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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Mar. Sci.</journal-id>
<journal-title>Frontiers in Marine Science</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Mar. Sci.</abbrev-journal-title>
<issn pub-type="epub">2296-7745</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/fmars.2025.1524885</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Marine Science</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Comprehensive analysis of ionomic profiling in <italic>Chlorella</italic> exposed to chlorpyrifos</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author" equal-contrib="yes">
<name>
<surname>Yu</surname>
<given-names>Qing-Qing</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="author-notes" rid="fn003">
<sup>&#x2020;</sup>
</xref>
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</contrib>
<contrib contrib-type="author" equal-contrib="yes">
<name>
<surname>Zhang</surname>
<given-names>Yulong</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<xref ref-type="author-notes" rid="fn003">
<sup>&#x2020;</sup>
</xref>
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</contrib>
<contrib contrib-type="author">
<name>
<surname>Zhao</surname>
<given-names>Shiyuan</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
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</contrib>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Pang</surname>
<given-names>Min</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<xref ref-type="author-notes" rid="fn001">
<sup>*</sup>
</xref>
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</contrib>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Jiang</surname>
<given-names>Pei</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="author-notes" rid="fn001">
<sup>*</sup>
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<contrib contrib-type="author" corresp="yes">
<name>
<surname>Qu</surname>
<given-names>Pei</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<xref ref-type="author-notes" rid="fn001">
<sup>*</sup>
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<aff id="aff1">
<sup>1</sup>
<institution>Translational Pharmaceutical Laboratory, Jining NO.1 People&#x2019;s Hospital</institution>, <addr-line>Jining</addr-line>, <country>China</country>
</aff>
<aff id="aff2">
<sup>2</sup>
<institution>Marine Ecology Research Center, First Institute of Oceanography, Ministry of Natural Resources of the People&#x2019;s Republic of China, Laoshan District</institution>, <addr-line>Qingdao</addr-line>, <country>China</country>
</aff>
<author-notes>
<fn fn-type="edited-by">
<p>Edited by: Marialetizia Palomba, University of Tuscia, Italy</p>
</fn>
<fn fn-type="edited-by">
<p>Reviewed by: Antonio Calisi, Universit&#xe0; del Piemonte Orientale, Italy</p>
<p>Maria Luisa Vannuccini, University of Tuscia, Italy</p>
</fn>
<fn fn-type="corresp" id="fn001">
<p>*Correspondence: Min Pang, <email xlink:href="mailto:pm_cat@hotmail.com">pm_cat@hotmail.com</email>; Pei Jiang, <email xlink:href="mailto:jiangpeicsu@sina.com">jiangpeicsu@sina.com</email>; Pei Qu, <email xlink:href="mailto:qupei@fio.org.cn">qupei@fio.org.cn</email>
</p>
</fn>
<fn fn-type="equal" id="fn003">
<p>&#x2020;These authors have contributed equally to this work and share first authorship</p>
</fn>
</author-notes>
<pub-date pub-type="epub">
<day>05</day>
<month>02</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="collection">
<year>2025</year>
</pub-date>
<volume>12</volume>
<elocation-id>1524885</elocation-id>
<history>
<date date-type="received">
<day>08</day>
<month>11</month>
<year>2024</year>
</date>
<date date-type="accepted">
<day>20</day>
<month>01</month>
<year>2025</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2025 Yu, Zhang, Zhao, Pang, Jiang and Qu</copyright-statement>
<copyright-year>2025</copyright-year>
<copyright-holder>Yu, Zhang, Zhao, Pang, Jiang and Qu</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/">
<p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p>
</license>
</permissions>
<abstract>
<sec>
<title>Introduction</title>
<p>Chlorpyrifos (CPF), a widely used organophosphorus insecticide, is highly toxic to non-target aquatic organisms and has relatively high persistence in water, posing a serious threat to marine ecosystems. However, little is known about the toxicological mechanism of CPF on marine microalgae, which is the main primary producer in the marine ecosystem.</p>
</sec>
<sec>
<title>Methods</title>
<p>This study explored the ion changes of microalgae <italic>Chlorella vulgaris</italic> under the stress of CPF through Inductively Coupled Plasma Mass Spectrometry (ICP-MS).</p>
</sec>
<sec>
<title>Results</title>
<p>Significant disparities in ionomics among control and treatment group were observed through pattern recognition analysis (principal component analysis, PCA; orthogonal partial least squares discriminant analysis, OPLS-DA), indicating that CPF may impede their growth by disrupting the homeostasis of crucial elements within algal cells.</p>
</sec>
<sec>
<title>Discussion</title>
<p>This study elucidated the inhibitory impact of CPF on green algae growth and its potential mechanism of toxicity through ICP-MS, providing crucial insights for a comprehensive understanding of the influence of organophosphorus pesticides on aquatic ecosystems.</p>
</sec>
</abstract>
<kwd-group>
<kwd>chlorpyrifos</kwd>
<kwd>ionomics</kwd>
<kwd>ICP-MS</kwd>
<kwd>microalgae</kwd>
<kwd>organophosphorus insecticide</kwd>
</kwd-group>
<counts>
<fig-count count="12"/>
<table-count count="3"/>
<equation-count count="0"/>
<ref-count count="43"/>
<page-count count="13"/>
<word-count count="5295"/>
</counts>
<custom-meta-wrap>
<custom-meta>
<meta-name>section-in-acceptance</meta-name>
<meta-value>Marine Molecular Biology and Ecology</meta-value>
</custom-meta>
</custom-meta-wrap>
</article-meta>
</front>
<body>
<sec id="s1" sec-type="intro">
<label>1</label>
<title>Introduction</title>
<p>Chlorpyrifos (CPF), a highly effective and broad-spectrum organophosphorus insecticide with contact, stomach poison and fumigation effects, is widely used globally to control crop damage from various insects, including borers, scale insects, armyworms, aphids and mites (<xref ref-type="bibr" rid="B21">Li et&#xa0;al., 2021</xref>). The insecticidal effect of CPF primarily involves the inhibition of acetylcholinesterase (AChE) (<xref ref-type="bibr" rid="B38">Ubaid ur Rahman et&#xa0;al., 2021</xref>). This inhibition results in the overstimulation of neuronal cells and disorders of central nervous system. In severe cases, it can lead torespiratory failure, paralysis, and even death (<xref ref-type="bibr" rid="B38">Ubaid ur Rahman et&#xa0;al., 2021</xref>).</p>
<p>Excessive utilization and misuse of CPF inevitably impacts the ecological environment (<xref ref-type="bibr" rid="B21">Li et&#xa0;al., 2021</xref>). When applied on farmland, CPF has an actual biological uptake rate of less than 1% (<xref ref-type="bibr" rid="B6">Bellas et&#xa0;al., 2023</xref>), with the majority being lost during field application. During its migration, CPF poses potential toxic effects on non-target organisms and even humans. Studies have demonstrated that CPF degrades soil fertility by inhibiting nitrogen and phosphorus metabolism, disrupting the acid-base balance, and contaminating groundwater (<xref ref-type="bibr" rid="B25">Nandi et&#xa0;al., 2022</xref>). In addition, CPF adversely affects non-target soil organisms such as earthworms, with LC<sub>50</sub> values ranging from 118.5 to 148 mg/kg (<xref ref-type="bibr" rid="B43">Zhou et&#xa0;al., 2008</xref>), leading to a decreases in soil biodiversity and consequently negatively impacting agroecosystems.</p>
