Microalgae Indicators of Metabolic Changes in Potamogeton perfoliatus L. Under Different Growing Conditions of Urban Territory Lakes in a Permafrost Area

Abstract

Under conditions of increasing anthropogenic load, aquatic ecosystems all over the world are undergoing a transformation, expressed in the growth of eutrophication, the overgrowing of water bodies with higher vegetation of macrophytes, cyanobacterial bloom, and the increased concentrations of different pollutants in these objects. In the region of Eastern Siberia that we studied, located in the middle reaches of the Lena River basin, there is the city of Yakutsk—the largest city in the world built in a permafrost region. Within the city and its surroundings, there are many small lakes (less than 1 km² in area) which over the past decades have been subject to varying degrees of pressure associated with human activity (nutrients and organic matter loads, urban landscape transformation). This study is the first to combine the metabolomic profiling of Potamogeton perfoliatus with microalgal bioindication to assess anthropogenic impacts in permafrost urban lakes, providing a novel framework for monitoring ecological resilience in extreme environments. We studied four lakes with varying degrees of anthropogenic pressure. Using a comprehensive assessment of the bioindicator properties of planktonic microalgae and the chemical parameters of water using statistical methods and principal component analysis (PCA), the lakes most susceptible to anthropogenic pressure were identified. Concentrations of pollutant elements in the tissues of the submerged macrophyte aquatic plant Potamogeton perfoliatus L., which inhabits all the lakes we studied, were estimated. Data on the content of pollutant elements in aquatic vegetation and the results of metabolomic analysis made it possible to identify the main sources of anthropogenic impact in the urbanized permafrost area. The pollution of water bodies with some key pollutants leads to Potamogeton perfoliatus’s metabolites decreasing, such as sucrose, monosaccharides (arabinose, mannose, fructose, glucose, galactose), organic acids (glyceric acid, malic acid, erythronic acid, fumaric acid, succinic acid, citric acid), fatty acids (linoleic and linolenic acids), myo-inositol, 4-coumaric acid, caffeic acid, rosmarinic acid, shikimic acid, and catechollactate, caused by pollution which may decrease the photosynthetic activity and worsen the sustainability of water ecosystems. Linkage was established between the accumulation of pollutants in plant tissues, the trophic status of the lake, and the percentage of eutrophic microalgae, which can be used in monitoring the anthropogenic load in the permafrost zone. Knowledge of the composition and concentration of secondary metabolites produced by macrophytes in permafrost lakes can be useful in organizing water resource management in terms of reducing the level of cyanobacterial blooms due to allelochemical compounds secreted by macrophytes. This new work makes possible the evaluation of the permafrost-zone small-lake anthropogenic load in the frame of a changing climate and the growing attention of the industry to Arctic resources.

1. Introduction

The contamination of freshwater ecosystems is a multifaceted issue, and the solutions to it require a deeper understanding of the ecosystem’s structure and the appropriateness of the methods used to assess its condition. The methods and indices used to evaluate the impact of pollution on natural water bodies are based on an ecological perspective, focusing on the relationship between the water and the biota, including algae and plants. The production of proteins is a crucial aspect of this process, as it involves the primary producers, such as algae and higher plants, which can serve as bioindicators of pollution.

The evaluation of the ecological condition of water bodies encompassed the examination of the composition and density of phytoplankton, macroalgae, and angiosperms [1,2]. Macrophytes, a diverse group of aquatic plants, are crucial for assessing the water quality of surface waters, as they can become abundant and often dominate the ecosystem, reflecting the trophic state of the water body [3,4]. The method of bioindication, which involves studying the reaction of living organisms to alterations in their surroundings, is crucial for assessing the impact of pollution on aquatic habitats [5,6]. Bioindicators are aquatic organisms, including plants, plankton, animals, and microorganisms, that are employed to assess the well-being of the natural environment [1]. Plants serve as highly responsive indicators for forecasting and identifying environmental stressors. In the modern era, industrialization and urbanization have exacerbated the issue of water contamination and pollution [7,8]. The presence or absence of certain plant species or other vegetation can serve as a reliable indicator of the state of the environment [8,9]. The saprobity index S is calculated for phytoplankton communities to evaluate the pollution of the ecosystem and assess the risk [10]. This approach is based on the specific bioindicator characteristics of species and the abundance of primary producers in the entire community. It is closely related to the trophic chemical components and is therefore used to assess the trophic state of water [11].

The saprobity index S can be determined for the macrophyte community and then integrated into the framework for evaluating water quality, particularly regarding organic pollution [4,12]. This approach is particularly relevant for lakes and wetlands, where macrophytes are frequently employed for assessing the impact of human activities [13]. Additionally, a system was created that utilized a combination of assessment techniques for diatoms, invertebrates, macrophytes, and other aquatic organisms [14,15].

The aquatic plant Potamogeton perfoliatus L. (clasping-leaf pondweed), a submerged macrophyte, is a keystone species in freshwater ecosystems, particularly in shallow, nutrient-rich lakes and ponds [16]. It plays a critical role in maintaining ecological balance through oxygen production, nutrient cycling, and habitat provision for aquatic organisms [17,18]. However, its survival and metabolic functions are increasingly threatened by urbanization, climate change, and the unique challenges of permafrost regions—factors that alter water chemistry, light availability, and sediment dynamics [19,20].

In urbanized areas, lakes face compounded stressors such as nutrient runoff, heavy metal contamination, and temperature fluctuations due to anthropogenic activities [20,21]. These perturbations disrupt the photosynthetic efficiency and nutrient uptake mechanisms of P. perfoliatus, as observed in studies linking periphyton accumulation to reduced light penetration and altered spectral quality, which force morphological and physiological acclimations in the plant [22]. For instance, P. perfoliatus exhibits shade-avoidance traits, such as elongated internodes and increased leaf area, under low red-to-infrared light ratios caused by periphyton coverage [18]. Such adaptations may divert energy from growth to stress mitigation, altering its metabolic priorities.

Permafrost thaw introduces additional complexity. Recent studies highlight that thawing permafrost mobilizes iron, sulfates, and toxic trace metals (e.g., Zn, Ni) into aquatic systems, lowering pH and increasing turbidity [20,23]. These changes impair macrophyte production by up to 80% in affected water bodies [22], while microbial shifts in permafrost soils further modify nutrient cycling pathways [23]. In Arctic regions, abrupt transitions from clear to iron-rich “orange streams” have been linked to declines in macroinvertebrate diversity and fish populations, indirectly destabilizing macrophyte habitats [20]. P. perfoliatus in such environments may face oxidative stress, as reactive oxygen species (ROS) and caspase-3-like proteins—key regulators of aerenchyma formation—are sensitive to redox imbalances [24].

This study investigates the metabolic responses of P. perfoliatus to pollutant elements as urban and permafrost-driven stressors in lakes across a permafrost urban gradient in parallel with planktonic microalgae and cyanobacteria. By analyzing photosynthetic efficiency, nutrient assimilation, and stress biomarkers (e.g., ROS activity), we aim to elucidate how synergistic pressures reshape the plant’s physiology in the environmental pollution and trophic base gradient defined by planktonic microorganisms and water chemistry. The findings will advance our understanding of ecological resilience in freshwater systems and inform conservation strategies for aquatic flora in rapidly changing Arctic and sub-Arctic urban landscapes [21].

2. Materials and Methods

2.1. Site Description

The study area is located in northeast Asia, in Yakutia, on the left bank of the middle reaches of the Lena River (Figure 1). The work was carried out on four different types of lakes (

Table 1. Brief characterization of the explored lakes.

