Diesel vehicle emissions are the major source of genotoxic compounds in ambient air from urban areas. These pollutants are linked to risks of cardiovascular diseases, lung cancer, respiratory infections and adverse neurological effects. Biological events associated with exposure to some air pollutants are widely unknown but applying omics techniques may help to identify the molecular processes that link exposure to disease risk. Most data on health risks are related to long-term exposure, so the aim of this study is to investigate the impact of short-term exposure (two hours) to air pollutants on the blood transcriptome and microRNA expression levels. We analyzed transcriptomics and microRNA expression using microarray technology on blood samples from volunteers participating in studies in London, the Oxford Street cohort, and, in Barcelona, the TAPAS cohort. Personal exposure levels measurements of particulate matter (PM10, PM2.5), ultrafine particles (UFPC), nitrogen oxides (NO2, NO and NOx), black carbon (BC) and carbon oxides (CO and CO2) were registered for each volunteer. Associations between air pollutant levels and gene/microRNA expression were evaluated using multivariate normal models (MVN). MVN-models identified compound-specific expression of blood cell genes and microRNAs associated with air pollution despite the low exposure levels, the short exposure periods and the relatively smallsized cohorts. Hsa-miR-197-3p, hsa-miR-29a-3p, hsa-miR-15a-5p, hsa-miR-16-5p and hsa-miR-92a-3p are found significantly expressed in association with exposures. These microRNAs target also relevant transcripts, indicating their potential relevance in the research of omics-biomarkers responding to air pollution. Furthermore, these microRNAs are also known to be associated with diseases previously linked to air pollution exposure including several cancers such lung cancer and Alzheimer's disease. In conclusion, we identified in this study promising compound-specific mRNA and microRNA biomarkers after two hours of exposure to low levels of air pollutants during two hours that suggest increased cancer risks.
Batch effects are technical sources of variation introduced by the necessity of conducting gene expression analyses on different dates due to the large number of biological samples in population-based studies. The aim of this study is to evaluate the performances of linear mixed models (LMM) and Combat in batch effect removal. We also assessed the utility of adding quality control samples in the study design as technical replicates. In order to do so, we simulated gene expression data by adding “treatment” and batch effects to a real gene expression dataset. The performances of LMM and Combat, with and without quality control samples, are assessed in terms of sensitivity and specificity while correcting for the batch effect using a wide range of effect sizes, statistical noise, sample sizes and level of balanced/unbalanced designs. The simulations showed small differences among LMM and Combat. LMM identifies stronger relationships between big effect sizes and gene expression than Combat, while Combat identifies in general more true and false positives than LMM. However, these small differences can still be relevant depending on the research goal. When any of these methods are applied, quality control samples did not reduce the batch effect, showing no added value for including them in the study design.
The Human BioMolecular Atlas Program (HuBMAP) aims to create a multi-scale spatial atlas of the healthy human body at single-cell resolution by applying advanced technologies and disseminating resources to the community. As the HuBMAP moves past its first phase, creating ontologies, protocols and pipelines, this Perspective introduces the production phase: the generation of reference spatial maps of functional tissue units across many organs from diverse populations and the creation of mapping tools and infrastructure to advance biomedical research.HuBMAP was founded with the goal of establishing state-of-the-art frameworks for building spatial multiomic maps of non-diseased human organs at single-cell resolution 1 . During the first phase (2018)(2019)(2020)(2021)(2022), the priorities of the project included the validation and development of assay platforms; workflows for data processing, management, exploration and visualization; and the establishment of protocols, quality control standards and standard operating procedures. Extensive infrastructure was established through a coordinated effort among the various HuB-MAP integration, visualization and engagement teams, tissue-mapping centres, technology and tools development and rapid technology implementation teams and working groups 1 . Single-cell maps, predominantly consisting of two-dimensional (2D) spatial data as well as data from dissociated cells, were generated for several organs. The HuBMAP Data Portal (https://portal.hubmapconsortium.org) was established for open access to experimental tissue data and reference atlas data.The infrastructure was augmented with software tools for tissue data registration, processing, annotation, visualization, cell segmentation and automated annotation of cell types and cellular neighbourhoods from spatial data. Computational methods were developed for integrating multiple data types across scales and interpretation 2 . Standard reference terminology and a common coordinate framework spanning anatomical to biomolecular scales were established to ensure interoperability across organs, research groups and consortia 3 . Guidelines to capture high-quality multiplexed spatial data 4 were established including validated panels of cell-and structure-specific antibodies 5 . The first phase produced a large number of manuscripts (https://commonfund.nih.gov/ publications?pid=43) including spatially resolved single-cell maps [6][7][8][9][10][11] .The production phase of HuBMAP was launched in the autumn of 2022. The focus is on scaling data production spanning diverse biological variables (for example, age and ethnicity) and deployment and enhancement of analytical, visualization and navigational tools to generate high-resolution 3D accessible maps of major functional tissue units from more than 20 organs. This phase involves over 60 institutions and 400 researchers with opportunities for active intra-and inter-consortia collaborations and building a foundational resource for new biological insights and precision medicine. Below, ...
Considering genetic variability in population studies focusing on the health risk assessment of exposure to environmental carcinogens may provide improved insights in individual environmental cancer risks. Therefore, the current study aims to determine the impact of genetic polymorphisms on the relationship between exposure and gene expression, by identifying exposure-dependently coregulated genes and genetic pathways. Statistical analysis based on mixed models, was performed to relate gene expression data from 134 subjects to exposure measurements of multiple carcinogens, 28 polymorphisms, age, sex and biomarkers of cancer risk. We evaluated the combined exposure to cadmium, lead, polychlorinated biphenyls, p,p′-dichlorodiphenyldichloroethylene, hexachlorobenzene and 1-OH-pyrene, and the outcome was biologically interpreted by using ConsensusPathDB, thereby focusing on carcinogenesis-related pathways. We found generic and carcinogenesisrelated pathways deregulated in both sexes, but males showed a stronger transcriptome response than females. We highlighted NOTCH1, CBR1, ITGB3, ITGA4, ADI1, HES1, NCOA2 and SMARCA2 in view of their direct link with cancer development. Two of these, NOTCH1 and ITGB3, are also known to respond to PCBs and cadmium chloride exposure in rodents and to lead in humans. Subjects carrying a high number of risk alleles appear more responsive to combined carcinogen exposure with respect to the induced expression of some of these cancer-related genes, which may be indicative of increased cancer risk as a consequence of environmental factors.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.