Quantitative Proteomic Analysis of Chikungunya Virus-Infected Aedes aegypti Reveals Proteome Modulations Indicative of Persistent Infection

The mosquito-borne chikungunya virus (CHIKV) poses a threat to human health in tropical countries throughout the world. The molecular interactions of CHIKV with its mosquito vector Aedes aegypti are not fully understood. Following oral acquisition of CHIKV via salinemeals, we analyzed changes in the proteome of Ae. aegypti in 12 h intervals by label-free quantification using a timsTOF Pro mass spectrometer. For each of the seven time points, between 2647 and 3167 proteins were identified among CHIKV-infected and noninfected mosquito samples, and fewer than 6% of those identified proteins were affected by the virus. Functional enrichment analysis revealed that the three pathways, Endocytosis, Oxidative phosphorylation, and Ribosome biogenesis, were enriched during CHIKV infection. On the other hand, three pathways of the cellular RNA machinery and five metabolism related pathways were significantly attenuated in the CHIKV-infected samples. Furthermore, proteins associated with cytoskeleton and vesicular transport, as well as various serine-type endopeptidases and metallo-proteinases, were modulated in the presence of CHIKV. Our study reveals biological pathways and novel proteins interacting with CHIKV in the mosquito. Overall, CHIKV infection caused minor changes to the mosquito proteome demonstrating a high level of adaption between the vector and the virus, essentially coexisting in a nonpathogenic relationship. The mass spectrometry data have been deposited to the MassIVE repository (https://massive.ucsd.edu/ProteoSAFe/dataset.jsp?task=abfd14f7015243c69854731998d55df1) with the data set identifier MSV000085115.

Ion Mobility Coupled to a Time-of-Flight Mass Analyzer Combined With Fragment Intensity Predictions Improves Identification of Classical Bioactive Peptides and Small Open Reading Frame-Encoded Peptides

Bioactive peptides exhibit key roles in a wide variety of complex processes, such as regulation of body weight, learning, aging, and innate immune response. Next to the classical bioactive peptides, emerging from larger precursor proteins by specific proteolytic processing, a new class of peptides originating from small open reading frames (sORFs) have been recognized as important biological regulators. But their intrinsic properties, specific expression pattern and location on presumed non-coding regions have hindered the full characterization of the repertoire of bioactive peptides, despite their predominant role in various pathways. Although the development of peptidomics has offered the opportunity to study these peptides in vivo, it remains challenging to identify the full peptidome as the lack of cleavage enzyme specification and large search space complicates conventional database search approaches. In this study, we introduce a proteogenomics methodology using a new type of mass spectrometry instrument and the implementation of machine learning tools toward improved identification of potential bioactive peptides in the mouse brain. The application of trapped ion mobility spectrometry (tims) coupled to a time-of-flight mass analyzer (TOF) offers improved sensitivity, an enhanced peptide coverage, reduction in chemical noise and the reduced occurrence of chimeric spectra. Subsequent machine learning tools MS2PIP, predicting fragment ion intensities and DeepLC, predicting retention times, improve the database searching based on a large and comprehensive custom database containing both sORFs and alternative ORFs. Finally, the identification of peptides is further enhanced by applying the post-processing semi-supervised learning tool Percolator. Applying this workflow, the first peptidomics workflow combined with spectral intensity and retention time predictions, we identified a total of 167 predicted sORF-encoded peptides, of which 48 originating from presumed non-coding locations, next to 401 peptides from known neuropeptide precursors, linked to 66 annotated bioactive neuropeptides from within 22 different families. Additional PEAKS analysis expanded the pool of SEPs on presumed non-coding locations to 84, while an additional 204 peptides completed the list of peptides from neuropeptide precursors. Altogether, this study provides insights into a new robust pipeline that fuses technological advancements from different fields ensuring an improved coverage of the neuropeptidome in the mouse brain.