Introduction:
Explaining emergent properties of complex organisms represents a grand
challenge of biology (Schwenk, Padilla, Bakken, & Full, 2009). Emergent
properties are often based on non-linear interactions of their molecular
constituents (Stillman et al., 2011). Despite the known non-linearity of
the genome to phenome continuum many biological studies rely heavily on
correlations of complex organismal phenotypes (traits) with genomic
variation (e.g. SNPs and other types of sequence variation) via QTL or
GWAS analyses (Kratochwil & Meyer, 2015; Wray et al., 2013) and mRNA
abundance changes via transcriptomics while proteome dynamics is less
commonly investigated (Evans, 2015).
Unlike the genome, transcriptomes and proteomes are spatially highly
heterogeneous and dynamic, i.e. they are highly responsive to
environmental stimuli. Currently, there is a large gap in the literature
regarding transcriptome versus proteome data and systematic comparisons
aimed at discerning the rules of non-linearity between these two levels
of biological organization that are not common (Buccitelli & Selbach,
2020). There is a great need for developing robust and comprehensive
quantitative proteomics approaches to facilitate such comparisons and
close the gap between genotypes and ecologically relevant phenotypes,
e.g. environmental stress tolerance. Understanding the connection
between mRNA and protein levels is fundamental to predicting how the
underlying genetic code impacts changes in phenotype due to
environmental changes. Non-linearity between transcriptome and proteome
levels of regulation is well documented (Franks, Airoldi, & Slavov,
2017). It can be based on differential mRNA processing and degradation
(Bentley, 2014), transcript-specific regulation of protein translation
through all stages including initiation, elongation, and localization
(Tahmasebi, Khoutorsky, Mathews, & Sonenberg, 2018), and/ or regulation
of protein degradation (Pohl & Dikic, 2019).
The proteome represents the core that is central to the genome to
phenome continuum. On the one hand, proteins are linked directly to
specific genes via proteotypic peptides, which allows unambiguous
association of each protein with a specific genomic locus (Keerthikumar
& Mathivanan, 2017). On the other, proteins represent the critical
molecular building blocks that define structure and carry out most
biochemical processes and functions of cells, tissues and organisms
(Ebhardt, Root, Sander, & Aebersold, 2015). Proteins represent the
molecular constituents giving rise to phenotypic variability that is
acted upon by natural selection (Clarke, 1971; Frömmel & Holzhütter,
1985; Mularoni, Ledda, Toll-Riera, & Albà, 2010). Moreover, most
targets of pharmaceutical drugs are proteins, which illustrates that
proteins control critical organismal phenotypes (Batchelor, Loewer,
Mock, & Lahav, 2011; Ebhardt et al., 2015).
Recent developments in biological mass spectrometry have enabled robust
gel- and label-free quantitative proteomics workflows that are well
suited for organismal biology and molecular ecology (Huang et al., 2015;
Vowinckel et al., 2013). In particular, the invention of
data-independent acquisition (DIA) liquid chromatography mass
spectrometry (DIA-LCMS2) holds great promise for molecular ecology
studies (Crowgey, Matlock, Venkatraman, Fert-Bober, & Van Eyk, 2017;
Schubert et al., 2015). The DIA approach is also referred to as
Sequentially Windowed Acquisition of all theoretically possible MSMS
spectra (SWATH)-MS (Arnhard, Gottschall, Pitterl, & Oberacher, 2015;
Huang et al., 2015).
DIA-LCMS2 represents a merger of pre-acquisition targeted mass
spectrometry approaches, i.e. selected reaction monitoring (SRM) or
multiple reaction monitoring (MRM), and non-targeted data acquisition
that is independent of precursor (MS1) spectra acquisition (Koopmans,
Ho, Smit, & Li, 2018). In DIA-LCMS2 the targeting of specific
transitions, precursors, peptides, and proteins is performed
post-acquisition by interrogating all theoretically possible fragment
ion (MS2 spectra) present in a sample against a previously validated DIA
assay library. Here, we have constructed a DIA assay library from raw
MS2 spectral libraries of Nile tilapia (Oreochromis niloticus )
kidney to facilitate quantitative studies of proteome dynamics in
response to environmental stress and other ecological contexts. We
demonstrate the utility of this DIA assay library by identifying
proteins, biological functions, and processes that are associated with
salinity acclimation in O. niloticus kidney. Furthermore, we
demonstrate that quantitative proteomics provides knowledge that cannot
be gained from transcriptomics data.
Methods :