# Proteomics in Allopolyploid Crops: Challenges and Prospects

Tanushree Halder, Roopali Bhoite, Shahidul Islam, Guijun Nurealam Yan, Md. Nurealam Siddiqui, Md. Omar Kayess, Kadambot H.M. Siddique

<figure><img src="image_1.png" /></figure>

## To cite this version:

Tanushree Halder, Roopali Bhoite, Shahidul Islam, Guijun Nurealam Yan, Md. Nurealam Siddiqui, et al.. Proteomics in Allopolyploid Crops: Challenges and Prospects. 2025. hal-05334628

HAL Id: hal-05334628

https://hal.inrae.fr/hal-05334628v1

Preprint submitted on 28 Oct 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# Proteomics in Allopolyploid Crops: Challenges and Prospects

Tanushree Halder *, Roopali Bhoite, Shahidul Islam, Guijun Yan, Md. Nurealam Siddiqui, Md. Omar Kayess, Kadambot H.M. Siddique

Posted Date: 20 October 2025

doi: 10.20944/preprints202510.1553.v1

Keywords: wheat; cotton; oilseed brassica; climate-resilient; disease; breeding; yield; quality

<figure><img src="image_3.png" /></figure>
Review

# Proteomics in Allopolyploid Crops: Challenges and Prospects

Tanushree Halder ¹,²*, Roopali Bhoite ³,², Shahidul Islam ⁴, Guijun Yan ⁵, Md. Nurealam Siddiqui ⁶,⁷, Md. Omar Kayess ⁸ and Kadambot H. M. Siddique ²

¹ Department of Genetics and Plant Breeding, Sher-e-Bangla Agricultural University, Dhaka 1207, Bangladesh

² The UWA Institute of Agriculture, The University of Western Australia, Crawley, 35 Stirling Highway, Crawley, WA 6009, Australia

³ Grains Genetic Improvement, Department of Primary Industries and Regional Development, 3 Baron-Hay Ct, South Perth, Western Australia 6151, Australia

⁴ Department of Plant Sciences, North Dakota State University, Loftsgard Hall 216, NDSU Fargo, ND 658102, USA

⁵ UWA School of Agriculture and Environment, The University of Western Australia, 35 Stirling Highway, Crawley, WA 6009, Australia

⁶ Department of Biochemistry and Molecular Biology, Gazipur Agricultural University, Gazipur 1706, Bangladesh

⁷ IPSiM, University of Montpellier, INRAE, CNRS, Institute Agro, Montpellier, 34060, France.

⁸ Department of Genetics and Plant Breeding, Hajee Mohammad Danesh Science and Technology University, Dinajpur-5200, Bangladesh

* Correspondence: halder.tanushree.gepb@sau.edu.bd

## Abstract

Polyploid crops such as wheat, Brassica and cotton are critical in global agricultural and economic system. However, their productivity is threatened increasingly by biotic stresses such as disease, and abiotic stresses such as heat, both exacerbated by climate change. Understanding the molecular basis of stress responses in these crops is crucial but remains challenging due to their complex genetic makeup—characterized by large sizes, multiple genomes and limited annotation resources. Proteomics is a powerful approach to elucidate molecular mechanisms, enabling the identification of stress-responsive proteins, cellular localization, physiological, biochemical and metabolic pathways, protein-protein interactions, and post-translational modifications. It also sheds light on the evolutionary consequences of genome duplication and hybridization. Breeders can improve stress tolerance and yield traits by characterizing the proteome of polyploid crops. Functional and subcellular proteomics, and the identification and introgression of stress-responsive protein biomarkers, are promising for crop improvement. Nonetheless, several challenges remain, including inefficient protein extraction method, limited organelle-specific data, insufficient protein annotations, low proteoform coverage, reproducibility and lack of target-specific antibodies. This review explores the genomic complexity of three key allopolyploid crops—wheat, oilseed Brassica, and cotton—summarizes recent proteomic insights into heat stress and pathogen response and discusses current challenges and future directions for advancing proteomics in polyploid crop improvement through proteomics.

**Keywords:** wheat; cotton; oilseed brassica; climate-resilient; disease; breeding; yield; quality

## 1. Introduction

Polyploid crops—with multiple sets of chromosomes—play an important role in global agriculture. They are the key drivers of crop evolution and contribute to global food security [1].
Polyploids exhibit greater vigor and yield potential than their diploid relatives. Whether natural or induced, polyploidization enhances heterozygosity, reduces inbreeding depression and mitigates deleterious mutations. Polyploids are classified into two major types: autoploids, with duplicated identical genomes [2], and alloplloids formed by hybridization of more than two copies of divergent genomes [3].

Examples of autoploid crops include potato (Solanum tuberosum; 2n = 4x = 48 [4]) and sugarcane (Saccharum spp.; 2n = 62, 80, 96, 112, or 128 [5]). In contrast, alloplloids include wheat (Triticum aestivum; AABBDD; 2n = 6x = 42 [6]), Brassica species (Brassica spp.; 2n = 2x = 34 (BBCC), 36 (AABB) and 38 (AACC) [7]) and cotton (Gossypium hirsitum; AADD; 2n = 2x = 52 [8]). Other notable alloplloids include oat (Avena sativa; AACCDD; 2n = 6x = 42), different millets (e.g., finger millet, porso millete, fonio millet, and Indian- and Japanese-barnyard millet) and quinoa (Chenopodium quinoa; AABB; 2n = 4x = 36) [8]. Due to their dietary, industrial, and economic value, this review focuses on three globally significant allopolyploid crops—wheat, oilseed Brassica, and cotton.

Wheat (Triticum aestivum) is the most widely grown cereal crop, critical for food, animal feed and trade. In 2023–2024, global wheat production was estimated 788 million tons (MT), with 209.6 MT traded [9]. Despite this, production has declined while demand continues to rise, highlighting the urgent need for yield improvement.

Oilseed Brassica, a member of the Brassicaceae family, includes 4,000 of species, many of which are cultivated for edible oil [10]. Among them, B. napus (rapeseed/canola), B. campestris, and B. juncea are the most important [11]. However, B. napus is widely grown globally due to its low erucic acid and glucosinolate levels and high oleic and linolenic acid content. In contrast, B. campestris and B. juncea, which have higher erucic acid, are more regionally cultivated—B. campestris in temperate countries such as Australia, Germany, Sweden, France and Canada, and B. juncea in Asia [12]. Global canola production reached 89.39 MT in 2023–2024, with a projected decline for 2024–2025 [13].

Cotton has industrial value in textiles, paper, oil, chipboard and livestock feed industries [14]. It is valued at around $600 billion to the global textile industry economy [15], with an estimated production of 24.12 MT in 2023–2024 [16].

The demand for these crops is rising due to global population growth—projected to increase by 25% by 2050 [17]. Wheat production must double by 2050 [18,19], cotton production must reach 28.1 MT by 2032 [20], and canola production in Canada is projected to increase by 3 MT by 2030 [21]. However, achieving these targets is hampered by biotic (e.g., pathogens and pests) and abiotic (e.g., heat, drought and salinity) stresses, which are becoming more severe with climate change. For instance, drought can reduce yields by 32% in wheat [22], ~60% in canola [23], and 31% in cotton [24]. A 1°C increase above 15 °C can reduce global wheat yields by 6% [25], and a similar increase above 30 °C can reduce canola and cotton yields by 7% [26] and 6.1% [27], respectively. These stresses disrupt plant phenotypes, physiology, biochemical, and metabolism, ultimately reducing yield and product quality.

For example, an individual heat stress (HS) and combined HS and drought stresses reduce canola yield and oil concentration significantly [28], and impair cotton ball development [29]. In wheat, HS stress at the reproductive stage can cause pollen malformation and male sterility, [30–32] and a combined HS and drought stresses can cause mitotic and meiotic arrest, ovule abortion and reduce stigma receptivity [33], drastically reducing grain number and quality. Biotic stresses are equally harmful, with powdery mildew in wheat causing up to 33% yield loss [34], and clubroot disease in canola [35] and cotton leafroll dwarf virus [36] causing up to 100% yield loss in severe cases.

Understanding the molecular basis of stress responses is essential to combat these threats. During stress, plant nucleus receives signals to alter gene expressions—exportation of mRNA from nucleus to cytoplasm and biosynthesis of stress-induced novel proteins [37–39]. To acclimatize stress, plant phenotype changes due to the biological function of the novel proteins. As biotic and abiotic stress response, the biological function of a protein is governed by proteoforms—the basic molecular forms of proteins produced from a single gene including genetic variants, alternative splice variants

<figure><img src="image_4.png" /></figure>
and post-translational modifications (PTMs) [40]—, their cellular localization, and protein-protein
and protein-other non-protein compound's interactions [38,39]. Therefore, while genomics,
transcriptomics, and phenomics have been useful to study changes in DNA, RNA [41], and
phenotypes [42], respectively, proteomics provides more direct insights into stress adaptation
mechanisms [43]. Unlike transcriptomics, which may not correlate with actual protein levels [44]. For
example, transcriptomics techniques such as microarray and RNA-seq covers 48–57% of phenotypic
variation and has moderate correlation with mRNA and protein (identified by label-free proteomics)
expression [45]. Proteomics can identify proteins and proteoforms actively involved in stress-related
physiological, biochemical, and metabolic processes [46], and protein–protein interactions [47].
Proteomics is also a valuable tool for understanding the consequences of polyploidization [48].

Several proteomic tools are popularly used in polyploid crops to get stress-responsive molecular insights (Figure 1). Mass spectrometry (MS) is the most extensively used tool in proteomics since its discovery in 1913 [49]. Techniques such as 2-dimensional polyacrylamide gel electrophoresis-MS (2DE-PAGE/MS) and its improved version, 2D-DIGE (2-dimensional difference gel electrophoresis), have evolved into advanced methods such as quadrupole (Q), time-of-flight (TOF), orbitrap, matrix-assisted laser desorption/ionization couple with TOF (MALDI-TOF), and shotgun proteomics [43,48]. Shotgun methods such as label-based method, isobaric tags for relative and absolute quantitation (iTRAQ), and label-free quantitative proteomics are widely used. While label-free approaches offer precise quantification of all peptides of a sample, they are data-dependent acquisition (DDA) method and therefore, often fail to identify extensive low abundant protein or peptide [50]. To overcome the challenge, sequential window acquisition of all theoretical fragment ion spectra mass spectrometry (SWATH-MS), a data-independent acquisition (DIA) method, is being applied in crops like, including wheat [51,52] and cotton [53].

Despite these advances, proteomic research in polyploid crops remains challenging due to
genome complexity, inefficient protein extraction methods, lack of organelle-specific databases,
insufficient protein annotations, and limited protein validation [43,54–56]. The genomic complexity
of allopolyploids arises from the genomic shocks due to hybridization causing genome
rearrangement and altered gene expression than their diploid parents [57]. Compared to diploids,
the proteomic data interpretation in the polyploids can be challenged by the “homeologs collapse” —
wrong assembly of two homeologs into a single gene [58]—and “homeologe expression bias” —
unequal expressions between homeologs caused by the duplicated gene actions [59]. For the first
time, identification of “homeologe expression bias” in the protein data between the A and B
subgenomes in *Arachis hypogaea* provides insights into the biological processes in the homeologous
and paralogous protein expression [60].

However, recent advancement in genome sequencing, functional and subcellular proteomics
improved extraction methods, and identification of stress-related protein markers highlight the
growing potential of proteomics for enhancing stress resistance in polyploid crops. Though
proteomics identifies the gene response for environmental stimuli, multi-omics—integration of other
omics such as genomics, transcriptomics, epigenomics and metabolomics with proteomics—could
provide more comprehensive understanding stress tolerance in polyploids than the single approach
[61].

<figure><img src="image_5.png" /></figure>
<figure><img src="image_6.png" /><figcaption>**Figure 1.** Proteomic approaches used in allopolyploid crops: Gel-based (bottom left) and shotgun (bottom right). The gel photos were modified from Gris and Baldoni (2013) and Nadeem et al. (2016). Crops were generated from BioRender (https://app.biorender.com/); 1-DE and 2-DE = one- and two-dimensional gel electrophoresis; MALDI-TOF = matrix assisted laser desorption ionization - time of flight; MS = Mass spectrophotometry; LC = liquid chromatography.</figcaption></figure>

This review highlights the genomic complexity of polyploid crops—wheat, oilseed *Brassica* and cotton—progress of their proteomics for HS and biotic stress (disease) focusing on comparative proteomics, challenges of proteomics in identifying functional proteins and proteoforms of polyploids, and the prospects of proteomics, particularly subcellular proteomics, in polyploid breeding.

## 2. Genome Complexity of Polyploidy

### 2.1. Wheat

Bread wheat (*T. aestivum*; AABBDD; 2n = 6x = 42), a hexaploidy species in the Poaceae family, is one of the world's most important staple cereal crops. It evolved naturally through two successive hybridization events. First, a diploid wheat (*T. urartu*; AA; 2n = 2x = 14) crossed with a wild goat grass (*Aegilops speltoides*; BB; 2n = 2x = 14), producing a tetraploid wild emmer wheat (*T. turgidum* spp. *diccoides*; AABB; 2n = 4x = 28). This tetraploid then hybridized with another goat grass (*A. tauschii*; DD; 2n = 2x = 14), resulting in modern hexaploidy bread wheat (**Figure 2a**) [6,62]. The estimated genome sizes of hexaploid, tetraploid and diploid wheat are approximately 17 Gb [63], 10.1 Gb [64] and 4.94 Gb [65], respectively.

### 2.2. Oilseed Brassica Crops

The genus *Brassica*, within the Brassicaceae family, includes economically important species cultivated for oilseeds, vegetables and condiments [66]. The *Brassica* genus has three diploid species – *B. nigra* (BB; 2n = 16), *B. oleracea* (CC; 2n = 18) and *B. rapa* (AA; 2n = 20). They evolved, by intercrossing into three amphidiploid species, *B. carinata* (BBCC; 2n = 34), *B. juncea* (AABB; 2n = 36) and *B. napus* (AACC; 2n = 38) (**Figure 2b**) [7]. Among the three diploids, *B. rapa* is the oldest and most widely distributed [67], with a genome size ranging from ~389.2–424.59 Mb [68,69]. The genome sizes of *B. oleracea* and *B. nigra* are estimated at ~587.7–630.7 Mb [70,71] and ~446.5–534.2 Mb [72,73],
respectively. The genome sizes of the amhidiploids are approximately 922–961.72 Mb for *B. juncea* [74,75], 921.5–975.8 Mb for *B. acarinata* [76].

<figure><img src="image_7.png" /><figcaption>**Figure 2.** Evolution and genome structure of three major allopolyploid crops: (a) wheat (*Triticum aestivum* L.), (b) oilseed *Brassica* crops, and (c) cotton (*Gossypium hirsutum* and *G. barbadense*); adapted from [77]).</figcaption></figure>

### 2.3. Cotton

Cotton belonging to the genus *Gossypium* and Malvaceae family has been cultivated for more than 7000 years [78] for fibres, medicine and animal feed [79]. Although there are around 50 species, only four are widely cultivated: two American species—*G. hirsutum* (A1A1D1D1; 2n = 4x = 52) and *G. barbadense* (A2A2D2D2; 2n = 4x = 52)—and two Asian and African species—*G. arboreum* (AA; 2n = 2x = 26) and *G. herbaceum* (A1A1; 2n = 2x = 26) (**Figure 2c**) [77,79–81]. Forty-three *Gossypium* species are diploid (n = 13), classified into genome groups A–G and K while seven are tetraploid (AADD) [82,83]. The A-genome originated in Africa and the D-genome in Mexico [82]. Allotetraploid or amphidiploid cottons species—*G. hirsutum* (2,305.24 Mbp), *G. barbadense* (2,195.8 Mbp), *G. tomentosum* (2,193.56 Mbp), *G. mustelinum* (2,315.09 Mbp), *G. darwinii* (2,182.96 Mbp) [84], *G. ekmanianum* (2,341.87 Mbp), and *G. stephensii* (2,291.84 Mbp) [85]—are the result of polyploidization between *G. arboreum* (A genome; 1,746 Mb; [86] or *G. herbaceum* (1,667 Mb; [87])) and *G. raimondii* (D genome; 885 Mb; [88–90] 2015). Among these cotton species, *G. hirsutum* is widely cultivated species globally [91].

## 3. Proteomic Studies on Heat Stress Tolerance in Polyploid Crops

Heat stress significantly impairs crop growth, development, yield, grain, seed or fibre quality. Although HS can adversely affect plants’ all developmental stages, the anthesis and post-anthesis phases are particularly sensitive in crops like wheat, oilseed brassica, and cotton. Protein—functional gene expression products—play key roles in a plant’s HS response by regulating various metabolic pathways. For example, HS increases biomolecule kinetics resulting protein degradation or

<figure><img src="image_8.png" /></figure>
misfolding risk; protective proteins or proteoforms accumulate to reduce the harmful effect of HS on the cells [38]. Different HS-induced proteins, particularly heat shock proteins (HSPs), and proteoforms are strongly associated with HS tolerance across different developmental stages. This section discusses how HS affects crops, and the molecular mechanisms of the HS response revealed by proteomics approaches.

### 3.1. Wheat

In wheat, HS adversely impacts every stage, from seedling emergence to maturity, ultimately reducing grain yield and quality [92]. Terminal HS—occurring during the reproductive stage—is detrimental to yield [93]. Heat stress during tillering disrupts pollen viability, spike formation, seed set, and embryo development [94,95]. Post anthesis HS inhibits the translocation of photosynthesates and starch accumulation into grains and reduces stem carbohydrate [95]. It also leads to increased reactive oxygen species (ROS), osmolyte accumulation, decreased RuBisCo activity, and degradation of seed proteins[96]. However, the accumulation of HSPs, antioxidant enzymes (e.g., catalase (CAT), peroxidase (H₂O₂), superoxide dismutase (SOD) and glutathione (GSH)) and proline, along with the suppression of anabolic and catabolic process, are critical for HS tolerance [43,95]. Numerous proteomic studies in recent years have examined HS responses at different growth stages in wheat (Table 1). Our previous review summarized recent progress in wheat proteomics under HS [43].

