Next Article in Journal
Angiotensin II and EDH Pathways Underlie the Vascular Sympatho-Modulation by 5-HT in Female Rats
Previous Article in Journal
Predictive Factors for Cervical Intraepithelial Neoplasia in Women with Abnormal Cytology According to Human Papillomavirus Genotype: An Observational Study
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJMS
Volume 26
Issue 19
10.3390/ijms26199613
ijms-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Genomic Signatures of Adaptive Evolution in Taenioides sp. During Northward Invasion

by
Kun Huang
,
Tianwei Liu
,
An Xu
,
Jing Yu
,
Yijing Yang
,
Jing Liu
,
Fenghui Li
,
Denghui Zhu
,
Li Gong
,
Liqin Liu
and
Zhenming Lü
*
National Engineering Laboratory of Marine Germplasm Resources Exploration and Utilization, College of Marine Sciences and Technology, Zhejiang Ocean University, Zhoushan 316022, China
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2025, 26(19), 9613; https://doi.org/10.3390/ijms26199613
Submission received: 4 August 2025 / Revised: 30 September 2025 / Accepted: 30 September 2025 / Published: 1 October 2025
(This article belongs to the Section Molecular Genetics and Genomics)
Browse Figures
Versions Notes

Abstract

The success and impact of biological invasions depend on adaptations to novel abiotic and biotic selective pressures. However, the genetic mechanisms underlying adaptations in invasive species are inadequately understood. Taenioides sp. is an invasive worm goby, originally endemic to brackish waters in the estuaries of Southeastern China, and now colonizes multiple inland freshwaters of North China within decades as a byproduct of the East Route of South-to-North Water Transfer (ESNT) project. However, the molecular mechanisms underlying their adaptations to the climate of North China, especially the temperature regime, are unknown. Here, we performed genomic resequencing analysis to assess genetic diversity and population genetic structure, and further investigated the genomic signatures of local adaptation in the invasive population of Taenioides sp. during their northward invasion. We revealed that all invasive populations exhibited no genetic differentiation but low gene flow and an obvious signal of population bottleneck. Yangtze River estuary may serve as the source population, while Gaoyou Lake serves as a potential bridgehead of the invasion. Selective sweep analyses revealed 117 genomic regions, containing 673 candidate genes, under positive selection in populations at the invasive front. Redundancy analysis suggested that local temperature variables, particularly the monthly minimum temperature, represent critical evolutionary forces in driving adaptive divergence. Functional enrichment analyses revealed that multiple biological processes, including metabolism and energy production, substance transmembrane transport, and neural development and synaptic transmission, may play important roles in adaptation to regional temperature conditions. Our findings revealed a scenario of adaptive evolution in teleost species that underpins their regional climate adaptation and successful establishment of invasive populations in a human-facilitated invasion context. Proper management strategies should be established to manage Taenioides sp invasion as soon as possible.

1. Introduction

Invasive species are a threat to native ecosystems, and can exert a rapid and severe impact on global biosafety, food security, and human health [1]. Understanding the causes of biological invasions may serve as a prerequisite to mitigate the impact of invasive species [2], but it is usually complex and requires detailed parallel field studies conducted in both native and introduced ranges to investigate how biogeographical shifts alter individual performance, population success, and the community-level impacts of the invading species [3]. An alternative step may lie in comprehending the demographic or evolutionary determinants underlying the successful establishment of invasive species, which would enable our improved predictability of their invasive potential and establish enhanced management strategies for their effective control [4]. Although several ecological or demographic determinants are known as important factors underlying the successful colonization and establishment of populations, including propagule pressure or plasticity in life history (e.g., faster growth, or younger age at first maturity) [5,6], other evolutionary factors, such as drift, selection, or adaptive phenotypic plasticity in this process, are still limitedly known [4]. However, well-documented cases have been recorded, including that of the invasive marine vertebrates cornetfish (Fistularia commersonii) [2] and invertebrate ascidian (Molgula manhattensis) [7], where the rapid evolution of adaptive loci or genomic regions facilitated rapid range expansion and invasion success. Fortunately, with the rapid progress of next-generation sequencing (NGS) technology, the use of genomic tools in invasion contexts has gained increasing recognition, and the provision of reference genomes of more and more non-model species is allowing for the accurate estimation of the role of pre- and post-introduction genetic variation, demographic history, and adaptive potential in invasion success from a genomics perspectives [3]. However, to date, the use of genomics in the context of biological invasion is still in its infancy, thus leaving many unanswered questions about the adaptive processes involved in successful invasions and associated ecological and evolutionary consequences.
Taenioides sp. is an invasive worm goby originally known to inhabit muddy bottoms of brackish waters in coastal areas of southeast China [8,9]. The estuary areas around Yangtze River and Lüsi fishing ground almost form the northernmost limits of its natural distribution, according to the historical records [10,11]. However, it has begun to invade northward recently, and successfully colonized multiple inland freshwater lakes in North China and caused severe damage to aquatic ecosystems [8,9]. The northward freshwater invasion of Taenioides sp. was hypothesized to be facilitated by the East Route of the South-to- North Water Transfer (ESNT) project, which pumps water from the lower reaches near the Yangtze River estuary and diverts it northward to resolve the water shortage problem in north China persisting since the 1960s [8,9]. The diverted water flow through several large lakes of North China, including Gaoyou Lake (GYL), Hongze Lake (HZL), Luoma Lake (LML), Nansi Lake (NSL), and finally reaching Dongping Lake (DPL) in Shandong province [8], makes these lakes the most affected areas of the invasion, except for Dongping Lake, where no colonization of Taenioides sp. populations has been recorded to date [8]. The establishment of invasive Taenioides sp. populations in these lakes occurred at an extremely rapid rate: it was first recorded in Gaoyou and Hongze lakes in the 1980s [8,10,11], and increased abundance and northward dispersal into Luoma Lake in 2005, and invaded further northward into NSL, and was first reported in the lower stream of NSL, Weishan Lake (WSL) in 2011, and gradually observed to colonize the upper stream of NSL, Zhaoyang Lake (ZYL) in 2014 [8]. As a result, within around 60 years since the 1960s, the distribution of Taenioides sp. in its invasive range has spanned the tropic and subtropic areas of Southeast China to the temperate areas of Shandong Province in North China. Such a northward invasion scenario of Taenioides sp. has offered a unique opportunity to investigate how the adaptive process has contributed to the successful colonization, regional expansion, and hence the invasion success of an invasive species. However, recent molecular studies on Taenioides sp. populations have mainly focused on characterizing the invasive origin and dispersal routes facilitated by the ESNT project [8,9]. All these attempts have primarily utilized only a few molecular markers, including mitochondrial [8] and microsatellite DNA [9]. To date, no adaptive process, including selection and adaptive phenotypic plasticity, has been characterized in invasive populations of Taenioides sp., aiming to reveal the molecular mechanism underlying their invasion success from genetic signatures, especially from a genomic point of view.
In the present study, based on our previously assembled genome of Taenioides sp., we performed whole-genomic resequencing on specimens collected from six invasive and native populations around the affected areas of ESNT projects. We also added two additional native populations around these areas as representatives of the potential invasive sources of the invasion. By performing the population genomics analysis of Taenioides sp. from its native and invasive regions, our study aimed to (1) systematically investigate the invasion process and hypothetical dispersal in the invasive range, which provides an evolutionary framework for better understanding the genetic adaptation of Taenioides sp. during their northward invasion; and (2) identify candidate loci under selection to determine their role in the northward invasion success of Taenioides sp. Our results could provide insights into how adaptive processes facilitate the successful establishment and range expansion of invasive species and thus, provide important information for establishing proper management strategies for their effective control.

2. Results

2.1. SNP and Genetic Diversity

A total of 116 fish were used, and their genomes were resequenced in the study. On average, 1.50 × 108 clean reads were yielded for each specimen and then mapped to the assembled reference genome of Taenioides sp. The mean coverage of each site was calculated, and revealed a mean depth of 29.84× (23.07~68.34×) in the mapped regions of all the samples. After a strict filtration and quality control process, a total of 6,365,664 high-quality SNP sites were generated across all samples and thereby used for the downstream analyses. Genome-wide estimates of diversity varied substantially between the native and invasive populations. In general, native populations showed much higher diversity in all statistical parameters, such as percentage of polymorphic loci (%), heterozygosity observed (Ho), heterozygosity expected (He), and nucleotide diversity (Pi), than invasive populations (Table 1), consistent with the “source population richness” phenomenon usually observed in the invasion context [4,12]. More specifically, the PYE population generally harbored the highest diversity in all parameters examined among the three native populations, while the YE population displayed the lowest. The ZYL population showed the lowest diversity in all parameters estimated among the five invasive populations, and was even the lowest across the regions, while GYL exhibited the highest diversity among the five invasive populations (Table 1). Accordingly, the genomic inbreeding coefficient (FROH) is generally higher in invasive populations than their native counterparts, with the ZYL population displaying the highest FROH value and the PYE population showing the lowest among all the studied populations (Figure 1). These results were, again, in line with the fact that the ZYL population represents the northernmost front of the invasion and likely experiences the greatest bottleneck events during the invasion.

