<i>Pseudomonas aeruginosa</i> Proteome under Hypoxic Stress Conditions Mimicking the Cystic Fibrosis Lung

Pseudomonas aeruginosa is a ubiquitous Gram-negative pathogen known to inhabit hypoxic mucus plugs of cystic fibrosis (CF) patient lungs. Despite the high prevalence and related patient mortality, the protein machinery enabling the bacterium to adapt to low oxygen environment remains to be fully elucidated. We investigated this by performing both SWATH mass spectrometry and data-dependent SPS-MS3 of TMT-labeled peptides to profile the proteomes of two P. aeruginosa CF isolates, PASS2 and PASS3, and a laboratory reference strain, PAO1, grown under hypoxic stress (O₂ < 1%) in media that mimic the nutrient components of the CF lung. Quantitated across all three strains were 3967 P. aeruginosa proteins, reflecting approximately 71% of predicted ORFs in PAO1 and representing the most comprehensive proteome of clinically relevant P. aeruginosa to date. Comparative analysis revealed 735, 640, and 364 proteins were altered by 2-fold or more when comparing low oxygen to aerobic growth in PAO1, PASS2, and PASS3, respectively. Strikingly, under hypoxic stress, all strains showed concurrent increased abundance of proteins required for both aerobic (<i>cbb</i>₃-1 and <i>cbb</i>₃-2 terminal oxidases) and anaerobic denitrification and arginine fermentation, with the two clinical isolates showing higher relative expression of proteins in these pathways. Additionally, functional annotation revealed that clinical strains portray a unique expression profile of replication, membrane biogenesis, and virulence proteins during hypoxia which may endow these bacteria with a survival advantage. These protein profiles illuminate the diversity of P. aeruginosa mechanisms to adapt to low oxygen and shows that CF isolates initiate a robust molecular response to persist under these conditions

Coverage and Consistency: Bioinformatics Aspects of the Analysis of Multirun iTRAQ Experiments with Wheat Leaves

The hexaploid genome of bread wheat (<i>Triticum aestivum</i>) is large (17 Gb) and repetitive, and this has delayed full sequencing and annotation of the genome, which is a prerequisite for effective quantitative proteomics analysis. Aware of these constraints we investigated the most effective approaches for shotgun proteomic analyses of bread wheat that would support large-scale quantitative comparisons using iTRAQ reagents. We used a data set that was generated by two-dimensional LC–MS of iTRAQ labeled peptides from wheat leaves. The main items considered in this study were the choice of sequence database for matching LC–MS data, the consistency of identification when multiple LC–MS runs were acquired, and the options for downstream functional analysis to generate useful insight. For peptide identification we examined the extensive NCBInr plant database, a smaller composite cereals database, the <i>Brachypodium distachyon</i> model plant genome, the EST-based SuperWheat database, as well as the genome sequence from the recently sequenced D-genome progenitor <i>Aegilops tauschii.</i> While the most spectra were assigned by using the SuperWheat database, this extremely large database could not be readily manipulated for the robust protein grouping that is required for large-scale, multirun quantitative experiments. We demonstrated a pragmatic alternative of using the composite cereals database for peptide spectra matching. The stochastic aspect of protein grouping across LC–MS runs was investigated using the smaller composite cereals database where we found that attaching the <i>Brachypodium</i> best BLAST hit reduced this problem. Further, assigning quantitation to the best <i>Brachypodium</i> locus yielded promising results enabling integration with existing downstream data mining and functional analysis tools. Our study demonstrated viable approaches for quantitative proteomics analysis of bread wheat samples and shows how these approaches could be similarly adopted for analysis of other organisms with unsequenced or incompletely sequenced genomes

Coverage and Consistency: Bioinformatics Aspects of the Analysis of Multirun iTRAQ Experiments with Wheat Leaves

The hexaploid genome of bread wheat (<i>Triticum aestivum</i>) is large (17 Gb) and repetitive, and this has delayed full sequencing and annotation of the genome, which is a prerequisite for effective quantitative proteomics analysis. Aware of these constraints we investigated the most effective approaches for shotgun proteomic analyses of bread wheat that would support large-scale quantitative comparisons using iTRAQ reagents. We used a data set that was generated by two-dimensional LC–MS of iTRAQ labeled peptides from wheat leaves. The main items considered in this study were the choice of sequence database for matching LC–MS data, the consistency of identification when multiple LC–MS runs were acquired, and the options for downstream functional analysis to generate useful insight. For peptide identification we examined the extensive NCBInr plant database, a smaller composite cereals database, the <i>Brachypodium distachyon</i> model plant genome, the EST-based SuperWheat database, as well as the genome sequence from the recently sequenced D-genome progenitor <i>Aegilops tauschii.</i> While the most spectra were assigned by using the SuperWheat database, this extremely large database could not be readily manipulated for the robust protein grouping that is required for large-scale, multirun quantitative experiments. We demonstrated a pragmatic alternative of using the composite cereals database for peptide spectra matching. The stochastic aspect of protein grouping across LC–MS runs was investigated using the smaller composite cereals database where we found that attaching the <i>Brachypodium</i> best BLAST hit reduced this problem. Further, assigning quantitation to the best <i>Brachypodium</i> locus yielded promising results enabling integration with existing downstream data mining and functional analysis tools. Our study demonstrated viable approaches for quantitative proteomics analysis of bread wheat samples and shows how these approaches could be similarly adopted for analysis of other organisms with unsequenced or incompletely sequenced genomes

