Systems-wide Functional Characterization of Time-resolved Protein Dynamics by Mass Spectrometry

Disease progression is governed by dynamic protein abundance changes altering molecular and phenotypic state of the cell. The usually acquired snapshot measurements are prone to random effect errors and might miss the most informative timepoint in the specific context. Contrary, time-resolved data acquisition is immensely helpful for the identification of genuinely altered abundance changes and these dynamics contain valuable mechanistic information. However, there is a lack of easy-to-use bioinformatic methods to apply on proteomic datasets identifying proteins that are differentially regulated over time and subsequently characterizing their dynamics. To generate a generally applicable approach, we investigated the effects induced by a key modulator of innate and adaptive immune responses, interferon alpha 2a (IFNα2a), on hepatoma cells in context of viral infections and cancer.

To identify and characterize protein dynamics at a global scale, we stimulated HepG2hNTCP cells with multiple doses of IFNα2a and acquired data across nine timepoints by DIA proteomics. A novel and unbiased data analysis method was applied identifying the induction of 74 proteins, including known IFNα-induced proteins, but also novel candidates, and the repression of 17 proteins, e.g. a negative regulator of the mTOR pathway.

To characterize the identified differential protein dynamics, a retarded transient function was fitted to the experimental data enabling to retrieve information regarding the time delay until induction, the timepoint of maximal induction and the extend of transient behavior. The trajectories of protein dynamics follow a similar sequential induction as previously observed in the transcription factor dynamics obtained from predictions of a mathematical model on IFNα2a signal transduction.

The curve characteristics were compared to dynamic profiles previously acquired by RT-qPCR. Overall, the distinct time clusters were confirmed at the protein level, however, a delayed maximal induction was observed compared to the mRNA profiles. Surprisingly, different from the mRNA profiles, for a large proportion of proteins the abundance was maintained after reaching maximal induction and only a few proteins, including the DNA damage response protein PARP10, showed a transient dynamic behavior.

We demonstrated a novel approach to analyze time-resolved proteome data as a proof-of-concept on IFNα-induced protein dynamics which now allows for application on other datasets.