2.2. Population Genetic Structure

Estimates of divergence among the 8 populations showed a genome-wide Fst of 0.021–0.129 across all autosomal SNPs (Table 2), suggesting low-to-moderate differentiation among the populations. Such differentiation among the populations resulted in a weak but significant population genetic structure, according to our downstream population structure analyses. Our principal component analyses (PCA) divided all genotyped samples into two specific clades, with the first two components explaining 9.96% and 5.74% of total genetic variance, respectively, according to a Tracy–Widom test (Figure 2). The first clade mainly comprised individuals from PYE populations, and the second clade primarily comprised individuals from the remaining seven populations. Such population substructure was also confirmed by the results of our admixture analyses, which also subdivided all genotyped samples into two specific clades when the number of clusters (K) was 2, i.e., optimal (Figure 3; Supplemental Figure S1). Among them, one clade was from all individuals of the PYE population, while the other clade was from the remaining seven populations. Further population substructuring was observed when K = 3, in which individuals of THL, YE, and GYL were subdivided from Clade 2 and formed a separate clade, indicating their genetic affinity between these native and invasive populations. When K = 4, the THL population was further divided from YE and GYL populations, and formed a specific clade. The YE population could be only divided from the GYL population when K = 5, also indicating their close relationship in genetic composition (Figure 3). When K = 6, individuals of HZL and LML were further subdivided from WSL and ZYL populations, and again formed a specific separate clade. A maximum likelihood (ML) tree further lent support for these differentiation patterns, with PYE (Clade 1) and the remaining 7 populations (Clade 2) reflecting the grouping of populations with high bootstrap support (100%) (Figure 4). Clade 2 included most of the (7/8) populations analyzed in this study, in which the five invasive populations of GYL, HZL, LML, WSL, and ZYL were more closely related to the native population of YE, other than THL, with YE forming the base group of the tree (Figure 4). These results indicated that YE may serve as the invasion source of the alien Taenioides sp. populations, which has also been implicated in previous studies [8,9]. However, as far as the five invasive populations were concerned, individuals of GYL more genetically resembled those of the source population YE than any other population, while individuals of HZL and LML most likely came from the GYL population, and individuals of WSL and ZYL formed the most specialized group, which likely came from the HZL and LML populations, as inferred from the ML tree (Figure 4). Such substructuring of the invasive populations accorded well with what should be expected from an invasion process through the stepping-stone dispersal model [8], possibly reflecting their historical invasion dynamic and model of gene flow among invasive populations. However, such substructuring of the invasive populations may predict limited gene flow among populations. To test our inference, an AYESASS analysis was further employed to estimate the contemporary gene flow among the native and invasive populations, and the results indicated that except for a relatively higher rate of gene flow being detected among four invasive populations (ZYL, WSL, LML, and HZ), a general low level of gene flow was found among all the native and invasive populations (Figure 5) studied.

2.3. Candidate Loci Under Selection

To identify the possible adaptive selection they may experience during their northward invasion, as expected from the heterogeneous population structure and the latitude gradients they span during the northward invasion, we first estimated the strength of positive selection or the rate of adaptive substitution (α) in Taenioides sp. populations using the standard McDonald–Kreitman test. The results revealed a comparatively low rate of adaptive substitution in invasive populations compared to their native counterparts (except for THL) (Table 3), possibly attributed to their potentially smaller population size (Ne). When the selection strength was compared within the invasive populations, the α value generally increased with latitude with the northernmost invasive populations of ZYL, SL, and LML possessing a higher rate of adaptive substitution despite their genetic bottleneck effect, indicating the possible adaptive selection they may experience during their northward invasion. To better capture these signatures of selection in invasive populations during their northward invasion, we selected ZYL and WSL at the invasive front as representatives of invasive populations, and YE as the source population to perform selective sweep analyses by using both metrics of Fst and pairwise nucleotide diversity (Pi). Our results identified 117 genomic regions displaying signatures of selection, containing 673 candidate genes under positive selection in WSL and ZYL populations (Figure 6; Supplemental Table S2). Functional enrichment (GO) analysis indicated that the top positively selected genes were significantly enriched in multiple biological processes, such as “regulation of hippo signaling”, “cell redox homeostasis”, “response to osmotic stress”, “regulation of oogenesis”, and “actin filament-based movement” (Figure 7A; Supplemental Table S3). KEGG enrichment analysis indicated that signal pathways such as “Insulin secretion (ko04911)”, “Glucagon signaling pathway (ko04922)”, “Diabetic cardiomyopathy (ko05415)”, “Polyketide sugar unit biosynthesis (ko00523)”, “Ion channels (ko04040)”, “Aldosterone synthesis and secretion (ko04925)”, “Proximal tubule bicarbonate reclamation (ko04964)”, “DNA repair and recombination proteins (ko03400)”, “Retrograde endocannabinoid signaling (ko04723)”, and “Growth hormone synthesis, secretion and action (ko04935)” were significantly enriched (p < 0.05) (Figure 7B; Supplemental Table S4), which suggested the crucial role of nutrient metabolism and energy production, osmotic regulation, DNA repairs, and hormone production and secretion in regional environmental adaptation during their northward invasion of Taenioides sp.

2.4. Gene–Environment Associations

To understand the possible role of regional temperature regime in shaping genetic differentiation and adaptive evolution among Taenioides sp. populations, we further performed an environmental association analysis based on RDA analyses. According to environmental correlation analyses (R2 > 0.7), only two water temperature variables, thetao_ltmin and thetao_mean, were preserved from the initial 6 parameters for subsequent analysis (Supplemental Figure S2). Our results indicated that the RDA was globally significant (p < 0.001) and explained about 3.32% of the total variation (adjusted R2 = 0.033). Among the two RDA axes analyzed, only RDA1 axes were significant (p =  0.001), and both of the temperature variables retained in RDA were significant (p =  0.001) as explanatory variables (Supplemental Figure S2). Considering the significantly constrained canonical axes, 11,487 unique candidate loci were finally identified, and the majority of the detected SNPs (6820; 59.37%) were strongly correlated with thetao_ltmin. The remaining candidate loci (4667; 40.63%) were associated with thetao_mean. Thetao_ltmin explained the largest proportion of RDA detections, suggesting that the minimum temperature range explained more allelic variation than any other temperature variable, and may likely play important roles in driving the genomic landscape evolution and hence regional climate adaptation of invasive Taenioides sp. populations.

2.5. Functional Annotation and Enrichment

Combining both data matrixes of RDA and selective sweep analyses, 1705 candidate SNPs were identified to be positively selected in overlap, corresponding to 51 candidate genes under positive selection (Supplemental Table S5). These positively selected genes were primarily associated with multiple biological processes, such as metabolism and energy production (G6PD, SLC25A14, CEL, PGS1), substance transmembrane transport (COG5, GOLT1B), cytoskeletal structure and organization (EFEMP1, EML6, TPRN), and neural development and synaptic transmission (SCN8A, CACNA2D1, CACNA1D, KCNQ2, SEMA3D), implying their critical roles in adaptation to regional temperature conditions, and hence the successful population colonization during their northward invasion.