Lake Name	Altitude Above Sea Level, m	Water Surface Area, km2	Depth, m	Latitude, N	Longitude, E	Type of Origin
Dachnoe	122	0.23	3.5	62°07′29.3″	129°37′22.9″	A
Saysary	96	0.40	6.5	62°01′17.3″	129°41′39.6″	O
Ytyk-Kyuyol	108	0.79	3	62° 01′22.02″	129°36′59.0″	O
Ierelyakh	258	0.10	1.2	62°06′46.2″	129°17′37.0″	K

Note: Classification of lakes by their type of origin: (O) oxbow lake, (K) thermokarst lake, and (A) artificial damming lake.

) in the vicinity of Yakutsk, which is the largest city in the world located in the permafrost zone. Some of the lakes are located on the floodplain terrace of the Lena River and are river lakes (of oxbow origin), representing channels that have separated from the river. Lake Ierelyakh is of thermokarst origin; its basin was formed as a result of thawing of underground ice of the permafrost. Lake Dachnoe is of artificial origin (Table 1). Lake Saysary is located within the city of Yakutsk; Lake Ytyk-Kyuyol is located in the suburbs, and on its banks there are experimental sowing fields of the academic botanical garden and a dacha village. Lakes Dachnoe and Ierelyakh are located outside the city limits: the first is surrounded by summer cottage villages, and the second was chosen as a control lake and is not subject to anthropogenic impact.

2.2. Water and Phytoplankton Sampling

Water samples were collected from the surface water layer of 0–0.3 m during the peak of the growing season, in the summer low-water period, on 22 July 2024. Samples were taken 1.5 m from the shore of each lake. The water temperature was measured by a Chektemp electronic thermometer (Hanna Instruments, Woonsocket, RI, USA). Samples for qualitative and quantitative analysis of phytoplankton were collected using an Apstein net (Sefar Nitex material with a mesh size of 15 μm). The initial sample volume for quantitative analysis was 20 L. The volume of the concentrated phytoplankton sample was 15 mL and was fixed with 3 drops of a 40% neutral formaldehyde solution. Water samples for hydrochemical analysis of 2 L were collected by directly scooping into glass bottles and sent to the laboratory for immediate analysis.

2.3. Collecting Potamogeton Specimens, Taxonomic Identification, and Sample Preparation

Plant identification was carried out according to the Identifier of Higher Plants of Yakutia [25]. The species name is given in accordance with S.K. Cherepanov [26]. At each lake, the material was collected from communities where the species dominated, in 4 random replicates at a depth of 0.5–1.7 m. The phytomass was collected during the period of pondweed bloom on 22 July 2024, on sites from 1 to 10 m². All selected specimens of P. perfoliatus were in the mature generative stage during the vegetative phase and exhibited no visible microbial colonization. The dry weight (DW) of the sample was 20 g. Freshly collected samples of Potamogeton perfoliatus were placed in polyethylene bags and frozen in the laboratory at −40 °C. The time from the sample collection to the freezer storage was approximately one hour. Subsequently, the samples were freeze-dried using a Jouan LP-3 lyophilizer (Jouan SA, Saint-Nazaire, France). The lyophilized material was then used directly for chemical analyses, including metabolomic profiling using gas chromatography–mass spectrometry (GC-MS) and elemental analysis by inductively coupled plasma (ICP) techniques.

2.4. Water Chemistry Analysis

The chemical analysis was carried out using the methods provided in the guidance by A.D. Semenov [27]. The water temperature was measured by a Chektemp electronic thermometer (Hanna Instruments, Woonsocket, RI, USA). With a Multitest IPL-101 device (LLC NPP “SEMIKO”, Novosibirsk, Russia), we detected the dissolved oxygen concentration using the titration method with iodometric determination and pH using the potentiometric method. Water color was defined using a photometric method with the spectrophotometer PE-5300VI (GK “EKROS”, Saint Petersburg, Russia). Water salinity was calculated as the sum of ions using different methods: turbidimetry for sulfate anions with a spectrophotometer PE-5300VI, electrothermal vaporization for potassium and sodium cations with an atomic absorption spectrometer AAnalyst 400 (PerkinElmer Inc., Waltham, MA, USA), mercurimetry for chloride ions, and titration for calcium, magnesium, and bicarbonate ions. The hardness of water was determined by complexometric titrations using eriochrome black T as an indicator. A photometric method with a spectrophotometer PE-5300VI was applied to determine nutrient concentrations. Nessler’s reagent, Griess reagent, salicylic acid, and sulfosalicylic acid were used for the measurement of ammonium ions, nitrite ions, nitrate ions, and total iron, respectively; ammonium molybdate was used for the measurement of phosphate ions and silicon (Si-SiO₂). A combined reagent composed of ammonium molybdate and ascorbic acid was used to determine the total phosphorus content. The Fluorat-02-2M device (LLC “Lumex-Marketing”, Saint Petersburg, Russia) was used to determine the chemical oxygen demand (COD).

2.5. Algological Analysis

Qualitative and quantitative analysis of phytoplankton was performed using an Olympus BH-2 light microscope (Olympus Corporation, Tokyo, Japan) with a magnification of up to 550 times. Biomass was determined as the product of the cell count and their volume, which was calculated stereometrically and based on our own cell measurements [28]. The specific gravity of algae was taken to be equal to one. The algal cell count was performed in a Nageotte counting chamber with a volume of 0.01 cm³. Identification of algal and cyanobacterial species was performed using identification guides [29,30,31,32,33,34,35,36,37]. Species taxonomy is given according to the data published on the algabase.com portal [38]. Bioindicator analysis of the lakes’ trophic state and organic saturation was performed according to [11] with planktonic abundant microalgae species-specific ecological preferences [39].

2.6. Elemental Analysis

To determine the elemental concentration in tissues of Potamogeton perfoliatus, dry tissues (100 ± 3 mg) were digested using a series of strong acids. The digestion process began by adding 1 mL of 40% hydrofluoric acid (HF) to the sample in a 50 mL polypropylene tube (UC475-WH, Environmental Express Inc., Charleston, SC, USA). The tube was tightly sealed and placed in a Hot Block heating system (SC154-240, Environmental Express Inc., USA) at 130 °C for 60 min. After cooling, 2 mL of 70% nitric acid (HNO₃) and 8 mL of 37% hydrochloric acid (HCl) were added to the tube. The mixture was then heated again in the Hot Block at 130 °C for an additional 60 min. To ensure the retention of reaction products involving hydrofluoric acid, 5 mL of 37% HCl and 20 mL of 4% boric acid (H₃BO₃) were introduced into the tube, followed by heating at 130 °C for 30 min. Once the solution had cooled, 1 mL of an indium (In³⁺) internal standard solution (1250 ppm in 3% nitric acid) was added. The final solution was diluted to 50 mL with deionized water and then filtered. Elemental analysis was performed using an ICP OES iCAP 7600 DUO instrument (Thermo Scientific, Waltham, MA, USA). For calibration and quantification, the multi-element standard solutions 71A (IV-ICPMS-71A-125 ML), 71B (IV-ICPMS-71B-125 ML), and 71C (IV-ICPMS-71D-125 ML) from Inorganic Ventures (Christiansburg, VA, USA) were used. These standards enabled the measurement of the following elements with method detection limits (mg kg⁻¹ DW): Al (5), As (0.1), Ba (5), Be (0.05), Ce (0.05), Cr (0.1), Cu (0.1), Fe (5), La (0.05), Li (0.1), Mn (0.1), Na (5), Nb (0.1), Nd (0.1), Ni (0.1), Rb (0.1), Sc (0.1), Si (5), Th (0.05), Ti (5), V (0.1) [40].