<p>Acute intoxication from short-term exposure to high doses of CPF can lead to symptoms including dizziness, headache, convulsions, increased sweating, salivation, nausea, unconsciousness, and even death (<xref ref-type="bibr" rid="B25">Nandi et&#xa0;al., 2022</xref>). Even at the does below the threshold for systemic toxicity, prolonged CPF exposure over months or years may result in chronic effects such as impaired neural cell development, birth defects, reproductive abnormalities, and an increased risk of cancer (<xref ref-type="bibr" rid="B25">Nandi et&#xa0;al., 2022</xref>).</p>
<p>CPF has been detected in freshwater worldwide due to its misuse, particularly in the water sources near the application area (<xref ref-type="bibr" rid="B1">Affum et&#xa0;al., 2018</xref>; <xref ref-type="bibr" rid="B18">Kar et&#xa0;al., 2024</xref>). The CPF content in Ankobra Basin (Ghana) groundwater ranged from30 to 2000ng/L (<xref ref-type="bibr" rid="B1">Affum et&#xa0;al., 2018</xref>), while those in stream near Paddyfields in Odisha, India)were 81.53 ng/L on average (<xref ref-type="bibr" rid="B18">Kar et&#xa0;al., 2024</xref>). Given its high toxicity to non-target aquatic organisms and relatively long persistence in water, CPF poses a significant threat to aquaponic systems (<xref ref-type="bibr" rid="B6">Bellas et&#xa0;al., 2023</xref>). Studies have shown that CPF can inhibit the growth of various freshwater microalgae, such as <italic>Scenedesmus quadricanda</italic>, with a 96 h EC<sub>50</sub> value of 708.01 &#x3bc;g/L (<xref ref-type="bibr" rid="B36">Sun et&#xa0;al., 2013</xref>). Toxicity of CPF to aquatic fauna includes disruption in steroid metabolism, hepatic system dysfunction, behavioral changes, epithelial hyperplasia, respiratory stress, hydropic degeneration, delayed metamorphosis, renal tubule degeneration and necrosis, and glomerular shrinkage (<xref ref-type="bibr" rid="B25">Nandi et&#xa0;al., 2022</xref>). At a concentration of 0.0528 &#x3bc;g/L, CPF caused mortality in Daphnia. The 90-hour LC<sub>50</sub> values for CPF in carp, crucian carp and silver carp were 3 170, 3 670 and 3 970 &#x3bc;g/L, respectively (<xref ref-type="bibr" rid="B41">Zhao et&#xa0;al., 2008</xref>).</p>
<p>The ocean has long been considered the ultimate sink for pesticides (<xref ref-type="bibr" rid="B6">Bellas et&#xa0;al., 2023</xref>). Due to the misuse of CPF, CPF can be detected in coastal and even polar marine areas worldwide (<xref ref-type="bibr" rid="B42">Zhong et&#xa0;al., 2012</xref>; <xref ref-type="bibr" rid="B34">Smalling et&#xa0;al., 2013</xref>; <xref ref-type="bibr" rid="B23">Montuori et&#xa0;al., 2015</xref>; <xref ref-type="bibr" rid="B22">Liu et&#xa0;al., 2018</xref>). According to the reported toxicological studies, CPF is toxic to many marine species. Microalgae, the main primary producer in the marine ecosystem, are not likely to be very sensitive to CPF because it does not have the processes, structures, or functional groups involved in the modes of action of CPF. The growth of the marine diatoms <italic>Skeletonema costatum</italic> and <italic>Minutocellus polymorus</italic> were affected by CPF at 640 and 240 &#x3bc;g/L, respectively (<xref ref-type="bibr" rid="B39">Walsh et&#xa0;al., 1988</xref>). Crustaceans, as &#x201c;Marine insects&#x201d;, have similar biochemical and physiological traits with target organisms, therefore it is especially sensitive to CPF. <italic>Palaemonetes pugio</italic> showed LC50 values of 0.37 &#x3bc;g/L (<xref ref-type="bibr" rid="B20">Key and Fulton, 1993</xref>), <italic>Neomysis integer</italic> showed LC50 values of 0.13&#x3bc;g/L (<xref ref-type="bibr" rid="B29">Roast et&#xa0;al., 1999</xref>). In general, Crustaceans is more sensitive to CPF than marine fish. Species and life-stage of marine fish depended its sensitivity to CPF, adults have been found to be more tolerated to CPF than juveniles and larvae. Acute lethal toxicity of CPF to <italic>Menidia peninsulae</italic> larvae and Cyprinodon variegatus has been reported at 0.42 and 270 &#x3bc;g/L (<xref ref-type="bibr" rid="B10">EPA, 1986</xref>), respectively.</p>
<p>To safeguard human health, ecological safety, and ensure food security globally, various countries, including China, have comprehensively banned the use of CPF (<xref ref-type="bibr" rid="B15">Huang et&#xa0;al., 2020</xref>). Nevertheless, CPF remains the most extensively employed organophosphorus insecticide worldwide (<xref ref-type="bibr" rid="B16">Jiang et&#xa0;al., 2021</xref>).The CPF levels now in the ocean has been found in small amounts (8~219.42 pg/L, <xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Table S1</bold>
</xref>) after undergoing processes such as dilution, photolysis, biodegradation, and adsorption settling in seawater, which are too low to cause significant acute toxic effects on marine organisms (<xref ref-type="bibr" rid="B6">Bellas et&#xa0;al., 2023</xref>). However, studies have shown that long-term exposure to low concentrations can result in CPF accumulation in various tissues of tilapia fish, leading to chronic toxicity effects over time (<xref ref-type="bibr" rid="B28">Rao et&#xa0;al., 2003</xref>). CPF has also been detected in different groups of marine organisms worldwide, confirming its ability to accumulate within these organisms (<xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Table S2</bold>
</xref>) (<xref ref-type="bibr" rid="B35">Sol&#xe9; et&#xa0;al., 2000</xref>; <xref ref-type="bibr" rid="B34">Smalling et&#xa0;al., 2013</xref>; <xref ref-type="bibr" rid="B30">Ros et&#xa0;al., 2015</xref>; <xref ref-type="bibr" rid="B4">Balmer et&#xa0;al., 2019</xref>). Given that global marine fisheries production has reached 91.03 million tons in 2022 (<xref ref-type="bibr" rid="B11">FAO, 2024</xref>), humans who rely on marine organisms as a source of high-quality protein are also at risk of exposure to CPF. Therefore, it is necessary to improve the knowledge of the mechanism of CPF toxicity on aquatic organisms. Microalgae are main primary producer in the marine ecosystems. They face, absorb, and accumulate CPF from seawater directly (<xref ref-type="bibr" rid="B24">Morris et&#xa0;al., 2016</xref>), but little has been done on the toxicological mechanism of CPF on marine microalgae.</p>
<p>Ionome includes all the mineral nutrients and trace elements existing in an organism and represents the inorganic component of cellular system, which are involved in a wide range of crucial biological processes, such as signaling, osmoregulation, enzymology, electrophysiology and transport (<xref ref-type="bibr" rid="B33">Singh et&#xa0;al., 2022</xref>). Ion changes in a process means large changes has happened to related metabolic pathways. Thus, ionomics, dealing with the changes in ion production, provides a new perspective to the study toxicological mechanisms of microalgae under varied external stimulus (<xref ref-type="bibr" rid="B40">Zargar et&#xa0;al., 2016</xref>). This study aims to investigate the impact of CPF on the biological behavior of microalgae <italic>Chlorella vulgaris</italic>, and to explore the toxicological mechanisms of CPF on <italic>C. vulgaris</italic> using ionomics. This study aims to provide novel insights into pollution research, focusing on the effects of bioaccumulation and biomagnification, their impacts along the trophic chains and the consequent alteration of marine ecosystems.</p>
</sec>
<sec id="s2">
<label>2</label>
<title>Methods</title>
<sec id="s2_1">
