RNA-sequencing and mass-spectrometry proteomic time-series analysis of T-cell differentiation identified multiple splice variants models that predicted validated protein biomarkers in inflammatory diseases
Kim, Min Jung
Na, Chan Hyun
Jenmalm, Maria C
Nestor, Colm E
KAUST DepartmentBiological and Environmental Science and Engineering (BESE) Division
Biological and Environmental Sciences and Engineering Division, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
Permanent link to this recordhttp://hdl.handle.net/10754/671300
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AbstractProfiling of mRNA expression is an important method to identify biomarkers but complicated by limited correlations between mRNA expression and protein abundance. We hypothesised that these correlations could be improved by mathematical models based on measuring splice variants and time delay in protein translation. We characterised time-series of primary human naïve CD4+ T cells during early T helper type 1 differentiation with RNA-sequencing and mass-spectrometry proteomics. We performed computational time-series analysis in this system and in two other key human and murine immune cell types. Linear mathematical mixed time delayed splice variant models were used to predict protein abundances, and the models were validated using out-of-sample predictions. Lastly, we re-analysed RNA-seq datasets to evaluate biomarker discovery in five T-cell associated diseases, further validating the findings for multiple sclerosis (MS) and asthma. The new models significantly out-performing models not including the usage of multiple splice variants and time delays, as shown in cross-validation tests. Our mathematical models provided more differentially expressed proteins between patients and controls in all five diseases. Moreover, analysis of these proteins in asthma and MS supported their relevance. One marker, sCD27, was validated in MS using two independent cohorts for evaluating response to treatment and disease prognosis. In summary, our splice variant and time delay models substantially improved the prediction of protein abundance from mRNA expression in three different immune cell types. The models provided valuable biomarker candidates, which were further validated in MS and asthma.
CitationMagnusson, R., Rundquist, O., Kim, M. J., Hellberg, S., Na, C. H., Benson, M., Gomez-Cabrero, D., Kockum, I., Tegnér, J. N., Piehl, F., Jagodic, M., Mellergård, J., Altafini, C., Ernerudh, J., Jenmalm, M. C., Nestor, C. E., Kim, M.-S., & Gustafsson, M. (2022). RNA-sequencing and mass-spectrometry proteomic time-series analysis of T-cell differentiation identified multiple splice variants models that predicted validated protein biomarkers in inflammatory diseases. Frontiers in Molecular Biosciences, 9. https://doi.org/10.3389/fmolb.2022.916128
SponsorsThis work was supported by the Swedish foundation for strategic research (SB16-0011), Swedish Cancer Society grants (CAN 2017/625), East Gothia Regional Funding, Åke Wiberg foundation, Neuro Sweden, the Swedish Research Council grants 2015-02575, 2015-03495, 2015-03807, 2016-07108, and 2018-02776, and National Research Foundation of Korea (NRF-2016K1A3A1A47921601, 2017M3C7A1027472). We would like to thank Jun Hyung Lee for his contribution to the proteomics sample preparation.
PublisherFrontiers Media SA
PubMed Central IDPMC9465313
Except where otherwise noted, this item's license is described as Archived with thanks to Frontiers in molecular biosciences under a Creative Commons license, details at: https://creativecommons.org/licenses/by/4.0/
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