1. Introduction
Cnidarians, such as scleractinian corals (Anthozoa) and hydrocorals (Hydrozoa), are responsible for the formation and maintenance of coral reefs, which are ecosystems that play an essential role in maintaining oceanic biodiversity and supporting human communities [1,2,3,4]. A key aspect of this ecological success lies in their mutualistic relationship with photosynthetic algae from the Symbiodiniaceae family. This symbiosis is fundamental for coral energy acquisition and skeletal development, as the algae provide over 95% of their photosynthetically fixed carbon to the host, facilitating calcium carbonate deposition and reef accretion [5,6,7]. However, this partnership is highly sensitive to environmental stressors associated with climate change, particularly ocean warming and acidification [8]. Such stress can disrupt the physiological equilibrium of the holobiont, impairing cellular homeostasis and triggering oxidative stress responses. A primary manifestation of this breakdown is coral bleaching, which is a process in which the accumulation of reactive oxygen species (ROS) causes the cnidarian to expel its symbiotic algae, resulting in a loss of pigmentation and exposure of the white calcium carbonate skeleton [9,10]. This disruption not only affects individual colonies but also contributes to broader declines in coral reef resilience. Climate-driven disturbances have severely impacted marine ecosystem resilience [11], and widespread bleaching events have resulted in significant coral mortality and rapid reef degradation worldwide [12]. According to the National Oceanic and Atmospheric Administration (NOAA), 2014–2017 were among the warmest years recorded in the Earth’s history. During this period of time, the third global-scale bleaching event was registered, affecting more than 75% of the coral reefs around the world, 30% of which suffered mortality [13]. Alarmingly, the frequency and severity of these events continue to rise. By 2080, coral bleaching is expected to commence in spring, overlapping with critical stages of the coral life cycle and increasing the risk of long-term population decline [14].
While several studies have explored the impact of elevated temperatures on reef-forming cnidarians using omics approaches, most of this research has focused on anthozoan species. Genomic and transcriptomic analyses have revealed that thermal stress modulates the expression of genes involved in diverse cellular processes. These include pathways related to growth regulation, protein homeostasis, metabolic reprogramming, and stress adaptation mechanisms. Specifically, changes have been observed in genes regulating growth arrest, chaperone activity, nucleic acid stabilization, macromolecule degradation, energy metabolism, oxidative stress response, calcium signaling, cytoskeletal dynamics, protein synthesis, apoptosis, phagocytic processes, and immune function [15,16,17,18,19,20,21,22,23]. In contrast, proteomic studies investigating the effects of thermal stress remain limited and have been primarily conducted on anthozoans such as Acropora microphthalma [24], Acropora palmata [25], Aiptasia spp. [26], Pocillopora acuta [27], Seriatopora hystrix [28], Acropora millepora [29], Montipora capitata [30], Orbicella faveolata [31,32,33], and Acropora hyacinthus [34]. Although some omics studies on M. complanata have recently emerged, further proteomic research is needed to enhance our understanding of its molecular response to natural thermal stress. This study contributes to that effort by providing a comparative analysis of bleached and non-bleached M. complanata fragments collected during a large-scale bleaching event.
Regarding reef-forming hydrozoans, two previous studies demonstrated bleaching-related changes in the abundance of soluble proteins extracted from Millepora alcicornis and M. complanata exposed to the 2015–2016 El Niño–Southern Oscillation in the Mexican Caribbean. A differential abundance of proteins related to exocytosis, calcium homeostasis, cytoskeleton organization, and toxins was detected in M. alcicornis [35]. Similarly, differentially abundant proteins involved in key cellular processes, such as glycolysis, DNA repair, stress response, calcium homeostasis, exocytosis, and cytoskeleton organization, were found in bleached M. complanata [36]. Although these studies have provided valuable insights into the effects of bleaching on hydrocorals, it is important to further investigate the impact of elevated water temperatures on the proteome of these organisms in order to gain a deeper understanding of the molecular processes involved in the heat stress response of reef-forming hydrozoans. In this study, we conducted a comparative proteomic analysis of bleached and non-bleached M. complanata specimens collected after the 2015–2016 El Niño–Southern Oscillation (ENSO) using liquid chromatography–tandem mass spectrometry (LC–MS/MS). Our aim was to provide new insights into the molecular phenotype changes in hydrocorals induced by the disruption of the Millepora–Symbiodiniaceae symbiosis.
2. Materials and Methods
2.1. Sample Collection
Non-bleached and bleached M. complanata fragments were collected during SCUBA dives using a chisel and hammer in the Parque Nacional Arrecife de Puerto Morelos, Quintana Roo, México (21°00′ N, 86°460′ W), in the area known as “La Bocana Chica”, on 25 November 2016 at 10:00 A.M., 5 months after the 2015–2016 ENSO event. Hydrocoral fragments were retrieved by divers from two different colonies at a depth of approximately 4 to 5 m: one colony provided ten fragments of non-bleached hydrocoral, and the other provided ten fragments of bleached hydrocoral. Due to permit restrictions and the logistical challenges of fieldwork in marine protected areas, sampling was limited to one colony per condition. Immediately after collection, samples were frozen in liquid nitrogen and kept at ultra-low temperatures until their processing at the Autonomous University of Querétaro, in Querétaro, Mexico. The bleaching condition of the colonies was later confirmed based on chlorophyll a and c2 content, as reported by Hernández-Elizárraga et al. [37]. Sea-water temperature records for November 2016 and previous years were obtained from
2.2. Proteome Extraction from Non-Bleached and Bleached Fragments of M. complanata Hydrocoral
Ten non-bleached hydrocoral fragments and ten bleached fragments of ≈10 g were placed in a sterile Petri dish at 4 °C. Subsequently, 5 mL of Tris-HCl extraction solution (50 mM Tris, 0.1 M NaCl, 5 mM EDTA, 20 mM β-mercaptoethanol; pH = 7.5) was added to each fragment and the surfaces were scraped using a Dremel Engraver glass scratcher [39]. The extracts obtained were collected in the Petri dish and then transferred to Falcon tubes for centrifugation at 10,000× g for 40 min at 4 °C to precipitate the excess CaCO3 and the largest cell components. Afterwards, the recovered supernatants were filtered through 0.45 µm Titan 3 nylon acrodisc filters (Thermo Fisher Scientific, Waltham, MA, USA) and the filtrates were lyophilized. Then, the lyophilized samples were prepared for dialysis by resuspending them in 1 mL of distilled and deionized water, followed by 24 h of dialysis using a 3 kDa membrane. Water was changed every 6–8 h. The dialyzed solutions were lyophilized and stored at −70 °C.
2.3. Proteomic Analysis of Non-Bleached and Bleached Extracts of M. complanata LC-MS/MS
Proteome extracts from non-bleached and bleached M. complanata fragments were resuspended in 0.1% RapiGest SF (Waters Corporation, Milford, MA, USA) dissolved in 50 mM ammonium bicarbonate buffer (Sigma-Aldrich, St. Louis, MO, USA). Subsequently, the samples were reduced with 10 mM dithiothreitol (Sigma-Aldrich, St. Louis, MO, USA) for 45 min at 60 °C and then alkylated with 55 mM iodoacetamide (Sigma-Aldrich, St. Louis, MO, USA) for 30 min at room temperature in the dark. Protein digestion was performed overnight at 37 °C using Trypsin Mass Spec Grade (Promega Corporation, Madison, WI, USA) at a protein-to-enzyme ratio of 50:1. Afterwards, salts were removed using a 1 cc Sep-Pak C18 Vac cartridge (Waters Corporation, Milford, MA, USA), and, finally, the samples were acidified with 0.1% formic acid (FA) (Sigma-Aldrich, St. Louis, MO, USA).
2.4. LC-MS/MS
Following sample preparation, liquid chromatography–tandem mass spectrometry analysis was performed on a Dionex Ultimate 3000 RSLC nano system coupled to an Orbitrap Fusion Lumos Tribrid Mass Spectrometer (Thermo Fisher Scientific, Waltham, MA, USA). Approximately 1.5 µg of digested peptides was dissolved in methanol (≥99.9% GC), suitable for LC/MS (LiChrosolv, Supelco, Merck, Darmstadt, Germany), and loaded onto an in-house packed column (15 cm length, 1.9 μm inner diameter; packed with Reprosil-Pur 120 C18, Dr. Maisch HPLC GmbH, Ammerbuch, Germany). Mobile phase A consisted of 98% water (LC-MS Chromasolv, Burdick & Jackson, Honeywell, Charlotte, NC, USA); 2% acetonitrile (ACN) (≥99.9% GC), suitable for LC/MS (LiChrosolv, Supelco, Merck, Darmstadt, Germany); and 0.1% formic acid (FA) (LC-MS-grade; Thermo Scientific, Waltham, MA, USA), while mobile phase B consisted of 98% ACN, 2% water, and 0.1% FA. The peptides were eluted using a 2–60% gradient of mobile phase B over 60 min at a flow rate of 400 nL/min. The mass spectrometer was operated in data-dependent acquisition (DDA) mode, and full MS scans were acquired from 350 to 1600 m/z at a resolution of 60,000 at m/z 200.
2.5. Database Search and Protein Identification
Raw MS files were processed using Sequest (Thermo Fisher Scientific, Waltham, MA, USA; version IseNode in Proteome Discoverer 1.4.1.14) and X! Tandem [The GPM, thegpm.org; version X! Tandem Alanine (2017.2.1.4)]. The protein sequence database was generated by retrieving all available Millepora protein entries from UniProtKB using the keyword “Millepora”. The resulting sequences were compiled in a custom FASTA file (Supplementary Materials, uniprot-Millepora-2022.09.09-Database.FASTA) for subsequent protein identification. Sequest was set up to search uniprot-Millepora-2022.09.09-Database.FASTA, assuming trypsin digestion, and X! Tandem were set up to search for a reverse concatenated Millepora UniProt 2023 database and a reverse concatenated Millepora UniProt 2023 database, also assuming trypsin digestion. The Sequest and X! Tandem search parameters were set to a parent ion mass tolerance of 20 ppm and a fragment ion mass tolerance of 0.6 Da. Carbamidomethyl of cysteine was specified in Sequest and X! Tandem as a fixed modification. Methionine oxidation and deamidation of asparagine and glutamine were set as variable modifications in Sequest and X! Tandem, while Glu->pyro-Glu of the N-terminus, ammonia-loss of the N-terminus, and Gln->pyro-Glu of the N-terminus were specified in X! Tandem as variable modifications.
2.6. Protein Identification and Validation Criteria
The Scaffold software (version Scaffold_5.2.0; Proteome Software Inc., Portland, OR, USA) was used to validate MS/MS-based peptide and protein identifications. Peptide matches were accepted if they achieved a probability greater than 87.0%, corresponding to a false discovery rate (FDR) of less than 5.0%. Percolator was used to estimate the posterior error probability (the probability of incorrect peptide assignment), and peptide probabilities were assigned by the Peptide Prophet algorithm [40]. Protein matches were accepted if they could be established at a probability greater than 99.0% and if they contained at least two identified peptides. Protein probabilities were calculated by the Protein Prophet algorithm [41]. Proteins sharing similar peptides that could not be distinguished based solely on MS/MS data were grouped to ensure the most parsimonious representation of the protein list.
2.7. Functional Annotation and Gene Ontology Term Assignment
The functional annotation of the identified proteins was conducted using the Blast2GO software (version 6.0.3; BioBam Bioinformatics, Valencia, Spain). Protein sequences in FASTA format were analyzed with BLASTp against the NCBI nr database, applying a taxonomy filter for Cnidaria (taxa ID: 6073) and an e-value threshold of 1.0 × 10−3. The top 5 alignments per sequence were retained for subsequent mapping to Gene Ontology (GO) terms. Annotation parameters included an annotation cutoff score of 55, a GO weight of 5, a taxonomy filter for Cnidaria, an e-value hit filter of 1.0 × 10−6, and a maximum of 500 hits per sequence. GO terms were classified into biological processes, molecular functions, and cellular components, providing insights into the functional roles of the identified proteins in the context of coral bleaching.