<label>2.1</label>
<title>Chemicals and reagents</title>
<p>Chlorpyrifos (<xref ref-type="fig" rid="f1">
<bold>Figure&#xa0;1</bold>
</xref>) (powder, 99.61% purity, CAS#: 2921-88-2) was obtained from Dr. Ehrenstorfer Co., Ltd (Augsburg, Germany). For the experiments, stock solutions of CPF was prepared in dimethyl sulfoxide (DMSO) and diluted into the culture media to the desired concentrations. In the culture media the final concentration of DMSO was &lt; 0.1%. F/2 medium (<xref ref-type="bibr" rid="B13">Guillard, 1975</xref>), and Lugo&#x2019;s iodine solution were used.</p>
<fig id="f1" position="float">
<label>Figure&#xa0;1</label>
<caption>
<p>Chlorpyrifos, IUPAC name is <italic>O</italic>, <italic>O</italic>-Diethyl-<italic>O</italic>-(3,5,6-trichloro-2-pyridyl) phosphorothioate, is a white crystal-like solid with a strong odor. It does not mix well with water, so it is usually mixed with oily liquids before it is applied to crops or animals.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fmars-12-1524885-g001.tif"/>
</fig>
</sec>
<sec id="s2_2">
<label>2.2</label>
<title>Culture condition</title>
<p>The First Institute of Oceanography (MNR, Qingdao, China) provided cultures of the unicellular microalga <italic>Chlorella vulgaris</italic>. The strains were used to prepare the stock culture. To obtain a culture in the early exponential growth phase, it was incubated for 3 days at a temperature of 23 &#xb1; 1&#xb0;C under fluorescent illumination of 4000 lx and a light/dark cycle of 12/12 hours in an illumination incubator. Manual swirling agitations were performed three times daily. The microalga strain was inoculated at a concentration of 25% (V<sub>inoculum</sub>/V<sub>media</sub>) in 150 mL sterile f/2 medium contained in glass Erlenmeyer flasks with a volume capacity of 250 mL. The culture medium was supplemented with CPF stock solutions to achieve nominal concentrations of the active ingredient: 0, 50, 100, 150, and 200 &#x3bc;g/L. A control treatment without any DMSO was also included in the same sterile f/2 medium. All experiments were conducted in triplicate without replacing the medium throughout the study period. To ensure uniform light and temperature conditions, all flasks containing different treatments were repositioned three times daily inside the illumination incubator during the course of the experiment. To assess the impacts of chlorpyrifos on the population growth, the density of <italic>Chlorella</italic> in each treatment was figured by direct counting in Neubauer chamber every 24 h during the entire experimental period (12 days).</p>
</sec>
<sec id="s2_3">
<label>2.3</label>
<title>Sample collection and preparation</title>
<p>At the end of the bioassay, the DMSO control culture and the treatment groups exposed to CPF concentrations of 0, 50, 100, 150, 200 &#x3bc;g/L were centrifuged at 4000 rpm for 10 min. The resulting pellet was refrigerated at -20 &#xb0;C for subsequent ICP-MS analysis. The CPF concentrations in the experimental groups were determined based on previous studies and preliminary experiments conducted for this study.</p>
</sec>
<sec id="s2_4">
<label>2.4</label>
<title>ICP-MS</title>
<p>Prior to the experiment, all experimental materials were soaked in 6M nitric acid overnight, followed by rinsing and drying with ultrapure water before use. A digestion tube was utilized to hold 50 &#x3bc;L of serum or 50 mg of tissue, which was then supplemented with 1ml of 65% nitric acid. The mixture underwent digestion on a heating plate at a temperature of 130 &#xb0;C for a duration of two hours. After evaporating the acid, it was diluted using ultrapure water. Before injection, the sample was cooled down to room temperature. In this study, detection was conducted using the NexION 1000G ICP-MS spectrometer (PerkinElmer, USA), which underwent tuning optimization through mass spectrometry tuning solution prior to analysis initiation. Calibration involved online internal standard addition and collision mode employed high-purity helium gas as the collision gas for interference removal purposes. The instrument&#x2019;s operating parameters were set as follows: nebulizer flow rate at 1.04 L/min, auxiliary gas flow rate at 1.20 L/min, plasma gas flow rate at15 L/min, ICP RF power at1600 W, simulated voltage -1862 V, pulse voltage at1250 V, and blank removal process applied. The chamber was supplied with an internal standard solution, which consisted of scandium, germanium, indium, and rhenium at a concentration of 20 &#x3bc;g/L. This solution was introduced into the chamber using a peristaltic pump. A total of 34 elements were identified in the analysis, including lithium (Li), beryllium (Be), boron (B), sodium (Na), magnesium (Mg), aluminum (Al), silicon (Si), phosphorus (P), potassium (K), calcium(Ga), titanium (Ti), vanadium (V), chromium (Cr), manganese (Mn), iron (Fe), cobalt (Co), nickel (Ni), copper (Cu), zinc (Zn), arsenic (As), selenium (Se), strontium (Sr), zirconium (Zr), molybdenum (Mo), silver (Ag), cadmium (Cd), tin(Sn), antimony (Sb), barium (Ba), thallium (Tl), lead (Pb), bismuth (Bi), mercury (Hg). After excluding elements that deviated from the standard curve or had concentrations below the limit of quantification, a total of 28 elements remained: Li, Na, Mg, Al, P, K, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Sr, Zr, Mo, Ag, Cd, Sn, Sb, Ba, Ir, Pb, Bi. The accuracy, reproducibility, and limit of quantification for this method were calculated and validated.</p>
</sec>
<sec id="s2_5">
<label>2.5</label>
<title>Multivariate statistical analysis</title>
<p>The data was normalized and subjected to pattern recognition analysis using SIMCA-P 14.0 (Umetrics, Ume&#xe5;, Sweden), employing principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA). Data analysis involved a two-tailed Student&#x2019;s t-test. The selection of differential variables was based on the following criteria: (1) <italic>p</italic> &lt; 0.05 and (2) Variable Importance in Projection (VIP) value obtained from OPLS-DA &gt; 1.</p>
</sec>
</sec>
<sec id="s3" sec-type="results">
<label>3</label>
<title>Results</title>
<sec id="s3_1">
<label>3.1</label>
<title>Establishment of <italic>Chlorella</italic> model treated by CFP</title>
<p>We counted the cells regularly every day during the culture period, and the density changes of <italic>Chlorella</italic> were recorded (<xref ref-type="fig" rid="f2">
<bold>Figure&#xa0;2</bold>
</xref>). Fisher-test was used to compare the mean value of <italic>Chlorella</italic> concentration data among the groups (<xref ref-type="fig" rid="f3">
<bold>Figure&#xa0;3</bold>
</xref>).</p>
<fig id="f2" position="float">
<label>Figure&#xa0;2</label>
<caption>
<p>Growth of alga among DMSO Control, 0 &#x3bc;g/L, 50 &#x3bc;g/L, 100 &#x3bc;g/L, 150 &#x3bc;g/L, 200 &#x3bc;g/L (n=3).</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fmars-12-1524885-g002.tif"/>
</fig>
<fig id="f3" position="float">
<label>Figure&#xa0;3</label>
<caption>
<p>Fisher-test results between the cell density of groups (n=3).</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fmars-12-1524885-g003.tif"/>
</fig>
<p>The <italic>Chlorella</italic> density map showed that the cell density of each group had an overall increasing trend, and the density of DMSO control group and 0 &#x3bc;g/L group was higher than that of CPF addition group, indicating that CPF had an effect on <italic>Chlorella</italic> growth (<xref ref-type="fig" rid="f2">
<bold>Figure&#xa0;2</bold>
