Chironomus Species Dna Barcording in Monitoring the Pollution Levels of the Nyanza Gulf in Lake Victoria, Kenya

doi:10.21203/rs.3.rs-3876695/v1

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Chironomus Species Dna Barcording in Monitoring the Pollution Levels of the Nyanza Gulf in Lake Victoria, Kenya

https://doi.org/10.21203/rs.3.rs-3876695/v1

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Chironomidae is a group of diptera insects, commonly known as “non-biting midges'' in the adult stage and “bloodworms” in the larval stage, represent a group of insects that thrive in various aquatic environment worldwide. Despite the plethora of information on Chironomidae in many parts of the world, there is a paucity of data regarding this indicator species in the polluted Kenyan Nyanza Gulf of Lake Victoria and molecular identification has not been explored. This study aimed to characterize Chironomidae species based on the mitochondrial DNA barcoding of the cytochrome oxidase subunit 1(COI) gene. Aquatic insects were collected from inshore and offshore in the Nyanza gulf, with a focus on pollution gradient. Chironomus larvae were subjected to analysis to discern divergence or convergence among conspecifics or intraspecific based on pollution gradients. The COI gene was amplified, sequenced using species-specific primers and compared to Gene Bank entries. Genetic analysis was done using MEGA version 11. Phylogenetic analyses employed Neighbor-joining and maximum parsimony algorithms with 1000 bootstrap replicates. The results revealed two known species, Chironomus transvaalensis from heavily polluted Kisumu station, and Chironomus pseudothummi from moderately polluted Kendu bay and Homabay stations, within the same biogeophysical environment. Additionally, a unique Chironomus species was identified on Ndere Island, an offshore station, presumed to be a clean site with restricted human activities. Sequences comparisons with global data indicated proximity but highlighted evolutionary significance and uniqueness. The study postulates that pollution serves as a selective pressure, driving the evolution of Chironomidae species in this particular region.

Chironomidae

Cytochrome oxidase I gene

DNA Barcoding

Mitochondria DNA

Chironomus, Meigen,1803 commonly known as the non-biting midge/lake flies belonging to the Family Chironomidae has been studied and 647 species identified globally, (Arnett, 2000; GBIF, 2018; Epler, 2019). The striking feature being the red coloration attributed to the hemoglobin protein, hence the name bloodworm, an adaptive characteristic for the survival in low oxygen habitats (Sagi et al., 1995; Klaus et al., 2008; Malgorzata et al., 2017; Karima, 2021). Although, the genus Chironomus is among the largest in class Insecta among the macroinvertebrates, morphological analysis of the larvae of the organism has been restricted by the concealed features of the organism to allow for further studies of the genus, hence only identifiable with the giant chromosomes using DNA barcodes (Chib & Jasrotia, 2022; Pape & Evenhuis, 2021).

Cytochrome I oxidase subunit 1 gene is a perfect conserved molecular marker in the distinction of immature and extremely resembling organisms with similar morphological features. The gene is strictly inherited and not easily distorted in the transfer of the genetic material in the lineage. Karima (2021) observed that previous studies on the Chironomus were based only on the adult stage rather than the larval stage providing the morphological features used in identification useful for adults only. Devotion to research on the larval stage, the non biting midge, deemed vital in the aquatic food chains and food webs as filter feeders and environment cleaners has received minimal attention. Other features include: physiological adaptability to harsh environmental cues, particularly, resistance to desiccations (Strachan et al., 2015; Frouz et al., 2015; Malgorzata et al., 2017); low oxygen levels (Sagi et al., 1995; Klaus et al., 2008), survival in high nutrients waters, polluted waters or clean waters regardless of the type: fresh, brackish or marine (Sagi et al., 1995; Klaus et al., 2008; Arnett, 2000; Epler, 2019).

Chironomus is tolerant to organic and inorganic pollutants which are associated with industrialization, sewerage and storm water flow common in urban waters. Chironomidae survives very dynamic conditions, therefore good in environmental bio-monitoring. Hence, the genus Chironomus is ecologically regarded as perfect bio indicators of change attributed to anthropogenic effects like urbanization, industrialization and climate change resulting from natural influence.

The conspecific or heterospecific in question has remained unclear due to the invisibility of the distinguishing features. Besides, whether the divergences and convergences resulting from the changing aquatic habitats exist remain obscured. This study sought to elucidate the occurrence and diversity of Chironomidae species based on Cytochrome oxidase subunit 1 gene and as influenced by pollution within the Kenyan Nyanza Gulf of Lake Victoria. Results of the present study will provide baseline data on environmental biomonitoring, influence policy on mitigation measures against degradation of aquatic habitats thereby bolstering environmental health.

Study Area and sampling design

The study took place in the Nyanza Gulf on the Kenyan side of Lake Victoria (Fig. 1). This catchment area contributes 30% of the riverine inflow into the lake, sourced from five major rivers: Nzoia, Yala, Kuja, Nyando, and Sondu, all originating in the highlands and passing through agricultural areas (Gikuma-Njiru & Hecky, 2005; Gikuma-Njiru et al., 2013). Additionally, smaller crucial rivers such as R. Kisat, R. Kisian, and R. Nyamasaria (a highly polluted river receiving influents from the City’s sewage treatment plant) flow from agricultural and suburban areas, respectively. Sondu and Miriu rivers are utilized for hydropower generation. These rivers traverse sugarcane agricultural and industrial zones, as well as rice paddies where rice cultivation is integrated with aquaculture, albeit on a small scale. The region, however, faces challenges due to the impact of industries like breweries, tanning, fish processing, agro-processing, and abattoirs, which discharge significant volumes of pollutants into the lake. Moreover, the area is known for activities such as livestock farming, gold mining, sand harvesting, and oil depots.

