Active Biomonitoring of Stream Ecosystems: Untargeted Metabolomic and Proteomic Responses and Free Radical Scavenging Activities in Mussels

doi:10.21203/rs.3.rs-4978395/v1

Download PDF

Research Article

Active Biomonitoring of Stream Ecosystems: Untargeted Metabolomic and Proteomic Responses and Free Radical Scavenging Activities in Mussels

https://doi.org/10.21203/rs.3.rs-4978395/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Many contaminants from scattered sources constantly endanger streams that flow through heavily inhabited areas, commercial districts, and industrial hubs. The responses of transplanted mussels in streams in active biomonitoring programs will represent the dynamic of environmental stream conditions. This study evaluated the untargeted metabolomic and proteomic responses and free radical scavenging activities of transplanted mussels Sinanodonta woodiana in the Winongo Stream at three stations (S1, S2, S3) representing different pollution levels: low (S1), moderate (S3), and high (S2). The investigation examined untargeted metabolomic and proteomic responses in the gills and 2,2-diphenyl-1-picrylhydrazyl (DPPH) and 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) activities in the gills, mantle, and digestive glands. Metabolomic analysis revealed a clear separation between mussel responses from the three stations after 28 days of exposure, with specific metabolites responding to different pollution levels. Proteomic analysis identified β-Actin protein in all stations. β-Actin protein sequence on unexposed mussels has coverage of 17%, increased to 23% at S1 Day 28 and 34% at S2 and S3 Day 28. All tissues showed increased DPPH and ABTS activities from day 3 to day 28, mainly in stations S2 and S3. These findings underscore the impact of pollution levels on the metabolomic and proteomic responses of S. woodiana and the importance of these discoveries as early indicators of long-term aquatic environmental problems. In the face of current environmental challenges, this research raises concerns about the health of water bodies. It underscores the importance of developing robust, standardized, and dependable analytical techniques for monitoring the health of aquatic environments.

Stream pollution

metabolomics

proteomics

radical scavenging activity

bivalve

Stream ecosystems provide homes for aquatic organisms, water sources, and recreational areas for humans. Nevertheless, growth that lacks consideration for the environment can harm the well-being of stream ecosystems. Various pollutants (fertilizers, pesticides, plastics/microplastics, and heavy metals) can constantly threaten streams. The water quality and aquatic biota can be influenced by the dispersion of these pollutants, which include industrial, agricultural, recreational/tourism, and domestic activities, as well as urbanization processes. (Meng et al., 2023; Sabilillah et al., 2023; Kadim and Risjani, 2022; Moussa et al., 2022; Brettschneider et al., 2019; Santana et al., 2018; Liu and Wang, 2012).

By assessing the response of living organisms to environmental contaminants, biomonitoring assesses the health of stream ecosystems (Capello et al., 2017). Active biomonitoring using organisms such as mussels and fish in cages from clean to polluted sites can be conducted to monitor the health of stream ecosystems. Compared to the passive approach, which takes organisms directly into the environment, the active approach allows the organisms to record changes in the surrounding environment, providing a more valid picture of the relationship between organism responses and pollutant exposure over time. Biomonitoring activities employ biomarkers, which are biological responses at the molecular or cellular level, to detect environmental pollutants and evaluate the overall health of stream ecosystems with great sensitivity. Aquatic habitats commonly disperse contaminants, rendering a single biomarker insufficient for a full assessment of the ecosystem's state (Kadim and Risjani, 2022; Brettschneider et al., 2019)

Freshwater mussels have become excellent water-quality bioindicators in aquatic ecosystem monitoring programs and ecotoxicological studies since this organism is immobile and obtains nutrients by filtering water, allowing it to collect and store different contaminants in its bodily tissues (Cappello et al., 2017; Ji et al., 2016; Queirós et al., 2021; Roznere et al., 2017). Mussels Sinanodonta woodiana has been used as a bioindicator of stream ecosystem quality (Al-Taher, 2024). Mussels demonstrate active reactions to changes in their environment by making dynamic adaptations in their physiology, gene expression, and metabolism. (Yang et al., 2019). One method of assessing the responses through biomonitoring is identifying and describing metabolites that result from metabolic activities in living systems (Rathahao-Paris et al., 2016). Untargeted metabolomics is a comprehensive approach that explores the toxicity mechanisms of environmental pollutants by analyzing the alterations in the relative concentrations of naturally occurring metabolites in the organism as well as the organism's metabolic homeostasis as a whole (Liu et al., 2022; Gauthier et al., 2018). Liquid chromatography-high resolution mass spectrometry (LC-HRMS) is a technique that can be used to analyze the profiles of metabolites. This method enables the detection and measurement of a wide variety of metabolites, allowing for a thorough comprehension of an organism's metabolic condition. (James et al., 2023; Serra-Compte et al., 2019; Rathahao-Paris et al., 2016).

Research on ecotoxicology has revealed that exposure of organisms to contaminants such as heavy metals, microplastics, and pharmaceutical waste transmitted through stream water has been shown to affect the physiology of organisms by stimulating oxidative stress (Magni et al., 2018; Machado et al.,2014). Oxidative stress has the potential to elevate levels of reactive oxygen species (ROS), damaging essential biomolecules like DNA, proteins, and membrane lipids. According to He et al. (2022), this damage causes oxidative injury and interferes with regular metabolic activities. Therefore, removing excessive reactive oxygen species (ROS) is imperative to maintain cellular homeostasis. In contemporary times, antioxidants are frequently employed to prevent and intervene in harm caused by reactive oxygen species (ROS). Antioxidants transfer electrons to free radicals, so rendering them harmless and counteracting their effects by decreasing oxidative damage in biological processes. (Gulcin, 2020). The molecule α,α-diphenyl-²-picrylhydrazyl (DPPH) is a long-lasting free radical used to evaluate the effectiveness of antioxidants in fighting against free radicals. 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) is a synthetic compound that, under certain conditions, can be oxidized to free radical forms and is often used in research to measure antioxidant activity. DPPH and ABTS are utilized to assess the efficacy of polar antioxidants and gauge their ability to contribute proton radicals, hence establishing the stability of mussels (Torres et al., 2018; Embuscado, 2015; Patil et al., 2015).

The Winongo Stream in the Yogyakarta Province of Indonesia, especially around densely populated settlements and industrial and business centers, receives inputs from various pollutants. Advection and dispersion mechanisms distribute multiple pollutants downstream, increasing pollution spread in stream waters. Active biomonitoring in the Winongo Stream has never been conducted, mainly through evaluating metabolomic and proteomic responses of transplanted mussels. This study assessed the untargeted metabolomic and proteomic responses and free radical scavenging activities of transplanted mussels Sinanodonta woodiana in the Winongo Stream at three stations representing different pollution levels.

2.1 Study Area and Test Organism

The Winongo Stream is located in the Yogyakarta Province of Indonesia. The stream traverses Sleman Regency, Yogyakarta City, and Bantul Regencies. Field studies were conducted from March 2024 to April 2024 at three sampling stations representing ecological conditions with different pollution levels. The determination of these levels was based on human activity in close proximity to the stream and then verified using remote sensing. Station 1 (S1) refers to the upstream of Winongo Stream, which is situated in Sleman at coordinates 7°38'38.0 “S, 110°23'33.6”E (Fig. 1). The selection of this location was based on the comparatively low population density in the surrounding area and the absence of any substantial human activities in the watershed. Consequently, this station is considered to have the lowest degree of pollution. Station 2 (S2) is situated in the central part of Winongo Stream, precisely in the core of Yogyakarta City at coordinates 7°48'28.9 "S, 110°21'13.5 "E. This station is surrounded by several high-intensity human activities, and it is located in a heavily populated area. Visually, the pollution level at this place is significantly higher compared to others. Station 3, also known as S3, is situated in the downstream area of the Winongo Stream at coordinates 7°52'3.3 "S, 110°20'51.0 "E. The vicinity of this station is characterized by an agricultural landscape and a comparatively sparse population, resulting in a lower level of pollution compared to S2. The S. woodiana mussels used in this study were obtained from the natural stream waters in the Pangandaran region, situated in West Java. The mussels' biochemical and physiological processes can be influenced by their developmental stage and age. Therefore, a mussel measuring 12 ± 2 centimeters (cm) in length and weighing 100 ± 10 grams (g) was chosen.

2.2 Sample Collection

At each site, 18 of S. woodiana mussels were transferred into bamboo cages of 1 meter by 2 meters. These cages were then submerged to a depth of 500 centimeters in the stream. A total of three mussels were gathered from every location on days 0, 3, 7, 14, 21, and 28. The samples were placed in a cooled container, transported to the laboratory, washed with distilled water, and thereafter stored in a freezer at a temperature of -20 degrees Celsius (°C) until they were ready for further examination. The gills, mantle, and digestive glands of the mussels were dissected and divided into three portions for the purpose of analyzing metabolomics, proteomics, and radical scavenging activity. The moist weight of each part was determined by weighing it..

2.3 Metabolomics Analysis using LC-HRMS

The procedure described by Windarsih et al. (2022) was employed to remove mussel gills, with slight adjustments. Ten milligrams of dried gill samples were put into a 2 mL microtube. Then, 1 mL of high-performance liquid chromatography (HPLC)-grade methanol was added. The mixture was vigorously mixed for 30 seconds using a vortex and then exposed to sonication for a duration of 30 minutes at 25°C. The sample was exposed to centrifugation at a force equivalent to 1400 times the acceleration due to gravity for a duration of 5 minutes to separate the liquid portion (the supernatant) from the solid portion (the pellet). The liquid portion was collected and passed through a 0.22 µm nylon filter to remove impurities. Subsequently, the filtered sample was examined for its metabolite composition using LC-HRMS. Methanol (mass spectrometry grade) was used as a metabolomics analysis blank.

Metabolomics analysis was performed using the LC-HRMS Thermo Scientific™ Orbitrap™ Exploris 240 HRMS (Bremen, Germany). A Thermo Scientific™ Vanquish™ Horizon UHPLC with Binary Pump (Germering, Germany) and a 100 mm × ID 2.1 mm × 2.6 εm Thermo Thermo Scientific™ AccucoreTM Phenyl Hexyl analytical column were used for the liquid chromatography. The mobile phases used were high-purity water with 0.1% formic acid (A) and high-purity acetonitrile with 0.1% formic acid (B). The mobile phases were employed in a gradient approach with a flow rate of 0.3 mL/min. Initially, the mobile phase B concentration was fixed at 5% and thereafter increased in a progressive manner to reach 90% within a span of 16 minutes. Afterwards, it was sustained at a level of 90% for a duration of 4 minutes, following which it was progressively reverted back to its initial state of 5% B over a period of 25 minutes. The column temperature was adjusted to 40°C, while the injection volume was 5 µL.

