between pairs of hydrophobic and polar residues were unfavorable, and the charged residues tended to pair subject to charge complementarity. Secondary structure composition analysis shows that neither helices nor beta-sheets are dominantly populated at interfaces. Analyzing the pairing preferences of the secondary structure elements revealed a higher affinity within the same elements and alludes to tight packings. In addition, the results for the side-chain and backbone interaction frequencies, which were measured under more stringent conditions, showed a high occurrence of side-chain-backbone interactions. Taking a closer look at the helix and beta-sheet binding frequencies for a given side-chain and backbone interaction underlined the relevance of tight packings. The polarity of interfaces increased with decreasing interface size. These types of information may be useful for scoring complexes in protein-protein docking studies or for prediction of protein-protein interfaces from the sequences alone.

Construction of biological networks from unstructured information based on a semi-automated curation workflow.

Capture and representation of scientific knowledge in a structured format are essential to improve the understanding of biological mechanisms involved in complex diseases. Biological knowledge and knowledge about standardized terminologies are difficult to capture from literature in a usable form. A semi-automated knowledge extraction workflow is presented that was developed to allow users to extract causal and correlative relationships from scientific literature and to transcribe them into the computable and human readable Biological Expression Language (BEL). The workflow combines state-of-the-art linguistic tools for recognition of various entities and extraction of knowledge from literature sources. Unlike most other approaches, the workflow outputs the results to a curation interface for manual curation and converts them into BEL documents that can be compiled to form biological networks. We developed a new semi-automated knowledge extraction workflow that was designed to capture and organize scientific knowledge and reduce the required curation skills and effort for this task. The workflow was used to build a network that represents the cellular and molecular mechanisms implicated in atherosclerotic plaque destabilization in an apolipoprotein-E-deficient (ApoE(-/-)) mouse model. The network was generated using knowledge extracted from the primary literature. The resultant atherosclerotic plaque destabilization network contains 304 nodes and 743 edges supported by 33 PubMed referenced articles. A comparison between the semi-automated and conventional curation processes showed similar results, but significantly reduced curation effort for the semi-automated process. Creating structured knowledge from unstructured text is an important step for the mechanistic interpretation and reusability of knowledge. Our new semi-automated knowledge extraction workflow reduced the curation skills and effort required to capture and organize scientific knowledge. The atherosclerotic plaque destabilization network that was generated is a causal network model for vascular disease demonstrating the usefulness of the workflow for knowledge extraction and construction of mechanistically meaningful biological networks.

A systems biology approach reveals the dose- and time-dependent effect of primary human airway epithelium tissue culture after exposure to cigarette smoke in vitro.

To establish a relevant in vitro model for systems toxicology-based mechanistic assessment of environmental stressors such as cigarette smoke (CS), we exposed human organotypic bronchial epithelial tissue cultures at the air liquid interface (ALI) to various CS doses. Previously, we compared in vitro gene expression changes with published human airway epithelia in vivo data to assess their similarities. Here, we present a follow-up evaluation of these in vitro transcriptomics data, using complementary computational approaches and an integrated mRNA-microRNA (miRNA) analysis. The main cellular pathways perturbed by CS exposure were related to stress responses (oxidative stress and xenobiotic metabolism), inflammation (inhibition of nuclear factor-κB and the interferon gamma-dependent pathway), and proliferation/differentiation. Within post-exposure periods up to 48 hours, a transient kinetic response was observed at lower CS doses, whereas higher doses resulted in more sustained responses. In conclusion, this systems toxicology approach has potential for product testing according to "21st Century Toxicology".

Causal biological network database: a comprehensive platform of causal biological network models focused on the pulmonary and vascular systems.