<figure><img src="image_9.png" /></figure>
<table><caption>**Table 1.** Summary of proteomic studies on heat stress tolerance in wheat, cotton and oilseed *Brassica* during the last five years (2019–2025).</caption><thead><tr><th>Crop</th><th>Species</th><th>Genotypes / varieties/ lines</th><th>Growth stages</th><th>Treatment condition</th><th>Tissue/ organ</th><th>Techniques</th><th>Validati on status</th><th>Total no. of DAPs/ protein groups</th><th>Unique DAPs/pe ptides</th><th>Common proteins</th><th>Summary</th><th>Key genes/proteins /Enzymes</th><th>Reference</th></tr></thead><tbody><tr><td>Wheat</td><td><i>Triticum aestivum</i> L.</td><td>Zhoumai 36</td><td>Tetrad stage, binuclear stage, and trinuclear stage</td><td>Field condition</td><td>Anther</td><td>TMT; Peptide identification by nano UPLC-MS/MS</td><td>qRT-PCR</td><td>2532 DAPs</td><td>Twenty-seven, 157, and 2348 DAPs in tetrad, binuclear, and trinuclear stages, respectively</td><td>Under HS, in the tetrad, binuclear and trinuclear stages 27, 157 and 2348 DAPs were identified, respectively. HS-disrupts different signaling pathways, including MAPK signaling pathway, phosphatidylinositol biosynthesis pathway, starch and sucrose synthesis pathway and flavinoid biosynthesis pathway, due to downregulation of related proteins resulting male sterility.</td><td><i>TraesCS4B02G1</i><br>93500.2<br>(thermosensiti ve male sterile 1),<br><i>TraesCS7A02G</i><br>272400.3,<br><i>TraesCS4B02G1</i><br>93500.2<br>(thermosensiti ve male sterile 1)</td><td>[30]</td></tr></tbody></table>
<table><tr><td>KX3302<br>(high<br>gluten)<br>and BN<br>208<br>(medium<br>gluten)</td><td>Maturit<br>y</td><td>Steam at<br>100–120<br>°C</td><td>Wheat<br>flour</td><td>4D label-free</td><td>3783<br>proteins<br>(2124<br>DAPs)</td><td>2861</td><td>Storage protein in wheat<br>grain is associated with<br>celiac disease, an<br>autoimmune disease.<br>Wheat genotypes with<br>high- and medium-gluten<br>have α-gliadin and<br>HMW-GSs, and γ-gliadin<br>and LMW-GSs as main<br>DAPs related to the<br>disease, respectively.<br>Heat stress during dough<br>stage reduces the<br>abundance of these<br>proteins in both<br>genotypes resulting in<br>improved diseases.</td><td>N/A</td><td>[97]</td></tr><tr><td>Xinchun 9</td><td>Maturit<br>y</td><td>38 °C for<br>1, 2, 8,<br>and 24 h.</td><td>Spike</td><td>Data<br>Independent<br>Acquisition<br>(DIA)</td><td>qRT-<br>PCR</td><td>78,885<br>peptides</td><td>19,503<br>peptides</td><td>Proteins associated with<br>proline synthesis, ABA<br>signaling, HSPs, and<br>MADS TFs play an<br>important role in<br>thermotolerance in wheat.</td><td>TraesCS7D02G<br>483200</td><td>[51]</td></tr><tr><td>Tainong 18</td><td>Maturit<br>y</td><td>Field<br>conditio<br>n</td><td>Leaf</td><td>iTRAQ</td><td></td><td>4411 (139<br>DAPs)</td><td></td><td>Grain weight and grain<br>yield increased due to<br>delayed sowing due to<br>improved electron<br>transport in</td><td></td><td>[98]</td></tr></table>
BWL4444 Anthesi 35/24 °C Grain 2D-PAGE; qRT- 153 DAPs
(HD2967+ s MALDI- PCR
Yr10; TOF/MS
tolerant)

photosynthetic channel
by upregulation of
proteins-related to
photosynthesis,
antioxidant activities and
stress. Delayed sowing
increases photosynthetic
electron transport
associated with proteins
such as PsbH, PsbR, and
PetB under HS, which
supports high
photosynthesis to
increase HS tolerance.

Day-night HS suppresses stress tolerance gene expression due to activation of multiple pathways for proteins associated with protein translation, gliadins, low- and high-molecular weight glutenin, glycolysis, photosynthesis and defense.

Downregulation of proteins associated with photosynthesis,

[99]
<table><tr><td>HD3086<br>(tolerant)<br>and BT-<br>Schombur<br>gk<br>(sensitive)</td><td>Floweri<br>ng and<br>grain<br>develop<br>ment<br>stages</td><td>32 °C and<br>38 °C for<br>2 h</td><td>Leaf<br>2D-PAGE;<br>MALDI-<br>TOF/MS</td><td>N/A</td><td>Approxim<br>ately<br>23,000<br>DAPs</td><td>Twenty-<br>two and<br>11<br>unique<br>DAPs in<br>tolerant<br>and<br>sensitive<br>varieties,<br>respectiv<br>ely</td><td>Twenty</td><td>glycolysis, metabolic<br>pathways and improper<br>protein folding increase<br>HS-sensitivity and<br>decreases grain weight,<br>yield and quality.<br>Mitogen-activated protein<br>kinase (MAPK) positively<br>increases HS tolerance by<br>increasing proline, H2O2,<br>gene expression of<br>antioxidant enzymes,<br>defence and osmolytes<br>and HSPs. MAPK also<br>regulates grain quality<br>under HS.</td><td>MAPK genes [100]</td></tr><tr><td>Chinese<br>Spring</td><td>Grain<br>filling</td><td>30 °C<br>and 40<br>°C</td><td>Grains<br>Tandem<br>mass tags<br>(TMT)</td><td>N/A</td><td>3742 (297<br>and 461<br>DAPs at<br>30 and 40<br>°C,<br>respectivel<br>y)</td><td></td><td>150</td><td>Posttranscriptional<br>regulation plays<br>important role in mild HS<br>(30 °C), while amino acid<br>frequency is important for<br>protein expression during<br>severe HS (40 °C). Under<br>HS, higher frequency of<br>AAG codon was found to<br>alter HS-induced protein<br>expression. Further,</td><td>[101]</td></tr></table>
Unnat
Halna

Seedling
38 °C for
4 h

Leaf
2-DE

HD2985
(tolerant)
and
HD2329
(sensitive)

Pollinat
on and
grain
filling

38 °C for
2 h

Leaf,
stem,
and
spike

iTRAQ

Vacuu
m-assisted
Agrobact
erium
transfor
mation
method

55 DAPs
(10 and 29,
upregulate
d and
downregu
lated,
respectivel
y)

qRT-PCR

318
groups

17 DAPs
associate
d with
stress
associate
d active
proteins

9,425
(3,600
and 5825
upregula
ted and
downreg
ulated,
respectiv
ely)

though ribosomal protein
influenced HS induced
proteins, but no
transcriptional regulation
was evident under 30 °C.

Heat stress induced 2-cysteine peroxiredoxin (2CP) increases HS-tolerance by increasing chlorophyll a and b content and decreasing H₂O₂ and ROX concentrations. The 2CP in wheat also interacts with protochlorophyllide reductase b, TaPORB, and play important role in chlorophyll biosynthesis.

Heat shock proteins, including HSP20, HSP26, and HSC70, signaling molecules including MAPKs and CDPKs, and antioxidant enzymes, including SOD, CAT, and APX, play an important role in HS-tolerance. On the other hand,

Ta2CP

[102]

Carboxylase
enzyme

[103]
<table><tr><td>Gaocheng 8901</td><td>Flowering to maturity</td><td>40 °C for 2 h</td><td>Milled grain</td><td>iTRAQ; LC-ESI/MS</td><td>qRT-PCR; immunobloting</td><td>207 DAPs</td><td></td><td></td></tr><tr><td>Cotton Gossypium hirsutum</td><td>GH-Hamaliya (tolerant) and GH-Hamaliya (susceptible)</td><td>Flowering</td><td>45/32 °C</td><td>Leaf Label-free</td><td>N/A</td><td>8005</td><td>494</td><td>13</td></tr></table>

[104]

[105]
<table><tr><td>TM-1 and W0</td><td>Seedling</td><td>42 °C for 24 h</td><td>Leaf</td><td>SDS-PAGE</td><td>Yeast two-hybrid assay for protein-protein interaction</td><td>2426</td><td>728</td><td>Three</td><td>Reduction of expression of GhHSP24.7 plays an important role in heat and drought stress tolerance through increasing ROS scavenging, decreasing acetylation, stomatal conductance and H2O2 content.</td><td>GhHSP24.7</td><td>[106]</td></tr><tr><td>Sicot 71</td><td>Reproductive</td><td>40/30 °C for five days</td><td>Leaf</td><td>SWATH-MS</td><td></td><td>880</td><td>728</td><td>Two</td><td>In mature pollen, HS suppresses translational processes by disturbing metabolic processes, such as starch biosynthesis, glycogen catabolism, and</td><td></td><td>[53]</td></tr></table>
<table><tr><td>Gossypium hirsutum</td><td>Sicot 71</td><td>Tetrads and binuclea te stage</td><td>36/25 °C and 40/30 °C for five days</td><td>Pollen grain</td><td>Label-free</td><td>876</td><td>106 DAPs</td></tr></table>

xyloglucon processes.
However, transcription remains active. In all pollen development stages, HSPs and peptidylprolyl isomerase were common.

Heat stress reduces the reproductive capacity of pollen grain of cotton decreases by decreasing the pollen grain size at the tetrad stage, increasing sugar percentage in the pollen grain and decreasing pollen viability.

Upregulation of HSPs, uncluding, HSP70 kDa, proteins related to starch and sucrose metabolism, and downregulation of late embryogenesis abundant proteins contribute to the HS tolerance in cotton's pollen.

[107]

<figure><img src="image_10.png" /></figure>
<table><tr><td>Gossypium hirsutum</td><td>Sikang 3</td><td>Bolling stage</td><td>38 °C and 40% field capacity</td><td>Boll shell</td><td>2D-PAGE; label-free quantitation</td><td>3,094 and 3,049</td><td>2,927</td><td>Under drought and HS, Bt gene expression in cotton caused increased starch hydrolysis ability and decreased fructose and glucose content. Proteins associated with protein export pathways, particularly signal recognition particles, play a crucial role in restricting the transportation of nascent peptide chain into the endoplasmic reticulum due to HS, resulting in decrease of Bt-insecticidal protein in cotton.</td><td>[108]</td></tr><tr><td>Gossypium robinsonii</td><td>Australian wild cotton</td><td>Pollen development stages (tetrad, uninucleate and binucleate)</td><td>36/25 °C and 40/30 °C for five days</td><td>Pollen grain</td><td>Nano LC-MS/MS; IDA-DIA SWATH-MS</td><td>2704</td><td>422, 489 and 94 DAPs at 36 °C and 297, 157 and 61 DAPs at 40 °C in tetrad, uninucleate</td><td>Heat stress at the late stage of pollen development reduces translational response and protein abundance. Plasmodesma related proteins play an important role in protein transport and cell-cell communication at the</td><td>[109]</td></tr></table>
<table><tr><td>Brassic<br>a</td><td><i>Brassica juncea</i></td><td>Seedling</td><td>45 °C for 4 and 8 h</td><td>Leaf</td><td>MALDI-TOF-MS</td><td>22954 DAPs</td><td>81 DAPs</td><td>Differentially accumulated proteins: 77 at 4h, 81 at 8h, 74 at both 4 and 8h</td><td>Under 4h and 8h of HS, 119 and 81 DAPs, 532 and 570 peptide sequences were identified, respectively. During 8h HS, accumulation of HSP, including HSPs and HS-induced TF A-4a increased. On the other hand, during 4h, MuTL like-protein-1, Cytochrome P450 85A1, CDT1_C domain-containing protein and B3 domain containing proteins, putative calcium-transporting ATPase 11 and an uncharacterized protein accumulation increased, and ribosome inactive</td><td>[110]</td></tr></table>
<table><tr><td>Brassica juncea</td><td>BPR 543-2</td><td>37 °C, 3 h interval for five days</td><td>Pericarp</td><td>Label-free</td><td>1078</td><td>209 DEP</td><td>78</td><td>N/A</td><td>protein accumulation decreased.<br>Under HS and combination of salt-stress and HS, 367 and 209 DAPs, including HSPs, proteins related to cell division, protein disulfide isomerase, calcium binding proteins and protease, were highly accumulated, respectively.</td><td>[111]</td></tr><tr><td>Brassica campestris</td><td>WS-1</td><td>Reproductive stage</td><td>40/30 °C</td><td>Leaf</td><td>TMT</td><td>qRT-PCR</td><td>2806 DAPs</td><td>1787 DAPs</td><td>1022</td><td>Glucose transporter gene, BccrGLU1, improves HS-tolerance by increasing ROS scavenging, and reducing glutathione content and ratio of glutathione/oxidized glutathione.</td><td>[112]</td></tr></table>

N/A = not applicable; TMT = tandem mass tag, UPLC = ultra-performance liquid chromatography, MS = mass spectrometry, 4D = four dimensional, iTRAQ = isobaric tags for relative and absolute quantitation, 2-DE = two-dimensional gel electrophoresis, SDS-PAGE = sodium dodecyl sulfate-polyacrylamide gel electrophoresis, LC = liquid chromatography, ESI = electrospray ionization, SWATH = sequential window acquisition of all theoretical mass spectra, MALDI = matrix assisted laser desorption/ionization, TOF = time-of-flight, qRT-PCR = quantitative real-time reverse-transcription-polymerase chain reaction, DAPs = differentially abundant proteins; TF = transcription factors, and HS = heat stress.
Heat stress causes wheat seedling leaves to roll and turn yellow [113]. During early stress stages, H₂O₂ levels rise while peroxidase drops; at later stages, antioxidant enzymes like ascorbate peroxidase (APX), glutathione reductase (GR) and SOD increase. Cytoplasmic proteins such as 2-cysteine peroxiredoxin (*Ta2CP*) contribute to HS tolerance and chlorophyll metabolism. *Arabidopsis* with overexpressed *Ta2CP* had normal green leaves with high chlorophyll a and b content, low H₂O₂ and ROS leading to thermotolerance. A 2-DE and MALDI TOF/TOF proteomics identified that upregulation of photosynthesis associated proteins, including RuBisCO activase A and PEP carboxylase, and signal transduction pathway in wheat seedling contributes to HS tolerance by increasing CO₂ assimilation [114]. A small HSP gene, *TaHSP23.9* (a member of the HSP20/ACD family) significantly upregulated in wheat leaves and developing grain under HS at both the transcription (mRNA) and translation levels, making it a promising marker for HS tolerance [102]. Phosphorylation, a major PTMs, also play important roles in HS tolerance. Due to the amino acid substitution in *TaSG-D1* allele, *TaSG-D1*E286K gene undergoes phosphorylation and stabilizes the expression of *TraesCS5B02G380200* (TaPIF4), resulting improved HS tolerance in seedling and mature stages of *T. sphaerococcum* [115].

Heat stress disrupts photosynthesis and pollination leading to male sterility and reduced grain yield and quality in wheat. Sowing time plays a crucial role in mitigating HS effects. For example, delayed sowing during 2018–2019 resulted in 11.16% and 10.10% increases in grain weight and yield in wheat, respectively, due to higher photosynthetic rates in flag leaves [98]. This improvement was linked to increased energy flow into the electron transport system and upregulation of differentially abundant proteins (DAPs) involved in photosynthetic electron transport (e.g., PsbH and PsbR), the Calvin cycle, and chlorophyll biosynthesis. Conversely, terminal HS under standard sowing dates significantly reduces photosynthesis and starch accumulation, diminishing grain yield and quality, respectively (Kumar, Dubey et al. 2021). Combined 2D-PAGE and transcriptomic analyses revealed that mitogen-activated protein kinase (MAPK) contributes to terminal HS tolerance by promoting proline, starch, and H₂O₂ accumulation, enhancing granule integrity, positively regulating stress-related gene expression and amylolytic activities while suppressing photosynthesis-related genes. Moreover, MAPK improves oxidative stress tolerance through its positive correlations with SOD and HSP17. MAPK signaling also involved in HS-induced male sterility in wheat [30]. Downregulation of MAPK related genes (e.g., *TraesCS3A02G149800.1*) under HS negatively affects ROS scavenging and pollen development. Further, DAPs related with phenylpropanoid biosynthesis, and starch and sucrose metabolism cause anther indehiscence and starch deficiency in it. Excess ROS accumulation in anthers inhibits starch and sucrose synthesis through phenylpropanoid biosynthesis. Upregulation of the HS-pathway thermosensitive male sterile 1 gene, *TraesCS4B02G193500.2*, causes male sterility under HS.

Heat stress also affects spike differentiation in wheat [51]. A comparative transcriptomic and proteomic study across three spike differentiation stages (pistil and primordium differentiation, anther separation and tetrad formation) identified 210 HS-associated transcripts/proteins, including key genes in abscisic acid (ABA) and proline biosynthesis pathways. Downregulation of 9-cis-epoxy carotenoid dioxygenases and upregulation of zeaxanthin epoxidase and ascorbate oxidation modulate ABA biosynthesis. Similarly, ornithine aminotransferase and pyrroline-5-carboxylate reductase enhance proline accumulation via ornithine and glutamate pathways. Phosphorylation, a major PTM, regulates reproductive organ development and photosynthetic apparatus in leaves in wheat under HS [116]. A DDA LC-MS/MS identified 14 phosphoproteins under HS, which activated phosphatases and kinases and generated their phosphoforms. A protein kinase, *TraesCS6B01G377500.3*, was associated with upregulated S711 and S762 phosphosites. Downregulated splicing variant of S12, *TraesCS2D01G281200.1*, was found in wheat spike under HS. Serine or threonine dominated phosphosites—S3236 and T3238—maintained protein functions by preserving phosphorylation pool under HS. However, identification of proteins and proteoforms is recommended to understand the co-regulation of these phosphosites to combat HS.

<figure><img src="image_11.png" /></figure>
While recent proteomic studies have focused on heat response during various growth stages, earlier work concentrated on HS effects on grain quality. For example, proteomic study using 2-DE and MALDI-TOF showed that HS reduced grain weight by decreasing starch synthesis proteins such as, glucose-1-phosphate adenyl transferase, although catalase protected cells from H₂O₂ [117]. Heat stress also weakens dough properties due to reductions in glutenin and gliadins, and increase in HSPs, although sHSPs improve thermotolerance during grain filling stage [118].