2.8. Statistical Analysis
A spectral counting method to infer protein abundance was employed, where the number of identified MS/MS spectra directly correlates with a protein’s relative abundance. To ensure accuracy and reliability, the spectral counting data underwent normalization and statistical treatment [42,43]. Due to the data’s uneven distribution, a generalized log transformation function (glog(y) = log(y + sqrt(y2 + lambda)) was applied, with λ set to 1 × 10−9 for optimal fit. The analysis involved both univariate and multivariate approaches. Univariate analyses included Fisher’s exact test with a Bonferroni correction for multiple testing and permutation tests. Multivariate analyses consisted of pairwise correlation analysis, principal component analysis (PCA), and partial least-squares discriminant analysis (PLS-DA). Cross-validation of multivariate analysis was performed using the venetian blinds method. Variable Importance in Projection (VIP) scores were used to assess the relative influence of latent variables and identify significant biomarkers. Fold changes, expressed as log2 values, were calculated to quantify protein abundance changes. Univariate statistical analyses were conducted in RStudio 2022.07.2 (RStudio, PBC, Boston, MA, USA) and MATLAB version R2019b (The MathWorks, Inc., Natick, MA, USA) using the Permutation Test extension (available at
3. Results
3.1. Protein Quantification and Environmental Context of the Bleaching Event
After sample collection, a proteomic analysis was conducted on ten fragments from each condition. Non-bleached fragments exhibited an average protein concentration of 38.96 ± 0.84 µg/mL, whereas bleached hydrocorals had an average protein concentration of 38.01 ± 1.37 µg/mL. Based on information from NOAA’s Coral Reef Watch satellite monitoring, the Mexican Caribbean experienced a level 1 alert for thermal stress between August and November 2016, with a 60% likelihood of coral bleaching (sources:
3.2. Proteomic Profiling of M. complanata Under Bleaching Conditions
Following sample preparation, liquid chromatography–tandem mass spectrometry (LC-MS) analysis was employed to compare the proteomes of bleached and non-bleached M. complanata fragments. A total of 1030 proteins were initially identified. After filtering out those that did not meet the decoy criteria, 639 proteins remained. Subsequently, to further refine the dataset and eliminate potentially false-positive peptide identifications, a 5% FDR threshold was applied, resulting in 102 biologically relevant proteins. These 102 biologically relevant proteins were selected for subsequent statistical analyses to identify proteins with differential abundance between the two conditions. These proteins spanned a broad range of molecular weights, ranging from 9 to 261 kDa, in the proteomes of non-bleached and bleached hydrocorals (Supplementary Table S1).
3.3. Statistical Analysis of Spectral Count Data
The strategy employed to identify proteins with significantly different abundances—aimed at understanding the molecular response of M. complanata to bleaching, as well as the relationships and correlations among these proteins—combined both univariate and multivariate statistical methods.
3.3.1. Univariate Analyses
Of the 102 proteins evaluated using Fisher’s exact test (Supplementary Table S1), 26 had an adjusted p-value below 0.05 and 41 had an adjusted p-value below 0.10. Therefore, a relaxed threshold of p < 0.10 was applied to enhance statistical power under conditions of high correlation, as commonly practiced in exploratory proteomic studies [44,45]. However, a complementary criterion for final protein selection, based on variable importance in projection (VIP) values from the multivariate analysis, was also considered, as described below. The results of the permutation tests used to assess differences in means and effect sizes are presented in Table 1. Notably, the results showed strong consistency with observed fold changes.
3.3.2. Multivariate Analyses
Pairwise correlation analysis explored the relationships between proteins, revealing predominantly significant positive correlations, along with some negative ones, across 5151 comparisons among the 52 selected proteins. A summary of the results is presented in Supplementary Figure S1. Principal component analysis (PCA) identified independent sources of variation in the dataset. A two-component model explained 84.80% of the total variability, with no outliers detected. Although some separation between bleached and non-bleached groups was observed, complete distinction was not achieved (Supplementary Figure S2).
Partial least-squares discriminant analysis (PLS-DA) enabled the selection of a three-latent variable model that explained 85.56% of protein expression variation and 81.56% of classification category variation (Figure 1). This model exhibited clear separation between the two sample groups and demonstrated robustness, as confirmed by receiver operating characteristic (ROC) curve analysis, with area under the curve (AUC) values of 1.0000 for validation and 0.8750 for cross-validation. Sensitivity and specificity reached 1.000 during calibration, while cross-validation values were 0.714 and 0.875, respectively.
Thirty-five proteins with variable importance in projection (VIP) scores greater than 1 were selected, following standard practice (Supplementary Figure S3; Soft Independent Modeling of Class Analogy, SIMCA, Umetrics, Umeå, Sweden) [46]. As not all proteins with significant differences in univariate tests had VIP scores > 1, this study highlights the complementary nature of univariate and multivariate methods. While univariate analyses assess independent changes, multivariate methods examine interrelationships and complementary patterns among variables involved in biological processes. Therefore, combining both approaches is recommended to obtain robust insights [47]. Comparison of the protein sets identified by both methods revealed 16 overlapping proteins with adjusted p < 0.05 in Fisher’s exact test (45.70% of the multivariate selection) and 23 with adjusted p < 0.10 (65.70%). By integrating proteins with VIP > 1 and those with adjusted p < 0.10, a final set of 52 proteins was established, thereby enhancing the reliability of the findings. This integrated approach enabled robust identification of key proteins involved in the hydrocoral’s response to bleaching, considering both individual protein changes and their complex interactions, as illustrated in the network analysis (Supplementary Figure S4 and Supplementary Table S2).
3.4. Functional Annotation and Gene Ontology Term Assignment
The functional annotation and Gene Ontology (GO) analysis of the 102 proteins provided insights into their biological roles. Key visualizations summarize the GO mapping distribution, sequence similarity distribution, and combined GO annotations for biological process, molecular function, and cellular component.
Figure 2 presents a histogram showing the distribution of the number of GO terms mapped per protein. The x-axis indicates the number of GO terms assigned to each protein, while the y-axis shows the frequency of proteins within each category. The distribution highlights the diversity of functional annotations, with most proteins being associated with multiple GO terms. On average, each protein was annotated with 5.03 GO terms, and the median was 5, indicating a relatively high degree of functional information across the dataset. These results support the relevance of the identified proteins to various biological processes, cellular components, and molecular functions (Figure 2).
In the biological process category, several proteins were associated with cellular processes. Protein disulfide isomerase, heat shock protein 70, and peptidyl-prolyl cis-trans isomerase were annotated with the term “protein folding” (GO:0006457), reflecting their roles in the heat stress response and in maintaining redox homeostasis.
In the molecular function category, proteins such as tubulin alpha chain, tubulin beta chain, and cell division control protein 42 homolog were associated with “GTP binding” (GO:0005525).
In the cellular component category, proteins including fibrocystin-L, heat shock cognate 70 kDa, and cationic amino acid transporter were localized to the “plasma membrane” (GO:0005886).
The Sequence Similarity Distribution highlights the high alignment quality (Supplementary Figure S5). Combined GO graphs illustrate hierarchical relationships: biological process emphasizes roles in metabolism and regulation (Supplementary Figure S6); molecular function highlights binding and catalytic activities (Supplementary Figure S7); and cellular component maps subcellular localization to organelles and membranes (Supplementary Figure S8).
3.5. Proteins Differentially Abundant in Bleached M. complanata
The results obtained from the uni- and multivariate statistical analyses determined that a total of 52 proteins were differentially abundant when comparing both groups of hydrocorals. A summary of the average differences in protein abundance, the effect sizes of the statistical comparisons obtained by permutation tests, and fold changes are shown in Table 1, as well as their role in diverse cellular processes.
Table 1Differentially abundant proteins in a bleached M. complanata colony identified through proteomic analysis. 1. ↑ indicates an increase in protein abundance, while ↓ indicates a decrease. These changes are directly related to the effect size. 2. Effect size values represent the magnitude of the difference in protein abundance between non-bleached and bleached fragments. 3. Fold-change values indicate the direction of differential protein abundance: positive values correspond to increased levels in bleached fragments, whereas negative values indicate decreased levels [48]. Fold-change values are log2-transformed; a value of ±1 represents a twofold difference in protein abundance.