Nyanza gulf, is densely populated at the rate of about 440 people per km² (KPHC, 2009), putting pressure on available land resulting to deforestation and increasing levels of pollutants in the gulf. Major towns along the lake shores, which include: Kisumu City, Homa Bay and Kendu Bay are particularly under greater pressure in terms of sewage facilities and refuse deposition which translates into nonpoint sources of pollution on lake water quality. The effluents through storm waters find their way to the lake, compromising on the water quality. The pollutants impact on the gulf ecosystem, causing destruction of habitats for aquatic flora and fauna. The zonation of lake ecosystem forming growth of emergent and submergent macrophytes in the littoral zone and the water column, create insect habitats, which form part of the aquatic food chains and food web, allowing energy flow up the trophic levels.

Sampling was done in September 8th -11th, 2020. Three sampling stations, Kisumu bay, Homabay and Kendu-bay located in urban environs and with an approximate maximum depth of 3.5m were identified to capture the effects of urban pollution. This was to include effects of domestic effluents, raw sewage, industrial wastes and the nonpoint sources like storm water from the towns. The water contains suspended and dissolved solids like oils, detergents from carwash, wasted batteries etc. Kisumu bay has a highly polluted river in-let Kisat, which is mainly sewage effluent. Ndere island was identified to represent the inshore stations in the gulf and presumably observed to be relatively clean, with limited human activities due to the presence of Ndere island National Reserve Park under the management of Kenya wildlife service, KWS.

A systematic random design was employed in the sampling. Approximately 50 m belt along the lake shores was estimated and a first point randomly located at the center. A transect was developed across and three points identified for random sampling of water and sediments in triplicates. The samples were pooled to obtain composite homogeneous sample for heavy metal analysis. Sediment samples were collected from the three sampling points per sampling station for heavy metal analysis. Insect samples were collected from three sampling points on the transects, pooled together to form a representative sample per stations.

Water samples for heavy metal analyses were collected using a polycarbonate sampling bar of polytetrafluoroethylene container which was thoroughly pre-washed with acid and deionized water. The samples were preserved in liquid HNO₃ at a pH of < 2, placed in cooler boxes at 4ºC and transported to the laboratory for analysis.

The U.S. EPA (2020) standard operating procedure (ID: LSASDROC-200-R4) and (Ohio EPA, 2001) was employed in collection of sediment samples. A 400cm² (20 x 20 cm) size AISI 316 stainless steel fabricated Eckman bottom corer grab was used. The spring-tensioned, scoop jaw-like part was mounted on pivot points and set with a trigger assembly activated from the surface by a messenger. A total of three grabs was made per sample, dumped on a tray and pooled to form a composite sample. The sample was then placed in wide bottom 1000 ml Polyvinyl chloride, PVC bottles and covered with aluminum foil paper, placed in a cooler box at 4ºC and transported to the laboratory for heavy metal analysis.

Analysis of insect samples was done using a profundal lake sampling procedure as outlined in the standard SFS 5076. A boat was used to reach a 50 m distance inshore for littoral zone sampling. At the anchoring site, Eckman grab–Birge dredge sampler was used to make random triplicate grabs of submerged insect larvae placed into the plastic bucket through a bucket sieve with um mesh and pooled to obtain a composite sample. The contents emptied for sorting aided by washing bottles to flash the remaining content using alcohol, then placed in paper slips and labeled (location, date, time, collector, sampling method, habitat, habitat description, weather and photographs of every site taken, sample number). These were then filled with 80% alcohol as in ISO-EN 5667-3, closed and packed in readiness for transportation in cooler boxes at 20ºC for further identification (Moller, 2013; Mehmet et al., 2015; McMurtrie et al., 2014). Sampling was carried out in the morning hours between 7 am-11.30 am.

Sample Analyses (water, sediment and insect)

Heavy metal analyses in water were done by Vacuum filtration with high particulate matter, high turbidity, suspended solids and with low levels in concentration. Pre-treatment of the water sample was carried out by adding a mixture of HNO₃:H₂O₂, in a ratio 1:3 for acid digestion. The contents were placed in a hollow solid fiber micro-extractant with graphene oxide silica coating. The extract was raised to the 100 ml mark in readiness for analysis by Inductively Coupled Plasma-Mass Spectrometry, ICP-MS technique (EPA Methods, 2008). Mercury analysis was done using the Cold Vapor Atomic Fluorescence Spectroscopy, CV-AFS (EPA Methods, 245.1).

