Conclusions
Understanding how organisms respond to environmental stresses and other ecological contexts requires analyzing multiple levels of biological organization. Here we introduce an approach to molecular ecology that allows targeted quantitation of proteins on a proteome-wide scale by employing DIA assay libraries. We demonstrate the utility of this approach by comparing kidney proteomes of O. niloticus exposed to different salinities. Our study shows that regulation at the proteome greatly differs from that at the transcriptome level. Therefore, analyses of these two levels of biological organization produce different types of knowledge on tissue-specific phenotypes and functions that are associated with salinity acclimation. Moreover, analyzing the mRNA:protein regulation ratio provides insight into mechanisms of regulation (transcriptional, post-transcriptional, translational, post-translational). The DIA assay library generated in this study enabled a global picture of kidney proteome dynamics during salinity stress. Clustering significantly regulated proteins with other proteins that had similar (but non-significant) patterns of regulation, expanded the data set and revealed interconnectedness between known and novel salinity stress responses. The role of the osmolyte myo -inositol as an important cellular protective molecule was highlighted andmyo -inositol degradation identified as an important mechanism inO. niloticus kidney. Cellular protection and detoxification by antioxidant mechanisms was another core function associated with BW acclimation of O. niloticus , which was revealed using the kidney DIA assay library. Another interesting finding was the potential of red blood cell damage to be the trigger for the increase in oxidative metabolism via the signaling molecule VFA.
In summary, this study introduces the DIA assay library approach as a novel tool for proteome-wide molecular ecology studies. The utility of DIA assay libraries for discerning different ecological contexts was demonstrated by using a kidney DIA assay library to analyze proteome dynamics in response to acclimation of O. niloticus from FW to BW and by highlighting large differences in the regulation at the proteome versus transcriptome levels of biological organization.
AcknowledgementsThis investigation was supported by the National Science Foundation (NSF-BSF) Grant IOS-1656371 to ACn and DK, the US-Israel Binational Agricultural Research and Development Fund (BARD) Grant (IS-4800-15 R) to ACn and DK, and AES projects CA-D-ASC-7690-H and CA-D-ASC-7624-RR to DK.Data Accessibility StatementAll proteomics data and metadata have been deposited and are publicly accessible at the following repositories: MassIVE (accession # MSV000085637) and ProteomeXchange (accession # PXD020056) for all DDA data, and PanoramaPublic (access link: Kueltz-Lab_2020-1.url) for all DIA data (including the DIA assay library). All transcriptomics data and metadata have been deposited and are publicly accessible at NCBI under accession number PRJNA669315 (https://www.ncbi.nlm.nih.gov/bioproject/669315).Author ContributionsA.Cn. and D.K. conceived the experiments. P.C. challenged and sampled the fish. A.Ca. conducted and analyzed the fish transcriptomics experiments. L.R. conducted and analyzed the proteomics experiments and performed proteome-transcriptome correlations with help from D.K. and L.M. L.R. wrote and D.K. edited the manuscript with help from A.Cn. and A.Ca. on the transcriptomics part. All authors reviewed the manuscript.References
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Tables and Figures
Table 1: STRING and KEGG enrichment. Heat map of STRING IDs from list of 175 queries (174 proteins and 1 mRNA) which are significantly enriched (FDR<.05) for keywords (green) assigned by Uniprot and protein domains assigned (orange) by Pfam, InterPro, and SMART databases. KEGG IDs (purple) with over-representation >5, defined as the proportion of KEGG IDs in a KEGG pathway versus total KEGG IDs in the list of 236 queries (229 proteins and 7 mRNA) divided by the proportion of KEGG IDs in a pathway from the complete list of 2114 proteins in the DIA assay library versus the total number of KEGG IDs in the DIA assay library. Additionally, individual subsets of the query list (sig. up, sig. down, cluster 1, cluster 6, individual STRING networks) shown in each column and analyzed in the same manner. Darker color indicates higher significance, with FDR (STRING) and over-representation (KEGG) shown for each term.
Figure 1: DIA assay library properties relative to raw spectral library. DIA assay library of O. niloticus kidney. The initial spectral library (SL) represents over 7,000 proteins, 90,000 precursors, and 450,000 million transitions. Seven QC filters were applied to create the DIA assay library containing 2120 proteins (A ), 9226 peptides (B ), 9226 precursors (C ), and 52,361 transitions (D ). Most proteins are represented by at least 2 diagnostic peptides. The remainder (25%) was identified by at least 2 unique peptides but only 1 remains after applying all DIA QC filters. The initial bar labeled SL depicts data for the raw spectral library and final bar (step 7) target depicts data for the DIA assay library. Library filtration steps one to six are explained in the text.E , Frequency distributions of fragment ion types represented in the final DIA assay library. F , Frequency distribution for the number of peptides per protein in the DIA assay library. The data were generated with Skyline 20.0 (MacCoss Lab., University of Washington).