An untargeted screening was performed using the full MS/dd-MS2 acquisition mode with polarity switching, where positive or negative ions were examined in a single scan. Nitrogen gas was used as sheath, auxiliary, and sweep gases, with a setting of 35 and 7 arbitrary units (AU). The spray voltage was adjusted to 3.50 kilovolts, the capillary temperature was kept at 320°C, and the auxiliary gas heater temperature was set to 30°C. The scanning range extended from 66.7 to 1000 m/z, with a resolution of 60,000 FWHM for full MS and 30,000 for dd-MS2. The system was configured using XCalibur 4.4 software from Thermo Scientific, located in Bremen, Germany. Weekly, the instrument underwent tuning and calibration using Thermo Scientific Pierce ESI ion calibration solution (Waltham, MA) in both positive and negative ESI modes. This procedure ensured optimal performance during mass accuracy analysis (< 5 ppm), ion transfer, ion isolation, and instrumental sensitivity, as described by Windarsih et al. (2022).

2.4 Sample extraction for proteomic analysis

Mussel gills were extracted using the methodology described by Windarsih et al. (2022). Gill samples weighing 20 mg were placed into 1.5 mL Eppendorf tubes. 1000 µL of deionized water was used to introduce the sample. The mixtures were homogenized for 1 minute, vortexed, and centrifuged at a speed of 3000 times the force of gravity for 5 minutes at a temperature of 15°C. The 500 µL of liquid remaining after centrifugation was collected and transferred to a small tube. Then, proteins were separated from the liquid by adding 500 µL of acetone. The solution was aggressively agitated using a vortex mixer for a duration of 30 seconds. Next, it was subjected to centrifugation at a force equivalent to 1400 times the acceleration due to gravity for a duration of 10 minutes at a temperature of 15 °C. The liquid portion was separated, and the solid portion was gathered. The protein pellet was dehydrated using Concentrator Plus (Eppendorf, Hamburg) at 60 °C for 1 hour. A solution containing 50 millimolar (mM) of ammonium bicarbonate with a pH of 8.0 was combined with a protein pellet of 500 µL. The mixture was vigorously agitated using a vortex for a duration of one minute. The solution was then heated using a water bath at a temperature of 90 °C for a period of 10 minutes. The protein digestion method entailed the introduction of 10 µL of proteomic-grade trypsin (20 µg/100 µL) from Thermo Pierce, USA. The mixture was then vortexed for one minute. The reaction was carried out for a duration of 17 hours in an incubator set at a temperature of 37 °C. The reaction was halted by introducing 500 µL of 1% trifluoroacetic acid, followed by centrifuging the mixture at a force of 12,000×g for 10 minutes. Before doing LC-HRMS analysis, the liquid component was gathered and purified by passing it through a 0.2 µm nylon filter. Ultra-pure water of MS-grade quality was utilized as a reference sample in the proteomics analysis.

2.5 Proteomics Analysis using LC-HRMS

The analysis was performed using the methodology outlined by Windarsih et al. (2022). The proteomics examination employed the same analytical instrumentation as the metabolomics study. The separation of peptides was carried out using a gradient method, in which a mobile phase composed of MS-grade water with 0.1% formic acid (A) and MS-grade acetonitrile with 0.1% formic acid (B) was employed. The mobile-phase conditions were as follows: The mobile phase B was initially set at a concentration of 5% for the first minute, and then gradually increased from 5–50% over the course of the next 30 minutes. It was maintained at this level for an additional 2 minutes. Subsequently, the mobile phase was restored to its original state and maintained for a duration of 47 minutes. The flow rate used was 0.075 mL/min. A Thermo ScientificTM AcclaimTM PepMapTM 100 C18 HPLC column, measuring 150 mm in length, 1 mm in inner diameter, and containing particles of 3 µm in size, was used for the analytical evaluation. The injection had a volume of 3 microliters. A comprehensive MS/dd-MS2 acquisition approach was employed to conduct a mass spectrometry measurement in positive ionization mode. The flow rate of the sheath gas was adjusted to 15 arbitrary units (AU), while the flow rate of the auxiliary gas was set to 5 AU. The capillary temperature was set to 300°C, and the spray voltage was adjusted to 4.00 kV. The scanning range was from 150 to 2000 m/z, with a resolution of 140,000 for complete MS and 17,500 for dd-MS2.

2.6 Protein Profiling Analysis

The total ion chromatogram (TIC) or raw data from protein analysis utilizing LC-HRMS was retrieved in order to comprehensively determine protein contents. The protein sequence of S. woodiana was obtained from the website www.uniprot.orgin FASTA format. The protein contents in the gills of mussels that were exposed to a certain condition were compared to the FASTA protein database using Proteome Discoverer® software (Thermo Scientific, USA).

2.7 Multivariate analysis

This investigation aimed to discover intricate patterns or correlations among the many metabolites detected as the metabolomic response of the mussels to Winongo stream pollution. The Principal Component Analysis (PCA) approach was used to reduce the dimensionality of the spectra data, facilitating the identification of patterns that may not be readily apparent and detecting noteworthy alterations in the metabolomics data. The chemometric study was conducted using the SIMCA program developed by Umetrics, a company based in Sweden. A total of 110 variables derived from the metabolomics analysis were utilized for chemometric analysis. The Compound Discoverer software utilized filters to identify compounds that exhibited the most accurate matches with mzCloud and DDA fragments for the chosen ions. This process aimed to minimize the number of variables from their initial magnitude. Before doing chemometric analysis, the data underwent Pareto scaling technique to ensure optimal variance in the data. The interpretation of PCA and PLS-DA was conducted using MetaboAnalyst 6.0 software. The Variable Importance for Prediction (VIP) scores were computed using the PLS-DA technique, and a heatmap was created for visualization purposes. The analysis was performed using the MetaboAnalyst 6.0 program. The data on DPPH and ABTS scavenging activity is presented as the mean ± standard deviation (SD). A significant difference was operationally defined as a p-value < 0.05.

2.8 DPPH Radical Scavenging Activity

DPPH radical scavenging activity was assessed using the method outlined by Frediansyah and Fitrianto (2023), with minor modifications. In summary, mix 40 µL of the sample (concentration of 10 mg/mL) with 160 µL of a 0.2 mM DPPH solution. The specimen tube was stored in a lightless environment for a duration of 30 minutes. The absorbance of the sample solution was quantified at a specific wavelength of 517 nm using a Multiskan® Go microplate reader by Thermo Scientific in Vantaa, Finland.. The calculation of radical scavenging activity was performed using the following equation:

DPPH radical scavenging capacity (%) = \(\:\frac{(\mathbf{A}\mathbf{C}-\mathbf{A}\mathbf{S})}{\mathbf{A}\mathbf{C}}\times\:100\)

The letter "AC" represents the absorbance of the reference solution, comprising 200 µL of DMSO instead of the test sample. The symbol "AS" denotes the absorbance of the test solution. The quantification of the DPPH radical scavenging activity was expressed as milligrams of ascorbic acid per gram of the sample.

2.9 ABTS Radical Scavenging Activity

The ABTS radical scavenging activity was evaluated using the method developed by Frediansyah and Aziz (2024) with some adjustments. The radical cation ABTS was produced by mixing a 7 mM solution of ABTS with 2.45 mM of potassium persulfate (final concentration) in equal proportions. The reaction occurred in a dimly lit chamber for a duration of 16 hours. In order to measure, a solution of ABTS was prepared by diluting the ABTS solution in a phosphate buffer solution with a pH of 7.4. The solution was diluted until it reached an absorbance of 0.70 ± 0.02 at a wavelength of 734 nm, as determined by a Multiskan® Go microplate reader (Thermo Scientific, Vantaa, Finland), at a temperature of 25°C. A 40 µL sample, with a concentration of 10 mg/mL, was combined with 160 µL of ABTS solution. The mixture was then placed in a light-free environment and incubated at room temperature (25°C) for 10 minutes. The absorbance at a wavelength of 734 nm was measured using the Multiskan® Go microplate reader. The experiment exhibited a linear decrease in color intensity as the concentration of Trolox increased. The ABTS radical scavenging activity was determined using the following calculation method:

ABTS ⁺ radical scavenging capacity (%) = \(\:\frac{(\mathbf{A}\mathbf{C}-\mathbf{A}\mathbf{S})}{\mathbf{A}\mathbf{C}}\times\:100\)

The letter "AC" represents the absorbance of the reference solution, which consists of 15 µL of DMSO instead of the actual test sample. Conversely, "AS" denotes the absorbance of the test solution. The reference standard utilized in this study was Trolox, and the results were expressed as milligrams of Trolox equivalent per gram of the sample.

3.1 Untargeted metabolomics and chemometrics analysis

Figure 2a displays the mussels' Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA) after being transplanted for 7 and 28 days. PCA analysis revealed that the first principle component (PC1) accounted for 24.7% of the total variance, while the second main component (PC2) accounted for 19%. Together, these two components explained 43.7% of the variation. The PCA analysis revealed a distinct clustering of samples taken on days 7 and 28 (S1 and S3), suggesting shared metabolic characteristics and distinguishing them from samples taken on unexposed mussels. PLS-DA, a method that enhances the distinctions between groups, demonstrated a more accurate distinction between samples using the first (15.9%) and second (22.8%) principal components (Fig. 2b). On day 28, the samples exhibited notable disparities compared to unexposed mussels, particularly for S2 and S3, thus validating the presence of constant and substantial metabolic alterations for the study. The overall findings suggest that the metabolic profile of S. woodiana undergoes adaptive or responsive alterations throughout time. Specifically, days 7 and 28 exhibit different and consistent metabolic patterns compared to unexposed mussels. This can be explained by the gradation of metabolites that show fluctuating metabolite conditions during the 28 days of exposure, as shown in Fig. 3. Environmental pollutants can have a substantial impact on mussels' metabolic systems, leading to decreased concentrations of important metabolites. The presence of pollutants can affect antioxidant synthesis (Tables 1 and 2). Furthermore, a one-way ANOVA was conducted using the average PCA score of PC1 to validate the statistical significance of the observed variations. This research revealed statistically significant variations (< 0.05) in every identified metabolite. The data demonstrated a high level of homogeneity, as evidenced by the tight clustering of all study sites (S1, S2, and S3) and the absence of any major outliers in the PCA score plots. This study found a total of 254 metabolites from all samples.