Combined day-night temperatures HS conditions are more detrimental than high daytime temperatures alone with 9% more grain weight lost under these conditions due to increased proline and H₂O₂ levels in flag leaves [99]. Using 2D-PAGE and MALDI-TOF, 153 and 95 DAPs were identified in wheat grain under day and day-night HS conditions, respectively. Proteins associated with starch biosynthesis, cell wall synthesis, defense, and storage were altered—particularly downregulation of starch and cell wall-related proteins—resulting in poor grain development and premature senescence in both conditions. Day-night temperatures reduced thermotolerance, grain yield and quality by restricting the expression of transcription factors (TFs), genes associated with signaling pathway and abiotic stress, and function of protein folding machinery

Heat stress also affects flour quality and by altering protein abundance in wheat grain [104]. Using an iTRAQ, 116 and 91 up- and down-regulated DPEs were identified in Gaocheng 8901, respectively. A decreased starch accumulation and increased gluten, gliadin, globulin and albumin content resulted in a reduced thousand grain yield in wheat. Downregulation of HSP90, low-molecular-weight glutenin subunits (LMW-GSs) and starch branching enzyme IIb reduced both grain yield and quality. Five DAPs—elicitor responsive gene 3, brassinosteroid-insensitive 1, histone cell cycle regulator, chaperone protein and splicing arginine serine-rich 7—were also linked to protein-protein interactions affecting grain yield and quality.

Finally, HS reduces dough quality by modulating the abundance of storage proteins. Approximately 57.5% of high-molecular-weight glutenin subunits, LMW-GSs, α- and γ-gliadins, and avenin-like proteins were upregulated, while 79.3% were downregulated under HS [97].

Moreover, peroxidase, RuBisCo activase, sHSPs and photosynthetic electron transport are crucial for thermotolerance during seedling stage, while proteins related to antioxidants, MAPK, proline and H₂O₂ accumulation, and phosphofroms are vital for during grain filling stage. Globulin, gliadin and albumin are essential in determining grain quality under HS.

## 3.2. Oilseed Brassica Crops

Heat stress affects all the growth stages of the oilseed *Brassica*, but flowering stage is the most sensitive [119]. It reduces gametophyte fertility, pollen viability, germination and causes seedless pod resulting in a decreased thousand grain weight, reduced yield and poor oil quality [119,120]. Among the cultivated species, *B. napus* is more sensitive to HS than *B. juncea* and *B. campestris* [120]. Proteins, such as HSPs (e.g., HSP101, HSP70 and HSP17.6) [119], gucosinolates, including glucobrassican, glucocarphanin and glucoraphasatin, gluconapin, sinigrin, and progoitrin [121] and glyoxalase I [122] contribute to thermotolerance. Phytohormone signaling pathways also play critical role in HS-tolerance.

Although proteomic study on oilseed *Brassica* started earlier [123,124], proteomic study in HS on *Brassica* started only a decade ago. The first proteomic study on HS in *B. napus* found ascorbate peroxidase as a key protein for thermotolerance in seedlings using 2-DE [125]. Proteins related to energy and metabolism reduced growth under HS through consuming major nutritive reserves. Additionally, HS severely impaired seed germination [122]. However, elevated levels of glyoxalase-I enhanced seed thermotolerance by detoxifying methylglyoxal, maintaining redox balance, and increasing SOD activity.

Beyond canola, proteomic studies have also been conducted on other oilseed *Brassica* species. Over the last five years, advancement of proteomic techniques has accelerated progress in HS responses in oilseed *Brassica* (Table 1). Role of protease accumulation in dead pericarps in *B. juncea* was identified by label-free proteomics [126]. Dead organs such as seed coat and pericarp affect yield

<figure><img src="image_12.png" /></figure>
[127]. A combined proteomic and metabolomic study on *B. juncea* under HS found the high abundance of several proteins, including HSP70-5, HSP104, and multiple sHSPs, with proteases being the most abundant in the pericarp [126]. Heat primarily induced the accumulation of cysteine proteases, 25 HS-induced proteins and sugars on its dead pericarp.

Heat stress also disrupts biochemical and metabolic pathways in *B. juncea* [110]. Increased antioxidants and flavonoid, with minimal impacts on photosynthetic performance, contribute to thermotolerance in mustard seedling. Phenylalanine ammonia lyase and respiratory burst oxidase homolog protein-A improves thermotolerance by synthesizing metabolites (e.g., flavonoids and lignins) and producing ROS, respectively. A combined proteomics and peptidomics—a comprehensive analysis of all peptides of an organism [128])—in oilseed *Brassica* under HS for 4 and 8 h found 25 and 24 upregulated, and 41 and 23 downregulated DAPs and 532 and 570 peptide sequences, respectively. Key thermotolerance associated proteins included HSPs (21.7, 14.7 and 17.5 kDa HSP groups), sHSPs and TFs, including HS transcription factor A-4a, Myb family Atlg14600, and WRKY—related to signal transductions, regulating transcriptions, repairing DNA and processing RNA.

In *B. campestris* seedling, thermotolerance under both heat and cold stress involves several molecular pathways, including redox homeostasis, photosynthesis, chaperons, HSPs, carbohydrate metabolism, and signal transduction [112]. Under both stresses, 1022 DAPs (172 upregulated and 324 downregulated) and 1784 DAPs under HS were found using a combined TMT and LC-MS. The critical role of redox homeostasis in thermotolerance was validated through the reduced glutathione-to-oxidized glutathione ratio and decreased oxidative damage in transgenic *Arabidopsis* overexpressing *GLU1*, a gene encoding a redox-associated protein.

### 3.3. Cotton

Heat stress critically affects all the developmental stages of cotton, with the reproductive stage the most sensitive [129]. It impairs seed germination, shoot and root growth, and induces male sterility, ultimately reducing yield and fiber quality. Increased leaf temperature due to HS disrupts photosynthesis by inhibiting RuBisCO activation [130]. In *G. barbadense*, HS restricts photosynthesis by limiting the regeneration capacity of ribulose-1,5-bisphosphate and impeding electron transport [131]. Traits such as membrane thermostability, high proline and soluble sugar can improve thermotolerance in cotton [132]. Additionally, regulation of microRNAs, signaling pathways, including calcium, ROS, carbohydrate, transcription factors, phytohormones and gene regulation pathway, and HSPs play critical role in its thermotolerance [129]. Despite numerous studies on the physiological impacts of HS on cotton, proteomic studies remain limited. However, recent proteomic studies have provided valuable insights on thermotolerance in cotton (Table 1).

In the leaves of cotton seedlings, expression of the mitochondrial protein GhHSP24.7 increased HS-sensitivity due to increased ROS, mitochondrial acetylation activity and decreased ATP levels [107]. However, suppressed GhHSP24.7 enhanced thermotolerance by decreasing stomatal conductance and H₂O₂, activating GhHDA14, maintaining chloroplast structure and improving ROS scavenging.

The reproductive stage is particularly vulnerable, as HS disrupts pollen development at tetrad, uninucleate and binucleate stages, pollen maturation, pollination and embryo formation [133]. Pollen at the tetrad stage is most susceptible to damage at 38 °C, but recovery is possible through the upregulation protective proteins such as HSP (15.7 kDa) and peptidylprolyl isomerase [53]. However, under 40 °C at tetrad stage, mature pollen grain size and pollen tube reduced by 36% and 120 µm, respectively, resulting two-thirds of yield reduction [107]. Under these HS conditions, HSP70, and HSP70-90 organizing protein 3-like upregulated, while late embryogenesis abundant protein (LEA) 2 downregulated. HSP70, heat shock cognate 70 kDa protein 2-like, luminal-binding protein 5-like and luminal-binding protein 5-like protect pollen from HS through protein export, folding and processing pathways.

<figure><img src="image_13.png" /></figure>
A recent proteomic study identified over 8000 HS-induced DAPs at the flowering stage [105]. Increased abundance of β-glucosidase, different photosynthetic proteins, NDH subunit of subcomplex B1, NADH ubiquinone oxidoreductase (Complex I), stress responsive proteins, 1-aminocyclopropane-1-carboxylate oxidase and HSPs (e.g., Hsp70, Hsp90 and Hsp80) are crucial for thermotolerance. These proteins contribute to HS-tolerance by maintaining membrane and cell wall integrity, producing energy, maintaining osmotic balance, enhancing proline accumulation and ROS scavenging, and regulating electron transport in photosynthesis channels, carbohydrate metabolism and protein folding, and preventing protein aggregation.

Heat stress also reduces insecticidal proteins in *Bt* cotton's bolls [108]. Under a combined heat (38 °C) and drought (40% field capacity) stress, the insecticidal proteins decreased by 38.3 and 30.7 ng/g fresh weight of boll shell in 2026 and 2017, respectively. That reduction may be the result of the downregulation of signal recognition particles (SRPs) and SRP receptors, restriction of peptide chain translocation into the endoplasmic reticulum and increase of ubiquitin-mediated proteolysis.

Overall, proteins associated with photosynthesis, carbohydrate metabolism, stress response, ROS regulation, protein folding and aggregation and electron transport play pivotal roles in cotton growth and HS tolerance.

## 4. Proteomic studies for biotic stress

Biotic stress—caused by living organisms such as bacteria, fungi, viruses, and insects— significantly threatens on global food security [134]. Emerging pathogenic strains further intensify this challenge, continuously threatening crop productivity worldwide. Plant resistance to pathogens is a complex trait governed by a multifaceted interplay of morphological, genetic, biochemical, and molecular processes, with proteomic insights playing pivotal role [135–137]. Therefore, a comprehensive understanding of these mechanisms for essential to addressing biotic stress in complex plant genomes. Proteins are crucial in interpreting multiple signals derived from biotic stress and activating stress-associated defense responses [138]. Proteins and PTMs contribute directly to plant immunity, while subcellular protein localization provides key insights into how plants respond to biotic stress. Therefore, proteomics—a powerful and evolving analytical tool—offers substantial potential for developing disease resistance, particularly in polyploid crops [139,140].

### 4.1. Wheat

Wheat production is challenged by different biotic stresses, including fungi, virus, nematodes and aphids [141]. Among these, fungal pathogens such as rusts, mildew, and blights are the most destructive. Numerous proteomic studies have explored the mechanisms underlying wheat-pathogen interactions to understand both compatible and incompatible responses [142–146]. For example, a comprehensive proteomic revealed key virulence and host defense proteins—including PR1, PR4, and GST—associated with early resistance mechanisms against wheat rust [146]. In the wheat apoplast, increased level of protein-modifying enzymes, particularly serine proteases, along with other stress associated proteins mitigate infection-induced damage by *Magnaporthe oryzae* [147].

Another proteomic study found the upregulation of 4100 and 447 DAPs in wheat infected by *Tilletia controvversa* and *T. foetida*, respectively [148]. Antioxidant-related proteins were upregulated in both cases, while those involved in plant-pathogen interactions and glutathione metabolism were downregulated. However, thaumatin-like proteins and HSPs contributed to increase the resistance against these pathogens.

Separately, 366 DAPs for wheat infected by *Fusarium pseudograminearum* (causal agent of Fusarium crown rot) were identified using TMT tool [149]. Resistant cultivars exhibited significant enrichment of proteins linked to different cellular activities—glucoside, electron transport, cellulose synthase and oxidoreductase—metabolic processes, plant-pathogen interactions, and antioxidant activities [149,150]. In the resistant variety Xinong 538, unique proteins encoded by *Chitinase IV*, *Thaumatin-like 1*, *PR1.1*, and *PR1.2* genes were strongly associated with resistance to Fusarium head blight (FHB) [150]. Further, an FHB resistant network—Ca²⁺, phytohormone- and phenylalanine-

<figure><img src="image_14.png" /></figure>
signaling pathways for stomatal closure, disease resistance and thickening cell wall, respectively — was proposed. Although wheat cultivars vary in FHB susceptibility, they share a core set of defense-related proteins [151]. During FHB infection, pectin-derived oligogalacturonides (produced via homogalacturonan degradation) act as damage-associated molecular patterns and are recognized by wall-associated kinase 1 in *Arabidopsis*, a plant receptor kinase functioning as a pattern recognition receptor. Similarly, in wheat, the *Stb6* gene encodes a wall-associated kinase -like protein that recognizes *AvrStb6*, the fungal avirulence effector, thereby conferring resistance to *Septoria tritici* blotch [142]. Under biotic stress, proteomic investigations have identified key proteins, including type II metacaspase and wheat cold shock domain protein family 1, in regulating carbohydrate metabolism and photosynthesis [152]. In the resilient wheat cultivar "Xingmin318," 741 DAPs were identified in response to *Puccinia striiformis* and *Blumeria graminis* infections, indicating distinct defense strategies against these pathogens [153].

A comparative proteomic study in a resistant wheat line "N9134" identified 394 DAPs in response to *Blumeria graminis* f. sp. *tritici* infection [154]. Among these, few DAPs were primarily associated with pathogenesis-related processes, oxidative stress responses, and primary metabolism. Key metabolic pathways for plant's defense response includes phenylpropanoid biosynthesis, phenylalanine metabolism, and photosynthesis-antenna proteins. The identification of protein motifs rich in leucine repeats and histidine sites, along with eight predicted PPI networks, provides insights into potential molecular mechanisms underlying resistance to powdery mildew.

Although proteomics has significantly advanced our understanding of plant-pathogen interactions at the molecular level, the narrow genetic diversity of modern wheat varieties remains a major challenge. Continuous research and breeding programs are essential to enhance wheat's resistance to evolving pathogens [141,155].

In summary, proteins involved with antioxidant defense, glutathione metabolism, carbohydrate metabolism, photosynthesis, calcium-, phytohormone- and phenylalanine-mediated signaling pathways play vital roles in wheat's resistance to biotic stress.

## 4.2. Oilseed Brassica Crops

Proteomic studies have revealed that biotic stress activates detoxification, redox homeostasis, and defense-related pathways in oilseed Brassica. Black rot, caused by *Xanthomonas campestris* pv. *campestris* (*Xcc*) severely affects cruciferous vegetables, including *Brassica napus* [156]. Label-free proteomic analysis identified 158 and 163 *Xcc*-responsive DAPs in the susceptible cultivar Mosa and the resistant cultivar Capitol, respectively, which were categorized into five major functional groups: antioxidative systems, proteolysis, photosynthesis, redox, and innate immunity. Mosa showed increased protein degradation and oxidative stress markers, while Capitol exhibited high redox potential and an enhanced innate immune response. Interestingly, reduced photosynthesis due to the downregulation of photosystem II-related proteins improved disease resistance.

Clubroot, caused by *Plasmodiophora brassicae*, has threatened *Brassica* productivity for over a century [157]. Researchers are employing advanced tools, including proteomics, to develop clubroot-resistant cultivars. Using LC with tandem MS, 73 putative proteins were identified on the quantitative trait loci in chromosomes A3 and A8 in the clubroot resistant *B. napus* cultivar [158]. A total of 937 proteins had significant changes in abundance, which were associated with pathways related to calcium signalling, reactive oxygen species, dehydrins, lignin, and phytohormones. Additionally, 73 orthologous proteins linked to clubroot resistant loci were identified, highlighting potential genetic targets for developing clubroot-resistant cultivars. Among them, BnaA02T0335400WE, BnaA03T0374600WE, BnaA03T0262200WE, and BnaA03T0464700WE associated with calcium signaling—play crucial role in defense against *Plasmodiophora brassicae*. In *B. oleracea*, proteins associated with ABA and glucose signaling, and fructose-bisphosphate aldolase are critical for clubroot defence [159]. Most of the abundant proteins were localized to outside the nucleus, particularly in the chloroplast, thylakoid, stroma, and membranes. Additionally, restriction of energy metabolism enhances resistance.

<figure><img src="image_15.png" /></figure>
Stem rot, caused by *Sclerotinia sclerotiorum* is another major disease of *Brassica* [160]. The first proteomic study on *S. sclerotiorum* infection in *B. napus*, identified upregulated antioxidant enzymes peroxidase and SOD as contributors to the disease resistance [161]. Additional proteins related to photosynthesis, metabolism, hormone signaling and protein folding were also important for defense mechanisms. Several proteomic studies have expanded the understanding of stem rot resistance mechanisms in *B. napus* [160,162–164]. A TMT-based analysis identified 221 unique proteins and 173 DAPs in response to stem rot infection, including proteins involved in ROS homeostasis, lipid signaling, DNA methylation, histone modification, defense responses, and cyanate lyase activity [164].

In *B. juncea*, 20–22 DAPs were identified for stem rot using SDS-PAGE [165]. Further, nano LC-MS revealed peptidyl-prolyl cis-trans isomerase as the key protein for stem rot defense, interacting with other proteins related with protein importation into nucleus and RNA exportation, including GTP binding nuclear proteins, 60S ribosomal protein, and protein kinase. Investigation into alternative splicing in *S. sclerotiorum*-infected *B. napus* across five infection stages identified a total of 130 genes with 98 differentially expressed genes for defense mechanism through alternate splicing [166]. Most dominant alternative splicing sites were on intron or alternative 3' splice sites. However, further proteomic investigations focusing on PTMs are needed to deepen our understanding of host–pathogen interactions.

Overall, proteins associated with photosynthesis, ROS homeostasis, lignin synthesis, lipid- and Ca²⁺- signaling, DNA methylation and histone modification, contribute to the biotic stress resistance in oilseed Brassica.

### 4.3. Cotton

Phytopathogens—fungi, bacteria and viruses, cause significant yield loss in cotton [167]. Among the fungi, *Verticilium dahlia*, *Fusarium oxysporum*, *Thielaviopsis basicola* [168] and *Rhizoctonia solani* [169] are major causal agents for cotton diseases. Bacterial blight by *Xanthomonas campestris* also cause significant yield loss in cotton [170]. Cotton proteomics has advanced significantly over the past decade, improving the understanding of pathogenicity and plant defense responses at the molecular level [54].