The sediment samples were dried at room temperature, ground and sieved using 2 mm sieve. About 2.00 g obtained from the dried sample was placed in a 100 ml beaker, 15 ml HNO₃ added and heated at 130 ºC till boiling point for 5 hrs. to obtain 3 ml. The heated content was filtered, washed in 0.1M HNO₃, made up to a volume of 100 ml using distilled water in readiness for analysis using Graphite Furnace Atomic Absorption Spectrophotometer - GF-AAS (EPA Methods, 200.9) for metals except Mercury and Arsenic, analyzed by Cold Vapor Absorption technique-CV- AFS, (EPA methods, 245.1)

Heavy metal analyses on preserved insect samples were done by oven drying at 120ºC and weighed on a Mettler Toledo microbalance, a highly precise weighing instrument used for determining the weight of extremely small samples in the microgram weighing range. Microbalances offer six decimal place readability (1 µg). Triplicate samples were digested in a flask by adding HNO₃ and H₂O₂, in the ratio 1:1, and heated at 130ºC till dissolution. Dilution was done by adding distilled water to obtain 100ml sub-samples. Then followed by metal analysis using Graphite furnace- Atomic Absorption spectrophotometer-GF-AAS (EPA Method 200.9) for metals except Mercury and Arsenic, analyzed by Cold Vapor Absorption technique-CV- AFS, (EPA method 245.1)

Molecular analysis of chironomids

Genomic DNA was extracted from insects’ isolates using the DNeasy Blood and Tissue Kit Qiagen™ kit according to the manufacturer’s specifications. The concentration and purity of DNA was estimated using a DNA Nanodrop™ Lite Spectrophotometer (Thermo Scientific Inc., USA) at 260–280 nm and by horizontal gel electrophoresis (Thistle Scientific Ltd, USA) on a 0.8% (w/v) agarose gel at 100 V for 30 min and visualized under UV after staining with GelRed™ (Thermo Scientific, USA).

Amplification was performed in a programmable master cycler thermocycler (C1000-BioRad, USA) using specific primers that target COI gene of mitochondrial DNA, LCO1490 5GGTCAACAAATCATAAAGATATTGG and HC02198 TAAACTTCAGGGTGACCAAAAAATCA. The thermal cycler conditions were as follows: initial denaturation at 98°C for 2 minutes followed by 35 cycles at 98°C for 30 seconds, annealing at 47.3°C for30 seconds, elongation at 75°C for 30 seconds and final elongation at 75°C for 10 minutes. 50µL PCR mix constituted DNA polymerase enzyme 1U/50µL reaction, 5X Buffer 10µL, 10pm dNTP, 50Mm Mg Cl₂ 1µL,10pm primers 1µL each and distilled water.

PCR products were separated by horizontal gel electrophoresis on 1.5% (w/v) agarose gel at 100 V for 45 mins and visualized under UV after staining with 2 µl Gel Red™ (Thermo Scientific). PCR amplicons purified using the Thermo Scientific®GeneJET Purification Kit (EU Lithuania). A ratio of 1:1 volume of binding buffer added to the completed PCR mixture and vortexed to mix properly. When the color of the mixture remained orange or- violet, 10 µl of 3M Sodium acetate (pH 5.2) was added to alter the color to yellow. Eight hundred microliters of the solution transferred to the Gene-JET purification column and centrifuged at 10,000rpm for 30 sec. and the flow-through was discarded. Seven hundred microliters of the buffer (diluted with ethanol) added and centrifuged at 10,000 rpm on a rotor for 30 sec. and the flow-through discarded. Additional centrifugation done to completely remove any residual buffer. The purification column was transferred to clean 1.5 ml microcentrifuge tubes and 50 µl of elution buffer added followed by centrifugation at 10,000 rpm for 1 min to obtain pure DNA amplicons.

Purified PCR products were sequenced by capillary sequencing on ABI 3730xl DNA Analyzer, (Applied Biosystems) using the same forward and reverse primers and ABI BigDye® Terminator v3.1 Cycle Sequencing reaction kit (Applied Biosystems, USA). Forward and reverse sequences were obtained and used to produce consensus sequences after editing using Chromas Pro v3.1. and further editing and alignment was achieved using Bio-edit Sequence Alignment Editor ver. 7.0. The sequences were converted into FASTA file format in preparation for analyses. Sequences obtained from the present study were submitted to the Gen Bank accession number ON455096-ON455103. Additional CO1 sequences for Chironomus from other parts of the world were obtained from the Gen Bank (www.ncbi.nlm.nih.gov) for phylogenetic analyses (Table 1).

Descriptive statistics were employed to evaluate data on heavy metals across stations. The data was analyzed by ANOVA and a Tukey’s pairwise post hoc test was used to confirm significant variations. The composition of aquatic insects was analyzed independently based on morphological and molecular approaches. The non-biting midge, chironomids a species representative sample was identified across stations for genetic analysis. Molecular analysis, assembled sequences were transferred to MEGA v.11 software and pairwise sequence alignment of the nucleotides done using CLUSTAL W according to Tamura et al., (2011). Sequences were submitted to the NCBI BLAST portal (www.ncbi.blast.nlm.nih.gov) for a sequence homology search, and sequences with greater than 97% similarity retrieved for phylogenetic analysis. Evolutionary history was inferred using the Neighbor-Joining algorithm and distances computed using the Maximum Composite Likelihood (Tamura et al., 2004; Tamura et al., 2011). Bootstrap tests (1000 replicates) were used to cluster associated taxa, with replicate trees showing likelihoods above 50% indicated on the branches.

Study Area

The description of the study area including geographical coordinates and economic activities within the region are summarized in Table 1 below.

