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
Anders S., Huber W. (2010). Differential expression analysis for
sequence count data. Genome Biology , 11, R106. doi:
10.1186/gb-2010-11-10-r106,
http://genomebiology.com/2010/11/10/R106/.
Anders S., Pyl P.T., Huber W. (2014). HTSeq – A Python framework to
work with high-throughput sequencing data. Bioinformatics doi:
10.1093/bioinformatics/btu638.
Andrews, S. (2010). FastQC: A Quality Control Tool for High Throughput
Sequence Data [Online]. Available online at:
http://www.bioinformatics.babraham.ac.uk/projects/fastqc/
Arner, R. J., Prabhu, K. S., Thompson, J. T., Hildenbrandt, G. R.,
Liken, A. D., & Reddy, C. C. (2001). myo-Inositol oxygenase: molecular
cloning and expression of a unique enzyme that oxidizes myo-inositol and
D-chiro-inositol. Biochemical Journal , 360 (Pt 2),
313–320.
Arnhard, K., Gottschall, A., Pitterl, F., & Oberacher, H. (2015).
Applying “Sequential Windowed Acquisition of All Theoretical Fragment
Ion Mass Spectra” (SWATH) for systematic toxicological analysis with
liquid chromatography-high-resolution tandem mass spectrometry.Anal Bioanal Chem , 407 (2), 405–414. doi:
10.1007/s00216-014-8262-1
Batchelor, E., Loewer, A., Mock, C., & Lahav, G. (2011).
Stimulus-dependent dynamics of p53 in single cells. Mol Syst
Biol , 7 , 488. doi: 10.1038/msb.2011.20
Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery
rate: A practical and powerful approach to multiple testing.Journal of the Royal Statistical Society. Series B
(Methodological) , 57 (1), 289–300.
Bentley, D. L. (2014). Coupling mRNA processing with transcription in
time and space. Nature Reviews Genetics , 15 (3), 163–175.
doi: 10.1038/nrg3662
Blattmann, P., Stutz, V., Lizzo, G., Richard, J., Gut, P., & Aebersold,
R. (2019). Generation of a zebrafish SWATH-MS spectral library to
quantify 10,000 proteins. Scientific Data , 6 , 190011. doi:
10.1038/sdata.2019.11
Bolger, A. M., Lohse, M., & Usadel, B. (2014). Trimmomatic: A flexible
trimmer for Illumina Sequence Data. Bioinformatics, btu170.
Buccitelli, C., & Selbach, M. (2020). mRNAs, proteins and the emerging
principles of gene expression control. Nature Reviews Genetics ,
1–15. doi: 10.1038/s41576-020-0258-4
Choi, M., Chang, C. Y., Clough, T., Broudy, D., Killeen, T., MacLean,
B., & Vitek, O. (2014). MSstats: an R package for statistical analysis
of quantitative mass spectrometry-based proteomic experiments.Bioinformatics , 30 (17), 2524–2526. doi:
10.1093/bioinformatics/btu305
Clarke, B. (1971). Natural Selection and the Evolution of Proteins.Nature , 232 (5311), 487. doi: 10.1038/232487a0
Cramb, G., Kalujnaia, S., Gellatly, S., Hazon, N., Villasenor, A., &
Yancey, P. (2013). Expression and functions of inositol monophosphatase
(IMPA) in seawater (SW)-acclimated euryhaline teleosts. The FASEB
Journal , 27 (S1), 937.7-937.7. doi:
10.1096/fasebj.27.1_supplement.937.7
Crowgey, E. L., Matlock, A., Venkatraman, V., Fert-Bober, J., & Van
Eyk, J. E. (2017). Mapping biological networks from quantitative
data-independent acquisition mass spectrometry: data to knowledge
pipelines. Methods Mol Biol , 1558 , 395–413. doi:
10.1007/978-1-4939-6783-4_19
Cui, W., Ma, A., Huang, Z., Wang, X., Liu, Z., Xia, D., … Zhao,
T. (2020). Comparative transcriptomic analysis reveals mechanisms of
divergence in osmotic regulation of the turbot Scophthalmus maximus.Fish Physiology and Biochemistry , 46 (4), 1519–1536. doi:
10.1007/s10695-020-00808-6
Dobin A, Davis CA, Schlesinger F, et al. (2013) STAR: ultrafast
universal RNA-seq aligner. Bioinformatics , 29(1):15-21.