The heat map can graphically depict the variations in metabolite gradients between the unexposed mussels group and research stations S1, S2, and S3. The heat map in Fig. 3 illustrates the normalized data set of differential metabolites in the gills of S. woodiana. The proteins that exhibited an increase in intensity from the unexposed mussels to the 28th day in all sites were thymidine 5'-monophosphate and anandamide. The metabolites pyroglutamic acid and propyl 2-(trimethylammonium) ethyl phosphate exhibited a decrease in intensity.

Figure 4a displays the TIC of LysoPC (17:0), which stands for 1-heptadecanoyl-sn-glycero-3-phosphocholine, the most common molecule, and Fig. 4b depicts the metabolic pathway. The production of LysoPC (17:0), a distinct variant of lysophospholipid (LyP), involves the breakdown of phospholipids, resulting in the removal of one fatty acid chain. This molecule's persistent presence indicates its vital role in the metabolism of S. woodiana. LysoPC can activate numerous signaling pathways that influence cellular metabolism and the physiological response to environmental stress.

Table 1

Potential metabolite identified in the *S. woodiana* gills
Pathway	Metabolite		Formula	Calculated mazz	Observed mazz	RT (min)	FC / Trend	VIP Value
Arginine, proline and glutathione metabolism	GABA	gamma-Aminobutyric acid	C₄H₉NO₂	103.120	104.070	0.72	14.66	\(\:\uparrow\:\)	0.44
	Glu	Glutamic acid	C₅H₉NO₄	147.129	146.045	0.75	6.96	\(\:\uparrow\:\)	0.17
	pGlu	Pyroglutamic acid	C₅H₇NO₃	129.114	130.049	0.73	1.20	\(\:\uparrow\:\)	2.01
	Orn	Ornithine	C₅H₁₂N₂O₂	132.161	133.096	0.67	0.66	\(\:\downarrow\:\)	0.78
	Pro	DL-Proline	C₅H₉NO₂	115.131	116.071	0.73	1.79	\(\:\uparrow\:\)	0.15
Glycine, serine and threonine metabolism	ALA	5-Aminolevulinic acid	C5H9NO3	131.130	132.065	0.72	10.20	\(\:\uparrow\:\)	0.07
	Cho	Choline	C₅H₁₄NO	104.170	104.106	0.73	6.57	\(\:\uparrow\:\)	0.93
	Thr	L-Threonine	C₄H₉¹⁵NO	120.113	120.065	0.74	3.01	\(\:\uparrow\:\)	0.04
Lysine degradation	LC	L-Carnitine	C₇H₁₅NO₃	161.199	162.112	0.73	1.30	\(\:\uparrow\:\)	0.84
	Lys	Lysine	C₆H₉N₃	146.188	147.112	0.67	1.29	\(\:\uparrow\:\)	0.15
	TML	N6,N6,N6-Trimethyl-L-lysine	C₉H₂₀N₂O₂	188.267	189.159	0.67	9.03	\(\:\uparrow\:\)	0.15
Phenylalanine, tyrosine, and tryptophan biosynthesis	Phe	Phenylalanine	C₉H₁₁NO₂	165.189	166.086	1.44	2.67	\(\:\uparrow\:\)	0.78
	Val	L-Valine	C₅H₁₁NO₂	117.146	118.086	0.73	0.61	\(\:\downarrow\:\)	0.16
	Tyr	L-Tyrosine	C₉H₁₁NO₃	181.189	182.080	0.71	0.29	\(\:\downarrow\:\)	1.22
Purine metabolism	dA	2'-Deoxyadenosine	C₁₀H₁₃N₅O₃	251.242	252.108	1.03	0.78	\(\:\downarrow\:\)	1.68
	Ade	Adenine	C₅H₅N₅	135.126	136.061	0.73	0.98	\(\:\downarrow\:\)	1.01
	Ado	Adenosine	C₁₀H₁₃N₅O₄	267.241	268.103	0.73	15.21	\(\:\uparrow\:\)	0.52
	dG	2'-Deoxyguanosine	C₁₀H₁₃N₅O₄	267.096	266.089	1.04	1.73	\(\:\uparrow\:\)	0.75
	Dl	2'-Deoxyinosine	C₁₀H₁₂N₄O₄	252.085	251.078	1.05	1.97	\(\:\uparrow\:\)	0.80
	Gua	Guanine	C₅H₅N₅O	151.126	152.056	0.73	2.39	\(\:\uparrow\:\)	0.88
	Hyp	Hypoxanthine	C₅H₂N₄O	134.096	137.045	0.73	6.11	\(\:\uparrow\:\)	0.34
	Xan	Xanthine	C₅H₄N₄O₂	152.033	151.025	0.75	0.69	\(\:\downarrow\:\)	0.24
Pyrimidine metabolism	dC	2'-Deoxycytidine	C₉H₁₃N₃O₄	227.217	228.097	0.72	70.65	\(\:\uparrow\:\)	0.22
	dU	Deoxyuridine	C₉H₁₂N₂O₅	228.202	263.043	0.75	6.68	\(\:\uparrow\:\)	0.30
	dUMP	dUMP	C₉H₁₃N₂O₈P	308.181	3 07.033	0.77	6.18	\(\:\uparrow\:\)	0.22
	dThd	Thymidine	C₁₀H₁₄N₂O₅	242.229	2 87.088	1.34	4.20	\(\:\uparrow\:\)	1.07
	dTMP	Thymidine 5-monophosphate	C₁₀H₁₅N₂O₈P	322.208	3 21.048	0.74	0.46	\(\:\downarrow\:\)	2.11
	Thy	Thymine	C₅H₆N₂O₂	126.113	1 27.050	1.33	0.81	\(\:\downarrow\:\)	0.61
Lipid metabolism	LXB4	Lipoxin B4	C₂₀H₃₂O₅	352.224	1 351.217	7.90	6.22	\(\:\uparrow\:\)	0.49
Antioxidant	EEPA	Ethyl eicosapentaenoic acid	C₂₂H₃₄O₂	330.255	329.248	14.09	3.87	\(\:\uparrow\:\)	0.29
	4-MCA	4-Methoxycinnamic acid	C₁₀H₁₀O₃	178.062	179.069	13.41	2.62	\(\:\uparrow\:\)	0.35

Potential metabolites identified were grouped based on metabolic pathways (Table 1). Trend fold change, or the ratio of changes between two different conditions or times, is usually used to describe changes in gene, protein, or metabolite expression levels (Tchitchek et al., 2012). It is known that each amino acid metabolite shows different variations (Table 1). This illustrates that the different conditions of Winongo Stream pollution have caused changes in the metabolism of S. woodiana.

3.1 Proteomic Response

The bottom-up proteomics technique is utilized for untargeted proteomics analysis. The untargeted method offers a thorough protein profile, which is valuable for identifying the maximum number of proteins in a specific sample. Sample homogenization is a crucial stage in the sample preparation process for untargeted proteomics. It ensures the success of extraction techniques in extracting proteins, particularly when working with low quantities of mussel gills. Greater homogeneity of the sample leads to improved protein extraction efficiency. A 21-hour digestion process utilized proteomic-grade trypsin to optimize protein digestion into peptides.

In this study, the proteome response is evident from the presence of peptides in samples collected from the unexposed mussels group and exposure stations S1, S2, and S3 on day 28. The proteome analysis using LC-HRMS revealed that the β-Actin peptide was the sole peptide detected (Table 2). The presence of diverse peptides in β-actin demonstrates the protein's ability to adapt and change following various biological requirements. According to Fig. 5, conservative peptides are stable and consistently present in all samples from unexposed mussels to S1, S2, and S3 day 28. This suggests these peptides may have essential functions or originate from unchanged proteins under the experimental conditions. These proteins contain signature peptides in S. woodiana, namely SYELPDGQVITIGNER, TTGIVLDSGDGVTHTVPIYEGYALPHAIMR, and VAPEEHPVLLTEAPLNPK.

Regarding specific peptides (Table 2), some metabolites are exclusively present in particular samples, such as DLYANTVLSGGSTMFPGIADR discovered in the S2 and S3 of day 28. Variances in peptide sequences were identified, suggesting their potential association with specific post-translational modifications or changes in protein expression that occur in response to particular environmental conditions. The exposure stations (S2 and S3) could show different peptide profiles compared to the unexposed mussels and each other, reflecting how varying environmental conditions affected the proteome.

Table 2

Peptide chain identified in *S. woodiana* gills
Code	Protein	Coverage (%)	Sekuens	Precursor m/z [Da]	\(\:\varDelta\:\)mazz [m/z]	RT (min)	\(\:\:\varDelta\:\)Score
Unexposed mussels	β-Actin	17	VAPEEHPVLLTEAPLNPK	652.02814	2.79	3.2811	1
			SYELPDGQVITIGNER	895.95325	4.08	2.2888	1
			TTGIVLDSGDGVTHTVPIYEGYALPHAIMR	1061.88098	4.42	2.7304	1
S1 D28	β-Actin	23	DLTDYLMK	499.74683	0.07	2.3709	1
			QEYDESGPSIVHR	758.85455	-0.05	2.3056	1
			SYELPDGQVITIGNER	895.95233	3.05	2.3000	1
			TTGIVLDSGDGVTHTVPIYEGYALPHAIMR	1061.87988	3.39	2.2057	1
			VAPEEHPVLLTEAPLNPK	652.02808	2.70	2.2001	1
S2 D28	β-Actin	34	DLYANTVLSGGSTMFPGIADR	1093.03491	1.44	2.2069	1
			GYSFTTTAER	566.76630	-1.42	2.2146	1
			HQGVMVGMGQK	586.29028	1.90	2.2128	1
			IWHHTFYNELR	505.92288	3.26	2.1904	1
			QEYDESGPSIVHR	758.85309	-2.48	2.2790	1
			SYELPDGQVITIGNER	895.95172	2.37	2.1923	1
			TTGIVLDSGDGVTHTVPIYEGYALPHAIMR	1061.88013	3.62	2.1960	1
			VAPEEHPVLLTEAPLNPK	652.02820	2.89	2.1863	1
S3 D28	β-Actin	34	DLYANTVLSGGSTMFPGIADR	1093.03833	4.57	2.2052	1
			GYSFTTTAER	566.76825	2.03	2.2129	1
			HQGVMVGMGQK	586.29144	3.88	2.2067	1
			IWHHTFYNELR	505.92310	3.68	2.1924	1
			QEYDESGPSIVHR	758.84979	-6.83	2.4283	1
			SYELPDGQVITIGNER	895.95251	3.26	2.2000	1
			TTGIVLDSGDGVTHTVPIYEGYALPHAIMR	1061.88062	4.08	2.7687	1
			VAPEEHPVLLTEAPLNPK	652.02887	3.92	2.1918	1

Table 2 shows the list of peptide chains identified in the gills of S. woodiana from the results of proteomic analysis. All identified peptide chains were derived from the β-Actin protein, essential in cell structure and cellular processes. Coverage (%) indicates the percentage of the overall β-Actin protein sequence covered by the identified peptides. At unexposed mussels, coverage of 17% increased to 23% at S1 Day 28 and 34% at S2 and S3 Day 28, indicating an increase in the number of peptides identified over time.