Table 1

Summary of the description of the study area.
Sampling Station		Description Of Site	Location			Gps: Southings	Gps Eastings	Elevation (Metres)			Substrate	Human activities
Kisumu Bay		Kisumu Town	Coca cola			00º05.686.097˝	034º44.086'	1134			Silt	Sewage disposal; Littoral/sublittoral vegetation; Phytoplankton blooms-water hyacinth/hippo grass
			Kisat River. Mouth			00º05.212˝	034º44.969'	1134			Sandy	Golf club; Phytoplankton blooms Littoral and sublittoral vegetation-water hyacinth/hippo grass
			Hippo Point			00º07.474˝	034º44.630'	1136			Clay	Auchi R., mouth; waste dumping site; hippo point; next to KIWASCO
Ndere island		Island	Ndere island 1			00º10.999˝	034º31.142'	1134			Mudy	Human activities prohibited; protected area.Home to wild animals eg.hippos,crocodlies,birds,fish
			Ndere island 2			00º11.662˝	034º31.187'	1138			Mudy	Human activities prohibited; home to wild animals eg.hippos,crocodlies,birds,fish
			Ndere island 3			00º11.450˝	034º31.168'	1137			Mudy	Human activities prohibited;home to wild animals e.g hippos;crocodiles;birds,fish
Kendu Bay		Kendu Town	Kendubay 1			00º20.971˝	034º39.321'	1139			Silty	Hotels; Fish Landing Beaches, littoral and sublittoral vegetation, floating vegetation
			Kendubay 2			00º20.968˝	034º39.095'	1138			Silty	Open waters
			Kendubay 3			00º20.968˝	034º39.264'	1135			Silty	Minimal floating vegetation; a lot of hippo grass and papyrus
Homa Bay	Homa Town			Homabay 1	00º31.3 34˝		034º27.206'		1133	Silty			Waste, disposal; town; settlements; town; sewage plant treatment entry point; beach activities; fishing;
				Homabay 2	00º30.784˝		034º26.735'		1129	Silty			Open waters;
				Homabay 3	00º30.337˝		034º26.203'		1132	Rocky			Undisturbed area; patchy thickets of papyrus and floating water hyacinth next to sikri island which provides a shading effect

Heavy metal concentration in water, sediment and insect samples

Heavy metal concentration in water, sediment and insect samples were as outlined in Table 2. Significant variations (p ≤ 0.05) were detected in the level of Pb from the water samples. However, no variations were observed in levels of Ar, Hg, and Cd between the sampling stations. All sediment samples significantly varied (p ≤ 0.05) in heavy metal concentrations, with Pb having highest (p ≤ 0.0001) variation. Insect samples differed significantly (p ≤ 0.0001) in Ar and Hg contents, although Pb and Cd displayed insignificant variations.

Overall mean concentration of 0.411 ± 0.15 for Ar, 0.019 ± 0.01 for Hg, 1.340 ± 0.52 for Pb, and 0.717 ± 0.08 for Cd was observed in insect samples, which was greater that KEBS recommended standards. The concentration of Ar, Pb, and Cd in insect, water and sediment were greater than KEBS standards while Hg concentration was within the standard limits.

The levels of arsenic (Ar) in water samples were exceedingly high in Kisumu bay (1.62 ± 0.04 mg/l) and Ndere island (< 0.34 ± 0.03 mg/l) while that of cadmium (Cd) was highest at Kisumu (0.731 ± 0.002 mg/l) followed by Ndere island (0.587 ± 0.1 mg/l). Mercury (Hg) recorded the lowest concentrations in water, which was below the KEBS standards.

The concentration of all heavy metals assessed in sediment samples were exceedingly elevated beyond the KEBS standards except Hg (< 0.05) in all stations. The highest level in Ar was in sediment samples from Kisumu bay (2.377 mg/l) while the lowest level was from Kendu bay (0.011 ± 0.0 mg/l). Similarly, highest levels of Pb were from Kisumu bay (2.78 ± 0.08 mg/l) samples, and the lowest concentrations were from Ndere Island (0.137 ± 0.0 mg/l) samples. Sediment samples from Ndere island, an offshore station, recorded the highest levels of Cd (0.763 ± 0.4 mg/l) while Kendu bay samples had the least concentrations (0.481 ± 0.0 mg/l).

Observations on heavy metal concentration in insect samples showed Ar, Pb and Cd were exceedingly high beyond acceptable KEBS limits in all stations. Insects from Kisumu bay had outstandingly the highest concentration in all heavy metals: Ar (1.337 ± 0.06 mg/l), Hg (0.087 ± 0.01 mg/l), Pb (4.387 ± 0.03 mg/l) and Cd (1.127 ± 0.04 mg/l), while Kendu bay displayed the lowest concentration of Hg (0.003 ± 0.0 mg/l) and Cd (0.497 ± 0.0 mg/l). Samples of insects from the two islands studied recorded the lowest concentration of Ar (0.035 ± 0.0 mg/l) at Maboko and Pb (0.087 ± 0.01 mg/l) at Ndere island. Levels of Ar exceeded KEBS acceptable limits in all stations except in Kendu-bay stations (0.018 ± 0.0 mg/l).