3. Discussion

3.1. Genetic Diversity and Large Bottleneck at the Invasion Front

High genetic diversity usually provides populations with evolutionary advantages to adapt to new environments [13]. However, introduced populations seem to always exhibit the opposite, where reduced genetic diversity relative to native populations is typically observed [4,12]. This phenomenon was also well confirmed in our results, because a large decrease in genetic diversity was observed in our invasive populations compared to the native populations. Actually, we found radical losses of genetic diversity in invasive populations, with the largest decrease in allelic diversity of 43.68–50.29% and observed heterozygosity of 52.35–53.43% at the invasive front: ZYL and WYL populations (Table 1), a degree of decline rarely observed in invasive species. Similar genetic loss of invasive Taenioides sp. was also observed in previous surveys based on nuclear microsatellites (SSRs) [8] and mitochondrial D-loop sequences [9]. Such losses of genetic diversity observed in invasive populations are typically attributed to a genetic bottleneck associated with founder effects: a common outcome of the establishment of new invasive populations from a small number of founding individuals [8,12]. And this has been partially verified by much higher values of genomic inbreeding coefficients (FROH) observed in invasive populations than their native counterparts (Figure 1). However, despite this loss of diversity and potential limited number of founders, Taenioides sp. populations established and expanded successfully following their introductions, as observed in their well-documented invasive history [8,9]. This phenomenon reflects a well-explained “genetic paradox”, where invasive species are highly successful despite their initially low genetic diversity due to bottleneck effects [13]. However, high genetic diversity is not always a prerequisite for population establishment success in the invasion context. Marchini (2016) suggested that the depletion of genetic diversity induced by bottleneck events could also potentially increase population fitness by purging deleterious mutations, resulting in the production of vigorous inbred offspring [14], which allows for rapid population growth. This bottleneck-induced population fitness has also been observed in several invasive fish, such as Serrasalmus brandtii [15] and Micropterus salmoides [16]. In addition, invasive species with low genetic diversity can also benefit from rapid evolutionary changes that are favorable in the invaded regions, contributing to their overall fitness, and hence invasion success [2,7], though other factors such as ecological preadaptation of invasive species cannot yet be excluded [2,13]. Nevertheless, not all the invasive populations exhibited a radical reduction in genetic diversity relative to that of the native population. Some populations, such as GYL, exhibited well-restored genetic diversity, as observed in our analyses (Table 1). This aligns well with the comparatively long invasion history of Taenioides sp. in GYL, where the first spot of the invasion can be dated back to the 1980s, likely introduced as a byproduct of the Northern Jiangsu Water Transfer Project (NJWT), the precursor of the current ESNT Project which has been in place since the 1960s [8]. The restoration of genetic diversity in GYL may have arisen from multiple introductions from the source populations, which favors the accumulation of mutations and hence the genetic diversity during long-term invasion. This has made the GYL population a potential bridgehead for new water body colonization and further northward invasions. Such a genetic diversity profile and the occurrence of invasive bridgehead may have important implications in establishing where the invasive populations should be effectively controlled in the future.

3.2. Genetic Structure and the Invasion Source Inference

To mitigate the impact of invasive species, successful identification of the invasion source is important, which is largely dependent on the surveillance of population structure in both native and invasive ranges [3,4]. We detected a similar invasion pattern of Taenioides sp., as observed in previous studies based on mitochondrial [9] and microsatellite [8] datasets: the freshwater infestations in GYL were predicted to have arisen from the YE area, likely introduced as a byproduct of the ESNT project. The infestations in HZL, LML, WSL, and ZYL were likely introduced from GYL following a stepping-stone dispersal model [8]. However, previous studies were unable to achieve adequate resolution regarding the exact source of the invasion due to the marker variability, though YE has been largely implicated as the invasion source in both analyses [8,9]. Using the genome-wide approach, we clearly indicated that the native YE population was more likely to serve as a potential source for freshwater infestations than any other population. This pattern was observed in the results of our phylogenetic analyses, where all freshwater infestations aggregated in one cluster with the native population of YE (Figure 4). Such a pattern of population structure was also confirmed by our late-on structure (Figure 3) and PCA analyses (Figure 2), where the genetic composition of freshwater infestations more resembled that of YE than rest of the native populations. Though the THL population was also clustered with freshwater infestations in our phylogenetic analysis (Figure 4), its genetic composition was not closely related to invasive populations like the YE population was (Figure 2, Figure 3 and Figure 4). In addition, it also seems reasonable that THL should be expelled as the invasion source when we consider the water body connection situation and invasion history of Taenioides sp. in affected areas. Firstly, there are no direct water body connections between THL and the affected lakes along the ESNT route, which makes invasion unlikely via individual dispersal. Secondly, the earliest record of Taenioides sp. in THL could be dated back to the 1960s [17], but no freshwater invasions have ever been recorded nearby, except for some occasional spots in the connecting water bodies [18]. This may indicate that the dispersal capacity of the THL population is somewhat low, which is unlikely to underpin fast regional colonization and hence population expansion through natural diffusion in an invasion context. Other invasion sources and driving factors, such as the ESNT project must be required to facilitate the dispersal and regional colonization of Taenioides sp. during their northward invasion. Even under the facilitation of the ESNT project, the active dispersal of Taenioides sp. also seemed limited, as inferred from the substructuring of invasive populations and low gene flow among them. Similar patterns of genetic differentiation and weak gene flow were also observed among pikeperch populations in river canal systems in previous studies [19]. The low active dispersal with limited gene flow among invasive populations could partially be attributed to the deep-burrowing lifestyle of Taenioides sp. [10,11]. In addition, the obvious seasonality of the water transfer in the ESNT project, in which the water was mainly transferred during the late winter to spring (December to May) [20], a period that has largely missed the spawning peak of Taenioides sp. (June to July) around this region [21], may further constrain the active dispersal and hence gene flow among Taenioides sp. populations through planktonic larvae. Such source and sink relationships, and the dynamics of gene flow among Taenioides sp. populations may have good implications for understanding the genetic mechanisms underlying Taenioides sp. invasion and thus making sound measurements for mitigating the impact of Taenioides sp. invasion in the future.

3.3. Adaptive Evolution Underlying the Northward Invasions

Though our phylogenetic analyses revealed much genetic similarity between the source and invasive populations of Taenioides sp. (Figure 4), adaptive changes could also accumulate at the invasion front via natural selection expected from the contrasting environments they inhabit. Consistent with this inference, our McDonald– Kreitman analysis revealed a higher strength of positive selection in northernmost invasive populations compared to their lower-latitude counterparts (Table 3), despite the larger genetic bottleneck effect they may experience during the northward invasion (Figure 1). Such a genetic bottleneck effect would have resulted in a much lower selection strength in populations [22,23]. Therefore, the observed higher selection strength in northernmost invasive populations may indicate the possible adaptive selection they experience during their northward invasion, though the overall strength of the selection was somewhat low (0.101–0.136) compared with other species [24]. To identify the exact genome-wide patterns of divergence and the possible signature of adaptations in the genome during their northward invasion, our selective sweep analyses revealed substantial genomic regions and candidate genes under positive selection in WSL and ZYL populations at the invasion front (Figure 6; Supplemental Table S2). The functional enrichment analysis revealed that the top positively selected genes were significantly enriched in pathways associated with nutrient metabolism and energy production, osmotic regulation, DNA repair, and hormone production and secretion (Figure 7; Supplemental Tables S3 and S4), indicating their crucial roles in regional climate adaptations in the invasive populations. Indeed, osmotic pressure may represent an important selective force during freshwater invasion from the estuary area. Therefore, for the adaptive evolution of the genomic regions associated with nutrient metabolism and energy production, osmotic regulation would possibly contribute to the enhanced energy production and osmotic regulatory ability, which would improve fitness at the hypotonic environment [25,26] of the invasion front. However, osmotic selection alone cannot explain why there were always intervals between each colonization of the affected freshwater where no salinity gradient is available. For instance, Taenioides sp. has colonized GYL, LML, WSL, and ZYL in 1980, 2005, 2011, and 2014, respectively, during their northward invasion [8]. Therefore, other environmental parameters, such as thermal conditions, at the invasion front may also play important roles, as expected from the latitude gradients they span during the northward invasion. To confirm this inference, we further performed an environmental association analysis based on RDA using temperature as the variable. Our results revealed 6820 and 4667 SNPs that were strongly correlated with the two temperature variables of thetao_ltmin and thetao_mean, respectively, in sampling sites, with the majority of SNPs closely related to a lower temperature (Supplemental Figure S2). Combining both data matrices of RDA and selective sweep analyses, 1705 candidate SNPs containing 51 candidate genes were identified to be positively selected in overlap (Supplemental Table S5). These positively selected genes were primarily associated with multiple biological processes, such as metabolism and energy production (G6PD, SLC25A14, CEL, PGS1), substance transmembrane transport (COG5, GOLT1B), cytoskeletal structure and organization (EFEMP1, EML6, TPRN), and neural development and synaptic transmission (SCN8A, CACNA2D1, CACNA1D, KCNQ2, SEMA3D), implying their important roles in adaptation to regional thermal conditions during their northward invasion.
Among them, G6PD, SLC25A14, and PGS1 are important candidates associated with mitochondrial genesis and aerobic respiration [27,28,29], while CEL is primarily associated with the hydrolysis of fatty acids and hence energy production [30], which may occur in thermal adaptation function. The most convincing candidate is G6PD for its encoding enzyme that catalyzes the rate-limiting step of the oxidative pentose–phosphate pathway, which is indispensable for maintaining the normal function of carbohydrate dissimilation and glycolysis, and hence aerobic energy production in mitochondria [29]. Mutations or abnormal expression of G6PD would cause severe alteration in energy production and thermal resistance in both vertebrates [31] and invertebrates [32]. In addition, adaptive alteration in G6PD has been observed to result in higher glycolysis catalytic efficiency and extreme low-temperature resistance in Antarctic fishes [33,34]. The apparent positive selection in these mitochondrion-associated genes in Taenioides sp. at the invasive front may reflect different energy requirements in adaptation to changed thermal conditions expected from the latitude gradient, although their actual role still awaits further verification. In addition, several candidate genes enriched in pathways of neural development and synaptic transmission have also been observed in invasive populations, indicating their roles in enhanced thermal adaptation and hence the success of northward invasion. This is consistent with what has already been observed in multiple teleost species, where neuronal signaling is closely related to their capacity for thermal tolerance [35,36], though the exact mechanism remains to be elucidated [37]. However, recent findings in nematode [38,39] and Drosophila [40] have begun to unravel such mysteries, and revealed that neuronal signaling could regulate the animal’s thermal tolerance through abnormal thermosensation by the thermosensory neurons, or via some neural-mediated metabolic pathways (e.g., insulin signaling), which led to altered thermal tolerance. Take KCNQ2, for instance: it encodes a KCNQ-type potassium channel abundantly expressed in nociceptive cold-sensing trigeminal ganglion neurons, and plays a critical role in regulating cold sensitivity in Caenorhabditis elegans [39]. Mutation of KCNQ2 has been observed to cause abnormal thermosensation in ADL chemosensory neurons, and thus supranormal cold acclimation in the animal [39]. Although the close links between neuronal signaling and thermal tolerance are less well established in vertebrates, KCNQ2 was also implicated in cold sensing and acclimation in rats [41]. Therefore, the observed positive selection in KCNQ2 may have implications in new thermal regime adaptation at the invasion front, although the causative effect of these mutations still awaits further verification.
Interestingly, we also identified one positively selected gene (ZP3) that is associated with reproduction in populations at the invasion front (Supplemental Table S5). It encodes a protein at the cell surface or zona pellucida of oocytes, which are essential for species-specific gamete recognition and fertilization in reproduction [42]. However, accumulating literature has revealed that ZP3 may also play important roles in the cold tolerance of teleosts due to their binding affinity to ice, and thus potentially depressing the melting point of a solution in the cell at freezing conditions [43]. The adaptive evolution of ZP3 has also been identified in several fish species inhabiting higher latitudes or Antarctic regions, and is largely implicated in their acclimation to the regional cold climate they inhabit [43,44]. Therefore, the obvious selection of ZP3 in invasive populations may indicate that it also functions in regional climate adaptation in teleost species inhabiting a normal temperature regime, though, again, their actual role still awaits further verification. In conclusion, our results revealed a scenario of adaptive evolution in teleost species that underpin their regional climate adaptation and fast population colonization in a human-facilitated invasion context. Proper management strategies, such as the monitoring and physical removal of the bridgehead GYL population (e.g., using local fyke nets), or installation of exclusion structures (e.g., benthic fish screen) near the pump area, are, therefore, urgently needed to prevent their potential further northward expansion and mitigate the impact of invasion in the future.