3.2 Measurement of DPPH and ABTS Scavenging Activity

DPPH scavenging activities on the third day, all organs showed significantly increased activity compared to unexposed mussels. At S1, DPPH activity tended to be higher in gills and digestive glands than S2 and S3. On day 28, DPPH activity in gills and mantles tended to be higher in stations S2 and S3 compared to unexposed mussels and S1. DPPH activity in the gills and mantles of S1 mussels tended to decrease over 28 days of treatment, except in the digestive glands. On the other hand, DPPH activity in all organs of S2 and S3 mussels tended to increase on day 28 (Table 3). In addition, ABTS scavenging activities on the third day, all stations showed significant changes in ABTS activity compared to unexposed mussels. In S1, ABTS activity tended to be higher in gills and digestive glands compared to S2 and S3. ABTS activity at station S1 on the 28th day showed a decrease in activity in the gills and mantle and a significant increase compared to the third day in the digestive glands. Meanwhile, S2 and S3 showed an overall rise in ABTS activity on the 28th day (Table 4).

Table 3

DPPH radical activity of water-soluble extract from *S. woodiana*
DPPH (mg of ascorbic acid/ g sample)
Station	Day 3			Day 28
Station	Gills	Mantle	Digestive glands	Gills	Mantle	Digestive glands
Unexposed mussels	0.25\(\:\pm\:\)0.03^a	0.29\(\:\pm\:\)0.04^a	0.18\(\:\pm\:\)0.02^a	0.25\(\:\pm\:\)0.03^a	0.29\(\:\pm\:\)0.04^a	0.18\(\:\pm\:\)0.02^a
S1	0.47\(\:\pm\:\)0.04^b	0.29\(\:\pm\:\)0.01^b	0.48\(\:\pm\:\)0.09^b	0.36\(\:\pm\:\)0.03^ab	0.09\(\:\pm\:\)0.04^a	0.57\(\:\pm\:\)0.01^b
S2	0.40\(\:\pm\:\)0.05^bc	0.41\(\:\pm\:\)0.01^bc	0.26\(\:\pm\:\)0.03^bc	0.43\(\:\pm\:\)0.01^ab	0.65\(\:\pm\:\)0.00^b	0.35\(\:\pm\:\)0.03^b
S3	0.18\(\:\pm\:\)0.05^bc	0.46\(\:\pm\:\)0.06^bc	0.28\(\:\pm\:\)0.02^bc	0.28\(\:\pm\:\)0.04^c	0.46\(\:\pm\:\)0.01^c	0.31\(\:\pm\:\)0.02^d
Note: The samples were analyzed in triplicate (n = 3) and expressed as mean\(\:\:\pm\:\:\)standard deviation. a, b, c, d, – significant difference among the levels in the same column on p < 0.05

Table 4

ABTS radical activity of water-soluble extract from *S. woodiana*
ABTS (mg Trolox/g sample)
Station	Day 3			Day 28
Station	Gills	Mantle	Digestive glands	Gills	Mantle	Digestive glands
Unexposed mussels	1.00\(\:\pm\:\)0.09^a	0.85\(\:\pm\:\)0.04^a	0.57\(\:\pm\:\)0.00^a	1.00\(\:\pm\:\)0.09^a	0.85\(\:\pm\:\)0.04^a	0.57\(\:\pm\:\)0.00^a
S1	1.40\(\:\pm\:\)0.02^b	1.09\(\:\pm\:\)0.04^ab	0.99\(\:\pm\:\)0.02^b	0.34\(\:\pm\:\)0.06^b	0.20\(\:\pm\:\)0.01^b	1.35\(\:\pm\:\)0.00^b
S2	0.68\(\:\pm\:\)0.00^c	1.05\(\:\pm\:\)0.05^ab	0.30\(\:\pm\:\)0.01^c	1.06\(\:\pm\:\)0.02^ca	1.35\(\:\pm\:\)0.03^c	0.86\(\:\pm\:\)0.02^c
S3	0.87\(\:\pm\:\)0.04^da	1.35\(\:\pm\:\)0.03^c	0.69\(\:\pm\:\)0.06^d	1.25\(\:\pm\:\)0.08^d	0.88\(\:\pm\:\)0.00^da	0.30\(\:\pm\:\)0.01^d
Note: The samples were analyzed in triplicate (n = 3) and expressed as mean\(\:\:\pm\:\) Standard deviation. a, b, c, d, – significant difference among the levels in the same column on p < 0.05

Figure 6 displays the change in peptide TIC intensity, indicating a variation in the number of peptides derived from actin proteins. The series of peaks and the area of the TIC curve indicate the relative difference of peptides that are not significantly different in the samples, as observed in Table 2.

4.1 Metabolomic response

Liquid chromatography-high-resolution mass spectrometry (LC-HRMS) allows for untargeted metabolomics, which may comprehensively examine and identify sample compounds. This study utilized an untargeted metabolomics approach to identify and quantify compounds in gill samples of S. woodiana. Several studies have shown that changing amounts of pollution at various station locations can modify the composition of metabolites in the gill tissue of mussels. Therefore, it is anticipated that there would be differences in the metabolite content between the unexposed mussels and treatment sites, including both polar and non-polar metabolite composition. The extraction in this investigation utilized 100% methanol of MS-grade quality as the solvent. Methanol is commonly recommended for metabolomics research because to its excellent efficiency in extracting polar and semi-polar molecules. In order to prevent any impact on the chromatogram for chemicals that elute early, a minimal injection volume of 3 µL was employed. To acquire clear and useful results from untargeted metabolomics, it is necessary to employ an efficient and robust statistical approach to handle the large volume of data. Chemometrics, a reliable statistical method, is widely used in metabolomics analysis, specifically for investigating data obtained from untargeted metabolomics research (Worley & Powers, 2013).

Metabolomics has been utilized as a developing tool in chemical toxicity research, particularly for evaluating the potential risks associated with low-level exposure to environmental toxins. Its high sensitivity allows it to deliver crucial information. Multiple research have been conducted on the toxicological consequences using metabolomics analysis, research on bivalves as bioindicators in active biomonitoring of stream ecosystems is still limited, and few data on bivalve metabolic mechanisms are available (Liu et al., 2022; Wei et al., 2022; Jiang et al., 2021). This study used LC-HRMS to obtain metabolite profiles of the transplanted S. woodiana gills at the unexposed mussels and exposure stations better to understand the toxic consequences of pollutans in the stream. Cappello et al. (2017) used gill tissues as selected as the target organ for metabolomic analysis on mussels since gills are the first organ to be directly exposed to its environment. This allows it to accumulate various metabolites and pollutants, making them effective indicators of environmental health. The gills are involved in critical metabolic processes, including respiration and filtration. This high metabolic activity can provide insights into the organism's overall health and metabolic state (Krishnamoorthy et al., 2019; Lobo et al., 2010).

Differences in the environmental conditions of the Winongo Stream in S1, S2, and S3 significantly impacted the metabolites and proteomes expressed by S. woodiana. As previously stated, the gill metabolite profiles exhibited site-specific and time-limited metabolic alterations, indicating the presence of toxicity in the stream (Cappello et al., 2016). Through pathway enrichment analysis of the divergent metabolites found in the mussels, exposure to pollutants at the exposure station has disrupted the metabolic regulatory networks of S. woodiana. The metabolic alterations were elucidated by the findings depicted on the heat map (Fig. 3), either directly or indirectly. There are variations in the intensity of metabolites between the unexposed mussels group and the exposure stations (S1, S2, S3), as well as changes in the duration of exposure (7 and 28 days after transplantation).

The PLS-DA score plot of the gills shows that the mussel groups from different places and periods consistently exhibited a distinct separation from the unexposed mussel group. Figure 3 displays metabolites whose degradation in red indicates an increase (near + 1.5), or blue indicates a decrease (near − 1.5) consistent with an increase or decrease in water quality; this can be viewed as a candidate biomarker. Several metabolites exhibit a similar fluctuation pattern, including 2-(diphenylmethoxy)-N-methylanamine, thymidine 5'-monophosphate, citric acid, anandamide, and ceramide (d18:1/20:0) (Fig. 3). The possible reason is that the different ability of S. woodiana groups to resist the toxicity of pollutants in the environment causes the mussel to show changes with increasing and decreasing metabolite intensity. Metabolite pathway impact (Fig. 4b) and changes in metabolite intensity can be comprehensively explained by constructing metabolic pathway modules (Fig. 7).

4.1.1 Nucleotide Metabolism

Nucleotide metabolism includes the processes involved in creating and breaking nucleotides. Nucleotides have crucial functions as precursors of DNA and RNA, as activated intermediates in various biosynthetic pathways, and as regulators of metabolism (Chandel et al., 2021; Gupta et al., 2021). This study detected many purine and pyrimidine nucleobase metabolites, including xanthine, hypoxanthine, guanine, and adenosine (Fig. 7). These metabolites exhibited a significant reduction in purine synthesis and breakdown compared to the unexposed mussels group. This implies that the amount of substances available for DNA and RNA production will be insufficient, potentially reducing the capacity of the gills to divide and repair themselves. As a result, this could impact tissue regeneration and repair (Dumas et al. 2020). Specifically, in the purine metabolic pathway, an increase in deoxyadenosine intensity was observed at stations S1 and S3, while in S2, it decreased. Deoxyadenosine is a nucleoside consisting of deoxyribose and adenine. An increase in intensity indicates that DNA metabolism or the process of deamination of adenosine into inosine is actively taking place regardless of environmental pollution conditions. This is because deoxyadenosine can occur from the salvage pathway process, where purines are recovered from nucleotide degradation products, so interference in the de novo pathway will not have a significant effect. This indicates that this metabolite is stable in the purine metabolic pathway in S. woodiana. The conditions that occurred in S2 can be explained by the research of Waller et al. (2023), who observed a decrease in deoxyadenosine in mussels Lampsilis cardium after being transplanted in the Mud River, which is a polluted area because it is located upstream of the city of Kokomo, Indiana which receives various sources of pollution. These changes may also affect other cellular operations, as nucleotides are crucial components in cellular energy production and the creation of nucleic acids.