doi:10.1093/bioinformatics/bts635
Ebhardt, H. A., Root, A., Sander, C., & Aebersold, R. (2015).
Applications of targeted proteomics in systems biology and translational
medicine. Proteomics , 15 (18), 3193–3208. doi:
10.1002/pmic.201500004
Elarabany, N., Bahnasawy, M., Edrees, G., & Alkazagli, R. (2017).
Effects of Salinity on Some Haematological and Biochemical Parameters in
Nile Tilapia, Oreochromus niloticus . Agriculture, Forestry
and Fisheries , 6 (6), 200. doi: 10.11648/j.aff.20170606.13
Escher, C., Reiter, L., MacLean, B., Ossola, R., Herzog, F., Chilton,
J., … Rinner, O. (2012). Using iRT, a normalized retention time
for more targeted measurement of peptides. Proteomics ,12 (8), 1111–1121. doi: 10.1002/pmic.201100463
Evans, T. G. (2015). Considerations for the use of transcriptomics in
identifying the ‘genes that matter’ for environmental adaptation.JOURNAL OF EXPERIMENTAL BIOLOGY , 218 (12, SI), 1925–1935.
doi: 10.1242/jeb.114306
Evans, T. G., & Somero, G. N. (2009). Protein-protein interactions
enable rapid adaptive response to osmotic stress in fish gills.Communicative & Integrative Biology , 2 (2), 94–96.
Franks, A., Airoldi, E., & Slavov, N. (2017). Post-transcriptional
regulation across human tissues. PLOS Computational Biology ,13 (5), e1005535. doi: 10.1371/journal.pcbi.1005535
Frömmel, C., & Holzhütter, H.-G. (1985). An estimate on the effect of
point mutation and natural selection on the rate of amino acid
replacement in proteins. Journal of Molecular Evolution ,21 (3), 233–257. doi: 10.1007/BF02102357
Gardell, A. M., Yang, J., Sacchi, R., Fangue, N. A., Hammock, B. D., &
Kültz, D. (2013). Tilapia (Oreochromis mossambicus) brain cells respond
to hyperosmotic challenge by inducing myo-inositol biosynthesis.The Journal of Experimental Biology , 216 (Pt 24),
4615–4625. doi: 10.1242/jeb.088906
Gehlenborg, N., O’Donoghue, S. I., Baliga, N. S., Goesmann, A., Hibbs,
M. A., Kitano, H., … Gavin, A.-C. (2010). Visualization of omics
data for systems biology. Nature Methods , 7 (3 Suppl),
S56-68. doi: 10.1038/nmeth.1436
Gillet, L. C., Navarro, P., Tate, S., Rost, H., Selevsek, N., Reiter,
L., … Aebersold, R. (2012). Targeted data extraction of the MS/MS
spectra generated by data-independent acquisition: a new concept for
consistent and accurate proteome analysis. Mol Cell Proteomics ,11 (6), O111 016717. doi: 10.1074/mcp.O111.016717
Gu, Y., Wang, Y., Zhang, H., Zhao, T., Sun, S., Wang, H., … Li,
P. (2017). Protective effect of dihydropteridine reductase against
oxidative stress is abolished with A278C mutation. Journal of
Zhejiang University. Science. B , 18 (9), 770–777. doi:
10.1631/jzus.B1600123
Huang, Q., Yang, L., Luo, J., Guo, L., Wang, Z., Yang, X., …
Zhang, Y. (2015). SWATH enables precise label-free quantification on
proteome scale. Proteomics , 15 (7), 1215–1223. doi:
10.1002/pmic.201400270
Jeffries, K. M., Brander, S. M., Britton, M. T., Fangue, N. A., &
Connon, R. E. (2015). Chronic exposures to low and high concentrations
of ibuprofen elicit different gene response patterns in a euryhaline
fish. Environmental Science and Pollution Research International ,22 (22), 17397–17413. doi: 10.1007/s11356-015-4227-y
Jovanovic, M., Rooney, M. S., Mertins, P., Przybylski, D., Chevrier, N.,
Satija, R., … Regev, A. (2015). Dynamic profiling of the protein
life cycle in response to pathogens. Science , 347 (6226).