4. Materials and Methods

4.1. Sample Collection and Genomic DNA Extraction

A total of 116 Taenioides sp. samples were collected from 5 invasive (GYL, HZL, LML, WSL, and ZYL) and 3 native populations (Yangze estuary, YE, Pingyang aojiang estuary, PYE, and Taihu lake, THL) using benthic fyke nets from April to May, 2023 (Figure 8, Table 1). Muscle tissues were collected, and the samples were stored in liquid nitrogen until use.
A Blood and Cell Culture DNA Mini Kit (QIAGEN, Hilden, Germany, cat. no. 13343) was thereafter used to isolate the genomic DNA from muscle tissues. The quality and concentration of the isolated DNA were evaluated using a Pultton DNA/Protein Analyzer (Plextech, New York, NY, USA) and stored at −80 °C prior to downstream library construction and sequencing. All the tissue sampling and DNA extraction procedures conformed to the ethical regulations formulated by the Institutional Animal Care and Use Committee of Zhejiang Ocean University (ZJOU).

4.2. Whole-Genome Resequencing and SNP Detection

A VAHTS Universal Plus DNA Library prep Kit (Illumina, San Diego, CA, USA) was used to construct a 150 bp paired-end sequencing library with an insertion size of 350 bp for each Taenioides sp. specimen. The libraries were thereby sequenced on a DNBSEQ-T7 platform (MGI Technology Co., Ltd., Shenzhen, China) with an expected target coverage of ~20×. The obtained raw sequence data were uploaded into the Sequence Read Archive (SRA) of NCBI with the BioProject accession number of JNA1290502. A Trimmomatic (version 0.39) [45] software was then used for the quality control of the raw data to remove adapters and low-quality reads with a base quality ≤ 20. The obtained clean data for each sample were then mapped to the reference genome of Taenioides sp. (figshare at: https://doi.org/10.6084/m9.figshare.22799645), previously assembled in our library using BWA (version 0.7.18) [46] with the parameter mem -M -k 19. The sequencing coverage and depth were then calculated using SAMtools (version 0.1.20) [47] software with the mapping data, and the potential PCR duplicates were removed using the command “MarkDuplicates” in PICARD (http://broadinstitute.github.io/picard/ accessed on 25 April 2025). SNP calling was thereafter performed using the Haplotype Caller protocol in Genome Analysis Toolkit (GATK, version 4.5.0.0) software [48], and we removed the SNPs with alow frequency of allele (MAF < 0.05) and low coverage (<10×) at the population level using GATK Variant Filtration to guarantee the reliability of the called SNPs. The remaining SNPs were then used for the downstream estimates.

4.3. Population Genetic Structure Inference

To infer the possible invasion process of Taenioides sp. within their invasive range, we first evaluated the profile of genetic diversity among populations along the route of the ESNT project by calculating the genome-wide diversity statistics (e.g., percentage of polymorphic loci (%), heterozygosity observed (Ho), heterozygosity expected (He), and nucleotide diversity (Pi)) using STACKS (version 2.68) [49]. Afterward, the genomic inbreeding based on ROH (FROH) was also estimated using PLINK (version 1.9) [50] to quantify how the observed genetic diversity was attributed to inbreeding, possibly due to a bottleneck in the invasive populations. To this end, the eligible SNPs with MAF ≥0.01 were first filtered for each population. Long homozygous fragments were scanned in the pruned data using PLINK according to the following parameters: −homozyg-snp 50; −homozyg-window-missing 5; −homozyg-window-het 3; −homozyg kb 300; −homozyg-density 50; −homozyg-gap 1000; −homozyg window-threshold 0.05. The genomic inbreeding coefficient based on ROH (FROH) was thereafter evaluated using PLINK according to standard methods [51]. In addition, the phylogenetic relationships and genetic structure among the populations were evaluated by clustering analyses performed using ADMIXTURE (version 1.3) [52]. To this end, sites with data missing less than 10% were used, and the number of coancestry clusters (K) was set ranging from 1 to 8. In addition, principal component analysis (PCA) was also conducted using PLINK (version 1.9) package [51] with the parameter “--−pca 10” using all SNPs. To adopt a phylogenetic perspective in the population structure inference, maximum likelihood (ML) trees were also constructed based on whole genome-wide SNPs using RAxML-NG (version 1.2.2) software [53], employing a rapid bootstrap procedure of 1000 replicates. To further estimate the magnitude of gene flow among the populations, BAYESASS 3.0 [54] was applied to calculate contemporary migration rates among populations. To this end, 90,000,000 iterations, with a burn-in of 10,000,000 iterations, and a sampling frequency of 2000 were used to ensure that the model’s starting parameters were sufficiently random. Five runs using different starting seed values were performed to ensure consistency between runs.

4.4. Genome-Wide Scan of Adaptive Signals

To identify the possible adaptive selection they may experience during their northward invasion, we first estimated the strength of positive selection, or the rate of adaptive substitution (α) in each population by using the classical McDonald–Kreitman test [55]. Briefly, the intraspecific diversity and divergence counts compared to the outgroup Odontamblyopus Rebecca (GenBank accession number: ASM3068695v1) were created from a multisequence alignment of single-copy orthologs. These alignments were first concatenated and then used as the input for imkt version 0.2 [56] to perform standard MKTs [56]. Secondly, to identify the exact genome-wide patterns of divergence and the possible signature of adaptations in the genome during their northward invasion, a combined approach involving both the Fst and genetic diversity (Pi) indexes was employed to define candidate-selected loci in the genome-wide regions in populations at the invasion front, relative to the source population. To this end, VCFtools (version 0.1.16) [57] was first used to estimate the Fst value across the genome to define candidate-selected regions using sliding windows of 100 kb (with a step size of 10 kb) if they contained window-based Fst values above the 95th percentile of the empirical distribution. Afterward, the Pi value in the same sliding windows was also calculated to identify windows above the 95th percentile of the Pi distribution. Putative selected regions were thereby identified as the overlapped windows with the top 5% of Fst and log2 (Pi) values [58]. The putatively selected regions were afterward mapped to the corresponding SNPs and genes in the genome. The enrichment of these putatively selected genes (PSGs) was also performed using Enrich GO and Rscript. A threshold of p-values < 0.05 was used to define the significantly over-represented GO terms and KEGG pathways.