Shotgun Proteomic Analysis of Long-distance Drought Signaling in Rice Roots

Rice (<i>Oryza sativa</i> L. cv. IR64) was grown in split-root systems to analyze long-distance drought signaling within root systems. This in turn underpins how root systems in heterogeneous soils adapt to drought. The approach was to compare four root tissues: (1) fully watered; (2) fully droughted and split-root systems where (3) one-half was watered and (4) the other half was droughted. This was specifically aimed at identifying how droughted root tissues altered the proteome of adjacent wet roots by hormone signals and how wet roots reciprocally affected dry roots hydraulically. Quantitative label-free shotgun proteomic analysis of four different root tissues resulted in identification of 1487 nonredundant proteins, with nearly 900 proteins present in triplicate in each treatment. Drought caused surprising changes in expression, most notably in partially droughted roots where 38% of proteins were altered in level compared to adjacent watered roots. Specific functional groups changed consistently in drought. Pathogenesis-related proteins were generally up-regulated in response to drought and heat-shock proteins were totally absent in roots of fully watered plants. Proteins involved in transport and oxidation–reduction reactions were also highly dependent upon drought signals, with the former largely absent in roots receiving a drought signal while oxidation–reduction proteins were strongly present during drought. Finally, two functionally contrasting protein families were compared to validate our approach, showing that nine tubulins were strongly reduced in droughted roots while six chitinases were up-regulated, even when the signal arrived remotely from adjacent droughted roots

Shotgun Proteomic Analysis of Long-distance Drought Signaling in Rice Roots

Rice (<i>Oryza sativa</i> L. cv. IR64) was grown in split-root systems to analyze long-distance drought signaling within root systems. This in turn underpins how root systems in heterogeneous soils adapt to drought. The approach was to compare four root tissues: (1) fully watered; (2) fully droughted and split-root systems where (3) one-half was watered and (4) the other half was droughted. This was specifically aimed at identifying how droughted root tissues altered the proteome of adjacent wet roots by hormone signals and how wet roots reciprocally affected dry roots hydraulically. Quantitative label-free shotgun proteomic analysis of four different root tissues resulted in identification of 1487 nonredundant proteins, with nearly 900 proteins present in triplicate in each treatment. Drought caused surprising changes in expression, most notably in partially droughted roots where 38% of proteins were altered in level compared to adjacent watered roots. Specific functional groups changed consistently in drought. Pathogenesis-related proteins were generally up-regulated in response to drought and heat-shock proteins were totally absent in roots of fully watered plants. Proteins involved in transport and oxidation–reduction reactions were also highly dependent upon drought signals, with the former largely absent in roots receiving a drought signal while oxidation–reduction proteins were strongly present during drought. Finally, two functionally contrasting protein families were compared to validate our approach, showing that nine tubulins were strongly reduced in droughted roots while six chitinases were up-regulated, even when the signal arrived remotely from adjacent droughted roots

Combining Protein Ratio <i>p</i>‑Values as a Pragmatic Approach to the Analysis of Multirun iTRAQ Experiments

iTRAQ labeling of peptides is widely used for quantitative comparison of biological samples using mass spectrometry. However, iTRAQ determined protein ratios have varying credibility depending on the number and quality of the peptide ratios used to generate them, and accounting for this becomes problematic particularly in the multirun scenario needed for larger scale biological studies. One approach to this problem relies on the use of sophisticated statistical global models using <i>peptide</i> ratios rather than working directly with the <i>protein</i> ratios, but these yield complex models whose solution relies on computational approaches such as stage-wise regression, which are nontrivial to run and verify. Here we evaluate an alternative pragmatic approach to finding differentially expressed proteins based on combining protein ratio <i>p</i>-values across experiments in a fashion similar to running a meta-analysis across different iTRAQ runs. Our approach uses the well-established Stouffer’s Z-transform for combining <i>p</i>-values, alongside a ratio trend consistency measure, which we introduce. We evaluate this method with data from two iTRAQ experiments using plant and animal models. We show that in the specific context of iTRAQ data analysis this method has advantages of simplicity, high tolerance of run variability, low false discovery rate, and emphasis on proteins identified with high confidence

Manipulating Root Water Supply Elicits Major Shifts in the Shoot Proteome

Substantial reductions in yield caused by drought stress can occur when parts of the root system experience water deficit even though other parts have sufficient access to soil water. To identify proteins associated to drought signaling, rice (<i>Oryza sativa</i> L. cv. IR64.) plants were transplanted into plastic pots with an internal wall dividing each pot into two equal compartments, allowing for equal distribution of soil and the root system between these compartments. The following treatments were applied: either both compartments were watered daily (“wet” roots), or water was withheld from both compartments (“dry” roots), or water was withheld from only one of the two compartments in each pot (“wet” and “dry” roots). The substantial differences in physiological parameters of different growth conditions were accompanied by differential changes in protein abundances. Label-free quantitative shotgun proteomics have resulted in identification of 1383 reproducible proteins across all three conditions. Differentially expressed proteins were categorized within 17 functional groups. The patterns observed were interesting in that in some categories such as protein metabolism and oxidation–reduction, substantial numbers of proteins were most abundant when leaves were receiving signals from “wet” and “dry” roots. In yet other categories such as transport, several key transporters were surprisingly abundant in leaves supported by partially or completely droughted root systems, especially plasma membrane and vacuolar transporters. Stress-related proteins behaved very consistently by increasing in droughted plants but notably some proteins were most abundant when roots of the same plant were growing in both wet and dry soils. Changes in carbohydrate-processing proteins were consistent with the passive accumulation of soluble sugars in shoots under drought, with hydrolysis of sucrose and starch synthesis both enhanced. These results suggest that drought signals are complex interactions and not simply the additive effect of water supply to the roots