Chromoanagenesis is a single catastrophic event in which one or few chromosomes are shattered and disorderly reassembled. Chromoanagenesis is common in many types of cancers. In this study, we utilize data from The Pan-Cancer Analysis of Whole Genome (PCAWG) to build a machine learning algorithm that detects chromoanagenesis with high accuracy. We applied the algorithm on ~10,000 samples from The Cancer Genome Atlas (TCGA), thereby providing, for the first time, chromoanagenesis status labels for the complete data set. An in-depth analysis of somatic and clinical chromoanagenesis features is presented for 20 cancer types. Mutual exclusivity patterns between genes impaired in chromoanagenesis versus non-chromoanagenesis cases might imply at distinct pathways involved in chromoanagenesis-driven tumorigenesis.During the past decade, whole-genome sequencing of tumor biopsies and individuals with congenital disorders highlighted the phenomenon of chromoanagenesis, a single chaotic event of chromosomal rearrangement. Chromoanagenesis was shown to be frequent in many types of cancers, to occur in early stages of cancer development, and significantly impact the tumor’s nature. However, an in-depth, cancer-type dependent analysis has been somewhat incomplete due to the shortage in whole genome sequencing of cancerous samples. In this study, we extracted data from The Pan-Cancer Analysis of Whole Genome (PCAWG) and The Cancer Genome Atlas (TCGA) to construct and test a machine learning algorithm that can detect chromoanagenesis with high accuracy (86%). The algorithm was applied to ~10,000 unlabeled TCGA cancer patients. We utilize the chromoanagenesis assignment results, to analyze cancer-type specific chromoanagenesis characteristics in 20 TCGA cancer types. Our results unveil prominent genes affected in either chromoanagenesis or non-chromoanagenesis tumorigenesis. The analysis reveals a mutual exclusivity relationship between the genes impaired in chromoanagenesis versus non-chromoanagenesis cases. We offer the discovered characteristics as possible targets for cancer diagnostic and therapeutic purposes. Over the past decade, the term chromoanagenesis (for chromosome rebirth) was coined to describe a catastrophic cellular event in which large numbers of complex rearrangements occur at one or a few chromosomal loci. A chromoanagenesis event consists of multiple chromosomal breakage and results in a variety of chromosomal abnormalities, including copy number alterations (CNA), inversions, and inter-and intra-chromosomal translocations. Chromoanagenesis was originally discovered in tumor cells and in individuals with congenital disorders . It was There are three subtypes of chromoanagenesis: chromothripsis, chromoplexy, and chromoanasynthesis ,4. ChromThe most exhaustive research on chromoanagenesis was performed as a part of The Pan-Cancer Analysis of Whole Genomes (PCAWG) study . PCAWG aIn this study we performed an in-depth analysis of chromoanagenesis’ somatic, cancer-type specific characteristics while focusing on coding genes. Many of the found somatic single nucleotide variants (SNV) and CNA patterns match previous studies, as we concur on chromoanagenesis related genes. Additionally, we identified CNA and cancer-type specific mutual-exclusivity patterns matching established observations that were reported regardless of chromoanagenesis. We offer our TCGA samples classification and novel discoveries as the basis for further investigating chromoanagenesis.https://portal.gdc.cancer.gov/ (accessed on 1 August 2020). The data does not include genes in the Y chromosome. The PCAWG project performed a whole genome analysis of 799 of those individuals. PCAWG thoroughly described each individual chromosomal state. The data was downloaded from the Chromothripsis Explorer site (http://compbio.med.harvard.edu/chromothripsis/ (accessed on 3 June 2020). The site includes chromothripsis labeling, as well as a labeling for other complex chromosomal events /[all positive + all negative]. This model presented the best results for the development testing data, and reached 85.7% accuracy on the final testing data.We applied Fisher’s exact test (using scipy stats module ) when teMatplotlib  and seabChromoanagenesis status for 799 cancer samples from 22 cancer types in the TCGA cohort was collected via PCAWG (see Methods). Overall, 371 of the samples (46.4%) had chromoanagenesis. The chromoanagenesis samples can be further divided: 64 have chromothripsis, 143 have both chromothripsis and other complex chromosomal events, and 164 with only other, non-chromothripsis, complex chromosomal events.Chromoanagenesis frequency varied greatly between cancer types, ranging from 3% in thyroid carcinoma (THCA) to 88% in glioblastoma (GBM). Chromoanagenesis subtype distributions also varied among cancer types. For example, 75% of kidney renal clear cell carcinoma (KIRC) chromoanagenesis samples had chromothripsis, while 57% of liver hepatocellular carcinoma (LIHC) chromoanagenesis samples had strictly non-chromothripsis events a.We collected masked genic CNA from TCGA for the 799 samples. Namely, for each sample and for 19,729 known coding genes, we know whether the number of copies in the somatic sample is higher, identical, or smaller than in the matching germline sample (see Methods). We chose to use genic CNA (derived from GISTIC  results)p-value of We examined the total number of genes with CNA for each of the four chromoanagenesis states: (i) no chromoanagenesis; (ii) chromothripsis; (iii) chromothripsis and other complex chromosomal events; (iv) non-chromothripsis complex chromosomal events. In all three chromoanagenesis groups, the number of genes with CNA was significantly higher. The mean number of genes with altered copy number is 559.2 for the no chromoanagenesis group, 701.2 for chromothripsis, 1268.5 for samples with both chromoanagenesis and other complex events and 964.1 for non-chromothripsis complex events. One-way ANOVA test yields a .5×10−24 a. Simila.5×10−24 .p-values of 0.42 and 0.88, respectively  and LUSC (lung squamous cell carcinoma), a one-way ANOVA test on chromoanagenesis number of genic CNA yields non-significant ectively b. The geTo overcome the variation in somatic background and chromoanagenesis proportions between cancer types, we examined more complex genic CNA attributes as chromoanagenesis status predictors. When examining adjacent genes on the same chromosomal arm, it is likely that a similar CNA status  is attributed to the same CNA event. The alternative possibility of having unrelated similar CNA events in adjacent genes is less probable. We used this assessment to measure different CNA features for each chromosomal arm. For example, the number of genes affected by the same CNA or the number of gene-affecting CNA per chromosomal arm.A key indicator for chromoanagenesis is CNA oscillations along the affected chromosomes. We calculated the number and length (in genes) of oscillations per chromosomal arm. Interestingly, the chromoanagenesis samples include one or few chromosomal arms with exceptionally high number of oscillations. Contrastingly, the non-chromoanagenesis samples (with many oscillations) include many chromosomal arms with a high number of oscillations . This atWe trained a decision tree on 85% of the data to identify whether a sample has chromoanagenesis (see Methods). We reached an 85.7% accuracy rate, with 88.9% accuracy, 83.6% specificity, and 78.4% sensitivity for having chromoanagenesis. This generic somatic chromoanagenesis detection module is applicable to any cancer type. However, due to the limited sample size and previously discussed data limitations, we were not able to distinguish between different chromoanagenesis subtypes .The machine learning model was applied on all remaining 9929 TCGA samples from all 33 cancer types reported in TCGA . Overallf 0.7461 c. NotablAn in-depth analysis was performed to uncover cancer type specific chromoanagenesis CNA patterns. For each gene, we tested whether the frequency of CNA in each sample type (chromoanagenesis and non-chromoanagenesis) was significantly different (see Methods). We limited the analysis to the 20 cancer types with at least 50 chromoanagenesis samples and 50 non-chromoanagenesis samples: BLCA , BRCA (Breast invasive carcinoma), CESC , COAD (Colon adenocarcinoma), ESCA , GBM (Glioblastoma multiforme), HNSC (Head and Neck squamous cell carcinoma), KIRC , LGG (Brain Lower Grade Glioma), LIHC (Liver hepatocellular carcinoma), LUAD (Lung adenocarcinoma), LUSC (Lung squamous cell carcinoma), OV (Ovarian serous cystadenocarcinoma), PAAD (Pancreatic adenocarcinoma), PRAD (Prostate adenocarcinoma), READ (Rectum adenocarcinoma), SARC (Sarcoma), SKCM (Skin Cutaneous Melanoma), STAD (Stomach adenocarcinoma), and UCEC .The emerging patterns mostly consisted of numerous adjacent genes and often included more than one chromosomal region. Some CNA regions showed clear distinction between copy number deletion and amplification, while other regions were significantly altered, but not specifically enriched with either deletions or amplifications . UnsurprVarious CNA association patterns are linked to the different cancer types. Notably, amplifications are more common than deletions . Despitep-values are summarized in Three of the examined cancer types—ESCA, PAAD, and READ—did not have any statistically significant copy number alterations, in either chromosomal region or gene level. The lack of significant results is mostly explained by the relatively small number of samples for either chromoanagenesis or non-chromoanagenesis samples. Applying a more relaxed significance threshold will likely reveal additional results, for all tested cancer types. p-value passes the predefined significance threshold of Some prominent and significant CNA consist only of a single gene. We consider a gene as a distinct CNA gene if the Fisher’s exact test  cancers . The ampIt is unclear whether these altered genes drive the chromoanagenesis and tumorigenesis processes forward, or simply accompany them. The different chromoanagenesis processes are less likely to alter the copy number of a single gene, and are more likely to affect a chromosomal region. Nevertheless, the chromoanagenesis process might abrupt fragile sites . Previous studies have identified some of the altered genes as fragile sites: DMD, WWOX, PARK2, and LRP1B ,23,24. Op-value of Many of the described tumor-specific CNA were previously detected and characterized in tumorigenesis studies. However, some of these CNA patterns were analyzed prior to the depiction of chromoanagenesis, and were not considered associated with the phenomenon. In BLCA, one of the four significantly amplified regions for chromoanagenesis is 6p22. There are four consecutive genes which pass the significance threshold, the most significant being E2F3 with a