Numerous proteomic studies have focused on unravelling the molecular mechanism of Verticillium wilt (VW), a devastating soil borne disease caused by *V. dahlia* that severely affects cotton yield and quality [171]. The first root proteomic study on VW in cotton (*G. barbadense*) identified 51 upregulated and 17 downregulated proteins using 2-DE [172]. This study highlighted the roles of the ethylene signaling pathway, pentose phosphate pathway, and Bet v 1 family proteins in defense. Further investigation found that one of the proteins of Bet v 1 family proteins, PR10 (GbPR10.5D1), contributed to resistance by modulating lipid metabolism and defense signaling pathways [173]. Moreover, the ethylene response factor, GbERF1-like enhanced VW resistance by promoting lignin synthesis [174]. More recently, a DIA-based proteomic comparison between two contrasting *G. barbadense* cultivars—XH21 (resistant) and XH7 (susceptible)—identified 885 DAPs associated with disease resistance [175]. Further, weighted gene co-expression network analysis revealed oxidoreductase and peroxidase activities significantly enriched pathways for VW resistance. Silencing ascorbate peroxidase proteins reduced VW resistance, underscoring the importance of APX-mediated redox metabolism in plant defense.

The nuclear protein, *GthGAPC2*, was identified in *Gossypium thurberi* via proteomic analysis as the key to VW resistance [176]. Silencing *GthGAPC2* increased susceptibility by reducing lignin content and disrupting redox balance, while its overexpression in tobacco and *Arabidopsis* confirmed its role in disease resistance, highlighting its potential as a promising candidate for breeding programs. Mi et al. (2024) [177] demonstrated that the GhMPK9-GhRAF39_1-GhWRKY40a signaling pathway enhanced cotton disease resistance by regulating the GhERF1b- and GhABF2-mediated pathways. *GhMAC3e*, a homologous gene of a key component of MOS4-associated complex, was implicated in growth and defense responses against VW [178]. A phosphoproteomic study identified

<figure><img src="image_16.png" /></figure>
359 and 287 differential phosphoproteins in one and three days post-VW inoculation in resistant line, respectively [179]. Analysis of PTMs revealed 37 enriched serine (Ser) and four threonine (Thr) motifs, of which 19 Ser and two Thr were novel. Moreover, the upregulation of burst oxidase homolog D (*GhRbohD*) increased phosphorylation after VW infection, increasing resistance by decreasing ROS, $H_2O_2$, nitric oxide and calcium levels while promoting lignin and callose accumulation [180].

Fusarium wilt (FW), caused by *F. oxysporum*, also results in considerable yield losses in cotton [181], though limited proteomic studies have explored this disease. Using an SDS-PAGE, it was found that proteins associated with polyketides, cellular process (e.g., lipid biosynthesis, protein and nucleotide metabolism, protein folding, carbohydrate metabolism and oxidative stress response), HSP70 and protease are significantly enriched in the *F. oxysporum* f.sp. *vasinfectum* [182]. These proteins are crucial in mediating host-pathogen interactions via extracellular vesicles. In contrast MAPK cascade involving *GhMPK20*, *GhMKK4* and *GhWRKY40* negatively regulated FW resistance [183]. However, *GhMPK20* plays key role in defence signal transduction pathway to improve FW resistance in cotton. Further, a phrosphoproteomics identified *GhMORG1* (a MAPK scaffold protein of the *GhMKK6*–*GhMPK4* cascade) that enhances FW resistance in cotton [184]. Overexpression of *GhMORG1* in cotton protoplasts significantly increased the activity of this pathway.

In response to *Rhizoctonia solani* infection, iTRAQ analysis identified 174 DAPs, with proteins involved in ROS homeostasis—such as glutathione S-transferases and glutathione peroxidase—and those linked to lignin and phenylpropanoid biosynthesis, DNA methylation, and histone modification significantly upregulated [185]. Particularly, the crucial role of secondary metabolic pathways in fungal pathogen defense was confirmed by the altered expression of phenylpropanoid biosynthesis-related proteins.

Bacterial blight, caused by *Xanthomonas campestris* pv. *Malvacearum*, defense mechanisms include the downregulation of photosynthesis-related proteins, such as ribulose-biphosphate carboxylase activase, ATP synthase β subunit, RuBisCo large subunit-binding protein, and glyceraldehyde-3-phosphate dehydrogenase B [186]. Further, ROS-regulating proteins, including peroxidase, peroxiredoxins, and Prx type 2-Cys, play important roles in enhancing resistance.

Overall, proteins associated with signaling pathways—ethylene, calcium and defense—, lipid and carbohydrate metabolism, DNA-methylation, histone modification and peroxidase activity contribute to the biotic stress resistance in cotton.

## 5. Challenges of Proteomics in Polyploid Crops

Proteomics have been used in allopolyploids, including wheat, oilseed Brassica and cotton, to harness the molecular insights of abiotic and biotic stress tolerance. However, these studies face several challenges, primarily due to the complex nature of polyploid genomes, difficulties in protein identification, reproducibility and the lack of comprehensive protein databases.

The genomic complexity of allopolyploid is the result of the combination of divergent genomes through interspecific hybridization. Gene loss during interspecific hybridization or further polyploidization (e.g., in wheat), continued recombination between the homoeologous chromosomes (e.g., Brassica), and chromosomal translocations between subgenomes increase the complexity of the genomic landscape [187,188]. While proteomics analysis can detect chromosomal rearrangements by comparing DAPs or proteoforms between progenitor lines, the specific parental contributions often remain unresolved [48].

For example, bread wheat has a large and complex genome (~17 Gb across three sets of chromosomes) [63], with 55–63% comprising repetitive sequences [189]. Paralogous genes in allopolyploids often share > 90% amino acid sequence identity. This high redundancy makes it difficult to identify novel proteins and protein-protein interactions associated with distinct phenotypes [48,190]. Studies have shown that 65–70% of transcriptomic data does not correspond to proteomic outcomes [210], potentially due to its amino acid composition and alternative splicing or PTMs. Protein isoforms, altered protein sequences by alternate splicing, and PTMs, biochemical modification of amino acid residues of proteins, are two major proteoforms for determining

<figure><img src="image_17.png" /></figure>
biological functions of proteins in plants [39]. Therefore, proteoforms vary in developmental stages of crops, tissue types and external stimulus [191].

Besides genetically functional proteins, identification of proteofroms is crucial due to their important role in allopolyploid evolution and stress tolerance. Homologous genes—orthologous and paralogous—and homeologous genes are a significant part of polyploid evolution. Homeologous genes or homeologs are the homologous gene pairs originated from different species but merged to an allopolyploid genome by allopolyploidization [192], while protein isoforms are formed due to their expression [193]. For example, 18.7% of expressed genes of allotetraploid wheat (AADD) have similar type alternate splicing in two subgenomes [193], while 90 novel protein isoforms of a HSP, *TaHSP90*, developed from three homeologs were found in hexaploid wheat [194]. Protein isoform of TaYRG1.1 (TaYRG1.6), increases susceptible to yellow rust disease in wheat [195], while *TaHSFA6e* originated from alternative splicing of a HS transcription factor gene increases HS-tolerance [196].

Similarly, PTMs also play an important role in stress tolerance in polyploids. Recently, the model of controlling cellular functions by PTM proposed as: enzymes such as kinases, “writer”, catalyze the PTM addition to amino acid residues, other enzymes such as phosphatases de-ubiquitinases and -acetylases, “erasers”, play role to deletion of PTMs from target amino acid residues, while a protein domains, “readers”, play role for interacting between the PTMs and the other proteins [197]. However, this PTM crosstalk is underexplored in plant proteomes, which can be addressed by incorporating of artificial intelligence (AI). Critical PTMs will be identified using AI in the PTM analysis to edit bases using CRISPR-Cas9 for altering the target traits [198]. Under different external stimuli, a single protein can develop the same or different kinds of PTMs, interacting with different target motifs [39]. For example, different temperature stresses activated phosphatase and kinase and produced different phosphofroms of a target protein in wheat [116]. However, progress in allopolyploid proteomics for stress-related proteoform identification lags far behind compared with other plants and human proteomics due to the inefficiency of the current proteomic approaches.

Protein enrichment protocol has been recommended to identify 1 to 5% proteoforms of the total protein in wheat [37]. One hundred and eighty-four proteoforms were identified in blood samples, using a nano-particle enrichment MS proteomics [199], which technique could be explored in alloploids. Further, despite having poor reproducibility, and limited ability to detect low-abundance and low-molecular-weight proteins precisely by 2-DE [207], it can identify proteoforms coupled with other proteomic approaches, such as LC-MS/MS [200]. 2-DE can identify the isoelectric point and relative mass of proteoforms combining with other approaches, such as Orbitrap MS, iTRAQ, TMT, and stable isotope labelling of amino acid in cell-culture (SILAC) [200,201], limitedly used in plant proteomics. Advanced proteomic technique, DIA, can quantify proteins and PTMs precisely, but it requires sophisticated software to overcome the data complexity challenges [202]. Recently DIA has been advanced due to its incorporation into Orbitrap (a high-resolution mass analyzer) [202]—mostly used for DDA—, trapped ion mobility spectrometry [203] and high-field asymmetric waveform ion mobility spectrometry [204], but limitedly applied in plant proteomics. With further optimization, this method can be adapted for improved protein quantification from the complex polyploid tissues.

In addition, sample preparation methods and different proteomic approaches influence true proteoform identification. Several recent review articles discussed the technical challenges of using top-down and bottom-up approaches for proteoform identifications [205–209]. Though top-down proteomics with MS identifies intact proteoforms [205], it has several major challenges, including proteoform mass limit of 30 kDa [210], and requirements of compulsory reproducible samples and high level of sample fractions [211], resulting in difficulties in finding exact localization [212]. However, improved liquid/gas-phase separation of MS instruments and bioinformatic tools [212]— e.g., TDPortal, Proteoform Suite and MetaMorpheus—, false-discovery rate determination, proteoform annotations and databases, and proteome validation [211] are required to overcome the challenges. “Super charging”—forcefully bringing a single charge state of all the molecules of a proteoform within the mass range of the MS instrument—and modified 2DE-PAGE improve the

<figure><img src="image_18.png" /></figure>
sensitivity of the top-down approach for higher proteoform coverage [206]. In contrast, though bottom-up proteomics is a sensitive, faster and easier approach than top-down proteomics to identify high and low abundant proteoforms [200,208], it has limitations in identifying complete proteoform information and distinguishing between multiple proteoforms originating from a single protein due to protein digestion and a lack of proteoform database [211,213]. SWATH-MS, and a combination of bottom-up and 2DE approaches can be useful for high-resolution proteome identification in bottom-up proteomics [208].

Despite significant advancement in proteomics techniques, data reproducibility is still a key challenge for proteomics. Though addressing the challenges in human proteomics has been initiated, it remains under-represented in plant proteomics. In human proteomics, reproducibility and data accuracy in DIA-MS has been improved by short-time data acquisition from different instruments in triplicates [214]. Furthermore, a pipeline (ProNorM), developed using computational modules, mitigates instrumental variations, resulting in high reproducibility and statistical analysis potential. On the other hand, DDA's reproducibility can be improved by dual-search approach—a spectral library is built using the searched sequence data, and further same data is searched in the library [215]. This method also minimizes the requirement of large database size, but two different sizes libraries result significant differences in protein and peptide identification. Improved software tools are required for the practical use of the proposed approach. Though the progress of top-down and bottom-up proteomics for proteoform identification techniques is mostly explored in animal proteomics, they can be explored in plant proteomics, including polyploids, with an improved protein extraction protocol, complete protein and proteoform databases and bioinformatic tools.

Protein extraction protocol from allopolyploid tissues is still being optimized. The complex cell wall composition and presence of secondary metabolites can hinder complete protein extraction [43,216]. For example, wheat tissues have polysaccharides, lipids, polyphenols, and other secondary metabolites [217], while cotton fibers contain polyphenols and lipids in the cell wall [54]. An improved TCA-B (trichloroacetic acid/acetone-borax/PVPP/phenol) protocol of protein extraction from polysaccharide and polyphenol rich tissues for further 2-DE and LC-MS/MS has been proposed [218]. Combination of two different protein extraction methods with an additional phenol extraction yielded high-quality protein from cotton leaves. A combination of multiple protein extraction methods also facilitates getting more protein coverage. Using a combination of two protein extraction buffers, isoelectric focusing and phenol, pairing pressure cycling technology for further label-free quantification yielded 66 higher proteins than the commonly used phenol extraction method in cotton fibre [219]. Protein extraction buffer and digestion enzymes can be modified according to the crops and tissues used for proteomics. Testing seven different protein extraction protocols in polyploid grains, including wheat and barley, Tris-HCL or urea-based extraction buffer coupled with trypsin-based membrane digestion with filter-aided sample preparation (FASP) found effective for a higher number of protein and peptide identifications than the conventional method [220]. However, trypsin digestion is limited to a shortage of lysine and arginine cleavage sites, often restricting precise proteoform identification, while multiple enzymatic digestions—trypsin and glutamyl endoproteinase—improve grain and metabolic proteins identification in wheat [221]. High quality single seed storage protein extraction from oilseed Brassica—*B. rapa*, *B. nigra*, *B. juncea* and *B. fruticulosa*—for 1D-SDS-PAGE based proteomics was improved by using a ball grinder with stainless steel beads in 2.0 mL Safe-Lock tubes with rounded bottoms and hinged lock lids with methanol for 1.2–5.5 mg of single seed grinding [222]. Tris-Glycine and tris-tricine based extraction buffers were found efficient for high (25–120 kDa) and low (2–25 kDa) molecular weight protein separation. Thus, optimized protein extraction protocols are crucial for successful proteomic studies in allopolyploids [43,54,222], while combination of different protein extraction strategies could be more efficient than a single method.

Another associated challenge is less precise and incomplete protein annotations. Like other diploids, annotation databases of allopolyploids are often incomplete. Although several genomic databases are available for allopolyploid crops—such as Wheat@URGI [223], Bolbase [224], BRAD

<figure><img src="image_19.png" /></figure>
[225], and CottonGen [226]—they often fall short of providing a comprehensive match between transcriptomic and proteomic data [44,48]. Inadequate annotation further complicates the identification of protein localization, PTMs, and protein variants in allopolyploids like wheat [216]. Multiomics databases—Wheatomics, BnIR and COTTONOMICS for wheat [227], *B. napus* [228] and cotton [229], respectively—offer better annotation. However, individual high-quality protein databases remain essential. For example, the wheat proteome database remains incomplete [43,230], limiting our ability to investigate complex, polygenic traits like abiotic stress tolerance [55], organelle-specific proteomics [chloroplast [231], mitochondria [232] and transmembrane [233]], and single-cell proteomics [234].

Difficulties in pathogenic protein identification, and lack of optimized in-vitro mimicking media as well as in-vivo models [235] often limit proteomics to understand the molecular mechanism of biotic stress tolerance of crops. Pathogenic protein extraction complexity from the host cell [236], MS-based instrument's limitations, including ionization suppression [237] and lack of complete genome sequence of the pathogens [238] are the major challenges of pathogenic protein identification. Consequently, protein abundance comparison becomes difficult due to the disproportionate host and pathogenic proteins [238], leading to challenges in proteoforms and PTMs identification. The challenges could be intensified if new pathogenic strains evolved. Use of advanced proteomic approaches, such as DIA, reducing heterogeneity among the infected cells through improving pathogen inoculation methods, using improved mimicking media, and improving pathogenic annotation databases, can improve the understanding of host-pathogen interactions. For example, a recently optimized in vitro-mimicking media improved the protein dynamics in *Botrytis cinerea* [239], a major pathogen of many crops, including cotton [240]. Protein-protein interactions (PPI) also play a crucial role in host-pathogen interactions through controlling cellular mechanisms. Recently developed TurboID-based proximity labeling method overcomes the limitations of low efficiency of PPI identification, using the conventional method [241]. Recently, a TurboID-based proximity protocol labeling has been proposed for a polyploid, potato [242], indicating the prospect of using this method for other polyploids with developing protocols. However, the requirement of wild variety as control, and optimization of biotin addition time and enzyme (Turbo) [241] needs to be considered during protocol development.

Proteomics often fail to contribute to breeding due to a lack of validation of the protein or proteoform biomarkers. Popular validation approach using antibody-based methods such as Western blotting remains extremely challenging [243] due to lack of strong affinity to the target protein or amino acid residues and the ability to mark only the target, selectivity [244]. While researchers are searching for an efficient protein validation method, using CRISPR/Cas knockdown/knockout of the target-protein encoding functional genes is a promising approach [245]. For proteoform validation, immunoassays [246] and Proteoform-predictor [247] can be explored in polyploids.

## 6. Prospects of Proteomics in Polyploid Breeding

Rapid advances of genome sequencing technologies have increased the availability of gene sequences for most of the crops. However, identification of protein coding regions in the genome and their functions in crop performances is essential. With large sequence data in databases, geneticists face the challenge of deciphering the protein products of gene expression. Proteomics, the large-scale study of proteins, holds great promise for revolutionizing plant breeding by enhancing our understanding of the genetic factors underlying complex traits in polyploid crops [48]. Despite major progress over the last two decades in understanding genetic and genomic consequences of polyploidy, research into proteomic-level changes in polyploids remains in its early stages. Protein isoforms and PTMs are key regulators of protein function, activity, localization, and interactions. Current research efforts focus increasingly on linking genotype to proteome to phenotype, and proteomics is expected to emerge as a critical field in polyploid breeding over the coming decade.
Polyploid crops are sensitive to abiotic stresses during flowering and grain filling, during which ROS causes oxidative stress and reduce yield [248]. These stresses affect photosynthesis and increase photorespiration by altering cellular homeostasis. However, the complex interactions among gene copy numbers, stress-induced expression changes, and the resulting impacts on the transcriptome, translatome, proteome, metabolome, phenotype, and plant fitness are not fully understood. Investigating the effect of gene content and expression changes in the proteome of in auto- and allopolyploids is crucial. Identification of biologically important proteins is also important. To get better understanding of the biologically significant low-abundance proteins, researchers often reduce sample complexity or remove high-abundance proteins.

Polyploidy involves whole-genome duplications, which enables, newly formed angiosperm polyploids to tolerate extensive genomic restructuring [188]. These large-scale genome alternations within just one or a few generations result in significant changes to the transcriptome, metabolome, and proteome, ultimately leading to altered phenotypes. High-throughput DNA sequencing technologies offer to determine the parental origin of sequences in both the genomic DNA and the transcriptome and to facilitate comparative analysis between natural and synthetic polyploids and their diploid relatives [188,249]. Synthetic allopolyploid studies have proven invaluable for investigating genome plasticity in angiosperms; the genomic response to polyploidy can be rapid, targeted, and highly species specific [250]. For example, synthetic *Brassica* allopolyploids have 25 to 38% of tissue-specific proteins exhibit deviate from the expected additive pattern of parent protein expression. Thus, rapid genomic changes resulting from interspecific hybridization drive adaptation and speciation [251].