</xref>). The result of Fisher-test showed that there were significant difference between 200 &#x3bc;g/L group and 0 &#x3bc;g/L group (<italic>p</italic> &lt; 0.05), and nonsignificant differences between other groups, which means that 200 &#x3bc;g/L CPF could significantly inhibit the growth of <italic>Chlorella</italic> population.</p>
</sec>
<sec id="s3_2">
<label>3.2</label>
<title>Quality control of ionomics</title>
<p>The Correlation coefficient for the standard samples was calculated, all of which exhibited values above 0.99. The element Cd was excluded due to a Trueness/Recovery rate of 71%. Additionally, Be, As, Se, Mo, Cd, Sb, Ti and Bi were eliminated as they had fewer than one instance of detection below the Limit of quantification. The remaining elements that could be assessed include Li, B, Na, Mg, Al, P, K, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Sr, Zr, Sn, Ba, and Pb. All indicators of QC are shown in <xref ref-type="table" rid="T1">
<bold>Table&#xa0;1</bold>
</xref>.</p>
<table-wrap id="T1" position="float">
<label>Table&#xa0;1</label>
<caption>
<p>Quality control of ionomics.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" align="center">Element</th>
<th valign="middle" align="center">Correlation coefficient (r)</th>
<th valign="middle" align="center">Limit of quantification</th>
<th valign="middle" align="center">Repeatability CV</th>
<th valign="middle" align="center">Trueness/Recovery</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">Li</td>
<td valign="top" align="left">0.999995</td>
<td valign="top" align="left">0.052</td>
<td valign="top" align="left">0.019</td>
<td valign="top" align="left">96.05%</td>
</tr>
<tr>
<td valign="top" align="left">B</td>
<td valign="top" align="left">0.999272</td>
<td valign="top" align="left">2.720</td>
<td valign="top" align="left">0.128</td>
<td valign="top" align="left">99.51%</td>
</tr>
<tr>
<td valign="top" align="left">Na</td>
<td valign="top" align="left">0.993609</td>
<td valign="top" align="left">91.013</td>
<td valign="top" align="left">0.092</td>
<td valign="top" align="left">198.33%</td>
</tr>
<tr>
<td valign="top" align="left">Mg</td>
<td valign="top" align="left">0.999765</td>
<td valign="top" align="left">0.601</td>
<td valign="top" align="left">0.019</td>
<td valign="top" align="left">101.81%</td>
</tr>
<tr>
<td valign="top" align="left">Al</td>
<td valign="top" align="left">0.999768</td>
<td valign="top" align="left">0.980</td>
<td valign="top" align="left">0.024</td>
<td valign="top" align="left">100.35%</td>
</tr>
<tr>
<td valign="top" align="left">P</td>
<td valign="top" align="left">0.999728</td>
<td valign="top" align="left">6.886</td>
<td valign="top" align="left">0.037</td>
<td valign="top" align="left">106.88%</td>
</tr>
<tr>
<td valign="top" align="left">K</td>
<td valign="top" align="left">0.999773</td>
<td valign="top" align="left">5.315</td>
<td valign="top" align="left">0.087</td>
<td valign="top" align="left">100.67%</td>
</tr>
<tr>
<td valign="top" align="left">Ca</td>
<td valign="top" align="left">0.993403</td>
<td valign="top" align="left">6.664</td>
<td valign="top" align="left">0.058</td>
<td valign="top" align="left">107.76%</td>
</tr>
<tr>
<td valign="top" align="left">Ti</td>
<td valign="top" align="left">0.999980</td>
<td valign="top" align="left">0.065</td>
<td valign="top" align="left">0.017</td>
<td valign="top" align="left">92.92%</td>
</tr>
<tr>
<td valign="top" align="left">V</td>
<td valign="top" align="left">0.999998</td>
<td valign="top" align="left">0.012</td>
<td valign="top" align="left">0.017</td>
<td valign="top" align="left">92.12%</td>
</tr>
<tr>
<td valign="top" align="left">Cr</td>
<td valign="top" align="left">0.999974</td>
<td valign="top" align="left">0.065</td>
<td valign="top" align="left">0.016</td>
<td valign="top" align="left">91.26%</td>
</tr>
<tr>
<td valign="top" align="left">Mn</td>
<td valign="top" align="left">0.999830</td>
<td valign="top" align="left">0.023</td>
<td valign="top" align="left">0.015</td>
<td valign="top" align="left">100.63%</td>
</tr>
<tr>
<td valign="top" align="left">Fe</td>
<td valign="top" align="left">0.999478</td>
<td valign="top" align="left">0.652</td>
<td valign="top" align="left">0.027</td>
<td valign="top" align="left">102.73%</td>
</tr>
<tr>
<td valign="top" align="left">Co</td>
<td valign="top" align="left">0.999995</td>
<td valign="top" align="left">0.004</td>
<td valign="top" align="left">0.014</td>
<td valign="top" align="left">91.75%</td>
</tr>
<tr>
<td valign="top" align="left">Ni</td>
<td valign="top" align="left">0.999968</td>
<td valign="top" align="left">0.099</td>
<td valign="top" align="left">0.017</td>
<td valign="top" align="left">90.35%</td>
</tr>
<tr>
<td valign="top" align="left">Cu</td>
<td valign="top" align="left">0.999882</td>
<td valign="top" align="left">0.075</td>
<td valign="top" align="left">0.015</td>
<td valign="top" align="left">87.58%</td>
</tr>
<tr>
<td valign="top" align="left">Zn</td>
<td valign="top" align="left">0.999826</td>
<td valign="top" align="left">0.456</td>
<td valign="top" align="left">0.015</td>
<td valign="top" align="left">101.36%</td>
</tr>
<tr>
<td valign="top" align="left">As</td>
<td valign="top" align="left">0.999957</td>
<td valign="top" align="left">0.396</td>
<td valign="top" align="left">0.017</td>
<td valign="top" align="left">93.79%</td>
</tr>
<tr>
<td valign="top" align="left">Se</td>
<td valign="top" align="left">0.999421</td>
<td valign="top" align="left">0.487</td>
<td valign="top" align="left">0.029</td>
<td valign="top" align="left">98.77%</td>
</tr>
<tr>
<td valign="top" align="left">Sr</td>
<td valign="top" align="left">0.999999</td>
<td valign="top" align="left">0.033</td>
<td valign="top" align="left">0.018</td>
<td valign="top" align="left">89.30%</td>
</tr>
<tr>
<td valign="top" align="left">Zr</td>
<td valign="top" align="left">0.999961</td>
<td valign="top" align="left">0.111</td>
<td valign="top" align="left">0.019</td>
<td valign="top" align="left">91.71%</td>
</tr>
<tr>
<td valign="top" align="left">Mo</td>
<td valign="top" align="left">0.999924</td>
<td valign="top" align="left">0.237</td>
<td valign="top" align="left">0.018</td>
<td valign="top" align="left">92.70%</td>
</tr>
<tr>
<td valign="top" align="left">Ag</td>
<td valign="top" align="left">0.998516</td>
<td valign="top" align="left">1.959</td>
<td valign="top" align="left">0.020</td>
<td valign="top" align="left">71.15%</td>
</tr>
<tr>
<td valign="top" align="left">Cd</td>
<td valign="top" align="left">0.999779</td>
<td valign="top" align="left">0.158</td>
<td valign="top" align="left">0.015</td>
<td valign="top" align="left">89.59%</td>
</tr>
<tr>
<td valign="top" align="left">Sn</td>
<td valign="top" align="left">0.998383</td>
<td valign="top" align="left">0.183</td>
<td valign="top" align="left">0.018</td>
<td valign="top" align="left">84.05%</td>
</tr>
<tr>
<td valign="top" align="left">Sb</td>
<td valign="top" align="left">0.995511</td>
<td valign="top" align="left">0.110</td>
<td valign="top" align="left">0.016</td>
<td valign="top" align="left">85.79%</td>
</tr>
<tr>
<td valign="top" align="left">Ba</td>
<td valign="top" align="left">0.999995</td>
<td valign="top" align="left">0.028</td>
<td valign="top" align="left">0.015</td>
<td valign="top" align="left">87.32%</td>
</tr>
<tr>
<td valign="top" align="left">Tl</td>
<td valign="top" align="left">0.999871</td>
<td valign="top" align="left">0.012</td>
<td valign="top" align="left">0.016</td>
<td valign="top" align="left">92.66%</td>
</tr>
<tr>
<td valign="top" align="left">Pb</td>
<td valign="top" align="left">0.999952</td>
<td valign="top" align="left">0.015</td>
<td valign="top" align="left">0.016</td>
<td valign="top" align="left">92.48%</td>
</tr>
<tr>
<td valign="top" align="left">Bi</td>