feration . The othferation , 3p25 [2feration , and 8q2feration .BRCA chromoanagenesis samples have three deleted regions, deleted 17q21 includes the oncogene BRCA1. Lettesier et al.  analyzedGBM chromoanagenesis has a small amplification of three consecutive genes in 12q15, including the gene MDM2. MDM2 is transcriptionally regulated by p53. It promotes tumor formation by targeting p53 protein for degradation. Overexpression or amplification of this locus is detected in a variety of different cancers. Amplification of MDM2 without TP53 mutations was observed in gliomas ,34; thisWe further analyzed somatic SNV in chromoanagenesis samples for each of the 20 examined cancer types (see Methods). We tested the total number of somatic exome SNV, the number of affected genes, how many occurrences of loss-of-function (LOF), missense and synonymous mutations occurred, as well as the number of affected driver genes . For thp-value smaller than For each gene, in each cancer type, we calculated separately the number of individuals with LOF, missense, non-synonymous (either LOF or missense), and synonymous mutations, in chromoanagenesis and non-chromoanagenesis samples. We tested the differences for each gene and each mutation type using the conservative Fisher’s exact test. Detailed results for genes with p-value: p-value: p-value: p-value: The top four genes detected as likely chromoanagenesis inducing genes are TP53, ATRX and to a lesser extent: PPP2R1A and ST6GAL2. TP53 and ATRX are two prominent, known chromoanagenesis causing genes ,37,38. Ielomeres ,40. PPP2p-value: p-value: p-value: Many prominent oncogenes are SNV impaired at a higher rate in non-chromoanagenesis samples. The most substantial non-chromoanagenesis genes are PTEN, CIC, CASP8, KMT2D, ARID1A, RNF213, and PIK3CA. PTEN, an established tumor suppressor , is assoq-value of <0.005 are presented. TP53, a top chromoanagenesis gene (and ATRX in LGG) is mutually exclusive from other cancer driver genes  in chromoanagenesis samples, and genes frequently impaired in non-chromoanagenesis samples. We performed a mutual exclusivity analysis for the differentially impaired genes in each cancer type using cBioPortal ,43. The er genes . RecurriMany of these mutually exclusive relationships were previously detected and studied. In LGG, the genes TP53 and ATRX are impaired in chromoanagenesis samples, while CIC and FUBP1 are impaired in non-chromoanagenesis samples, these mutually exclusive genes were connected to specific pathological and clinical characteristics . In HNSCAmong the genes mutually exclusive with TP53 are several members of the ARID family  causes certain chromoanagenesis effects in infected individuals . We collWe performed CNA, somatic SNV, and clinical data chromoanagenesis analyses for 20 cancer types. Chromoanagenesis samples presented distinct CNA patterns, mostly cancer-type specific. The somatic SNV analysis, however, revealed similar genic phenoms. Many of the observed CNA and somatic SNV patterns were previously independently reported, but some were not associated with chromoanagenesis. We offer these reported patterns as further evidence to the validity of our methodology and discoveries and suggest chromoanagenesis as a possible driving force for known oncogenic CNA phenoms. Providing this additional context can aid in better defining subtypes of cancer, as well as revealing underlying shared tumorigenesis mechanisms. Surprisingly, we hardly found any distinguishing clinical features between the proposed tumorigenesis routes, despite existing reports on a diminished survival rate in chromoanagenesis ,52. It iTP53 is the most common gene damaged in many chromoanagenesis samples. TP53 has a much higher rate of missense or LOF mutations in chromoanagenesis , while sThe high frequency of the chromoanagenesis phenomenon in cancer became evident in recent years . It was


Angelica dahurica belongs to the Apiaceae family, whose dry root is a famous traditional Chinese medicine named as “Bai zhi”. There are two cultivars (A. dahurica cv. ‘Hangbaizhi’ and A. dahurica cv. ‘Qibaizhi’), which have been domesticated for thousands of years. Long term artificial selection has led to great changes in root phenotypes of the two cultivars, and also decreased their adaptability to environment. We proposed hypothesis that the cultivars may have lost some of the genetic diversity found in the wild species and may be highly differentiated from the latter during the domestication process. However, few studies have been carried out on how domestication affected the genetic variation of this species. Here, we accessed the levels of genetic variation and differentiation within and between wild A. dahurica populations and two cultivars using 12 microsatellite markers.A. dahurica, and A. dahurica cv. ‘Qibaizhi’ had lower genetic diversity compared to A. dahurica cv. ‘Hangbaizhi’. AMOVA analysis showed significant genetic differentiation between the wild and cultivated A. dahurica populations, and between A. dahurica cv. ‘Hangbaizhi’ and A. dahurica cv. ‘Qibaizhi’. Results from Bayesian, UPGMA, NJ and PcoA clustering analysis indicated that all 15 populations were assigned to two genetic clusters corresponding to the wild and cultivated populations. Bayesian clustering analysis further divided the cultivated populations into two sub-clusters corresponding to the two cultivars.The results revealed that the genetic diversity of the cultivars was much lower than that of wild A. dahurica populations and in significant genetic differentiation from the wild populations due to founder effect and/or artificially directional selections. This large-scale analysis of population genetics could provide valuable information for genetic resources conservation and breeding programs of Angelica dahurica.Our study suggests that the domestication process is likely the major factor resulting in the loss of genetic diversity in cultivated  Medicinal plants have been used as important sources of medicine to prevent and treat many human diseases in traditional cultures all over the world for thousands of years , 2. HoweScrophularia ningpoensis [Atractylodes macrocephala [Cannabis species [Plant domestication is an episode in which human-mediated selection favors phenotypic modification of wild resources to meet human needs, mainly through artificial selection , 12. Domgpoensis , Atractyocephala  and Cann species , numerouAngelica dahurica (Hoffm.) Benth. et Hook. f. ex Franch. & Sav., is a perennial herb belonging to the genus Angelica of the family Apiaceae, mainly distributed in North and Northeast China, Japan, Korea, Russia (Siberia) [A. dahurica, which is an effective mechanism to encourage outcrossing and avoid selfing [Angelica species, such as A. biserrata [A. dahurica is considered to be outcrossing. The dry root of the species, named as “bai zhi” (Angelicae Dahuricae Radix), is a famous traditional Chinese medicine, which has been used as a food additive as well as a folk medicinal therapy of headache, rhinitis, cold and toothache amongst others in East Asian countries  for thousands of years [A. dahurica cv. ‘Hangbaizhi’ and A. dahurica cv. ‘Qibaizhi’, widely cultivated in China, have gone through evolution by artificial selection for more than 1000 and 200–400 years, respectively [A. dahurica (the two cultivars) may have lost some of the genetic diversity found in the wild species and may be highly differentiated from the latter during the domestication process in the past decades. However, so far, few studies have concerned on how domestication affects the genetic variation of cultivars of A. dahurica. There is still lack of studies on genetic diversity and population structure of A. dahurica and it culitvars.Siberia) . Dichoga selfing . The pol selfing . Such meiserrata . Therefoof years –32. Two ectively , 30, 33.ectively , 34. HerA. dahurica [A. dahurica. In this study, we aim to: (1) access the levels of genetic variation and differentiation within and between wild A. dahurica and its cultivars; (2) clarify how the domestication process influence on genetic variation of this species. Hopefully, the outcomes of this study could provide valuable information for genetic resource conservation and breeding programs of A. dahurica.In a previous study, we developed highly polymorphic SSR markers by transcriptome sequencing for dahurica , providiA. dahurica, allele frequency = 100%) in A. dahurica. Wild and cultivated A. dahurica displayed the same allele frequency distribution patterns. Among all populations except for two cultivated populations (ZC and YZ), low-to-medium frequency allele accounted for the highest proportion (41.30–80.49%), followed by rare allele (4.88–38.46%) and medium-to-high frequency allele (12.96–25.00%). Common alleles were the least prevalent one (0–13.33%).A total of 336 individuals from 15 populations were genotyped with 12 SSR markers, resulted in 108 alleles, of which 90 and 80 were detected in wild and cultivated populations, respectively (Table Ho) and Shannon’s index (I) were 0.412 and 0.735, respectively  . For each population analyzed, genetic diversity parameters varied widely among populations , and the lowest in the cultivated population JN . Four populations, including one wild population (BJ) and three cultivated populations , had significantly positive FIS values  of the total variation was partitioned among populations of A. dahurica  in cultivated A. dahurica (Table A. dahurica is much stronger than that in wild A. dahurica (Fst: 0.486 vs 0.194) , and between A. dahurica cv. ‘Hangbaizhi’ and A. dahurica cv. ‘Qibaizhi’  , whereas the wild populations remained relatively uniform . Anthropic activities also have a huge impact on genetic diversity of wild plants as they are usually accompanied with dramatically increasing loss and damage of natural habitats [A. dahurica due to anthropic activities caused by urbanization, agricultural and silvicultural practices. In addition, the wild resources are in decline due to over-exploitation. Thus, over-exploitation of the wild resources and habitat destruction are the most likely factors responsible for the moderate level of genetic diversity in A. dahurica. Among the seven wild populations, DH, AS and BX showed relatively high genetic diversity, which could provide abundant genetic variation for breeding. In addition, most of populations preserve private alleles, which are also valuable genetic resources for breeding.Wild progenitors are always an important genetic resource for plant breeding . However species , 37, 38. species –41. Our analyses . As an U insects , 29, whig plants . However to 0.85 –47, wildhabitats , 49. TheA. dahurica was approximately reduced by a third compared to the wild resource, respectively. The findings of our study suggested that domestication had a negative effect on genetic diversity of cultivated