Table 2

Heavy metal concentrations in water, sediment and insect samples expressed as mean (± SE) in milligrams/liter
Heavy metal (mg/l)	Sample type	Maboko Island	Kisumu Bay	Fish LB	Ndere Island	Kendu Bay	Homa Bay	KEBS STDS	F Stat	p value	Sig.
Arsenic (Ar)	Insect	0.035 ± 0.00^c	1.33 ± 0.06^a	0.387 ± .03^b	0.350 ± 0.03^b	0.018 ± 0.00^c	0.343 ± 0.01^b	0.02	2016.70	< 0.001	HS
	Water	0.021 ± 0.00	1.62 ± 0.04	0.22 ± 0.01	0.34 ± 0.03	0.013 ± 0.00	0.147 ± 0.01	0.02	1.55369	0.245913	NS
	Sediment	0.031 ± 0.00^c	2.37 ± 0.06^a	0.307 ± 0.02^b	0.58 ± 0.03^b	0.011 ± 0.00^c	0.253 ± 0.03^b	0.02	3.78681	0.0273018	S
Mercury (Hg)	Insect	0.035 ± 0.00^a	0.08 ± 0.01^a	0.007 ± 0.00^b	0.087 ± 0.00^a	0.003 ± 0.00^b	0.006 ± 0.00^b	0.05	543.452	< 0.001	HS
	Water	0.001 ± 0.00	0.001 ± 0.00	0.00 ± 0.00	0.003 ± 0.00	0.001 ± 0.00	0.002 ± 0.00	0.05	1.57884	0.239117	NS
	Sediment	0.002 ± 0.00^b	0.005 ± 0.0^a	0.002 ± 0.00^b	0.001 ± 0.00^b	0.003 ± 0.00^b	0.0017 ± 0.0^b	0.05	4.72680	0.012847	S

Lead (Pb)	Insect	0.257 ± 0.00	4.38 ± 0.03	0.740 ± 0.03	0.087 ± 0.01	0.314 ± .020	2.253 ±0.01	0.01	0.00019	0.99981	NS
	Water	0.192 ± 0.00^c	2.43 ± 0.01^a	0.443 ± 0.01^c	0.0867 ± 0.01 ^d	0.254 ± 0.00^c	1.877 ± 0.01^b	0.01	5.70992	0.006356	S
	Sediment	0.250 ± 0.00^b	2.78 ± 0.08^a	0.503 ± 0.04^b	0.137 ± 0.00^b	0.336 ± 0.02^b	2.177 ± 0.01^a	0.01	896.344	< 0.001	HS
Cadmium, (Cd)	Insect	0.512 ± 0.02	1.12 ± 0.04	0.573 ± 0.01	0.639 ± 0.01	0.497 ± 0.00	0.953 ± 0.01	0.01	0.00019	0.99981	NS
	Water	0.01 ± 0.01	0.73 ± 0.002	0.320 ± 0.00	0.587 ± 0.01	0.520 ± 0.00	0.573 ± 0.00	0.01	1.07426	0.4216749	NS
	Sediment	0.507 ± 0.02	0.701 ± .025	0.397 ± .007	0.763 ± 0.04	0.481 ± 0.00	0.763 ± 0.04	0.01	3.21848	0.0450694	S

Characterization of chironomids-non biting midge

The use of morphological characteristics was employed and non biting midge without setae to define the family; subfamily and even the tribe were identified. The details were analyzed in reference to guides and works outlined in (Olafsson, 1992; Cranston, 1995; Armitage et al., 1995; Brooks et al., 2008; Andersen et al., 2013) but not limited reference material. The study revealed the presence of non-biting midge larvae (Chironomids), across all the study sites (Plate 1). However, similarity and differences in Chironomus species was unclear (as outlined in plate 1). Hence, the need for molecular techniques to establish whether the species were similar or dissimilar across the stations. Morphological characteristics used for the identification of Chironomidae include the distinctive bright red color, hence the name blood fluke. The segmented elongated body lacked the thoracic legs. In addition, the non-retractile head capsule is known to have long antennae. The presence and absence of teeth, their location and number distinguish one family from the other. The location of teeth whether medial or lateral of both also offers a separating line. Besides description of the mentum and ventral plates, shape, length with or lateral of both also offers a separating line.

Plate 1: Samples of Chironomid lavea collected from Winam gulf, Lake Victoria.

Eight sequences (CH1 – CH8) of the cytochrome oxidase 1 (COx1) gene were obtained from Chironomus insects which were distributed throughout Winam Gulf (Fig. 2). Comparison of the sequences with those from NCBI database through BLAST analysis resulted in above 97% similarity with Chironomus pseudothummi except isolate CH1 and CH7 which had 100% similarities with Chironomus transvaalensis strain and Chironomus februarius respectively (Fig. 2).

A neighbor-joining phylogenetic tree built from eight (8) sequences of the COx1 gene showed Chironomus insect species coalesced into three clades (Fig. 2). Insect species identified as CH1 from Kisumu bay and CH7 from Ndere island separated into their own clades with NCBI species identified as Chironomus februarius strain AF1922314 from Russia and Chironomus transvaalensis strain EM5 from insect species CH2, CH3, CH4, CH5, CH6 and CH8 appear to have had similarity in ancestry and clustered within the same clade.

A phylogenetic tree was constructed from sequences, revealing distinct clades and clusters with independent operational taxonomic units (OTUs) among insects. Reconstruction employed Neighbor Joining, Maximum Likelihood, and Maximum Parsimony algorithms, with the latter chosen for its informativeness. A bootstrapping test, setting a threshold of > 50% for significance, supported the analysis. Figure 2 results indicated CH7 as an outlier from an offshore station—Ndere Island, within Ndere National Park, known for minimal human disturbances and presumed cleanliness. CH1, CH2, CH3, CH4, CH5, CH6, and CH8 shared similar ancestry, evolving over 0.02 million years from CH7. CH7's evolution led to CH1 and CH2, followed by CH6, CH3, and finally CH5 and CH8. CH4 evolved into CH5 and CH8. CH1's mutation from a highly polluted station, possibly due to urbanization and industrialization in Kisumu, highlighted human-induced impact.