| No. | Protein Name | Accession Number | MW (kDa) | Protein Level 1 | Effect Size 2 | Fold Change 3 |
|---|---|---|---|---|---|---|
| Amino acid biosynthesis | ||||||
| 1 | Hcy-binding domain-containing protein | A0A3M6UII3 | 64 | ↑ | 0.8106 | 4.07 |
| Carbohydrate metabolism | ||||||
| 2 | Phosphopyruvate hydratase | A0A3M6UH98 | 37 | ↑ | 0.8544 | 3.72 |
| Cell communication | ||||||
| 3 | F5/8 type C domain-containing protein | A0A3M6TA43 | 2261 | ↓ | −0.732 | −3.83 |
| 4 | PDZ domain-containing protein 2 | A0A3M6TV70 | 403 | ↓ | −0.4818 | −3.7 |
| Cell cycle | ||||||
| 5 | Cell division control protein 42 homolog | A0A3M6UBV6 | 21 | ↑ | 0.8021 | 4.45 |
| Cytoskeleton component | ||||||
| 6 | Actin | A0A3M6UP43 | 42 | ↑ | 0.5179 | 0.18 |
| 7 | Actin | A0A3M6UP81 | 42 | ↑ | 0.0439 | 0.02 |
| 8 | Actin | A0A3M6UH97 | 56 | ↓ | −0.3848 | −0.31 |
| 9 | Tubulin beta chain | A0A3M6TMY2 | 17 | ↓ | −0.5705 | −1.27 |
| 10 | Microtubule-associated proteins 1A/1B light chain 3C-like | A0A3M6U8T6 | 15 | ↓ | −0.8054 | −2.49 |
| 11 | Tubulin alpha chain | A0A3M6TAX7 | 99 | ↓ | −0.121 | −0.25 |
| 12 | Myosin motor domain-containing protein | A0A3M6UZZ8 | 228 | ↑ | 0.5668 | 4.84 |
| 13 | Myosin regulatory light chain ef-hand protein | A0A3M6UI41 | 15 | ↓ | −0.214 | −0.96 |
| 14 | Actin-related protein 3 | A0A3M6UDC7 | 44 | ↑ | 0.8317 | 3.25 |
| 15 | Alpha-actinin, sarcomeric | A0A3M6U9E3 | 94 | ↑ | 0.7684 | 3.85 |
| DNA repair | ||||||
| 16 | PDDEXK_1 domain-containing protein | A0A3M6TW03 | 38 | ↓ | −0.4818 | −2.64 |
| Extracellular matrix component | ||||||
| 17 | Collagen alpha chain | B8V7R6 | 88 | ↓ | −0.4818 | −3.22 |
| 18 | ZP domain-containing protein | A0A3M6U3D9 | 39 | ↓ | −0.4818 | −2.96 |
| Protein modification and heat shock response | ||||||
| 19 | Heat shock protein 70 | Q5FB18 | 74 | ↑ | 0.1967 | 0.42 |
| 20 | Ubiquitin-60S ribosomal protein L40 | Q93116 | 15 | ↓ | −1.1759 | −1.79 |
| 21 | Protein disulfide-isomerase | A0A3M6V013 | 37 | ↓ | −0.5419 | −1.52 |
| 22 | Protein disulfide-isomerase | A0A3M6TV91 | 56 | ↓ | −0.6721 | −2.17 |
| 23 | Heat shock 70 kDa protein cognate 5 | A0A3M6U177 | 71 | ↓ | −0.6242 | −3.15 |
| 24 | PX domain-containing protein | A0A3M6TEA6 | 39 | ↑ | 0.6118 | 2.52 |
| 25 | Peptidyl-prolyl cis-trans isomerase | A0A3M6UIN0 | 18 | ↑ | 0.4965 | 3.3 |
| 26 | WD repeat-containing protein 11 | A0A3M6UW24 | 141 | ↓ | −0.4818 | −3.7 |
| 27 | Calreticulin | A0A3M6TB88 | 51 | ↑ | 0.7684 | 3.35 |
| 28 | UBC core domain-containing protein | A0A3M6UGY3 | 17 | ↑ | 0.7684 | 3.35 |
| Redox homeostasis | ||||||
| 29 | Thioredoxin domain-containing protein | A0A3M6T9K8 | 24 | ↓ | −0.7799 | −1.49 |
| 30 | Thioredoxin-dependent peroxiredoxin | A0A3M6U7L4 | 22 | ↑ | 0.6057 | 2.26 |
| 31 | Aldedh domain-containing protein | A0A3M6UUA9 | 58 | ↑ | 0.7784 | 4.35 |
| 32 | Unspecific monooxygenase | A0A3M6UWA6 | 59 | ↓ | −0.4818 | −3.7 |
| Signaling | ||||||
| 33 | EF-hand domain-containing protein | A0A3M6USF3 | 12 | ↓ | −0.7136 | −1.7 |
| 34 | EF-hand domain-containing protein | A0A3M6TLA1 | 14 | ↓ | −0.6555 | −1.56 |
| 35 | PRKG1 interact domain-containing protein | A0A3M6TTF0 | 14 | ↓ | −0.9043 | −4.84 |
| 36 | Serine/threonine-protein kinase TOR | A0A3M6UZI7 | 272 | ↑ | 0.6923 | 2.45 |
| 37 | EGF-like domain-containing protein | A0A3M6TYF4 | 198 | ↓ | −0.4818 | −3.7 |
| 38 | Fibrocystin-L | A0A3M6UHX3 | 719 | ↑ | 0.6551 | 5.11 |
| 39 | RZ-type domain-containing protein | A0A3M6TXJ4 | 600 | ↓ | −0.4818 | −2.64 |
| 40 | TNFR-Cys domain-containing protein | A0A3M6UKT4 | 56 | ↓ | −0.4818 | −3.7 |
| Transcription | ||||||
| 41 | Histone H4 | A0A3M6UZ06 | 11 | ↓ | −0.7419 | −1.12 |
| 42 | Histone H2B | A0A3M6THD8 | 14 | ↓ | −1.0012 | −2.13 |
| 43 | Histone H3 | A0A3M6T926 | 15 | ↓ | −1.1167 | −6.35 |
| 44 | DUF3715 domain-containing protein | A0A3M6TH31 | 437 | ↑ | 0.5847 | 2.89 |
| 45 | CCR4-NOT transcription complex subunit 1 | A0A3M6T4M6 | 278 | ↑ | 0.668 | 5.94 |
| Transport | ||||||
| 46 | Ras-related protein Rab-1A | A0A3M6U070 | 25 | ↑ | 0.7684 | 4.35 |
| 47 | Amino acid transporter | A0A3M6TNM3 | 181 | ↓ | −0.4818 | −3.22 |
| 48 | Cationic amino acid transporter C-terminal domain-containing protein | A0A3M6UX14 | 96 | ↑ | 0.6237 | 5.62 |
| 49 | Ras-related protein Rab-2A | A0A3M6UCJ4 | 29 | ↑ | 0.837 | 4.99 |
| 50 | Ras-like protein 2 | A0A3M6TKM7 | 22 | ↑ | 0.7684 | 3.35 |
| Unknown | ||||||
| 51 | Uncharacterized protein | A0A3M6V3A1 | 130 | ↓ | −0.3604 | −0.91 |
| 52 | Uncharacterized protein | A0A3M6U169 | 23 | ↑ | 0.7108 | 3.79 |
4. Discussion
Among the differentially abundant proteins identified, one of the most biologically relevant was actin, a key cytoskeletal component consistently linked to cytoskeletal disruption and remodeling during bleaching in cnidarians [24,25,26,27,29]. Its altered abundance highlights the central role of cytoskeletal dynamics in the stress response of M. complanata. In addition to actin, other structural proteins such as tubulins were also affected, underscoring widespread cytoskeletal reorganization under thermal stress.
Beyond structural proteins, the 52 differentially abundant proteins identified reveal broader aspects of the cellular response to bleaching. Functional categorization showed that the most represented groups were protein modification and heat shock response (10 proteins) and cytoskeleton components (10 proteins), followed by signaling (8), transcription (5), transport (5), redox homeostasis (4), cell communication (2), extracellular matrix component (2), unknown (2), and amino acid biosynthesis, carbohydrate metabolism, DNA repair, each one with 1 protein. These categories are commonly disrupted in proteomic studies of bleaching and reflect a combination of damage and compensatory mechanisms [24,25,26,27,28,29,30,31,32,33,34]. Such functional shifts suggest that bleached hydrocorals undergo active molecular reorganization involving both injury and potential adaptation. Overall, 24 proteins were upregulated and 28 were downregulated in bleached compared to non-bleached fragments (Table 1), indicating a substantial shift in the hydrocoral proteome in response to stress. These proteins are discussed below grouped by functional category, starting with cytoskeletal components due to the central role of actin and the relevance of cytoskeletal dynamics in the bleaching response highlighted above.
4.1. Cytoskeleton Components
Differential abundance of actin has previously been reported in proteomic studies aimed at evaluating the effect of heat stress and bleaching on diverse anthozoan cnidarians [24,25,26,27,29]. The decreased abundance of actin found in this study is consistent with what we had previously demonstrated when comparing the soluble proteomes of bleached M. complanata [36] and M. alcicornis [35] and the transcriptomes of bleached and non-bleached M. complanata [36]. Actin has been recognized as a key biomarker of bleaching and heat stress [49,50], and, depending on the specific proteoform, it plays an essential role in cellular morphology, motility, and cytoskeletal organization [51,52,53,54]. The expression of actin genes is highly responsive to thermal stress in cnidarian cells, which undergo cytoskeletal reorganization in response to thermal stress and bleaching. While this reorganization has often been interpreted as a sign of cytoskeletal instability or degradation, it may also reflect a coordinated and potentially adaptive remodeling process aimed at maintaining or restoring cellular architecture during stress recovery. The removal of Symbiodiniaceae from cnidarian gastrodermal cells due to bleaching influences diverse cellular functions which depend on the actin cytoskeleton, including intracellular transport, interactions with the plasma membrane, maintenance of cell shape, and the transcriptional control of proteins associated with the cytoskeleton (Figure 3) [15,19,36,49,55,56,57,58,59,60].
Our investigation also revealed that the abundance of alpha-tubulin and beta-tubulin, which play a critical role in the formation of microtubules, was decreased in bleached hydrocorals. The decrease in the abundance of alpha- and beta-tubulin could imply a decrease in the intracellular transport of nutrients and poor cell division, which is necessary for tissue regeneration under stress conditions (Figure 3) [26]. On the other hand, the decreased abundance of Microtubule-associated protein 1A/1B light chain 3C (LC3C) suggests a reduction in autophagy under bleaching conditions. While autophagy has been proposed as a response mechanism for the elimination of endosymbionts [61,62,63,64], it has been reported that proteins associated with these processes may exhibit decreased abundances under thermal stress-induced bleaching conditions (Figure 3) [55,57].
The response of the cytoskeleton to a phenomenon such as thermal stress varies among organisms, and it has even been reported that genes encoding cytoskeletal-associated proteins can exhibit distinct differential expressions within the same study [65]. Additionally, post-translational modifications can result in the existence of multiple proteoforms, which increase proteome complexity, making proteomic analysis an even greater challenge [54,66]. This could explain the differences in the differential abundances of the other identified actin proteins. However, the observed differential abundance of cytoskeletal components in bleached hydrocorals could indicate a reorganization or disruption of the actin cytoskeleton in response to heat and oxidative stress and endosymbiont expulsion. This reorganization might be part of a broader cellular remodeling response, possibly aimed at adapting cellular structure and function to new physiological conditions during or after bleaching events (Figure 3).
4.2. Extracellular Matrix Component
Collagen showed a decreased differential abundance in bleached M. complanata fragments. Numerous transcriptomic and proteomic studies have shown differential abundance of collagen in cnidarians subjected to heat stress [17,19,20,25,35,49,55,67]. A decrease in the production of this protein could lead to loss of structural integrity and alterations in cell signaling and healing (Figure 3) [68,69]. A ZP domain-containing protein with differentially decreased abundance in bleached M. complanata fragments was also identified. This protein takes part in various biological processes such as fertilization and ECM formation [17,70,71,72]. Some ZP domain-containing proteins also interact with other ECM components to form stable complexes important for cellular structure and development (Figure 3) [73].
Bleaching in M. complanata leads to significant disruptions in cellular functions, including the stability and repair of tissues. Processes such as cell communication, regeneration, and structural organization are adversely affected. Additionally, the capacity to maintain proper cellular development and cohesion within the extracellular matrix is diminished, highlighting the extensive impact of thermal stress on fundamental biological systems.
4.3. Heat Shock Response
The 70-kilodalton (kDa) heat shock protein (HSP70) chaperones have been considered as molecular biomarkers to measure the physiological state of reef-forming cnidarians exposed to thermal stress [50]. In this study HSP70 was increased in bleached M. complanata, which is consistent with our previous study [36]. Several transcriptomic studies have analyzed the gene-expression responses to thermal stress in corals and symbiotic cnidarians [23,29,58,74,75]. The results obtained in transcriptomic investigations have also been corroborated in proteomic studies carried out to examine the heat stress response of coral holobionts [26,29,33,76,77,78]. Differential protein abundance patterns of HSP70 indicate that the modulation of this HSP is a key response of the coral holobiont to preserve protein structure and function and to stimulate cellular repair processes (Figure 3).
Thermal stress in M. complanata disrupts various cellular processes essential for maintaining homeostasis and coping with environmental challenges. The resulting stress affects protein folding and repair mechanisms, leading to potential dysfunctions in cell signaling, stress response, and the regulation of the cell cycle. Additionally, alterations in protein stability and degradation pathways can impair cellular repair and regeneration. These modifications reflect an adaptive yet strained cellular response to prolonged environmental stress, underscoring both the challenges faced by the holobiont and the limitations of its stress-response capacity.
4.4. Redox Homeostasis
Numerous studies have clearly shown that oxidative stress is closely linked to coral bleaching [79,80,81,82,83]. Thus, it is imperative for cnidarians to possess robust defense mechanisms to counteract ROS-induced damage [82,83]. The aldedh domain (increased in bleached M. complanata) is found in aldehyde dehydrogenase (ALDHs) enzymes. ALDHs are part of the metabolic pathway for ethanol degradation, converting toxic aldehydes into non-reactive carboxylic acids [84]. Acute thermal stress leads to a decrease in fatty acid concentration and an increase in alcohol concentration, attributed to an increase in energy reserve consumption and the generation of wax ester decomposition products, respectively [85,86]. The increased abundance of aldedh domain-containing proteins as a result of oxidative stress conditions suggests a rise in detoxification processes to counteract ROS-induced amino acid dissociation (Figure 3) [87].
Thioredoxin, which was found to be decreased in bleached hydrocorals, is related to cellular antioxidant defense mechanisms. Decreased thioredoxin reductase abundance could further compromise antioxidant defenses, suggesting an overwhelmed ROS response affecting stress-responsive protein abundance. Reduced thioredoxin activity may elevate ROS and caspase-3 activity, potentially increasing apoptosis and tissue damage in cnidarians (Figure 3) [26,33,88,89].