<td valign="top" align="left">0.999833</td>
<td valign="top" align="left">0.044</td>
<td valign="top" align="left">0.016</td>
<td valign="top" align="left">89.98%</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>The relevant parameters of the ions that meet the quality control criteria outlined in the research design include: correlation coefficient (r), limit of quantification (LOQ), repeatability coefficient of variation (CV), trueness/recovery rate, with n=6 for each parameter.</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="s3_3">
<label>3.3</label>
<title>General linear model of ionomics</title>
<p>To better assess inter-group ion differences, we initially employed a linear model to evaluate disparities between the Control group and CPF groups. The results, presented in <xref ref-type="table" rid="T2">
<bold>Table&#xa0;2</bold>
</xref>, revealed statistically significant variations in ionomics across the five groups. However, no statistically significant distinctions were observed when comparing Control with DMSO (<xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Table S3</bold>
</xref>). Subsequent pairwise comparisons between the Control and CPF groups unveiled notable discrepancies in Li, P, Ca, Ti, and Mn (<xref ref-type="table" rid="T3">
<bold>Table&#xa0;3</bold>
</xref>).</p>
<table-wrap id="T2" position="float">
<label>Table&#xa0;2</label>
<caption>
<p>General linear model analysis of ionomic profile across Control, CPF 50 &#x3bc;g/L, CPF 100 &#x3bc;g/L, CPF 150 &#x3bc;g/L, and CPF 200 &#x3bc;g/L groups.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" align="center"/>
<th valign="middle" align="center">Statistic</th>
<th valign="middle" align="center">F</th>
<th valign="middle" align="center">
<italic>p</italic>-value</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="middle" align="center">Wilks&#x2019; lambda</td>
<td valign="middle" align="center">3.594</td>
<td valign="middle" align="center">2.815</td>
<td valign="middle" align="center">0.001*</td>
</tr>
<tr>
<td valign="middle" align="center">Pillai&#x2019;s trace</td>
<td valign="middle" align="center">0.000</td>
<td valign="middle" align="center">3.740</td>
<td valign="middle" align="center">0.001*</td>
</tr>
<tr>
<td valign="middle" align="center">Lawley-Hotelling trace</td>
<td valign="middle" align="center">165.588</td>
<td valign="middle" align="center">4.704</td>
<td valign="middle" align="center">0.005*</td>
</tr>
<tr>
<td valign="middle" align="center">Roy&#x2019;s largest root</td>
<td valign="middle" align="center">132.069</td>
<td valign="middle" align="center">42.022</td>
<td valign="middle" align="center">0.000**</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>(* indicates <italic>p</italic>&lt;0.05, while ** indicates <italic>p</italic>&lt;0.01; n=6).</p>
</fn>
</table-wrap-foot>
</table-wrap>
<table-wrap id="T3" position="float">
<label>Table&#xa0;3</label>
<caption>
<p>The pairwise comparisons between Control and CPF 50 &#x3bc;g/L, CPF 100 &#x3bc;g/L, CPF 150 &#x3bc;g/L, and CPF 200 &#x3bc;g/L.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" rowspan="2" align="center">Element</th>
<th valign="middle" rowspan="2" colspan="2" align="center">Group</th>
<th valign="middle" rowspan="2" align="left">Average-variance</th>
<th valign="middle" rowspan="2" align="left">
<italic>p</italic>-value</th>
<th valign="middle" colspan="2" align="center">95%CI</th>
</tr>
<tr>
<th valign="middle" align="left">Lower</th>
<th valign="middle" align="left">Upper</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="middle" rowspan="4" align="center">Li</td>
<td valign="middle" rowspan="4" align="center">Control</td>
<td valign="middle" align="left">CPF 50 &#x3bc;g/L</td>
<td valign="middle" align="left">0.004</td>
<td valign="middle" align="left">0.971</td>
<td valign="middle" align="left">-0.201</td>
<td valign="middle" align="left">0.209</td>
</tr>
<tr>
<td valign="middle" align="left">CPF 100 &#x3bc;g/L</td>
<td valign="middle" align="left">-0.007</td>
<td valign="middle" align="left">0.948</td>
<td valign="middle" align="left">-0.212</td>
<td valign="middle" align="left">0.199</td>
</tr>
<tr>
<td valign="middle" align="left">CPF 150 &#x3bc;g/L</td>
<td valign="middle" align="left">-0.236</td>
<td valign="middle" align="left">0.026*</td>
<td valign="middle" align="left">-0.441</td>
<td valign="middle" align="left">-0.031</td>
</tr>
<tr>
<td valign="middle" align="left">CPF 200 &#x3bc;g/L</td>
<td valign="middle" align="left">-0.075</td>
<td valign="middle" align="left">0.458</td>
<td valign="middle" align="left">-0.280</td>
<td valign="middle" align="left">0.130</td>
</tr>
<tr>
<td valign="middle" rowspan="4" align="center">P</td>
<td valign="middle" rowspan="4" align="center">Control</td>
<td valign="middle" align="left">CPF 50 &#x3bc;g/L</td>
<td valign="middle" align="left">1054.407</td>
<td valign="middle" align="left">0.040*</td>
<td valign="middle" align="left">51.877</td>
<td valign="middle" align="left">2056.937</td>
</tr>
<tr>
<td valign="middle" align="left">CPF 100 &#x3bc;g/L</td>
<td valign="middle" align="left">983.920</td>
<td valign="middle" align="left">0.054</td>
<td valign="middle" align="left">-18.610</td>
<td valign="middle" align="left">1986.450</td>
</tr>
<tr>
<td valign="middle" align="left">CPF 150 &#x3bc;g/L</td>
<td valign="middle" align="left">126.757</td>
<td valign="middle" align="left">0.797</td>
<td valign="middle" align="left">-875.773</td>
<td valign="middle" align="left">1129.287</td>
</tr>
<tr>
<td valign="middle" align="left">CPF 200 &#x3bc;g/L</td>
<td valign="middle" align="left">-13.235</td>
<td valign="middle" align="left">0.979</td>
<td valign="middle" align="left">-1015.765</td>
<td valign="middle" align="left">989.294</td>
</tr>
<tr>
<td valign="middle" rowspan="4" align="center">Ca</td>
<td valign="middle" rowspan="4" align="center">Control</td>
<td valign="middle" align="left">CPF 50 &#x3bc;g/L</td>
<td valign="middle" align="left">81.074</td>
<td valign="middle" align="left">0.519</td>
<td valign="middle" align="left">-174.054</td>
<td valign="middle" align="left">336.202</td>
</tr>
<tr>
<td valign="middle" align="left">CPF 100 &#x3bc;g/L</td>
<td valign="middle" align="left">96.601</td>
<td valign="middle" align="left">0.443</td>
<td valign="middle" align="left">-158.527</td>
<td valign="middle" align="left">351.729</td>
</tr>
<tr>
<td valign="middle" align="left">CPF 150 &#x3bc;g/L</td>
<td valign="middle" align="left">-317.652</td>
<td valign="middle" align="left">0.017*</td>
<td valign="middle" align="left">-572.780</td>
<td valign="middle" align="left">-62.525</td>
</tr>
<tr>
<td valign="middle" align="left">CPF 200 &#x3bc;g/L</td>
<td valign="middle" align="left">-288.736</td>
<td valign="middle" align="left">0.028*</td>
<td valign="middle" align="left">-543.864</td>
<td valign="middle" align="left">-33.608</td>
</tr>
<tr>
<td valign="middle" rowspan="4" align="center">Ti</td>
<td valign="middle" rowspan="4" align="center">Control</td>
<td valign="middle" align="left">CPF 50 &#x3bc;g/L</td>
<td valign="middle" align="left">-6.863</td>
<td valign="middle" align="left">0.271</td>
<td valign="middle" align="left">-19.411</td>
<td valign="middle" align="left">5.686</td>
</tr>
<tr>
<td valign="middle" align="left">CPF 100 &#x3bc;g/L</td>
<td valign="middle" align="left">-1.506</td>
<td valign="middle" align="left">0.807</td>
<td valign="middle" align="left">-14.055</td>
<td valign="middle" align="left">11.042</td>
</tr>
<tr>
<td valign="middle" align="left">CPF 150 &#x3bc;g/L</td>
<td valign="middle" align="left">-24.506</td>
<td valign="middle" align="left">0.000*</td>