A. dahurica, and the extent was comparable to many crop plants, e. g. maize and soybean [Many experiential studies have revealed that domestication processes caused a massive loss of genetic diversity in domestication crop plants , 50. How soybean , 21.A. dahurica, which was most likely descended from a limited number of individuals from the wild since more than 1000 years ago [A. dahurica, the phenotype of thick and unbranched root might be continuously selected, resulting in further loss of diversity by the reduction of the effective population size and the increasing genetic drift. Moreover, inbreeding caused further genetic erosion, which is a recent phenomenon caused by the decrease of the efficient population size after domestication [It has been demonstrated that founder effect or bottleneck effect, selective sweeps and/or directional selection, and intensive breeding are the main factors causing the reduction of genetic diversity , 23, 51.ears ago –32. Furtears ago . During tication , 52. ObvA. dahurica cv. ‘Hangbaizhi’ to A. dahurica cv. ‘Qibaizhi’ was observed. STRUCTURE cluster analysis showed that the two cultivars clustered into a genetic group, indicating that cultivated A. dahurica have possibly originated from the same genetic resource. According to the time of initial cultivation, A. dahurica cv. ‘Hangbaizhi’ (more than 1000 years) has a longer domestication history compared to A. dahurica cv. ‘Qibaizhi’ (about 200–400 years) [A. dahurica cv. ‘Qibaizhi’ may have originally being introduced from A. dahurica cv. ‘Hangbaizhi’. Our result is likely consistent to the fact that genetic diversity decreased along the diffusion history [For the two cultivars, a reduction of genetic diversity from 0 years) , 30, 33. history , 53, 54.A. dahurica, and between A. dahurica cv. ‘Hangbaizhi’ and cv. ‘Qibaizhi’ [A. dahurica. PCoA analysis almost confirmed the partitioning results of NJ tree, UPGMA dendrogram and Bayesian genetic structure. Significant genetic differentiation was also detected between the wild and cultivated A. dahurica  in the AMOVA analysis. Furthermore, the Bayesian genetic structure analysis also indicated an obvious genetic clustering between the two cultivars (A. dahurica cv. ‘Hangbaizhi’ and A. dahurica cv. ‘Qibaizhi’). Also, AMOVA analysis showed significant genetic differentiation between them . Artificial selection as a driving force of divergence between wild and domesticated plants has been documented [A. dahurica and its cultivars are most probably derived from artificial selection of root characteristics during the domestication process. Although the two cultivars possibility originated from the same genetic resource, they are cultivated in different regions since initial cultivation. Therein, A. dahurica cv. ‘Hangbaizhi’ was mainly cultivated in Zhejiang and Sichuan Provinces, China, while A. dahurica cv. ‘Qibaizhi’ was planted in Henan, Hebei and Anhui Provinces, China  indicated that the wild resources could be used as a source of novel alleles for improvement of the future genetic improvement of cultivated A. dahurica. However, the wild resources are in decline. Based on the knowledge of genetic diversity and structure of A. dahurica, an appropriate conservation and management strategy can be formulated here. Conservation of genetic diversity should concentrate on the maintenance of large populations in outcrossing plants due to the fact that most genetic diversity reside within populations [A. dahurica  is a key to preserve diversity due to lower differentiation among populations and greater variation within population. These populations with high levels of diversity could provide abundant genetic variation for future breeding program.Our results suggested that the long time artificial selection has resulted in the loss of genetic diversity, and this may lead to the decline of disease and insect resistance in cultivated  efforts . The sigulations , 10. In A. dahurica and its cultivars from 15 locations. Our results revealed that cultivated A. dahurica harbored lower genetic diversity, and showed significantly high genetic differentiation from wild A. dahurica. The domestication process through artificial selection is the major factor resulting in the loss of genetic diversity in cultivated A. dahurica, considering that significant genetic divergence has been found in the wild resources. Our results could provide genetic insight to improve conservation and management strategies for A. dahurica, and advance understanding of effects of domestication on genetic diversity of medicinal plants.In the present study, we used 12 SSR markers to access the levels of genetic variation and differentiation within and between wild Angelica dahurica wild populations from 7 locations which covered all the species natural distribution area in China. Specimens of A. dahurica cv. ‘Hangbaizhi’ and A. dahurica cv. ‘Qibaizhi’ were collected from 3 and 5 locations, respectively, which included almost all the main cultivated area , morphologically identified all the voucher specimens mentioned above, which then were deposited in the herbarium of SMU , 2.5 mM Mg2+, 1 unit Taq DNA polymerase  with the following procedure: initial denaturation at 95 °C for 5 min, 35 cycles of denaturation at 94 °C for 30 s, annealing at 50 to 55 °C for 60 s, extension at 72 °C for 45 s and a final extension of 72 °C for 8 min. The PCR products were visualized using capillary electrophoresis on an ABI PRISM 3100 Genetic Analyser . The size of all alleles was determined on Genotyper 4.0, with LIZ 500  as an internal product size standard.Twelve microsatellite markers, previously developed for dahurica , were seNa), effective alleles (Ne) and private alleles (Ap), Shannon’s information index (I), observed heterozygosity (Ho), expected heterozygosity (He) over loci were calculated using GenAlEx v6.502 [F-statistics inbreeding coefficient (FIS) [To explore the level of genetic diversity, number of observed alleles (x v6.502 . GenAlExnt (FIS)  was calcnt (FIS) , with siTo understand the genetic relationship among all populations, Bayesian cluster analysis was conducted in the program STRUCTURE v2.1  to assig


Aedes koreicus was recently reported to have established a population in western Germany (Wiesbaden) in 2016. The species is difficult to distinguish morphologically from its close relative, the invasive Japanese bush mosquito Ae. japonicus, which is already widely distributed in many parts of Germany, including the area colonised by Ae. koreicus. Genetic confirmation of morphologically identified “Ae. japonicus” collection material, however, had only been done exceptionally before the German Ae. koreicus population became known.The Korean bush mosquito Ae. japonicus” specimens both from the municipality of Wiesbaden and from deliberately and randomly selected distribution sites all over Germany were re-examined morphologically and genetically for admixture by Ae. koreicus. Moreover, cemeteries in the greater Wiesbaden area were sampled in 2019 and 2020 to check for Ae. koreicus spread. Korean and Japanese bush mosquitoes submitted to the German citizen science mosquito monitoring scheme “Mueckenatlas” in 2019 and 2020 were also subjected to particularly thorough species identification. The ND4 DNA sequences generated in this study in the context of species identification were phylogenetically compared to respective GenBank entries of Ae. koreicus. As a by-product, several genetic markers were evaluated for their suitability to identify Ae. koreicus.Dried archived “Aedes koreicus specimens could be identified in mosquito collection material and submissions from Wiesbaden from 2015 onwards, suggesting establishment to have happened in the same year as Ae. japonicus establishment. Detections of Ae. koreicus from 2019 and 2020 in Wiesbaden indicate a negligible enlargement of the populated area as described for 2018. Two Ae. koreicus specimens were also submitted from the city of Munich, southern Germany, in 2019 but further specimens could not be identified during immediate local inspections. Comparison of ND4 sequences generated in this and other studies demonstrate a high degree of homology, suggesting that this DNA region is not informative enough for clarification of origins and relationships of Ae. koreicus populations. For genetic identification of Ae. koreicus, PCR primers used for classical CO1 barcoding were found to lead to mismatches and produce no or incorrect amplicons. Alternative CO1 primers or a validated ND4 marker should be used instead.Aedes koreicus is probably introduced into Germany every now and then but rarely succeeds in becoming established. As with most European populations, the German population is characterised by a limited expansion tendency. Since Ae. koreicus is a potential vector, however, Asian bush mosquitoes found at new places should be examined quite carefully and known distribution areas of Ae. japonicus regularly checked for the presence of Ae. koreicus. Aedes (Hulecoeteomyia) koreicus , has its native distribution area in Japan, Korea, northern China and southern Russia ) or the shorter fragment only  showed mismatches pertaining to two bases in the forward and four bases in the reverse primer, although not at the 3′-ends. PCRs with the same primers and lower annealing temperatures (50 and 52 °C instead of 54 °C) to compensate for the base mismatches did not improve the results. Newly constructed primers LCO1490mod (5′-TCTCAACAAATCATAAAGATATTGG-3′) and HCO2198mod (5′-TAAACTTCTGGGTGTCCGAAGAATCA-3′), 100% homologous to the annealing sites of the Ae. koreicus GenBank sequences, however, produced the expected PCR products .PCR amplification of the CO1 marker region described by Hébert et al.  using thnly Fig. , with atAe. koreicus without problems.PCR amplifications using CO1-specific primer pairs according to Kumar et al.  