The possible reduction of metabolites in the nucleotide metabolic pathway suggests a shift of resources from DNA/RNA production to other processes, such as protein synthesis and antioxidants (Yan et al., 2019). These processes are considered more important during times of environmental stress or adaptation. Prior research has established that adenosine is a crucial signaling molecule released under inflammatory circumstances (Fredholm, 2007). On the other hand, a decrease in pyrimidine metabolites, specifically dTMP and thymine, was observed (Fig. 7). dTMP is an important component in DNA and RNA synthesis, so a decrease in its intensity can also indicate damage to gill tissues that causes the process of DNA replication and RNA transcription to be disrupted (Chon et al., 2017). Decreased dTMP intensity can be indicated as a response of mussels to environmental stress, such as exposure to pollutants, changes in temperature, pH, or low oxygen availability (Erlania and Radiarta, 2011). These stresses can affect the ability of mussels to carry out normal metabolic processes. Several related studies have reported that the decrease in dTMP metabolite intensity results from disrupting mussel's metabolic processes induced by environmental pollutants (Walter and Herr, 2022; Krungkrai and Krungkrai, 2016). This disruption may cause S. woodiana to have limited sources of basic materials such as aspartate, glutamine, and carbon dioxide needed for dTMP synthesis through the de novo pyrimidine pathway. This makes the gill tissues more dependent on the salvage pathway that uses free bases already present in the tissue, thus not requiring basic materials such as aspartate and glutamine (Dumas et al., 2022; Hartati et al., 2020; Leija et al., 2016).

This study observed a significant decrease in thymine in all samples (Fig. 7), except in S2 and S3 on day 7 of exposure. Waller et al. (2023) and Stalin et al. (2011) found that physico-chemical environmental factors such as high temperature, unstable water pH, and low oxygen availability can affect mussel metabolism, where thymine intensity was observed. The results of this study show that S2 and S3, which have physical-environmental factors included in the heavily polluted category, show an increase in thymine metabolites. In addition, an indication of the increased intensity of these metabolites may occur due to disruption in the de novo pyrimidine pathway caused by environmental stress, especially in water quality and exposure to pollutants. A previous research report showed a similar mechanism, where the study sites categorized as heavily polluted also expressed increased thymidine and thymine metabolites indicated due to exposure to various contaminants such as pharmaceutical pollutants and heavy metals (O'Rourke et al., 2023).

4.1.2 Amino Acid Metabolism

The processes involved in the synthesis and degradation of proteins can be used to explain amino acid metabolism. An increase in protein synthesis may be indicated by an increase in amino acid proteins (Poortmants and Carpentier, 2016). Mussels rely on structural and enzymatic proteins to carry out biochemical processes and preserve the integrity of their gill tissues. The levels of some amino acid proteins, such as 5-aminolevulinic acid valine and threonine, were shown to have increased (Fig. 7). Enhancing the synthesis or utilization of amino acids related to lipid and protein pathways is greatly aided by this increase. However, this increase suggests that gills are adjusting to ensure the protein synthesis required for the repair and maintenance of cellular structure (Wang et al., 2021). In particular, the intensity of threonine and valine metabolites increased in S2 and S3 and decreased in S1, both on day 3 and 28 of exposure. Threonine synthesizes and produces proteins critical for maintaining cellular integrity and pollutant inhibitory functions (Tang et al., 2021). The study stations S2 and S3, classified as heavily and moderately polluted, can explain the increase in threonine intensity, presumably due to increased metabolic activity for detoxification and stress response. This condition was also reported by Kwon et al. (2012), where Mytilus edulis mussels transplanted in Onsan Bay, an area polluted with heavy metals, showed increased concentrations of threonine and valine, compared to mussels transplanted in the Dokdo area, which is categorized as clean or unpolluted. In addition, a research report by Ding et al. (2023), which evaluated the metabolomic response in Apostichopus japonicus, found an increase in threonine and valine concentrations in polluted environments indicated by increased protein synthesis and cellular repair processes. These mechanisms may occur in the metabolism of S. woodiana mussels to maintain homeostasis and adapt to changes in severe stress conditions. Additionally, the increased ornithine is involved in the urea cycle for ammonia detoxification to prevent the accumulation of toxic nitrogen compounds. Concurrently, there was also an increase in proline, which is important for synthesizing collagen and other structural proteins. This may suggest that the gills exhibit increased detoxification and tissue repair activity, which could indicate a disturbance caused by contaminants in the Winongo stream (Li et al., 2018; Milner, 2018).

4.1.3 Lipid Metabolism

Lipids are essential for cell signaling, energy storage, and membrane shape and function (Moreau & Bayer, 2023; Yoon et al., 2021). As inflammatory mediators, polyunsaturated fatty acid metabolism produces specialized pro-resolving lipid mediators (SPMs). According to Rasquel-Oliveira et al. (2023), SPMs are presently divided into four categories: lipoxins (LX), resolvins (Rv), maresins (MaR), and protectins (PD). Lipoxin A4 (LXB4) was present in each sample of gills (Table 1). This metabolite signals "stop" to body tissues, which serves as an immune resolver. There has been scant information about LXB4's protective mechanism in mussels' gills. However, the identification of aspirin involved in SPM synthesis, specifically the lipoxin family (LX; LXA4 and LXB4), and the discovery of leukotriene and prostaglandin metabolite groups as pro-inflammatory mediators for SPM formation suggest that SPM biosynthesis processes occur in S. woodiana gill tissues. LXB4, though, plays a significant role in the tissues of the mussels' gills. Since mussels are filter-feeding creatures that take food from the water through their gills, LXB4 might have a role in the defense mechanism of the animal by stimulating immune cells in the gills and helping to identify and remove pollution particles that enter the body through filtration. According to this study, exposure stations S2 and S3 had greater levels of LXB4 than the unexposed mussels. Station S2 showed the most vigorous intensity of LXB4 among the samples. Day 7 was the height of intensity, which S3 and S1 followed. LXB4 serves as an indicator of contamination in the water environment and indicates heightened oxidative stress in the gill tissues. Furthermore, it has been proven that LXB4 possesses antioxidant and anti-inflammatory characteristics. (Ye et al., 2019; Karra et al., 2015).

This study identified many metabolites linked to lipid metabolism (Fig. 4a), including LysoPC (17:0) and lysophosphatidylcholine (LPC). LysoPC (17:0) significantly increased in S2 and S3 from day 7 to 28, while its significantly decreased in S1 over the same period. According to previous reports, the increase in LysoPC (17:0) indicates high metabolic activity related to the mussel's effort to cope with pollutants. In contrast, the decrease in S1 may indicate an environment less supportive of metabolic activity under pollution conditions. According to Fokina et al. (2014) investigation, oil pollution and decreased salinity caused alterations in the lipid composition of mussels, resulting in elevated levels of cholesterol and triacylglycerols. Therefore, the contaminated environment in S2 and S3 is thought to cause high levels of inflammation and antioxidant metabolites.

The levels of glutathione (GSH) and carnitine significantly increased in the treatment groups, S2 and S3, which were exposed for 7 and 28 days, respectively. This signifies an augmentation in the transportation of fatty acids to mitochondria for oxidation, implying that lipids are utilized as a substantial energy supply. The antioxidative role of l-carnitine and GSH has been verified in mussel species (Danielli et al., 2017). Such antioxidative composition biologically adapts to oxidative stress, consistent with the hypothesis that oxidative stress in gill tissues increases in groups exposed to polluted streams, confirmed by the increase in DPPH and ABTS scavenging activities in the gills of S. woodiana.

4.2 Proteomic Respons

Untargeted proteomics analysis utilizes a bottom-up proteomics technique. The untargeted approach offers a thorough protein profile, facilitating the identification of a maximum number of proteins in a specific sample (Windarsih et al., 2022; Manes & Nita-Lazar, 2018). Protein digestion into peptides is optimized by performing a 21-hour digestion using proteomics-grade trypsin. Protein profiling was conducted using comprehensive or untargeted techniques to detect proteins isolated from mussel gill samples collected on the unexposed mussels and 28 (treated with S1, S2, S3). The presented figure shows a Total Ion Chromatogram (TIC) plot (Fig. 6) depicting the profile of peptides detected during the analysis, where each peak on the graph reflects a group of peptides eluted at a specific time and detected by the mass spectrometer. A comparison between the TIC profiles on unexposed mussels and day 28 for sample S1 shows a change in the distribution of peptides over time. In addition, comparing TIC profiles between different samples (S1, S2, and S3) after 28 days showed variation or consistency of proteomic responses under the same conditions. These intensity changes in TIC are related to variations in the number of peptides derived from the actin protein, the only protein detected. Although there were variations in peak intensities and areas under the TIC curves, these differences were not statistically significant among the samples tested, indicating that the proteomic response was relatively stable.