2.7. GC-MS Metabolite Profiling

For metabolomic analysis, 10 mg of lyophilized-dried Potamogeton perfoliatus tissues was extracted in 1 mL of methanol. The extract was evaporated at 40 °C using a rotary evaporator, and the resulting dry residue was dissolved in 50 μL of pyridine in a vial. Trimethylsilyl (TMS) derivatization was then performed by adding 50 μL of N,O-bis(trimethylsilyl)trifluoroacetamide (BSTFA) with 1% trimethylchlorosilane (TMCS) to the vial, followed by heating at 100 °C for 15 min. The analysis was performed using a gas chromatography–mass spectrometry (GC-MS) system (Agilent 7820A/5975, Agilent Technologies, Lexington, MA, USA) equipped with an HP-5MS column (30 m × 0.25 mm × 0.25 μm). The injector temperature was set to 250 °C, and the column temperature was programmed with a linear gradient, starting at 70 °C and increasing to 320 °C at a rate of 4 °C/min. Helium was used as the carrier gas at a constant flow rate of 1 mL min⁻¹.

2.8. Statistical Procedures and Data Analysis

The results obtained are presented as the arithmetic mean and its standard deviation (mean ± SD). The Pearson linear correlation coefficient was calculated using the AnalystSoft, StatPlus 2007 software, version 4.7.5.0. Data acquisition for GC-MS metabolite profiling was performed using Agilent ChemStation software version E.02.02.1431. Quantitative interpretation of the chromatograms was conducted using the internal standardization method. Tricosane C23 standard solution was used as the internal standard [41]. Mass spectrometric data were processed and interpreted using the NIST 2020 standard library version 2.4. Principal component analysis (PCA) was performed using 15 observations for 72 metabolites, with the quantitative composition resulting from GC-MS analysis of metabolites used as parameters. Sample data were normalized using an internal standard (tricosane C23). Samples were normalized by the median with a base-10 logarithmic transformation, followed by auto-scaling (mean-centering and division by the standard deviation of each variable) in the publicly available MetaboAnalyst resource [42]. Loadings plot and heatmaps were used to assess the statistical relationship between organism functioning and the exposure factor. A comparison of the metabolites in the tissues of Potamogeton perfoliatus with the content of polylutantic elements was carried out using the calculation of the correlation coefficient of Pearson [43].

3. Results

3.1. Water Chemistry

The lake water warmed up in accordance with the observation period. The water temperature did not differ significantly between the studied water bodies (

Table 2. Average values of physical and chemical parameters of water of the studied lakes.

Variables	Dachnoe	Saysary	Ytyk-Kyuyol	Ierelyakh
T °C	23.4	26.9	25.5	23.8
pH	7.98	8.44	8.28	8.47
Dissolved O2, mg L−1	20.15	7.31	11.37	14.63
COD, mgO L−1	104.7	104.9	118.3	118.0
Color, Pt/Co°	85	44	75	47
TDS, mg L−1	235.09	609.96	512.86	337.28
Hardness, mg L−1	2.48	5.84	4.80	3.12
Ca, mg L−1	30.46	49.70	49.70	19.24
Mg, mg L−1	11.66	40.82	28.19	26.24
Na, mg L−1	14.3	60.0	50.6	30.4
K, mg L−1	4.97	8.24	7.72	12.10
HCO3, mg L−1	130	33	274	189
Cl, mg L−1	25.70	100.00	77.35	54.20
SO4, mg L−1	18.00	19.20	25.30	<10.0
N-NH4, mg L−1	0.92	0.71	0.85	0.60
N-NO2, mg L−1	0.021	0.006	0.009	0.006
N-NO3, mg L−1	0.15	0.10	0.16	0.12
Ptot, mg L−1	0.02	0.02	<0.02	<0.02
P-PO4, mg L−1	0.9	0.8	0.6	0.4
Si, mg L−1	3.88	1.88	3.55	1.51
Fetot, mg L−1	0.57	0.40	0.57	0.53

). All the studied water bodies have a slightly alkaline reaction. The oxygen regime corresponds to normal values. In the lakes located in the city and suburbs (Saysary and Ytyk-Kyuyol), the concentration of dissolved oxygen is lower than in the suburban lakes (Dachnoe and Ierelyakh).

The COD index is high in all the lakes surveyed, its concentration varied within relatively narrow limits. Lakes Ytyk-Kyuyol and Dachnoe stand out with an increased color index; the waters of the other lakes are less colored.

The surveyed lakes belong to the hydrocarbonate class, calcium–magnesium group, type II. The waters of the city and suburban lakes (Ytyk-Kyuyol and Saysary) are medium-hard, fresh, and have increased mineralization. The other surveyed objects (Dachnoe and Ierelyakh) are fresh, medium-mineralized, and have a hardness index of “soft–medium-hard”.

The content of nitrite nitrogen, nitrate nitrogen, and mineral phosphorus is characterized by relatively low values. The concentration of ammonium nitrogen, total phosphorus, and total iron is high in all the surveyed lakes. The maximum values of ammonium nitrogen content are noted in Lakes Ytyk-Kyuyol and Dachnoe. The highest concentrations of total phosphorus are found in Lakes Dachnoe and Saysary. The content of total iron differs insignificantly in the surveyed lakes.

The trophic status of lakes is determined by the concentration of nutrient elements. For the studied lakes, the concentration of phosphates decreases in the series Dachnoe—Saysary—Ytyk-Kyuyol—Ierelyakh, i.e., the trophicity of the lakes decreases in this sequence. It is in this order that we have arranged the lakes in Table 1. Confirmation of the trophic load with a maximum in Lake Dachnoe is also confirmed by a decrease in the concentration of ammonium and nitrites, indicating the decomposition of organic pollutants.

3.2. Composition of Planktonic Microalgae and Cyanobacteria Communities and Level of Potamogeton Vegetation

A total of 84 species of algae and cyanobacteria from six phyla were identified in the plankton of the lakes. The greatest species diversity was found in the phytoplankton of Lake Ytyk-Kyuyol (53 species and varieties); in Lakes Saysary and Ierelyakh, the phytoplankton was less diverse in terms of species (37 and 29 species and varieties, respectively). The smallest number of species was found in Lake Dachnoe (21 species and varieties). The following seven species, representing three phyla (Chlorophyta, Dinoflagellata, and Heterokontophyta), are common to the plankton of all the studied lakes: Botryococcus braunii Kützing, Ceratium hirundinella (O.F.Müller) Dujardin, Desmodesmus armatus (Chodat) E.H.Hegewald, Mucidosphaerium pulchellum (H.C.Wood) C.Bock, Proschold & Krienitz, Pandorina morum (O.F.Müller) Bory, Pseudopediastrum boryanum (Turpin) E.Hegewald, Ulnaria acus (Kützing) Aboal.

Cyanobacteria formed the basis of phytoplankton in most lakes, reaching from 53 to 98% of its total biomass (Figure 2). The only exception was the reference Lake Ierelyakh, where 95% of the phytoplankton biomass was made up of algae of the phylum Dinoflagellata.

In the plankton of most lakes, the dominant plankton species were representatives of cyanobacteria (

Table 3. Dominant species in phytoplankton communities of studied lakes.