4.5. Genotype–Environment Association Analyses

Water temperature may vary substantially among the lakes along the ESNT route (e.g., the monthly minimum lake surface water temperature varies from ~1.69 °C in ZYL and WSL to ~6.81 °C in YE, see Supplemental Table S1) due to the latitude gradients they span, and potentially exert large selective pressure on the Taenioides sp. genome. To understand the possible evolutionary changes associated with water–temperature adaptation in invasive populations of Taenioides sp., we further performed an environmental association analysis based on Redundancy Analysis (RDA) following the approach detailed in Dixon (2003) [59]. Briefly, we first downloaded a total of 6 water temperature variables, including monthly maximum lake surface water temperature (thetao_ltmax), monthly minimum lake surface water temperature (thetao_ltmin), annual maximum lake surface water temperature (thetao_max), annual minimum lake surface water temperature (thetao_min), annual mean lake surface water temperature (thetao_mean), and annual range of lake surface water temperature (thetao_range) in these sampling localities, which are the only water parameters available in these lakes in database of the National Earth System Science Data Center (https://www.geodata.cn accessed on 18 March 2025). To avoid theredundancy of these environmental factors, the “pairs.panels” function in R package was used to calculate the correlation among these 6 water temperature variables. For environmental factors with a correlation greater than 0.7, only one factor was retained. RDA analysis was then performed based on all the SNPs called and the preserved water temperature variables using vegan v2.6-10 program in R package [48]. To this end, the permutation was set to 999 by default, and the standard deviation was set to 3.5 (two-tailed p-value = 0.0005) [60] to reduce the false positive rate, and the candidate-adaptive SNPs were classified according to their highest correlation with different environmental factors.

4.6. Functional Annotation of Adaptive Loci

The highly selected regions detected by FST-Pi were then overlapped with the adaptive SNPs identified by RDA using the BEDtools software (version 2.31.1) [61], and only the highly adaptive regions and SNPs that were detected by both analyses were recognized as the real adaptive loci. The candidate genes corresponding to these adaptive regions and SNPs were thereby extracted based on the annotation (gff) file of the assembled genome using BEDtools. The GO and KEGG enrichment for these candidate-selected genes was then performed via Enrich GO and Rscript. Significantly over-represented GO terms and KEGG pathways were then identified with a p-value of <0.05.

5. Conclusions

In conclusion, our study demonstrated the strengths of using genomic tools to accurately estimate the role of pre- and post-introduction genetic variation, demographic history, and adaptive potential in invasion success from a genomics perspective. Using large-scale genomic re-sequencing data, we revealed that YE may serve as the invasion source, and the ESNT project serves as the main driving force facilitating their northward invasion of Taenioides sp. Adaptive evolution has been identified in invasive populations, despite their young invasive history, which enables their rapid range expansion, and hence their northward invasion. Regional climate, particularly the monthly minimum temperature, represented critical evolutionary forces that drive the adaptive divergence of the invasive population. Our results, for the first time, reveal a scenario of adaptive evolution in teleost species that underpin their regional climate adaptation and successful establishment of invasive populations in a human-facilitated invasion context. Our findings not only provide valuable information for accurately predicting the potential risks that hydraulic projects bring about in terms of biological invasion, but also contribute to establishing enhanced management strategies for the effective control of invasive Taenioides sp. in the future.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijms26199613/s1.

Author Contributions

Z.L. conceived and supervised the project; T.L., A.X., J.Y. and Y.Y. collected the samples and carried out the experiments; J.L., F.L., D.Z. and L.G. performed bioinformatic analyses; Z.L., K.H. and L.L. wrote the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Natural Science Foundation of China (NSFC): 42171069 and 41976121.

Institutional Review Board Statement

All tissue sampling and DNA isolation procedures in this paper conforms to all relevant ethical regulations provided by the Institutional Animals Care and Use Committee of Zhejiang Ocean University. All procedures were conducted in accordance with the Institutional Animals Care and Use Committee of Zhejiang Ocean University (Protocol Code: ZOUMGREU-2025-000114; Approval 24 January 2025).

Informed Consent Statement

Not applicable.