<td valign="middle" align="left">-37.055</td>
<td valign="middle" align="left">-11.958</td>
</tr>
<tr>
<td valign="middle" align="left">CPF 200 &#x3bc;g/L</td>
<td valign="middle" align="left">-22.122</td>
<td valign="middle" align="left">0.001*</td>
<td valign="middle" align="left">-34.670</td>
<td valign="middle" align="left">-9.573</td>
</tr>
<tr>
<td valign="middle" rowspan="4" align="center">Mn</td>
<td valign="middle" rowspan="4" align="center">Control</td>
<td valign="middle" align="left">CPF 50 &#x3bc;g/L</td>
<td valign="middle" align="left">125.105</td>
<td valign="middle" align="left">0.018*</td>
<td valign="middle" align="left">23.257</td>
<td valign="middle" align="left">226.953</td>
</tr>
<tr>
<td valign="middle" align="left">CPF 100 &#x3bc;g/L</td>
<td valign="middle" align="left">123.385</td>
<td valign="middle" align="left">0.020*</td>
<td valign="middle" align="left">21.538</td>
<td valign="middle" align="left">225.233</td>
</tr>
<tr>
<td valign="middle" align="left">CPF 150 &#x3bc;g/L</td>
<td valign="middle" align="left">57.817</td>
<td valign="middle" align="left">0.253</td>
<td valign="middle" align="left">-44.030</td>
<td valign="middle" align="left">159.665</td>
</tr>
<tr>
<td valign="middle" align="left">CPF 200 &#x3bc;g/L</td>
<td valign="middle" align="left">54.414</td>
<td valign="middle" align="left">0.282</td>
<td valign="middle" align="left">-47.434</td>
<td valign="middle" align="left">156.261</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>(* indicates <italic>p</italic>&lt;0.05; n=6).</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="s3_4">
<label>3.4</label>
<title>PCA of ionomics</title>
<p>We initially conducted a principal component analysis (PCA) using the data from the Control group and different doses of CPF treatment groups. The PCA model identified 2 principal components (<xref ref-type="fig" rid="f4">
<bold>Figure&#xa0;4A</bold>
</xref>) with main parameters: R2X=0.734, Q2 = 0.615. No significant outliers were observed in DModX (<xref ref-type="fig" rid="f4">
<bold>Figures&#xa0;4B-D</bold>
</xref>), indicating that the included indicator data was stable and free from anomalies. However, this PCA model did not effectively classify the data from each group (<xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Figure S1</bold>
</xref>). Therefore, we will proceed to perform orthogonal partial least squares discriminant analysis (OPLS-DA) between Control group and different doses of CPF treatment groups.</p>
<fig id="f4" position="float">
<label>Figure&#xa0;4</label>
<caption>
<p>PCA of ionomics for Control, CPF 50&#x3bc;g/L, CPF 100&#x3bc;g/L, CPF 150&#x3bc;g/L, and CPF 200&#x3bc;g/L treatments. <bold>(A)</bold> The principal components of the PCA model. <bold>(B)</bold> DModX values of the PCA model. <bold>(C)</bold> Ion parameters contributing to DModX. <bold>(D)</bold> DModX values for each sample. (n=6).</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fmars-12-1524885-g004.tif"/>
</fig>
</sec>
<sec id="s3_5">
<label>3.5</label>
<title>OPLS-DA of ionomics</title>
<p>General linear model of ionomics showed that statistically significant variations in ionomics across the five groups. Further PCA model of ionomics revealed that no significant outliers in the data from the Control group and different doses of CPF treatment groups. But this PCA model did not effectively classify the data from each group, we then established OPLS-DA model of ionomics of Control <italic>vs</italic>. CPF 50 &#x3bc;g/L, Control <italic>vs</italic>. CPF 100 &#x3bc;g/L, Control <italic>vs</italic>. CPF 150 &#x3bc;g/L, and Control <italic>vs</italic>. CPF 200 &#x3bc;g/L, which parameters approaching 1.0 indicate that the models were stable and predictably reliable. And the significantly different element (OPLS-DA: VIP &gt; 1 and Student&#x2019;s t-test: <italic>p</italic> &lt; 0.05) was Mn, P, Zn, and K in Control <italic>vs</italic>. CPF 50 &#x3bc;g/L; Zn, Mn, and P in Control <italic>vs</italic>. CPF 100 &#x3bc;g/L; Ti and Ca in Control <italic>vs</italic>. CPF 150 &#x3bc;g/L; Ti, Li and Ca in Control <italic>vs</italic>. CPF 200 &#x3bc;g/L.</p>
<sec id="s3_5_1">
<label>3.5.1</label>
<title>Control <italic>vs.</italic> CPF 50 &#x3bc;g/L</title>
<p>The OPLS-DA model of ionomics between Control and CPF 50 &#x3bc;g/L identified 1 + 4 principal components (<xref ref-type="fig" rid="f5">
<bold>Figure&#xa0;5A</bold>
</xref>) with main parameters: R2X=0.924, R2Y=0.92, Q2 = 0.726. <xref ref-type="fig" rid="f5">
<bold>Figure&#xa0;5B</bold>
</xref> showed this model classified the data of two groups well. Statistical validation using OPLS-DA demonstrated no signs of overfitting, as indicated by the blue regression line intersecting the vertical axis on the left side below zero. Furthermore, all Q2-values on the left were found to be lower than the original points (<xref ref-type="fig" rid="f5">
<bold>Figure&#xa0;5C</bold>
</xref>). The parameters approaching a value of 1.0 suggest that this model exhibited stability and reliable predictability. Then we calculated the VIP predictive value of each element with significant changes based on the Student&#x2019;s <italic>t</italic>-test (<italic>p</italic> &lt; 0.05) and VIP score (VIP &gt; 1, <xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Table S4</bold>
</xref>) screening between Control and CPF 50 &#x3bc;g/L (<xref ref-type="fig" rid="f5">
<bold>Figure&#xa0;5D</bold>
</xref>). As <xref ref-type="fig" rid="f6">
<bold>Figure&#xa0;6</bold>
</xref> showed, the significantly different element was Mn, P, Zn, and K.</p>
<fig id="f5" position="float">
<label>Figure&#xa0;5</label>
<caption>
<p>OPLS-DA of ionomics between Control and CPF 50 &#x3bc;g/L groups. <bold>(A)</bold> Score plot of the OPLS-DA model. <bold>(B)</bold> Results of 200 permutation tests validating the OPLS-DA model. <bold>(C)</bold> The 3D score scatter plot of OPLS-DA. <bold>(D)</bold> The VIP values for ions in the OPLS-DA model. (n=6).</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fmars-12-1524885-g005.tif"/>
</fig>
<fig id="f6" position="float">
<label>Figure&#xa0;6</label>
<caption>
<p>OPLS-DA (VIP&gt; 1) and Student&#x2019;s <italic>t</italic>-test analysis of Mn <bold>(A)</bold>, P <bold>(B)</bold>, Zn <bold>(C)</bold> and K <bold>(D)</bold> between Control group and the CPF 50 &#x3bc;g/L group(* <italic>p</italic> &lt; 0.05; ** <italic>p</italic>&lt;0.01; n=6).</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fmars-12-1524885-g006.tif"/>
</fig>
</sec>
<sec id="s3_5_2">
<label>3.5.2</label>
<title>Control <italic>vs.</italic> CPF 100 &#x3bc;g/L</title>
<p>The OPLS-DA model of ionomics between Control and CPF 100 &#x3bc;g/L identified 1 + 2 principal components (<xref ref-type="fig" rid="f7">
<bold>Figure&#xa0;7A</bold>
</xref>) with main parameters: R2X=0.843, R2Y=0.829, Q2 = 0.502. <xref ref-type="fig" rid="f7">
<bold>Figure&#xa0;7B</bold>
</xref> showed this model also classified the data of two groups well. Similarly to the OPLS-DA model of ionomics between Control and CPF 50 &#x3bc;g/L, statistical validation using OPLS-DA model of ionomics between Control and CPF 100 &#x3bc;g/L revealed no overfitting; all Q2-values on the left were lower than the original points (<xref ref-type="fig" rid="f7">
<bold>Figure&#xa0;7C</bold>
</xref>). The parameters approaching 1.0 indicate that this model was stable and predictably reliable. Then the VIP predictive value of each element with significant changes based on the Student&#x2019; s <italic>t</italic>-test (<italic>p</italic> &lt; 0.05) and VIP score (VIP &gt; 1, <xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Table S5</bold>