Species	Dachnoe	Saysary	Ytyk-Kyuyol	Ierelyakh
Aphanizomenon flos-aquae Ralfs ex Bornet & Flahault	28.6	14.0	1.5	-
Ceratium hirundinella (O.F.Müller) Dujardin	0.002	1.8	2.9	95.0
Dolichospermum sp.	-	20.0	-	-
Dolichospermum lemmermannii (Richter) P.Wacklin, L.Hoffmann & J.Komárek	0.2	10.0	0.3	-
Microcystis aeruginosa (Kützing) Kützing	5.0	-	30.2	-
Microcystis flos-aquae (Wittrock) Kirchner	60.0	-	40.2	-

Note: “-“, species not found. If the species is dominated in the lake, the percentage of it biomass is highlighted in bold and italic.

). In Lakes Ytyk-Kyuyol and Dachnoe, up to two-thirds of the total biomass were species of the genus Microcystis. Aphanizomenon flos-aquae was among the dominant species in Lakes Saysary and Dachnoe. Representatives of the genus Dolichospermum dominated only in the plankton of Lake Saysary. Only in the control Lake Ierelyakh was the dominant species of the Dinoflagellata phylum: Ceratium hirundinella. For Lakes Dachnoe and Ytyk-Kyuyol, during sampling, water bloom was noted in the form of observed clusters of cyanobacteria in the near-surface layer. In these two reservoirs, we noted the highest phytoplankton biomass (Figure 2). In the other lakes studied, we did not notice any signs of water bloom visually.

The ecology of species shows the most favorable conditions for them when the species develops in mass. Thus, mass species in Lakes Saysary, Ytyk-Kyuyol, and Dachnoe belonged only to rapidly developing cyanobacteria, which were indicators of eutrophic or meso-eutrophic waters, in the third quality class. The only mass species in Lake Ierelyakh belonged to the dinoflagellates and also preferred eutrophic waters. The saprobic indices S calculated for the plankton communities of the studied lakes showed that all lakes have third-class water quality with the highest index S = 2.04 in Lake Ytyk-Kyuyol. This indicates not only the eutrophic state of the studied lakes, but also that their water is sufficiently saturated with organic matter.

The data on the ecology of the identified microphyte species (Appendix A,

Table A1. Average abundance (10³ cells L⁻¹) and trophic indicators of algae and cyanobacteria species from studied lakes of urban territory in permafrost area of Yakutia sampling in 2024.

Species	Dachnoe	Saysary	Ytyk-Kyuyol	Ierelyakh	TRO	Index S
Charophyta
Closterium acutum var. linea (Perty) West & G.S.West			5.6		m	2.2
Closterium leibleinii Kützing ex Ralfs				0.0002	e	2.6
Cosmarium formosulum Hoff	0.002		0.1	0.0008	me	1.8
Staurastrum boreale West & G.S.West			1.5		m
Staurastrum gracile Ralfs ex Ralfs				0.004	m
Staurastrum manfeldtii Delponte				0.0007	e
Staurastrum tetracerum Ralfs ex Ralfs		1.6		0.02	om	1.3
Chlorophyta
Actinastrum hantzschii Lagerheim		363.9	162.3			2.3
Ankistrodesmus arcuatus Korshikov			180.7			2.1
Ankistrodesmus fusiformis Corda		158.4			e	2.0
Botryococcus braunii Kützing	620.7	366.6	161.1	53.7		1.5
Coelastrum astroideum De Notaris		60.2	22.4	0.3	e	2.2
Desmodesmus armatus (Chodat) E.H.Hegewald	3.1	599.6	59.6	0.3	e	1.9
Desmodesmus spinosus (Chodat) E.Hegewald		522.2		2.2		2.0
Dicellula geminata (Printz) Korshikov			30.0		me	2.2
Kirchneriella lunaris (Kirchner) Möbius			160.4		e
Lagerheimia genevensis (Chodat) Chodat		200.4				2.1
Lagerheimia subsalsa Lemmermann			211.5			1.2
Lemmermannia tetrapedia (Kirchner) Lemmermann		306.7	94.0		e	2.0
Micractinium pusillum Fresenius		164.0	52.9		m	0.3
Monoraphidium griffithii (Berkeley) Komárková-Legnerová		142.7	53.7		e	2.5
Mucidosphaerium pulchellum (H.C.Wood) C.Bock, Proschold & Krienitz	17.4	164.0	54.0	2.4		1.8
Oocystis borgei J.W.Snow		90.1			e	1.7
Pandorina morum (O.F.Müller) Bory	14.6	135.2	22.5	133.2	m
Parapediastrum biradiatum (Meyen) E.Hegewald			7.9			2.9
Pediastrum duplex Meyen	0.2	14.7	5.3		e
Pseudopediastrum boryanum (Turpin) E.Hegewald	4.1	23.8	42.4	0.9	e	2.1
Scenedesmus obtusus f. disciformis (Chodat) Compère			70.4	0.6	e	1.9
Scenedesmus obtusus Meyen		177.7			e	1.8
Schroederia setigera (Schröder) Lemmermann		67.4	19.7		e	1.7
Selenastrum bibraianum Reinsch			25.6		e	1.7
Tetradesmus lagerheimii M.J.Wynne & Guiry		497.3	282.8		e	2.15
Tetradesmus obliquus (Turpin) M.J.Wynne			52.2		ot	2.4
Tetraëdron caudatum (Corda) Hansgirg			13.2		e	2.0
Tetraëdron minimum (A.Braun) Hansgirg		29.0	11.6		e	2.1
Treubaria planctonica (G.M.Smith) Korshikov		363.5				1.9
Cyanobacteria
Anathece clathrata (West & G.S.West) Komárek, Kaštovský & Jezberová				6250.4	me	1.8
Aphanizomenon flos-aquae Ralfs ex Bornet & Flahault	22,091.8	3322.8	530.5		m	1.95
Aphanocapsa conferta (West & G.S.West) Komárková-Legnerová & Cronberg				8.7	me
Aphanocapsa incerta (Lemmermann) G.Cronberg & Komárek	5540.5		8575.2	255.7	me	2.2
Aphanocapsa planctonica (G.M.Smith) Komárek & Anagnostidis			3434.5		o-e
Dolichospermum lemmermannii (Richter) P.Wacklin, L.Hoffmann & J.Komárek	10.3	158.1	8.0		e
Dolichospermum perturbatum (H.Hill) Wacklin, L.Hoffmann & Komárek	173.6				m
Dolichospermum planctonicum (Brunnthaler) Wacklin, L.Hoffmann & Komárek	87.7		32.4		e	2.0
Dolichospermum sigmoideum (Nygaard) Wacklin, L.Hoffmann & Komárek		382.4			e	1.7
Dolichospermum spiroides (Klebahn) Wacklin, L.Hoffmann & Komárek			241.7		e	1.3
Merismopedia tranquilla (Ehrenberg) Trevisan			635.6			2.3
Microcystis aeruginosa (Kützing) Kützing	1736.1		4847.8		me	2.2
Microcystis flos-aquae (Wittrock) Kirchner	76,325.1		23,607.9		e	1.6
Microcystis wesenbergii (Komárek) Komárek ex Komárek	300.0	320.4	553.8			2.3
Phormidium chalybeum (Mertens ex Gomont) Anagnostidis & Komárek		97.0			e	3.3
Snowella lacustris (Chodat) Komárek & Hindák			954.9	9.5	me	1.6
Woronichinia naegeliana (Unger) Elenkin	7777.8		1923.1		e	1.8
Dinoflagellata
Ceratium hirundinella (O.F.Müller) Dujardin	0.04	0.6	1.4	22.7	e	1.3
Peridinium cinctum (O.F.Müller) Ehrenberg				0.0004		1.4
Euglenophyta
Lepocinclis acus (O.F.Müller) B.Marin & Melkonian		5.4	1.6	0.01		2.4
Lepocinclis oxyuris (Schmarda) B.Marin & Melkonian			0.2	0.003		2.3
Phacus longicauda (Ehrenberg) Dujardin			0.2			2.8
Trachelomonas dybowskii Dreżepolski	0.4					2.3
Trachelomonas granulosa Playfair				0.01		2.2
Trachelomonas hispida (Perty) F.Stein				0.005		2.2
Trachelomonas planctonica Svirenko			2.0			2.1
Trachelomonas woycickii Koczwara		3.6				2.3
Heterokontophyta
Acanthoceras zachariasii (Brun) Simonsen		4.5				1.4
Aulacoseira granulata (Ehrenberg) Simonsen			19.1		e	2.0
Dinobryon divergens O.E.Imhof			10.6	0.3		1.2
Dinobryon sociale (Ehrenberg) Ehrenberg		50.6				1.3
Nitzschia acicularis (Kützing) W.Smith			57.2		om	1.4
Pseudostaurastrum limneticum (Borge) Guiry			2.8		e
Rhoicosphenia abbreviata (C.Agardh) Lange-Bertalot				0.1	me	1.9
Ulnaria acus (Kützing) Aboal	18.3	18.1	7.0	2.1	me	1.85