With the wealth of publications and data available, powerful and transparent computational approaches are required to represent measured data and scientific knowledge in a computable and searchable format. We developed a set of biological network models, scripted in the Biological Expression Language, that reflect causal signaling pathways across a wide range of biological processes, including cell fate, cell stress, cell proliferation, inflammation, tissue repair and angiogenesis in the pulmonary and cardiovascular context. This comprehensive collection of networks is now freely available to the scientific community in a centralized web-based repository, the Causal Biological Network database, which is composed of over 120 manually curated and well annotated biological network models and can be accessed at http://causalbionet.com. The website accesses a MongoDB, which stores all versions of the networks as JSON objects and allows users to search for genes, proteins, biological processes, small molecules and keywords in the network descriptions to retrieve biological networks of interest. The content of the networks can be visualized and browsed. Nodes and edges can be filtered and all supporting evidence for the edges can be browsed and is linked to the original articles in PubMed. Moreover, networks may be downloaded for further visualization and evaluation. Database URL: http://causalbionet.com

CSEO - the Cigarette Smoke Exposure Ontology.

BACKGROUND: In the past years, significant progress has been made to develop and use experimental settings for extensive data collection on tobacco smoke exposure and tobacco smoke exposure-associated diseases. Due to the growing number of such data, there is a need for domain-specific standard ontologies to facilitate the integration of tobacco exposure data. RESULTS: The CSEO (version 1.0) is composed of 20091 concepts. The ontology in its current form is able to capture a wide range of cigarette smoke exposure concepts within the knowledge domain of exposure science with a reasonable sensitivity and specificity. Moreover, it showed a promising performance when used to answer domain expert questions. The CSEO complies with standard upper-level ontologies and is freely accessible to the scientific community through a dedicated wiki at https://publicwiki-01.fraunhofer.de/CSEO-Wiki/index.php/Main_Page. CONCLUSIONS: The CSEO has potential to become a widely used standard within the academic and industrial community. Mainly because of the emerging need of systems toxicology to controlled vocabularies and also the lack of suitable ontologies for this domain, the CSEO prepares the ground for integrative systems-based research in the exposure science.

A 28-day rat inhalation study with an integrated molecular toxicology endpoint demonstrates reduced exposure effects for a prototypic modified risk tobacco product compared with conventional cigarettes.

Towards a systems toxicology-based risk assessment, we investigated molecular perturbations accompanying histopathological changes in a 28-day rat inhalation study combining transcriptomics with classical histopathology. We demonstrated reduced biological activity of a prototypic modified risk tobacco product (pMRTP) compared with the reference research cigarette 3R4F. Rats were exposed to filtered air or to three concentrations of mainstream smoke (MS) from 3R4F, or to a high concentration of MS from a pMRTP. Histopathology revealed concentration-dependent changes in response to 3R4F that were irritative stress-related in nasal and bronchial epithelium, and inflammation-related in the lung parenchyma. For pMRTP, significant changes were seen in the nasal epithelium only. Transcriptomics data were obtained from nasal and bronchial epithelium and lung parenchyma. Concentration-dependent gene expression changes were observed following 3R4F exposure, with much smaller changes for pMRTP. A computational-modeling approach based on causal models of tissue-specific biological networks identified cell stress, inflammation, proliferation, and senescence as the most perturbed molecular mechanisms. These perturbations correlated with histopathological observations. Only weak perturbations were observed for pMRTP. In conclusion, a correlative evaluation of classical histopathology together with gene expression-based computational network models may facilitate a systems toxicology-based risk assessment, as shown for a pMRTP.

On Crowd-verification of Biological Networks.

Biological networks with a structured syntax are a powerful way of representing biological information generated from high density data; however, they can become unwieldy to manage as their size and complexity increase. This article presents a crowd-verification approach for the visualization and expansion of biological networks. Web-based graphical interfaces allow visualization of causal and correlative biological relationships represented using Biological Expression Language (BEL). Crowdsourcing principles enable participants to communally annotate these relationships based on literature evidences. Gamification principles are incorporated to further engage domain experts throughout biology to gather robust peer-reviewed information from which relationships can be identified and verified. The resulting network models will represent the current status of biological knowledge within the defined boundaries, here processes related to human lung disease. These models are amenable to computational analysis. For some period following conclusion of the challenge, the published models will remain available for continuous use and expansion by the scientific community.