</xref>) screening between Control and CPF 100 &#x3bc;g/L (<xref ref-type="fig" rid="f7">
<bold>Figure&#xa0;7D</bold>
</xref>). As <xref ref-type="fig" rid="f8">
<bold>Figure&#xa0;8</bold>
</xref> showed, the significantly different element was Zn, Mn, and P.</p>
<fig id="f7" position="float">
<label>Figure&#xa0;7</label>
<caption>
<p>OPLS-DA analysis of ionomics between Control and CPF 100 &#x3bc;g/L. <bold>(A)</bold> Score plot of the OPLS-DA model. <bold>(B)</bold> Results of 200 permutation tests for the OPLS-DA. <bold>(C)</bold> The 3D score scatter plot of OPLS-DA model. <bold>(D)</bold> The VIP values of ions from OPLS-DA model. (n=6).</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fmars-12-1524885-g007.tif"/>
</fig>
<fig id="f8" position="float">
<label>Figure&#xa0;8</label>
<caption>
<p>OPLS-DA (VIP &gt; 1) and Student&#x2019;s <italic>t</italic>-test analysis of Zn <bold>(A)</bold>, Mn <bold>(B)</bold> and P <bold>(C)</bold> between Control and CPF 100 &#x3bc;g/L groups(* <italic>p</italic> &lt; 0.05; n=6).</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fmars-12-1524885-g008.tif"/>
</fig>
</sec>
<sec id="s3_5_3">
<label>3.5.3</label>
<title>Control <italic>vs.</italic> CPF 150 &#x3bc;g/L</title>
<p>The OPLS-DA model of ionomics between Control and CPF 150 &#x3bc;g/L identified 1 + 1 principal components (<xref ref-type="fig" rid="f9">
<bold>Figure&#xa0;9A</bold>
</xref>) with main parameters: R2X = 0.76, R2Y = 0.713, Q2 = 0.541. <xref ref-type="fig" rid="f9">
<bold>Figure&#xa0;9B</bold>
</xref> showed this model also classified the data of two groups well. And statistical validation using OPLS-DA model of ionomics between Control and CPF 150 &#x3bc;g/L revealed no overfitting; all Q2-values on the left were lower than the original points (<xref ref-type="fig" rid="f9">
<bold>Figure&#xa0;9C</bold>
</xref>). Then the VIP predictive value of each element with significant changes based on the Student&#x2019;s <italic>t</italic>-test (<italic>p</italic> &lt; 0.05) and VIP score (VIP &gt; 1, <xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Table S6</bold>
</xref>) screening between Control and CPF 150 &#x3bc;g/L (<xref ref-type="fig" rid="f9">
<bold>Figure&#xa0;9D</bold>
</xref>). As <xref ref-type="fig" rid="f10">
<bold>Figure&#xa0;10</bold>
</xref> showed, the significantly different element was Ti and Ca.</p>
<fig id="f9" position="float">
<label>Figure&#xa0;9</label>
<caption>
<p>OPLS-DA analysis of ionomics between Control and CPF 150 &#x3bc;g/L groups. <bold>(A)</bold> OPLS-DA score plot. <bold>(B)</bold> Results of 200 permutation tests for OPLS-DA model validation. <bold>(C)</bold> The 3D scatter plot of OPLS-DA scores. <bold>(D)</bold> The VIP values for ions in the OPLS-DA model. (n=6).</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fmars-12-1524885-g009.tif"/>
</fig>
<fig id="f10" position="float">
<label>Figure&#xa0;10</label>
<caption>
<p>Comparison of elemental concentrations, including Ti <bold>(A)</bold> and Ca <bold>(B)</bold>, between the Control group and the CPF 150 &#x3bc;g/L group using OPLS-DA (VIP &gt; 1) and Student&#x2019;s <italic>t</italic>-test (* <italic>p</italic> &lt; 0.05; ** <italic>p</italic>&lt;0.01; n=6).</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fmars-12-1524885-g010.tif"/>
</fig>
</sec>
<sec id="s3_5_4">
<label>3.5.4</label>
<title>Control <italic>vs.</italic> CPF 200 &#x3bc;g/L</title>
<p>The OPLS-DA model of ionomics between Control and CPF 200 &#x3bc;g/L identified 1 + 1 principal components (<xref ref-type="fig" rid="f11">
<bold>Figure&#xa0;11A</bold>
</xref>) with main parameters: R2X = 0.759, R2Y = 0.792, Q2 = 0.559. <xref ref-type="fig" rid="f11">
<bold>Figure&#xa0;11B</bold>
</xref> showed this model also classified the data of two groups well. And statistical validation using OPLS-DA model of ionomics between Control and CPF 200 &#x3bc;g/L revealed no overfitting; all Q2-values on the left were lower than the original points (<xref ref-type="fig" rid="f11">
<bold>Figure&#xa0;11C</bold>
</xref>). Then the VIP predictive value of each element with significant changes based on the Student&#x2019;s <italic>t</italic>-test (<italic>p</italic> &lt; 0.05) and VIP score (VIP &gt; 1, <xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Table S7</bold>
</xref>) screening between Control and CPF 200 &#x3bc;g/L (<xref ref-type="fig" rid="f11">
<bold>Figure&#xa0;11D</bold>
</xref>). As <xref ref-type="fig" rid="f12">
<bold>Figure&#xa0;12</bold>
</xref> showed, the significantly different element was Ti, Li&#xa0;and Ca.</p>
<fig id="f11" position="float">
<label>Figure&#xa0;11</label>
<caption>
<p>OPLS-DA analysis of ionomics between Control and CPF 200 &#x3bc;g/L groups. <bold>(A)</bold> OPLS-DA score plot. <bold>(B)</bold> Results of 200 permutation tests for OPLS-DA model validation. <bold>(C)</bold> The 3D score scatter plot of OPLS-DA. <bold>(D)</bold> The VIP plot for ions in OPLS-DA. (n=6).</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fmars-12-1524885-g011.tif"/>
</fig>
<fig id="f12" position="float">
<label>Figure&#xa0;12</label>
<caption>
<p>Comparison of elemental concentrations, including Ti <bold>(A)</bold>, Li <bold>(B)</bold>, and Ca <bold>(C)</bold>, between the Control group and the CPF 200 &#x3bc;g/L group using OPLS-DA (VIP &gt; 1) and Student&#x2019;s t-test (* p &lt; 0.05; ** p &lt; 0.01; n = 6).</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fmars-12-1524885-g012.tif"/>
</fig>
</sec>
</sec>
</sec>
<sec id="s4" sec-type="discussion">
<label>4</label>
<title>Discussion</title>
<p>Microalgae exhibit higher tolerance to the toxic effects of CPF due to the absence of processes, structures, or functional groups involved in insecticidal action (<xref ref-type="bibr" rid="B9">Dupraz et&#xa0;al., 2019</xref>). But the certain concentrations of CPF can also impact physiological characteristics of marine phytoplankton by potentially altering cell membrane permeability in marine microalgae, affecting photosynthetic activity, and increasing reactive oxygen species leading to oxidative stress that affects their growth (<xref ref-type="bibr" rid="B8">DeLorenzo and Serrano, 2003</xref>; <xref ref-type="bibr" rid="B37">Tato and Beiras, 2019</xref>). We investigated the impact of 200 &#x3bc;g/L CPF on the growth of <italic>Chlorella vulgaris</italic> and found significant inhibition. Similar toxic responses were observed in <italic>Dunaliella tertiolecta</italic> exposed to 600 &#x3bc;g/L CPF (<xref ref-type="bibr" rid="B8">DeLorenzo and Serrano, 2003</xref>). The EC50 values for inhibition of growth by CPF were determined as 132 &#x3bc;g/L for <italic>Isochrysis galbana</italic> and 746 &#x3bc;g/L for <italic>Phaeodactylum tricornutum</italic> (<xref ref-type="bibr" rid="B37">Tato and Beiras, 2019</xref>). It appears that there is a critical concentration of CPF beyond which significant changes occur in the biological behavior of microalgae.</p>