Note. Abbreviations: Trophic state indicators (Tro) according to Van Dam et al., 1994 [39,44]: (ot—oligotraphentic; om—oligomesotraphentic; m—mesotraphentic; me—mesoeutraphentic; e—eutraphentic; o-e—oligo to hypereutraphentic); Index S, species-specific index saprobity according to Sládeček, 1986 [10,39].

) were compared by us with the phosphate concentrations in the lake water. Figure 3 shows that the lakes are arranged in descending order of phosphate concentration, i.e., decreasing trophic base feeding microorganisms and vegetation. The percentage of trophic state indicators in phytoplankton communities shows the prevalence of eutrophic and mesotrophic species in terms of their abundance in each lake. For better presentation, intermediate groups were combined with the major groups as follows: ot (ot + om), m (m + me), e (e + o-e) (Appendix A, Table A1). In Figure 3, it is difficult to recognize the value of the ot indicators because their number are very small. It is also clear that the phosphate concentrations and the change in microphyte community from eutrophic in Lake Dachnoe to mesotrophic in Lake Ierelyakh are closely related.

Potamogeton perfoliatus with a species-specific saprobity index S = 2.30 was an indicator of water saturated by organic matter [4]. It dominated the higher aquatic vegetation in Lake Dachnoe and was one of the dominants in the community in Saysary Lake. In Lakes Ytyk-Kyuyol and Ierelyakh, Potamogeton perfoliatus vegetated in patches.

3.3. Data on Concentration of Pollutants in Tissues of Potamogeton perfoliatus

The tissues of Potamogeton perfoliatus growing in the studied lakes were found to contain elevated levels of such pollutant elements as aluminum (Al), sodium (Na), silicon (Si), iron (Fe), manganese (Mn), titanium (Ti), barium (Ba), neodymium (Nd), copper (Cu), chromium (Cr), rubidium (Rb), vanadium (V), cerium (Ce), lithium (Li), arsenic (As), lanthanum (La), thorium (Th), nickel (Ni), niobium (Nb), scandium (Sc), and beryllium (Be) (

Table 4. The concentrations of pollutant elements in the tissues of Potamogeton perfoliatus under different growing conditions in studied lakes in the permafrost region.

Pollutant Elements, mg kg−1 DW	Dachnoe	Saysary	Ytyk-Kyuyol	Ierelyakh
Al	6229 ± 431	2260 ± 264	360 ± 94	251 ± 65
Na	4620 ± 387	4782 ± 294	4822 ± 263	2302 ± 394
Si	3921 ± 560	2068 ± 275	1106 ± 151	885 ± 93
Fe	3157 ± 678	1186 ± 253	485 ± 65	822 ± 179
Mn	1294 ± 291	630 ± 87	737 ± 94	646 ± 154
Ti	397 ± 64	124 ± 21	24 ± 4	18 ± 4
Ba	182 ± 28	177 ± 35	160 ± 21	180 ± 30
Nd	50 ± 7	27 ± 3	18 ± 1	18 ± 1
Cu	14.4 ± 1.7	5.6 ± 0.8	4.2 ± 0.5	4.3 ± 1
Cr	12.2 ± 2	9.4 ± 2.1	4.1 ± 0.3	3.8 ± 0.6
Rb	11.3 ± 1.9	5.6 ± 0.6	3.6 ± 0.2	5.9 ± 1.2
V	9.8 ± 1.6	4 ± 0.6	2.2 ± 0.1	1.5 ± 0.1
Ce	8 ± 1.2	2.4 ± 0.5	1.7 ± 0.3	0.9 ± 0.2
Li	8 ± 1.6	2.4 ± 0.3	1.6 ± 0.2	0.8 ± 0.1
As	5.9 ± 2.1	2.5 ± 1.8	3.1 ± 0.5	3.8 ± 2.1
La	6.5 ± 1.2	2.4 ± 0.4	1.6 ± 0.1	1.2 ± 0.1
Th	5.2 ± 1.2	1.8 ± 0.8	2 ± 0.6	2.4 ± 0.9
Ni	5.2 ± 0.8	3.4 ± 0.7	1.1 ± 0.3	0.7 ± 0.2
Nb	2.63 ± 0.51	1.92 ± 0.48	0.53 ± 0.24	0.35 ± 0.11
Sc	1.32 ± 0.25	0.49 ± 0.06	0.3 ± 0.01	0.25 ± 0.02
Be	0.22 ± 0.05	0.03 ± 0.01	0.01 ± 0	0.01 ± 0.01
∑	19940 ± 2289	11296 ± 1578	7737 ± 448	5147 ± 342

Note: n = 4.

). It was found that the highest content of most pollutant elements was observed in the tissues of Potamogeton perfoliatus from Lake Dachnoe.

The total content of pollutant elements (∑) in the tissues of Potamogeton perfoliatus was maximum in Lake Dachnoe and amounted to 19,940 mg kg⁻¹ of DW, and the minimum was noted in Lake Ierelyakh—5147 mg kg⁻¹ DW. It should be noted that there was a 24.8-fold increase in the concentration of Al in the tissues of Potamogeton perfoliatus in Lake Dachnoe compared to Lake Ierelyakh. It was found that the content of Na and Si in the tissues of Potamogeton perfoliatus in Lake Dachnoe was 2- and 4.4-times higher, respectively, relative to Lake Ierelyakh. Heavy metals such as copper (Cu), chromium (Cr), nickel (Ni), and vanadium (V) also show an increase in concentrations in Lake Dachnoe: copper increases by 3.3 times, chromium by 3.2 times, nickel by 7.4 times, and vanadium by 6.5 times relative to Lake Ierelyakh. Rare earth elements such as neodymium (Nd), cerium (Ce), and lanthanum (La) also show an increase in concentrations in Lake Dachnoe: neodymium increases by 2.8 times, cerium by 8.9 times, and lanthanum by 5.4 times.

Thus, from Table 4 it can be seen that the accumulation of pollutant elements in plant tissues decreases in order of decreasing trophic status of the lake and decreasing eutrophic microalgae indicators.