The increased abundance of thioredoxin-dependent peroxiredoxin found in this study is in accordance with previous reports in other cnidarians and our previous study [19,24,31,36]. An increase in the abundance of peroxiredoxin proteins suggests an enhanced capacity to reduce peroxides, which may aid in neutralizing oxidative damage, maintaining redox balance, and increasing cellular protection (Figure 3) [36].
Thermal stress induces significant alterations in redox homeostasis in M. complanata, contributing to oxidative damage, disrupting the balance between reactive oxygen species (ROS) production and antioxidant defenses. The resulting oxidative stress leads to increased detoxification processes to mitigate ROS damage, while simultaneously impairing antioxidant mechanisms, reducing the cell’s ability to neutralize peroxides effectively. These changes may elevate ROS levels, contributing to cellular damage, apoptosis, and compromised tissue integrity. Despite some compensatory upregulation of ROS-scavenging processes, the overall imbalance highlights the extent of oxidative damage that precedes and contributes to bleaching, as well as its broader impact on cellular stability and resilience.
4.5. Signaling
Two EF-hand domain-containing proteins with decreased abundance were found in bleached M. complanata fragments. These types of proteins are important due to their ability to bind calcium. The EF-hand domain is present in various proteins that regulate essential biological processes, mainly cellular signaling [90]. Calmodulin (an EF-hand domain-containing protein) was reported with decreased abundance in the soluble proteome of bleached M. complanata fragments [36]. EF-hand proteins, in general, are important for intracellular Ca2+ homeostasis, and this homeostasis, in turn, is crucial for proper cellular functioning. The oxidative stress produced during thermal exposure can disrupt this homeostasis, resulting in a sustained elevation of intracellular Ca2+ due to release from intracellular stores and entry from the extracellular environment [91,92]. Given the structural diversity of EF-hand proteins, these can exist as different proteoforms, which may exhibit distinct functional properties and contribute to the complexity of calcium regulation under stressful conditions that may ultimately culminate in bleaching [93]. The decreased abundance of two EF-hand proteins, coupled with the previous finding of decreased calmodulin abundance, could indicate that Ca2+-linked cellular signaling processes may be affected due to bleaching, as well as processes such as apoptosis, energy metabolism, and protein synthesis (Figure 3) [94].
Our study found that a TNFR-Cys domain-containing protein involved in apoptosis regulation [95,96] was decreased in bleached M. complanata. Proteins containing these domains are involved in the regulation of processes such as apoptosis [96]. The variability in TNFR expression, along with their role in apoptosis regulation, enhances stress tolerance and disease resistance in corals [23,67,97,98,99]. Therefore, this protein family emerges as a principal candidate in multiple pathways related to health and stress tolerance in these marine organisms [23,67,99]. Our findings allow us to speculate that the decreased levels of this protein in bleached fragments of M. complanata may indicate the inhibition of processes such as apoptosis (Figure 3).
Bleaching in M. complanata significantly impacts key cellular processes, including calcium homeostasis, signal transduction, and stress response pathways. Disruptions to calcium signaling may alter processes such as cell proliferation, apoptosis, and energy metabolism [90,100,101]. Changes in signaling pathways, including those associated with growth factors and kinases, suggest impaired cell growth, survival mechanisms, and stress adaptation [102,103,104]. Additionally, the regulation of apoptosis and immune responses appears compromised, potentially reducing the resilience of these organisms to environmental stressors [96,97,98,99]. These findings underscore the broad cellular disruptions caused by bleaching and the need for further research to clarify their implications.
4.6. Transcription
Transcriptional control undergoes significant changes in response to coral bleaching conditions, with variations in transcripts, transcription factors, and related protein expression resulting from thermal and oxidative stress [16,21,105,106]. In our study, we identified three histones linked to the transcriptional process which showed differential abundance (H4, H2B, and H3). It has been proposed that diminished levels of H4 might imply a generalized stress response that contributes to survival in adverse environments, favoring the abundance of proteins essential for stress adaptation (Figure 3) [107,108].
4.7. Transport
Cellular transport represents another fundamental cellular function commonly affected during bleaching events, often attributed to a rise in the response of cnidarians or an increased utilization of energy reserves [109,110,111]. Rab proteins predominantly participate in vesicle formation and transport, serving as crucial regulators of intracellular membrane trafficking [112,113,114]. Rab1A and Rab2A, proteins with increased levels in bleached hydrocorals, are involved in key cellular processes such as vesicular protein transport and migration, among others [115,116,117,118,119,120,121]. The increase in the abundance of these proteins, also linked to stress adaptation in plants [117], suggests a role in temperature stress responses and compensatory carbohydrate transport due to the loss of symbiotic algae in cnidarians, potentially influenced by changes in cytoskeletal protein abundance critical for vesicle transport (Figure 3) [16,110,111]. Bleaching in M. complanata disrupts intracellular transport, affecting vesicle movement, nutrient distribution, and protein trafficking. These changes reflect an increased energy demand and a response to the loss of symbiotic algae. Additionally, alterations in cytoskeletal organization may further impair transport efficiency, highlighting the extensive cellular adjustments required to cope with thermal stress and bleaching.
4.8. Carbohydrate Metabolism
Regarding phosphopyruvate hydratase, also referred to as enolase, which was found to be increased in bleached fragments, it is an enzyme that participates in the glycolysis pathway [122,123]. In our previous study on the soluble proteome of M. complanata, we observed differential abundance in two glycolysis-related proteins: alpha-enolase (upregulated) and triosephosphate isomerase (downregulated) [36]. The upregulation of alpha-enolase likely supports energy production under conditions of reduced symbiont density [36,124], whereas the downregulation of triosephosphate isomerase may suggest a metabolic shift toward the pentose phosphate pathway to mitigate oxidative stress [36,125,126,127]. However, in the present study, enolase was the only glucose metabolism-related protein exhibiting differential abundance. This discrepancy may reflect the presence of distinct proteoforms with variable expression patterns [54]. Despite the absence of other proteins showing differential abundances linked to carbohydrate metabolism, the increase in enolase observed in this study may be related to this phenomenon (Figure 3).
4.9. Final Considerations
Overall, the proteomic changes observed in bleached Millepora complanata fragments point to a widespread disruption of key cellular processes, including cytoskeletal dynamics, protein folding and modification, redox balance, signaling, and intracellular transport. These molecular alterations reflect a complex response to thermal stress, in which structural damage, oxidative stress, and loss of symbionts trigger compensatory mechanisms aimed at maintaining cellular homeostasis. The reorganization of cytoskeletal proteins, particularly actins and tubulins, suggests both disassembly and remodeling of the cytoskeleton, possibly linked to changes in vesicular transport, cell shape, and tissue integrity. Meanwhile, the upregulation of stress-related proteins, such as HSP70 and peroxiredoxins, indicates an attempt to counteract protein misfolding and oxidative damage. Despite these adaptive responses, the downregulation of antioxidant proteins and signaling regulators points to a reduced capacity to mitigate stress and maintain normal function. Taken together, these findings highlight that the bleaching response of M. complanata involves not only stress-induced damage but also active cellular reorganization processes that may reflect an attempt to restore physiological equilibrium in the face of environmental perturbation.
4.10. Limitations
This study faced several limitations, with the sampling strategy being the most relevant. Ideally, sampling multiple colonies per condition would have provided a greater genetic diversity, allowing for a more comprehensive understanding of proteomic variability and enhancing the biological relevance of the findings. However, due to the inherent challenges of working with marine organisms—such as limited accessibility and permit restrictions at the time of collection—sampling was restricted to a single colony per condition. Previous omics studies have conducted analyses employing specimens derived from single colonies or clonal laboratory stocks, thereby reducing genetic variability [16,24,38,88,128]. To mitigate this limitation and improve the robustness of the analysis, we employed a combination of univariate and multivariate statistical approaches, which have proven effective in detecting biologically meaningful patterns, even in studies with reduced sample sizes [129,130,131].
Nevertheless, we acknowledge the need for caution when interpreting the findings, as they may reflect colony-specific responses. Future research should prioritize increasing the number of biological replicates from multiple colonies to better account for inter-colony variability and improve statistical power and generalizability. In parallel, further efforts are needed to functionally validate the proteomic results and expand their biological interpretation. Although the identified proteins give us a broad overview of some of the processes that are related to bleaching in M. complanata, additional studies are required to evaluate protein activity and gain deeper understanding of the cellular survival processes of these holobionts. Another important limitation to consider is the lack of orthogonal validation. Future studies should aim to confirm the differential abundance of key proteins using methods such as Western blotting and the Proximity Extension Assay, which would support the proteomic findings at the protein level.
5. Conclusions
This study reveals proteomic alterations associated with bleaching in M. complanata following the 2015–2016 ENSO event, highlighting key differences in the abundance of proteins linked to stress response, cytoskeletal stability, signaling, metabolism, and cell maintenance. These changes reflect the host’s molecular adjustments to thermal stress and suggest potential mechanisms contributing to differential bleaching susceptibility.
The identified proteins are involved in critical biological processes, including redox regulation, heat shock response, intracellular transport, and extracellular matrix preservation. While these findings do not constitute direct evidence of thermal resilience, they point to physiological traits that may support short-term survival or increase vulnerability under prolonged stress.
Our results expand current knowledge of hydrocoral stress responses and contribute to the broader understanding of cnidarian bleaching mechanisms. Future studies integrating complementary omics tools, such as metabolomics and targeted proteomics, and functional assays will be key to validating these proteomic trends and clarifying their physio-logical implications. Moreover, increasing biological replication will enhance the robustness and ecological relevance of future analyses.
A.R.-M. and C.I.-A. conceptualized the project; E.d.J.A.-O., J.E.V.-T. and V.H.H.-E. performed the experiments; A.R.-M., V.H.H.-E. and J.C.-P. supervised the experiments; E.R.d.S.M. and J.C.-P. conducted the data analysis; E.R.d.S.M. and E.d.J.A.-O. plotted the figures; E.d.J.A.-O., J.E.V.-T., E.R.d.S.M. and A.R.-M. wrote the manuscript; A.R.-M., C.I.-A., E.R.d.S.M. and J.C.-P. revised the manuscript; A.R.-M. was responsible for project administration and funding acquisition. All authors have read and agreed to the published version of the manuscript.
The following information was provided regarding field study approvals (i.e., the approving body and reference numbers): This investigation was approved by the Research Ethics Committee of the Faculty of Chemistry at the Autonomous University of Querétaro, Mexico (approval code CBQ19/058) (26 April 2021). The collection of M. complanata fragments was authorized by the Secretaría del Medio Ambiente y Recursos Naturales de México (SEMARNAT) (permit number PFP-DGOPA-139/15) (19 October 2015).
Not applicable.
The mass spectrometry proteomics data have been deposited with the ProteomeXchange Consortium via the PRIDE [
Esteban de Jesús Alcantar Orozco acknowledges Consejo Nacional de Humanidades, Ciencias y Tecnologías (CONAHCYT), for his graduate grant (CVU: 852933). We would like to thank the biologist Erika Patricia Meneses Romero and the Instituto de Biotecnología of the Universidad Autónoma de México for the academic and technical assistance with the proteomic analysis.
The authors declare no conflicts of interest.
Footnotes
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Figure 1 Score plot of a three-PLS-DA latent variable model showing the distinction between both groups of samples.
Figure 2 GO mapping distribution histogram.