doi: 10.1126/science.1259038
Kalujnaia, S., McVee, J., Kasciukovic, T., Stewart, A. J., & Cramb, G.
(2010). A role for inositol monophosphatase 1 (IMPA1) in salinity
adaptation in the euryhaline eel (Anguilla anguilla). Faseb
Journal , 24 (10), 3981–3991. doi: 10.1096/fj.10-161000
Kanehisa, M., Sato, Y., & Morishima, K. (2016). BlastKOALA and
GhostKOALA: KEGG Tools for Functional Characterization of Genome and
Metagenome Sequences. Journal of Molecular Biology ,428 (4), 726–731. doi: 10.1016/j.jmb.2015.11.006
Keerthikumar, S., & Mathivanan, S. (2017). Proteotypic Peptides and
Their Applications. Methods in Molecular Biology (Clifton, N.J.) ,1549 , 101–107. doi: 10.1007/978-1-4939-6740-7_8
Koopmans, F., Ho, J. T. C., Smit, A. B., & Li, K. W. (2018).
Comparative analyses of data independent acquisition mass spectrometric
approaches: DIA, WiSIM-DIA, and untargeted DIA. Proteomics ,18 (1), 1–6. doi: 10.1002/pmic.201700304
Kratochwil, C. F., & Meyer, A. (2015). Closing the genotype-phenotype
gap: emerging technologies for evolutionary genetics in ecological model
vertebrate systems. BioEssays: News and Reviews in Molecular,
Cellular and Developmental Biology , 37 (2), 213–226. doi:
10.1002/bies.201400142
Kültz, D. (2020). Evolution of cellular stress response mechanisms.Journal of Experimental Zoology Part A: Ecological and Integrative
Physiology , n/a (n/a). doi: 10.1002/jez.2347
Kültz, D., Li, J., Gardell, A., & Sacchi, R. (2013). Quantitative
molecular phenotyping of gill remodeling in a cichlid fish responding to
salinity stress. Molecular & Cellular Proteomics , 12 (12),
3962–3975. doi: 10.1074/mcp.M113.029827
Lang, K. S., Duranton, C., Poehlmann, H., Myssina, S., Bauer, C., Lang,
F., … Huber, S. M. (2003). Cation channels trigger apoptotic
death of erythrocytes. Cell Death and Differentiation ,10 (2), 249–256. doi: 10.1038/sj.cdd.4401144
Laskar, A. A., & Younus, H. (2019). Aldehyde toxicity and metabolism:
the role of aldehyde dehydrogenases in detoxification, drug resistance
and carcinogenesis. Drug Metabolism Reviews , 51 (1),
42–64. doi: 10.1080/03602532.2018.1555587
Li, J., Levitan, B., Gomez-Jimenez, S., & Kültz, D. (2018). Development
of a Gill Assay Library for Ecological Proteomics of Threespine
Sticklebacks (Gasterosteus aculeatus). Molecular & Cellular
Proteomics: MCP , 17 (11), 2146–2163. doi:
10.1074/mcp.RA118.000973
Lindros, K. O., Oinonen, T., Kettunen, E., Sippel, H., Muro-Lupori, C.,
& Koivusalo, M. (1998). Aryl hydrocarbon receptor-associated genes in
rat liver: Regional coinduction of aldehyde dehydrogenase 3 and
glutathione transferase Ya. Biochemical Pharmacology ,55 (4), 413–421. doi: 10.1016/S0006-2952(97)00495-4
Liu, H., Sun, W., Dong, X., Chi, S., Yang, Q., Li, Y., & Tan, B.