3.4. Causation Patterns Behind Metabolomic Observations

Based on the results of metabolomic analysis of Potamogeton perfoliatus tissue samples growing in the lakes studied, statistical analysis was performed, and graphs of scores (Figure 4A) and loads (Figure 4B) were created, including 16 observations for 72 metabolites (Appendix A,

Table A2. Metabolites in the tissues of Potamogeton perfoliatus under different growing conditions in urban water bodies in the permafrost region.

Compound, mg/g DW	Dachnoe Lake	Ierelyakh Lake	Saysary Lake	Ytyk-Kyuyol Lake
1-Monopalmitin	0.0306	0.0268	0.0242	0.0284
2,3-Dihydroxybutanedihydrazide	0.1352	0.6499	0.1524	0.4004
3,4-Dihydroxybenzaldehyde	0.0124	0.0234	0.0130	0.0171
4-Coumaric acid	0.0220	0.0458	0.0143	0.0463
4-Hydroxybenzoic acid	0.0581	0.0842	0.0474	0.0526
5-Oxoproline	0.2232	0.1154	0.0906	0.0847
Adonitol	0.0637	0.0656	0.0482	0.0515
Alanine	0.0781	0.1000	0.1212	0.1064
Arabinose	0.0470	0.0553	0.0382	0.0759
Arbutin	0.0046	0.0120	0.0069	0.0044
Aspartic acid	0.0396	0.0323	0.0228	0.0403
Aspartic acid	0.0845	0.0552	0.0348	0.0653
Asterbatanoside A	0.6027	1.2284	0.0971	0.1671
Benzoic Acid	0.0281	0.0285	0.0305	0.0225
Caffeic acid	0.5244	1.9675	0.8558	1.8949
Carbamate	0.2517	0.2480	0.2309	0.2455
Catechollactate	0.5801	3.3695	0.8796	2.7182
Cholesterol	0.0067	0.0143	0.0154	0.0282
Citric acid	0.7486	3.5633	0.7815	2.0912
Erythronic acid	0.0352	0.1171	0.0269	0.1008
Erythrono-1,4-lactone	0.0308	0.1051	0.0203	0.0648
Ethanolamine	0.0163	0.0438	0.0359	0.0451
Ferulic acid	0.0295	0.0330	0.0129	0.0239
Fructose	1.0079	2.0094	0.9995	1.4924
Fumaric acid	0.0144	0.0177	0.0137	0.0205
Galactonic acid	0.0302	0.0402	0.0224	0.0240
Galactose	0.0136	0.0450	0.0295	0.0455
Glucose	0.5427	2.0461	1.1397	1.3495
Glyceric acid	0.0193	0.0375	0.0217	0.0327
Glycerol-3-phosphate	0.2357	0.2119	0.1170	0.1010
Glyceryl-glycoside	0.0785	0.0741	0.2950	0.1091
Isoleucine	0.0430	0.0679	0.0444	0.0174
Lactic Acid	0.6390	0.4702	0.4462	0.5055
Linoleic acid	0.0399	0.0894	0.0524	0.0962
Linolenic acid	0.3415	0.6994	0.5428	0.7561
Maleic acid	0.1852	0.2809	0.3348	0.3406
Malic acid	1.0152	3.1479	0.6446	2.7880
Malonic acid	0.0234	0.0227	0.0081	0.0136
Mannose	0.7329	2.0926	1.1535	1.3879
Melibiose	0.0441	0.0614	0.0213	0.0462
Methylmalonic acid	0.0579	0.0652	0.0794	0.0982
Myo-Inositol	0.3554	0.7140	0.4267	0.6558
N/I disaccharide	0.5113	0.0829	0.0296	0.0589
N/I disaccharide	1.6299	0.2275	0.0478	0.1050
N/I disaccharide	0.0255	0.0389	0.1266	0.2020
N/I monosaccharide	0.3323	0.6894	0.3168	0.5952
N/I monosaccharide	0.0300	0.1383	0.0303	0.0629
N/I monosaccharide	0.0369	0.0820	0.0465	0.0591
N/I monosaccharide	0.1266	0.1652	0.1051	0.1428
N/I nitrous compound	0.0250	0.0599	0.0211	0.0168
N/I phenolic compound	0.4567	1.7767	0.8327	1.8669
N/I phenolic compound	0.5675	2.4728	0.2178	0.2081
N/I terpene	0.2353	0.1642	0.1139	0.1733
Oleic Acid	0.0313	0.0319	0.0538	0.0589
Oxalic acid	0.0333	0.0821	0.0463	0.0417
Palmitelaidic acid	0.0258	0.0301	0.0630	0.0948
Palmitic Acid	0.7967	0.9205	0.9661	1.1808
Phosphoric acid	1.0341	0.8817	0.8247	1.0885
Phytol	0.0425	0.0416	0.0774	0.0369
Rosmarinic acid	1.7401	5.5661	1.1818	2.6691
Serine	0.0714	0.1014	0.0588	0.0625
Shikimic acid	0.1181	0.2144	0.1837	0.1920
Stearic acid	0.1205	0.1575	0.1539	0.1891
Stigmasterol	0.2063	0.3347	0.1479	0.1610
Succinic acid	0.0067	0.0248	0.0084	0.0160
Sucrose	25.1072	29.3950	26.1119	29.1054
Threonine	0.0478	0.0777	0.0399	0.0329
Threonine	0.0598	0.0971	0.0384	0.0283
Trehalose	0.0658	0.0551	0.0283	0.0166
Valine	0.0757	0.0647	0.0572	0.0316
Xylose	0.0276	0.0480	0.0212	0.0261
γ-Aminobutyric acid	0.0810	0.0974	0.1367	0.0895

Note: n = 4. In the table, 1 mg of TMS-derivative is considered as 1 mg of the studied compound. N/I—not identified.

). The resulting data set was processed using the PCA (principal component analysis) method.

The data points corresponding to the Potamogeton perfoliatus tissue metabolomes were grouped into clusters based on the degree of contamination with pollutant elements (Figure 4A), with the dispersion for Components 1 and 2 being 34% and 22%, respectively. The loadings plot shows that the metabolites that contribute most to the clustering of Potamogeton perfoliatus metabolomes based on the degree of contamination include mono- and disaccharides, polyols and free fatty acids, phenolic and nitrous compounds, inorganic and organic acids, sterols, and terpenes (Figure 4B).

The metabolites that had the greatest impact on the differentiation of metabolomes are presented in the heat map (Figure 5). It is shown that in the tissues of Potamogeton perfoliatus, there is a decrease in the content of sucrose, monosaccharides (arabinose, mannose, fructose, glucose, galactose), organic acids (glyceric acid, malic acid, erythronic acid, fumaric acid, succinic acid, citric acid), fatty acids (linoleic and linolenic acids), myo-inositol, 4-coumaric acid, caffeic acid, rosmarinic acid, shikimic acid, and catechollactate with an increase in the level of pollutant elements in the tissues of Potamogeton perfoliatus growing in the studied lakes.

It was shown that the content of sucrose (r = −0.83; p = 0.02), mannose (r = −0.85; p = 0.02), glucose (r = −0.91; p = 0.01), and fructose (r = −0.85; p = 0.02) significantly correlated with the content of pollutant elements in the tissues of Potamogeton perfoliatus (Figure 6).

Inverse correlations were also found between citric acid (r = −0.78; p = 0.03), fumaric acid (r = −0.61; p = 0.05), succinic acid (r = −0.79; p = 0.03), and malic acid (r = −0.72; p = 0.03) relative to the content of pollutant elements in the tissues of Potamogeton perfoliatus (Figure 7).