MTFN/MT, the corAe. koreicus specimens collected in this study  were deposited in GenBank under the accession numbers MZ397946–MZ397949. These were phylogenetically analysed against corresponding Ae. koreicus sequences found in GenBank: nine from Belgium, nine from Italy, five from South Korea, three from Slovenia, one from Austria and two from previous studies in Germany. The resulting phylogenetic tree, based on DNA fragments trimmed to a consistent length of 190 bp, suggests a particularly close genetic relationship of almost all specimens, with only minor differences even between individuals from Asia and Europe  and southeastern Wiesbaden (Nordenstadt)  and their genetic make-up therefore remains a question for further studies. ND4 sequence data, at least as far as the trimmed fragment available for comparison from GenBank entries is concerned, do not appear to be informative enough to deduce genetic relationships and origins, and thus to conclude on displacement and migration routes, of geographic populations.Even though two species as closely related and as similar in appearance as  in Asia  and can observed , 25, Eurlishment , 45. Thurman one , 46. Contzerland . Due to Ae. koreicus specimens in this study. However, the above primers obviously did not anneal correctly which could be attributed to base pair mismatching with the rDNA of this specific mosquito species. By contrast, three other primer pairs, MTFN/MTRN, PanCuli-COX1-211F/PanCuli-COX1-727R and N4J-8502/ND4R1X [Ae. koreicus. A slight modification of the LCO1490/HCO2198 nucleotide sequences solved the annealing problem but prevented functionality with other mosquito species, while an equal mixture of original and modified primers worked well with Ae. koreicus and other mosquito species tested. Accordingly, studies applying genetic identification of mosquitoes from an area in which Ae. koreicus occurs, but not specifically focusing on that species, should well consider which genetic marker and primer pairs to use in order to obtain unambiguous identification results and to minimise work at the same time.CO1-barcoding, based on PCR with LCO1490 and HCO2198 primers according to Hébert et al.  and Folm2/ND4R1X , 32, 37,Ae. koreicus sampling sites from 2016 to 2018 and the finding of this species at new sites in the Wiesbaden area in 2019 and 2020, it appears that the only currently known German population of Ae. koreicus is more or less stable five years after its establishment. Apparently, an expansion, if it has taken place, is negligible. However, the present evaluation was based on cemetery inspections alone, as cemeteries simultaneously offer plenty of potential mosquito breeding sites and unrestricted access to investigators [Ae. koreicus in the Wiesbaden area than detected in this study. Spreading capacity as well as origin and introduction route(s) of Ae. koreicus are major topics that need to be addressed in more detail in the future. The demonstrated establishment and occasional introduction of this potential mosquito vector species in Germany, however, should be reason enough to continue mosquito monitoring, paying special attention to the distinction between Ae. koreicus and Ae. japonicus.Based on the documentation of tigators . Includi


The health of the world's oceans is intrinsically linked to the biodiversity of the ecosystems they sustain. The importance of protecting and maintaining ocean biodiversity has been affirmed through the setting of the UN Sustainable Development Goal 14 to conserve and sustainably use the ocean for society's continuing needs. The decade beginning 2021–2030 has additionally been declared as the UN Decade of Ocean Science for Sustainable Development. This program aims to maximize the benefits of ocean science to the management, conservation, and sustainable development of the marine environment by facilitating communication and cooperation at the science–policy interface. A central principle of the program is the conservation of species and ecosystem components of biodiversity. However, a significant omission from the draft version of the Decade of Ocean Science Implementation Plan is the acknowledgment of the importance of monitoring and maintaining genetic biodiversity within species. In this paper, we emphasize the importance of genetic diversity to adaptive capacity, evolutionary potential, community function, and resilience within populations, as well as highlighting some of the major threats to genetic diversity in the marine environment from direct human impacts and the effects of global climate change. We then highlight the significance of ocean genetic diversity to a diverse range of socioeconomic factors in the marine environment, including marine industries, welfare and leisure pursuits, coastal communities, and wider society. Genetic biodiversity in the ocean, and its monitoring and maintenance, is then discussed with respect to its integral role in the successful realization of the 2030 vision for the Decade of Ocean Science. Finally, we suggest how ocean genetic diversity might be better integrated into biodiversity management practices through the continued interaction between environmental managers and scientists, as well as through key leverage points in industry requirements for Blue Capital financing and social responsibility. Genetic5The principle objectives of the Decade affirm the need to better understand our oceans and to improve the interface between science and policy in order to support their sustainable management Table  IOC, 20. UnderstRates of change in the marine environment underline the urgent need for increased monitoring and baseline surveying of genetic diversity in the coming decade  has likewise been widely acknowledged, in particular through the work of groups such as the High Level Ocean Panel and the FAO ‐Include and consider marine genetic diversity in concepts of Blue Nature Capital and “no net loss” of biodiversity and encourage its inclusion in corporate social responsibility and certification schemes.‐Improve the monitoring of potential genetic effects from overharvesting and large‐scale hatchery and release programs in the marine environment.‐Improve understanding of the role of genetic diversity in adaptation and resilience, and integrate this aim in the implementation of Decade resources, including ocean observation platforms and remote sensing‐Support frequent and improved knowledge transfer of genetic diversity between scientists, environmental managers, policymakers, and wider stakeholders regarding genetic diversityCrucially, the monitoring and maintenance of genetic diversity should work in synergy with other forms of environmental and biodiversity management. In doing so, management geared toward the preservation and enhancement of genetic diversity will help achieve the central vision of the Decade program of supporting ecosystem resilience and adaptability alongside the sustainable management and conservation of the ocean's resources for society's continuing needs.None declared.Supplementary MaterialClick here for additional data file.


Catostomus commersonii, yellow perch Perca flavescens, walleye Sander vitreus, smallmouth bass Micropterus dolomieu, and rock bass Ambloplites rupestris. We detected significant differentiation within each species, but patterns of population structure varied substantially. Interestingly, genetic structure did not appear to be solely the result of fragmentation by the dam. While genetic structure in yellow perch and walleye generally coincided with “above dam” and “below dam” sampling locations, samples from our other three species did not. Specifically, samples from rock bass, smallmouth bass, and, to a much lesser extent, white sucker, aligned with a putative Great Lakes (GL) group that contained mostly individuals sampled below the dam and a putative Boardman River (BR) group that contained individuals sampled both above and below the dam, with some evidence of admixture among groups. We hypothesize that the GL and BR groups formed prior to dam construction and our samples largely represent a mixed stock that was sampled sympatrically outside of the spawning season. Support for this hypothesis is especially strong in smallmouth bass, where GL fish were 151 mm smaller than BR fish on average, suggesting a potential ontogenetic habitat shift of young GL fish into the lower river for feeding and/or refuge. Our study illuminates the complex dynamics shaping genetic structure in fragmented river systems and indicates that conclusions drawn for a single species cannot be generalized.Fragmentation of river systems by dams can have substantial genetic impacts on fish populations. However, genetic structure can exist naturally at small scales through processes other than isolation by physical barriers. We sampled individuals from five native fish species with varying life histories above and below a dam in the lower Boardman River, Michigan, USA, and used RADseq to investigate processes influencing genetic structure in this system. Species assessed were white sucker  Some of these processes include allopatric divergence resulting from isolation in different glacial refugia  explored the population structure and diversity of our study species above and below the dam using genotypes from thousands of loci generated with restriction site‐associated DNA (RAD) sequencing, (2) conducted genetic migration simulations to compare our empirical data to simulated data generated under a variety of divergence scenarios, and (3) evaluated the relationship between observed patterns in population genetic structure and ecological attributes such as fish size and date of capture. Our study illustrates the importance of including multiple species when investigating the relative influence of different factors on contemporary genetic structure. Additionally, our approach is highly applicable to other species and systems and should provide valuable information that will aid management of genetic diversity in fragmented habitats.22.1® 96 Blood & Tissue Kits (Qiagen).We collected tissue samples from fish above and below the Union Street Dam for each of our five study species from 2017 to 2019. All sampling was performed with IACUC approval under University of Wisconsin—Stevens Point protocol number 2019.03.05. Fish below the dam were captured by boat electrofishing from the mouth of the Boardman River up to the Union Street Dam. Samples were taken during routine monitoring surveys, with the vast majority of fish being captured between April and July, and sampling spread relatively equally across 2017 and 2018, with only a few samples collected in 2019 Figure . Field c2.2SbfI restriction enzyme following the BestRAD procedure , ustacks , cstacks . These STACKs parameters were derived based on review papers by Mastretta‐Yanes et al. (We prepared restriction site‐associated DNA (RAD) libraries with the s et al.  and Paris et al.  and haves et al. . SNPs ges et al.  and incls et al.  to inves2.3K) contributing to contemporary structure for each species using the program ADMIXTURE v1.3 , expected heterozygosity (He), and inbreeding coefficients (FIS). We tested the significance of each population comparison with a test for genetic differentiation  using the R package adegenet Jombart, . We alsoR Figure . We thenNe) of each population was estimated with the bias‐corrected linkage disequilibrium method  and implemented a formula for correcting bias based on chromosome number for the other species  population and a putative “Boardman River” (BR) population (see Results for further explanation). To assess this unexpected structure and compare population‐level statistics using sampled populations and inferred populations, we reformed genetic groups based on population assignment from ADMIXTURE Figure  to eithe−10 were retained.We used the program BAYESCAN  or longer‐term isolation