Data Availability Statement

All raw genome re-sequencing data for Taenioides sp. genome are deposited at the NCBI in the sequence read archive (SRA) under accession number BioProject number: PRJNA1290502.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Jin, M.; North, H.L.; Peng, Y.; Liu, H.; Liu, B.; Pan, R.; Zhou, Y.; Zheng, W.; Liu, K.; Yang, B. Adaptive evolution to the natural and anthropogenic environment in a global invasive crop pest, the cotton bollworm. Innov. 2023, 4, 100454. [Google Scholar] [CrossRef]
  2. Bernardi, G.; Azzurro, E.; Golani, D.; Miller, M.R. Genomic signatures of rapid adaptive evolution in the bluespotted cornetfish, a Mediterranean Lessepsian invader. Mol. Ecol. 2016, 25, 3384–3396. [Google Scholar] [CrossRef]
  3. Li, H.; Wang, J.; Peng, Y.; Guo, C.; Qu, W.; Yang, N.; Zhu, Y.; Jeong, I.; Li, X.; Ghanim, M. Invasion genomics uncover complex introduction patterns of the globally invasive whitefly, Bemisia tabaci MED. Divers. Distrib. 2023, 29, 1172–1189. [Google Scholar] [CrossRef]
  4. Baltazar-Soares, M.; Blanchet, S.; Cote, J.; Tarkan, A.S.; Záhorská, E.; Gozlan, R.E.; Eizaguirre, C. Genomic footprints of a biological invasion: Introduction from Asia and dispersal in Europe of the topmouth gudgeon (Pseudorasbora parva). Mol. Ecol. 2020, 29, 71–85. [Google Scholar] [CrossRef] [PubMed]
  5. Britton, J.R.; Gozlan, R.E. How many founders for a biological invasion? Predicting introduction outcomes from propagule pressure. Ecology 2013, 94, 2558–2566. [Google Scholar] [CrossRef] [PubMed]
  6. Simberloff, D. The role of propagule pressure in biological invasions. Annu. Rev. Ecol. Evol. Syst. 2009, 40, 81–102. [Google Scholar] [CrossRef]
  7. Chen, Y.; Gao, Y.; Huang, X.; Li, S.; Zhan, A. Local environment-driven adaptive evolution in a marine invasive ascidian (Molgula manhattensis). Ecol. Evol. 2021, 11, 4252–4266. [Google Scholar] [CrossRef]
  8. Qin, J.; Bjorn Victor, S.; Zhang, L.; Cheng, F.; Xie, S. Patterns of genetic diversity: Stepping-stone dispersal of an invasive fish introduced by an inter-basin water transfer project. Freshw. Biol. 2022, 67, 2078–2088. [Google Scholar] [CrossRef]
  9. Sun, C.; Lü, Z.; Fang, J.; Yao, C.; Zhao, S.; Liu, Y.; Gong, L.; Liu, B.; Liu, L.; Liu, J. Population structure of Taenioides sp. (Gobiiformes, Gobiidae) reveals their invasion history to inland waters of China based on mitochondrial DNA control region. ZooKeys 2024, 1203, 239. [Google Scholar] [CrossRef]
  10. Ni, Y.; Wu, H.L. Fishes in the Jiangsu Province; China Agriculture Press: Beijing, China, 2006; p. 699. [Google Scholar]
  11. Wu, H.; Zhong, J. Fauna Sinica: Osteichthyes Perciformes (V), Gobioidei; Science Press: Beijing, China, 2008; p. 747. [Google Scholar]
  12. Dlugosch, K.M.; Parker, I.M. Founding events in species invasions: Genetic variation, adaptive evolution, and the role of multiple introductions. Mol. Ecol. 2008, 17, 431–449. [Google Scholar] [CrossRef]
  13. Cui, M.; Roe, A.D.; Boyle, B.; Keena, M.; Wu, Y.; Braswell, W.E.; Smith, M.T.; Gasman, B.; Shi, J.; Javal, M. Tracking the North American Asian longhorned beetle invasion with genomics. Evol. Appl. 2024, 17, e70036. [Google Scholar] [CrossRef] [PubMed]
  14. Marchini, G.L.; Sherlock, N.C.; Ramakrishnan, A.P.; Rosenthal, D.M.; Cruzan, M.B. Rapid purging of genetic load in a metapopulation and consequences for range expansion in an invasive plant. Biol. Invasions 2016, 18, 183–196. [Google Scholar] [CrossRef]
  15. Teixeira, D.F.; Neto, F.R.A.; Gomes, L.C.; Beheregaray, L.B.; Carvalho, D.C. Invasion dynamics of the white piranha (Serrasalmus brandtii) in a Neotropical river basin. Biol. Invasions 2020, 22, 983–995. [Google Scholar] [CrossRef]
  16. Hargrove, J.S.; Weyl, O.L.; Austin, J.D. Reconstructing the introduction history of an invasive fish predator in South Africa. Biol. Invasions 2017, 19, 2261–2276. [Google Scholar] [CrossRef]
  17. Ni, Y.; Zhu, C.D. Fishes of Taihu Lake; Shanghai Scientific and Technical Publishers: Beijing, China, 2005; p. 255. [Google Scholar]
  18. Yuan, C.M.; Xie, H.G. Freshwater Fishes in Jiangsu Province; Jiangsu Scientific and Technical Publishers: Nanjing, China, 1987; p. 315. [Google Scholar]
  19. Poulet, N.; Balaresque, P.; Aho, T.; Björklund, M. Genetic structure and dynamics of a small introduced population: The pikeperch, Sander lucioperca, in the Rhône delta. Genetica 2009, 135, 77–86. [Google Scholar] [CrossRef]
  20. Zhang, Z.F.; Zhang, X.; Xu, J.; Zhang, N.; Liu, Z.H.; Zhang, Y.H. Characteristics and influencing factors of planktonic crustacean community in Dongping Lake under the operation of the South-to-North water diversion project. Oceanol. Limnol. Sin. 2025. in print. Available online: https://link.cnki.net/urlid/37.1149.P.20250922.0938.002 (accessed on 3 August 2025).
  21. Liang, Y.Y.; Fang, T.; Li, J.; Yang, K.; Zhao, X.X.; Cui, K. Age, growth and reproductive traits of invasive goby Taenioides cirratus in the Chaohu Lake, China. J. Appl. Ichthyol. 2020, 36, 219–226. [Google Scholar] [CrossRef]
  22. Raas, M.W.D.; Dutheil, J.Y. The rate of adaptive molecular evolution in wild and domesticated Saccharomyces cerevisiae populations. Mol. Ecol. 2024, 33, e16980. [Google Scholar] [CrossRef]
  23. Gossmann, T.I.; Keightley, P.D.; Eyre-Walker, A. The effect of variation in the effective population size on the rate of adaptive molecular evolution in eukaryotes. Genome Biol. Evol. 2012, 4, 658–667. [Google Scholar] [CrossRef]
  24. Angst, P.; Ebert, D.; Fields, P.D. Demographic history shapes genomic variation in an intracellular parasite with a wide geographical distribution. Mol. Ecol. 2022, 31, 2528–2544. [Google Scholar] [CrossRef]
  25. Ruan, S.; Lu, Z.; Huang, W.; Zhang, Y.; Shan, X.; Song, W.; Ji, C. Renal metabolomic profiling of large yellow croaker Larimichthys crocea acclimated in low salinity waters. Comp. Biochem. Physiol. Part D Genom. Proteom. 2023, 46, 101083. [Google Scholar] [CrossRef]
  26. Liu, Y.; Gu, L.; Zhao, J.; Liu, M.; Wang, K.; Zhou, Q.; Cao, Y.; Hu, R.; Wang, W.; Liu, Q. Comprehensive multi-omics and biochemical analysis to elucidate the molecular response mechanisms of gill and kidney tissues under acute salinity stress in Pseudobagras ussuriensis. BMC Genom. 2025, 26, 590. [Google Scholar] [CrossRef] [PubMed]
  27. Džugasová, V.; Obernauerová, M.; Horváthová, K.; Vachová, M.; Žáková, M.; Šubík, J. Phosphatidylglycerolphosphate synthase encoded by the PEL1/PGS1 gene in Saccharomyces cerevisiae is localized in mitochondria and its expression is regulated by phospholipid precursors. Curr. Genet. 1998, 34, 297–302. [Google Scholar] [CrossRef] [PubMed]
  28. Gorgoglione, R.; Porcelli, V.; Santoro, A.; Daddabbo, L.; Vozza, A.; Monné, M.; Di Noia, M.A.; Palmieri, L.; Fiermonte, G.; Palmieri, F. The human uncoupling proteins 5 and 6 (UCP5/SLC25A14 and UCP6/SLC25A30) transport sulfur oxyanions, phosphate and dicarboxylates. Biochim. Biophys. Acta (BBA)-Bioenerg. 2019, 1860, 724–733. [Google Scholar] [CrossRef] [PubMed]
  29. Li, B.; Wang, X.; Yu, M.; Yang, P.; Wang, W. G6PD, bond by miR-24, regulates mitochondrial dysfunction and oxidative stress in phenylephrine-induced hypertrophic cardiomyocytes. Life Sci. 2020, 260, 118378. [Google Scholar] [CrossRef]
  30. Fjeld, K.; Beer, S.; Johnstone, M.; Zimmer, C.; Mössner, J.; Ruffert, C.; Krehan, M.; Zapf, C.; Njølstad, P.R.; Johansson, S. Length of variable numbers of tandem repeats in the carboxyl ester lipase (CEL) gene may confer susceptibility to alcoholic liver cirrhosis but not alcoholic chronic pancreatitis. PLoS ONE 2016, 11, e0165567. [Google Scholar] [CrossRef]
  31. Sohn, J.H.; Ji, Y.; Cho, C.-Y.; Nahmgoong, H.; Lim, S.; Jeon, Y.G.; Han, S.M.; Han, J.S.; Park, I.; Rhee, H.-W. Spatial regulation of reactive oxygen species via G6PD in brown adipocytes supports thermogenic function. Diabetes 2021, 70, 2756–2770. [Google Scholar] [CrossRef]
  32. Liu, J.; Sun, Z.; Wang, Z.; Peng, Y. A comparative transcriptomics approach to analyzing the differences in cold resistance in Pomacea canaliculata between Guangdong and Hunan. J. Immunol. Res. 2020, 2020, 8025140. [Google Scholar] [CrossRef]
  33. Ciardiello, M.A.; Camardella, L.; di Prisco, G. Glucose-6-phosphate dehydrogenase from the blood cells of two Antarctic teleosts: Correlation with cold adaptation. Biochim. Biophys. Acta (BBA)-Protein Struct. Mol. Enzymol. 1995, 1250, 76–82. [Google Scholar] [CrossRef]
  34. Ciardiello, M.A.; Camardella, L.; Carratore, V.; di Prisco, G. Enzymes in Antarctic fish: Glucose-6-phosphate dehydrogenase and glutamate dehydrogenase. Comp. Biochem. Physiol. Part A Physiol. 1997, 118, 1031–1036. [Google Scholar] [CrossRef]