(2016). Profiling of Up-Regulated Genes Response to Acute Hypo-Osmotic
Stress in Hepatopancreas and Gill of the Pacific White Shrimps
(Litopenaeus vannamei). International Journal of Biology ,8 (2), p43. doi: 10.5539/ijb.v8n2p43
Lü, A., Hu, X., Wang, Y., Shen, X., Li, X., Zhu, A., … Feng, Z.
(2014). iTRAQ analysis of gill proteins from the zebrafish (Danio rerio)
infected with Aeromonas hydrophila. Fish & Shellfish Immunology ,36 (1), 229–239. doi: 10.1016/j.fsi.2013.11.007
Lu, X.-J., Zhang, H., Yang, G.-J., Li, M.-Y., & Chen, J. (2016).
Comparative transcriptome analysis on the alteration of gene expression
in ayu (Plecoglossus altivelis) larvae associated with salinity change.Zoological Research , 37 (3), 126–135. doi:
10.13918/j.issn.2095-8137.2016.3.126
Luo, M., Ma, W., Sand, Z., Finlayson, J., Wang, T., Brinton, R. D.,
… Mandarino, L. J. (2020). Von Willebrand factor A
domain-containing protein 8 (VWA8) localizes to the matrix side of the
inner mitochondrial membrane. Biochemical and Biophysical Research
Communications , 521 (1), 158–163. doi:
10.1016/j.bbrc.2019.10.095
Marlatt, V. L., & Martyniuk, C. J. (2017). Biological responses to
phenylurea herbicides in fish and amphibians: New directions for
characterizing mechanisms of toxicity. Comparative Biochemistry
and Physiology Part C: Toxicology & Pharmacology , 194 , 9–21.
doi: 10.1016/j.cbpc.2017.01.002
Mularoni, L., Ledda, A., Toll-Riera, M., & Albà, M. M. (2010). Natural
selection drives the accumulation of amino acid tandem repeats in human
proteins. Genome Research , 20 (6), 745–754. doi:
10.1101/gr.101261.109
Pino, L. K., Searle, B. C., Bollinger, J. G., Nunn, B., MacLean, B., &
MacCoss, M. J. (2017). The Skyline ecosystem: Informatics for
quantitative mass spectrometry proteomics. Mass Spectrom Rev ,39 (3), 229–244. doi: 10.1002/mas.21540
Pohl, C., & Dikic, I. (2019). Cellular quality control by the
ubiquitin-proteasome system and autophagy. Science ,366 (6467), 818–822. doi: 10.1126/science.aax3769
R Core Team (2020). R: A language and Environment for Statistical
Computing. R foundation for Statistical Computing , Vienna,
Austria. URL https://www.R-project.org
Reiter, L., Rinner, O., Picotti, P., Huttenhain, R., Beck, M., Brusniak,
M. Y., … Aebersold, R. (2011). mProphet: automated data
processing and statistical validation for large-scale SRM experiments.Nat Methods , 8 (5), 430–435. doi: 10.1038/nmeth.1584
Sacchi, R., Gardell, A. M., Chang, N., & Kültz, D. (2014). Osmotic
regulation and tissue localization of the myo-inositol biosynthesis
pathway in tilapia (Oreochromis mossambicus) larvae. Journal of
Experimental Zoology. Part A, Ecological Genetics and Physiology ,321 (8), 457–466. doi: 10.1002/jez.1878
Sacchi, R., Li, J., Villarreal, F., Gardell, A. M., & Kültz, D. (2013).