Figure 2: Volcano plot and QC of kidney DIA assay data from FW and BW acclimated fish. A , Volcano plot comparing relative protein abundances in FW and BW acclimated fish. Blue circles represent proteins that are significantly reduced on BW fish at adjusted p < 0.05 and FC ≥ 1.85. Orange circles represent proteins that are significantly elevated on BW at adjusted p < 0.05 and FC ≥ 1.85. Gray circles represent non-significant proteins. B , Mass error distribution of all transitions. C , Retention time (RT) reproducibility of all peptides in the DIA assay library based on internal RT (iRT) standards. D , Fold change (FC) and coefficient of variation (CV) depending on number of biological replicates at a statistical power of 0.8 and false discovery rate (FDR) of 0.05. E , mProphet peak score distribution indicating that the great majority of peaks meet the threshold for inclusion in MSstats quantitative DIA data (q < 0.01, MacCoss Lab., University of Washington).
Figure 3: Correlation between protein and mRNA regulation.Correlation between difference in mRNA abundance and protein abundance between treatments expressed as -log2 of fold change (FC). Correlation between all 2114 proteins in the DIA assay library and corresponding mRNA transcripts (left) and correlation between all 42 significant proteins defined as p<0.05 and FC>1.85 (right).
Figure 4: Genesis clustering of protein expression. Non-biased clustering of all proteins in the DIA assay. Patterns of protein abundance within replicates was clustered using K-means method. Proteins were clustered into 6 groups with most significant proteins clustered in cluster 1 (down-regulated) and cluster 6 (up-regulated) (left). Individual protein patterns and cluster average indicate the general regulation patterns within clusters, with replicate identifiers (right)
Figure 5: Visual depiction of analysis following Genesis clustering. Of total 2114 proteins in the DIA assay library, 263 were analyzed using KEGG and STRING online tools. These were: 205 proteins in cluster 1, 53 proteins in cluster 6, and 5 additional significant proteins which were not in clusters 1 & 6. Of these proteins, 174 had unique STRING identifiers and 229 had KEDD IDs. Additionally, 14 significantly regulated mRNA were added to the query lists, of which 1 matched a STRING ID and 7 had matching KEGG IDs.
Figure 6: STRING network map containing all networks with at least one significantly regulated protein (p<.05, FC>1.85). Nodes represent one STRING protein ID and edges represent a connection based on protein-protein interactions including known or predicted interactions based on published literature. The network map is split into 7 different networks based on score of combined connections, with a Markov Cluster (MCL) inflation factor of 1.3. Large circle bolding represents proteins with greater than 4-fold difference between treatments, while smaller bolding represents proteins with significant regulation and between 1.85 and 4-fold difference between treatments.
Figure 7: Expansion of STRING networks 1 and 3 from the full network map. Blue circles represent significantly downregulated proteins while orange circles represent significantly upregulated proteins. The legend indicates the fold change difference between treatments, with the largest circle representing greater than 5-fold difference, the next largest representing between 3 and 5-fold difference, the next largest representing between 1.85 and 3-fold difference, and the smallest bolded circle representing proteins which were statistically significant (p<.05) but which did not meet the fold change cut-off (FC>1.85).
Figure 8: Ascorbate and alderate metabolism KEGG pathway.Significant proteins found in the KEGG pathway of ascorbate and alderate metabolism (map00053). Dark orange indicates significant upregulation, dark blue indicates significant downregulation, and light blue indicates non-significant downregulation. Pathway is taken from portion of KEGG pathway provided by Kanehisa Laboratories 11/9/2018.
Figure 9: Protein isoform comparison. Volcano plots of all proteins and sets of isoforms for several important proteins. All proteins in DIA assay library are shown with log2 transformed Fold Change along the X-axis and log10 transformed p-values along the Y-axis (A). Orange points in the upper right represent significantly upregulated proteins and blue points in the upper left represent significantly downregulated proteins. Dashed lines represent p-value cutoff (p<.05, horizontal line) and FC cutoff (FC>±1.85, vertical lines). Individual proteins shown include one significant isoform and related non-significant isoforms.