Stations CH2, CH3, CH4, CH5, and CH6 exhibited close ancestry, linked to similar environmental and biogeographical characteristics in Homa Bay and Kendu Bay. Mutations occurred, indicating the influence of environmental closeness on clades. Additional CO1 sequences from NCBI GenBank showed spatial variations in Chironomus sp., identifying nine species—three from the study and six from GenBank. Chironomus pseudothummi predominated in Kendu Bay and Homa Bay (CH2; CH4; CH5; CH6), aligning with the accession from Finland. Probability > 55% in Chironomus spp. in Kendu Bay and Homa Bay suggested similar bio-geophysical characteristics, confirming intraspecific divergences and evolutionary histories in the Chironomus genus.

Pairwise distances

Estimation of average evolutionary divergences over sequence pairs within groups was carried out. The base substitution per station from the average of all sequence pairs within each group was approximated as in Table 3 below with maximum value and minimum values of base substitution rate is in a range of 0.002-0. 175.The species were closely related with a divergence value of 0.05 among the clades.

Table 3

Estimates of Evolutionary Divergence between Sequences
	CH1_kisumu	CH2_kendu_Bay	CH3_kendu _bay	CH4_kendu_ Bay	CH5_kendu_ Bay	CH6_kendu_ Bay	CH7_Ndere_ Island
CH1_kisumu
CH2_kendu_bay	0.144
CH3_kendu_bay	0.142	0.011
CH4_kendu_bay	0.140	0.011	0.002
CH5_kendu_bay	0.142	0.008	0.002
CH6_kendu_bay	0.140	0.004	0.006	0.006	0.004
CH7_Ndere_Island	0.156	0.178	0.173	0.175	0.175	0.173
CH8_Homa_Bay	0.142	0.015	0.008	0.008	0.006	0.011	0.173

The number of base differences per site from between sequences are shown. This analysis involved 8 nucleotide sequences. All positions containing gaps and missing data were eliminated (complete deletion option). There was a total of 473 positions in the final dataset. Evolutionary analyses were conducted in MEGA11 (Tamura et al. 2021)

Table 4

Estimates of nucleotide substitution between sequences
From\To	A	T	C	G
A	-	7.7826	4.4320	9.3727
T	11.2474	-	4.8275	5.3534
C	11.2474	8.4772	-	5.3534
G	19.6919	7.7826	4.4320	-

The number of base substitutions per site from between sequences are shown in Table 4 above. The substitution pattern and rates were estimated under the Tamura-Nei model, (Tamura,1993). Analysis was conducted using the Maximum Composite Likelihood model [Tamura et al. 2004]. This analysis involved 8 nucleotides sequences. The nucleotide frequencies were: A = 39.03%, T/U = 27.01%, C = 15.38%, and G = 18.58%. A tree topology was automatically computed. The maximum Log likelihood for this computation was − 1179.657. Codon positions included were 1st + 2nd + 3rd + Noncoding. All ambiguous positions were removed for each sequence pair (pairwise deletion option). There was a total of 473 positions in the final dataset. Evolutionary analyses were conducted in MEGA11 (Tamura et al., 2021).

Heavy metals in water, sediment and in insect samples-non biting midge larvae

Metallic pollution result to toxicity and impairment of water ecological integrity. Hence, the ecological paradigms. Moreover, affects vulnerable and pollution intolerant aquatic species, changing the biological community structure. The present study revealed the availability of toxic metallic elements which included: Pb, Ar, Cd, Zn and Hg. The elevated Pb in water was exceedingly higher than the WHO STDS unlike Hg which was in low levels within the limits. Cd levels were higher in concentration in Kisumu, Homa bay and Kendu Bay, areas situated in the urban centers and associated with sewerage, urbanization, industrial effluents. In general, all toxic metallic components were exceedingly higher in sediment samples across all sampling stations except for Mercury where the low levels were experienced below WHO STDS limits. The results were in agreement with previously observed patterns showing heterogeneity in metallic pollution which vary seasonally and spatially (Downing et al., 2014; Su et al., 2022; Batool et al., 2020; Yuan et al., 2022; Olando et al., 2020). For instance, studies carried out on freshwater lakes established the presence of Pb, Zn, Ni, Hg, though within the acceptable limits, (Kunduz et al., 2014; Hashima et al., 2014; Olando et al., 2020; Showqi et al., 2018; Sigh et al., 2020). Besides, studies by Rehman et al., (2018), Gutierrez-Ravelo et al., (2020), Balalai-Mood et al., (2021) and Stephania (2016) also identified Pb and Cd as toxic metals, and further categorized metallic components into three: essential (micronutrients -Cu, Zn, Fe, Mn, co, Mo, Cr, Se and Macronutrients-Ca, Na, Mg, P, S), non-essential (Pb, Cd, Ni, As, Hg) and the toxic metals as Pb and Cd. In addition, Hare (1992) observed high levels in Mercury concentrations exceeding the WHO STDS while (Tchounwou et al. 2012) and (Shuhaimi-Othman et al., 2012) documented and affirmed that metallic components were toxic in freshwaters.