Several studies have found certain protein markers in the gills of mussels. These markers, such as Actin, Antimicrobials (AMPs), Heat Shock Proteins (HSPs), Metallothioneins (MTs), Cytochrome P450 Enzymes, Ferritin, Glutathione S-Transferase (GST), Transferrin, and Superoxide Dismutase (SOD), are used to investigate different aspects of mussel physiology and their immune responses to the environment and pathogens (Giarratano et al., 2014; Campos et al., 2013). This study revealed that the mussel protein β-actin was the sole protein detected and was present in all samples (Table 2). The β-actin protein is a multifunctional globular protein that plays crucial functions in diverse physiological activities, such as cell migration, membrane transport, and cytoskeleton construction. Research has revealed multiple proteins involved in mollusks' physiology and immune responses. One such protein is β-actin, which plays a vital role in the functioning of the immune system (Yanuhar and Khumaidi, 2017). Although specific peptides, such as β-Actin, remain stable, differences in environmental conditions between stations and sampling days may affect the expression of other proteins. This suggests that different environmental conditions may trigger changes in the proteome response, but some proteins, such as β-Actin, remain consistent. The β--actin peptides detected in S1 show a response to relatively stable environmental conditions (Fig. 6). These peptides can stay stable and consistent because there is no significant change in environmental conditions, consisting of only five peptides with 23% coverage compared to the unexposed mussels with three peptides with 17% coverage. In samples S2 and S3, β-actin peptides show the same response, each having eight peptides with 34% coverage (Table 2). The study of Bultelle et al. (2021) showed a significant increase in the abundance and coverage of β-actin protein, indicating that temperature stress increases protein expression to activate ciliary function, filtration function, and respiratory process in mussel gills. This suggests that different environmental conditions in the Winongo Stream may have an impact on actin proteins that are often found to be upregulated in stressed bivalves (David et al., 2005)

The most frequently detected β-actin protein peptides at all sites were SYELPDGQVITIGNER, TTGIVLDSGDGVTHTVPIYEGYALPHAIMR, and VAPEEHPVLLTEAPLNPK (Fig. 5). The specific peptide markers, DLYANTVLSGGSTMFPGIADR, were found in the day 28 samples S2 and S3. The peptide had a m/z value of 1093.03491 in S2 and 1093.03833 (single fill) in sample S3. It was detected during retention times of 1.44 and 4.57 minutes. Peptide sequences were found to have variations, indicating a possible link to certain post-translational modifications or protein production changes triggered by particular environmental conditions. The existence of a variety of peptides in β-actin is hypothesized that changes in environmental conditions leading to heightened levels of pollution can significantly influence protein expression in organisms. This, in turn, can facilitate the emergence of biomarkers, which serve as critical indicators of biological responses to environmental stressors. A recommended method for completing a thorough protein analysis and finding specific peptide markers in a sample is to use an untargeted proteomics approach with LC-HRMS. This approach provides a strong, dependable, and consistent means of studying the metabolic response of organisms to environmental conditions.

4.2 Free Radical Scavenging Activity

The latest investigation has focused on the antioxidant activity of several aquatic organisms (Wang et al., 2019; Miller et al., 2015). Antioxidants can protect the body of an organism against the harmful impact of free radicals and reactive oxygen species. It also prevents the development of numerous chronic illnesses and lipid oxidation. Several investigations have proven that mussels demonstrate significant antioxidant capabilities. (Mamelona et al., 2010; Esmat et al., 2013; Pachaiyappan et al., 2014). Radical scavenging activity assays such as DPPH and ABTS are widely used to evaluate the antioxidant capacity of organic extracts of aquatic organisms.

This study employed three tissues, including the gills, mantles, and digestive glands of S. woodiana, for antioxidant analysis (Tables 3 and 4). This study observed mussel gills as a tissue that absorbs particles from water; the DPPH scavenging activity decreased from day 3 to 28 in S1 but increased in S2 and S3. The DPPH scavenging activity in the mantle decreased in S1 and S3 and increased in S2 day 28. At all stations, the digestive gland tissue showed increased DPPH scavenging activity. Exposure to heavy metals Cu, Fe, Cd, Pb, and other chemical pollutants from industrial and domestic waste can cause an increase in DPPH scavenging activity (Abdel-Mohsen et al., 2024). The increase in all mussel tissues from stations S2 and S3 shows that pollutants are putting stress on the stream, which can throw off the balance of the ecosystem (Garg et al., 2015).

ABTS scavenging activity in mussels showed fluctuations in the ability of mussels to capture free radicals, which depended on the location and time of exposure. It reflected the antioxidant resistance of mussels to oxidative stress. The highest activity in the gills and mantle reflected a similar pattern to DPPH scavenging activity. Meanwhile, the digestive gland showed high activity, increasing at sites S1 and S2 and decreasing at station S3. In general, mussels filter pollutants through their gills, distributed throughout the mussel's organs, especially in the digestive glands. This process leads to the accumulation of pollutants in the digestive glands. As a result, free radical scavenging activities for both ABTS and DPPH scavenging activities were highest in the digestive glands, as observed in this study (Tables 3 and 4). This indicates that the digestive glands are the place of highest accumulation and detoxification of radicals (Faggio et al., 2018). According to other studies, the activity of DPPH free radicals leads to less harmful or harmless and can also stop radical chain reactions (Wang et al., 2019; Wang et al., 2013). The increase in DPPH and ABTS activities indicates that contaminants have accumulated in the mussel's body. In addition, the increase also shows exposure to higher concentrations of pollutants at stations 2 and 3, confirming that S2 and S3 are more polluted than S1.

This study identified ethyl eicosapentaenoic acid and 4-methoxycinnamic acid, increasing at all stations (Table 1). Ethyl eicosapentaenoic acid (EPA), an omega-3 fatty acid, is known to have antioxidant properties that can help neutralize free radicals. EPA can enhance the absorption ability of DPPH and ABTS radicals, thereby enhancing cellular protection against oxidative damage (Rajasekaran et al., 2024). 4-Methoxycinnamic acid, as a derivative of cinnamic acid, is a natural phenolic acid with multiple effects, such as neuroprotection and cancer inhibition (Wang et al., 2023). This metabolite can increase radical scavenging activity by increasing DPPH and ABTS, contributing to mussel tissues' total antioxidant capacity. This is evidenced by increased DPPH and ABTS scavenging activities in all the mussel tissues. This also proves that exposure to pollutants affects the metabolomic and proteomic responses of S. woodiana.

Active biomonitoring of stream ecosystems with an untargeted metabolomics and proteomics approach using LC-HRMS was successfully applied to analyze the metabolic responses of S. woodiana to environmental conditions. An untargeted metabolomics approach combined with PCA and PLS-DA proved effective in evaluating the differentiation and classification of metabolites in S. woodiana gills. Proteomic analysis was successfully performed to detect mussel-specific peptide markers (SYELPDGQVITIGNER), (TTGIVLDSGDGVTHTVPIYEGYALPHAIMR), and (VAPEEHPVLTEAPLNPK) at all study sites with different pollution levels (lightly, moderately, and heavily polluted). This study confirmed that S. woodiana can significantly reduce free radicals through DPPH and ABTS assays, potentially protecting the body from the detrimental effects of free radicals and reactive oxygen species.

Exposure to pollution causes various toxicological effects on the gills of S. woodiana. This can be used as an early warning signal regarding environmental conditions in the long term. This study supports the development of aquatic ecological health biomonitoring research methods to obtain robust, standardized, and reliable analytical techniques that allow the description of several metabolites involved in different metabolic pathways, including energy and protein metabolism, cell stabilization/repair processes, and antioxidant defense systems in bioindicator organisms that are modulated in response to environmental conditions.

Author Contribution

A.P.N. conceived the idea, planned and designed the active biomonitoring research with mussels, meticulously verified the data and results of metabolomics and proteomics analysis, ensured the highest reliability level, and drafted and corrected the manuscript. M.R. collected data, conducted a meticulous and rigorous study of the data, ensuring the highest level of thoroughness and quality of the research, and drafted and revised the manuscript. A.F. verified the data of DPPH and ABTS and corrected the manuscript. S.A.A.A. conducted the DPPH and ABTS analysis.

Acknowledgement

The authors would like to thank colleagues for their assistance in collecting data in the field and acknowledge the Final Project Recognition Grant, Universitas Gadjah Mada, Number [5286/UN1.P1/PT.01.03/2024].

