Dataset Information

EDNAir: proof of concept that animal DNA can be collected from air sampling.

ABSTRACT: Environmental DNA (eDNA) is one of the fastest developing tools for species biomonitoring and ecological research. However, despite substantial interest from research, commercial and regulatory sectors, it has remained primarily a tool for aquatic systems with a small amount of work in substances such as soil, snow and rain. Here we demonstrate that eDNA can be collected from air and used to identify mammals. Our proof of concept successfully demonstrated that eDNA sampled from air contained mixed templates which reflect the species known to be present within a confined space and that this material can be accessed using existing sampling methods. We anticipate this demonstration will initiate a much larger research programme in terrestrial airDNA sampling and that this may rapidly advance biomonitoring approaches. Lastly, we outline these and potential related applications we expect to benefit from this development.

SUBMITTER: Clare EL

PROVIDER: S-EPMC8019316 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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eDNAir: proof of concept that animal DNA can be collected from air sampling.

Clare Elizabeth L EL Economou Chloe K CK Faulkes Chris G CG Gilbert James D JD Bennett Frances F Drinkwater Rosie R Littlefair Joanne E JE

PeerJ 20210331

Environmental DNA (eDNA) is one of the fastest developing tools for species biomonitoring and ecological research. However, despite substantial interest from research, commercial and regulatory sectors, it has remained primarily a tool for aquatic systems with a small amount of work in substances such as soil, snow and rain. Here we demonstrate that eDNA can be collected from air and used to identify mammals. Our proof of concept successfully demonstrated that eDNA sampled from air contained mix ...[more]

PMID: 33850648

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Project description:PurposeCombined radiotherapy (RT) and immune checkpoint-inhibitor (ICI) therapy can act synergistically to enhance tumor response beyond what either treatment can achieve alone. Alongside the revolutionary impact of ICIs on cancer therapy, life-threatening potential side effects, such as checkpoint-inhibitor-induced (CIP) pneumonitis, remain underreported and unpredictable. In this preclinical study, we hypothesized that routinely collected data such as imaging, blood counts, and blood cytokine levels can be utilized to build a model that predicts lung inflammation associated with combined RT/ICI therapy.Materials and methodsThis proof-of-concept investigational work was performed on Lewis lung carcinoma in a syngeneic murine model. Nineteen mice were used, four as untreated controls and the rest subjected to RT/ICI therapy. Tumors were implanted subcutaneously in both flanks and upon reaching volumes of ~200 mm3 the animals were imaged with both CT and MRI and blood was collected. Quantitative radiomics features were extracted from imaging of both lungs. The animals then received RT to the right flank tumor only with a regimen of three 8 Gy fractions (one fraction per day over 3 days) with PD-1 inhibitor administration delivered intraperitoneally after each daily RT fraction. Tumor volume evolution was followed until tumors reached the maximum size allowed by the Institutional Animal Care and Use Committee (IACUC). The animals were sacrificed, and lung tissues harvested for immunohistochemistry evaluation. Tissue biomarkers of lung inflammation (CD45) were tallied, and binary logistic regression analyses were performed to create models predictive of lung inflammation, incorporating pretreatment CT/MRI radiomics, blood counts, and blood cytokines.ResultsThe treated animal cohort was dichotomized by the median value of CD45 infiltration in the lungs. Four pretreatment radiomics features (3 CT features and 1 MRI feature) together with pre-treatment neutrophil-to-lymphocyte (NLR) ratio and pre-treatment granulocyte-macrophage colony-stimulating factor (GM-CSF) level correlated with dichotomized CD45 infiltration. Predictive models were created by combining radiomics with NLR and GM-CSF. Receiver operating characteristic (ROC) analyses of two-fold internal cross-validation indicated that the predictive model incorporating MR radiomics had an average area under the curve (AUC) of 0.834, while the model incorporating CT radiomics had an AUC of 0.787.ConclusionsModel building using quantitative imaging data, blood counts, and blood cytokines resulted in lung inflammation prediction models justifying the study hypothesis. The models yielded very-good-to-excellent AUCs of more than 0.78 on internal cross-validation analyses.

Dataset Information

EDNAir: proof of concept that animal DNA can be collected from air sampling.

Publications

eDNAir: proof of concept that animal DNA can be collected from air sampling.

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