The present study established a significant positive correlation between the metals in water and those in sediment, particularly Arsenic, Lead, Zinc and Iron. Consequently, the same association was observed in metallic components in water and insects, notably in Arsenic and Iron which significantly correlated positively. Same pattern was observed in the association between metals in insects and in sediment with a very strong and very significant correlation.

The investigations on macronutrients in insect samples observed a negative correlation between metallic components in water and in sediment samples. A stronger correlation existed in metallic components in sediment and in insect samples rather than the relationship in water versus insect samples. This was attributed to the fact that the larval stage of a non- biting midge occurs and feeds on sediments as filter feeders known to contain high concentration of metals. Hence, the strong positive correlation.

Non biting midge undergoes four developmental stages which starts by laying eggs on the water surface affected by the dynamics in the properties of water. The eggs sink and hatch at the bottom where the larvae feed on suspended organic matter from the mud which forms the sediment where it develops. The component of the sediment forms the external environment in which it develops. This impacts on the insect holistically, and explains the strong correlation between the metallic components in the insects and the sediment. This implies that any effluent into the water particularly, the insoluble matter impacts negatively on the life of the non biting midge larva.

Chironomidae (Non-Biting midge larvae)

The present study on aquatic insects revealed the presence of non biting midge larvae also known as chironomids, amongst all sampling stations in Winam gulf. The presence of non-biting midge, a pollution tolerant species attributed to the fact that it could stand harsh conditions like low oxygen levels, nutrient rich systems, decomposing organic matter, winter drawdown, (Maggio, 2022; Mehlman et al., 2018, Waldvogel et al., 2019). The attributes allowed the non-biting midge assemblages to be used in bio indication of environmental quality, and also in assessment of the ecosystem integrity. The results are in agreement with previous studies as revealed in works published by (Nicacio & Juen, 2015; Floss et al., 2012; Popovic et al., 2022). For instance, the non-biting midge larvae’s hemoglobin, the red color, allows respiration in anaerobic environment and feeds through filter feeding from the suspended organic matter and mud. In addition, the development into the pupa which migrates through the water column into the surface to adulthood and flies off the waters increases survival, (Vogel et al., 2019).

Besides being an indicator of water quality, non-biting midge cleans an environment through recycling of organic matter. Therefore, key in the aquatic food chains and food webs. For instance, predatory fisheries like bottom feeders found within the benthic and profound zones are dependent on the larvae besides other predaceous insects like beetles, dragon, birds, decomposers etc., (Elmer et al., 2011; Anderson et al., 2006; Andersen et al., 2013; Raunio et al., 2010). Consequently, the midge larvae key in energy flow up the food chains, a fact that has received minimal attention despite the fact that the adult is human delicacies.

Besides, the present research attempted to explore the possible effects of pollution on the non-biting midge, a pollution tolerant, indicator and economically viable species to the environmental health and the use as food and as a feed.

Phylogenetic analyses of chironomids

Distinguishing species using morphological similarities or dissimilarities among the Dipterans, remains a big challenge to Biologists and entomologists. Advancement in molecular biology has greatly resolved the problem of misidentification. Use of DNA sequences and development in bioinformatics has increased the speed, simplicity and accuracy (Yang et al., 2012; Waldvogel et al., 2018; Waldvogel et al., 2020). Moreover, species can be identified from partial remains and where species numbers have declined and facing the threat of extinction. Non-invasive methods such as eDNA can be employed for identification using species-specific primers, (Ghaffar et al., 2018; Shalaby and Ghaffar, 2018).In the present study, morphological characteristics were used for identification of chironomids. The species being tolerant were detected in all stations presumably influenced by human activities. This inclination probably occurs due to adaptability and tolerance of the species. Pollution could also be a factor driving evolution acting as a selective pressure. This is supported by the rate of nucleotide substitution between the sequences (Table 4).

Sequencing of approximately 500 bp of the mt CO1 region revealed three species‑specific differences. The study was able to delineate the genus Chironomus into three species Chironomus transvaalensus; Chironomus pseudothummi and Chironomus species using cytochrome I oxidase subunit 1 as a vital barcode region. The study corroborates with previous studies (Herbert et al., 2003; Rodrigues et al., 2013; Kher et al., 2011; Chip et al., 2022) confirming that COX1 is a barcode for animal life for with the ability to discriminate closely related species. This also agrees with the study of (Sperling et al., 1994; Tan et al., 2010; Harvey et al., 2004; Dawnay et al., 2007; Sontigun et al., 2018) who demonstrated that mtDNACO1 sequences (along with COII and tRNA leu) could be used to identify the immature larvae of other forensically important Diptera (such as Lucilia illustris, Phormia regina, and Phaenicia sericata).

The results also prove that morphological and physiological characteristics are not conclusive in the study of diversity; rather a more reliable technique is to be used to distinguish the specimen in question that is Chironomus. This was in agreement with writings of Wink ,2007 which further indicated that genetic barcodes ids were more reliable (Rodrigues et al., 2017). COI gene is a perfect biomarker for the ability of detecting species with higher precision often misidentified manually, similar looking groups and with immature specimens (Deagle et al., 2014; Kullar et al., 2016; Xia et al., 2011).