Figure 3 Schematic representation of cellular pathways and metabolic processes affected by the differential abundance of proteins in M. complanata under bleaching conditions. The figure illustrates the impact of thermal stress on host cellular functions, highlighting alterations in redox homeostasis, cytoskeletal remodeling, energy metabolism (glycolysis, pentose phosphate pathway, and beta-oxidation), vesicular trafficking, autophagy, and apoptotic signaling. ↑ indicates increased protein and processes; ↓ indicates decreased protein and processes. White crosses denote counteraction. Acronyms: thioredoxin (TRX), heat shock protein 70 (HSP70), heat shock protein 70 cognate (HSC70), reactive oxygen species (ROS), pentose phosphate pathway (PPP), aldehyde dehydrogenase (ALDH), thioredoxin-dependent peroxiredoxin (TRX-d PRX), EF-hand domain-containing protein (EF), myosin motor protein (MMP), myosin regulatory protein (MRP), microtubule-associated protein 1A/1B light chain 3C-like (LC3C), ZP domain-containing protein (ZP).
Supplementary Materials
The following supporting information can be downloaded at
1. Stambler, N. Zooxanthellae: The Yellow Symbionts Inside Animals. Coral Reefs: An Ecosystem in Transition; Dubinsky, Z.; Stambler, N. Springer: Dordrecht, The Netherlands, 2011; pp. 87-106. ISBN 978-94-007-0114-4
2. Lough, J.M. Turning Back Time. Nature; 2016; 531, pp. 314-315. [DOI: https://dx.doi.org/10.1038/nature17302] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/26909580]
3. Rhodes, E.R.; Naser, H.; Naser, H. Natural Resources Management and Biological Sciences; IntechOpen: London, UK, 2021; ISBN 978-1-83880-465-7
4. Van de Water, J.A.; Tignat-Perrier, R.; Allemand, D.; Ferrier-Pagès, C. Coral Holobionts and Biotechnology: From Blue Economy to Coral Reef Conservation. Curr. Opin. Biotechnol.; 2022; 74, pp. 110-121. [DOI: https://dx.doi.org/10.1016/j.copbio.2021.10.013]
5. LaJeunesse, T.C.; Parkinson, J.E.; Gabrielson, P.W.; Jeong, H.J.; Reimer, J.D.; Voolstra, C.R.; Santos, S.R. Systematic Revision of Symbiodiniaceae Highlights the Antiquity and Diversity of Coral Endosymbionts. Curr. Biol.; 2018; 28, pp. 2570-2580.e6. [DOI: https://dx.doi.org/10.1016/j.cub.2018.07.008]
6. González-Pech, R.A.; Bhattacharya, D.; Ragan, M.A.; Chan, C.X. Genome Evolution of Coral Reef Symbionts as Intracellular Residents. Trends Ecol. Evol.; 2019; 34, pp. 799-806. [DOI: https://dx.doi.org/10.1016/j.tree.2019.04.010]
7. Cziesielski, M.J. Making a Model—Investigating the Molecular Machinery of the Coral Symbiosis Model System Aiptasia. Ph.D. Thesis; King Abdullah University of Science and Technology: Thuwal, Saudi Arabia, 2019.
8. Hein, M.Y.; Birtles, A.; Willis, B.L.; Gardiner, N.; Beeden, R.; Marshall, N.A. Coral Restoration: Socio-Ecological Perspectives of Benefits and Limitations. Biol. Conserv.; 2019; 229, pp. 14-25. [DOI: https://dx.doi.org/10.1016/j.biocon.2018.11.014]
9. Rinkevich, B. The Active Reef Restoration Toolbox is a Vehicle for Coral Resilience and Adaptation in a Changing World. J. Mar. Sci. Eng.; 2019; 7, 201. [DOI: https://dx.doi.org/10.3390/jmse7070201]
10. Lesser, M.P. Oxidative Stress in Marine Environments: Biochemistry and Physiological Ecology. Annu. Rev. Physiol.; 2006; 68, pp. 253-278. [DOI: https://dx.doi.org/10.1146/annurev.physiol.68.040104.110001]
11. Montero-Serra, I.; Garrabou, J.; Doak, D.F.; Ledoux, J.-B.; Linares, C. Marine Protected Areas Enhance Structural Complexity but Do Not Buffer the Consequences of Ocean Warming for an Overexploited Precious Coral. J. Appl. Ecol.; 2019; 56, pp. 1063-1074. [DOI: https://dx.doi.org/10.1111/1365-2664.13321]
12. Eakin, C.M.; Sweatman, H.; Brainard, R. The 2014–2017 Global-Scale Coral Bleaching Event: Insights and Impacts. Coral Reefs; 2019; 38, pp. 539-545. [DOI: https://dx.doi.org/10.1007/s00338-019-01844-2]
13. Bissolli, P.; Ganter, C.; Li, T.; Mekonnen, A.; Sanchez-Lugo, A. 7. Regional Climates. Bull. Am. Meteorol. Soc.; 2018; 99, S193.
14. Mellin, C.; Brown, S.; Cantin, N.; Klein-Salas, E.; Mouillot, D.; Heron, S.F.; Fordham, D.A. Cumulative Risk of Future Bleaching for the World’s Coral Reefs. Sci. Adv.; 2024; 10, eadn9660. [DOI: https://dx.doi.org/10.1126/sciadv.adn9660]
15. Richier, S.; Rodriguez-Lanetty, M.; Schnitzler, C.E.; Weis, V.M. Response of the Symbiotic Cnidarian Anthopleura elegantissima Transcriptome to Temperature and UV Increase. Comp. Biochem. Physiol. Part D Genom. Proteom.; 2008; 3, pp. 283-289. [DOI: https://dx.doi.org/10.1016/j.cbd.2008.08.001]
16. Desalvo, M.K.; Voolstra, C.R.; Sunagawa, S.; Schwarz, J.A.; Stillman, J.H.; Coffroth, M.A.; Szmant, A.M.; Medina, M. Differential Gene Expression during Thermal Stress and Bleaching in the Caribbean Coral Montastraea faveolata. Mol. Ecol.; 2008; 17, pp. 3952-3971. [DOI: https://dx.doi.org/10.1111/j.1365-294X.2008.03879.x] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/18662230]
17. Moya, A.; Ganot, P.; Furla, P.; Sabourault, C. The Transcriptomic Response to Thermal Stress Is Immediate, Transient and Potentiated by Ultraviolet Radiation in the Sea Anemone Anemonia viridis. Mol. Ecol.; 2012; 21, pp. 1158-1174. [DOI: https://dx.doi.org/10.1111/j.1365-294X.2012.05458.x]
18. Kenkel, C.D.; Meyer, E.; Matz, M.V. Gene Expression under Chronic Heat Stress in Populations of the Mustard Hill Coral (Porites astreoides) from Different Thermal Environments. Mol. Ecol.; 2013; 22, pp. 4322-4334. [DOI: https://dx.doi.org/10.1111/mec.12390]
19. Maor-Landaw, K.; Karako-Lampert, S.; Ben-Asher, H.W.; Goffredo, S.; Falini, G.; Dubinsky, Z.; Levy, O. Gene Expression Profiles during Short-Term Heat Stress in the Red Sea Coral Stylophora pistillata. Glob. Change Biol.; 2014; 20, pp. 3026-3035. [DOI: https://dx.doi.org/10.1111/gcb.12592]
20. Seneca, F.O.; Palumbi, S.R. The Role of Transcriptome Resilience in Resistance of Corals to Bleaching. Mol. Ecol.; 2015; 24, pp. 1467-1484. [DOI: https://dx.doi.org/10.1111/mec.13125]
21. Pinzón, J.H.; Kamel, B.; Burge, C.A.; Harvell, C.D.; Medina, M.; Weil, E.; Mydlarz, L.D. Whole Transcriptome Analysis Reveals Changes in Expression of Immune-Related Genes during and after Bleaching in a Reef-Building Coral. R. Soc. Open Sci.; 2015; 2, 140214. [DOI: https://dx.doi.org/10.1098/rsos.140214] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/26064625]
22. Helmkampf, M.; Renee Bellinger, M.; Geib, S.M.; Sim, S.B.; Takabayashi, M. Draft Genome of the Rice Coral Montipora capitata Obtained from Linked-Read Sequencing. Genome Biol. Evol.; 2019; 11, 3094. [DOI: https://dx.doi.org/10.1093/gbe/evz135] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31243452]
23. Cleves, P.A.; Krediet, C.J.; Lehnert, E.M.; Onishi, M.; Pringle, J.R. Insights into Coral Bleaching under Heat Stress from Analysis of Gene Expression in a Sea Anemone Model System. Proc. Natl. Acad. Sci. USA; 2020; 117, pp. 28906-28917. [DOI: https://dx.doi.org/10.1073/pnas.2015737117]
24. Weston, A.J.; Dunlap, W.C.; Beltran, V.H.; Starcevic, A.; Hranueli, D.; Ward, M.; Long, P.F. Proteomics Links the Redox State to Calcium Signaling During Bleaching of the Scleractinian Coral Acropora microphthalma on Exposure to High Solar Irradiance and Thermal Stress[S]. Mol. Cell. Proteom.; 2015; 14, pp. 585-595. [DOI: https://dx.doi.org/10.1074/mcp.M114.043125]
25. Ricaurte, M.; Schizas, N.V.; Ciborowski, P.; Boukli, N.M. Proteomic Analysis of Bleached and Unbleached Acropora palmata, a Threatened Coral Species of the Caribbean. Mar. Pollut. Bull.; 2016; 107, pp. 224-232. [DOI: https://dx.doi.org/10.1016/j.marpolbul.2016.03.068]
26. Oakley, C.A.; Durand, E.; Wilkinson, S.P.; Peng, L.; Weis, V.M.; Grossman, A.R.; Davy, S.K. Thermal Shock Induces Host Proteostasis Disruption and Endoplasmic Reticulum Stress in the Model Symbiotic Cnidarian Aiptasia. J. Proteome Res.; 2017; 16, pp. 2121-2134. [DOI: https://dx.doi.org/10.1021/acs.jproteome.6b00797]
27. Mayfield, A.B.; Chen, Y.-J.; Lu, C.-Y.; Chen, C.-S. The Proteomic Response of the Reef Coral Pocillopora acuta to Experimentally Elevated Temperatures. PLoS ONE; 2018; 13, e0192001. [DOI: https://dx.doi.org/10.1371/journal.pone.0192001]
28. Mayfield, A.B. Proteomic Signatures of Corals from Thermodynamic Reefs. Microorganisms; 2020; 8, 1171. [DOI: https://dx.doi.org/10.3390/microorganisms8081171] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32752238]
29. Petrou, K.; Nunn, B.L.; Padula, M.P.; Miller, D.J.; Nielsen, D.A. Broad Scale Proteomic Analysis of Heat-Destabilised Symbiosis in the Hard Coral Acropora millepora. Sci. Rep.; 2021; 11, 19061. [DOI: https://dx.doi.org/10.1038/s41598-021-98548-x] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34561509]
30. Axworthy, J.B.; Timmins-Schiffman, E.; Brown, T.; Rodrigues, L.J.; Nunn, B.L.; Padilla-Gamiño, J.L. Shotgun Proteomics Identifies Active Metabolic Pathways in Bleached Coral Tissue and Intraskeletal Compartments. Front. Mar. Sci.; 2022; 9, 797517. [DOI: https://dx.doi.org/10.3389/fmars.2022.797517]
31. Mayfield, A.B.; Aguilar, C.; Kolodziej, G.; Enochs, I.C.; Manzello, D.P. Shotgun Proteomic Analysis of Thermally Challenged Reef Corals. Front. Mar. Sci.; 2021; 8, 660153. [DOI: https://dx.doi.org/10.3389/fmars.2021.660153]
32. Mayfield, A.B.; Lin, C. Field-Testing a Proteomics-Derived Machine-Learning Model for Predicting Coral Bleaching Susceptibility. Appl. Sci.; 2023; 13, 1718. [DOI: https://dx.doi.org/10.3390/app13031718]
33. Ricaurte, M.; Schizas, N.V.; Weil, E.F.; Ciborowski, P.; Boukli, N.M. Seasonal Proteome Variations in Orbicella faveolata Reveal Molecular Thermal Stress Adaptations. Proteomes; 2024; 12, 20. [DOI: https://dx.doi.org/10.3390/proteomes12030020]
34. Zhao, H.; Liu, X.; Zhang, J.; Zhu, W.; Su, C.; Wang, A.; Li, X. An Integrative Analysis of Proteomics and Metabolomics Reveals the Effects of Active Restoration on Acropora hyacinthus. J. Exp. Mar. Biol. Ecol.; 2024; 570, 151972. [DOI: https://dx.doi.org/10.1016/j.jembe.2023.151972]
35. Olguín-López, N.; Hérnandez-Elizárraga, V.H.; Hernández-Matehuala, R.; Cruz-Hernández, A.; Guevara-González, R.; Caballero-Pérez, J.; Ibarra-Alvarado, C.; Rojas-Molina, A. Impact of El Niño-Southern Oscillation 2015–2016 on the Soluble Proteomic Profile and Cytolytic Activity of Millepora alcicornis (“Fire Coral”) from the Mexican Caribbean. PeerJ; 2019; 7, e6593. [DOI: https://dx.doi.org/10.7717/peerj.6593]
