The purpose of WP10 is to provide the NANOSOLUTIONS consortium with appropriate methodologies to support WP5-8 and provide data for data analysis in WP11. It will identify nucleic acids bound to nanomaterial particles by employing highly sensitive single-cell DNA sequencing protocols. WP10 will analyse proteins, RNA and DNA from the model cells, as well as organisms from WP6, WP7 and WP8.

Outcomes of the workpackage 

WP10 was focused on the large-scale analysis of mRNA, microRNA and protein molecules, and in particular in the changes in the transcripts or proteins in response to treatment with different ENMs. WP10 had three specific tasks: 1) to study the potential binding of nucleic acids to ENM, 2) to determine the effect of different ENMs on the transcriptomes and proteomes of selected cell types and different organisms, and 3) to determine the changes in microRNA profiles in response to different ENMs. To do this, WP10 worked closely with WP6 and WP7 that supplied the treated biological material (the human cell lines, THP.1 and BEAS-2B, mouse lung RNA, and E. coli) and WP11, who bioinformatically analysed the data to produce a classifier for nanotoxicity.

The binding of nucleic acids to ENMs was tested experimentally by incubating DNA or RNA together with gold ENMs with different surface modifications. It was shown that negatively charged DNA and RNA bind more readily to positively charged gold ENMs coated with amino groups than to negatively charged gold ENMs coated with carboxyl groups. The interactions do not seem to be sequence specific. Unfortunately, there exist no methods available to retrieve ENMs from cells or tissues without disrupting the nucleic acid or protein core bound to ENMs and therefore it was not possible to study the bindings in in vivo situations. However, the protein coronas generated in vitro were determined and reported by WP5.

Monocytic THP.1 and lung epithelial BEAS-2B cells were exposed for 24 hours to 31 to different ENMs by WP6. RNA was extracted and used both to determine the transcriptome (i.e., the total mRNA profiles) and microRNA in Task 3. The transcriptome was determined by STRT-RNA sequencing and DNA microarrays. In parallel experiments using cells treated in the same way, proteins were extracted to determine the proteome changes (i.e. the changes in the amounts of the different proteins). Further, C57BL/6 female mice were exposed to all 31 ENM and total RNA extracted from the lungs by WP7. The transcriptomes of mice samples were analysed by STRT-RNA sequencing and by DNA microarrays. E.coli was treated with a set of ENMs by WP7 and sent to Karolinska Institutet/b for proteomics analysis. The transcriptome of E.coli was analysed by global RNA sequencing at the University of Plymouth. In total, Karolinska Institutet/c has sequenced 510 samples and Karolinska Institutet/b determined the proteomes of 354 samples. The normalised data from both sets of analyses have been submitted to WP11 to be integrated in the safety classifier and to data repository.

The main goal of the University of Turku (U. TURKU) partner was to study the expression profile of microRNA in monocytic THP.1 and lung epithelial BEAS-2B cells exposed for 24 hours to 31 different ENM. microRNA are small non-coding RNA molecules (RNA that does not encode for a protein) which are known to regulate more than 60% of all protein coding genes. They are involved in several important biological mechanisms including early development, cell proliferation and cell death, fat metabolism and stem cell pluripotency. Dysregulation of microRNA has been associated with diseases such as cancer, heart diseases, kidney diseases, diseases related to development and function of the nervous system and obesity. A total of 210 RNA samples isolated from the exposed cells were delivered to U.TURKU partner who studied the expression profile of the genome-wide microRNA molecules in each exposed sample using next-generation sequencing system. In order to generate good quality microRNAseq data with higher number of sequencing reads, all samples were sequenced twice. Using different bioinformatics approaches, U.TURKU performed basic analysis which included data processing and normalisation of the pooled data from both sequencing process on each sample. The normalised data were forwarded to WP11 to be integrated in the Nanosafety Classifier (WP11). Moreover, all the raw data have been made accessible and been submitted to the NANOSOLUTIONS data repository server system supervised by WP11.