4. Discussion

The concentration of nitrogen and phosphorus compounds was high in all of the studied lakes, which is consistent with the eutrophic assessment based on microalgae. However, the highest content of these biogenic substances was found in Lakes Ytyk-Kyuyol and Dachnoe. Lake Ierelyakh is not subject to anthropogenic load, and in Lake Saysary, periodic work is carried out to clear the bottom sediments and aerate the water as part of the municipal program for the reclamation of urban water bodies. Apparently, this explains the fact that in these two lakes the concentration of nitrogen and phosphorus compounds, color, and COD were the lowest among the studied water bodies. According to the classification of S. P. Kitaev [45], based on the content of mineral nitrogen, Lakes Ytyk-Kyuyol and Dachnoe were classified as hypertrophic water bodies, while the other lakes were classified as β-eutrophic. Based on the concentration of total phosphorus, all water bodies were classified as hypertrophic according to the classification of R. G. Wetzel [46]. In this case, the greatest biomass of phytoplankton was achieved only in Lakes Dachnoe and Ytyk-Kyuyol, where the bloom of water bodies was noted visually by the greenish tint of the water. According to the classification of G. K. Nürnberg [47], Lakes Dachnoe and Ytyk-Kyuyol were β-eutrophic water bodies according to the biomass of phytoplankton, while the other lakes were α-β-mesotrophic. It should be noted that all lakes were characterized by a high amount of total iron. It is known that in waters rich in oxygen and iron, the proportion of biologically available phosphorus can be only a part of its total amount [48]. Therefore, despite the increased content of biogenic substances in the studied water bodies, their biological availability could be limited. Based on the bioindicator properties of planktonic microalgae, we have noted the strongest anthropogenic pressure for Lakes Dachnoe and Ytyk-Kyuyol.

All the studied lakes are in the stage of overgrowing with higher aquatic vegetation. The ecological state for this stage is normal. The influx of biogenic elements into the lakes was compensated for by the rapid development of aquatic vegetation in the Lakes Ierelyakh and Saysary, where it played the role of a biological filter and prevented “water bloom” (development of planktonic microalgae). Potamogeton is widespread in lakes and is a good indicator of organic pollution. There are 12 known species of Potamogeton, for which the species-specific saprobity index S [4,49] has been calculated. Moreover, P. perfoliatus has one of the highest indices (S = 2.30), being an indicator of rather high organic pollution and, thus, a high trophic status of the lake. The dynamics of phosphate concentration and other production indicators is associated with changes in the saprobity indices of the aquatic community [50]. Thus, having data on phosphates, one can judge the trophic status of the reservoir, and the saprobity indices show how actively the processes of self-purification and their utilization proceed. In our case, phosphates decrease in the series of Lakes Dachnoye–Ierelyakh, where the phytoplankton saprobity indices were about 2.04, while Potamogeton perfoliatus has a species saprobity index of 2.30 [4]. This shows how much more actively macrophytes, which live for a long time and form a large biomass during the year, purify the water of the lakes [12,51].

Aquatic macrophytes of the genus Potamogeton, which play a crucial role in the functioning of freshwater ecosystems, exhibit diverse adaptive mechanisms in response to anthropogenic stressors such as heavy metal pollution, eutrophication, and changes in hydrological regimes. Studies have shown that the toxicity of metals to these plants varies in the order Zn > Cu > Pb, with species differing in their ability to accumulate elements: P. pectinatus accumulates more copper, while P. perfoliatus accumulates more lead, highlighting the importance of metal phytotoxicity in sediments rather than their total content [52,53]. The impact of metals on plant physiology, including reduced photosynthetic activity (Fv/Fm) and oxidative stress, is modulated by both their combined effects (e.g., the antagonism between Zn and Cu) and external factors such as light quality. For instance, blue light reduces cadmium toxicity in P. crispus by enhancing antioxidant defenses and pigment synthesis [54,55]. Simultaneously, eutrophication combined with shading disrupts the carbon–nitrogen metabolism of macrophytes, leading to the accumulation of free amino acids and oxidative stress, which explains their decline in eutrophic water bodies [56]. Stress resistance also depends on morphological plasticity: P. perfoliatus alters leaf and shoot structure in response to water depth, trophic status, and wave exposure, demonstrating adaptability critical for survival in dynamic conditions [57,58]. Life strategies such as vegetative reproduction through turions and the presence of rhizomes ensure long-term resilience, as observed in narrow-leaved Potamogeton species in changing river ecosystems [59]. However, prolonged droughts, even for relatively resilient species like P. nodosus, result in high mortality, necessitating water resource management to minimize risks [60]. Biochemical adaptations, including the restructuring of membrane lipid composition and the activation of antioxidant systems, play a key role in cadmium tolerance, where low metal concentrations may stimulate photosynthetic pigment synthesis, while high concentrations disrupt tissue structural integrity [61]. Considerable variations were noted in the studied lakes in terms of the content of pollutant elements accumulated in the tissues of Potamogeton perfoliatus and the intensity of the associated metabolic processes in the plant. The pronounced enrichment in aluminum (Al), silicon (Si), iron (Fe), and rare earth elements (Nd, Ce, La) in plants from Lake Dachnoe, as well as elevated levels of heavy metals (Cu, Cr, Ni, V) indicate a complex interaction of geogenic and anthropogenic factors. The total concentration of pollutant elements (∑) in the tissues of P. perfoliatus from Lake Dachnoe was almost four-times higher than in the relatively unpolluted Lake Ierelyakh, which emphasizes the heterogeneity of the ecological conditions of water bodies in urbanized permafrost areas. It is noteworthy that the 24.8-fold increase in Al content in Lake Dachnoe compared to Lake Ierelyakh is consistent with regional geochemical features, where the weathering of aluminosilicate rocks and cryogenic processes in permafrost soils promote the natural release of Al and Si into aquatic systems [62]. However, the simultaneous increase in concentrations of Cu, Cr, Ni, and V—elements typically associated with anthropogenic activities such as urban runoff, transport emissions, and industrial waste—indicates local human impact in areas near Lake Dachnoe [63]. Thus, Lake Dachnoe stands out as the reservoir with the highest concentrations of most elements compared to the control Lake Ierelyakh. This may be due to natural geochemical processes. However, it is also likely to be due to anthropogenic impact. In the basin of Lake Dachnoe, there are two potential sources of man-made impact: a municipal controlled landfill dump and a sand quarry (Figure 1). Geographically, these objects are quite remote from Lake Dachnoe, but the runoff into the lake is possible from their territory. And given the features of the permafrost zone, where frozen soils are impermeable to water and with surface runoff, dissolved substances may not be immediately absorbed by the soil and can be transported over significant distances.

From the dynamics of Potamogeton development in the studied lakes, its biomass is greater where the trophic status of the lake is higher in phosphorus and where there are most pollutants in the bodies of plants. Consequently, Potamogeton perfoliatus can be not only a good indicator of the concentration of organic pollutants, but also an indicator of the high ability of the lake ecosystem to self-purify, which is also indicated by the high biomass of phytoplankton.

The partitioning of metabolomes between lakes revealed by PCA, particularly the clear clustering of samples from Lake Dachnoe, highlights the sensitivity of P. perfoliatus to environmental gradients. The contribution of sugars, organic acids, and fatty acids to the observed variance supports the role of these metabolites in mediating plant responses to pollutant stress. Such metabolic plasticity may enhance the species’ resilience in polluted habitats, although long-term exposure may impair physiological functions, as evidenced by the sharp decline in energy-rich compounds.