Beyond the evolutionary research challenge, subcellular proteins or multiplex proteins, which are often located in two or more locations, are important for stress tolerance, identification remains challenging [252]. In wheat, isolating specific organelles from whole tissue complicates subcellular proteomic analyses. As a result, many subcellular proteins—particularly stress-related and housekeeping proteins—remain unclassified. A recent artificial intelligence (AI)-based predictor, pLoc-mPlant, has been advanced to pLoc bal-mPlant to identify subcellular proteins in plants using Gene Ontology (GO) annotations and balanced training datasets [253]. The subcellular location resources in The Crop Proteins of Annotated Location database (https://croppal.org/) can be used to improve the model and develop crop-specific datasets for wheat and canola, but need to include other polyploids, including cotton. To understand the host-pathogen interactions, couple with crop predictors, pathogen specific subcellular protein predictors such as Gram-LocEN [254] and FunSecKB2 [255], can be considered. Furthermore, recently proposed dual-perspective protein profiling in clinical proteomics [256] coupled with bioinformatic tools can be applied in crops, including polyploids, to identify novel resistance mechanism, host-pathogen relationships, and disease protection strategies [198]. Therefore, AI-based predictors can help identify organelle-specific proteins, improving our understanding of stress responses and cellular functions in polyploids.

Understanding polyploidization serves as a complementary tool in crop improvement, particularly in identifying differential proteins and their roles in functional expression and evolution. Studying proteome alterations caused by polyploidization gives breeders insights into developing crops with improved traits, boosting productivity and resilience. For example, proteomic studies on hexaploid *Brassica* identified 452 DAPs with a significant expression bias toward the tetraploid progenitor [257]. In wheat, comparative proteomic analyses across diploid, tetraploid, and hexaploid genotypes revealed that protein expression in hexaploid wheat is influenced not just by individual donor genomes but also by complex interactions among these genomes [258]. Similarly, in cotton, allopolyploidization has led to altered gene expression patterns and diversified proteomic profiles. Hu et al. (2015) [259] reported that 4.4–12.8% of differential protein expression between allopolyploids and their diploid progenitors with A and D genome while over 80% of them were additive in nature. Additionally, proteogenomics—combination of proteomics, genomics and transcriptomics—is highly promising to refine gene annotations of these complex polyploids, discover protein coding regions in the genes and identify novel peptides [260]. For example, using
proteogenomics, tissue specific proteomes including novel proteins and their subcellular locations were identified in the 24 organs in *T. aestivum* [230]. Thus, proteomics combining with other omics, multi-omics, can provide better insights into the molecular mechanisms of the stress response of the polyploids for improving their stress-resilience.

Comparative proteomic studies have significantly contributed to identifying and validating candidate genes for crop improvement. These analyses provide molecular insights into grain yield increase and stress tolerance mechanisms, helping breeders to develop high-yielding varieties with improved stress resilience. For example, in wheat, Daba et al. (2020) [261] identified 3,182 DAPs involved in tillering, spike initiation, and kernel development after anthesis using quantitative proteomics. These proteins are promising targets for boosting yield potential. Moreover, DAPs have been identified in relation to various stress tolerances, including heat [110], salinity [262], cold [263], drought [264], and disease resistance [158,265]. In cotton, proteomic research revealed key proteins associated with high-quality fiber development [54,266], and DAPs linked to drought [267,268], cold [269] and salinity tolerance [270].

Overall, proteomics helps elucidate how environmental stresses modify protein structure in polyploid crops. Future advancements in functional and subcellular proteomics will enhance our understanding of stress response pathways and facilitate the development of resilient crop varieties. Notably, breakthroughs in wheat genomics have greatly supported breeding efforts, and the integration of new stress-tolerant genes into breeding pipelines will accelerate the development of homozygous lines with high genetic gains. As proteomic separation techniques improve, proteomics will become increasingly central to modern crop breeding programs.

## 7. Conclusion

Allopolyploid crops such as wheat, oilseed Brassica, and cotton are vital for global food security and economic growth. However, HS and pathogenic infections drastically reduce grain yield, highlighting the need to improve climate resilience and yield stability. Advancement of proteomic approaches have been utilized for unravelling the molecular mechanisms underpinning stress responses in these crops. Among the three allopolyploids, proteomic research in wheat progressed most. Across these allopolyploid crops, several proteins—particularly those involved in photosynthesis, ROS scavenging, carbohydrate metabolism, peroxidase activity, and HSPs—and protein isoforms have been commonly associated with HS tolerance without grain yield penalty. On the other hand, the proteins involved in responses to biotic stress vary according to the disease specific pathogens. Nonetheless, proteins linked to photosynthesis, defense mechanisms, calcium signaling, lipid metabolism, and lignin biosynthesis commonly contribute to pathogen resistance in the allopolyploids.

Understanding stress-induced proteins at different growth stages is critical for breeding climate-resistant cultivars. In wheat, for example, upregulated proteins such as RuBisCo activase A and PEP carboxylase, along with antioxidant enzymes like SOD, CAT, and GSH, contribute to HS-tolerance in seedling. Additionally, proteins related to starch biosynthesis, low-molecular-weight glutenin subunits, and gliadin help maintain grain yield and quality under HS. Despite the promising potential of proteomics to enhance our understanding of HS responses and host-pathogen interactions in allopolyploid crops, several limitations remain. Lack of improved protein extraction methodologies, comprehensive protein annotation databases, investigation into protein isoforms, PTMs, and PPI, reproducibility, functional validation of candidate protein biomarkers and subcellular proteomics are major challenges of proteomics. Combinations of multiple protein extraction methods can offer better protein coverage than the single extraction method. Advanced DIA or double-search DDA used in other biological systems can be adapted for improved proteoform identification and reproducibility. An improved TurboID-based proximity labeling method can decipher the PPI to understand the cellular functions of the crops during stress conditions. CRISPR/Cas, immunoassays, and Proteoform-predictor can be potential proteoform validation methods.

<figure><img src="image_20.png" /></figure>
Despite challenges, proteomics has great prospects in polyploid breeding. Proteomics can not only be successfully used in discovering the evolution of the crops and stress associated genes but also identify the subcellular localization of the candidate proteins. Integration of AI into the existing MS-based proteomic approaches and other predictive tools are promising for identifying stress-induced proteoforms and their subcellular locations. Proteomics coupled with other omics can be also useful for clear understanding of the stress-resilience mechanisms of the allopolyploids. Moreover, addressing the current challenges and gaps of proteomics will be the key to translating molecular insights into practical tools for stress-resilient breeding programs in allopolyploid crops.

**Author Contributions:** T. H conceptualized, wrote and reviewed majority sections of the original draft through interpreting literatures. R. B. wrote a section, reviewed, and edited the original draft. M. N. S. and M. O. K. wrote another section. K. H. M. S. critically reviewed, commented on and edited the manuscript. G. Y. reviewed and provided ideas for generating figures, and S. I. commented on and edited the draft. All the authors read and approved the final manuscript.

**Data Availability Statement:** No new data were created or analyzed in this study.

**Acknowledgments:** The author is cordially thankful to Samalka Wijeweera, Postdoctoral Research Associate at the University of Western Australia, for helping search for relevant literatures.

**Conflicts of Interest:** The authors declare no conflict of interest.

## Abbreviations

The following abbreviations are used in this manuscript:

<table><tr><td>HS</td><td>Heat stress</td></tr><tr><td>HSPs</td><td>Heat shock proteins</td></tr><tr><td>ROS</td><td>Reactive oxygen species</td></tr><tr><td>MAPK</td><td>Mitogen-activated protein kinase</td></tr><tr><td>SOD</td><td>Superoxide dismutase</td></tr><tr><td>PPI</td><td>Protein-protein interaction</td></tr><tr><td>AI</td><td>Artificial intelligence</td></tr></table>

## References

1. Sattler, M.C.; Carvalho, C.R.; Clarindo, W.R. The polyploidy and its key role in plant breeding. *Planta* **2016**, 243, 281-296.

2. Rédei, G.P. *Encyclopedia of genetics, genomics, proteomics, and informatics*; Springer Science & Business Media: 2008.

3. Tossi, V.E.; Martínez Tosar, L.J.; Laino, L.E.; Iannicelli, J.; Regalado, J.J.; Escandón, A.S.; Baroli, I.; Causin, H.F.; Pitta-Álvarez, S.I. Impact of polyploidy on plant tolerance to abiotic and biotic stresses. *Frontiers in Plant Science* **2022**, 13, 869423.

4. Kui, L.; Majeed, A.; Ahmed, S.; Khan, M.S.S.; Islam, F.; Chen, J.; Dong, Y. *Solanum tuberosum* (potato). *Trends in Genetics* **2022**, 38, 1193-1195.

5. Sforça, D.A.; Vautrin, S.; Cardoso-Silva, C.B.; Mancini, M.C.; Romero-da Cruz, M.V.; Pereira, G.d.S.; Conte, M.; Bellec, A.; Dahmer, N.; Fourment, J. Gene duplication in the sugarcane genome: a case study of allele interactions and evolutionary patterns in two genic regions. *Frontiers in Plant Science* **2019**, 10, 553.

6. El Baidouri, M.; Murat, F.; Veyssiere, M.; Molinier, M.; Flores, R.; Burlot, L.; Alaux, M.; Quesneville, H.; Pont, C.; Salse, J. Reconciling the evolutionary origin of bread wheat (*Triticum aestivum*). *New Phytologist* **2017**, 213, 1477-1486, doi:10.1111/nph.14113.

7. Branca, F.; Cartea, E. Brassica. In *Wild crop relatives: genomic and breeding resources: oilseeds*; Springer: 2010; pp. 17-36.

8. Akagi, T.; Jung, K.; Masuda, K.; Shimizu, K.K. Polyploidy before and after domestication of crop species. *Current Opinion in Plant Biology* **2022**, 69, 102255.
9. FAO. World food situation: FAO Cereal Supply and Demand Brief; FAO (Food and Agriculture Organization of the United States): Roam, Italy, 2/12/2022 2025.

10. Friedt, W.; Tu, J.; Fu, T. Academic and economic importance of Brassica napus rapeseed. In *The Brassica Napus Genome*, Shengyi Liu, Rod Snowdon, Chalhoub, B., Eds.; Springer Cham: 2018; pp. 1-20.

11. Rathore, S.S.; Babu, S.; Shekhawat, K.; Singh, V.K.; Upadhyay, P.K.; Singh, R.K.; Raj, R.; Singh, H.; Zaki, F.M. Oilseed Brassica species diversification and crop geometry influence the productivity, economics, and environmental footprints under semi-arid regions. *Sustainability* **2022**, *14*, 2230.

12. Rai, P.; Yadav, P.; Kumar, A.; Sharma, A.; Kumar, V.; Rai, P. *Brassica juncea*: A crop for food and health. In *The Brassica juncea Genome*; Springer: 2022; pp. 1-13.

13. USDA. Oilseeds: world markets and trade; United States Department of Agriculture Foreign Agricultural Service: 2025; pp. 13-21.

14. Akın, S. The strategic importance of cotton production for the world and Türkiye. In *Best Crop Management and Processing Practices for Sustainable Cotton Production*, Gürsoy, S., and, Akın, S., Eds.; IntechOpen: 2024.

15. Khan, M.A.; Wahid, A.; Ahmad, M.; Tahir, M.T.; Ahmed, M.; Ahmad, S.; Hasanuzzaman, M. World cotton production and consumption: An overview. In *Cotton production and uses: Agronomy, crop protection, and postharvest technologies*, Ahmad, S., Hasanuzzaman, M., Eds.; Springer: Singapore, 2020; pp. 1-7.

16. ICAC. Current Global Cotton Market Outlook for 2024/2025; Washington, DC, 2025.

17. Hickey, L.T.; Hafeez, A.N.; Robinson, H.; Jackson, S.A.; Leal-Bertioli, S.C.M.; Tester, M.; Gao, C.; Godwin, I.D.; Hayes, B.J.; Wulff, B.B.H. Breeding crops to feed 10 billion. *Nature Biotechnology* **2019**, *37*, 744-754.

18. FAO. Cereal production, utilization, and trade reaching record levels in 2021/22; Food and Agriculture Organization: Rome, Italy, 3/02/2022 2022.

19. CIMMYT. Wheat research. Available online: https://www.cimmyt.org/work/wheat-research/ (accessed on

20. OECD/FAO. OECD-FAO Agricultural outlook 2023-2032. Cotton **2023**, 236-247, doi:doi.org/10.1787/08801ab7-en.

21. OECD-FAO. OECD-FAO Agricultural Outlook 2021-2030; Organisation for Economic Co-operation and Development (OECD) and the Food and Agriculture Organization (FAO): Paris, 2021.

22. Zhang, J.; Zhang, S.; Cheng, M.; Jiang, H.; Zhang, X.; Peng, C.; Lu, X.; Zhang, M.; Jin, J. Effect of drought on agronomic traits of rice and wheat: A meta-analysis. *International Journal of Environmental Research and Public Health* **2018**, *15*, 839.

23. Elferjani, R.; Soolanayakanahally, R. Canola responses to drought, heat, and combined stress: shared and specific effects on carbon assimilation, seed yield, and oil composition. *Frontiers in Plant Science* **2018**, *9*, 1224.

24. Wang, R.; Ji, S.; Zhang, P.; Meng, Y.; Wang, Y.; Chen, B.; Zhou, Z. Drought effects on cotton yield and fiber quality on different fruiting branches. *Crop Science* **2016**, *56*, 1265-1276.

25. Asseng, S.; Ewert, F.; Martre, P.; Rötter, R.P.; Lobell, D.B.; Cammarano, D.; Kimball, B.A.; Ottman, M.J.; Wall, G.W.; White, J.W.; et al. Rising temperatures reduce global wheat production. *Nature Climate Change* **2014**, *5*, 143-147.

26. Kutcher, H.; Warland, J.; Brandt, S. Temperature and precipitation effects on canola yields in Saskatchewan, Canada. *Agricultural and Forest Meteorology* **2010**, *150*, 161-165.

27. Sharma, R.K.; Dhillon, J.; Kumar, P.; Reddy, K.R.; Reed, V.; Dodds, D.M.; Reddy, K.N. Modelling the climate change and cotton yield relationship in Mississippi: Autoregressive distributed lag approach. *Ecological Indicators* **2024**, *166*, 112573.

28. Secchi, M.A.; Fernandez, J.A.; Stamm, M.J.; Durrett, T.; Prasad, P.V.; Messina, C.D.; Ciampitti, I.A. Effects of heat and drought on canola (*Brassica napus* L.) yield, oil, and protein: A meta-analysis. *Field Crops Research* **2023**, *293*, 108848.

29. Bista, M.K.; Adhikari, B.; Sankarapillai, L.V.; Pieralisi, B.; Reddy, K.R.; Jenkins, J.; Bheemanahalli, R. Drought and heat stress induce differential physiological and agronomic trait responses in cotton. *Industrial Crops and Products* **2024**, *222*, 119540.

30. Liu, H.; Li, J.; Xie, L.; Wu, H.; Han, S.; Hu, L.; Zhang, F.; Wang, H. Quantitative proteomic analysis reveals hub proteins for high temperature-induced male sterility in bread wheat (*Triticum aestivum* L.). *Frontiers in Plant Science* **2024**, *15*, 1426832.

<figure><img src="image_21.png" /></figure>
52. Shen, C.-C.; Chen, M.-X.; Xiao, T.; Zhang, C.; Shang, J.; Zhang, K.-L.; Zhu, F.-Y. Global proteome response to Pb (II) toxicity in poplar using SWATH-MS-based quantitative proteomics investigation. *Ecotoxicology and Environmental Safety* **2021**, *220*, 112410.

53. Masoomi-Aladizgeh, F.; McKay, M.J.; Asar, Y.; Haynes, P.A.; Atwell, B.J. Patterns of gene expression in pollen of cotton (Gossypium hirsutum) indicate downregulation as a feature of thermotolerance. *The Plant Journal* **2022**, *109*, 965-979.

54. Zhou, M.; Sun, G.; Sun, Z.; Tang, Y.; Wu, Y. Cotton proteomics for deciphering the mechanism of environment stress response and fiber development. *Journal of Proteomics* **2014**, *105*, 74-84.

55. Barkla, B.J.; Vera-Estrella, R.; Pantoja, O. Progress and challenges for abiotic stress proteomics of crop plants. *Proteomics* **2013**, *13*, 1801-1815.

56. Sharif, I.; Aleem, S.; Junaid, J.A.; Ali, Z.; Aleem, M.; Shahzad, R.; Farooq, J.; Khan, M.I.; Arshad, W.; Ellahi, F. Multiomics approaches to explore drought tolerance in cotton. *Journal of Cotton Research* **2024**, *7*, 32.

57. D'Agostino, N.; Fasano, C. Editorial: Genetics and Genomics of Polyploid Plants. *Genes* **2024**, *15*, 1377.

58. Aufiero, G.; Fruggiero, C.; D'Angelo, D.; D'Agostino, N. Homoeologs in Allopolyploids: Navigating Redundancy as Both an Evolutionary Opportunity and a Technical Challenge—A Transcriptomics Perspective. *Genes* **2024**, *15*, 977.

59. Grover, C.; Gallagher, J.; Szadkowski, E.; Yoo, M.; Flagel, L.; Wendel, J. Homoeolog expression bias and expression level dominance in allopolyploids. *New Phytologist* **2012**, *196*, 966-971.

60. Duan, Z.; Zhang, Y.; Zhang, T.; Chen, M.; Song, H. Proteome evaluation of homolog abundance patterns in *Arachis hypogaea* cv. Tifrunner. *Plant Methods* **2022**, *18*, 1-11.

61. Zhang, B.; Kuster, B. Proteomics is not an island: multi-omics integration is the key to understanding biological systems. *Molecular & Cellular Proteomics* **2019**, *18*, S1-S4.

62. Peng, J.H.; Sun, D.; Nevo, E. Domestication evolution, genetics and genomics in wheat. *Molecular Breeding* **2011**, *28*, 281-301.