36. Hernández-Elizárraga, V.H.; Olguín-López, N.; Hernández-Matehuala, R.; Ocharán-Mercado, A.; Cruz-Hernández, A.; Guevara-González, R.G.; Caballero-Pérez, J.; Ibarra-Alvarado, C.; Sánchez-Rodríguez, J.; Rojas-Molina, A. Comparative Analysis of the Soluble Proteome and the Cytolytic Activity of Unbleached and Bleached Millepora complanata (“Fire Coral”) from the Mexican Caribbean. Mar. Drugs; 2019; 17, 393. [DOI: https://dx.doi.org/10.3390/md17070393]
37. Hernández-Elizárraga, V.H.; Olguín-López, N.; Hernández-Matehuala, R.; Caballero-Pérez, J.; Ibarra-Alvarado, C.; Rojas-Molina, A. Transcriptomic Differences between Bleached and Unbleached Hydrozoan Millepora complanata Following the 2015–2016 ENSO in the Mexican Caribbean. PeerJ; 2023; 11, e14626. [DOI: https://dx.doi.org/10.7717/peerj.14626]
38. Sproles, A.E.; Oakley, C.A.; Matthews, J.L.; Peng, L.; Owen, J.G.; Grossman, A.R.; Weis, V.M.; Davy, S.K. Proteomics Quantifies Protein Expression Changes in a Model Cnidarian Colonised by a Thermally Tolerant but Suboptimal Symbiont. ISME J.; 2019; 13, pp. 2334-2345. [DOI: https://dx.doi.org/10.1038/s41396-019-0437-5]
39. Wang, W.; Scali, M.; Vignani, R.; Spadafora, A.; Sensi, E.; Mazzuca, S.; Cresti, M. Protein Extraction for Two-Dimensional Electrophoresis from Olive Leaf, a Plant Tissue Containing High Levels of Interfering Compounds. Electrophoresis; 2003; 24, pp. 2369-2375. [DOI: https://dx.doi.org/10.1002/elps.200305500] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/12874872]
40. Käll, L.; Storey, J.D.; Noble, W.S. Non-Parametric Estimation of Posterior Error Probabilities Associated with Peptides Identified by Tandem Mass Spectrometry. Bioinformatics; 2008; 24, pp. i42-i48. [DOI: https://dx.doi.org/10.1093/bioinformatics/btn294]
41. Nesvizhskii, A.I.; Keller, A.; Kolker, E.; Aebersold, R. A Statistical Model for Identifying Proteins by Tandem Mass Spectrometry. Anal. Chem.; 2003; 75, pp. 4646-4658. [DOI: https://dx.doi.org/10.1021/ac0341261] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/14632076]
42. Liu, H.; Sadygov, R.G.; Yates, J.R. A Model for Random Sampling and Estimation of Relative Protein Abundance in Shotgun Proteomics. Anal. Chem.; 2004; 76, pp. 4193-4201. [DOI: https://dx.doi.org/10.1021/ac0498563]
43. Colinge, J.; Chiappe, D.; Lagache, S.; Moniatte, M.; Bougueleret, L. Differential Proteomics via Probabilistic Peptide Identification Scores. Anal. Chem.; 2005; 77, pp. 596-606. [DOI: https://dx.doi.org/10.1021/ac0488513] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/15649059]
44. Mosquim Junior, S.; Siino, V.; Rydén, L.; Vallon-Christersson, J.; Levander, F. Choice of High-Throughput Proteomics Method Affects Data Integration with Transcriptomics and the Potential Use in Biomarker Discovery. Cancers; 2022; 14, 5761. [DOI: https://dx.doi.org/10.3390/cancers14235761] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/36497242]
45. Morretta, E.; D’Agostino, A.; Cassese, E.; Maglione, B.; Petrella, A.; Schiraldi, C.; Monti, M.C. Label-Free Quantitative Proteomics to Explore the Action Mechanism of the Pharmaceutical-Grade Triticum Vulgare Extract in Speeding Up Keratinocyte Healing. Molecules; 2022; 27, 1108. [DOI: https://dx.doi.org/10.3390/molecules27031108]
46. Cho, H.W.; Kim, S.B.; Jeong, M.K.; Park, Y.; Miller, N.G.; Ziegler, T.R.; Jones, D.P. Discovery of Metabolite Features for the Modelling and Analysis of High-Resolution NMR Spectra. Int. J. Data Min. Bioinform.; 2008; 2, 176. [DOI: https://dx.doi.org/10.1504/IJDMB.2008.019097] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/18767354]
47. Saccenti, E.; Hoefsloot, H.C.J.; Smilde, A.K.; Westerhuis, J.A.; Hendriks, M.M.W.B. Reflections on Univariate and Multivariate Analysis of Metabolomics Data. Metabolomics; 2014; 10, pp. 361-374. [DOI: https://dx.doi.org/10.1007/s11306-013-0598-6]
48. Cano-Corres, R.; Sánchez-Álvarez, J.; Fuentes-Arderiu, X. The Effect Size: Beyond Statistical Significance. Electron. J. Int. Fed. Clin. Chem.; 2012; 23, pp. 19-23.
49. Kenkel, C.D.; Aglyamova, G.; Alamaru, A.; Bhagooli, R.; Capper, R.; Cunning, R.; deVillers, A.; Haslun, J.A.; Hédouin, L.; Keshavmurthy, S.
50. Louis, Y.D.; Bhagooli, R.; Kenkel, C.D.; Baker, A.C.; Dyall, S.D. Gene Expression Biomarkers of Heat Stress in Scleractinian Corals: Promises and Limitations. Comp. Biochem. Physiol. Part C Toxicol. Pharmacol.; 2017; 191, pp. 63-77. [DOI: https://dx.doi.org/10.1016/j.cbpc.2016.08.007]
51. Drazic, A.; Aksnes, H.; Marie, M.; Boczkowska, M.; Varland, S.; Timmerman, E.; Foyn, H.; Glomnes, N.; Rebowski, G.; Impens, F.
52. Schrank, B.R.; Aparicio, T.; Li, Y.; Chang, W.; Chait, B.T.; Gundersen, G.G.; Gottesman, M.E.; Gautier, J. Nuclear ARP2/3 Drives DNA Break Clustering for Homology-Directed Repair. Nature; 2018; 559, pp. 61-66. [DOI: https://dx.doi.org/10.1038/s41586-018-0237-5]
53. Ramsey, J.S.; Ammar, E.-D.; Mahoney, J.E.; Rivera, K.; Johnson, R.; Igwe, D.O.; Thannhauser, T.W.; MacCoss, M.J.; Hall, D.G.; Heck, M. Host Plant Adaptation Drives Changes in Diaphorina Citri Proteome Regulation, Proteoform Expression, and Transmission of ‘Candidatus Liberibacter Asiaticus’, the Citrus Greening Pathogen. Phytopathology®; 2022; 112, pp. 101-115. [DOI: https://dx.doi.org/10.1094/PHYTO-06-21-0275-R]
54. Carbonara, K.; Andonovski, M.; Coorssen, J.R. Proteomes Are of Proteoforms: Embracing the Complexity. Proteomes; 2021; 9, 38. [DOI: https://dx.doi.org/10.3390/proteomes9030038]
55. DeSalvo, M.K.; Sunagawa, S.; Voolstra, C.R.; Medina, M. Transcriptomic Responses to Heat Stress and Bleaching in the Elkhorn Coral Acropora Palmata. Mar. Ecol. Prog. Ser.; 2010; 402, pp. 97-113. [DOI: https://dx.doi.org/10.3354/meps08372]
56. DeSalvo, M.K.; Estrada, A.; Sunagawa, S.; Medina, M. Transcriptomic Responses to Darkness Stress Point to Common Coral Bleaching Mechanisms. Coral Reefs; 2012; 31, pp. 215-228. [DOI: https://dx.doi.org/10.1007/s00338-011-0833-4]
57. Maor-Landaw, K.; Levy, O. Survey of Cnidarian Gene Expression Profiles in Response to Environmental Stressors: Summarizing 20 Years of Research, What Are We Heading For?. The Cnidaria, Past, Present and Future: The World of Medusa and Her Sisters; Goffredo, S.; Dubinsky, Z. Springer International Publishing: Cham, Switzerland, 2016; pp. 523-543. ISBN 978-3-319-31305-4
58. Lee, S.T.M.; Keshavmurthy, S.; Fontana, S.; Takuma, M.; Chou, W.-H.; Chen, C.A. Transcriptomic Response in Acropora muricata under Acute Temperature Stress Follows Preconditioned Seasonal Temperature Fluctuations. BMC Res. Notes; 2018; 11, 119. [DOI: https://dx.doi.org/10.1186/s13104-018-3230-z]
59. Zheng, B.; Han, M.; Bernier, M.; Wen, J. Nuclear Actin and Actin-Binding Proteins in the Regulation of Transcription and Gene Expression. FEBS J.; 2009; 276, pp. 2669-2685. [DOI: https://dx.doi.org/10.1111/j.1742-4658.2009.06986.x] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/19459931]
60. Fletcher, D.A.; Mullins, R.D. Cell Mechanics and the Cytoskeleton. Nature; 2010; 463, pp. 485-492. [DOI: https://dx.doi.org/10.1038/nature08908] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/20110992]
61. Kabeya, Y.; Mizushima, N.; Ueno, T.; Yamamoto, A.; Kirisako, T.; Noda, T.; Kominami, E.; Ohsumi, Y.; Yoshimori, T. LC3, a Mammalian Homologue of Yeast Apg8p, is Localized in Autophagosome Membranes after Processing. EMBO J.; 2000; 19, pp. 5720-5728. [DOI: https://dx.doi.org/10.1093/emboj/19.21.5720]
62. Mizushima, N.; Yoshimori, T.; Levine, B. Methods in Mammalian Autophagy Research. Cell; 2010; 140, pp. 313-326. [DOI: https://dx.doi.org/10.1016/j.cell.2010.01.028]
63. Dunn, S.R.; Schnitzler, C.E.; Weis, V.M. Apoptosis and Autophagy as Mechanisms of Dinoflagellate Symbiont Release during Cnidarian Bleaching: Every Which Way You Lose. Proc. R. Soc. B Biol. Sci.; 2007; 274, pp. 3079-3085. [DOI: https://dx.doi.org/10.1098/rspb.2007.0711]
64. Downs, C.A.; Kramarsky-Winter, E.; Martinez, J.; Kushmaro, A.; Woodley, C.M.; Loya, Y.; Ostrander, G.K. Symbiophagy as a Cellular Mechanism for Coral Bleaching. Autophagy; 2009; 5, pp. 211-216. [DOI: https://dx.doi.org/10.4161/auto.5.2.7405]
65. Fan, T.; Wang, R.; Xiang, Y.; An, L.; Cao, S. Heat Stress Induces Actin Cytoskeletal Reorganization and Transcript Profiles of Vegetative Profilins and Actin Depolymerizing Factors (ADFs) in Arabidopsis. Acta Physiol. Plant.; 2016; 38, 37. [DOI: https://dx.doi.org/10.1007/s11738-016-2061-6]
66. Harper, J.W.; Bennett, E.J. Proteome Complexity and the Forces That Drive Proteome Imbalance. Nature; 2016; 537, pp. 328-338. [DOI: https://dx.doi.org/10.1038/nature19947] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/27629639]
67. Barshis, D.J.; Ladner, J.T.; Oliver, T.A.; Seneca, F.O.; Traylor-Knowles, N.; Palumbi, S.R. Genomic Basis for Coral Resilience to Climate Change. Proc. Natl. Acad. Sci. USA; 2013; 110, pp. 1387-1392. [DOI: https://dx.doi.org/10.1073/pnas.1210224110] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/23297204]
68. Rittié, L.; Fisher, G.J. UV-Light-Induced Signal Cascades and Skin Aging. Ageing Res. Rev.; 2002; 1, pp. 705-720. [DOI: https://dx.doi.org/10.1016/S1568-1637(02)00024-7]
69. Park, G.; Oh, M.S. Acceleration of Heat Shock-Induced Collagen Breakdown in Human Dermal Fibroblasts with Knockdown of NF-E2-Related Factor 2. BMB Rep.; 2015; 48, pp. 467-472. [DOI: https://dx.doi.org/10.5483/BMBRep.2015.48.8.215]
70. Jovine, L.; Qi, H.; Williams, Z.; Litscher, E.; Wassarman, P.M. The ZP Domain Is a Conserved Module for Polymerization of Extracellular Proteins. Nat. Cell Biol.; 2002; 4, pp. 457-461. [DOI: https://dx.doi.org/10.1038/ncb802] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/12021773]
71. Wassarman, P.M. Zona Pellucida Glycoproteins. J. Biol. Chem.; 2008; 283, pp. 24285-24289. [DOI: https://dx.doi.org/10.1074/jbc.R800027200] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/18539589]
72. Bokhove, M.; Jovine, L. Chapter Thirteen—Structure of Zona Pellucida Module Proteins. Current Topics in Developmental Biology; Litscher, E.S.; Wassarman, P.M. Academic Press: Cambridge, MA, USA, 2018; Volume 130, pp. 413-442.