Metabolomic analysis revealed distinct changes in primary and secondary metabolites in P. perfoliatus tissues under different pollutant loads. The data obtained may be associated with the adaptation of Potamogeton perfoliatus to stressful growing conditions. It is known that sucrose and monosaccharides play a key role in the energy metabolism of plants, and their decrease may be associated with an increase in energy costs for detoxification and the body’s defense mechanisms.

One of the key changes in the metabolome of Potamogeton perfoliatus was a decrease in the content of sucrose, monosaccharides, and polyols as a result of an increase in pollutant elements. It is known that carbohydrates play an important role in the energy metabolism of plants, and their decrease may indicate the inhibition of photosynthetic activity and energy balance disturbance. This can be confirmed by the inverse correlation of the content of sucrose (r = −0.83; p = 0.02), mannose (r = −0.85; p = 0.02), glucose (r = −0.91; p = 0.01), and fructose (r = −0.85; p = 0.02) relative to the content of pollutant elements in the tissues of Potamogeton perfoliatus (Figure 6). Similar changes were observed in other studies, where heavy metal pollution led to a decrease in the content of sugars and polyols in the tissues of aquatic plants [19,64]. Under ocean acidification (OA) conditions, the macroalgae Ulva prolifera exhibited a reduction in sucrose concentration, which was associated with enhanced growth rates and elevated energy costs for protein biosynthesis [65]. These findings indicate a shift in sugar resource allocation towards growth-related processes, despite the presence of environmental stress.

The glucose concentration showed a negative correlation with the trophic status of the lake in the form of phosphate concentration (r = −0.96; p = 0.01), as well as with the development of microalgae in terms of the biomass (r = −0.85; p = 0.07) and abundance (r = −0.85; p = 0.07) of phytoplankton.

Organic acids such as glyceric, malic, erythronic, fumaric, succinic, and citric acids also showed a decrease in concentration in the tissues of Potamogeton perfoliatus with an increase in the content of pollutant elements and trophic state of the lake. These acids are involved in key metabolic pathways, including the tricarboxylic acid cycle (TCA), which is the main source of energy for cells. A decrease in their content may indicate a disruption in energy metabolism and oxidative phosphorylation, which is consistent with data on the toxic effects of heavy metals on plant enzymatic systems [66]. This fact can be confirmed by the inverse correlation of citric acid (r = −0.78; p = 0.03), fumaric acid (r = −0.61; p = 0.05), succinic acid (r = −0.79; p = 0.03), and malic acid (r = −0.72; p = 0.03) with respect to the content of pollutant elements in the tissues of Potamogeton perfoliatus (Figure 7). Under copper (Cu) stress, the marine algae Sargassum fusiforme exhibited a decline in malate, succinate, and citrate levels, likely driven by the generation of reactive oxygen species (ROS). This reduction in key metabolites is thought to impair the efficiency of the tricarboxylic acid (TCA) cycle [67].

Fatty acids such as linoleic (r = −0.88; p = 0.06) and linolenic (r = −0.95; p = 0.05) also showed a decrease in concentration in the tissues of Potamogeton perfoliatus with an increase in pollutant elements. These acids are important components of cell membranes and are involved in the regulation of fluidity and permeability. A decrease in their content can lead to a disruption of membrane integrity and an increase in the sensitivity of cells to oxidative stress. Similar changes were observed in studies where heavy metal pollution caused oxidative stress and damage to lipid membranes [19,68]. Under OA conditions, Ulva prolifera exhibited triglyceride remodeling, marked by a reduction in polyunsaturated fatty acids (PUFAs) and an increase in palmitic acid content. This shift may reflect adaptations to optimize membrane fluidity and energy storage [65]. In a separate study, cadmium exposure in Ulva lactuca resulted in a threefold rise in n-6 PUFAs (18:3 ω-6 and 18:2 ω-6), potentially mitigating oxidative stress by modulating lipoxygenase activity [69]. These findings underscore the dual role of lipids as both structural elements and key players in signaling pathways and antioxidant protection.

Phenolic compounds, which also showed a decrease in concentration in the tissues of Potamogeton perfoliatus with increasing pollutant elements, are known to play an important role in protecting plants from oxidative stress and pathogens. Their decrease may indicate a disruption of antioxidant defenses and an increase in the vulnerability of plants to the toxic effects of pollutants. Phenolic compounds, including flavonoids and tannins, have been shown to play a critical role in mitigating heavy metal toxicity by chelating metal ions and reducing their bioavailability. However, with long-term exposure to high concentrations of heavy metals, the production of these compounds may decrease due to the suppression of key enzymes involved in their biosynthesis [70].

5. Conclusions

The degree of anthropogenic load on four water bodies of urbanized territory in the zone of continuous permafrost distribution was estimated by methods of bioindication and direct analysis of the concentrations of the main chemical components of lake waters. The anthropogenic impact on the studied water bodies is accompanied by an increase in their trophic level. Only in the lake located within the city limits was the anthropogenic impact compensated for by reclamation works. Data on the concentrations of pollutant elements in the tissues of Potamogeton perfoliatus confirm the results based on the bioindication properties of planktonic microalgae. Pollutant accumulation in P. perfoliatus tissues reached 19,940 mg kg⁻¹ DW in Lake Dachnoe, with aluminum concentrations 24.8-fold higher than the control. The results of metabolomic analysis indicate that the pollution of water bodies with pollutant elements has a significant effect on the metabolism of Potamogeton perfoliatus, leading to a decrease in the content of key metabolites, such as carbohydrates, organic acids, fatty acids, and phenolic compounds. Significant inverse correlations to the sum levels of pollutant elements of carbohydrates such as sucrose (r = −0.83; p = 0.02), mannose (r = −0.85; p = 0.02), glucose (r = −0.91; p = 0.01), and fructose (r = −0.85; p = 0.02), as well as tricarboxylic acid cycle metabolites including citric acid (r = −0.78; p = 0.03), fumaric acid (r = −0.61; p = 0.05), succinic acid (r = −0.79; p = 0.03), and malic acid (r = −0.72; p = 0.03), highlight the disruption of metabolism. These changes may be associated with the disruption of energy metabolism, oxidative stress, and damage to cell membranes caused by an increase in the concentration of pollutant elements in the cells of Potamogeton perfoliatus.

In a comprehensive study of the response of primary producers (planktonic microalgae and Potamogeton) to the level of anthropogenic stress and the trophic status of lakes in the permafrost region, a positive correlation was found between the trophic status of the lake, the accumulation of pollutants in the body of higher plants, and the biomass of planktonic microalgae. However, a negative correlation was observed between these indicators and the metabolic activity of Potamogeton, indicating its suppression during water body pollution. The study also showed the high Potamogeton accumulation capacity, which makes it possible to recommend its colonization for the remediation of water bodies in the permafrost zone. As a practical recommendation to implement urban lake restoration programs, for the better removal of phosphates and nitrates, planting is proposed of Potamogeton and removal of its biomass in autumn, at the end of the growing season, together with accumulated pollutants.

As a result of this research, it was found that the accumulation of pollutant elements in plant tissues decreases in order of decreasing trophic status of the lake and decreasing eutrophic microalgae indicators. This study has shown the relevance of complex work based on the bioindicator properties of microalgae and metabolomic analysis of higher aquatic vegetation to assess the anthropogenic load on aquatic ecosystems of permafrost territories.