63. Borrill, P.; Harrington, S.A.; Uauy, C. Applying the latest advances in genomics and phenomics for trait discovery in polyploid wheat. *The Plant Journal* **2019**, *97*, 56-72.

64. Avni, R.; Nave, M.; Barad, O.; Baruch, K.; Twardziok, S.O.; Gundlach, H.; Hale, I.; Mascher, M.; Spannagl, M.; Wiebe, K. Wild emmer genome architecture and diversity elucidate wheat evolution and domestication. *Science* **2017**, *357*, 93-97.

65. Ling, H.-Q.; Zhao, S.; Liu, D.; Wang, J.; Sun, H.; Zhang, C.; Fan, H.; Li, D.; Dong, L.; Tao, Y. Draft genome of the wheat A-genome progenitor *Triticum urartu*. *Nature* **2013**, *496*, 87-90.

66. Cheng, F.; Wu, J.; Wang, X. Genome triplication drove the diversification of Brassica plants. *Horticulture research* **2014**, *1*.

67. Vélez-Gavilán, J. *Brassica rapa* (field mustard). *CABI Compendium*. CABI International. doi **2022**, 10.

68. Cai, C.; Wang, X.; Liu, B.; Wu, J.; Liang, J.; Cui, Y.; Cheng, F.; Wang, X. *Brassica rapa* genome 2.0: a reference upgrade through sequence re-assembly and gene re-annotation. *Molecular Plant* **2017**, *10*, 649-651.

69. Zhang, L.; Liang, J.; Chen, H.; Zhang, Z.; Wu, J.; Wang, X. A near-complete genome assembly of *Brassica rapa* provides new insights into the evolution of centromeres. *Plant Biotechnology Journal* **2023**, *21*, 1022-1032.

70. Guo, N.; Wang, S.; Gao, L.; Liu, Y.; Wang, X.; Lai, E.; Duan, M.; Wang, G.; Li, J.; Yang, M. Genome sequencing sheds light on the contribution of structural variants to *Brassica oleracea* diversification. *BMC Biology* **2021**, *19*, 93.

71. Ji, G.; Long, Y.; Cai, G.; Wang, A.; Yan, G.; Li, H.; Gao, G.; Xu, K.; Huang, Q.; Chen, B. A new chromosome-scale genome of wild *Brassica oleracea* provides insights into the domestication of Brassica crops. *Journal of Experimental Botany* **2024**, *75*, 2882-2899.

72. Perumal, S.; Koh, C.S.; Jin, L.; Buchwaldt, M.; Higgins, E.E.; Zheng, C.; Sankoff, D.; Robinson, S.J.; Kagale, S.; Navabi, Z.-K. A high-contiguity *Brassica nigra* genome localizes active centromeres and defines the ancestral Brassica genome. *Nature Plants* **2020**, *6*, 929-941.

73. Paritosh, K.; Pradhan, A.K.; Pental, D. A highly contiguous genome assembly of *Brassica nigra* (BB) and revised nomenclature for the pseudochromosomes. *BMC Genomics* **2020**, *21*, 1-12.

<figure><img src="image_22.png" /></figure>
74. Yang, J.; Liu, D.; Wang, X.; Ji, C.; Cheng, F.; Liu, B.; Hu, Z.; Chen, S.; Pental, D.; Ju, Y. The genome sequence of allopolyplloid *Brassica juncea* and analysis of differential homoeolog gene expression influencing selection. *Nature Genetics* **2016**, *48*, 1225-1232.

75. Heng, S.; Cui, M.; Li, X.; Zhang, S.; Mao, G.; Xing, F.; Wan, Z.; Wen, J.; Shen, J.; Fu, T. High quality genome of potherb mustard XC (*Brassica juncea* var. multiceps) provides new insight into leaf shape variation1. *Journal of Integrative Agriculture* **2024**.

76. Niu, Y.; Liu, Q.; He, Z.; Raman, R.; Wang, H.; Long, X.; Qin, H.; Raman, H.; Parkin, I.A.; Bancroft, I. A *Brassica carinata* pan-genome platform for Brassica crop improvement. *Plant Communications* **2024**, *5*.

77. Emani, C. *Gossypium*. *Wild Crop Relatives: Genomic and Breeding Resources: Industrial Crops* **2011**, 109-122.

78. Egbuta, M.A.; McIntosh, S.; Waters, D.L.; Vancov, T.; Liu, L. Biological importance of cotton by-products relative to chemical constituents of the cotton plant. *Molecules* **2017**, *22*, 93.

79. Wegier, A.; Alavez, V.; Piñero, D. Cotton: traditional and modern uses. *Ethnobotany of Mexico: interactions of people and plants in Mesoamerica* **2016**, 439-456.

80. Adams, K.L.; Wendel, J.F. Exploring the genomic mysteries of polyploidy in cotton. *Biological Journal of the Linnean Society* **2004**, *82*, 573-581.

81. Zhang, X.; Zhai, C.; He, L.; Guo, Q.; Zhang, X.; Xu, P.; Su, H.; Gong, Y.; Ni, W.; Shen, X. Morphological, cytological and molecular analyses of a synthetic hexaploid derived from an interspecific hybrid between *Gossypium hirsutum* and *Gossypium anomalum*. *The Crop Journal* **2014**, *2*, 272-277.

82. Percival, A.; Wendel, J.; Stewart, J. Taxonomy and germplasm resources. *Cotton: origin, history, technology, and production* **1999**, *33*, 63.

83. Wendel, J.; Clark Cronn, R.; Clark, R.; Cronn, R. Polyploidy and the Evolutionary History of Cotton. *Advances in Agronomy* **2003**, *78*.

84. Chen, Z.J.; Sreedasyam, A.; Ando, A.; Song, Q.; De Santiago, L.M.; Hulse-Kemp, A.M.; Ding, M.; Ye, W.; Kirkbride, R.C.; Jenkins, J. Genomic diversifications of five *Gossypium* allopolyplloid species and their impact on cotton improvement. *Nature Genetics* **2020**, *52*, 525-533.

85. Peng, R.; Xu, Y.; Tian, S.; Unver, T.; Liu, Z.; Zhou, Z.; Cai, X.; Wang, K.; Wei, Y.; Liu, Y. Evolutionary divergence of duplicated genomes in newly described allotetraploid cottons. *Proceedings of the National Academy of Sciences* **2022**, *119*, e2208496119.

86. Li, F.; Fan, G.; Wang, K.; Sun, F.; Yuan, Y.; Song, G.; Li, Q.; Ma, Z.; Lu, C.; Zou, C. Genome sequence of the cultivated cotton *Gossypium arboreum*. *Nature Genetics* **2014**, *46*, 567-572.

87. Ramaraj, T.; Grover, C.E.; Mendoza, A.C.; Arick, M.A.; Jareczek, J.J.; Leach, A.G.; Peterson, D.G.; Wendel, J.F.; Udall, J.A. The *Gossypium herbaceum* L. Wagad genome as a resource for understanding cotton domestication. *G3* **2023**, *13*, jkac308.

88. Hendrix, B.; Stewart, J.M. Estimation of the nuclear DNA content of *Gossypium* species. *Annals of Botany* **2005**, *95*, 789-797.

89. Grover, C.E.; Gallagher, J.P.; Jareczek, J.J.; Page, J.T.; Udall, J.A.; Gore, M.A.; Wendel, J.F. Re-evaluating the phylogeny of allopolyplloid *Gossypium* L. *Molecular Phylogenetics and Evolution* **2015**, *92*, 45-52.

90. Wendel, J.F.; Grover, C.E. Taxonomy and evolution of the cotton genus, *Gossypium*. *Cotton* **2015**, *57*, 25-44.

91. Meng, Q.; Gu, J.; Xu, Z.; Zhang, J.; Tang, J.; Wang, A.; Wang, P.; Liu, Z.; Rong, Y.; Xie, P. Comparative analysis of genome sequences of the two cultivated tetraploid cottons, *Gossypium hirsutum* (L.) and *G. barbadense* (L.). *Industrial Crops and Products* **2023**, *196*, 116471.

92. Hasanuzzaman, M.; Nahar, K.; Alam, M.; Roychowdhury, R.; Fujita, M. Physiological, biochemical, and molecular mechanisms of heat stress tolerance in plants. *International Journal of Molecular Sciences* **2013**, *14*, 9643-9684.

93. Sarkar, S.; Islam, A.A.; Barma, N.C.D.; &; Ahmed, J.U. Tolerance mechanisms for breeding wheat against heat stress: A review. *South African Journal of Botany* **2021**, *138*, 262-277.

94. Xu, J.; Lowe, C.; Hernandez-Leon, S.G.; Dreisigacker, S.; Reynolds, M.P.; Valenzuela-Soto, E.M.; Paul, M.J.; and; Heuer, S. The effects of brief heat during early booting on reproductive, developmental, and chlorophyll physiological performance in common wheat (*Triticum aestivum* L.). *Frontiers in Plant Science* **2022**, *13*, 886541.

<figure><img src="image_23.png" /></figure>
95. Khan, A.; Ahmad, M.; Ahmed, M.; Iftikhar Hussain, M. Rising atmospheric temperature impact on wheat and thermotolerance strategies. *Plants* **2020**, *10*, 43.

96. Yadav, M.R.; Choudhary, M.; Singh, J.; Lal, M.K.; Jha, P.K.; Udawat, P.; Gupta, N.K.; Rajput, V.D.; Garg, N.K.; Maheshwari, C.; et al. Impacts, tolerance, adaptation, and mitigation of heat stress on wheat under changing climates. *International Journal of Molecular Sciences* **2022**, *23*, 2838.

97. Feng, J.; Jia, Y.; Xu, B.; Bi, X.; Ge, Z.; Ma, G.; Xie, Y.; Wang, C.; Ma, D. Quantitative proteomic analysis for characterization of protein components related to dough quality and celiac disease in wheat flour, dough, and heat-treated dough. *Food Chemistry* **2024**, *461*, 140924.

98. Fei, L.; Chu, J.; Zhang, X.; Dong, S.; Dai, X.; He, M. Physiological and proteomic analyses indicate delayed sowing improves photosynthetic capacity in wheat flag leaves under heat stress. *Frontiers in Plant Science* **2022**, *13*, 848464.

99. Chunduri, V.; Kaur, A.; Kaur, S.; Kumar, A.; Sharma, S.; Sharma, N.; Singh, P.; Kapoor, P.; Kaur, S.; Kumari, A. Gene expression and proteomics studies suggest an involvement of multiple pathways under day and day-night combined heat stresses during grain filling in wheat. *Frontiers in Plant Science* **2021**, *12*, 1-16.

100. Kumar, R.R.; Dubey, K.; Arora, K.; Dalal, M.; Rai, G.K.; Mishra, D.; Chaturvedi, K.K.; Rai, A.; Kumar, S.N.; Singh, B. Characterizing the putative mitogen-activated protein kinase (MAPK) and their protective role in oxidative stress tolerance and carbon assimilation in wheat under terminal heat stress. *Biotechnology Reports* **2021**, *29*, e00597.

101. Wu, B.; Qiao, J.; Wang, X.; Liu, M.; Xu, S.; Sun, D. Factors affecting the rapid changes of protein under short-term heat stress. *BMC Genomics* **2021**, *22*, 1-11.

102. Wang, J.; Gao, X.; Dong, J.; Tian, X.; Wang, J.; Palta, J.A.; Xu, S.; Fang, Y.; Wang, Z. Over-expression of the heat-responsive wheat gene *TaHSP23.9* in transgenic *Arabidopsis* conferred tolerance to heat and salt stress. *Frontiers in Plant Science* **2020**, *11*, 243.

103. Kumar, R.R.; Singh, K.; Ahuja, S.; Tasleem, M.; Singh, I.; Kumar, S.; Grover, M.; Mishra, D.; Rai, G.K.; Goswami, S. Quantitative proteomic analysis reveals novel stress-associated active proteins (SAAPs) and pathways involved in modulating tolerance of wheat under terminal heat. *Functional and Integrative Genomics* **2019**, *19*, 329-348.

104. Zhang, Y.; Lou, H.; Guo, D.; Zhang, R.; Su, M.; Hou, Z.; Zhou, H.; Liang, R.; Xie, C.; You, M. Identifying changes in the wheat kernel proteome under heat stress using iTRAQ. *The Crop Journal* **2018**, *6*, 600-610.

105. Perveen, A.; Sheheryar, S.; Ahmad, F.; Mustafa, G.; Moura, A.A.; Campos, F.A.; Domont, G.B.; Nishan, U.; Ullah, R.; Ibrahim, M.A. Integrative physiological, biochemical, and proteomic analysis of the leaves of two cotton genotypes under heat stress. *PloS one* **2025**, *20*, e0316630.

106. Ma, W.; Li, J.; Liu, F.; Zhang, T.; Guan, X. *GhHSP24.7* mediates mitochondrial protein acetylation to regulate stomatal conductance in response to abiotic stress in cotton. *The Crop Journal* **2023**, *11*, 1128-1139.

107. Masoomi-Aladizgeh, F.; Najeeb, U.; Hamzelou, S.; Pascovici, D.; Amirkhani, A.; Tan, D.K.; Mirzaei, M.; Haynes, P.A.; Atwell, B.J. Pollen development in cotton (*Gossypium hirsutum*) is highly sensitive to heat exposure during the tetrad stage. *Plant, Cell & Environment* **2021**, *44*, 2150-2166.

108. Zhang, X.; Tian, Q.; Zhao, Z.; Dong, Z.; Chen, Y.; Chen, D. Analysis of differentially expressed proteins affecting insecticidal protein content in Bt cotton under high-temperature and water deficit stress using label-free quantitation. *Journal of Agronomy and Crop Science* **2021**, *207*, 1-11.

109. Masoomi-Aladizgeh, F.; Kamath, K.S.; Haynes, P.A.; Atwell, B.J. Genome survey sequencing of wild cotton (*Gossypium robinsonii*) reveals insights into proteomic responses of pollen to extreme heat. *Plant, Cell & Environment* **2022**, *45*, 1242-1256.

110. Rani, R.; Mawlong, I.; Balbeer, B.; Kumar, M.S.; Rai, P.K.; Singh, V.V. Proteomic, biochemical and peptidomics based analysis reveals heat responsive changes in the seedlings of *Brassica juncea*. *Journal of Plant Biochemistry and Biotechnology* **2024**, 1-20.

111. Singiri, J.R.; Swetha, B.; Sikron-Persi, N.; Grafi, G. Differential response to single and combined salt and heat stresses: Impact on accumulation of proteins and metabolites in dead pericarps of *Brassica juncea*. *International Journal of Molecular Sciences* **2021**, *22*, 7076.

<figure><img src="image_24.png" /></figure>
112. Yuan, L.; Wang, J.; Xie, S.; Zhao, M.; Nie, L.; Zheng, Y.; Zhu, S.; Hou, J.; Chen, G.; Wang, C. Comparative proteomics indicates that redox homeostasis is involved in high-and low-temperature stress tolerance in a novel Wucai (Brassica campestris L.) genotype. *International Journal of Molecular Sciences* **2019**, *20*, 3760.

113. Mishra, D.; Shekhar, S.; Chakraborty, S.; Chakraborty, N. Wheat 2-Cys peroxiredoxin plays a dual role in chlorophyll biosynthesis and adaptation to high temperature. *The Plant Journal* **2021**, *105*, 1374-1389.

114. Gupta, O.P.; Mishra, V.; Singh, N.; Tiwari, R.; Sharma, P.; Gupta, R.; Sharma, I. Deciphering the dynamics of changing proteins of tolerant and intolerant wheat seedlings subjected to heat stress. *Molecular Biology Reports* **2015**, *42*, 43-51.

115. Cao, J.; Qin, Z.; Cui, G.; Chen, Z.; Cheng, X.; Peng, H.; Yao, Y.; Hu, Z.; Guo, W.; Ni, Z. Natural variation of STKc_GSK3 kinase TaSG-D1 contributes to heat stress tolerance in Indian dwarf wheat. *Nature Communications* **2024**, *15*, 2097.

116. Vu, L.D.; Zhu, T.; Verstraeten, I.; Van De Cotte, B.; Consortium, I.W.G.S.; Gevaert, K.; De Smet, I. Temperature-induced changes in the wheat phosphoproteome reveal temperature-regulated interconversion of phosphoforms. *Journal of Experimental Botany* **2018**, *69*, 4609-4624.

117. Majoul, T.; Bancel, E.; Triboï, E.; Ben Hamida, J.; Branlard, G. Proteomic analysis of the effect of heat stress on hexaploid wheat grain: Characterization of heat-responsive proteins from total endosperm. *Proteomics* **2003**, *3*, 175-183.

118. Skylas, D.; Cordwell, S.; Hains, P.; Larsen, M.; Basseal, D.; Walsh, B.; Blumenthal, C.; Rathmell, W.; Copeland, L.; Wrigley, C. Heat shock of wheat during grain filling: proteins associated with heat-tolerance. *Journal of Cereal Science* **2002**, *35*, 175-188.

119. Young, L.W.; Wilen, R.W.; Bonham-Smith, P.C. High temperature stress of *Brassica napus* during flowering reduces micro-and megagametophyte fertility, induces fruit abortion, and disrupts seed production. *Journal of Experimental Botany* **2004**, *55*, 485-495.

120. Angadi, S.; Cutforth, H.; Miller, P.; McConkey, B.; Entz, M.; Brandt, S.; Volkmar, K. Response of three Brassica species to high temperature stress during reproductive growth. *Canadian Journal of Plant Science* **2000**, *80*, 693-701.

121. Ahmad, M.; Waraich, E.A.; Skalicky, M.; Hussain, S.; Zulfiqar, U.; Anjum, M.Z.; Habib ur Rahman, M.; Brestic, M.; Ratnasekera, D.; Lamilla-Tamayo, L. Adaptation strategies to improve the resistance of oilseed crops to heat stress under a changing climate: An overview. *Frontiers in Plant Science* **2021**, *12*, 767150.

122. Yan, G.; Lv, X.; Gao, G.; Li, F.; Li, J.; Qiao, J.; Xu, K.; Chen, B.; Wang, L.; Xiao, X. Identification and characterization of a *glyoxalase I* gene in a rapeseed cultivar with seed thermotolerance. *Frontiers in Plant Science* **2016**, *7*, 150.

123. Subramanian, B.; Bansal, V.K.; Kav, N.N. Proteome-level investigation of *Brassica carinata*-derived resistance to *Leptosphaeria maculans*. *Journal of Agricultural and Food Chemistry* **2005**, *53*, 313-324.