Abdel-Mohsen HA, Ismail MM, Moussa R (2024) Hazardous impacts of heavy metal pollution on biometric and biochemical composition of pearl oyster Pinctada radiata from five sites along Alexandria coast, with reference to its potential health risk assessment. Environment Science Pollution Research 31,23262–23282. DOI: https://doi.org/10.1007/s11356-024-32571-z
Al-taher QM (2024) Locational and Seasonal Bioaccumulation of Heavy Metals in Bivalves Sinanodonta woodiana and Unio tigridis from the Al-massab alam River, Thi-qar province. Journal of Multidisciplinary Research, Biology, Chemistry and Medicine 8,1-11. DOI: https://doi.org/10.47577/biochemmed.v8i.10549
Brettschneider DJ, Misovic A, Schulte-Oehlmann U, Oetken M, Oehlmann J (2019) Detection of chemically induced ecotoxicological effects in streams of the Nidda catchment (Hessen, Germany) and development of an ecotoxicological, Water Framework Directive–compliant assessment system. Environmental Sciences Europe 31,1-22. DOI: https://doi.org/10.1186/s12302-019-0190-4
Bultelle F, Boutet I, Devin S, Caza F, St-Pierre Y, Péden R, Rocher B (2021) Molecular response of a sub-Antarctic population of the blue mussel (Mytilus edulis platensis) to a moderate thermal stress. Marine Environmental Research 169,105393. DOI: https://doi.org/10.1016/j.marenvres.2021.105393
Campos A, Puerto M, Prieto A, Cameán A, Almeida AM, Coelho AV, Vasconcelos V (2013) Protein extraction and two‐dimensional gel electrophoresis of proteins in the marine mussel Mytilus galloprovincialis: an important tool for protein expression studies, food quality and safety assessment. Journal of the Science of Food and Agriculture 93(7),1779-1787. DOI: https://doi.org/10.1002/jsfa.5977
Cappello T, Pereira P, Maisano M, Mauceri A, Pacheco M, Fasulo S (2016) Advances in understanding the mechanisms of mercury toxicity in wild golden grey mullet (Liza aurata) by 1H NMR-based metabolomics. Environmental pollution 219,139-148. DOI: https://doi.org/10.1016/j.envpol.2016.10.033
Cappello T, Maisano M, Mauceri A, Fasulo S (2017) 1H NMR-based metabolomics investigation on the effects of petrochemical contamination in posterior adductor muscles of caged mussel Mytilus galloprovincialis. Ecotoxicology and Environmental Safety 142,417-422. DOI: https://doi.org/10.1016/j.ecoenv.2017.04.040
Chandel NS (2021) Nucleotide metabolism. Cold Spring Harbor Perspectives in Biology 13(7),a040592.
Danielli NM, Trevisan R, Mello DF, Fischer K, Deconto VS, da Silva AD, Dafre AL (2017) Upregulating Nrf2-dependent antioxidant defenses in Pacific oysters Crassostrea gigas: Investigating the Nrf2/Keap1 pathway in bivalves. Comparative Biochemistry and Physiology Part C: Toxicology & Pharmacology 195,16-26. DOI: https://doi.org/10.1016/j.cbpc.2017.02.004
David E, Tanguy A, Pichavant K, Moraga D (2005) Response of the Pacific oyster Crassostrea gigas to hypoxia exposure under experimental conditions. Federation of European Biochemical Societies Journal. 272,5635–5652. DOI: https://doi.org/10.1111/j.1742-4658.2005.04960.x
Ding K, Xu Q, Zhang X, Liu S (2023) Metabolomic insights into neurological effects of BDE-47 exposure in the sea cucumber Apostichopus japonicus. Ecotoxicology and Environmental Safety 266,115558. DOI: https://doi.org/10.1016/j.ecoenv.2023.115558
Dumas T, Bonnefille B, Gomez E, Boccard J, Castro NA, Fenet H, Courant F (2020) Metabolomics approach reveals disruption of metabolic pathways in the marine bivalve Mytilus galloprovincialis exposed to a WWTP effluent extract. Science of the Total Environment 712,136551. DOI: https://doi.org/10.1016/j.scitotenv.2020.136551
Embuscado ME (2015) Spices and herbs: Natural sources of antioxidants–a mini review. Journal of functional foods 18,811-819. DOI: https://doi.org/10.1016/j.jff.2015.03.005
Esmat AY, Said MM, Soliman AA, El-Masry KS, Badiea EA (2013) Bioactive compounds, antioxidant potential, and hepatoprotective activity of sea cucumber (Holothuria atra) against thioacetamide intoxication in rats. Nutrition 29(1),258-267. DOI: https://doi.org/10.1016/j.nut.2012.06.004
Faggio C, Tsarpali V, Dailianis S (2018) Mussel digestive gland as a model tissue for assessing xenobiotics: an overview. Science of the Total Environment 636,220-229. DOI: https://doi.org/10.1016/j.scitotenv.2018.04.264
Fokina NN, Bakhmet IN, Shklyarevich GA, Nemova NN (2014) Effect of seawater desalination and oil pollution on the lipid composition of blue mussels Mytilus edulis L. from the White Sea. Ecotoxicology and environmental safety 110,103-109. DOI: https://doi.org/10.1016/j.ecoenv.2014.08.010
Fredholm BB (2007) Adenosine, an endogenous distress signal, modulates tissue damage and repair. Cell Death & Differentiation 14(7),1315-1323. DOI: https://doi.org/10.1038/sj.cdd.4402132
Frediansyah A, Aziz SAA (2024) Phytochemical properties of Medinilla speciosa leaf extract and its antibacterial activity against Burkholderia sp. AIP Conference Proceedings 2957:AIP Publishing. DOI: https://doi.org/10.1063/5.0183974
Frediansyah A, Azzahra FL, Fitrianto N (2023) Antioxidant properties of Syzygium polycephalum leaf extract and its antibacterial activity against Serratia marcescens and Burkholderia sp. AIP Conference Proceedings 2972:AIP Publishing. DOI: https://doi.org/10.1063/5.0182735
Garg A, Yadav H (2016) Impacts of Air Pollution on Human Health, Plant and Vegetation. International Journal of Advanced Engineering, Management and Science 1(2),239313.
Gauthier PT, Evenset A, Christensen GN, Jorgensen EH, Vijayan MM (2018) Lifelong exposure to PCBs in the remote Norwegian Arctic disrupts the plasma stress metabolome in arctic charr. Environmental science & technology 52(2),868-876. DOI: https://doi.org/10.1021/acs.est.7b05272
Giarratano E, Gil MN, Malanga G (2014) Biomarkers of environmental stress in gills of ribbed mussel Aulacomya atra atra (Nuevo Gulf, Northern Patagonia). Ecotoxicology and Environmental Safety 107:111-119. DOI: https://doi.org/10.1016/j.ecoenv.2014.05.003
Gulcin İ (2020) Antioxidants and antioxidant methods: An updated overview. Archives of toxicology 94(3),651-715. DOI: https://doi.org/10.1007/s00204-020-02689-3
Gupta R, Gupta N (2021) Nucleotide biosynthesis and regulation. Fundamentals of Bacterial Physiology and Metabolism 525-554. DOI: https://doi.org/10.1007/978-981-16-0723-3_19
He X, Xue J, Shi L, Kong Y, Zhan Q, Sun Y, Dai Y (2022) Recent antioxidative nanomaterials toward wound dressing and disease treatment via ROS scavenging. Materials Today Nano 17,100149. DOI: https://doi.org/10.1016/j.mtnano.2021.100149
James L, Gomez E, Ramirez G, Dumas T, Courant F (2023) Liquid chromatography-mass spectrometry based metabolomics investigation of different tissues of Mytilus galloprovincialis. Comparative Biochemistry and Physiology Part D: Genomics and Proteomics 45,101051. DOI: https://doi.org/10.1016/j.cbd.2022.101051
Ji C, Li F, Wang Q, Zhao J, Sun Z, Wu H (2016) An integrated proteomic and metabolomic study on the gender-specific responses of mussels Mytilus galloprovincialis to tetrabromobisphenol A (TBBPA). Chemosphere 144,527-539. DOI: https://doi.org/10.1016/j.marpolbul.2015.11.037
Jiang W, Fang J, Du M, Gao Y, Fang J, Jiang Z (2021) Integrated transcriptomics and metabolomics analyses reveal benzo [a] pyrene enhances the toxicity of mercury to the Manila clam, Ruditapes philippinarum. Ecotoxicology and Environmental Safety 213,112038. DOI: https://doi.org/10.1016/j.ecoenv.2021.112038
Kadim MK, Risjani Y (2022) Biomarker for monitoring heavy metal pollution in aquatic environment: An overview toward molecular perspectives. Emerging Contaminants 8,195-205. DOI: https://doi.org/10.1016/j.emcon.2022.02.003
Karra L, Haworth O, Priluck R, Levy BD, Levi-Schaffer F (2015) Lipoxin B4 promotes the resolution of allergic inflammation in the upper and lower airways of mice. Mucosal immunology 8(4),852-862. DOI: https://doi.org/10.1038/mi.2014.116
Krishnamoorthy V, Chuen LY, Sivayogi V, Kathiresan S, Bahari MB, Raju G, Parasuraman S (2019) Exploration of antioxidant capacity of extracts of Perna viridis, a marine bivalve. Pharmacognosy Magazine 15(66). DOI: : https://doi.org/10.4103/pm.pm_301_19
Krungkrai SR, Krungkrai J (2016) Insights into the pyrimidine biosynthetic pathway of human malaria parasite Plasmodium falciparum as chemotherapeutic target. Asian Pacific Journal of Tropical Medicine 9(6),525-534. DOI: https://doi.org/10.1016/j.apjtm.2016.04.012
Kwon KY, Jung JY, Park PJ, Seo SJ, Choi CM, Hwang HG (2012) Characterizing the effect of heavy metal contamination on marine mussels using metabolomics. Marine Pollution Bulletin 64,1874-1879. DOI: https://doi.org/10.1016/j.marpolbul.2012.06.012
Leija C, Rijo-Ferreira F, Kinch LN, Grishin NV, Nischan N, Kohler JJ, Phillips MA (2016) Pyrimidine salvage enzymes are essential for de novo biosynthesis of deoxypyrimidine nucleotides in Trypanosoma brucei. PLoS pathogens 12(11),e1006010. DOI: https://doi.org/10.1371/journal.ppat.1006010
Li P, Wu G (2018) Roles of dietary glycine, proline, and hydroxyproline in collagen synthesis and animal growth. Amino acids 50,29-38. DOI: https://doi.org/10.1007/s00726-017-2490-6
Liu F, Wang WX (2012) Proteome pattern in oysters as a diagnostic tool for metal pollution. Journal of hazardous materials 239,241-248. DOI: https://doi.org/10.1016/j.jhazmat.2012.08.069
Liu L, Wu Q, Miao X, Fan T, Meng Z, Chen X, Zhu W (2022) Study on toxicity effects of environmental pollutants based on metabolomics: A review. Chemosphere, 286, 131815. DOI: https://doi.org/10.1016/j.chemosphere.2021.131815
Lobo V, Patil A, Phatak A, Chandra N (2010) Free radicals, antioxidants and functional foods: Impact on human health. Pharmacognosy reviews 4(8),118. DOI: https://doi.org/10.4103/0973-7847.70902
Machado AADS, Wood CM, Bianchini A, Gillis PL (2014) Responses of biomarkers in wild freshwater mussels chronically exposed to complex contaminant mixtures. Ecotoxicology 23,1345-1358. DOI: https://doi.org/10.1007/s10646-014-1277-8
Magni S, Gagné F, André C, Della TC, Auclair J, Hanana H, Binelli A (2018) Evaluation of uptake and chronic toxicity of virgin polystyrene microbeads in freshwater zebra mussel Dreissena polymorpha (Mollusca: Bivalvia). Science of the Total Environment 631,778-788. DOI: https://doi.org/10.1016/j.scitotenv.2018.03.075
Mamelona J, Saint‐Louis R, Pelletier É (2010) Nutritional composition and antioxidant properties of protein hydrolysates prepared from echinoderm byproducts. International journal of food science & technology 45(1),147-154. DOI: https://doi.org/10.1111/j.1365-2621.2009.02114.x
Manes NP, Nita-Lazar A (2018) Application of targeted mass spectrometry in bottom-up proteomics for systems biology research. Journal of proteomics 189,75-90. DOI: https://doi.org/10.1007/s12161-023-02472
Meng H, Lin Y, Zhong W, Zhao Z, Shen L, Ling Z, Xu S (2023) Fish Biomonitoring and Ecological Assessment in the Dianchi Lake Basin Based on Environmental DNA. Water 15(3),399. DOI: https://doi.org/10.3390/w15030399
Miller DR, Spahn JE, Waite JH (2015) The staying power of adhesion-associated antioxidant activity in Mytilus californianus. Journal of The Royal Society Interface, 12(111),20150614. DOI: https://doi.org/10.1098/rsif.2015.0614
Milner JA (2018) Arginine: A dietary modifier of ammonia detoxification and pyrimidine biosynthesis. In Absorption and utilization of amino acids pp.25-40.CRC Press.
Moreau H, Bayer EM (2023) Lipids: plant biology’s slippery superheroes. Frontiers in Plant Physiology 1,1273816. DOI: https://doi.org/10.3389/fphgy.2023.1273816
Moussa MA, Mohamed HRH, Abdel-Khalek AA (2022) Metal Accumulation and DNA Damage in Oreochromis niloticus and Clarias gariepinus After Chronic Exposure to Discharges of the Batts Drain: Potential Risk to Human Health. Bulletin of Environmental Contamination and Toxicology 108(6),1064-1073. DOI: https://doi.org/10.1007/s00128-022-03512-8
O’Rourke K, Virgiliou C, Theodoridis G, Gika H, Grintzalis K (2023) The impact of pharmaceutical pollutants on daphnids–A metabolomic approach. Environmental Toxicology and Pharmacology 100,104157. DOI: https://doi.org/10.1016/j.etap.2023.104157
Pachaiyappan A, Muthuvel A, Sadhasivam G, Sankar VJV, Sridhar N, Kumar M (2014) In vitro antioxidant activity of different gastropods, bivalves and echinoderm by solvent extraction method. International Journal of Pharmaceutical Sciences and Research 5(6),2539. DOI: https://doi.org/10.13040/IJPSR.0975-8232.5(6).2529-35
Patil S, Rao RS, Majumdar B, Anil S (2015) Clinical appearance of oral Candida infection and therapeutic strategies. Frontiers in Microbiology 6,1391. DOI: https://doi.org/10.3389/fmicb.2015.01391
Poortmans JR, Carpentier A (2016) Protein metabolism and physical training: any need for amino acid supplementation?. Nutrire 41,1-17. DOI: https://doi.org/10.1016/B978-0-323-66162-1.00002-0
Queirós V, Azeiteiro UM, Barata C, Santos JL, Alonso E, Soares AMVM, Freitas R (2021) Effects of the antineoplastic drug cyclophosphamide on the biochemical responses of the mussel Mytilus galloprovincialis under different temperatures. Environmental Pollution 288:117735. DOI: https://doi.org/10.1016/j.envpol.2021.117735
Rajasekaran B, Gulzar S, Gopalrajan S, Karunanithi M, Benjakul S (2024) Omega-3 Enriched Fish and Shellfish Oils: Extraction, Preservation, and Health Benefits. In Fish Waste to Valuable Products, Singapore: Springer Nature Singapore pp.195-229. DOI: https://doi.org/10.1007/978-981-99-8593-7_9
Rasquel-Oliveira FS, Silva MDVD, Martelossi-Cebinelli G, Fattori V, Casagrande R, Verri JrWA (2023) Specialized Pro-Resolving Lipid Mediators: Endogenous Roles and Pharmacological Activities in Infections. Molecules 28(13),5032. DOI: https://doi.org/10.3390/molecules28135032
Rathahao-Paris E, Alves S, Junot C, Tabet JC (2016) High resolution mass spectrometry for structural identification of metabolites in metabolomics. Metabolomics 12,1-15. DOI: https://doi.org/10.1007/s11306-015-0882-8
Sabilillah AM, Palupi FR, Adji BK, Nugroho AP (2023) Health risk assessment and microplastic pollution in streams through accumulation and interaction by heavy metals. Global Journal of Environmental Science and Management 9,719–740. DOI: https://doi.org/10.22035/gjesm.2023.04.05
Santana MS, Yamamoto FY, Sandrini-Neto L, Neto FF, Ortolani-Machado CF, Ribeiro CAO, Prodocimo MM (2018) Diffuse sources of contamination in freshwater fish: detecting effects through active biomonitoring and multi-biomarker approaches. Ecotoxicology and Environmental Safety 149,173-181. DOI: https://doi.org/10.1016/j.ecoenv.2017.11.036
Serra-Compte A, Álvarez-Muñoz D, Solé M, Cáceres N, Barceló D, Rodríguez-Mozaz S (2019) Comprehensive study of sulfamethoxazole effects in marine mussels: Bioconcentration, enzymatic activities and metabolomics. Environmental Research 173,12-22. DOI: https://doi.org/10.1016/j.envres.2019.03.021
Tang Q, Tan P, Ma N, Ma X (2021) Physiological functions of threonine in animals: beyond nutrition metabolism. Nutrients 13(8),2592. DOI: https://doi.org/10.3390/nu13082592
Tchitchek N, Golib DJF, Targat B, Noth S, Benecke A, Lesne A (2012) CDS: a fold-change based statistical test for concomitant identification of distinctness and similarity in gene expression analysis. Genomics, Proteomics and Bioinformatics 10(3),127-135. DOI: https://doi.org/10.1016/j.gpb.2012.06.002
Torres P, Santos JP, Chow F, Ferreira MJP, dos Santos DY (2018) Comparative analysis of in vitro antioxidant capacities of mycosporine-like amino acids (MAAs). AlgalResearch 34,57-67. DOI: https://doi.org/10.1016/j.algal.2018.07.007
Walter M, Herr P (2022) Re-discovery of pyrimidine salvage as target in cancer therapy. Cells 11(4),739. DOI: https://doi.org/10.3390/cells11040739
Wang Y, Yin M, Gu L, Yi W, Lin J, Zhang L, Zhao G (2023) The therapeutic role and mechanism of 4-Methoxycinnamic acid in fungal keratitis. International Immunopharmacology 116,109782. DOI: https://doi.org/10.1016/j.intimp.2023.109782
Wang J, Zhang J, Li J, Dawuda MM, Ali B, Wu Y, Xie J (2021) Exogenous application of 5-aminolevulinic acid promotes coloration and improves the quality of tomato fruit by regulating carotenoid metabolism. Frontiers in plant science 12,683868. DOI: https://doi.org/10.3389/fpls.2021.683868
Wang J, Suhre MH, Scheibel T (2019) A mussel polyphenol oxidase-like protein shows thiol-mediated antioxidant activity. European Polymer Journal 113,305-312. DOI: https://doi.org/10.1016/j.eurpolymj.2019.01.069
Wang B, Li L, Chi CF, Ma JH, Luo HY, Xu YF (2013) Purification and characterisation of a novel antioxidant peptide derived from blue mussel (Mytilus edulis) protein hydrolysate. Food Chemistry 138(2-3),1713-1719. DOI: https://doi.org/10.1016/j.foodchem.2012.12.002
Windarsih A, Warmiko HD, Indrianingsih AW, Rohman A, Ulumuddin YI (2022) Untargeted metabolomics and proteomics approach using liquid chromatography-Orbitrap high resolution mass spectrometry to detect pork adulteration in Pangasius hypopthalmus meat. Food Chemistry 386,132856. DOI: https://doi.org/10.1016/j.foodchem.2022.132856
Wei S, Wei Y, Gong Y, Chen Y, Cui J, Li L, Yi L (2022) Metabolomics as a valid analytical technique in environmental exposure research: application and progress. Metabolomics 18(6),35. DOI: https://doi.org/10.1007/s11306-022-01895-7
Worley B, Powers R (2013) Multivariate analysis in metabolomics. Current metabolomics 1(1),92-107. DOI: 10.2174/2213235X11301010092
Yan LL, Zaher HS (2019) How do cells cope with RNA damage and its consequences?. Journal of Biological Chemistry 294(41),15158-15171. DOI: https://doi.org/10.1074/jbc.REV119.006513
Yang C, Du X, Hao R, Wang Q, Deng Y, Sun R (2019) Effect of vitamin D3 on immunity and antioxidant capacity of pearl oyster Pinctada fucata martensii after transplantation: insights from LC–MS-based metabolomics analysis. Fish & Shellfish Immunology 94,271-279. DOI: https://doi.org/10.1016/j.fsi.2019.09.017
Yanuhar U, Khumaidi A (2017) The application of pigment-protein fraction from Nannochloropsis oculata on β-actin response of Cromileptes altivelis infected with viral nervous necrosis. Jurnal Akuakultur Indonesia 16(1),22-32. DOI: https://doi.org/10.19027/JAI.16.1.22-32
Ye W, Zheng C, Yu D, Zhang F, Pan R, Ni X, Zhou M (2019) Lipoxin A4 Ameliorates Acute Pancreatitis‐Associated Acute Lung Injury through the Antioxidative and Anti‐Inflammatory Effects of the Nrf2 Pathway. Oxidative Medicine and Cellular Longevity 2019(1),2197017. DOI: https://doi.org/10.1155/2019/2197017
Yoon H, Shaw JL, Haigis MC, Greka A (2021) Lipid metabolism in sickness and in health: Emerging regulators of lipotoxicity. Molecular Cell 81(18),3708-3730. DOI: https://doi.org/10.1016/j.molcel.2021.08.027