that existed before construction of the dam. We did not consider walleye in this analysis as they were likely stocked from multiple nonlocal sources (see Results). To approximate the construction of a dam, we simulated scenarios where a barrier was placed between two panmictic populations 30 generations ago. We chose a generation value of 30 as our species have a generation time of approximately five years Becker, , and theNes on genetic differentiation (FST). Scenarios were simulated 10 times each in the coalescent‐based simulation in program fastsimcoal2  that approximated the resulting Ne for the species we analyzed . For each Ne, we simulated a migration rate of zero and asymmetric migration rates of zero upstream migration and downstream migration rates of 0.01, 0.05, and 0.1, or 1%, 5%, and 10%. Although little information on downstream movement of cool water fishes, such as our study species, through dams is available, a recent study suggested that emigration of walleye out of Iowa reservoirs ranged between 2 and 26% , we performed a one‐way ANOVA and then post hoc Tukey's HSD test for multiple comparisons. Because we saw a mix of genetic groups within each sampling location for smallmouth bass, rock bass, and white sucker, we further split fish into groups for each combination of genetic group and sample location and compared lengths for each species using a one‐way ANOVA and post hoc Tukey's HSD after testing assumptions using the same methods listed above. For all tests, we used α = 0.05.To assess any correlation between genetic structure and ecological attributes, we first compared total lengths of fish between populations within each species. We made comparisons between the originally sampled above‐ and below‐dam populations and between the putative BR and GL genetic groups for each species. We evaluated test assumptions with the Shapiro–Wilk test for normality, visualization of Q‐Q plots, and Levene's test for equality of variances. Length comparisons were performed using Student's two‐sample We also qualitatively assessed whether the proportion of GL and BR fish within each sampling event varied across sample date because several collection events occurred for most species. We plotted the proportion of each genetic group within each sampling event for each species and visually assessed how proportions differed across dates. It is important to note that sampling was not performed evenly across time, sampling effort may not have been consistent between events, and some dates have very low sample sizes (as few as one fish in some instances); therefore, we did not perform a statistical analysis for this assessment.33.1A total of 428 individuals were RAD sequenced and 346 remained after filtering Table . We succ3.2K) from ADMIXTURE was 2 for all species except white sucker (K = 1). However, white sucker did display subtle evidence of population structure at K = 2 and the same subtle structure was apparent in the PCA. Therefore, we split white sucker into two genetic groups based on population assignment from ADMIXTURE as we did with rock bass, yellow perch, and smallmouth bass. For walleye, PCA and ADMIXTURE demonstrated three distinct groupings, with two GL groups separated from the BR group along PC1 and separated from each other along PC2. Although K = 2 was the best fit according to ADMIXTURE cross‐validation for walleye, we suspect this was because the upstream group was highly diverged from both downstream groups due to stocking (see below), and we therefore retained the three genetic groups for plots and analyses.Visualization of ADMIXTURE plots Figure  and PCAsWe also observed highly variable levels of putative admixture across species, ranging from no putatively admixed individuals in walleye to over 50% putatively admixed individuals in the above‐dam samples of white sucker Table . As expeAfter reforming populations using ADMIXTURE assignments, several general patterns became apparent: (1) For rock bass and smallmouth bass, we generally found a single and relatively homogenous Boardman River (BR) group above the dam and mixture of the BR group with the putative Great Lakes (GL) group below the dam. Both of these species also displayed some evidence of putative admixture or representation from more than two populations. In particular, the smallmouth bass data displayed evidence for one or two other intermediate genetic groups (or possibly hybrids). These groups likely correspond to unsampled populations in Lake Michigan, but we did not analyze them separately due to low sample sizes. (2) For white sucker, we found a somewhat similar but much weaker pattern of genetic structure compared to rock bass and smallmouth bass. Specifically, we observed a group of ~20 individuals sampled below the dam that appeared to be differentiated from other individuals in the study and may represent a GL group, but we also observed high levels of admixture in the other samples and very low genetic structure overall. (3) For yellow perch, our above‐ and below‐dam sample sites generally coincided with observed population structure, with low but significant structure observed between samples taken in Traverse Bay and Boardman Lake. However, we did observe that 30% of individuals in the downstream collection were putatively admixed, indicating that either our data do not have the resolution to consistently differentiate Boardman Lake and Traverse Bay yellow perch or that migrants from Boardman Lake have interbred with the Traverse Bay population. (4) For walleye, we observed three distinct genetic groups, a BR group consisting of mostly upstream fish as well as five fish caught downstream that likely originated in Boardman Lake and passed downstream through the dam, and two distinct GL groups consisting solely of individuals caught below the dam. Investigation of the stocking history for this system suggests these three groups are likely the product of decades of stocking from multiple sources GLFC, . These rFST) for all species was highly significant in both original population groupings  and population groupings based on ADMIXTURE analysis. The FST of original populations ranged from 0.0046 (white sucker) to 0.0671  to 0.1053 . Because we changed walleye from the original two populations to three genetic groups, we assessed pairwise FST between the three groups in addition to overall FST. The two GL groups had a pairwise FST of 0.057, and the BR group had a pairwise FST of 0.112 and 0.118 between GL1 and GL2, respectively, providing further evidence to support our hypothesis that the BR group is derived from a highly divergent out‐of‐basin source (New York).Genetic differentiation (Ho) differed by 0.01 or less between original above‐ and below‐dam populations for all species  for all populations grouped based on ADMIXTURE except for white sucker, where FIS was ~0.12 in all populations for all groupings.Observed heterozygosity . Many of these loci were called as homozygotes in STACKs, leading to high frequencies of alternate homozygotes, few heterozygotes, and high FIS values. Unfortunately, few genomic resources exist for Catostomidae, making it difficult to confirm our hypotheses, but we hope that sequenced genomes for this family will clarify the pattern in the future. We do, however, believe that analyses based on our full dataset are robust, as we analyzed population structure with three different datasets  and found the same patterns . We therefore decided to retain our full dataset for all analyses with the caveat that true FIS values may be lower than our estimates.We conducted a number of analyses to attempt to understand why Ne ranged from 14 to over 12,000 and generally increased when populations were grouped according to ADMIXTURE rather than sampling location  and this may have potentially reduced the Ne estimate for this group. Estimates of Ne were generally similar between BR and GL groups, but slightly higher estimates were observed in the BR group for smallmouth bass, white sucker, yellow perch, and a slightly lower estimate was observed in the BR group for rock bass. Alignments to the yellow perch genome were successful for 1970 tags (44%) in walleye and 3746 tags (98%) in yellow perch; these alignments were used to remove bias due to physical linkage when calculating Ne for these species.Estimates of Ne, as species with larger Nes  did not have any related individuals. We retained related pairs for all analyses as we have no reason to believe that rates of relatedness that we observed are nonrepresentative of each population  were present in some but not all species in our dataset, with yellow perch and white sucker containing zero related pairs, walleye containing two, and smallmouth bass and rock bass each containing four Table . RelatedFST were relatively continuous and did not reveal large breaks with highly differentiated loci. Additionally, the fact that only two populations were included in each analysis likely led to low power for detecting outliers. We were able to successfully align four out of 15 outliers to protein sequences, one locus for smallmouth bass, one locus for rock bass, and two loci for white sucker  migration. This indicates that if the population differentiation that we observed in these species was caused by the Union Street Dam, migration between above‐ and below‐dam populations would need to be extremely small (near 1%). However, we observed a large number of individuals of both GL and BR origin below the dam. We therefore hypothesize that the most probable explanation for the patterns of genetic structure that we observed in rock bass and smallmouth bass is the existence of separate BR and GL populations, with GL populations mixing with BR populations in the lower river but rarely interbreeding.Simulations of zero and asymmetric migration (m) revealed that after 30 generations, two populations with y Figure . The twoNes of 1000 unsurprisingly produced smaller FST values, averaging 0.016, 0.012, 0.005, 0.003 under migration rates of 0, 0.01, 0.05, and 0.1, respectively . However, the fact that genetically similar white suckers are found above and below the dam suggests that some other factor may be causing the structure that we observed. Specifically, the ~20 fish sampled downstream that were identified by PCA and ADMIXTURE appear somewhat diverged from the rest  was over two times higher than the FST estimate for the m = 0 scenario, therefore providing the strongest evidence for differentiation before dam construction among our study species. However, we did observe some putative admixture among the two yellow perch groups below the dam, suggesting that some gene flow may be occurring, although based on our simulation results it does not appear to be enough to homogenize the populations.Simulations of two populations with y Figure . The two3.4Lengths of fish were significantly different between BR and GL groups defined by ADMIXTURE for all species Figure . WalleyeFor length comparisons among combinations of genetic group and sample location, the GL‐downstream white suckers were significantly shorter than both the BR‐downstream and the BR‐upstream groups Figure . The samFor most species, the main general pattern that emerged when we visually assessed the proportion of each genetic group by sampling date was the relatively higher numbers of fish caught below the dam during months when spawning was likely occurring Figure . For exa4While dams are ubiquitous worldwide, few studies have assessed the genetic structure of multiple fish species across a small spatial scale (<5 km) in a dammed river. Our results demonstrate some genetic differentiation on the scale of a few km within all five of our study species  in the fragmented lower section of the Boardman River. Patterns of differentiation were variable across species and are likely the result of multiple natural and anthropogenic processes that include fragmentation by the Union Street Dam but were likely more influenced by other factors. In general, we observed three major patterns: (1) in rock bass, smallmouth bass, and, to a much lesser extent, white sucker, we observed that individuals sampled above the dam were generally genetically homogenous and individuals below the dam seem to represent a mixed stock of fish from the above‐dam  and below‐dam  genetic groups, with some admixture among groups, especially in white sucker. (2) In yellow perch, we observed significant but subtle population structure between fish sampled above the dam and in Traverse Bay with a large degree of putative admixture between groups, which may be the result of either true admixture, a lack of resolution in the data, or a combination of both factors. (3) In walleye, we observed high genetic differentiation caused by historic stocking from multiple geographical locations, and no apparent reproduction between the different genetic groups. Our overall results also suggest that, for at least some of our species, individuals from the putative GL genetic group leave their natal habitat in Lake Michigan and enter the lower Boardman River in certain life stages where they coexist with individuals from the putative BR genetic group. Before the Union Street Dam was constructed, both putative BR and GL fish within our study species were able to move freely throughout the Boardman River watershed and Grand Traverse Bay, and each genetic group likely used these different habitats during specific times of the year or during different stages of growth and development. Without our genetic assessment, the overlap of distinct genetic groups in some of our study species such as rock bass and smallmouth bass in the lower Boardman River would have likely continued undetected. These results also highlight the importance of tributary and river mouth habitats for preserving fish populations in the Great Lakes and emphasize that a conservation approach centered around maintaining high connectivity and habitat quality of tributaries is vital for ecosystem health. Finally, our results highlight the fact that patterns of genetic structure can be complex and highly variable across species and caution against over‐interpretation based on data from one or even a few species.4.1Timema cristinae) adapted to different host species exhibit IBA at both neutral loci and loci under putative selection. IBA has also been documented in aquatic systems, including a study by Bond et al.  populations within a small watershed lacking physical barriers, and hypothesized that IBA due to varying selective pressures across different environments was the main driver of observed structure.While geographic isolation is often the largest factor influencing the landscape of genetic differentiation, research has highlighted that isolation by distance (IBD) may not always be the main variable driving contemporary population genetic structure. For example, adjacent populations may experience isolation by adaptation (IBA), in which gene flow is reduced as a result of local adaptation along differing ecological or environmental gradients , and we also observed substantial admixture in samples taken from Traverse Bay. This admixture may be the result of gene flow from Boardman Lake, a lack of resolution due to the large Nes and correspondingly low structure in yellow perch, or a combination of these two factors.Other mechanisms, such as natal homing and site fidelity, can help to create, increase, and maintain differentiation , a nonexistent or extremely low level of downstream gene flow seems highly unlikely in this system, and therefore, it is improbable that the dam alone caused the differentiation we observed. However, the Union Street Dam is definitely a barrier to upstream migration and has substantially altered connectivity in this system. Thus, while we believe that most of the genetic structure that we observed in our study species formed before the dam was built, the dam has likely influenced genetic structure since it was built and will continue to influence structure until it is removed and/or fish passage is facilitated.Although fragmentation by artificial barriers can impact the genetic structure and diversity of fish populations . For rock bass and especially smallmouth bass, GL fish in the lower Boardman River were significantly smaller than the BR fish that they were mixing with. However, it is important to note that our length data must be interpreted with caution due to gear selectivity, variable capture dates and sample sizes, and a lack of age data; nevertheless, the trends we observed in these two species are unlikely to be statistical artifacts as they were highly consistent across the different groups we assessed. Data on variables like fish length and sampling date are available for many population genetics studies, but these data are rarely incorporated into conservation genomics research. We thus demonstrate the utility of incorporating ecological data to gain a clearer picture of fish life history and movement patterns, and we suggest that conservation genomic studies should explore incorporating this type of data more frequently.Most fish species are known to demonstrate ontogenetic niche shifts, switching to different food sources or habitats during different life‐history stages, and movement to nearshore, wetland, or tributary habitats represent a common ontogenetic shift for growing fishes , to maintenance of unique genetic groups despite mixing below the dam , to subtle differentiation with potentially high degrees of admixture (white sucker and yellow perch). It is important to note that even if the Union Street Dam is not the primary driver of these patterns of population structure, it is likely still influencing them, as it has hindered the ability of fish to migrate above the dam into the Boardman River watershed.Without our genetic assessment, the presence of the cryptic genetic diversity that exists across small spatial scales in the lower Boardman River would have continued undetected. Our findings highlight the fact that fish from genetically distinct groups may sometimes overlap spatiotemporally and may also have distinctive life histories with unique population dynamics and habitat requirements during different life stages. These multiple life histories are likely important components of a robust portfolio of within‐species diversity that promotes population stability and resilience in the face of environmental stochasticity (Schindler et al., Even if river fragmentation does not result in population declines or drastic genetic impacts as some studies have observed, the restoration of connectivity between lake and tributary habitat is still essential for conservation and restoration of fish populations. Connectivity between Lake Michigan, Boardman Lake, and the entire Boardman watershed was likely historically important for various components of the life histories of all our study species. Species that migrate from the Great Lakes into tributaries to spawn like white sucker and walleye are more obviously impacted by barriers, but our research suggests that species like rock bass and smallmouth bass, which may not exhibit similarly distinct spawning runs over long distances, may also be negatively impacted by fragmentation. Even if their utilization of river habitat is less obvious, populations of these species in Lake Michigan likely rely upon tributary and DRM habitats, especially as juveniles, for feeding and refuge. The role of tributary and DRM habitat in Great Lakes fishery production has been underappreciated, but studies like ours are illuminating the importance of protecting these unique ecosystems feeding into the Great Lakes, which play a vital role in fish recruitment, growth, and reproduction. Based on our results, we suggest that fisheries managers in the Great Lakes and beyond adopt a more holistic viewpoint of fish populations and the habitats they occupy that considers the existence of unique and partially sympatric genetic groups as well as the importance of habitat connectivity across multiple life stages. For example, fisheries managers could conduct studies to understand the full range of habitats that species and genetic groups occupy throughout their lifetimes and attempt to protect all habitats rather than just spawning sites or areas where fish are harvested.In conclusion, our study combined genetic and ecological data to illuminate cryptic population diversity and heterogeneous patterns of differentiation that could have major implications for how fish populations in the Great Lakes are managed. Specifically, identifying cryptic genetic groups will allow managers to design strategies that ensure these groups are not overharvested and conserve the habitats that they rely on, thereby preserving a robust portfolio of diversity that will lead to more sustainable fisheries and more consistent ecosystem services. Additionally, our workflow which included ecological data, genetic data, and simulations can be applied to other systems to investigate the relative importance of dams and other factors in shaping population structure and how it may vary among species. In conjunction with traditional survey methods, genetics has the power to elucidate cryptic and underappreciated diversity. We suggest that resource managers seek to incorporate genetic analysis into their toolbox more frequently to better understand and conserve important habitats and populations.5The authors declare no conflict of interest.Fig S1Click here for additional data file.Fig S2Click here for additional data file.Fig S3Click here for additional data file.Fig S4Click here for additional data file.Table S1Click here for additional data file.Table S2Click here for additional data file.Table S3Click here for additional data file.


Fermented plant foods are gaining wide interest worldwide as healthy foods due to their unique sensory features and their health-promoting potentials, such as antiobesity, antidiabetic, antihypertensive, and anticarcinogenic activities. Many fermented foods are a rich source of nutrients, phytochemicals, bioactive compounds, and probiotic microbes. The excellent biological activities of these functional foods, such as anti-inflammatory and immunomodulatory functions, are widely attributable to their high antioxidant content and lactic acid-producing bacteria (LAB). LAB contribute to the maintenance of a healthy gut microbiota composition and improvement of local and systemic immunity. Besides, antioxidant compounds are involved in several functional properties of fermented plant products by neutralizing free radicals, regulating antioxidant enzyme activities, reducing oxidative stress, ameliorating inflammatory responses, and enhancing immune system performance. Therefore, these products may protect against chronic inflammatory diseases, which are known as the leading cause of mortality worldwide. Given that a large body of evidence supports the role of fermented plant foods in health promotion and disease prevention, we aim to discuss the potential anti-inflammatory and immunomodulatory properties of selected fermented plant foods, including berries, cabbage, and soybean products, and their effects on gut microbiota. Chronic inflammatory diseases are the leading cause of mortality worldwide ,2. InflaHistorically, natural products have been known to exert significant biological and pharmacological properties and play a valuable role in drug discovery and treating many diseases ,7,8,9. DTraditionally, fermentation was a method to preserve foods for a longer time; however, this process recently has attracted great attention due to the increase in the nutritional value of foods and the production of health-promoting components ,18,19,20S. boulardii, these microorganisms may be destroyed during the heating process yielding fermented products with no live microbes. However, even in these cases, the fermentation process before pasteurization will enrich the fermented foods with small compounds released from existing phytonutrients or by releasing active metabolites from the fermentation process itself [Nowadays, a wide range of fermented products is produced and consumed worldwide ,23. Alths itself ,29. The gut remains the most important organ in which fermented foods exert their beneficial effects, either by systemically modulating the immune response or positively influencing the gut microbiota. ,29. The The anti-inflammatory and immunomodulatory properties of plant-based fermented foods are well documented ,41,42. TMoreover, fermentation will lead to the degradation of complex phytochemical molecules into smaller bioactive polyphenols. Studies have shown that polyphenolic compounds found in fermented products are beneficial in microbiota metabolism and growth  and can Different plants, such as fruits, vegetables, tea, grains, legumes, and starchy roots, are used to produce plant-based fermented foods ,23,29,53Berry fruits are well known for their significant health benefits ,59. VariFermentation may increase the positive effects of berries due to an increase in polyphenols and the antioxidant capacity of fermented products . Figure Fermented blueberries and blackberries modulate the gut microbiota populations by increasing beneficial bacteria. Moreover, they improve the production of short-chain fatty acids (SCFAs) and enhance mucosal immunity by promoting secretory IgA (sIgA) cells through increasing TGF-β activity. Fermented blueberries and blackberries also induce antioxidant enzymes like superoxidase dismutase (SOD), which increase the radical scavenging capacity. Furthermore, the inflammatory responses are inhibited by inhibiting macrophage pro-inflammatory mediators release . It also influences immune cells by inhibiting Th17 activity and the differentiation of Tregs. The phenolic compounds released by the digestion of blackberries and blueberries inhibit PI3K/Akt/NF-κB signaling pathway and improve the gut barrier. Moreover, phenolic compounds decrease gut permeability by inhibiting TNF-α and its downstream, including ERK1/2 and MLCK. Created with Biorender.com (accessed on 29 April 2021).Blueberries are among the richest sources of phenolic compounds, such as anthocyanins, flavonols, and proanthocyanidins which possess high antioxidant capacity ,68. BecaSerratia vaccinii, isolated from blueberry microflora) have been reported to suppress NO production activated by LPS/interferon-gamma (INF-γ) in mouse macrophage [Lipopolysaccharide (LPS) is the main outer layer component of Gram-negative bacteria which can stimulate the innate immune system and inflammation by activation of the Toll-like receptor-4 (TLR4)/NF-κB signaling pathway . LPS-sticrophage . Also, fcrophage . Fermented blueberries may counteract obesity and diabetes, at least partly, through anti-inflammatory and antioxidant activities . The admSerratia vaccinii induced antioxidant enzymes activities and prevented neuronal cell death through the upregulation of cell survival signaling pathways such as MAPK family enzymes p38 and c-Jun N-terminal kinase (JNK) and the downregulation of cell death pathways such as ERK1/2 and MAPK/ERK kinase (MEK1/2) [Lactobacillus plantarum (L. plantarum) has been found in human cervical carcinoma HeLa cells [Fermented blueberries may prevent neurodegenerative disease through anti-inflammatory and antioxidant activity . ROS-ind(MEK1/2) . FurtherLa cells .Lactobacillus casei (L. casei) increased its antioxidant activity by a significant increase in superoxide dismutase activity and radical scavenging capacity. Fermented blueberry pomace also improved gut function by altering fecal microbial composition through inhibiting Escherichia coli, Enterococcus, and increasing the abundance of beneficial microbiota such as Bifidobacterium, Ruminococcus, Lactobacillus, Akkermansia genera, and butyrate-producing bacteria, and increasing the short-chain fatty acid (SCFAs) production [L. casei-fermented blueberry pomace was assayed on gut immunity and microbiota [Evidence has shown the health benefits of fermented blueberries may be to some extent attributable to gut microbiota modulation ,85. In aoduction . In the crobiota . Besidescrobiota  and the crobiota ,88.L. plantarum DSM 15,313 significantly decreased blood pressure in healthy rats and rats with L-NAME induced hypertension, while a change in gut cecal microbiome was observed in healthy rats [L. plantarum fermented blueberries [The antihypertensive activity of fermented blueberries through gut microbiota modulation has been studied in rats ,90. In athy rats . In a siZhong et al. (2020) investigated the effect of blueberry products on metabolic syndrome by regulating the gut microbial population . They suBlackberry is known for its high content of antioxidant compounds, particularly anthocyanins, ellagitannins, gallic acid, and significant antioxidant capacity based on its high oxygen radical absorbance capacity ,94. PrecSome studies have shown the anti-inflammatory potential of anthocyanins and proanthocyanidins from fermented blueberry–blackberry beverages through NF-κB signaling inhibition . AdiposeL. plantarum increased the antioxidant capacity of the fruit, inhibited activation of NF-κB signaling, and reduced the production of iNOS and COX-2 [Oxidative stress contributes to the photoaging process. The high expression of iNOS and cyclooxygenase 2 (COX-2) in photoaged skin has been reported ,102. UVBnd COX-2 .Although we could not find published research investigating the fermented blackberry products’ influence on the gut microbial composition, the beneficial impact of non-fermented blackberry and its compounds on gut microbiota and the mitigation of inflammatory conditions related to gut microbial dysbiosis has been investigated . For exaCabbage is a rich source of phenolic compounds and is well known due to its unique health benefits such as anti-inflammatory, antioxidant, and cancer-protective properties . StudiesLeuconostoc mesenteroides (L. mesenteroides), L. plantarum, Lactobacillus brevis, Pediococcus pentosaceus, and Enterococcus are the main bacterial species involved in the fermentation process of cabbage [Sauerkraut is a nutritious fermented cabbage product widely consumed as a traditional dish in many European and Asian nations and the United States ,111. It  cabbage ,113,114. cabbage ,112. Sci cabbage ,115,116. cabbage . This fe cabbage . In a st cabbage . Allyl i cabbage .L. mesenteroides, the key bacteria in the initiation fermentation process of sauerkraut, to sauerkraut enhances the innate and adaptive immune response in Escherichia coli-infected BALB/c mice [Furthermore, sauerkraut is a unique source of LAB ,120,121.B/c mice .L. plantarum and L. brevis were significantly higher in fecal samples of the group consuming unpasteurized sauerkraut, indicating that prebiotic bacteria were partly responsible for favorable effects of sauerkraut in IBS [There are limited human studies concerning the health benefits of sauerkraut and its effect on human gut microbiota. In a clinical trial involving 8 patients with mesenteric angina, significant differences were observed in disease burden between the 2 groups receiving French cassoulet or international sauerkraut . In anott in IBS . D-phenyt in IBS . 8–9 CFU/g LAB remaining alive in kimchi after the fermentation [Kimchi is a naturally fermented vegetable food with LAB ,131. It entation . Differeentation ,132,139.A large body of research has proven the positive health effects of kimchi ,140,141.On the other hand, preclinical and clinical studies show that kimchi’s medicinal benefits might be associated with gut microbiota modulation ,145. ForLactobacillus sakei (L. sakei) WIKIM30, a probiotic strain extracted from kimchi, to mice with 2,4-dinitrochlorobenzene-induced atopic dermatitis inhibited Th2 immune response and Th2 related cytokines , regulated Th1/Th2 balance, induced Tregs differentiation and decreased skin lesions [L. sakei WIKIM30 against atopic dermatitis could be mediated by its effect on gut microbiota. Treatment with this probiotic restored the changes in gut microbiota composition induced by atopic dermatitis toward an elevation in Arthromitus and Ralstonia and a reduction in Ruminococcus abundance [L. sakei probio-65, extracted from Kimchi, ameliorated skin inflammation and lesions by reducing serum IgE and/or inhibition of Th2-related cytokines [L. plantarum K-1 isolated from kimchi may mitigate inflammation and alleviate allergic diseases by suppressing the TNF-α and IL-4 expression and inhibiting NF-κB activation [Anti-inflammatory and immunomodulatory functions of LAB derived from kimchi through the regulation of gut microbiota have been exhibited in animal models of allergic skin disorders . Atopic  lesions . Dysbios lesions . Microbibundance . Besidesytokines . L. plantivation . Lactobacillus paracasei (L. paracasei) LS2, a lactic acid bacterium derived from kimchi, increased CD4+FOXP3+ Treg cells and anti-inflammatory cytokine IL-10 production in mice with colitis. It also decreased IL-6, TNF, and INF-γ levels in colon tissue. The colonic activity of myeloperoxidase (MPO) was significantly reduced in mice fed with L. paracasei LS2. Colonic MPO activity indicates neutrophil infiltration and tissue damage [L. mesenteroides and L. sakei are other kimchi-extracted LAB with significant potential for attenuation inflammation in experimental colitis [Kimchi-derived LAB display potential for alleviating IBD in mice ,153. Adm colitis . Figure L. mesenteroides was reported in participants’ fecal samples [In the clinical setting, Kim et al. (2016) found that kimchi could affect gut microbiota composition for its singular synbiotic content . They ex samples . Ferment samples ,155. In  samples .L. paracasei) inhibit the pro-inflammatory mediators and inflammatory enzymes, including IL-6, TNF, IFN-γ, and myeloperoxidase (MPO), and alleviate inflammatory bowel disease. LAB also prevent obesity by modulating gut microbiota toward decreasing Firmicutes’ abundance and increasing Bacteroidetes frequency. Furthermore, LAB extracted from fermented cabbage foods  restore gut microbiota and modulate immune responses by regulating Th1/Th2 balance, inhibiting Th2-related cytokines, inducing Tregs differentiation, and reducing IgE level, alleviating allergic reactions such as atopic dermatitis. Created with Biorender.com (accessed on 29 April 2021).Fermented cabbage-derived LAB  is considered a pro-inflammatory condition. Various factors such as increased inflammatory cytokines, oxidative stress, and gut dysbiosis contribute to CKD’s inflammatory state . He et aol group . A decrereported ,195. Fern in CKD . Bacillus amyloliquefaciens have been seen in an Asian type 2 diabetic animal model. Feeding rats with this product improved glucose metabolism, insulin secretion, and sensitivity. The number of beneficial gut bacteria such as lactobacillales and Akkermensia muciniphila increased, leading to the maintenance of mucin content, the villi area, and the frequency of goblet cells in the gut [Antidiabetic features of the short-term fermented soybean with  the gut .L. plantarum, L. casei, Lactococcus lactis, L. mesenteroides, and Saccharomyces florentinus, could increase the frequency of beneficial bacteria, bifidobacteria, and lactobacilli, in the feces of participants, while a decrease in abundance of fecal clostridia was observed [Research evidence has elucidated the health-promoting features of fermented soy in humans as well . Due to observed . Similarobserved . Besides, recent evidence suggested that short-term fermented soybean intake can be protective against memory impairment and Alzheimer’s disease in humans by suppressing insulin resistance in the brain, preventing neuroinflammation, and modulating the gut–microbiome–brain axis .Furthermore, large population-based studies have proved the health benefits of fermented soy in humans. For example, a population-based cohort study conducted by The Japan Public Health Centre-based Prospective Study evaluated the association of soybean product consumption and all-cause and cause-specific mortalities during 15 years. They showed that the higher intake of fermented soy products significantly correlated with a lower risk of mortality. However, there was no significant association between a higher intake of total soy and mortality rate .Scientific research highlights the significance of gut microbiota-directed interventions by diet enrichment with functional natural products such as probiotics and phytochemicals as a promising strategy to promote immune system performance, modulate inflammatory responses, and improve human health. Fermented fruits, vegetables, and grains enrich our diet with numerous live microorganisms, phytochemicals, and bioactive compounds. These compounds play a key role in the functional and health-promoting properties of fermented products. Due to the high content of phenolic compounds with strong antioxidant activity, fermented blueberries and blackberries may protect against chronic inflammatory disorders by decreasing oxidative stress, modulating inflammatory signaling and responses, and improving immunity. Regarding fermented cabbage products, sauerkraut and kimchi, live LAB are the key player in improving health and preventing chronic diseases through improving a healthy gut microbial balance and modulating inflammatory and immune responses. Fermented soybeans are an excellent source of isoflavones with known anti-inflammatory properties. Furthermore, probiotics found in fermented soy products contribute to the health benefits of these nutritious foods. Overall, growing evidence is strongly supporting the health benefits of fermented plant foods. However, existing evidence has been chiefly generated from in vitro and animal studies, and there exist rare clinical studies in this field. Therefore, the potential role of fermented plant products in human health remains to be determined by randomized, controlled clinical trials.


Bottom-up synthesis of β-glucans such as callose, fungal β--glucan and cellulose, can create the defined compounds that are needed to perform fundamental studies on glucan properties and develop applications. With the importance of β-glucans and cellulose in high-profile fields such as nutrition, renewables-based biotechnology and materials science, the enzymatic synthesis of such relevant carbohydrates and their derivatives has attracted much attention. Here we review recent developments in enzymatic synthesis of β-glucans and cellulose, with a focus on progress made over the last five years. We cover the different types of biocatalysts employed, their incorporation in cascades, the exploitation of enzyme promiscuity and their engineering, and reaction conditions affecting the production as well as in situ self-assembly of (non)functionalised glucans. The recent achievements in the application of glycosyl transferases and β-1,4- and β-1,3-glucan phosphorylases demonstrate the high potential and versatility of these biocatalysts in glucan synthesis in both industrial and academic contexts.  •Carbohydrate-active enzymes are vital for the bottom-up preparation of β-glucans.•Glycosyltransferases and glycosyl hydrolases widened the scope for β-glucan synthesis.•Glycoside phosphorylases can produce various well-defined cellulose-like materials.•Recent applications demonstrate their high potential, robustness and versatility. Amongsoduction . In thisoduction , thus em4.2Bifidobacterium longum subsp. longum and 1,2-β-oligoglucan phosphorylase from Listeria innocua expressed in E. coli [. A similar scale was also achieved utilising sophorose as the acceptor [In the presence of inorganic phosphate >100 g scale β-1,2-glucan synthesis was achieved enzymatically using a two-enzyme system of sucrose phosphorylase from  E. coli . A similacceptor . Few tooacceptor .4.3Euglena gracilis [E. gracilis and circumvent the laborious preparation of extract as source of catalyst, the Field group identified the algal β-1,3-glucan phosphorylase and uncovered a new family of phosphorylases (GH149). The candidate phosphorylase sequence (EgP1) was selected through proteomic analysis from cellular protein lysate and expressed in E. coli for in vitro characterisation and confirmation of its catalytic activity. Moreover, EgP1 orthologous sequences led to a bacterial metagenomic sequence that was expressed in E. coli (Pro_7066) and assessed to validate its function as β-1,3-D-glucan phosphorylase. Despite comparable activity in the synthesis of short oligosaccharides (DP up to 12) from D-Glc and α-D-Glc-1P, kinetic data suggested EgP1 preference for glucose and cellobiose whereas Pro_7066 showed similar catalytic efficiency for all tested acceptors (DP1 to DP6) [Ochromonas spp (OcP1) was identified along with bacterial orthologs, thus leading to the identification of a new GH family (GH161). The activity of GH161 family members was established from a bacterial GH161 gene sequence  cloned and expressed in E. coli. Recombinant PapP showed neither synthetic activity in the presence of D-Glc and α-D-Glc-1P, nor phosphorolysis activity towards Glc-Glc disaccharides with different linkages. Nonetheless, phosphorolysis assays with DP3 to DP6 revealed its preference for longer substrates. These results strongly suggest that PapP can only operate on β-1,3 linear oligosaccharide acceptors with DP ≥ 2, evidencing its different specificity from GH149 glucan phosphorylases [The families GH94 and GH112 have been previously reported to contain inverting GPs with regio- and stereospecificity towards the synthesis of β-1,3-linked disaccharide or oligosaccharides. The in vitro preparation of disaccharide has been catalysed by bacterial laminaribiose phosphorylase  ,125 whergracilis . In orde to DP6) . In a coorylases . These s6HQ6) and in complex with laminarihexaose (PDB code 6HQ8) were solved. Although the overall domain organisation is similar to GH94, Pro_7066 enzyme contains two additional distinct domains flanking its catalytic region and a surface oligosaccharide binding site where laminarihexaose was accommodated, which is distant from the catalytic site and may be involved in the recognition of longer substrates [Paenibacillus sp YM-1  demonstrated its specificity for disaccharides. Interestingly, PsLBP was not only limited to act on d-laminaribiose as previously described [d-mannopyranosyl-1,3-d-glucopyranose from D-Glc and α-D-Man-1P, albeit with reduced catalytic efficiency (ca. 150-fold). Structural data of PsLBP in complex with α-D-Glc-1P and α-D-Man-1P, together with saturation transfer difference (STD) NMR studies, revealed a similar binding mode for α-D-Man-1P due to its close overlapping with α-D-Glc-1P. However, it was observed the loss of an important hydrogen bond between the axial hydroxy group at C2 and a key residue in the active site, thus possibly contributing to the low reaction turnover [d-laminaribiose and d-cellobiose, though the transference of glucose onto both acceptors was effective. By producing mixed new β-1,4- and β-1,3-linkages from different donor substrates, the idea of ‘one enzyme to one linkage’ can no longer be applied to these phosphorylases in spite of their stereo- and regiospecific. Despite the natural tendency to generate long oligosaccharides, single turnover also was observed in some reactions. Considering that the lack of suitable enzymes hampers the synthesis of novel oligosaccharides, these results are highly encouraging as they uncover new features of GPs and start unfolding access to unique structures.In order to gain understanding of β-1,3-D-glucan phosphorylases at the molecular level and elucidate the basis for oligosaccharides chain length specificity of GH149 enzymes, the X-ray crystallographic structures of Pro_7066 in the absence of substrate  in the presence of external D-Glc. The maltose generated as by-product of α-glucan phosphorylase reaction was recycled into malto-oligosaccharides by 4-α-glucanotransferase for continuous production of α-D-Glc-1P (d-laminaribiose (>80%) from low and high concentrations of starch (Despite all the advantages of using sucrose as substrate and SCP as biocatalyst towards the synthesis of functional disaccharides, alternative multi-enzyme approaches have been also investigated using starch. Starch is cheaper than sucrose, the phosphorolysis produces more α-D-Glc-1P and the ratio α-D-Glc-1P/D-Glc can be tuned for optimisation . In thisD-Glc-1P D. High cf starch B. Some d5The use of natural glycosyltransferases and unnatural glycoside hydrolase mutants has broadened the scope for β-glucan synthesis, which, in tandem with phosphorylases, offers great diversity in producing designer β-glucans. The reconstitution of glycosyltransferases in vitro has enabled the polymerisation of glucose to form a variety of cellulose morphologies, whilst the advancements seen with glycosynthases offer great potential for the design of β-glucans with tailored functionalities. The current applications of β-1,4- and β-1,3-D-glucan phosphorylases highlight their relevance along with other important carbohydrate-active enzymes. From solo catalysis to multi-enzymatic cascades and from long-chain insoluble to short-chain soluble oligosaccharides, these wild-type enzymes are capable of producing a broad range of well-defined natural and unnatural structures in a controlled manner due to their tolerance to different reaction conditions and substrates. In particular, the oligomerisation-induced self-assembly of insoluble β-1,4 oligomers into highly ordered hierarchical nanostructures can be directed by simply tuning reaction conditions in the presence of natural substrates or using chemically functionalised substrates, thus representing a promising approach towards the bottom-up preparation of tailored cellulose-like materials. Considering what has been recently achieved, but also the prospect discovery of new phosphorylases , the greThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.