124. Hajduch, M.; Casteel, J.E.; Hurrelmeyer, K.E.; Song, Z.; Agrawal, G.K.; Thelen, J.J. Proteomic analysis of seed filling in *Brassica napus*. Developmental characterization of metabolic isozymes using high-resolution two-dimensional gel electrophoresis. *Plant Physiology* **2006**, *141*, 32-46.

125. Ismaili, A.; Salavati, A.; Pour Mohammadi, P. A comparative proteomic analysis of responses to high temperature stress in hypocotyl of canola (*Brassica napus* L.). *Protein and Peptide Letters* **2015**, *22*, 285-299.

126. Swetha, B.; Singiri, J.R.; Novoplansky, N.; Grandhi, R.; Srinivasan, J.; Khadka, J.; Galis, I.; Grafi, G. Single and combined salinity and heat stresses impact yield and dead pericarp priming activity. *Plants* **2021**, *10*, 1627.

127. Wu, Y.; Li, Y.; Li, Y.; Ma, Y.; Zhao, Y.; Wang, C.; Chi, H.; Chen, M.; Ding, Y.; Guo, X. Proteomic analysis reveals that sugar and fatty acid metabolisms play a central role in sterility of the male-sterile line 1355A of cotton. *Journal of Biological Chemistry* **2019**, *294*, 7057-7067.

128. Schulte, I.; Tammen, H.; Selle, H.; Schulz-Knappe, P. Peptides in body fluids and tissues as markers of disease. *Expert Review of Molecular Diagnostics* **2005**, *5*, 145-157.

129. Zahid, K.R.; Ali, F.; Shah, F.; Younas, M.; Shah, T.; Shahwar, D.; Hassan, W.; Ahmad, Z.; Qi, C.; Lu, Y. Response and tolerance mechanism of cotton *Gossypium hirsutum* L. to elevated temperature stress: a review. *Frontiers in Plant Science* **2016**, *7*, 937.

<figure><img src="image_25.png" /></figure>
130. Salvucci, M.E.; Crafts-Brandner, S.J. Relationship between the heat tolerance of photosynthesis and the thermal stability of Rubisco activase in plants from contrasting thermal environments. *Plant Physiology* **2004**, *134*, 1460-1470.

131. Wise, R.; Olson, A.; Schrader, S.; Sharkey, T. Electron transport is the functional limitation of photosynthesis in field-grown Pima cotton plants at high temperature. *Plant, Cell & Environment* **2004**, *27*, 717-724.

132. Ashraf, M.; Saeed, M.; Qureshi, M. Tolerance to high temperature in cotton (Gossypium hirsutum L.) at initial growth stages. *Environmental and Experimental Botany* **1994**, *34*, 275-283.

133. Majeed, S.; Rana, I.A.; Mubarik, M.S.; Atif, R.M.; Yang, S.-H.; Chung, G.; Jia, Y.; Du, X.; Hinze, L.; Azhar, M.T. Heat stress in cotton: a review on predicted and unpredicted growth-yield anomalies and mitigating breeding strategies. *Agronomy* **2021**, *11*, 1825.

134. Sahu, B.; Choudhary, V.K.; Sahu, M.; Kumar, K.K.; Sujayanand, G.; Gopi, R.; Prakasam, V.; Sridhar, J.; Mallikarjuna, J.; Singh, H. Biotic Stress Management. In *Trajectory of 75 years of Indian Agriculture after Independence*; Springer: 2023; pp. 619-653.

135. Dixit, S.; Sivalingam, P.N.; Baskaran, R.M.; Senthil-Kumar, M.; Ghosh, P.K. Plant responses to concurrent abiotic and biotic stress: unravelling physiological and morphological mechanisms. *Plant Physiology Reports* **2024**, *29*, 6-17.

136. Kayess, O.; Ashrafuzzaman, M.; Khan, M.A.R.; Siddiqui, M.N. Functional phenomics and genomics: Unravelling heat stress responses in wheat. *Plant Stress* **2024**, 100601.

137. Singh, B.K.; Delgado-Baquerizo, M.; Egidi, E.; Guirado, E.; Leach, J.E.; Liu, H.; Trivedi, P. Climate change impacts on plant pathogens, food security and paths forward. *Nature Reviews Microbiology* **2023**, *21*, 640-656.

138. Liu, Y.; Lu, S.; Liu, K.; Wang, S.; Huang, L.; Guo, L. Proteomics: a powerful tool to study plant responses to biotic stress. *Plant Methods* **2019**, *15*, 1-20.

139. Mmbando, G.S. Omics: a new, promising technologies for boosting crop yield and stress resilience in African agriculture. *Plant Stress* **2024**, 100366.

140. Haq, S.A.U.; Bashir, T.; Roberts, T.H.; Husaini, A.M. Ameliorating the effects of multiple stresses on agronomic traits in crops: Modern biotechnological and omics approaches. *Molecular Biology Reports* **2024**, *51*, 41.

141. Bakala, H.S.; Mandahal, K.S.; Sarao, L.K.; Srivastava, P. Breeding wheat for biotic stress resistance: Achievements, challenges and prospects. *Current trends in Wheat Research* **2021**, *12*, 11-34.

142. Kema, G.H.; Mirzadi Gohari, A.; Aouini, L.; Gibriel, H.A.; Ware, S.B.; van Den Bosch, F.; Manning-Smith, R.; Alonso-Chavez, V.; Helps, J.; Ben M'Barek, S. Stress and sexual reproduction affect the dynamics of the wheat pathogen effector AvrStb6 and strobilurin resistance. *Nature Genetics* **2018**, *50*, 375-380.

143. Yang, F.; Melo-Braga, M.N.; Larsen, M.R.; Jørgensen, H.J.; Palmisano, G. Battle through signaling between wheat and the fungal pathogen Septoria tritici revealed by proteomics and phosphoproteomics. *Molecular & Cellular Proteomics* **2013**, *12*, 2497-2508.

144. Yang, F.; Yin, Q. Comprehensive proteomic analysis of the wheat pathogenic fungus Zymoseptoria tritici. *Proteomics* **2016**, *16*, 98-101.

145. Vannini, C.; Domingo, G.; Fiorilli, V.; Seco, D.G.; Novero, M.; Marsoni, M.; Wisniewski-Dye, F.; Bracale, M.; Moulin, L.; Bonfante, P. Proteomic analysis reveals how pairing of a Mycorrhizal fungus with plant growth-promoting bacteria modulates growth and defense in wheat. *Plant, Cell & Environment* **2021**, *44*, 1946-1960.

146. Maytalman, D.; Mert, Z.; Baykal, A.; Inan, C.; Gunel, A.; Hasançebi, S. Proteomic analysis of early responsive resistance proteins of wheat (Triticum aestivum) to yellow rust (Puccinia striiformis f. sp. tritici) using ProteomeLab PF2D. *Plant Omics* **2013**, *6*, 24-35.

147. Wang, J.; Diaz, J.; Hua, K.; Bellizzi, M.; Qi, L.; Zhu, L.; Qu, M.; Wang, G.-L. Proteomic identification of apoplastic proteins from rice, wheat, and barley after Magnaporthe oryzae infection. *Phytopathology Research* **2024**, *6*, 55.

148. He, T.; Xu, T.; Muhae-Ud-Din, G.; Guo, Q.; Liu, T.; Chen, W.; Gao, L. iTRAQ-based proteomic analysis of wheat (Triticum aestivum) spikes in response to *Tilletia controversa* Kühn and *Tilletia foetida* Kühn infection, causal organisms of dwarf bunt and common bunt of wheat. *Biology* **2022**, *11*, 865.

<figure><img src="image_26.png" /></figure>
149. Qiao, F.; Yang, X.; Xu, F.; Huang, Y.; Zhang, J.; Song, M.; Zhou, S.; Zhang, M.; He, D. TMT-based quantitative proteomic analysis reveals defense mechanism of wheat against the crown rot pathogen *Fusarium pseudograminearum*. *BMC Plant Biology* **2021**, *21*, 1-17.

150. Yang, M.; Wang, X.; Dong, J.; Zhao, W.; Alam, T.; Thomashow, L.S.; Weller, D.M.; Gao, X.; Rustgi, S.; Wen, S. Proteomics reveals the changes that contribute to Fusarium head blight resistance in wheat. *Phytopathology®* **2021**, *111*, 386-397.

151. Fabre, F.; Urbach, S.; Roche, S.; Langin, T.; Bonhomme, L. Proteomics-based data integration of wheat cultivars facing *Fusarium graminearum* strains revealed a core-responsive pattern controlling Fusarium head blight. *Frontiers in Plant Science* **2021**, *12*, 644810.

152. Agrawal, Y.; Agarwal, D.; Ilyas, A.; Sharma, S.; Myskova, A.; Poddar, N.K. Patterns of protein expression in wheat under stress conditions and its identification by proteomics tools. In *Abiotic Stresses in Wheat*; Elsevier: 2023; pp. 247-258.

153. Wang, Q.; Guo, J.; Jin, P.; Guo, M.; Guo, J.; Cheng, P.; Li, Q.; Wang, B. Glutathione S-transferase interactions enhance wheat resistance to powdery mildew but not wheat stripe rust. *Plant Physiology* **2022**, *190*, 1418-1439.

154. Fu, Y.; Zhang, H.; Mandal, S.N.; Wang, C.; Chen, C.; Ji, W. Quantitative proteomics reveals the central changes of wheat in response to powdery mildew. *Journal of Proteomics* **2016**, *130*, 108-119.

155. Bhatta, M.; Morgounov, A.; Belamkar, V.; Wegulo, S.N.; Dababat, A.A.; Erginbas-Orakci, G.; Bouhssini, M.E.; Gautam, P.; Poland, J.; Akci, N. Genome-wide association study for multiple biotic stress resistance in synthetic hexaploid wheat. *International Journal of Molecular Sciences* **2019**, *20*, 3667.

156. Islam, M.T.; Lee, B.-R.; La, V.H.; Bae, D.-W.; Jung, W.-J.; Kim, T.-H. Label-free quantitative proteomics analysis in susceptible and resistant *Brassica napus* cultivars infected with *Xanthomonas campestris* pv. campestris. *Microorganisms* **2021**, *9*, 253.

157. Struck, C.; Rüsch, S.; Strehlow, B. Control strategies of clubroot disease caused by *Plasmodiophora brassicae*. *Microorganisms* **2022**, *10*, 620.

158. Adhikary, D.; Mehta, D.; Uhrig, R.G.; Rahman, H.; Kav, N.N. A Proteome-level investigation into Plasmodiophora brassicae resistance in *Brassica napus* canola. *Frontiers in Plant Science* **2022**, *13*, 860393.

159. Moon, J.Y.; Kim, S.T.; Choi, G.J.; Kwon, S.-Y.; Cho, H.S.; Kim, H.-S.; Moon, J.S.; Park, J.M. Comparative proteomic analysis of host responses to *Plasmodiophora brassicae* infection in susceptible and resistant *Brassica oleracea*. *Plant Biotechnology Reports* **2020**, *14*, 263-274.

160. Singh, J.; Yadav, P.; Budhlakoti, N.; Mishra, D.C.; Bhardwaj, N.R.; Rao, M.; Sharma, P.; Gupta, N.C. Exploration of the *Sclerotinia sclerotiorum-Brassica* pathosystem: advances and perspectives in omics studies. *Molecular Biology Reports* **2024**, *51*, 1-15.

161. Liang, Y.; Srivastava, S.; Rahman, M.H.; Strelkov, S.E.; Kav, N.N. Proteome changes in leaves of *Brassica napus* L. as a result of *Sclerotinia sclerotiorum* challenge. *Journal of Agricultural and Food Chemistry* **2008**, *56*, 1963-1976.

162. Garg, H.; Li, H.; Sivasithamparam, K.; Barbetti, M.J. Differentially expressed proteins and associated histological and disease progression changes in cotyledon tissue of a resistant and susceptible genotype of *Brassica napus* infected with *Sclerotinia sclerotiorum*. *PLoS One* **2013**, *8*, e65205.

163. Liang, Y.; Rahman, M.H.; Strelkov, S.E.; Kav, N.N. Developmentally induced changes in the sclerotial proteome of *Sclerotinia sclerotiorum*. *Fungal Biology* **2010**, *114*, 619-627.

164. Cao, J.-Y.; Xu, Y.-P.; Cai, X.-Z. TMT-based quantitative proteomics analyses reveal novel defense mechanisms of *Brassica napus* against the devastating necrotrophic pathogen *Sclerotinia sclerotiorum*. *Journal of Proteomics* **2016**, *143*, 265-277.

165. Singh, M.; Avtar, R.; Lakra, N.; Hooda, E.; Singh, V.K.; Bishnoi, M.; Kumari, N.; Punia, R.; Kumar, N.; Choudhary, R.R. Genetic and proteomic basis of sclerotinia stem rot resistance in Indian mustard [*Brassica juncea* (L.) czern & coss.]. *Genes* **2021**, *12*, 1784.

166. Cheng, X.; Zhao, C.; Gao, L.; Zeng, L.; Xu, Y.; Liu, F.; Huang, J.; Liu, L.; Liu, S.; Zhang, X. Alternative splicing reprogramming in fungal pathogen *Sclerotinia sclerotiorum* at different infection stages on *Brassica napus*. *Frontiers in Plant Science* **2022**, *13*, 1008665.

<figure><img src="image_27.png" /></figure>
167. Kamburova, V.; Salakhutdinov, I.; Abdurakhmonov, I.Y. Cotton breeding in the view of abiotic and biotic stresses: challenges and perspectives. In *Cotton*, Abdurakhmonov, I.Y., Ed.; IntechOpen: 2022.

168. Beckman, C. Cell irritability and localization of vascular infections in plants. *Phytopathology* **1966**, *56*, 821-824.

169. Aydın, M.H. *Rhizoctonia solani* and its biological control. *Türkiye Tarımsal Araştırmalar Dergisi* **2022**, *9*, 118-135.

170. Jalloul, A.; Sayegh, M.; Champion, A.; Nicole, M. Bacterial blight of cotton. *Phytopathologia Mediterranea* **2015**, *54*, 3-20.

171. Palanga, K.K.; Liu, R.; Ge, Q.; Gong, J.; Li, J.; Lu, Q.; Li, P.; Yuan, Y.; Gong, W. Current advances in pathogen-plant interaction between *Verticillium dahliae* and cotton provide new insight in the disease management. *Journal of Cotton Research* **2021**, *4*, 1-13.

172. Wang, F.X.; Ma, Y.P.; Yang, C.L.; Zhao, P.M.; Yao, Y.; Jian, G.L.; Luo, Y.M.; Xia, G.X. Proteomic analysis of the sea-island cotton roots infected by wilt pathogen *Verticillium dahliae*. *Proteomics* **2011**, *11*, 4296-4309.

173. Guo, J.; Cao, P.; Yuan, L.; Xia, G.; Zhang, H.; Li, J.; Wang, F. Revealing the contribution of *GbPR10.5D1* to resistance against *Verticillium dahliae* and its regulation for structural defense and immune signaling. *The Plant Genome* **2022**, *15*, e20271.

174. Guo, W.; Jin, L.; Miao, Y.; He, X.; Hu, Q.; Guo, K.; Zhu, L.; Zhang, X. An ethylene response-related factor, GbERF1-like, from *Gossypium barbadense* improves resistance to *Verticillium dahliae* via activating lignin synthesis. *Plant Molecular Biology* **2016**, *91*, 305-318.

175. Lu, T.; Zhu, L.; Liang, Y.; Wang, F.; Cao, A.; Xie, S.; Chen, X.; Shen, H.; Wang, B.; Hu, M. Comparative proteomic analysis reveals the ascorbate peroxidase-mediated plant resistance to *Verticillium dahliae* in *Gossypium barbadense*. *Frontiers in Plant Science* **2022**, *13*, 877146.

176. Umer, M.J.; Batool, R.; Yang, M.; Zheng, J.; Nazir, M.F.; Wang, H.; Cai, X.; Hou, Y.; Xu, Y.; Wang, Y. Unravelling the Functional Role of GthGAPC2 in Cotton's Defense Against Verticillium dahliae through Proteome. *Physiologia Plantarum* **2024**, *176*, e14127.

177. Mi, X.; Li, W.; Chen, C.; Xu, H.; Wang, G.; Jin, X.; Zhang, D.; Guo, W. *GhMPK9-GhRAF39_1-GhWRKY40a* regulates the *GhERF1b-and GhABF2-mediated* pathways to increase cotton disease resistance. *Advanced Science* **2024**, 2404400.

178. Han, Z.; Qiu, Y.; Pan, T.; Wang, L.; Wang, J.; Liu, K. *GhMAC3e* is involved in plant growth and defense response to *Verticillium dahliae*. *Plant Cell Reports* **2024**, *43*, 259.

179. Zhang, Y.; Shi, Y.; Zhao, L.; Wei, F.; Feng, Z.; Feng, H. Phosphoproteomics profiling of cotton (*Gossypium hirsutum* L.) roots in response to *Verticillium dahliae* inoculation. *ACS omega* **2019**, *4*, 18434-18443.

180. Huang, W.; Zhang, Y.; Zhou, J.; Wei, F.; Feng, Z.; Zhao, L.; Shi, Y.; Feng, H.; Zhu, H. The respiratory burst oxidase homolog protein D (*GhRbohD*) positively regulates the cotton resistance to *Verticillium dahliae*. *International Journal of Molecular Sciences* **2021**, *22*, 13041.

181. Khan, M.A.; Khan, S.A.; Waheed, U.; Raheel, M.; Khan, Z.; Alrefaei, A.F.; Alkhamis, H.H. Morphological and genetic characterization of *Fusarium oxysporum* and its management using weed extracts in cotton. *Journal of King Saud University-Science* **2021**, *33*, 101299.

182. Bleackley, M.R.; Samuel, M.; Garcia-Ceron, D.; McKenna, J.A.; Lowe, R.G.; Pathan, M.; Zhao, K.; Ang, C.-S.; Mathivanan, S.; Anderson, M.A. Extracellular vesicles from the cotton pathogen *Fusarium oxysporum* f. sp. *vasinfectum* induce a phytotoxic response in plants. *Frontiers in Plant Science* **2020**, *10*, 1610.