<p>Generally, trace elements and heavy metal elements primarily affect algae in terms of their growth metabolism rate, photosynthesis, changes in cell size and morphology, as well as enzyme activity (<xref ref-type="bibr" rid="B17">Jin-fen et&#xa0;al., 2000</xref>). Mn is a necessary element for physiological and biochemical reactions in microalgae and often serves as an important factor inducing red tide. Photosynthesis has an absolute demand for Mn, because it can exist in four oxidation states, allowing sequential electron transfer from photosystem II-water oxidizing system (<xref ref-type="bibr" rid="B3">Andresen et&#xa0;al., 2018</xref>; <xref ref-type="bibr" rid="B19">Kaur et&#xa0;al., 2023</xref>). Besides, Mn plays an important role in diverse processes (e.g. chloroplast development, phospholipid biosynthesis, ROS scavenging, respiration and hormone signaling (<xref ref-type="bibr" rid="B14">Hashimoto et&#xa0;al., 2012</xref>)) and is required as a cofactor in many enzymes such as decarboxylases and dehydrogenases (<xref ref-type="bibr" rid="B19">Kaur et&#xa0;al., 2023</xref>). Zn is an important component of various dehydrogenases and proteases within cellular bodies. Some enzymes are highly sensitive to Zn deficiency, exhibiting strong specificity during metabolic processes. Studies have found that <italic>Eugiena graci</italic> is cytoplasmic nucleoids contain abundant zinc; when experiencing zinc deficiency, these organelles become unstable (<xref ref-type="bibr" rid="B27">Prask and Plocke, 1971</xref>). It is generally believed that K can stimulate and control the production of acidic stimulation by ATPase on the plasma membrane, leading to cell wall relaxation and hydrolase activation, thereby promoting cell growth (<xref ref-type="bibr" rid="B26">Oosterhuis et&#xa0;al., 2014</xref>; <xref ref-type="bibr" rid="B2">Ameen et&#xa0;al., 2024</xref>). Besides, K performs a critical function in regulating the osmotic pressure, facilitating the transport of photosynthates, sustaining ion homeostasis and activating enzymes. In addition, P is also a common element required for the growth of microalgae. The content of P in natural seawater is small, but a certain concentration of P is an essential nutrient for the growth of microalgae (<xref ref-type="bibr" rid="B7">Biao and Kaijin, 2007</xref>). We observed a reduction in the aforementioned elements in the lower dose groups (50 &#x3bc;g/L and 100 &#x3bc;g/L), which may explain why CPF inhibits their growth. Interestingly, no changes were observed in these ions when CPF concentration was increased to 150 &#x3bc;g/L and 200 &#x3bc;g/L; instead, there was an increase in Ti, Li, and Ca ion levels.</p>
<p>External abiotic stresses can disrupt intracellular ion homeostasis. Under salty stress, activation of potassium channels by reactive oxygen species (ROS) will low the cytosolic K pool (<xref ref-type="bibr" rid="B2">Ameen et&#xa0;al., 2024</xref>). The photosynthetic activity was shown to induce increases in oxygen concentrations and pH of the colonial cells, and those increases were suggested to favor the precipitation and accumulation of Mn (<xref ref-type="bibr" rid="B31">Schoemann et&#xa0;al., 2001</xref>). The exist of function groups like amine, hydroxyl, carboxyl, and phosphoryl group (-NH<sub>2</sub>, -OH, -COO and -PO<sub>3</sub>
<sup>2-</sup>, respectively) makes the cell surface of microalgae constitute a negative charge, which facilitates the attachment of toxic compounds with positive charge and allows their sorption (<xref ref-type="bibr" rid="B12">Fayaz et&#xa0;al., 2024</xref>). Previous studies have shown that exposure to CPF can harm the cellular membranes and photosystem of microalgae and led to a significant rise in ROS (<xref ref-type="bibr" rid="B5">Baruah et&#xa0;al., 2024</xref>). Reasons led to the changes in ion levels of <italic>Chlorella vulgaris</italic> in this study may include the following: (1) CPF destroys the cell membrane structure, resulting in restricted ion transport; (2) CPF binds to the functional groups on the cell surface and saturates the binding sites, which is not conducive to the adsorption of ions by microalgae; (3) CPF exposure damages the photosynthesis system of microalgae, affecting the accumulation of ions (4) CPF stress causes an increase in ROS, resulting in a decrease in ion levels.</p>
<p>When exposed to different concentrations of CPF, the changes in the ion levels of <italic>Chlorella</italic> are different. Changes in individual ion levels or ratios of multiple ions in microalgae may be used as a biomarker to identification the degree of CPF contamination. Compared with other biomarkers (SOD, pigments of microalgae (<xref ref-type="bibr" rid="B32">Shao et&#xa0;al., 2016</xref>), etc.), ionomic analysis is more convenient and quick, and this deserves further exploration and verification.</p>
</sec>
<sec id="s5" sec-type="conclusions">
<label>5</label>
<title>Conclusions</title>
<p>The present study not only elucidates the direct impact of CPF on green algae growth but also uncovers potential toxic mechanisms through ionomic analysis, indicating that CPF may impede their growth by disrupting the homeostasis of crucial elements within algal cells. This finding is pivotal for comprehending the potential threat posed by CPF to marine ecosystems and devising effective environmental protection strategies and provides a valuable insights and directions for further research into the mechanism of action of CPF on microalgae. Subsequent investigations should further explore the effects of CPF on other aquatic organisms and entire ecosystems, as well as seek efficient pollution mitigation measures.</p>
</sec>
</body>
<back>
<sec id="s6" sec-type="data-availability">
<title>Data availability statement</title>
<p>The original contributions presented in the study are included in the article/<xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Material</bold>
</xref>. Further inquiries can be directed to the corresponding author/s.</p>
</sec>
<sec id="s7" sec-type="author-contributions">
<title>Author contributions</title>
<p>QY: Investigation, Writing &#x2013; original draft, Writing &#x2013; review &amp; editing, Data curation, Formal analysis, Resources. YZ: Data curation, Investigation, Writing &#x2013; original draft, Writing &#x2013; review &amp; editing. SZ: Data curation, Funding acquisition, Investigation, Resources, Writing &#x2013; original draft. MP: Formal analysis, Funding acquisition, Methodology, Resources, Supervision, Writing &#x2013; original draft, Writing &#x2013; review &amp; editing. PJ: Data curation, Formal analysis, Investigation, Supervision, Writing &#x2013; original draft. PQ: Conceptualization, Methodology, Project administration, Supervision, Writing &#x2013; review &amp; editing.</p>
</sec>
<sec id="s8" sec-type="funding-information">
<title>Funding</title>
<p>The author(s) declare financial support was received for the research, authorship, and/or publication of this article.</p>
</sec>
<ack>
<title>Acknowledgments</title>
<p>This work was financially supported by Laoshan Laboratory (Grant No. LSKJ202203902), the Natural Science Foundation of Shandong Province, China (Grant No. ZR2021MD014 and ZR2022MD027), and Key R&amp;D Program of Jining (Grant No. 2022YXNS121).</p>
</ack>
<sec id="s9" sec-type="COI-statement">
<title>Conflict of interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec id="s10" sec-type="ai-statement">
<title>Generative AI statement</title>
<p>The author(s) declare that no Generative AI was used in the creation of this manuscript.</p>
</sec>
<sec id="s11" sec-type="disclaimer">
<title>Publisher&#x2019;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
<sec id="s12" sec-type="supplementary-material">
<title>Supplementary material</title>
<p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type="uri" xlink:href="https://www.frontiersin.org/articles/10.3389/fmars.2025.1524885/full#supplementary-material">https://www.frontiersin.org/articles/10.3389/fmars.2025.1524885/full#supplementary-material</ext-link>
</p>
<supplementary-material xlink:href="DataSheet1.docx" id="SM1" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document"/>
</sec>
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