73. Drees, L.; Schneider, S.; Riedel, D.; Schuh, R.; Behr, M. The Proteolysis of ZP Proteins Is Essential to Control Cell Membrane Structure and Integrity of Developing Tracheal Tubes in Drosophila. eLife; 2023; 12, e91079. [DOI: https://dx.doi.org/10.7554/eLife.91079]
74. Császár, N.B.M.; Seneca, F.O.; van Oppen, M.J.H. Variation in Antioxidant Gene Expression in the Scleractinian Coral Acropora millepora under Laboratory Thermal Stress. Mar. Ecol. Prog. Ser.; 2009; 392, pp. 93-102. [DOI: https://dx.doi.org/10.3354/meps08194]
75. Rosic, N.N.; Pernice, M.; Dove, S.; Dunn, S.; Hoegh-Guldberg, O. Gene Expression Profiles of Cytosolic Heat Shock Proteins Hsp70 and Hsp90 from Symbiotic Dinoflagellates in Response to Thermal Stress: Possible Implications for Coral Bleaching. Cell Stress Chaperones; 2011; 16, pp. 69-80. [DOI: https://dx.doi.org/10.1007/s12192-010-0222-x]
76. Sharp, V.A.; Brown, B.E.; Miller, D. Heat Shock Protein (Hsp 70) Expression in the Tropical Reef Coral Goniopora djiboutiensis. J. Therm. Biol.; 1997; 22, pp. 11-19. [DOI: https://dx.doi.org/10.1016/S0306-4565(96)00029-0]
77. Downs, C.A.; Mueller, E.; Phillips, S.; Fauth, J.E.; Woodley, C.M. A Molecular Biomarker System for Assessing the Health of Coral (Montastraea faveolata) During Heat Stress. Mar. Biotechnol.; 2000; 2, pp. 533-544. [DOI: https://dx.doi.org/10.1007/s101260000038]
78. Fitt, W.K.; Gates, R.D.; Hoegh-Guldberg, O.; Bythell, J.C.; Jatkar, A.; Grottoli, A.G.; Gomez, M.; Fisher, P.; Lajuenesse, T.C.; Pantos, O.
79. Lesser, M.P. Oxidative Stress Causes Coral Bleaching during Exposure to Elevated Temperatures. Coral Reefs; 1997; 16, pp. 187-192. [DOI: https://dx.doi.org/10.1007/s003380050073]
80. Jones, R.J.; Hoegh-Guldberg, O.; Larkum, A.W.D.; Schreiber, U. Temperature-Induced Bleaching of Corals Begins with Impairment of the CO2 Fixation Mechanism in Zooxanthellae. Plant Cell Environ.; 1998; 21, pp. 1219-1230. [DOI: https://dx.doi.org/10.1046/j.1365-3040.1998.00345.x]
81. Downs, C.A.; Fauth, J.E.; Halas, J.C.; Dustan, P.; Bemiss, J.; Woodley, C.M. Oxidative Stress and Seasonal Coral Bleaching. Free Radic. Biol. Med.; 2002; 33, pp. 533-543. [DOI: https://dx.doi.org/10.1016/S0891-5849(02)00907-3]
82. Weis, V.M. Cellular Mechanisms of Cnidarian Bleaching: Stress Causes the Collapse of Symbiosis. J. Exp. Biol.; 2008; 211, pp. 3059-3066. [DOI: https://dx.doi.org/10.1242/jeb.009597]
83. Oakley, C.A.; Davy, S.K. Cell Biology of Coral Bleaching. Coral Bleaching: Patterns, Processes, Causes and Consequences; van Oppen, M.J.H.; Lough, J.M. Springer International Publishing: Cham, Switzerland, 2018; pp. 189-211. ISBN 978-3-319-75393-5
84. Harada, S. Classification of alcohol metabolizing enzymes and polymorphisms—Specificity in Japanese. Nihon Arukoru Yakubutsu Igakkai Zasshi; 2001; 36, pp. 85-106.
85. Benson, A.A.; Patton, J.S.; Abraham, S. Energy Exchange in Coral Reef Ecosystems. Atoll Res. Bull.; 1978; 220, pp. 33-54. [DOI: https://dx.doi.org/10.5479/si.00775630.220-5.33]
86. Tolosa, I.; Treignier, C.; Grover, R.; Ferrier-Pagès, C. Impact of Feeding and Short-Term Temperature Stress on the Content and Isotopic Signature of Fatty Acids, Sterols, and Alcohols in the Scleractinian Coral Turbinaria reniformis. Coral Reefs; 2011; 30, pp. 763-774. [DOI: https://dx.doi.org/10.1007/s00338-011-0753-3]
87. Beepat, S.S.; Davy, S.K.; Oakley, C.A.; Mashini, A.; Peng, L.; Bell, J.J. Increased Cellular Detoxification, Cytoskeletal Activities and Protein Transport Explain Physiological Stress in a Lagoon Sponge. J. Exp. Biol.; 2021; 224, jeb242820. [DOI: https://dx.doi.org/10.1242/jeb.242820]
88. Starcevic, A.; Dunlap, W.C.; Cullum, J.; Shick, J.M.; Hranueli, D.; Long, P.F. Gene Expression in the Scleractinian Acropora microphthalma Exposed to High Solar Irradiance Reveals Elements of Photoprotection and Coral Bleaching. PLoS ONE; 2010; 5, e13975. [DOI: https://dx.doi.org/10.1371/journal.pone.0013975]
89. Ren, X.; Ma, H.; Qiu, Y.; Liu, B.; Qi, H.; Li, Z.; Kong, H.; Kong, L. The Downregulation of Thioredoxin Accelerated Neuro2a Cell Apoptosis Induced by Advanced Glycation End Product via Activating Several Pathways. Neurochem. Int.; 2015; 87, pp. 128-135. [DOI: https://dx.doi.org/10.1016/j.neuint.2015.06.009]
90. Domínguez, D.C.; Guragain, M.; Patrauchan, M. Calcium Binding Proteins and Calcium Signaling in Prokaryotes. Cell Calcium; 2015; 57, pp. 151-165. [DOI: https://dx.doi.org/10.1016/j.ceca.2014.12.006] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/25555683]
91. Loven, D.P. A Role for Reduced Oxygen Species in Heat Induced Cell Killing and the Induction of Thermotolerance. Med. Hypotheses; 1988; 26, pp. 39-50. [DOI: https://dx.doi.org/10.1016/0306-9877(88)90111-9] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/2840559]
92. Orrenius, S.; Burkitt, M.J.; Kass, G.E.N.; Dypbukt, J.M.; Nicotera, P. Calcium Ions and Oxidative Cell Injury. Ann. Neurol.; 1992; 32, pp. S33-S42. [DOI: https://dx.doi.org/10.1002/ana.410320708]
93. Smith, L.M.; Kelleher, N.L. Proteoform: A Single Term Describing Protein Complexity. Nat. Methods; 2013; 10, pp. 186-187. [DOI: https://dx.doi.org/10.1038/nmeth.2369]
94. Verma, N.; Pink, M.; Schmitz-Spanke, S. A New Perspective on Calmodulin-Regulated Calcium and ROS Homeostasis upon Carbon Black Nanoparticle Exposure. Arch. Toxicol.; 2021; 95, pp. 2007-2018. [DOI: https://dx.doi.org/10.1007/s00204-021-03032-0]
95. Locksley, R.M.; Killeen, N.; Lenardo, M.J. The TNF and TNF Receptor Superfamilies: Integrating Mammalian Biology. Cell; 2001; 104, pp. 487-501. [DOI: https://dx.doi.org/10.1016/S0092-8674(01)00237-9]
96. Vanamee, É.S.; Faustman, D.L. The Benefits of Clustering in TNF Receptor Superfamily Signaling. Front. Immunol.; 2023; 14, 1225704. [DOI: https://dx.doi.org/10.3389/fimmu.2023.1225704]
97. Reed, K.C.; Muller, E.M.; Woesik, R. van Coral Immunology and Resistance to Disease. Dis. Aquat. Org.; 2010; 90, pp. 85-92. [DOI: https://dx.doi.org/10.3354/dao02213] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/20662364]
98. Ainsworth, T.; Wasmund, K.; Ukani, L.; Seneca, F.; Yellowlees, D.; Miller, D.; Leggat, W. Defining the Tipping Point. A Complex Cellular Life/Death Balance in Corals in Response to Stress. Sci. Rep.; 2011; 1, 160. [DOI: https://dx.doi.org/10.1038/srep00160] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/22355675]
99. Shen, H.-M.; Pervaiz, S. TNF Receptor Superfamily-Induced Cell Death: Redox-Dependent Execution. FASEB J.; 2006; 20, pp. 1589-1598. [DOI: https://dx.doi.org/10.1096/fj.05-5603rev]
100. Kobayashi, T.; Solaro, R.J. Calcium, thin filaments, and the integrative biology of cardiac contractility. Annu. Rev. Physiol.; 2005; 67, pp. 39-67. [DOI: https://dx.doi.org/10.1146/annurev.physiol.67.040403.114025]
101. Clapham, D.E. Calcium Signaling. Cell; 2007; 131, pp. 1047-1058. [DOI: https://dx.doi.org/10.1016/j.cell.2007.11.028]
102. Surks, H.K.; Mochizuki, N.; Kasai, Y.; Georgescu, S.P.; Tang, K.M.; Ito, M.; Lincoln, T.M.; Mendelsohn, M.E. Regulation of Myosin Phosphatase by a Specific Interaction with cGMP- Dependent Protein Kinase Iα. Science; 1999; 286, pp. 1583-1587. [DOI: https://dx.doi.org/10.1126/science.286.5444.1583]
103. Casteel, D.E.; Zhuang, S.; Gudi, T.; Tang, J.; Vuica, M.; Desiderio, S.; Pilz, R.B. cGMP-Dependent Protein Kinase Iβ Physically and Functionally Interacts with the Transcriptional Regulator TFII-I. J. Biol. Chem.; 2002; 277, pp. 32003-32014. [DOI: https://dx.doi.org/10.1074/jbc.M112332200]
104. Ahn, C.S.; Han, J.-A.; Lee, H.-S.; Lee, S.; Pai, H.-S. The PP2A Regulatory Subunit Tap46, a Component of the TOR Signaling Pathway, Modulates Growth and Metabolism in Plants. Plant Cell; 2011; 23, pp. 185-209. [DOI: https://dx.doi.org/10.1105/tpc.110.074005]