No competing interests reported.

Download PDF

Editorial decision: Revision requested
28 Oct, 2024
Reviews received at journal
25 Oct, 2024
Reviews received at journal
20 Oct, 2024
Reviewers agreed at journal
11 Oct, 2024
Reviewers agreed at journal
17 Sep, 2024
Reviewers invited by journal
09 Sep, 2024
Editor assigned by journal
27 Aug, 2024
Submission checks completed at journal
27 Aug, 2024
First submitted to journal
26 Aug, 2024

You are reading this latest preprint version

Active Biomonitoring of Stream Ecosystems: Untargeted Metabolomic and Proteomic Responses and Free Radical Scavenging Activities in Mussels

Status:

Version 1

Abstract

Figures

1. Introduction

2. Materials and Methods

2.1 Study Area and Test Organism

2.2 Sample Collection

2.3 Metabolomics Analysis using LC-HRMS

2.4 Sample extraction for proteomic analysis

2.5 Proteomics Analysis using LC-HRMS

2.6 Protein Profiling Analysis

2.7 Multivariate analysis

2.8 DPPH Radical Scavenging Activity

2.9 ABTS Radical Scavenging Activity

3. Results

3.1 Untargeted metabolomics and chemometrics analysis

3.1 Proteomic Response

3.2 Measurement of DPPH and ABTS Scavenging Activity

4. Discussion

4.1 Metabolomic response

4.1.1 Nucleotide Metabolism

4.1.2 Amino Acid Metabolism

4.1.3 Lipid Metabolism

4.2 Proteomic Respons

4.2 Free Radical Scavenging Activity

5. Conclusion

Declarations

Author Contribution

Acknowledgement

References

Additional Declarations

Status:

Version 1