The present study showed interspecific variation between the three distinct Chironomid species as supported by calculations of pairwise differences between individuals providing validity of a grouping in the analysis. Each species formed distinct conspecific and monophyletic clusters. In the phylogenetic tree, the monophyletic separation of C. transvalensis (Kisumu), C. psedothummi (Kendu Bay) and Chironomus sp. (Ndere Island) supported by over 50% bootstrapping values confirmed the power of resolution of the genetic marker used in the present study to distinguish between closely related species. The observed nucleotide divergence as confirmed by evolutionary divergence between sequences or groups is consistent with the findings of (Yadong et al., 2010). However, separation by COI was not supported significantly when considering sequences deposited in NCBI from other continents e.g., C. psedothummi from Kenya were separated from those of Canada and Russia with weak bootstrap values below 50%. The lack of support in this scenario could be due to geographical separation or allopatric speciation, each group evolving separately as a unique taxonomic unit. Moreover, species from regions with the similar climatic conditions e.g. Russia and Poland were closely related, supported at 100% bootstrap values.

The present study therefore, delineated the genus Chironomus into three species Chironomus transvaalensis, Chironomus pseudothummi and Chironomus species using cytochrome I oxidase subunit 1 as a vital barcode region. The research in line with a study by Herbert et al., (2003) confirmed COX1 barcode for animal life for the ability to discriminate closely related species. The results also proved that morphological and physiological characteristics were not conclusive in the study of diversity. Furthermore, a reliable technique was employed to distinguish the specimen in question - Chironomus. The results from the study were in agreement with writings of Wink, (2007) which further indicated that genetic barcodes ids were more reliable (Rodrigues et al., 2017).

In conclusion, the water analysis indicated significantly elevated levels of lead (Pb) beyond WHO/KEBS standards, while mercury (Hg) levels remained within limits. Cadmium (Cd) exhibited notable variations in Kisumu, Homa Bay, and Kendu Bay. Sediment analysis revealed excessive levels of metallic components, except for Hg, which was within limits. Insect analysis demonstrated exceedingly high Cd and Pb levels across all stations, with elevated arsenic (Ar) levels in water and sediment, in contrast to Hg staying below limits. Comparisons between inshore and offshore stations revealed significant variations in Pb and Zn. Overall, the results indicated a trend towards homogeneity in water quality parameters with minimal differences between inshore and offshore stations. The presence of heavy metals, including Ar, Hg, Cd, and Pb, suggested escalating toxicity levels in the Nyanza Gulf environment, signaling a deteriorating health state. The study employed COX 1 to identify three Chironomidae species, namely Chironomus transvaalensis, Chironomus pseudothummi, and Chironomus sp., in Nyanza Gulf. DNA sequencing indicated distinct species from different locations, revealing mutations in CH3 Kendubay. The findings affirmed the significance of COX 1 in identifying larval Chironomidae, offering a more precise solution than traditional morphological identification. The evolutionary history results showed divergences likely linked to environmental stressors, particularly pollutants. Chironomus emerged as effective indicators of environmental changes, supporting biomonitoring. Early warnings from these organisms can inform conservation and mitigation efforts in aquatic systems, preventing potential health hazards through food chains and webs. This study underscores the importance of monitoring and intervention for the sustainability of aquatic ecosystems and human health.

Ethics approval and consent to participate

National Commission for Science, Technology and Innovation, NACOSTI in partnership with Jaramogi Oginga University of Science and Technology, JOOUST provided ethical approval and consent to participate in current research.

Availability of Data and Material

All data will be availed upon request. In addition, all generated sequences are available to the public on the NCBI-website: https://www.ncbi.nim.nih.gov/genbank Accession number ON455096-ON455103.

Competing interests

Authors declare no competing interest.

Funding statement

The study was not funded but received support from Directorate of The African Centre of excellence of sustainable use of insects for food and feed (Insefoods) of Jaramogi Oginga Odinga University of Science and Technology (JOOUST).

Authors contribution

Monicah Florence Misiko data collection, data analysis and drafting the manuscript. Benson Onyango and Taurai Bere conceptualization, designing, acquisition and analysis of data. Darius Andika and Taurai Bere, general supervision analysis of data, interpretation and review. Paul Oyieng Angienda and Patrick Okoth, analysis, interpretation, uploading of molecular data to NCBI, review and editing of the manuscript. All authors discussed the results and approved the final manuscript.

Acknowledgements

We wish to acknowledge the following for their immense contribution. Mr. Zedekiah Okweny of National Museums of Kenya for the laboratory works on molecular analysis, Molecular and Infectious Disease Research (MIDR) Lab, Center of excellence in HIV medicine project, University of Nairobi team. Mr. Phillip Ochieng of Maseno University, Mr. Josephat Mwanchi of Kenya Marine and Fisheries Institute and the team from Centre for Disease Control (CDC) and Kenya Medical Research Institute (KEMRI) Kisumu for their role in sampling and laboratory analysis.

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Plate 1 is available in the Supplementary Files section.

No competing interests reported.

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Plate 1: Samples of Chironomid lavea collected from Winam gulf, Lake Victoria.

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Chironomus Species Dna Barcording in Monitoring the Pollution Levels of the Nyanza Gulf in Lake Victoria, Kenya

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Abstract

Figures

Introduction

Materials and methods

Study Area and sampling design

Sample Analyses (water, sediment and insect)

Molecular analysis of chironomids

Results

Study Area

Heavy metal concentration in water, sediment and insect samples

Characterization of chironomids-non biting midge

Pairwise distances

Discussion

Heavy metals in water, sediment and in insect samples-non biting midge larvae

Chironomidae (Non-Biting midge larvae)

Phylogenetic analyses of chironomids

Conclusion

Declarations

References

plates

Additional Declarations

Supplementary Files

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