Salinity-induced regulation of the myo-inositol biosynthesis pathway in
tilapia gill epithelium. The Journal of Experimental Biology ,216 (Pt 24), 4626–4638. doi: 10.1242/jeb.093823
Schubert, O. T., Gillet, L. C., Collins, B. C., Navarro, P.,
Rosenberger, G., Wolski, W. E., … Aebersold, R. (2015). Building
high-quality assay libraries for targeted analysis of SWATH MS data.Nat Protoc , 10 (3), 426–441. doi: 10.1038/nprot.2015.015
Schwanhäusser, B., Busse, D., Li, N., Dittmar, G., Schuchhardt, J.,
Wolf, J., … Selbach, M. (2011). Global quantification of
mammalian gene expression control. Nature , 473 (7347),
337–342. doi: 10.1038/nature10098
Schwenk, K., Padilla, D. K., Bakken, G. S., & Full, R. J. (2009). Grand
challenges in organismal biology. Integrative and Comparative
Biology , 49 (1), 7–14. doi: 10.1093/icb/icp034
Stillman, J. H., Denny, M., Padilla, D. K., Wake, M. H., Patek, S., &
Tsukimura, B. (2011). Grand opportunities: strategies for addressing
grand challenges in organismal animal biology. Integrative and
Comparative Biology , 51 (1), 7–13. doi: 10.1093/icb/icr052
Sturn, A., Quackenbush, J., & Trajanoski, Z. (2002). Genesis: cluster
analysis of microarray data. Bioinformatics , 18 (1),
207–208.
Szklarczyk, D., Gable, A. L., Lyon, D., Junge, A., Wyder, S.,
Huerta-Cepas, J., … Mering, C. von. (2019). STRING v11:
protein–protein association networks with increased coverage,
supporting functional discovery in genome-wide experimental datasets.Nucleic Acids Research , 47 (D1), D607–D613. doi:
10.1093/nar/gky1131
Tahmasebi, S., Khoutorsky, A., Mathews, M. B., & Sonenberg, N. (2018).
Translation deregulation in human disease. Nature Reviews
Molecular Cell Biology , 19 (12), 791–807. doi:
10.1038/s41580-018-0034-x
Vowinckel, J., Capuano, F., Campbell, K., Deery, M. J., Lilley, K. S.,
& Ralser, M. (2013). The beauty of being (label)-free: sample
preparation methods for SWATH-MS and next-generation targeted
proteomics. F1000Res , 2 , 272. doi:
10.12688/f1000research.2-272.v2
Williams, T. D., Gensberg, K., Minchin, S. D., & Chipman, J. K. (2003).
A DNA expression array to detect toxic stress response in European
flounder (Platichthys flesus). Aquatic Toxicology (Amsterdam,
Netherlands) , 65 (2), 141–157. doi:
10.1016/s0166-445x(03)00119-x
Wray, N. R., Yang, J., Hayes, B. J., Price, A. L., Goddard, M. E., &
Visscher, P. M. (2013). Pitfalls of predicting complex traits from SNPs.Nature Reviews Genetics , 14 (7), 507–515. doi:
10.1038/nrg3457
Zeng, L., Ai, C.-X., Wang, Y.-H., Zhang, J.-S., & Wu, C.-W. (2017).
Abrupt salinity stress induces oxidative stress via the Nrf2-Keap1
signaling pathway in large yellow croaker Pseudosciaena crocea.Fish Physiology and Biochemistry , 43 (4), 955–964. doi:
10.1007/s10695-016-0334-z
Zhang, L., Sun, W., Chen, H., Zhang, Z., & Cai, W. (2020).
Transcriptomic Changes in Liver of Juvenile Cynoglossus semilaevis
following Perfluorooctane Sulfonate Exposure. Environmental
Toxicology and Chemistry , 39 (3), 556–564. doi: 10.1002/etc.4633
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.