  35. Liang, L.; Chang, Y.; He, X.; Tang, R. Transcriptome analysis to identify cold-responsive genes in amur carp (Cyprinus carpio haematopterus). PLoS ONE 2015, 10, e0130526. [Google Scholar] [CrossRef]
  36. Udayantha, H.; Lee, S.; Liyanage, D.; Lim, C.; Jeong, T.; Omeka, W.; Yang, H.; Kim, G.; Kim, J.; Lee, J. Identification of candidate variants and genes associated with temperature tolerance in olive flounders by Genome-Wide Association Study (GWAS). Aquaculture 2023, 576, 739858. [Google Scholar] [CrossRef]
  37. Lü, Z.; Liu, T.; Liu, Y.; Wang, Y.; Liu, J.; Liu, B.; Gong, L.; Liu, L. Climate adaptation and drift shape the genomes of two eel-goby sister species endemic to contrasting latitude. Animals 2023, 13, 3240. [Google Scholar] [CrossRef]
  38. Cho, S.-W.; Choi, K.Y.; Park, C.-S. A new putative cyclic nucleotide-gated channel gene, cng-3, is critical for thermotolerance in Caenorhabditis elegans. Biochem. Biophys. Res. Commun. 2004, 325, 525–531. [Google Scholar] [CrossRef]
  39. Okahata, M.; Wei, A.D.; Ohta, A.; Kuhara, A. Cold acclimation via the KQT-2 potassium channel is modulated by oxygen in Caenorhabditis elegans. Sci. Adv. 2019, 5, eaav3631. [Google Scholar] [CrossRef]
  40. Himmel, N.J.; Letcher, J.M.; Sakurai, A.; Gray, T.R.; Benson, M.N.; Donaldson, K.J.; Cox, D.N. Identification of a neural basis for cold acclimation in Drosophila larvae. Iscience 2021, 24, 102657. [Google Scholar] [CrossRef]
  41. Ling, J.; Erol, F.; Gu, J.G.G. Role of KCNQ2 channels in orofacial cold sensitivity: KCNQ2 upregulation in trigeminal ganglion neurons after infraorbital nerve chronic constrictive injury. Neurosci. Lett. 2018, 664, 84–90. [Google Scholar] [CrossRef]
  42. Buffone, M.G.; Zhuang, T.; Ord, T.S.; Hui, L.; Moss, S.B.; Gerton, G.L. Recombinant mouse sperm ZP3-binding protein (ZP3R/sp56) forms a high order oligomer that binds eggs and inhibits mouse fertilization in vitro. J. Biol. Chem. 2008, 283, 12438–12445. [Google Scholar] [CrossRef] [PubMed]
  43. Cao, L.; Huang, Q.; Wu, Z.; Cao, D.-d.; Ma, Z.; Xu, Q.; Hu, P.; Fu, Y.; Shen, Y.; Chan, J. Neofunctionalization of zona pellucida proteins enhances freeze-prevention in the eggs of Antarctic notothenioids. Nat. Commun. 2016, 7, 12987. [Google Scholar] [CrossRef] [PubMed]
  44. Spies, I.; Drinan, D.P.; Petrou, E.L.; Spurr, R.; Tarpey, C.; Hartinger, T.; Larson, W.; Hauser, L. Evidence for selection and spatially distinct patterns found in a putative zona pellucida gene in Pacific cod, and implications for management. Ecol. Evol. 2021, 11, 16661–16679. [Google Scholar] [CrossRef] [PubMed]
  45. Bolger, A.M.; Lohse, M.; Usadel, B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 2014, 30, 2114–2120. [Google Scholar] [CrossRef]
  46. Li, H.; Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 2009, 25, 1754–1760. [Google Scholar] [CrossRef]
  47. Li, H.; Handsaker, B.; Wysoker, A.; Fennell, T.; Ruan, J.; Homer, N.; Marth, G.; Abecasis, G.; Durbin, R.; Subgroup, G.P.D.P. The sequence alignment/map format and SAMtools. Bioinformatics 2009, 25, 2078–2079. [Google Scholar] [CrossRef] [PubMed]
  48. McKenna, A.; Hanna, M.; Banks, E.; Sivachenko, A.; Cibulskis, K.; Kernytsky, A.; Garimella, K.; Altshuler, D.; Gabriel, S.; Daly, M. The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010, 20, 1297–1303. [Google Scholar] [CrossRef] [PubMed]
  49. Catchen, J.; Hohenlohe, P.A.; Bassham, S.; Amores, A.; Cresko, W.A. Stacks: An analysis tool set for population genomics. Mol. Ecol. 2013, 22, 3124–3140. [Google Scholar] [CrossRef] [PubMed]
  50. McQuillan, R.; Leutenegger, A.L.; Abdel-Rahman, R.; Franklin, C.S.; Pericic, M.; Barac-Lauc, L.; Smolej-Narancic, N.; Janicijevic, B.; Polasek, O.; Tenesa, A.; et al. Runs of Homozygosity in European populations. Am. J. Hum. Genet. 2008, 83, 359–372. [Google Scholar] [CrossRef]
  51. Chang, C.C.; Chow, C.C.; Tellier, L.C.A.M.; Vattikuti, S.; Purcell, S.M.; Lee, J.J. Second-Generation PLINK: Rising to the Challenge of Larger and Richer Datasets. Gigascience 2015, 4, s13742-015. [Google Scholar] [CrossRef]
  52. Alexander, D.H.; Novembre, J.; Lange, K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 2009, 19, 1655–1664. [Google Scholar] [CrossRef]
  53. Kozlov, A.M.; Darriba, D.; Flouri, T.; Morel, B.; Stamatakis, A. RAxML-NG: A fast, scalable and user-friendly tool for maximum likelihood phylogenetic inference. Bioinformatics 2019, 35, 4453–4455. [Google Scholar] [CrossRef]
  54. Wilson, G.A.; Rannala, B. Bayesian inference of recent migration rates using multilocus genotypes. Genetics 2003, 163, 1177–1191. [Google Scholar] [CrossRef]
  55. McDonald, J.H.; Kreitman, M. Adaptive protein evolution at the Adh locus in Drosophila. Nature 1991, 351, 652–654. [Google Scholar] [CrossRef]
  56. Murga-Moreno, J.; Coronado-Zamora, M.; Hervas, S.; Casillas, S.; Barbadilla, A. iMKT: The integrative McDonald and Kreitman test. Nucleic Acids Res. 2019, 47, W283–W288. [Google Scholar] [CrossRef]
  57. Danecek, P.; Auton, A.; Abecasis, G.; Albers, C.A.; Banks, E.; DePristo, M.A.; Handsaker, R.E.; Lunter, G.; Marth, G.T.; Sherry, S.T. The variant call format and VCFtools. Bioinformatics 2011, 27, 2156–2158. [Google Scholar] [CrossRef]
  58. Zhang, H.; Zhang, X.; Li, M.; Yang, Y.; Li, Z.; Xu, Y.; Wang, H.; Wang, D.; Zhang, Y.; Wang, H. Molecular mapping for fruit-related traits, and joint identification of candidate genes and selective sweeps for seed size in melon. Genomics 2022, 114, 110306. [Google Scholar] [CrossRef]
  59. Dixon, P. VEGAN, a package of R functions for community ecology. J. Veg. Sci. 2003, 14, 927–930. [Google Scholar] [CrossRef]
  60. Faske, T.M.; Agneray, A.C.; Jahner, J.P.; Sheta, L.M.; Leger, E.A.; Parchman, T.L. Genomic and common garden approaches yield complementary results for quantifying environmental drivers of local adaptation in rubber rabbitbrush, a foundational Great Basin shrub. Evol. Appl. 2021, 14, 2881–2900. [Google Scholar] [CrossRef]
  61. Quinlan, A.R.; Hall, I.M. BEDTools: A flexible suite of utilities for comparing genomic features. Bioinformatics 2010, 26, 841–842. [Google Scholar] [CrossRef]
Ijms 26 09613 g001
Figure 1. Genomic inbreeding coefficients inferred from the proportion of the genome within ROH (FROH). The y-axis represents the FROH value for each population. ZYL, WSL, LML, HZL, GYL, YE, THL, and PYE in the x-axis represent samples collected from Zhaoyang Lake, Weishan Lake, Luoma Lake, Hongze Lake, Gaoyou Lake, Yangzhi River Estuary, Thaihu Lake, and Pingyang Aojiang Estuary, respectively. The minima, maxima, center, and the upper and lower bounds of the box represent the maximum, minimum, median value, and upper and lower quartiles, respectively.
Figure 1. Genomic inbreeding coefficients inferred from the proportion of the genome within ROH (FROH). The y-axis represents the FROH value for each population. ZYL, WSL, LML, HZL, GYL, YE, THL, and PYE in the x-axis represent samples collected from Zhaoyang Lake, Weishan Lake, Luoma Lake, Hongze Lake, Gaoyou Lake, Yangzhi River Estuary, Thaihu Lake, and Pingyang Aojiang Estuary, respectively. The minima, maxima, center, and the upper and lower bounds of the box represent the maximum, minimum, median value, and upper and lower quartiles, respectively.
Ijms 26 09613 g001
Ijms 26 09613 g002
Figure 2. Principal component analysis (PCA) showing genetic distance among samples of Taenioides sp. Sampling sites are colored according to the population to which they belong. Individuals are labeled according to the location of sampling as coded in Table 1 (ZYL, WSL, LML, HZL, GYL, YE, THL, and PYE represent samples collected from Zhaoyang Lake, Weishan Lake, Luoma Lake, Hongze Lake, Gaoyou Lake, Yangtze River Estuary, Thaihu Lake, and Pingyang Aojiang Estuary, respectively).
Figure 2. Principal component analysis (PCA) showing genetic distance among samples of Taenioides sp. Sampling sites are colored according to the population to which they belong. Individuals are labeled according to the location of sampling as coded in Table 1 (ZYL, WSL, LML, HZL, GYL, YE, THL, and PYE represent samples collected from Zhaoyang Lake, Weishan Lake, Luoma Lake, Hongze Lake, Gaoyou Lake, Yangtze River Estuary, Thaihu Lake, and Pingyang Aojiang Estuary, respectively).
Ijms 26 09613 g002
Ijms 26 09613 g003
Figure 3. Genetic structure of Taenioides sp. populations within their native and invasive ranges inferred using ADMIXTURE. Each bar represents an individual, with different colors corresponding to one of the K ancestry clusters and length proportional to the assignment to that particular cluster. Individuals are grouped according to the location of sampling (ZYL, WSL, LML, HZL, GYL, YE, THL, and PYE represent samples collected from Zhaoyang Lake, Weishan Lake, Luoma Lake, Hongze Lake, Gaoyou Lake, Yangzhi River Estuary, Thaihu Lake, and Pingyang Aojiang Estuary, respectively).