105. Meyer, E.; Aglyamova, G.V.; Wang, S.; Buchanan-Carter, J.; Abrego, D.; Colbourne, J.K.; Willis, B.L.; Matz, M.V. Sequencing and de Novo Analysis of a Coral Larval Transcriptome Using 454 GSFlx. BMC Genom.; 2009; 10, 219. [DOI: https://dx.doi.org/10.1186/1471-2164-10-219] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/19435504]
106. Rose, N.H.; Seneca, F.O.; Palumbi, S.R. Gene Networks in the Wild: Identifying Transcriptional Modules That Mediate Coral Resistance to Experimental Heat Stress. Genome Biol. Evol.; 2016; 8, pp. 243-252. [DOI: https://dx.doi.org/10.1093/gbe/evv258]
107. Tang, H.M.; Talbot, C.C., Jr.; Fung, M.C.; Tang, H.L. Molecular Signature of Anastasis for Reversal of Apoptosis. F1000Research; 2017; 6, 43. [DOI: https://dx.doi.org/10.12688/f1000research.10568.1]
108. Kumar, K.; Moirangthem, R.; Kaur, R. Histone H4 Dosage Modulates DNA Damage Response in the Pathogenic Yeast Candida Glabrata via Homologous Recombination Pathway. PLoS Genet.; 2020; 16, e1008620. [DOI: https://dx.doi.org/10.1371/journal.pgen.1008620]
109. Hughes, A.D.; Grottoli, A.G.; Pease, T.K.; Matsui, Y. Acquisition and Assimilation of Carbon in Non-Bleached and Bleached Corals. Mar. Ecol. Prog. Ser.; 2010; 420, pp. 91-101. [DOI: https://dx.doi.org/10.3354/meps08866]
110. Wooldridge, S.A. Formalising a Mechanistic Linkage between Heterotrophic Feeding and Thermal Bleaching Resistance. Coral Reefs; 2014; 33, pp. 1131-1136. [DOI: https://dx.doi.org/10.1007/s00338-014-1193-7]
111. Tremblay, P.; Gori, A.; Maguer, J.F.; Hoogenboom, M.; Ferrier-Pagès, C. Heterotrophy Promotes the Re-Establishment of Photosynthate Translocation in a Symbiotic Coral after Heat Stress. Sci. Rep.; 2016; 6, 38112. [DOI: https://dx.doi.org/10.1038/srep38112]
112. Stenmark, H. Rab GTPases as Coordinators of Vesicle Traffic. Nat. Rev. Mol. Cell Biol.; 2009; 10, pp. 513-525. [DOI: https://dx.doi.org/10.1038/nrm2728]
113. Hutagalung, A.H.; Novick, P.J. Role of Rab GTPases in Membrane Traffic and Cell Physiology. Physiol. Rev.; 2011; 91, pp. 119-149. [DOI: https://dx.doi.org/10.1152/physrev.00059.2009]
114. Saraste, J. Spatial and Functional Aspects of ER-Golgi Rabs and Tethers. Front. Cell Dev. Biol.; 2016; 4, 28. [DOI: https://dx.doi.org/10.3389/fcell.2016.00028] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/27148530]
115. Zoppino, F.C.M.; Militello, R.D.; Slavin, I.; Alvarez, C.; Colombo, M.I. Autophagosome Formation Depends on the Small GTPase Rab1 and Functional ER Exit Sites. Traffic; 2010; 11, pp. 1246-1261. [DOI: https://dx.doi.org/10.1111/j.1600-0854.2010.01086.x] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/20545908]
116. Gyurkovska, V.; Murtazina, R.; Zhao, S.F.; Shikano, S.; Okamoto, Y.; Segev, N. Dual Function of Rab1A in Secretion and Autophagy: Hypervariable Domain Dependence. Life Sci. Alliance; 2023; 6, e202201810. [DOI: https://dx.doi.org/10.26508/lsa.202201810]
117. Pramod, S.; Annamalainathan, K.; Xavier, S.M.; Sumesh, K.V.; Thulaseedharan, A. Expression Analysis of Genes Encoding sHSP and Rab1 in Hevea brasiliensis. Indian J. Biotechnol.; 2017; 13, pp. 391-402.
118. O’Mahony, P.J.; Oliver, M.J. Characterization of a Desiccation-Responsive Small GTP-Binding Protein (Rab2) from the Desiccation-Tolerant Grass Sporobolus Stapfianus. Plant Mol. Biol.; 1999; 39, pp. 809-821. [DOI: https://dx.doi.org/10.1023/A:1006183431854]
119. Cheung, A.Y.; Chen, C.Y.-h.; Glaven, R.H.; de Graaf, B.H.J.; Vidali, L.; Hepler, P.K.; Wu, H. Rab2 GTPase Regulates Vesicle Trafficking between the Endoplasmic Reticulum and the Golgi Bodies and Is Important to Pollen Tube Growth. Plant Cell; 2002; 14, pp. 945-962. [DOI: https://dx.doi.org/10.1105/tpc.000836]
120. Zhang, J.-M.; Sylvester, A.W.; Li, D.-Q.; Sun, X.-P. Complementation and Expression Analysis of SoRab1A and SoRab2A in Sugarcane Demonstrates Their Functional Diversification. J. Integr. Plant Biol.; 2006; 48, pp. 1450-1457. [DOI: https://dx.doi.org/10.1111/j.1744-7909.2006.00403.x]
121. Maringer, K.; Yarbrough, A.; Sims-Lucas, S.; Saheb, E.; Jawed, S.; Bush, J. Dictyostelium Discoideum RabS and Rab2 Colocalize with the Golgi and Contractile Vacuole System and Regulate Osmoregulation. J. Biosci.; 2016; 41, pp. 205-217. [DOI: https://dx.doi.org/10.1007/s12038-016-9610-4]
122. Li, X.; Meng, X.; Luo, K.; Luan, S.; Cao, B.; Kong, J. cDNA Cloning and Expression Analysis of a Phosphopyruvate Hydratase Gene from the Chinese Shrimp Fenneropenaeus chinensis. Fish Shellfish. Immunol.; 2017; 63, pp. 173-180. [DOI: https://dx.doi.org/10.1016/j.fsi.2017.01.042]
123. Yang, Y.; Liu, H.; Zeng, W.; Yang, Y.; Zhang, J.; Yin, J.; Wu, J.; Lai, K. Characterization and Epitope Prediction of Phosphopyruvate Hydratase from Penaeus Monodon (Black Tiger Shrimp). J. Food Sci.; 2021; 86, pp. 3457-3466. [DOI: https://dx.doi.org/10.1111/1750-3841.15819]
124. Leggat, W.; Seneca, F.; Wasmund, K.; Ukani, L.; Yellowlees, D.; Ainsworth, T.D. Differential Responses of the Coral Host and Their Algal Symbiont to Thermal Stress. PLoS ONE; 2011; 6, e26687. [DOI: https://dx.doi.org/10.1371/journal.pone.0026687]
125. Nogae, I.; Johnston, M. Isolation and Characterization of the ZWF1 Gene of Saccharomyces cerevisiae, Encoding Glucose-6-Phosphate Dehydrogenase. Gene; 1990; 96, pp. 161-169. [DOI: https://dx.doi.org/10.1016/0378-1119(90)90248-P]
126. Pollak, N.; Dölle, C.; Ziegler, M. The Power to Reduce: Pyridine Nucleotides—Small Molecules with a Multitude of Functions. Biochem. J.; 2007; 402, pp. 205-218. [DOI: https://dx.doi.org/10.1042/BJ20061638]
127. Ralser, M.; Wamelink, M.M.; Kowald, A.; Gerisch, B.; Heeren, G.; Struys, E.A.; Klipp, E.; Jakobs, C.; Breitenbach, M.; Lehrach, H.
128. Ishii, Y.; Maruyama, S.; Takahashi, H.; Aihara, Y.; Yamaguchi, T.; Yamaguchi, K.; Shigenobu, S.; Kawata, M.; Ueno, N.; Minagawa, J. Global Shifts in Gene Expression Profiles Accompanied with Environmental Changes in Cnidarian-Dinoflagellate Endosymbiosis. G3 Genes|Genomes|Genet.; 2019; 9, pp. 2337-2347. [DOI: https://dx.doi.org/10.1534/g3.118.201012] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31097480]
129. Durbin, B.; Hardin, J.; Hawkins, D.; Rocke, D. A Variance-Stabilizing Transformation for Gene-Expression Microarray Data. Bioinformatics; 2002; 18, pp. S105-S110. [DOI: https://dx.doi.org/10.1093/bioinformatics/18.suppl_1.S105]
130. Huber, W.; von Heydebreck, A.; Sültmann, H.; Poustka, A.; Vingron, M. Variance Stabilization Applied to Microarray Data Calibration and to the Quantification of Differential Expression. Bioinformatics; 2002; 18, pp. S96-S104. [DOI: https://dx.doi.org/10.1093/bioinformatics/18.suppl_1.S96]
131. Worley, B.; Powers, R. Multivariate Analysis in Metabolomics. Curr. Metabolomics; 2013; 1, pp. 92-107. [DOI: https://dx.doi.org/10.2174/2213235X11301010092]
132. Perez-Riverol, Y.; Bandla, C.; Kundu, D.J.; Kamatchinathan, S.; Bai, J.; Hewapathirana, S.; John, N.S.; Prakash, A.; Walzer, M.; Wang, S.
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; Caballero-Pérez, Juan 4
; Rodríguez de San Miguel Eduardo 5
; Rojas-Molina, Alejandra 3
1 Posgrado en Ciencias Químico-Biológicas, Facultad de Química, Universidad Autónoma de Querétaro, Querétaro 76010, Mexico; [email protected] (E.d.J.A.-O.); [email protected] (V.H.H.-E.); [email protected] (J.E.V.-T.)
2 Posgrado en Ciencias Químico-Biológicas, Facultad de Química, Universidad Autónoma de Querétaro, Querétaro 76010, Mexico; [email protected] (E.d.J.A.-O.); [email protected] (V.H.H.-E.); [email protected] (J.E.V.-T.), University of Minnesota Genomics Center, Minneapolis, MN 55455, USA
3 Laboratorio de Investigación Química y Farmacológica de Productos Naturales, Facultad de Química, Universidad Autónoma de Querétaro, Querétaro 76010, Mexico; [email protected]
4 Max Planck Institute for Immunobiology and Epigenetics, 79108 Freiburg, Germany; [email protected]
5 Departamento de Química Analítica, Facultad de Química, Universidad Nacional Autónoma de México, Ciudad Universitaria, Mexico City 04510, Mexico; [email protected]