183. Wang, C.; He, X.; Li, Y.; Wang, L.; Guo, X.; Guo, X. The cotton MAPK kinase GhMPK20 negatively regulates resistance to *Fusarium oxysporum* by mediating the MKK4-MPK20-WRKY40 cascade. *Molecular Plant Pathology* **2018**, *19*, 1624-1638.

184. Wang, C.; Guo, H.; He, X.; Zhang, S.; Wang, J.; Wang, L.; Guo, D.; Guo, X. Scaffold protein GhMORG1 enhances the resistance of cotton to *Fusarium oxysporum* by facilitating the MKK6-MPK4 cascade. *Plant Biotechnology Journal* **2020**, *18*, 1421-1433.

185. Zhang, M.; Cheng, S.-T.; Wang, H.-Y.; Wu, J.-H.; Luo, Y.-M.; Wang, Q.; Wang, F.-X.; Xia, G.-X. iTRAQ-based proteomic analysis of defence responses triggered by the necrotrophic pathogen *Rhizoctonia solani* in cotton. *Journal of Proteomics* **2017**, *152*, 226-235.

<figure><img src="image_28.png" /></figure>
206. Padula, M.P.; Berry, I.J.; O'Rourke, M.B.; Raymond, B.B.; Santos, J.; Djordjevic, S.P. A comprehensive guide for performing sample preparation and top-down protein analysis. *Proteomes* **2017**, *5*, 11.

207. Duong, V.-A.; Lee, H. Bottom-up proteomics: advancements in sample preparation. *International Journal of Molecular Sciences* **2023**, *24*, 5350.

208. Carbonara, K.; Andonovski, M.; Coorssen, J.R. Proteomes are of proteoforms: embracing the complexity. *Proteomes* **2021**, *9*, 38.

209. Coorssen, J.R.; Padula, M.P. Proteomics—the state of the field: the definition and analysis of proteomes should be based in reality, not convenience. *Proteomes* **2024**, *12*, 14.

210. Fornelli, L.; Toby, T.K. Characterization of large intact protein ions by mass spectrometry: What directions should we follow? *Biochimica et Biophysica Acta (BBA)-Proteins and Proteomics* **2022**, *1870*, 140758.

211. Schaffer, L.V.; Millikin, R.J.; Miller, R.M.; Anderson, L.C.; Fellers, R.T.; Ge, Y.; Kelleher, N.L.; LeDuc, R.D.; Liu, X.; Payne, S.H. Identification and quantification of proteoforms by mass spectrometry. *Proteomics* **2019**, *19*, 1800361.

212. Kaulich, P.T.; Tholey, A. Top-Down Proteomics: Why and When? *Proteomics* **2025**, e202400338.

213. Cassidy, L.; Kaulich, P.T.; Tholey, A. Proteoforms expand the world of microproteins and short open reading frame-encoded peptides. *IsScience* **2023**, *26*.

214. Poulos, R.C.; Hains, P.G.; Shah, R.; Lucas, N.; Xavier, D.; Manda, S.S.; Anees, A.; Koh, J.M.; Mahboob, S.; Wittman, M. Strategies to enable large-scale proteomics for reproducible research. *Nature Communications* **2020**, *11*, 3793.

215. Fernández-Costa, C.; Martínez-Bartolomé, S.; McClatchy, D.; Yates III, J.R. Improving proteomics data reproducibility with a dual-search strategy. *Analytical Chemistry* **2019**, *92*, 1697-1701.

216. Bacala, R.; Dave W. Hatcher; Hélène Perreault; and; Fu, B.X. Challenges and opportunities for proteomics and the improvement of bread wheat quality. *Journal of Plant Physiology* **2022**, *275*, 153743.

217. Niu, L.; Yuan, H.; Gong, F.; Wu, X.; Wang, W. Protein extraction methods shape much of the extracted proteomes. *Frontiers in Plant Science* **2018**, *9*, 802.

218. Jin, X.; Zhu, L.; Tao, C.; Xie, Q.; Xu, X.; Chang, L.; Tan, Y.; Ding, G.; Li, H.; Wang, X. An improved protein extraction method applied to cotton leaves is compatible with 2-DE and LC-MS. *BMC genomics* **2019**, *20*, 1-11.

219. Mujahid, H.; Pendarvis, K.; Reddy, J.S.; Nallamilli, B.R.R.; Reddy, K.; Nanduri, B.; Peng, Z. Comparative proteomic analysis of cotton fiber development and protein extraction method comparison in late stage fibers. *Proteomes* **2016**, *4*, 7.

220. Bose, U.; Broadbent, J.A.; Byrne, K.; Hasan, S.; Howitt, C.A.; Colgrave, M.L. Optimisation of protein extraction for in-depth profiling of the cereal grain proteome. *Journal of Proteomics* **2019**, *197*, 23-33.

221. Liu, Z.; Sun, Z.; Mao, J.; Zhang, Y.; Zhou, L.; Wei, J.; Liu, Y.; Wang, L.; Liu, F.; Cui, F. Accelerating wheat (*Triticum aestivum*) proteomics using a tandem digestion approach. *Journal of Cereal Science* **2025**, *123*, 104133.

222. Rahman, M.; Lei Liu; and; Barkla, B.J. A single seed protein extraction protocol for characterizing Brassica seed storage proteins. *Agronomy* **2021**, *11*, 107.

223. Alaux, M.; Rogers, J.; Letellier, T.; Flores, R.; Alfama, F.; Pommier, C.; Mohellibi, N.; Durand, S.; Kimmel, E.; Michotey, C. Linking the International Wheat Genome Sequencing Consortium bread wheat reference genome sequence to wheat genetic and phenomic data. *Genome Biology* **2018**, *19*, 111.

224. Yu, J.; Zhao, M.; Wang, X.; Tong, C.; Huang, S.; Tehrim, S.; Liu, Y.; Hua, W.; Liu, S. Bolbase: a comprehensive genomics database for *Brassica oleracea*. *BMC Genomics* **2013**, *14*, 1-7.

225. Chen, H.; Wang, T.; He, X.; Cai, X.; Lin, R.; Liang, J.; Wu, J.; King, G.; Wang, X. BRAD V3.0: an upgraded Brassicaceae database. *Nucleic Acids Research* **2022**, *50*, D1432-D1441.

226. Yu, J.; Jung, S.; Cheng, C.-H.; Lee, T.; Zheng, P.; Buble, K.; Crabb, J.; Humann, J.; Hough, H.; Jones, D. CottonGen: the community database for cotton genomics, genetics, and breeding research. *Plants* **2021**, *10*, 2805.

227. Ma, S.; Wang, M.; Wu, J.; Guo, W.; Chen, Y.; Li, G.; Wang, Y.; Shi, W.; Xia, G.; Fu, D. WheatOmics: A platform combining multiple omics data to accelerate functional genomics studies in wheat. *Molecular Plant* **2021**, *14*, 1965-1968.

<figure><img src="image_29.png" /></figure>
228. Yang, Z.; Wang, S.; Wei, L.; Huang, Y.; Liu, D.; Jia, Y.; Luo, C.; Lin, Y.; Liang, C.; Hu, Y. BnIR: A multi-omics database with various tools for Brassica napus research and breeding. *Molecular Plant* **2023**, *16*, 775-789.

229. Dai, F.; Chen, J.; Zhang, Z.; Liu, F.; Li, J.; Zhao, T.; Hu, Y.; Zhang, T.; Fang, L. COTTONOMICS: a comprehensive cotton multi-omics database. *Database* **2022**, 2022, baac080.

230. Duncan, O.; Trösch, J.; Fenske, R.; Taylor, N.L.; Millar, A.H. Resource: mapping the *Triticum aestivum* proteome. *The Plant Journal* **2017**, *89*, 601-616.

231. Kamal, A.H.M.; Cho, K.; Choi, J.-S.; Bae, K.-H.; Komatsu, S.; Uozumi, N.; Woo, S.H. The wheat chloroplastic proteome. *Journal of Proteomics* **2013**, *93*, 326-342.

232. Jacoby, R.P.; Millar, A.H.; Taylor, N.L. Opportunities for wheat proteomics to discover the biomarkers for respiration-dependent biomass production, stress tolerance and cytoplasmic male sterility. *Journal of Proteomics* **2016**, *143*, 36-44.

233. Takemori, N.; Takemori, A.; Matsuoka, K.; Morishita, R.; Matsushita, N.; Aoshima, M.; Takeda, H.; Sawasaki, T.; Endo, Y.; Higashiyama, S. High-throughput synthesis of stable isotope-labeled transmembrane proteins for targeted transmembrane proteomics using a wheat germ cell-free protein synthesis system. *Molecular BioSystems* **2015**, *11*, 361-365.

234. Schoof, E.M.; Benjamin Furtwängler; Nil Üresin; Nicolas Rapin; Simonas Savickas; Coline Gentil; Eric Lechman; Ulrich auf dem Keller; John E. Dick; and; et al. Quantitative single-cell proteomics as a tool to characterize cellular hierarchies. *Nature Communications* **2021**, *12*, 3341.

235. Fels, U.; Gevaert, K.; Van Damme, P. Proteogenomics in aid of host-pathogen interaction studies: a bacterial perspective. *Proteomes* **2017**, *5*, 26.

236. Balotf, S.; Wilson, R.; Tegg, R.S.; Nichols, D.S.; Wilson, C.R. Shotgun proteomics as a powerful tool for the study of the proteomes of plants, their pathogens, and plant-pathogen interactions. *Proteomes* **2022**, *10*, 5.

237. Elmore, J.M.; Griffin, B.D.; &; Walley, J.W. Advances in functional proteomics to study plant-pathogen interactions. *Current Opinion in Plant Biology* **2021**, *63*.

238. Ravikumar, V.; Jers, C.; Mijakovic, I. Elucidating host-pathogen interactions based on post-translational modifications using proteomics approaches. *Frontiers in Microbiology* **2015**, *6*, 1313.

239. Singh, S.; Hegde, M.; Kaur, I.; Adlakha, N. Temporal proteome profiling of *Botrytis cinerea* reveals proteins involved in plant invasion and survival. *Scientific Reports* **2025**, *15*, 11857.

240. Gao, W.; Long, L.; Xu, L.; Lindsey, K.; Zhang, X.; Zhu, L. Suppression of the homeobox gene *HDTF1* enhances resistance to *Verticillium dahliae* and *Botrytis cinerea* in cotton. *Journal of Integrative Plant Biology* **2016**, *58*, 503-513.

241. Feng, L.; Zhou, J.; Zhu, D.; Gao, C. TurboID-based proximity labeling accelerates discovery of neighboring proteins in plants. *Trends in Plant Science* **2024**, *29*, 383-384.

242. Shi, L.; Ferrando, T.M.; Villanueva, S.L.; Joosten, M.H.; Vleeshouwers, V.G.; Bachem, C.W. Protocol to identify protein-protein interaction networks in *Solanum tuberosum* using transient TurboID-based proximity labeling. *STAR Protocols* **2023**, *4*, 102577.

243. Stevenson, S.E.; Ye Chu; Peggy Ozias-Akins; and; Thelen, J.J. Validation of gel-free, label-free quantitative proteomics approaches: applications for seed allergen profiling. *Journal of Proteomics* **2009**, *72*, 555-566.

244. Kwon, D. The antibodies don't work! The race to rid labs of molecules that ruin experiments. *Nature* **2024**, *635*, 26-28.

245. FR, T.; MOLEK, O. When antibodies mislead: the quest for validation. *Nature* **2020**, *585*, 313.

246. Nierves, L.A.; Lin, T.-T.; Moradian, A.; Shen, Q.; Sechi, S.; MacCoss, M.J.; Qu, J.; van Eyk, J.E.; Hoofnagle, A.N.; Qian, W.-J. Biomarkers, Proteoforms, and Mass Spectrometry-based Assays for Diabetes Clinical Research. *The Journal of Clinical Endocrinology & Metabolism* **2025**, dgaf159.

247. Su, T.; Fellers, R.T.; Greer, J.B.; LeDuc, R.D.; Thomas, P.M.; Kelleher, N.L. Proteoform-predictor: Increasing the Phylogenetic Reach of Top-Down Proteomics. *Journal of Proteome Research* **2025**, *24*, 1861-1870.

248. Alotaibi, F.; Alharbi, S.; Alotaibi, M.; Al Mosallam, M.; Motawei, M.; Alrajhi, A. Wheat omics: Classical breeding to new breeding technologies. *Saudi Journal of Biological Sciences* **2021**, *28*, 1433-1444.

249. Ma, X.-F.; Fang, P.; Gustafson, J.P. Polyploidization-induced genome variation in triticale. *Genome* **2004**, *47*, 839-848.

<figure><img src="image_30.png" /></figure>
250. Adams, K.L.; Wendel, J.F. Novel patterns of gene expression in polyploid plants. *TRENDS in Genetics* **2005**, *21*, 539-543.

251. Leitch, A.; Leitch, I. Genomic plasticity and the diversity of polyploid plants. *Science* **2008**, *320*, 481-483.

252. Schwanhäusser, B.; Busse, D.; Li, N.; Dittmar, G.; Schuchhardt, J.; Wolf, J.; Chen, W.; Selbach, M. Global quantification of mammalian gene expression control. *Nature* **2011**, *473*, 337-342.

253. Cheng, X.; Xiao, X.; Chou, K.-C. pLoc-bal-mPlant: predict subcellular localization of plant proteins by general PseAAC and balancing training dataset. *Current Pharmaceutical Design* **2018**, *24*, 4013-4022.

254. Wan, S.; Mak, M.-W.; Kung, S.-Y. Gram-LocEN: Interpretable prediction of subcellular multi-localization of Gram-positive and Gram-negative bacterial proteins. *Chemometrics and Intelligent Laboratory Systems* **2017**, *162*, 1-9.

255. Meinken, J.; Asch, D.K.; Neizer-Ashun, K.A.; Chang, G.H.; Cooper JR, C.R.; &; Min, X.J. FunSecKB2: a fungal protein subcellular location knowledgebase. *Computational Molecular Biology* **2014**, *4*.

256. Sukumaran, A.; Woroszchuk, E.; Ross, T.; Geddes-McAlister, J. Proteomics of host-bacterial interactions: new insights from dual perspectives. *Canadian Journal of Microbiology* **2021**, *67*, 213-225.

257. Shen, Y.; Zhang, Y.; Zou, J.; Meng, J.; Wang, J. Comparative proteomic study on Brassica hexaploid and its parents provides new insights into the effects of polyploidization. *Journal of proteomics* **2015**, *112*, 274-284.

258. Islam, N.; Tsujimoto, H.; Hirano, H. Proteome analysis of diploid, tetraploid and hexaploid wheat: towards understanding genome interaction in protein expression. *Proteomics* **2003**, *3*, 549-557.

259. Hu, J.; Rampitsch, C.; Bykova, N.V. Advances in plant proteomics toward improvement of crop productivity and stress resistance. *Frontiers in Plant Science* **2015**, *6*, 209.

260. Song, Y.-C.; Das, D.; Zhang, Y.; Chen, M.-X.; Fernie, A.R.; Zhu, F.-Y.; Han, J. Proteogenomics-based functional genome research: approaches, applications, and perspectives in plants. *Trends in Biotechnology* **2023**, *41*, 1532-1548.

261. Daba, S.D.; Liu, X.; Aryal, U.; Mohammadi, M. A proteomic analysis of grain yield-related traits in wheat. *AoB Plants* **2020**, *12*, plaa042.

262. Shu, J.; Ma, X.; Ma, H.; Huang, Q.; Zhang, Y.; Guan, M.; Guan, C. Transcriptomic, proteomic, metabolomic, and functional genomic approaches of Brassica napus L. during salt stress. *PLoS One* **2022**, *17*, e0262587.

263. Mi, W.; Liu, Z.; Jin, J.; Dong, X.; Xu, C.; Zou, Y.; Xu, M.; Zheng, G.; Cao, X.; Fang, X. Comparative proteomics analysis reveals the molecular mechanism of enhanced cold tolerance through ROS scavenging in winter rapeseed (Brassica napus L.). *PLoS one* **2021**, *16*, e0243292.

264. Koh, J.; Chen, G.; Yoo, M.-J.; Zhu, N.; Dufresne, D.; Erickson, J.E.; Shao, H.; Chen, S. Comparative proteomic analysis of *Brassica napus* in response to drought stress. *Journal of Proteome Research* **2015**, *14*, 3068-3081.

265. Lan, M.; Li, G.; Hu, J.; Yang, H.; Zhang, L.; Xu, X.; Liu, J.; He, J.; Sun, R. iTRAQ-based quantitative analysis reveals proteomic changes in Chinese cabbage (Brassica rapa L.) in response to *Plasmodiophora brassicae* infection. *Scientific Reports* **2019**, *9*, 12058.

266. Jiang, J.; Shi, Z.; Ma, F.; Liu, K. Identification of key proteins related to high-quality fiber in Upland cotton via proteomics analysis. *Plant Cell Reports* **2022**, *41*, 893-904.

267. Xiao, S.; Liu, L.; Zhang, Y.; Sun, H.; Zhang, K.; Bai, Z.; Dong, H.; Liu, Y.; Li, C. Tandem mass tag-based (TMT) quantitative proteomics analysis reveals the response of fine roots to drought stress in cotton (Gossypium hirsutum L.). *BMC Plant Biology* **2020**, *20*, 1-18.

268. Zheng, M.; Meng, Y.; Yang, C.; Zhou, Z.; Wang, Y.; Chen, B. Protein expression changes during cotton fiber elongation in response to drought stress and recovery. *Proteomics* **2014**, *14*, 1776-1795.

<figure><img src="image_31.png" /></figure>
269. Zhang, X.; Feng, Y.; Khan, A.; Ullah, N.; Li, Z.; Zaheer, S.; Zhou, R.; Zhang, Z. Quantitative proteomics-based analysis reveals molecular mechanisms of chilling tolerance in grafted cotton seedlings. *Agronomy* **2022**, *12*, 1152.

270. Peng, Z.; He, S.; Gong, W.; Xu, F.; Pan, Z.; Jia, Y.; Geng, X.; Du, X. Integration of proteomic and transcriptomic profiles reveals multiple levels of genetic regulation of salt tolerance in cotton. *BMC Plant Biology* **2018**, *18*, 1-19.

**Disclaimer/Publisher's Note:** The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

<figure><img src="image_32.png" /></figure>