Figure 3. Genetic structure of Taenioides sp. populations within their native and invasive ranges inferred using ADMIXTURE. Each bar represents an individual, with different colors corresponding to one of the K ancestry clusters and length proportional to the assignment to that particular cluster. Individuals are grouped according to the location of sampling (ZYL, WSL, LML, HZL, GYL, YE, THL, and PYE represent samples collected from Zhaoyang Lake, Weishan Lake, Luoma Lake, Hongze Lake, Gaoyou Lake, Yangzhi River Estuary, Thaihu Lake, and Pingyang Aojiang Estuary, respectively).
Ijms 26 09613 g003
Ijms 26 09613 g004
Figure 4. Maximum likelihood (ML) trees were constructed from the allele-shared matrix of SNPs among Taenioides sp. populations. Sampling sites are colored according to the population to which they belong. Individuals are labeled according to the location of sampling as coded in Table 1 (ZYL, WSL, LML, HZL, GYL, YE, THL, and PYE represent samples collected from Zhaoyang Lake, Weishan Lake, Luoma Lake, Hongze Lake, Gaoyou Lake, Yangzhi River Estuary, Thaihu Lake, and Pingyang Aojiang Estuary, respectively).
Figure 4. Maximum likelihood (ML) trees were constructed from the allele-shared matrix of SNPs among Taenioides sp. populations. Sampling sites are colored according to the population to which they belong. Individuals are labeled according to the location of sampling as coded in Table 1 (ZYL, WSL, LML, HZL, GYL, YE, THL, and PYE represent samples collected from Zhaoyang Lake, Weishan Lake, Luoma Lake, Hongze Lake, Gaoyou Lake, Yangzhi River Estuary, Thaihu Lake, and Pingyang Aojiang Estuary, respectively).
Ijms 26 09613 g004
Ijms 26 09613 g005
Figure 5. Recent gene flow among eight sampling localities for Taenioides sp. based on genomic resequencing. The gene flow direction was from population j–i, as indicated in x- and y-axes. Dark and light colors indicate a relatively high and low level of gene flow, respectively. Recent gene flow was represented by the migration rate (m) calculated from BayesAss. ZYL, WSL, LML, HZL, GYL, YE, THL, and PYE represent samples collected from Zhaoyang Lake, Weishan Lake, Luoma Lake, Hongze Lake, Gaoyou Lake, Yangzhi River Estuary, Thaihu Lake, and Pingyang Aojiang Estuary, respectively.
Figure 5. Recent gene flow among eight sampling localities for Taenioides sp. based on genomic resequencing. The gene flow direction was from population j–i, as indicated in x- and y-axes. Dark and light colors indicate a relatively high and low level of gene flow, respectively. Recent gene flow was represented by the migration rate (m) calculated from BayesAss. ZYL, WSL, LML, HZL, GYL, YE, THL, and PYE represent samples collected from Zhaoyang Lake, Weishan Lake, Luoma Lake, Hongze Lake, Gaoyou Lake, Yangzhi River Estuary, Thaihu Lake, and Pingyang Aojiang Estuary, respectively.
Ijms 26 09613 g005
Ijms 26 09613 g006
Figure 6. Distribution of log2 (Pi) ratios and Fst values, which were calculated in 100 kb windows sliding in 10 kb steps. Data points located to the left and right of the vertical dashed lines (corresponding to the 5% left and right tails of the empirical log2 (Pi) ratio distribution), and above the horizontal dashed line (the 5% right tail of the empirical FST distribution) were identified as selected regions for populations at the invasive front (green points).
Figure 6. Distribution of log2 (Pi) ratios and Fst values, which were calculated in 100 kb windows sliding in 10 kb steps. Data points located to the left and right of the vertical dashed lines (corresponding to the 5% left and right tails of the empirical log2 (Pi) ratio distribution), and above the horizontal dashed line (the 5% right tail of the empirical FST distribution) were identified as selected regions for populations at the invasive front (green points).
Ijms 26 09613 g006
Ijms 26 09613 g007
Figure 7. Go (A) and KEGG (B) enrichment results of candidate genes under selection identified in populations at the invasive front by selective sweeps. Only top 20 GO terms and KEGG pathways enriched from the candidate genes under selection are shown. The rich factor represents the ratio of differentially expressed genes to the total number of genes in each pathway. The color of the dots indicates the significance level (p-value) of enrichment, and the size of the dots represents the number of genes enriched in each pathway.
Figure 7. Go (A) and KEGG (B) enrichment results of candidate genes under selection identified in populations at the invasive front by selective sweeps. Only top 20 GO terms and KEGG pathways enriched from the candidate genes under selection are shown. The rich factor represents the ratio of differentially expressed genes to the total number of genes in each pathway. The color of the dots indicates the significance level (p-value) of enrichment, and the size of the dots represents the number of genes enriched in each pathway.
Ijms 26 09613 g007
Ijms 26 09613 g008
Figure 8. Sampling localities of invasive and native Taenioides sp. populations around the affected areas of ESNT projects in this study: red spots in the figure indicate the localities where Taenioides sp. samples were collected.
Figure 8. Sampling localities of invasive and native Taenioides sp. populations around the affected areas of ESNT projects in this study: red spots in the figure indicate the localities where Taenioides sp. samples were collected.
Ijms 26 09613 g008
Table 1. The sampling locations and the statistics of genetic diversity in Taenioides sp. populations within their native and invasive ranges.
Table 1. The sampling locations and the statistics of genetic diversity in Taenioides sp. populations within their native and invasive ranges.
PopulationSample SizeHoHePiPolymorphic Loci (%)
ZYL120.1290.1220.12844.493
WSL150.1320.1280.13350.406
LML150.1330.1300.13454.124
HZL140.1310.1260.13151.655
GYL150.1810.1740.18064.380
THL150.2170.2120.22079.435
YE150.2140.2040.21277.162
PYE150.2770.2780.28889.498
Note: ZYL, WSL, LML, HZL, GYL, YE, THL, and anPYE represent samples collected from Zhaoyang Lake, Weishan Lake, Luoma Lake, Hongze Lake, Gaoyou Lake, Yangtze River Estuary, Thaihu Lake, and Pingyang Aojiang Estuary, respectively.
Table 2. Pairwise Fst values among the eight populations of Taenioides sp. within their native and invasive ranges.
Table 2. Pairwise Fst values among the eight populations of Taenioides sp. within their native and invasive ranges.
PopulationZYLWSLLMLHZLGYLTHLYEPYE
ZYL
WSL0.021
LML0.0290.024
HZL0.0300.0250.018
GYL0.0560.0530.0470.049
THL0.0750.0740.0700.0720.054
YE0.0570.0560.0520.0530.0310.035
PYE0.1300.0970.0940.0950.0740.0550.052
Note: ZYL, WSL, LML, HZL, GYL, YE, THL, and PYE represent samples collected from Zhaoyang Lake, Weishan Lake, Luoma Lake, Hongze Lake, Gaoyou Lake, Yangtze River Estuary, Thaihu Lake, and Pingyang Aojiang Estuary, respectively.
Table 3. Proportion of adaptive amino acid substitutions (α) estimated for different Taenioides sp. populations with standard McDonald– Kreitman tests (MKTs).
Table 3. Proportion of adaptive amino acid substitutions (α) estimated for different Taenioides sp. populations with standard McDonald– Kreitman tests (MKTs).
Populationα-ValueFisher p-Value
ZYL0.1353.17 × 10−21
WSL0.1262.14 × 10−20
LML0.1366.68 × 10−25
HZL0.1231.36 × 10−19
GYL0.1011.43 × 10−16
THL0.0994.56 × 10−19
YE0.1208.54 × 10−28
PYE0.1491.23 × 10−52
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Huang, K.; Liu, T.; Xu, A.; Yu, J.; Yang, Y.; Liu, J.; Li, F.; Zhu, D.; Gong, L.; Liu, L.; et al. Genomic Signatures of Adaptive Evolution in Taenioides sp. During Northward Invasion. Int. J. Mol. Sci. 2025, 26, 9613. https://doi.org/10.3390/ijms26199613

AMA Style

Huang K, Liu T, Xu A, Yu J, Yang Y, Liu J, Li F, Zhu D, Gong L, Liu L, et al. Genomic Signatures of Adaptive Evolution in Taenioides sp. During Northward Invasion. International Journal of Molecular Sciences. 2025; 26(19):9613. https://doi.org/10.3390/ijms26199613

Chicago/Turabian Style

Huang, Kun, Tianwei Liu, An Xu, Jing Yu, Yijing Yang, Jing Liu, Fenghui Li, Denghui Zhu, Li Gong, Liqin Liu, and et al. 2025. "Genomic Signatures of Adaptive Evolution in Taenioides sp. During Northward Invasion" International Journal of Molecular Sciences 26, no. 19: 9613. https://doi.org/10.3390/ijms26199613

APA Style

Huang, K., Liu, T., Xu, A., Yu, J., Yang, Y., Liu, J., Li, F., Zhu, D., Gong, L., Liu, L., & Lü, Z. (2025). Genomic Signatures of Adaptive Evolution in Taenioides sp. During Northward Invasion. International Journal of Molecular Sciences, 26(19), 9613. https://doi.org/10.3390/ijms26199613

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop