As the world continues to urbanize, it is necessary to identify and implement new urban development models and strategies in order to meet the challenges of sustainable development. As cities continue to face challenges in becoming fully circular, the need to establish a framework to measure the circular economy in urban areas grows. Many definitions for circular cities have been developed and addressed in recent years, as have numerous indicators. To make the transition to a circular city, we must integrate the findings and develop a general definition and measurement framework. This article aims at outlining a framework for circular cities indicators based on their key characteristics, as well providing directions for fostering circularity at the city level. To accomplish this goal, we conducted a systematic review and analyzed key papers published in the field of circular economy to determine how circular cities are measured. Choosing the right indicators to use for developing, monitoring, and evaluating circular cities is a difficult task for urban policymakers, managers, and planners. This highlights the significance of standardized frameworks for urban indicators. As a result, the authors propose a framework and highlight some key points about circular cities and smart urban metabolism. The majority of the world’s population currently lives in cities, with the 50% mark being broken in 2007. The global urban population share was 55% in 2018 . Cities The present linear model of production and consumption in modern society is unsustainable. It is urgent to address the socioeconomic issues of population increase and a growing middle class . The cirIndicators to assess the efficiency of the circular economy model are necessary to support the transition towards the circular city model. Currently, there is not a set of indicators for assessing how a city is effective in moving towards circularity, nor are there tools for supporting it.While acknowledging that many metrics have already been developed that could be used to measure circularity, the present review focuses on the frameworks, indicators, and indices specifically developed for the environment, economics, and social and smart technologies at a macro level.The concept of sustainable development presented by the World Commission on Environment and Development (WCED) in the report “Our Common Future”, often known as the Brundtland Report, is undoubtedly the most well-known definition: “sustainable development is growth that seeks to meet the needs of the present without compromising the ability of future generations to meet their own needs” . This inCradle to Cradle: Remaking the Way We Make Things) divide materials into two categories: biological materials designed to safely re-enter the biosphere and contribute to the restoration of natural capital, and nonbiological materials [In terms of circular economy, the researchers discovered 114 different definitions on 17 dimensions after analyzing the literature . The Ellaterials ,11.The circular economy is an economic model that was created as an alternative to the linear economic model that is presently used in most industries. The linear model comprises the production and consumption of products and services without assigning significant weight to environmental externalities, such as waste generation and pollution resulting from the exploitation of virgin resources, a model in which economic goals take precedence. The circular economy, on the other hand, refers to the production and consumption of goods and services through closed-loop material flows that account for environmental externalities that occur during the design phase of products and goods with the goal of decoupling economic prosperity from resource consumption .According to official data, the European Union generates more than 2.5 billion tons of waste each year. It is actively adjusting its waste management regulations in order to facilitate the transition to a more sustainable circular economy. In March 2020, the European Commission announced a new action plan for the circular economy, which includes ideas for a more sustainable product design, waste reduction, and consumer empowerment, as part of the European Green Agreement and in line with a planned new industrial strategy (repairs).Parliament passed a resolution on the new circular economy action plan in February 2021, calling for additional measures to achieve a carbon-neutral, environmentally sustainable, nontoxic, and fully circular economy by 2050, including rigorous recycling rules and mandatory material use and consumption targets by 2030.“You can’t improve what you can’t measure!” as the saying goes. There are four key objectives in measuring the state of the art, progress, and impact of a circular economy, according to the Organization for Economic Co-operation and Development (OECD) report “Circular Economy in Cities and Regions”, which looked at 51 cities and regions. The four key objectives are: raising awareness, supporting the circular economy, triggering actions, performance monitoring, and evaluation of results .Over 90% of raw materials used around the world are not recycled, resulting in tremendous overexploitation of our planet’s finite natural resources and a harmful impact on our climate. Every year, Earth Overshoot Day, an annual measure of the planet’s overexploitation, gets closer. It fell on 29 July this year, although it was 23 September before the turn of the century. Greenhouse gas emissions and pollution have risen to historic levels, posing a threat to the planet’s overall well-being .In their search for solutions, the forerunners of sustainability began to shift away from the widely held linear economy of products and services production and use  toward circular solutions use, recycle). By decreasing the usage of raw materials and keeping materials in the loop for as long as is feasible, circularity offers the potential to address our world’s global sustainability concerns. Every human-made product’s ecological footprint is reduced and minimized, protecting the world. According to UN Environmental Statistics, a fully circular economy could reduce resource use by 28% while also reducing carbon emissions by 72% .The local government can encourage the reuse of building materials from its own construction, renovations, and demolition projects. As a result, the market for recycled building materials is stimulated.Grants, incentives, and tax exemptions can be used by local governments to encourage the development of new technology.Cooperation with other advanced cities to facilitate circular economy implementation. Supporting bottom–up efforts .Cities are growth engines that require monitoring and control. These are the primary causes of climate change, accounting for up to 76% of all carbon emissions. Despite occupying fewer than 2% of the Earth’s area, they account for 75% of world natural resource usage and 50% of global garbage generation. In terms of solutions, cities attract creative talent and so allow societal transformations toward sustainability in both the public and commercial sectors. Cities are among the most powerful entities that can impact development favorably if they become circular:“A circular city is based on closing, slowing and narrowing the resource loops as far as possible after the potential for conservation, efficiency improvements, resource sharing, servitization and virtualization has been exhausted, with remaining needs for fresh material and energy being covered as far as possible based on local production using renewable natural resources”An integrative definition for circular cities is the one synthesized by Paiho et al. (2020):Cities are one of the largest consumers of resources, but at the same time, they are incubators for innovation and have a huge potential to lead the transition to a circular economy. It has become really important to understand and define operable circular city indicators in order to understand where cities and citizens can have a real impact.The word “indicator” is derived from the Latin verb “indicare”, which means “to reveal” or “to indicate”, as well as “to announce” or “to make public”. According to Gallopin, indicators are valuable instruments for analyzing conditions and trends , comparing places and situations, providing early warning information, and forecasting conditions and trends . In termIn the context of circular cities, indicators can be used for monitoring, evaluation, and decision making. In reality, while establishing plans for the development and implementation of the circular economy, they can aid decision making by government officials and policymakers at both the local and national levels ,20.At the corporate level, however, indicators can assist managers in resolving operational difficulties, such as recognizing prospects for circular cities that are not currently (completely) explored and enhancing efficiency in utilizing existing synergies .It is vital to build a measurement system in order to determine whether the CE principles result in meaningful changes . “The abMany indicators are at the national level rather than at the level of the product ,26. The The circular economy (CE) is being adopted by a number of countries throughout the world. Material flow analysis (MFA), energy analysis, and input–output analysis are currently used in macroscale CE monitoring . In 2008Despite these efforts, there is no one-size-fits-all circular economy model at this time. Different players have different views of what CE can or should describe , and theHowever, because the concept is unclear and the indicators could lead to varied or even contradictory findings, what has to be monitored is being contested. Some authors have looked at the tools and approaches that have already been employed. Elia et al.  assessedThe monitoring framework for the circular economy is a concept for measuring development in the EU and its member states . The indThe indicators proposed by the European Commission are intended to assess and track the progress of the circular economy strategies announced by the European Union in the CE Action Plan 2015 , which mThis study aims to see how the indicators of circular cities are distributed and to see which main themes are repeated frequently. This article aims at outlining a framework for circular cities indicators based on their key characteristics, as well as to further investigation and providing directions for fostering circularity at the city level. In order to do that, this study analyzes the most important research papers within the database of Web of Science (WOS). The goal is to see all the indicators and categories discussed in the research papers in order to understand the current situation in the academic world and how far the sector is from measuring circularity at the city level.We used the stages suggested by Kitchenham  and adapIn order to provide an overview of how the scientific community responds to methodological issues in order to measure the circularity of cities, a review of the literature on the WOS search engine was conducted using keywords in different combination . The search focused on (1) scientific articles or book chapters published in English from 2010 to 2021; (2) topics of interest ; (3) the comparison between the measurement of circular cities in Europe versus the world. Metrics available online without a published methodological background were not considered in the present study.This report, which is structured in the form of a review of circular city indicators, consisted of an advanced search, with the aim of exploring the instruments used to measure circularity with a focus on the city. The search was followed by a screening process, performed by reading the title and summary of each result.The search returned 1065 results of which only 130 articles were considered the most representative at the first screening process . In the Results came from examining research articles, including a series of other reviews in which indicators of circular cities were studied, both established by researchers in scientific journals and by analyzing a series of official documents and reports of cities that have adopted the circular city model. Thus, a number of interesting elements for aspects related to city measurement were observed after taking further steps in the transition to a circular economy.“A circular city is based on closing, slowing and narrowing the resource loops as far as possible after the potential for conservation, efficiency improvements, resource sharing, servitization and virtualization has been exhausted, with remaining needs for fresh material and energy being covered as far as possible based on local production using renewable natural resources”Following our analysis, we discovered that there are various perspectives and definitions of circular cities. It would be extremely beneficial if everyone starts using a comprehensive definition that takes into account the various perspectives that researchers have when addressing circular cities. As a result of these various perspectives and definitions, we discovered that Paiho et al.  developeHowever, even this definition which tried to synthetize more perspectives is referring mostly to the environmental dimension and it does not include the economic and social components. In order to make the definition more complex, we added to the above vision, as follows: It also integrates a way across all its functions in collaboration with researchers, citizens, and businesses in order to improve human well-being, reduce emissions, protect and enhance biodiversity, and promote social justice, in line with Sustainable Development Goals.Additionally, not only were there many definitions for circular cities, this was also the case with indicators that were not very clearly defined or transparent. They often appear in articles, but there is no definition to understand exactly what they measure. To make the analysis easier, we attempted to categorize the information identified into four categories: environmental, social, economic, and smart.Analyzing the indicators according to the framework proposed by us, it emerged that the indicators reported in scientific studies are primarily concerned with the environmental aspect, with little attention paid to the economic and social aspects. In the review conducted by Girard and Nocca , a totalIn addition, we discovered some smart city indicators in the literature. These cities indicators, along with economic, environmental, and social indicators, are especially important at this time because we can now discuss smart urban metabolism. Considering the pandemic as well as the importance of technologies and digitalization, they should be incorporated into the measurement of circular cities.Smart urban metabolism is a hybrid approach to developing smart and sustainable cities that takes into account technological, economic, environmental, and social perspectives. Smart urban metabolism becomes a strategic tool for decision makers, urban managers, and planners as a result of this feature. However, little is known about smart urban metabolism indicators.To understand the functioning of cities and improve the efficiency of the processes that characterize urban metabolism, a holistic and multidimensional vision is required in order to support the following: waste management ; energy 2 emissions, toxicity, and land use); in addition, there were 46 social indicators identified  and a series of standards and indicators for smart cities.However, the proportion of indicators identified in official documents and city-level reports is slightly different. As a result, a series of 96 environmental indicators were identified , as well as 35 economic indicators  is gaining attention, and monitoring frameworks and assessment tools are critical for documenting and measuring progress.This systematic review examined the state of circular indicators at the city level, focusing on the issue of a large number of indicators that are currently more focused on the economic components in the scientific literature, with less emphasis on the social or environmental components and often exclude the smart technology factor.Additionally, computerization, digitalization, and the efficiency of urban processes have become a priority for the development of residential areas, with the concept of “intelligent urban metabolism”/smart sustainable cities allowing us to achieve this goal ,139. In This paper contributes to the theoretical development of the concept of circular cities by providing an integrated framework for circularity in cities and proposing some direction in order to make city measurement possible.Circular cities indicators can be criticized for their lack of transparency and scientific basis. It was almost impossible to see how an indicator with similar label differs from another because of the lack of definitions. In order to solve this problem, we suggest that future studies standardize categories of indicators in order to have an appropriate level of transparency, quality, reliability, and consistency in their results. As a result, moving forward in measuring cities is extremely difficult if a widely accepted academic concept for the circular economy, circular cities, and indicators is not yet available. To figure out how to organize the indicators or how to use them to help with the transition to a more circular economy, we must first figure out what we want to monitor.The authors believe that analyzing all of the criteria used to construct the indicators for circular cities would be beneficial to a future study as well. The authors tried to organize them by level of concern  in this review; however, in the literature we can see that there are many different ways in which these indicators have been organized and developed. We consider that the way we organize is simple and easy to use by everyone. Future studies could include more databases and articles to see which indicators are used the most according to the areas proposed by us and to improve this framework.Future research should also consider using indicators that already exist and incorporating them into models such as Lakatos et al. , as wellAside from the many different types of categories, we believe that having so many indicators can be a problem, making the implementation and measurement of cities more difficult and overwhelming.


Nesoenas mayeri) is an endemic species of Mauritius that has made a remarkable recovery after a severe population bottleneck in the 1970s to early 1990s. Prior to this bottleneck, an ex situ population was established from which captive‐bred individuals were released into free‐living subpopulations to increase population size and genetic variation. This conservation rescue led to rapid population recovery to 400–480 individuals, and the species was twice downlisted on the International Union for the Conservation of Nature (IUCN) Red List. We analyzed the impacts of the bottleneck and genetic rescue on neutral genetic variation during and after population recovery (1993–2008) with restriction site‐associated sequencing, microsatellite analyses, and quantitative genetic analysis of studbook data of 1112 birds from zoos in Europe and the United States. We used computer simulations to study the predicted changes in genetic variation and population viability from the past into the future. Genetic variation declined rapidly, despite the population rebound, and the effective population size was approximately an order of magnitude smaller than census size. The species carried a high genetic load of circa 15 lethal equivalents for longevity. Our computer simulations predicted continued inbreeding will likely result in increased expression of deleterious mutations  and severe inbreeding depression. Without continued conservation actions, it is likely that the pink pigeon will go extinct in the wild within 100 years. Conservation rescue of the pink pigeon has been instrumental in the recovery of the free‐living population. However, further genetic rescue with captive‐bred birds from zoos is required to recover lost variation, reduce expression of harmful deleterious variation, and prevent extinction. The use of genomics and modeling data can inform IUCN assessments of the viability and extinction risk of species, and it helps in assessments of the conservation dependency of populations.The pink pigeon ( The pink pigeon is an endemic species of Mauritius that experienced a population decline over several centuries due to habitat fragmentation and destruction and invasive species Jones, . These fThe GDEWS has been a source of genetic variation for the free‐living metapopulation, and it was used to establish all current subpopulations, except for the subpopulation in Pigeon Wood, which survived in the wild in the 1970s , Plaine Lievre (PL) (n = 12), Bel Ombre (BO) (n = 12), Pigeon Wood (PW) (n = 10), and Combo (CO) (n = 25) sampled from 1994 to 2008.Genome‐wide genetic diversity of the free‐living pink pigeon metapopulation was analyzed by restriction site‐associated DNA sequencing (RAD‐seq) of 175 birds from 5 subpopulations: Ile aux Aigrettes (IAA)  buffer solution, prior to DNA extraction, using the Agencourt GenFind V2 Blood & Serum Genomic DNA Isolation Kit. The RAD‐seq libraries (SbfI digests) were constructed from each DNA sample  with RaGOO (1.1)  recapitulating the families in the IAA population was created in a Jupyter notebook with the pandas framework  was calculated in GenAlEx 6.5  was calculated using rarefaction. Effective population size (Ne) was calculated for the entire free‐living metapopulation from the earliest year of sampling (1993) to the latest year (2010) and for each subpopulation for the 2 sample groups (1990s and 2000s) with the linkage disequilibrium method (LDM) in NeEstimator 2.01  and 2003 to 2011 (2000s), were genotyped at 22 loci. The samples comprised 571 free‐living birds from 5 subpopulations in Mauritius, 36 birds from the captive GDEWS in Mauritius, 25 birds from U.S. zoo collections, and 27 birds from European zoos Appendix . Mean unFST and DJOST, between captive populations and subpopulations of pink pigeons of the 1990s and 2000s was calculated with GenAlEx  among captive and wild subpopulations in the 1990s and 2000s. The most likely number of clusters were identified by evaluating log likelihood and delta K scores  on the genetic variation in the combined gene pool of 64 free‐living pink pigeons sampled in 2010. We quantified the effects of reintroduction by analyzing the effective number of alleles (Ae) and the actual number of alleles (A).We employed additional computer simulations coded in a Minitab 12.1 macro to evaluate the effects of genetic supplementation on microsatellite variation. Using 52 captive birds from Europe and the United States genotyped at 22 loci, we examined the impact of reintroducing a random subsample of those birds (F). The inbreeding coefficient was calculated based on studbook data with PMX 1.4.2  and B, the slope of the regression line, equals the number of LEs in a haploid gamete. In a diploid individual, 2B equals the number of LEs  and calculated using a logistic regression of (ln transformed) longevity of birds (number of days lived at death + 1) against inbreeding coefficient  to nucleotide diversity  in all years after population extinction. Extinct populations contributed a realized load identical to that recorded at the time of their extinction, and this load was used for all subsequent years. Due to the extremely high computational demands, we simulated only 40 replicates and report the 90% confidence limits of the summary statistics. Details on the SLiM model are in Appendix We tested 4 scenarios after the population reached the bottom of the bottleneck: (1) no intervention, wild population remained at the bottleneck size; (2) demographic rescue, population increased to 400 individuals as recorded in the wild population, but without contribution from the captive population; (3) genetic rescue, wild population received translocations from the captive population but remained at a low size; and (4) genetic and demographic rescue, which was most similar to the conservation rescue of the pink pigeon. Translocations occurred at rates reported by GDEWS until 2019 Appendix  and therhttps://github.com/pink‐pigeon‐conservation/Genomic_erosion_during_conservation_rescue.Code is availabe from F1,8 = 42.14, p < 0.001, R2 adjusted = 82.1%) .Population genetic analysis of 43,967 loci revealed considerable geographic structure consistent with isolation by distance (regression: ) Figure . However) Figure  and the Ho) of 44 offspring (0.294) was significantly lower than that of the 20 parental birds (Ho = 0.314)   of 70–90 in those years and an Ne/Nc = 0.084–0.109. Furthermore, the observed genome‐wide heterozygosity declined steeply from 1994 to 2008 in the IAA subpopulation  Figure . The 6.1n Figure  and in on Figure  and S3g.F1,108 = 45.02, p < 0.0001, adjusted R2 = 29.0%)    Figure . Individ) Figure . This su) Figure , allele ) Figure , after wFST values, which ranged from 0.009 (PW vs. BO) to 0.053 (PL vs. IAA). This was further supported by the coancestry analyses  had long ROH, occasionally spanning the length of nearly half a chromosome  Figure . We paraF = 0.0615 ) per generation was calculated by comparing the genome‐wide heterozygosity of 20 parents and their 44 offspring in the IAA. The level of inbreeding in 2020 was calculated using F = 1 – (1 – ΔF)t, where t is the number of generations since 1995. The generation time was assumed to be 5.6 years. Due to the increase in inbreeding coefficient between 1995 (white bars) and 2020 (gray bars), extinction probability of the population also increased.Varying the level of inbreeding and genetic load on the 100‐year extinction probability resulted in the population likely becoming extinct within the next 100 years Figure . In FiguThe SLiM simulations showed that the counterfactual scenario (no population intervention) resulted in 100% extinction before 2120, shortly after the bottleneck Figure . The demFour decades of intensive conservation management have increased the census population size of the free‐living pink pigeon metapopulation in Mauritius from 12 to over 400 birds. Consequently, the species has been downlisted twice on the IUCN Red List. However, we found that the metapopulation continues to lose genetic variation at an alarming rate. This conclusion is supported by the population genetic analysis of both RAD‐sequencing and microsatellite data. In addition, an analysis of longevity data of 1112 birds in zoos collected over 4 decades revealed a high genetic load of 15 LEs for this trait. Vortex simulations indicated that without renewed genetic rescue, the free‐living population in Mauritius is likely to become extinct within the next 100 years. However, given the results of our sensitivity analysis and uncertainty about some parameter settings, the Vortex results should be interpreted with caution. Nevertheless, we can be relatively confident that extinction is likely if supplementation is stopped altogether . Furthermore, computer simulations indicated that genetic rescue from European and U.S. zoo populations could elevate the genetic variation. Computer simulations in SLiM   . Given that the birds in this ex situ gene pool are closely related to individuals in the free‐living metapopulation, we assume this is a reasonable approximation of the genetic load in the wild population. The species’ load for longevity is slightly higher than the average of 12 diploid LEs found across the life history of bird and mammal species    from releases of captive‐bred individuals. By focusing on population size, changes in demography, habitat fragmentation, and geographic range size (IUCN criteria A–D) and not explicitly assessing genomic erosion, the IUCN potentially underestimates the extinction risk in such cases. Evidence of genomic erosion can be used in quantitative analyses of extinction probability (IUCN assessment criterion E), but unfortunately, this criterion is rarely used when evaluating the extinction risk of birds Appendix  Collen . Until rNevertheless, we do not call for a reassessment or uplisting of the IUCN Red List status of the pink pigeon. Considerable resources have been dedicated to enable the analyses summarized in this study, and this is only feasible for few threatened species. Given that the IUCN assessment criteria must remain similar across species (and over time), one should not apply scientific tools with higher sensitivity for just a small subset of species. Increasingly, calls are made to integrate genetics in IUCN assessment (e.g., Garner et al. We propose that the recently developed IUCN Green Status of Species (Akçakaya et al., Appendix A1Click here for additional data file.Appendix A2Click here for additional data file.Appendix A3Click here for additional data file.Appendix A4Click here for additional data file.


Schizothorax biddulphi, we developed and used 20 pairs of polymorphic microsatellite primers to analyze the genetic diversity and structure of populations. A total of 126 samples were collected from the Qarqan River (CEC), Kizil River (KZL), and Aksu River (AKS) in Xinjiang, China. The results showed that 380 alleles were detected in 20 pairs of primers and the average number of alleles was 19.0. The effective allele numbers and Nei’s gene diversity ranged from 1.1499 to 1.1630 and 0.0962 to 0.1136, respectively. The Shannon index range suggested low levels of genetic diversity in all populations. The genetic distance between the CEC and AKS populations was the largest, and the genetic similarity was the smallest. There was a significant genetic differentiation between CEC and the other two populations. The UPGMA clustering tree was constructed based on population genetic distance, and the clustering tree constructed by individuals showed that the AKS population and KZL population were clustered together, and the CEC population was clustered separately. Also, the group structure analysis also got the same result. It can be seen that although the three populations of S. biddulphi do not have high genetic diversity, the differentiation between the populations was high and the gene flow was limited, especially the differentiation between the CEC population and the other two populations. This study not only provided genetic markers for the research of S. biddulphi but the results of this study also suggested the need for enhanced management of S. biddulphi populations.To protect the germplasm resources of  Schizothorax biddulphi, in the order Cypriniformes and family Cyprinidae, is a symbolic species distributed only in the Tarim River system of Xinjiang Uygur Autonomous Region, China , Asian elephant (Elephas maximus), Arabian oryx (Oryx leucoryx), and land snail , to guide the management and conservation of endangered animals  and short tandem repeats (STRs), is a nucleotide sequence consisting of 1–6 nucleotides in the repeat motif . SSR has animals .S. biddulphi, the complexity of polyploidy was ignored, and the analyses followed the diploid approaches , 69 from Kizil River , and 13 from Akesu River , Xinjiang, China . All samS. biddulphi from Cherchen River. Five DNA libraries of 270 bp(2), 350 bp(2), and 500 bp(1) insert size were constructed and sequenced by Illumina Hi-Seq 2000.The samples for the genome survey were randomly selected from 3 http://pgrc.ipk-gatersleben.de/misa/misa.html) was used for genic-SSR markers with perfect repeat units of 2–6 nucleotides. The minimum SSR length criteria were defined as six reiterations for dinucleotide and five reiterations for other repeat units. About 288 microsatellite loci were selected, of which 288 with enough flanking sequence were used for primer design using Primer Premier 5.0 .The Perl script MIcroSAtellite .S. biddulphi. Each 50 μL PCR contained 2 μL genomic DNA, 25 µL RTaq DNA Premix, 1 µL forward and reverse primers, and 21 µL double-distilled water. The thermocycling conditions were as follows: initial denaturation for 5 min at 94°C, followed by 35 cycles of 30 s at 94°C, 30 s at the annealing temperature . PCR amplification was carried out using genomic DNA extracted from 126 perature , and 40 This study referred to the method of Na), effective number of alleles (Ne), Nei’s genetic distance (H), and Shannon’s Information index (I) were analyzed using GenePop v 4.14. Poptree V2 was used to establish an NJ clustering tree representing population genetic relationships based on allele frequency . Pcoa-genalex V6.5 analyzed genetic relationships among populations based on genetic distance (http://pritch. bsd. uchicago. edu/) was used to analyze the genetic structure of the population, and the mixed model and frequency correlation model were selected. Online software Structure Harvest was used to estimate the theoretical population number according to the ΔK algorithm , followed by mono-nucleotide repeat units (39.07%), and the content of tri-nucleotide (9.09%) and tetra-nucleotide (7.94%) repeat units was similar, the content of penta-nucleotide (0.92%) and hexa-nucleotide (0.24%) repeat units was less. The distribution density of the six nucleotide repeat types was positively correlated with the corresponding SSR content . AC/TG (30.02%) was more in di-nucleotide repeat SSRs. AAT/TTA (29.44%) and ATT/TTA (23.57%) had high frequency of tri-nucleotide repeats. TCTA/AGAT and GATA/CTAT appeared more frequently in tetra-nucleotide repeats. TATTA/ATAAT and AAAAT/TTTTA appeared more frequently in penta-nucleotide repeats. GTGTGA/CACACT and ATATAC/TATATG appeared more frequently in hexa-nucleotide repeat microsatellite.Among the 288 pairs of primers, 30 pairs of primers with high polymorphism were screened by agarose gel electrophoresis and 8% non-denaturing polyacrylamide gel electrophoresis. According to the detection results of capillary electrophoresis, 20 pairs of SSR primers with high polymorphism were screened out from the above 30 pairs of primers . The polNa) were detected, and the number of alleles per SSR locus ranged from 5 (T166) to 56 (T269), with an average of 19 alleles. The number of effective alleles (Ne) ranged from 1.0595 (T269) to 1.5804 (T166), and the average number was 1.2517. Nei’s genetic distance (H) ranged from 0.0540 (T269) to 0.3449 (T166), with an average of 0.1635. Shannon’s Information index (I) for each SSR locus ranged from 0.1206 (T269) to 0.5188 (T166), with an average of 0.2773 , and there was moderate genetic differentiation between AKS and KZL (0.05 < Fst < 0.15). The gene flow between AKS and KZL was the highest, while the gene flow between CEC and the other two populations was low , and the genetic identity was the least. The Nei’s genetic distance between AKS and KZL was the closest (0.021), and the genetic identity was the largest .S. biddulphi. The results showed that 126 samples of S. biddulphi were roughly divided into two clusters, with AKS and KZL individuals clustered into one cluster and CEC individuals clustered into another cluster. At the same time, the UPGMA tree constructed according to population genetic distance was consistent with the clustering tree constructed by individuals, the AKS and KZL clustered into a cluster, and the CEC clustered into a single cluster.Cluster analysis was performed on an individual  and popuS. biddulphi came from inter-population and 80% of the genetic variation came from intra-population (AMOVA results showed that 20% of the genetic variation of pulation . TherefoK = 2) produced the best assignment of individuals to subclusters, and subsequent analysis of each subset determined the appropriate number of populations within each subset , Schizopygopsis malacanthus chengi (66.36%), Schizothorax wangchiachii (61.21%), Schizothorax griseus (61.24%), Schizothorax grahami (60.63%), Schizothorax dolichonema (57.01%), and Schizopygopsis stoliczkae (57.48%), the genetic diversity of S. biddulphi is relatively lower , eventually resulting in an irreversible decline of the population and risk of extinction. The genetic variation obtained by microsatellite markers was similar among different populations of S. biddulphi, and the genetic variation mainly occurred within populations rather than between populations. CEC may retain the original population resources, or the result may be caused by the small number of AKS samples.Genetic diversity parameters, such as the effective number of alleles, Nei’s genetic distance, and Shannon’s Information index, can be used to reflect the size of the genetic diversity; in a fixed parameter range, the greater the value of these parameters, the higher the gene richness of the population . In thisly lower . Molecully lower . At the ly lower . TherefoFst and Nm are two important indicators to measure population genetic structure  in the Itkillik, Kuparuk, and Sagavanirktok basins in the foothills of the Brooks Mountains undergo altered dispersal and gene flow due to movement restrictions caused by river drying (tructure . In thistructure . The Kastructure . Althougtructure . The gentructure . For exar drying .S. biddulphi populations and individuals. The results showed that AKS and KZL clustered into a cluster, and CEC clustered into a cluster. Structure cluster analysis also shows that the CEC population had great genetic differences from the other two populations, which further verifies the Cherchen River due to early break with the Tarim River, cutoff of the genes within or between is a kind of communication, increase the genetic distance, and then the genetic similarity between CEC and the other two populations was small. After 1950s, although the Kizil River also gradually lost its connection with the Tarim River, the time of geographical isolation between KZL and AKS was relatively short, so the genetic distance between the two populations was relatively close and the genetic similarity coefficient was high.Based on Nei’s genetic distance, the UPMGA method was used for cluster analysis of three S. biddulphi were developed by genome survey. These markers used were informative enough and could detect genetic diversity among S. biddulphi. The results of this study indicated that Cherchen River had preserved relatively primitive S. biddulphi population resources due to its early separation from the mainstream of Tarim River, and showed higher genetic diversity than the other two populations. However, the genetic diversity of S. biddulphi was still relatively low compared with other schizothoracin. If it is not its protection and intervention, S. biddulphi gene flow between populations appears greatly reduced or even cutoff and will lead to further genetic differentiation between populations, and make it into a vicious circle. Therefore, we must protect the genetic resources of S. biddulphi scientifically and rationally, strengthen the protection of its ecological water environment, and improve gene exchange between populations (In this study, 20 new SSR markers of ulations .


These particles belonged to different Geldart groups and exhibited varied hysteresis phenomena and fluidization indices. The local collapse dynamics in the lower, lower-middle, upper-middle, and upper regions were carefully monitored in addition to the distributor pressure drop to obtain greater insight into the deaeration behavior of the bed. While the collapse dynamics of glass beads revealed high bed homogeneity, the upper middle region controlled the collapse process in the case of titania due to the size-based segregation along the bed height. The segregation behavior was very strong for nanosilica, with the slow settling fine agglomerates in the upper bed regions controlling its collapse dynamics. The collapse time of the upper region was 25 times slower than that of the lower region containing mainly large agglomerates. The spectral analysis confirmed the trend that was observed in the pressure transients. The clear presence of high frequency events at 20 and 40 Hz was observed in the nanosilica due to agglomerate movements. The residual air exiting the plenum was strongly affected by the bed voidage, being lowest for the nanosilica and highest for the glass beads.Bed collapse experiments provide vital information about fluidized bed hydrodynamics. In this study, the region-wise bed collapse dynamics of glass beads, titania (TiO Fluidized beds are often preferred over packed beds due to their superior gas–solid contact, higher mass and heat transfer rates, and efficient gas–solid handling and mixing ,3,4,5,6.An important tool for analyzing the fluidized bed hydrodynamics is the collapse bed study, which can provide meaningful insight into the bed’s dynamic behavior. The fluid flow is instantly stopped in a steady fluidized bed, and the solid particles are allowed free fall under gravity, until the bed attains complete rest ,28,29,30By monitoring the local pressure drop transients, Asif and Ali  noted thThe deaeration of the residual air during the collapse process critically governs the collapse process. The residual air moves upward during a bed collapse in a conventional fluidized bed ,29,31,33While the investigation of the collapse dynamics in the past mainly focused on the effect of the drainage configuration for the deaeration of the residual gas ,26,34, tThe experimental set up schematic is shown in The dual drainage deaeration scheme was implemented by allowing another residual air exit from the bottom of the fluidized bed during the bed collapse. This was carried out by using a normally closed 2-way solenoid valve, SV2 , placed immediately below the plenum chamber. With the start of the collapse process, the solenoid valve was simultaneously energized, thus providing an alternate passage to the escaping residual air from the collapsing bed. As shown in Several sensitive bidirectional differential pressure transducers  with a response time of 1-ms and appropriate ranges were used in the experiment. As shown in the figure, the local pressure drop transients in the lower (A data acquisition (DAQ) system (NI-DAQ-USB-6289) was used to acquire the analog input of the pressure transducers and flow totalizer and control the solenoid valve by using digital output signal. The data sampling rate was set to 100 Hz to capture the high frequency events taking place during the bed collapse.The segregation tendency was very strong for the nanosilica, with the slow settling of fine agglomerates in the upper bed regions controlling its collapse dynamics, which was five to six times higher than that of glass and titania under similar conditions.3. Its median agglomerate size was 12.5 μm (approx.), which was approximately thousand-fold larger than its primary size. The size range of titania was 0.5–90 μm. The particles’ sizes were obtained using Microtrac particle size analyzer .Three different powders, namely glass beads, titania, and nanosilica, were used in the present work. The physical properties of the particles are listed in Two different sets of experiments were carried out. In the first set of experiments, the conventional fluidization was carried to determine the minimum fluidization velocity . On the contrary, the normalized pressure drop never exceeded 0.76 for the fluidization of titania. This behavior indicates the presence of non-homogeneities caused by the channeling and rat-holes through which the gas bypasses the bed solids, resulting in lower frictional losses. The fluidization behavior of the titania powder is illustrated in Faster transients were observed for the lower layer that contained large agglomerates.  U0=2Umf b. HigherThe data analysis in the frequency domain can identify the presence of periodically recurring events in the bed, which cannot otherwise be clearly identified from the time domain data analysis . Figure This situation is substantially different for the titania, with a significantly higher voidage  showed the highest fluidization index of 0.92 with no hysteresis effects. The cohesive powder of titania (Geldart group C) showed severe gas-bypassing due to local non-homogeneities comprising cracks and channels, thereby resulting in a low fluidization index of 0.76 and a strong hysteresis effect. The fluidization index of nanosilica was even lower at 0.61 due its poor fluidization hydrodynamics with a stronger hysteresis behavior than that observed for the titania. A dual drainage scheme was implemented by allowing the downward movement of the residual air through a purge passage located below the plenum chamber. The flowrate of the exiting air through the purge was monitored using an electronic flowmeter. Owing to the bed homogeneity, all the regions in the glass bed exhibited almost identical two-stage collapse processes, i.e., the bubbling and sedimentation stages. The bubbling time decreased with the distance from the distributor. The spectral analysis of the transients clearly indicated the bubble movement as low frequency events. The bubbling was less intense and irregular due to the gas-bypassing through the cracks and channels in the bed of the titania particles. Its transient response decreased with the distance from the distributor owing to size segregation with the upper middle layer (i.e., For glass beads, the maximum negative distributor pressure drop attained during the collapse was a function of both the velocity and bed height. The same behavior was observed with the titania bed with a change of the bed height. For the case of the nanosilica, however, the backward flow of the residual air through the distributor was rather insignificant, resulting in a low pressure drop.Hydrophilic nanosilica with strong agglomeration behavior were used in this study. Any extension of these results to nanoparticles with APF behavior, even with similar primary dimensions and comparable bulk density, should be treated with caution.


Rosa species in Morocco was assessed using Inter-Simple Sequence Repeat (ISSR) and Directed Amplification of Minisatellites DNA (DAMD) markers.Morocco is considered one of the main biodiversity hotspots in the Mediterranean region and contains various plant species including wild and domestic Rosa. This genus is the most important among cultivated ornamental plants in the world, with a high economic value in cosmetics, pharmaceutical industries, and floriculture. In the present study, genetic diversity among the collected accessions of wild Results confirmed that both markers used have a good efficiency to assess genetic diversity in wild roses. Ten ISSR and eight DAMD primers amplified 276 and 203 loci, with an average of 27.4 and 25 polymorphic alleles per primer, respectively. The polymorphic information content (PIC) values were 0.34 with ISSR and 0.31 with DAMD. Analysis of molecular variance (AMOVA) showed that genetic variation in wild rose occurs mainly within populations (86%) rather than between populations (14%). The region of Azrou (Middle Atlas of Morocco) is the area that registered the highest genetic diversity in the present study with He = 0.21. The 39 rose accessions were divided into three main groups with among-group similarity of 30%. Principal component analysis and the hierarchical classification were consistent with genetic relationships derived by structure analysis.The findings revealed that the patterns of grouping are weakly correlated with geographical origin. ISSR and DAMD markers showed that the accessions have a good genetic diversity. Rosa represents one of the largest genera in the Rosaceae family and is comprised of more than 200 terrestrial species and more than 18,000 cultivars but only ten species have contributed to modern commercial roses [Rosa distribution concern the temperate and sub-tropical regions of the northern hemisphere, specifically in Europe, Asia, the Middle East, and North America [The genus al roses , and medal roses . Rose cual roses . Several America , 7. HoweRosa into four subgenera: Hulthemia, Platyrhodon, Hesperhodos, which are monotypic or include two species, and Rosa genus, which contains all the remaining species, grouped into 10 sections [Rosa genus has posed many problems to taxonomists as a gradation between many subdivisions is slight, and in many instances, segregation is a matter of opinion [Rosa genus for use in genetic improvement [Recent research has classified the genus sections . General opinion . For thirovement , 13. Molrovement .Since the mid-1980s, genome identification and selection has progressed rapidly with the help of PCR technology. A large number of marker protocols that are rapid and require only small quantities of DNA have been developed . The widIn the 1990s, molecular markers were developed for rose cultivar identification , 22 and ISSR and DAMD markers are becoming important tools for genetic diversity analysis in plants. They collectively provide a comprehensive description of the nature and the extent of diversity, its genetic relationship, and germplasm management –31.In Morocco, studies on wild roses are very rare even absent at morphological, anatomical, chemical, or genetic levels. The only studies provided to date concern morphological aspects . To fillA study of polymorphism among wild rose accessions was carried out using ISSR and DAMD markers. Therefore, the aim of this study is to characterize the genetic variability of wild rose species available in Morocco and the relationship between wild genetic pools. This will allow of performing breeding programs.Rosa sp. including 4 accessions of hybrid rose  and unknown (TOGH)) were collected from different geographical provinces of Morocco  ranging from 1260 to 2358 m altitude  were freeze-dried. Total genomic DNA was extracted from the young leaves using the CTAB method . The lea2), 0.5 U of TaqDNA polymerase , 0.5 μM of each forward and reverse primers and 5.3 μl of pure water. Amplification was carried out in thermocycler (AerisTM) with the following program: an initial denaturing step of 5 min at 95 °C, followed by 35 cycles, each had a 95 °C denaturing step for 30 s, a hybridization at 52 to 58.5 °C (depending on primers) for 1 min and extension step at 72 °C for 2 min. The final elongation step was set to 72°C for 8 min. The amplification products were stored at 4 °C until visualization using polyacrylamide gel electrophoresis. After electrophoresis, the gel was removed from the plates and stained with ethidium bromide solution for 5 min. A 50-bp ladder (BIOLINE) was used to evaluate the approximate molecular weight of amplification products.All 39 accessions were analyzed with 10 ISSR primers Table . DNA amp2), 0.5 U of TaqDNA polymerase , 0.5 μM of each forward and reverse primers and 5.3 μl of pure water. Amplification was performed in thermocycler (AerisTM) with the following program: an initial denaturing step of 3 min at 94°C, followed by 35 cycles, each had a 92°C denaturing step for 45 s, a hybridization at 40 to 70°C (depending on primers) for 2 min and extension step at 72°C for 2 min. The final elongation step was set to 72°C for 5 min. The amplification products were stored at 4°C until visualization using polyacrylamide gel electrophoresis.Using eight DAMD primers Table , all 39 ISSR and DAMD profiles were scored as present (1) or absent (0) for each entry and a binary qualitative data matrix was constructed. Very faint and non-reproducible bands were omitted from scoring. The genetic parameters such as observed number of alleles (na), effective number of alleles (ne) , Shannonhttps://irscope.shinyapps.io/iMEC/).The performance of the molecular markers was measured by calculating various parameters including polymorphism information content (PIC), effective multiplex ratio (EMR), marker index (MI), and resolving power (Rp) for each primer by program iMEC  method in DARWIN program version 6.0.4. The bootstrap analysis running 1000 replication was employed to determine a sampling variance of the genetic similarities calculated from the data sets gained of different marker systems .http://taylor0.biology.ucla.edu/structureHarvester/).The Bayesian-based model carried out the genetic makeup of rose accessions. This was analyzed by STRUCTURE (version 2.3.4) and was compressed into a single “Zip-Rar” file and then uploaded online to “Structure Harvester”  and eight DAMD primers  were selected as they gave better amplification products and showed clear polymorphism  followed by UBC811 (4.15) while the lowest for UBC834 (1.17) along with a mean of 3.05 per primer. Resolving power average value (Rp) was 11.95. UBC842 primer counted the highest value (16.92) while UBC834 showed the lowest value (5.74) (Table ISSR primers produced 276 fragments among which 274 (99.27%) were polymorphic. A set of 10 ISSR primers detected polymorphic bands that varied from 13 (ISSR2) to 39 (UBC842) with an average of 27.4 bands per primer. Altogether, eight primers  presented 100% of polymorphic rates, while the lowest polymorphic percent (94.11%) was detected at UBC834. The highest PIC value was recorded for ISSR2 (0.37) followed by ISSR15 (0.36) while the lowest (0.29) was for UBC842 with an average of PIC of 0.34. Effective multiplex ratio (EMR) spanned from 3.9 (UBC834) to 12.1 (ISSR15) with 9.4 as the average. Marker index (MI) was calculated to recognize the usefulness of the ISSR primers system on Two hundred and three fragments were generated by DAMD primers among which 200 (98.52%) were polymorphic. A set of eight DAMD primers detected polymorphic bands which varied from 6 (URP2R) to 44 (M13) with an average of 25 bands per primer. However, all primers generated multiple patterns ranging from 9 to 44 with an average of 25.37 alleles per loci. Absolutely, seven primers  registered 100% of polymorphic rates, while the polymorphic percent observed was as low as 66.66% for URP2R. URP2R recorded the highest PIC value of 0.374 followed by HBV3 (0.36) whereas the lowest was URP25F (0.25) with a mean of PIC of 0.31. Effective multiplex ratio (EMR) spanned from 2.77 (URP2R) to 11.77 (M13) with an average of 7.9 per primer. The marker index (MI) was maximum (3.82) for HBV5 followed by M13 with 3.71, while the lowest was 1.03 for URP2R with a mean of 2.11. The highest value counted for resolving power (Rp) is 16.15 for M13 while the lowest was 2.61 detected in URP2R with an average of 9.85 per primer  (Table p = 0.001).Variations among and within the A) Table . Based oPercentage of polymorphic loci (PPL) of Roses using the combined ISSR and DAMD markers Table  spanned Rosa sp. based on the combined ISSR and DAMD matrix that ranged from a minimum of 0.05 to a maximum of 0.19. The highest genetic distance (0.19) was registered for the pair of regions such as Kela Mgona and Akka Ait Ouyed. However, the minimum genetic distance (0.05) was recorded for the pair regions of Bouadel and Agoudim.Table The dissimilarity index calculated with data from the combined matrix of ISSR and DAMD markers and used to build a Weighted Neighbor-Joining tree ranged from 0.26 to 0.84. At 70% of dissimilarity, the dendrogram divided the 39 accessions into three major clusters  made it possible to graphically represent the relationship between the ISSR and DAMD markers, and the different accessions Fig. . Derivedk in order to characterize the genetic structure of 39 accessions. The program detected the highest value of delta k obtained from the structure harvester, k=3. Based on the best k=3, each assessed accession is often positioned into three different subgroups  by [Rosa and by [Rosa canina, reaching 75% and 77%, respectively. The mean of polymorphic band obtained in this study using ISSR and DAMD were 27.4 and 25, respectively, and this is much higher than the results reported by [ISSR and DAMD markers produced high levels of polymorphism, such as the average number of scored bands, polymorphism rates, and PIC were slightly higher with the ISSR primers  compared to those of the DAMD primers . The high rate of polymorphism revealed by both markers may have been due to the difference in ploidy level, mutations, recombination, natural hybridization, or random segregation of heterozygous chromosomes in the process of meiosis [(90%) by ; this ra(90%) by  using ISa and by  on 23 georted by  using DAIn our study, the efficiency of markers was determined by effective multiplex ratio (EMR), marker index (MI), and resolving power (Rp). The values detected for EMR, MI and Rp  using the ISSR primers are higher than those obtained by DAMD primers . These results show that the ISSR marker is very efficient compared to the DAMD marker. In accordance with , the resAccording to the AMOVA analysis, 86% of genetic variations within regions were shown by the combined ISSR and DAMD markers, indicating that the variation within accessions was higher (86%) than among them (14%). Many researches obtained similar results. Indeed,  using URMaximum values of indices in relation to genetic diversity  were reported for the Azrou region using combined data. This result indicates that this region could be an important source of diversity for breeding projects which can benefit from new alleles and genes of interest.Variable genetic distances among rose populations were registered in this study. Results revealed that regions with the lowest distance (Bouadel and Agoudim: 0.05) are found in adjacent geographic regions. This shows a possible existence of seed movement among these areas; unlike populations Kela Mgona (included hybrid accessions) and Akka Ait Ouyed, which exhibited large genetic distances (0.19).The dissimilarity index calculated with data from the combined matrix of ISSR and DAMD markers ranged from 0.265 to 0.84 with a similarity of 30%. Various studies revealed a high similarity value compared to our findings . using RRosa mesatlantica [Three clusters were recorded in this study. In cluster I, there is an overlap between the regions Ait Touda, Taoudaat, Tamalout, Tirghist, and Akka Ait Ouyed. On the other hand, the accessions of the Azrou region are grouped together in a single sub-clade with the integration of KMG2 accession from Kela Mgona. As a result, the region of Azrou where the endemic species of Morocco tlantica  was inclIn the present studies, the results of PCoA analysis were consistent with those of the cluster analysis. The accessions were well distributed in the biplot, which could be due to the relevance of primers used and the wide variety between accessions. The PCoA revealed that Bouadel, Tirghist, Tounfite, Agoudim, and Akka Ait Ouyed regions are very close to each other, which indicates that there is a high genetic flow among them, while this did not follow their origins. The results of PCoA indicated that the wild rose populations were expanded. There may be multiple refuges for wild rose, such as the Azrou and Bouadel populations.Rosa canina populations. These authors stated that this variation was due to the polyploidy, outcrossing, and seed dispersal systems. That is in accordance with our study which also showed that most of the species present in Morocco belong to the caninae section [Roses usually cross-pollinate and are self-incompatible which makes them more genetically diverse between and within populations –46. anal section .Using the structure harvester software, the best K were identified. The best level of genetic structure in the combined data system was K=3. In this study, high gene flow was detected among rose accessions. This is probably because of plant transmissions by humans or genetic flow and displacement by natural factors . This geRosa damascena in Morocco, especially since this country is known as one of the main producers of these roses in the world.This study showed the genetic diversity of Moroccan wild rose accessions using ISSR and DAMD markers. These markers detected a high level of polymorphism in these accessions. The PIC average values for both markers were 0.34 and 0.31, respectively, which indicates the efficiency of used markers in revealing polymorphism among wild rose accessions. Furthermore, the findings confirmed the efficiency of combined data in estimating the genetic diversity among the populations. The study of genetic diversity is a very important and crucial step that can help us to think of how to use this high level of variation in the breeding program and also how to manage and conserve those wild genetic resources. This study shows also that it is important to exploit wild resources for the genetic improvement of hybrid roses, especially


Fagus sylvatica L.) is a dominant European forest tree species that increasingly suffers from climate change-related die-back. Here, we conducted a systematic literature review of neutral genetic diversity in European beech and created a meta-data set of expected heterozygosity (He) from all past studies providing nuclear microsatellite data. We propose a novel approach, based on population genetic theory and a min–max scaling to make past studies comparable. Using a new microsatellite data set with unprecedented geographic coverage and various re-sampling schemes to mimic common sampling biases, we show the potential and limitations of the scaling approach. The scaled meta-dataset reveals the expected trend of decreasing genetic diversity from glacial refugia across the species range and also supports the hypothesis that different lineages met and admixed north of the European mountain ranges. As a result, we present a map of genetic diversity across the range of European beech which could help to identify seed source populations harboring greater diversity and guide sampling strategies for future genome-wide and functional investigations of genetic variation. Our approach illustrates how to combine information from several nuclear microsatellite data sets to describe patterns of genetic diversity extending beyond the geographic scale or mean number of loci used in each individual study, and thus is a proof-of-concept for synthesizing knowledge from existing studies also in other species.Genetic diversity influences the evolutionary potential of forest trees under changing environmental conditions, thus indirectly the ecosystem services that forests provide. European beech (The online version contains supplementary material available at 10.1007/s11295-022-01577-4. In 1988, Paul Ehrlich predicted that the loss of genetic diversity was already “at least as important a problem as the loss of entire species” (p. 22) and that this loss would cause a “disruption of the course of evolution, insofar as speciation processes will have to work with a greatly reduced pool of species and their genetic materials” . Indeed, today, there is abundant evidence that forest tree populations harboring lower genetic diversity suffer negative fitness consequences and compromised disease resistance  sequencing  and other genomic markers. Reference nuclear  summarize literature on the genetic diversity of F. sylvatica in terms of geographic distribution, numbers of studies and studied populations, sample sizes, and molecular marker systems; (2) combine available data from the most abundantly used marker system, nuclear microsatellites, to compile a meta-dataset; (3) apply a scaling approach to the meta-dataset to describe spatial genetic diversity patterns across the species range; and (4) furthermore, include a novel microsatellite data set with unprecedented geographic coverage, for which we also use various re-sampling schemes to mimic common sampling biases and show the potential and limitations of the scaling approach.Here, we consolidate current knowledge on the genetic diversity of Fagus sylvatica section of the database Dryad, to identify potentially relevant publications from peer-reviewed journals and/or additional datasets. The following keywords were used: (“Fagus sylvatica” OR “European beech”) and . Additional papers were identified by topical suggestions from Mendeley, which was used to keep, track, and organize the bibliography (https://www.mendeley.com/), and 75 articles were received by a subject expert . Reference lists from each article were examined for additional suitable papers, which were then included in the database. In addition, publications by the first and last authors of all initially selected articles were searched on PubMed. Relevant publications were initially retained based on title assessment, and the search was conducted until no new relevant publications were identified, which resulted in 123 publications in addition to the 75 from expert recommendation . Four duplicates were then removed, and abstracts, tables, and figures were screened according to the list of criteria below, which excluded an additional 29 publications. The full text of the remaining 165 publications was assessed based on the same criteria as used for the initial screening: published in a peer-reviewed academic journalFagus sylvatica L. reporting on the genetics of  including empirical studies with data collected from field sampling  reported allelic richness or genetic diversity  estimates publication language confined to EnglishWe followed the guidelines for systematic literature review provided by Pullin and Stewart  were accepted. The final dataset comprised 50 articles published between 1982 and 2020 . Data from published studies were extracted directly from PDFs as described below. Additionally, raw datasets of genotyped trees were retrieved from the Dryad database for 11 publications .When the source dataset was not available, data were extracted from the PDF text or from tables using the R package tabulizer Leeper  or the T• name of the first author• year of publication• country or countries of the sampled population(s)• name and location of the sampled population(s) • year of sample collection• number of populations studied• number of sampled individuals as a measure of sampling size• stand age• biological sample/type of specimen• plot size (km2)• molecular marker• number of loci• identity of lociNa), allelic richness (Ar), and the standard sample size used for the rarefaction, where reported; expected heterozygosity (He), and nucleotide diversity (π) or haplotype diversity (Hd) for the studies based on dominant markers (cpDNA)• proxies of genetic diversity including allelic diversity (Fst) and coefficient of inbreeding (Fis)• global fixation index . Genetic indices were also reported in a variable manner, and in 8 publications, genetic indices were averaged across all studied populations (Table S1).Methods and data-reporting customs varied, with many papers providing only the approximate location of the sampled populations described in text or indicated on maps. In several cases, the reported coordinates did not match with the name of the sampling area or were not available within the articles  to prevent redundancy . When He and genotypic data were not available, He was calculated from the allelic frequencies, if provided , analyses were conducted separately for each marker system. Genetic diversity from SNP data, reported as He or as nucleotide diversity, was analyzed as described in the Supplementary Material. Since nuclear microsatellites were the most frequently used marker types among the considered studies (Table S1), we focused further analysis on this marker type. Mean genetic diversity per locus was recorded from the studies or computed from allelic frequencies, where reported.For studies using nuclear microsatellites, primer sequences (listed in Table S4) were aligned on BLAST . Studies were grouped according to the set of microsatellite loci used for genotyping, resulting in five clusters of studies using a similar set of loci within each cluster . To test whether the array of loci chosen by the authors influenced the calculated He, a Kruskal–Wallis test and subsequent Dunn’s post-hoc test with the Bonferroni correction were performed on He values from these different clusters. Since He is derived from loci with alleles which differ in number and frequencies and therefore polymorphism, the metrics cannot be compared across studies. In other words, the same population genotyped with highly polymorphic loci would present a higher He than if it were genotyped with less polymorphic loci. This makes values of He computed by studies using different genotyping kits incomparable and a method for reducing the difference across-studies desirable.Among the 24 studies based on nuclear microsatellites, 11 original genotype datasets were retrieved and compiled in a meta-dataset together with the dataset from the present study (Table H), valid for a variable polymorphic genetic locus treated as indeterminate, with at most K alleles. Because H and He sum to one, we used this formula to standardize the He values  across studies. We retained data from loci with unusually low levels of polymorphism which were estimated by comparing the mean He across the loci with a Kruskal–Wallis test and post-hoc test, to avoid further loss of genetic information. Using this method, the differences in scaled He are expected to reflect the population size and its historical changes. According to Theorem 2 from Reddy and Rosenberg  and max are the minimum and maximum theoretical values of H for each locus i and each population j. Scaled He was subsequently calculated as 1–H and averaged across loci. We conducted a Kruskal–Wallis test between the He values from different studies before and after scaling, in order to check whether scaling reduced study-to-study differences as expected.Reddy and Rosenberg  derived osenberg , the theHe may significantly depend on both locus identity and geographic location. In order to test the utility of the scaling approach for correcting these types of bias, we used the novel, range-wide nuclear microsatellite dataset described above and in Ulaszewski et al. We sought to understand whether the certainty in assessing geographic patterns could be reduced by potential differences in polymorphism between loci (locus effect) and by varying polymorphism of the same locus across the species range (discovery effect). Furthermore, authors sampling in regions with high diversity may preferably have used one set of loci, while authors in regions with low diversity may have tended to use a different set. Therefore, He was computed considering data from these three kits separately and scaled with our approach . Afterward, raw and scaled He values from the different kits were correlated with latitude, longitude, and distance from origin.First, we tested whether the use of loci developed in different parts of the range (discovery effect) could lead to ascertainment bias. The pool of loci used by Ulaszewski et al. could be split into three groups of kits developed independently in different studies, as shown in Fig. S4. Mean He values, and compared the results . We chose longitude because a westward expansion from an eastern origin should result in a positive correlation of genetic diversity with longitude (from west to east in Europe). For the hypothesized origin, we chose Slovenia and the eastern Alps, locations which were proposed by Magri  was computed with the R function idw (gstat package) using a power parameter of 5. The map was subsequently masked to the distribution range of beech in Europe provided by EUFORGEN (http://www.euforgen.org). A leave-one-out cross-validation approach was subsequently used to check the effectiveness of kriging parameters and to construct confidence intervals on the estimates . Nevertheless, for several loci, the alleles recorded were different only for one repeat, showing an alternate fashion across publications, which could suggest inconsistencies in PCR fragment binning and allele naming across laboratories, rather than different allele identities. Moreover, the genotyping kits used in the 26 publications varied in terms of the number and identity of loci , hampering the comparison of results from different publications. As expected, He values differed significantly among clusters of studies defined by different sets of loci  = 297.65, p < 2.2e-16). In this comparison, the difference in polymorphism among investigated loci cannot be separated from differences in the population size and in the local demographic history. Therefore, we proceeded with a scaling approach to ameliorate these study-, population-, and location-specific differences.In this study, we present scaled He values between 0.41 and 0.76, with an average of 0.57 ± 0.067 . Again, scaling was effective in eliminating spurious significant correlations and reducing the geographic bias for the relationships between He and latitude and distance from origin .The effect of scaling was also evident after splitting the dataset into different subsets by geographic origin and by locus . Instead, the scaled He values have a similar spread among studies. The Kruskal–Wallis test between the He values from similar genotyping kits  showed statistically significant differences for both raw and scaled He; nevertheless, this difference is reduced for the scaled data  = 121.42, p < 2.2e-16, scaled He x group of studies, Kruskal–Wallis’s test H(3) = 58.155, p = 1.457e-12). This finding indicates that the scaling removed most of the locus-specific effects related to the number of alleles, but did not completely eliminate differences among studies. Scaling was effective in reducing potential ascertainment bias, which we simulated by splitting the largest nuclear microsatellite dataset reported here, from Ulaszewski, using two criteria. When mean He was computed only from a subset of loci belonging to specific genotyping kits , scaling the data resolved the likely spurious, statistically significant correlation between raw mean He from kit 2 and distance from origin, while all other correlations with latitude and distance from origin were very weak (r < 0.1) or positive and not statistically significant. In the case of longitude, the scaling method strengthened the positive correlation to mean He computed from each kit, resulting in Spearman’s correlation coefficients between 0.213 and 0.328, with all p-values < 0.5. In all cases, the scaled mean He values were smaller on average than the raw mean He values.Furthermore, we observed that raw He from the large single dataset reported here against latitude, longitude, and distance from the refugia proposed by Magri were not statistically significant, except for the case of scaled He values against longitude .Correlations of Fagus sylvatica genetic diversity, based on 50 publications spanning nearly 4 decades, and one additional study introduced here, as a reference for population genetic studies in European beech. The field of molecular genetics has dramatically changed during this time, and it is challenging to combine these studies in order to improve general knowledge about the spatial distribution of genetic diversity across the species range. Genetic diversity metrics generally vary as a function of marker type, genomic location, and numbers of analyzed DNA sequences. Using a larger number of loci from different original studies is a good first step toward the estimate of genome-wide diversity allowed by approaches using thousands of SNPs and other genomic markers. The geographic coverage of SNP-based studies in F. sylvatica is still patchy as a proportion of the total range, concentrated in the French Alps and Switzerland . Interestingly, scaling has relatively less effect on the study from Cvrčková and colleagues . The geographic patterns which emerge from the scaled He data . Very few or no data were available for these southeastern populations, and thus, our interpolated map is expected to be less accurate in those areas. In line with other markers, southern French and Italian regions display relatively high nuclear microsatellite-derived He, with less dense nuclear microsatellite data in Italy. The trends revealed by these combined, filtered and scaled microsatellite datasets were not clearly detectable when considering only one single dataset which incorporates the largest geographic variation in F. sylvatica yet published in a single study (Ulaszewski and Burczyk reported here), except for the strongest correlation (longitude)  Fig. , Table 2F. sylvatica colonized its current habitats from the south-west of the Balkan Peninsula, Slovenia and north Dalmatia, and from a central European refugium, southern Moravia and southern Bohemia . Our scaled genetic diversity derived from nuclear microsatellites is not in accordance with the genetic diversity map proposed by this study, where the highest genetic diversity is in French populations, while the area of Czech Republic and Southern Poland show a lower-then-average He. This may indicate differences between patterns of polymorphism for genes involved in adaptation to diverse environmental conditions across the range of European beech, versus neutral genetic structure revealed by microsatellites. We also note that both SNP sets used by Postolache et al. . The number of alleles is fixed as K ≥ 2, and M, the frequency of the most frequent allele, is given as ∈ . Upper and lower bounds of H, the homozygosity, are functions of M and vice versa, and their range is dependent on K Supplementary file2 (JPG 454 KB)Supplementary file3 (JPG 486 KB)Supplementary file4 (JPG 343 KB)Supplementary file5 (JPG 3126 KB)Supplementary file6 (JPG 1651 KB)Supplementary file7 (PNG 7402 KB)Supplementary file8 (PNG 4572 KB)Supplementary file9 (PNG 4371 KB)Supplementary file10 (JPG 336 KB)Supplementary file11 (JPG 147 KB)Supplementary file12 (DOCX 114 KB)Below is the link to the electronic supplementary material.


C. elegans genes. In microarray experiments, few of the HSE-containing genes are specifically upregulated during the heat-shock response. Most of the 4000 HSE-containing genes instead are unaffected by elevated temperatures and coexpress with genes unrelated to the HSR. This is also the case for several genes related to the HSP chaperone system, like dnj-12, dnj-13, and hsp-1. Interestingly, several promoters of the dedicated HSR-genes, like F44E5.4p, hsp-16.48p or hsp-16.2p, contain extended HSEs in their promoter region, composed of four or five HSE-elements instead of the common trimeric HSEs. We here aim at understanding how HSF-1 interacts with the different promoter regions. To this end we purify the nematode HSF-1 DBD and investigate the interaction with DNA sequences containing these regions. EMSA assays suggest that the HSF-1 DBD interacts with most of these HSE-containing dsDNAs, but with different characteristics. We employ sedimentation analytical ultracentrifugation (SV-AUC) to determine stoichiometry, affinity, and cooperativity of HSF-1 DBD binding to these HSEs. Interestingly, most HSEs show cooperative binding of the HSF-1 DBD with up to five DBDs being bound. In most cases binding to the HSEs of inducible promoters is stronger, even though the consensus scores are not always higher. The observed high affinity of HSF-1 DBD to the non-inducible HSEs of dnj-12, suggests that constitutive expression may be supported from some promoter regions, a fact that is evident for this transcription factor, that is essential also under non-stress conditions.The protein HSF-1 is the controlling transcription factor of the heat-shock response (HSR). Its binding to the heat-shock elements (HSEs) induces the strong upregulation of conserved heat-shock proteins, including Hsp70s, Hsp40s and small HSPs. Next to these commonly known HSPs, more than 4000 other HSEs are found in the promoter regions of  Hsf proteins are further involved in many developmental processes, like embryonic placenta development4, female meiotic division5, and general transcription6. Hsf proteins are also reported as negative regulators of RNA polymerase II promoters and modulate protein homeostasis, cellular proliferation8 and the regulation of multicellular organism growth10. While these functions are governed by the binding of Hsf proteins to HSEs distributed throughout the genome, they also dependent on the chromatin accessibility11, as well as the general environment in which these Hsf proteins are activated13. In the mammalian genome several Hsf-like proteins are encoded that control individual sets of target genes. In nematodes, the expressed, full-length Hsf-like gene (HSF-1) is encoded, while another Hsf-homolog gene, termed hsf-2, is represented by the pseudogene Y53C10A.3. With HSF-1 likely being the sole expressed and functional homolog of the Hsf-proteins, the interaction between HSF-1 and the many detected HSEs can be investigated without the necessity to differentiate between several Hsf-proteins15.The heat shock transcription factor (Hsf) is the essential transcriptional activator of the heat-shock response (HSR). It activates the genes of the classical HSR like Hsp70, small HSPs and Hsp40s16. Further, larvae up to the L2 stage show a reduced expression of the HSR in contrast to older larval forms. The aging adult then is characterized by lower inducibility of the HSR17. These differences imply a complex regulation of HSF-1 activity during aging, which is thought to ensure that the nematode’s reproductive phase is best protected from stressful events18. Beyond that, nematode HSF-1 is participating in the innate immune response by upregulating specific target genes and in aging, where HSF-1 cooperates with the transcription factor DAF-1622. The observation that several thousand of HSEs are present in the promoter regions throughout the nematode genome, even though the canonical HSR seems to be restricted to few genes24, is puzzling. Several of the canonical heat-shock proteins, like HSP-90 are not even heat-inducible in C. elegans and also the canonical Hsp40-like proteins are not upregulated strongly upon heat-shock25.Despite this simplicity, the regulation of the heat-shock response in nematodes is complex, being influenced by the age of the animal and active mostly in muscular and intestinal tissues28. Despite these regulatory events, the interaction of Hsf proteins with consensus dsDNA is observable also for the non-activated Hsf. In this respect, it is mostly unclear, how Hsf proteins distinguish the various HSE-containing target genes.HSF-1 in nematodes is a protein of 671 amino acids. Like other Hsf proteins, HSF-1 consist of several conserved domains, including the N-terminal DNA-binding domain (DBD), an oligomerization domain and a carboxyl-terminal regulatory domain. Nematode HSF-1 further contains an 82-amino-acids extension of unknown function at its N-terminus. Under normal growth conditions Hsf proteins are monomeric and form cytosolic complexes with Hsp90 and Hsc70. This interaction prevents the trimerization and activation of Hsf proteins. Under heat-stress or other inducing conditions, Hsf proteins are released from the protecting chaperones and oligomerize. In most cases Hsf binds as a trimeric protein to the HSE-containing DNA sequences. The phosphorylation of Hsf proteins triggers the translocation to the nucleus and initiates the transcriptionC. elegans and aim at resolving its interaction with differently regulated HSEs from the nematode genome. To this end we first define the HSE-containing genes that are strongly upregulated upon heat-shock. We then use HSE-containing dsDNA constructs from these HSE regions to investigate to what extent the interaction parameters of the HSF-1 DBD with dsDNA are influenced by HSEs of different sequences and structural organization.Here we focus on the DNA binding domain of HSF-1 from 31, were used to identify genes with strong overexpression. Here, L2 stage C. elegans larvae were heat-shocked for 20 min at 33 °C, followed by a recovery at 20 °C for 40 min. As expected, and previously published15, the strongest upregulated genes were hsp-16.1, hsp-16.48 and F44E5.4 and their duplicated loci. Individual genes that show elevated expression under heat-shock conditions were determined. To obtain information on whether these genes commonly express together, genome-wide clique set analysis was performed as described before30. The heat-shock data sets were used together with the publicly available coexpression cliques. Altogether 307 cliques had been obtained before, with the largest clique containing 1200 genes and the smallest clique containing 6 genes and the publicly available information was used (www.richterlab.de/DataSets/ and https://github.com/klarichter/clusterEX_cliques_Celegans)30. We then used each of the three microarray replicates to assign their values to the genes in the coexpression cliques and analyzed those in respect to significant induction or repression as previously described for yeast and nematode expression studies30. As these heat-shock data and the clique set were both based on the GPL200 platform (Affymetrix C. elegans genome st-1.0), each Probe Set ID was represented by exactly one value in the described clique set. Analyses were performed for each replicate and average values for each clique were calculated to rank the cliques according to their average induction and p-values for induction significance as described30.Initially three microarray data sets investigating the heat-shocked  versus non-shocked condition , which can be obtained from the GEO microarray depository under the GSE2862 tag32 was investigated as well as heat-shock experiments based on RNA sequencing17 . The Subio64 software package 1.24.5853  was used to derive annotated, normalized expression data from the publicly available SRA-files in cases where the annotated data were not available from GEO repository.Given the complexity of the heat-shock response, we compared these data to other genome-wide expression data sets. As such a heat-shock time course defined by microarray data33, which is represented by the following PWM pattern:A | 0.41 -3.20 1.13 1.05 -0.86 -0.55 -2.10 -1.61 -0.17 -1.28 0.58 -5.77 1.14 1.11 0.26C | -0.57 -5.19 -5.19 -3.58 0.84 0.73 0.02 0.34 1.14 0.06 -0.53 -5.19 -5.19 -5.19 -0.21G | 0.84 1.70 -3.11 -1.20 -0.18 0.37 -0.70 -4.50 -0.27 -2.63 0.58 1.71 -5.19 -1.83 0.23T | -5.77 -5.77 -5.77 -2.37 -0.08 -0.58 0.77 0.76 -2.40 0.79 -5.77 -5.77 -5.77 -5.77 -0.41HSE-detection in the promoter region 1000 bp upstream of the ATG was performed based on the PWM-models published for the human Hsf1’s DNA binding sequencewww.wormbase.org)34 and searched with this HSF-1 consensus description. As recommended, a threshold level of 9 was used as lower limit for detection35. In several cases HSEs were detected in the same promoter and located only 5 bp from each other, which implies that the investigated HSE actually is a tetrameric HSE. If this pattern is observed a second time, a pentameric HSE-element was detected or in rare cases even larger arrays of HSEs were detected.The 1000 bp promoter regions were obtained from Wormbase  and thereby fused with a GST-tag. A GST-trap column was used for purification and the GST-tag was cleaved off by TEV-protease before the HSF-1 DBD was further purified via ion exchange chromatography and size-exclusion chromatography . Purity was determined by SDS-PAGE and peptide fingerprinting using mass spectrometry on a Bruker ultra-flex III MALDI-TOF/TOF instrument  was employed to confirm the identity of the protein.Fragmentation was performed based on hydropathy plots and expression tests, which indicated that fragments, which contained additional domains outside the DBD showed either very weak expression or insoluble expression and that full-length HSF-1 could also not be obtained in soluble amounts sufficient for biochemical analysis. Due to our plan to investigate the direct interaction with the DNA, we chose the isolated DBD as model protein for interaction analysis. Therefore, the N- and C-terminus of 2HPO4, 150 mM KCl). CD-spectra were recorded in the Far-UV region between 215 and 260 nm. To analyze the thermal stability of the fragment an unfolding transition was recorded at 220 nm in a temperature range between 25–95 °C.CD-spectroscopy on a Jasco J-715 was performed to obtain information on the structure and stability of the HSF-1 fragment. The folding state and the thermal stability of the expressed HSF-1 fragment was assessed at a concentration of 0.2 mg/mL in storage buffer . Thermal shift assays were performed at a protein concentration of 0.2 mg/mL after addition of SYPRO orange  at a dilution of 1:1000. The total volume was adjusted to 20 µL with storage buffer. The emission of SYPRO orange is recorded by excitation at 470 nm at a wavelength of 570 nm to monitor the temperature induced transition in the temperature range of 25 to 95 °C.F44E5.5, hsp-16.2a, hsp16.2b, hsp-1, hsp-70, dnj-12 and dnj-13 were obtained from Eurofins MWG Biotech . An equal amount of forward and reverse complementary strand was incubated at 95 °C and aligned at room temperature. The interaction between these dsDNAs and the HSF-1 DBD was then monitored by EMSA shift assays, by performing a native PAGE after DBD was added to the dsDNA. Gels were incubated in SYBR green for DNA detection and analyzed in a Typhoon Fluorescence Scanner  using the Alexa Fluor filter at 532 nm and stained with Coomassie for visualization of the protein complex.ssDNA probes representing the promoter regions of Analytical ultracentrifugation was performed in a ProteomeLab XL-A analytical ultracentrifuge  to determine the binding of HSF-1 DBD to dsDNA sequences. To this end single strand DNA sequences from different promoter regions of the same length were mixed with equal amounts of their complement strand in storage buffer, heated up to 95 °C and then cooled to RT to generate the double stranded DNA product that represents the promoter region. HSF-1 DBD was added to 1.5 µM dsDNA at different concentrations  and the absorbance of these samples was detected in analytical ultracentrifugation sedimentation velocity experiments at 260 nm and 280 nm at 42,000 rpm.https://ultrascan3.aucsolutions.com/)36. All experiments were analyzed with the 2DSA-IT model employing the same settings . This way two species distributions were obtained for each experiment, one for the data at 280 nm and one for 260 nm. The complexity of these distributions did not allow a unanimous assignment of solutes to species, which suggests that for a unifying solution a further reduction in search space has to be enforced. A reduced model therefore contained only the most abundant species of the binding reaction  at defined s20,w values. These values were known for HSF-1 DBD, dsDNA and ssDNA from control experiments, while the other species were estimated from a stepwise optimization of these values. Given that all DNA strands were of the same size, a unique value for the sedimentation coefficient  of each assembly intermediate was assigned independent of the dsDNA used.Data analysis of individual samples was performed with UltraScan III Version 4.0 17. Bedgraph files were searched to retrieve the values for specified regions and the reads identified in HSF-1 IP under various conditions were summarized in Excel to display the regions relevant for the genes of interest.ChIPseq data as available from the GEO repository were obtained as bedgraph-filesC. elegans coexpression cliques. These groups of genes (or gene sets) were obtained based on coexpression analysis of more than 2000 microarray experiments recently30 and found to contain many coexpressing tissue specific, phenotype specific and GO-term specific gene sets.We initially aimed at identifying those HSE-containing promoters that are most strongly upregulated under heat-stress conditions. Given that the HSR is complex in nematodes we used data from several heat-shock studies based on microarray and RNAseq analysis. Besides defining the individual genes, which are induced upon heat-shock in the different experiments, we also tested, whether the differently regulated genes are enriched in one or more of the 307 31, we defined the gene sets (or cliques) that represent the heat-shock response under these conditions . To this end, we used the method previously described30 to determine significant coregulation units responding to heat shock. The procedure searches the 307 predefined coexpression cliques and identifies those with significant expression changes. In all three replicates of GSE286231 in particular one gene set out of the 307 cliques was highly induced (log2 > 2), the clique termed hsp-16.2-F44E5.4_19238, which contains the well described heat-shock genes hsp-16.1, hsp-16.2, hsp-16.48, hsp-16.41, hsp-70, F44E5.4 and F44E5.5 and in addition unc-23 and lact-4 , dnj-13 (two probes in cliques unc-116_2109-zfp-1_3976 and tars-1-AFFX-r2-3026-5_at) and the constitutively expressed Hsc70-homolog hsp-1 (clique dld-1-skn-1_16701) are not part of the HSR-clique hsp-16.2-F44E5.4_19238 and we individually tested their induction to confirm that they are indeed not coregulated with the induced heat-shock proteins  and dnj-13 (tetrameric site) representing the non-induced heat-shock related proteins. Since both sequence and position in the promoter region of the following genes are identical the probe for F44E5.4 also represents F44E5.5, while hsp-16.2 represents hsp-16.11, hsp-16.41, hsp-16.48. The sequences of the probes were obtained from the respective promoter regions. Here only HSEs were considered that locate within 1000 bps upstream of the starting point of transcription (Table F44E5.4p contains more HSEs in its sequence than synthesized in this study (comparison of the promoter regions), but here likewise the probes with the highest consensus score were synthesized.dsDNA probes were then generated by us from the heat-shock responsive cluster, in order to gain a better insight into the differential expression form the chaperone-gene derived HSEs. F44E5.4, which also contains the highest amount of HSEs compared to the promoters used in this study. To this end, we titrated the DBD of HSF-1 with concentrations ranging from 0–22.5 µM to 1.5 µM of promoter DNA, which represents a 15-fold molar excess at the highest concentration. Notably a saturated complex of protein and DNA was reached at a concentration of 10 µM HSF1-DBD, at which the complex bands could be observed on the Coomassie stained gel, while at the same time no further reduction in free DNA was visible. Following this initial analysis, we also tested the dsDNA probes of hsp-70, hsp-1, hsp-16.2, unc-23, dnj-12 and dnj-13 under the same conditions. 10 µM HSF-1 DBD was added to each probe to determine the formation of the respective protein-DNA complex  were performed to test the interaction between purified HSF-1 DBD and dsDNAs Fig. a. We setF44E5.4p was performed. Addition of HSF-1 DBD resulted in an increase in the sedimentation coefficient, indicating the binding of HSF-1 DBD to dsDNA  and with a very constrained model, where a custom grid was designed containing one sue Table  for eachhsp-70, hsp-16.2a, hsp-16.2b and F44E5.4) show a second binding step with submicromolar affinity, while the first binding step is weaker , which are also in accordance with previous studies17.Comparing the binding ability of HSF-1 to the promoter regions and the observed response to heat-stress may be far fetching, given that only the DBD of HSF-1 was studied and further regulation will surely come from the other regions of this complex protein. Nevertheless, for the strongest inducible genes, also the highest affinities are observed , HSF-1 sites are occupied in a similar or even reduced manner with and without heat-shock implying a constitutive expression and possible constitutive function of HSF-1 responsible for the high expression levels observed for these genes under stressed and non-stressed conditions. This logic may be relevant for several of the 4120 HSE-binding sites found in promoter regions. Despite the correlations observed, it is important to note, that the approach employed in this study solely considers the DBD of HSF-1 and that HSF-1 HSE binding in the cell is further regulated by other regulatory domains, oligomerization domains and posttranslational modifications, like phosphorylation40 and deacetylation41. Due to these limitations further studies with longer fragments or full-length protein will need to be performed to unravel the full relationship between promoter sequences and HSF-1 binding.One exception among the probes studied here is Therefore, the here applied approach shows the direct affinity of the unmodified DBD to the DNA, but will require adaptations, when used for the dsDNA binding analysis of full-length HSF-1 in the future.Supplementary Figures.