Lactobacillus bacteria were the most abundant. Lactobacillus plantarum was the most abundant species, followed by Lactobacillus fermentum, Lactobacillus pentosaceus, and Weissella cibaria. These species were present in each sample with average absolute content values greater than 1% and were thus defined as core microbiota. Analysis results based on the alpha and beta diversities of the microbial communities showed that the microbial profiles of the fermented vegetables differed significantly based on the regions and raw materials used, and the species of the vegetables had a greater effect on the microbial community structure than the region from where they were harvested. Regarding microbial functional metabolism, we observed an enrichment of metabolic pathways, including membrane transport, replication and repair and translation, which implied that the microbial metabolism in the fermented vegetables tended to be vigorous. In addition, Lactobacillus plantarum and Lactobacillus fermentum were calculated to be major metabolic pathway contributors. Finally, we constructed a network to better explain correlations among the core microbiota and metabolic pathways. This study facilitates an understanding of the differences in microbial profiles and fermentation pathways involved in the production of fermented vegetables, establishes a basis for optimally selecting microorganisms to manufacture high-quality fermented vegetable products, and lays the foundation for better utilizing tropical microbial resources.Fermented vegetables are typically traditional foods made of fresh vegetables and their juices, which are fermented by beneficial microorganisms. Herein, we applied high-throughput sequencing and culture-dependent technology to describe the diversities of microbiota and identify core microbiota in fermented vegetables from different areas of Hainan Province, and abundant metabolic pathways in the fermented vegetables were simultaneously predicted. At the genus level,  Vegetables are a primary source of nutrition for the human body, as they provide a variety of necessary vitamins and minerals. In addition to being cooked, vegetables can be pickled, dried, canned, frozen, and converted into liquid forms. These processes extend their storage life, facilitate their transport, increase their value, and improve their flavor. In addition, they can also increase the variety of vegetable products to meet the growing demand for vegetable foods.Leuconostoc mesenteroides, Pediococcus cerevisiae, Lactobacillus brevis and Lactobacillus plantarum were active in the fermentation process. Several strains with excellent performance have also been isolated and screened from Chinese pickles, including Lactobacillus plantarum, Leuconostoc mesenteroides, Leuconostoc mesenteroides, Lactobacillus casei, Lactobacillus brevis and Lactobacillus acidophilus. Jiang et al. , Hainan has become an important region for the development of tropical biotechnology and tropical resource studies. The populates of Hainan have inhabited the island for generations and have used rich microbial resources to produce various fermented foods for a long time. Fermented vegetables are generally welcomed by a vast number of consumers as a kind of traditionally fermented food with high nutritional value, and have naturally become the best model for studying the microbial diversities of tropically fermented foods.In our previous research, we studied the microbial diversity of Yucha, a traditionally fermented cereal of Hainan Province  and all regions in combination. The evolutionary relationship of each microbe is shown in Figure s Figure ; P-Firmis Figure ; and C-Bs Figure . LactobaLactobacillus plantarum, Lactobacillus fermentum, Lactobacillus pentosaceus, Weissella cibaria, Lactobacillus panis, Pseudomonas veronil, Lactococcus lactis, Lactobacillus namurensis, Lactobacillus acetotolerans, and Lactobacillus brevis all exceeded 1%  and Lactobacillus fermentum (116 isolates) existed in nearly all of the fermented vegetable samples from each region and were the predominant Lactobacillus species in fermented vegetables from Hainan. Additionally, Enterococcus faecalis and Lactobacillus nantensis were isolated from only samples collected from Wanning, Lactobacillus nasuensis was isolated from only the Wuzhishan group, and Lactobacillus suntoryeus was isolated from only the Changjiang group.Using culture-dependent technology, 353 Lactobacillus plantarum and Lactobacillus fermentum were the main metabolic pathway contributors, contributing 10.417 and 5.837% to amino acid metabolism and 13.106 and 7.344% to carbohydrate metabolism, respectively.To better understand the important roles of specific microbes in the fermented vegetables, the levels of various metabolic pathways among the different types of fermented vegetable samples were compared. As shown in Figure Lactobacillus plantarum, the most abundant species in the fermented vegetables, and carbohydrate metabolism, and a negative correlation was found between Lactobacillus plantarum and the metabolism of cofactors and vitamins. Meanwhile, a general positive correlation was observed among Lactobacillus fermentum, nucleotide metabolism and lactic acid. Additionally, a significant positive correlation was found between nucleotide metabolism and TTA, and a significant negative correlation was found between nucleotide metabolism and pH.We further explored correlations among the core microbiota, metabolic pathways, geographical conditions and physicochemical indices using Spearman's rank correlation coefficient analysis. As shown in Figure Lactobacillus was the most abundant genus, and Lactobacillus metabolism was the most vigorous. Lactobacillus are Gram-positive bacteria that ferment glucose and lactose to lactic acid and are beneficial to human health. The abundant Lactobacillus found in our samples likely originated on the vegetable surfaces and surrounding environment and played multiple roles during fermentation. After harvesting, vegetable surfaces generally contain many microorganisms, although the number of beneficial microorganism is generally low. With the addition of salt to fermented vegetable processing, salt-sensitive microorganisms fail to grow, and reproduce. Lactobacillus has a high salt tolerance , fermented green beans (FGB), fermented bamboo shoots (FBS), and fermented watermelons (FW)] were aseptically collected from different regions of the Hainan Province in China . The samples were refrigerated at 4°C before being transferred to the laboratory. The pH and lactic acid contents were determined, and the total titratable acidity values were measured in a relatively short time according to previous methods  and then homogenized into a homogenous suspension for 10 min. The QIAGEN DNA Mini-Kit  and bead-beating method were used in combination to extract DNA from the suspension. The integrity and purity of the extracted DNA were evaluated by 0.8% agarose gel electrophoresis, and the OD 260/280 value was measured using a micro-ultraviolet spectrophotometer to determine the DNA concentration. All extracted DNA samples were stored at −20°C.Amplification of theV3-V4 regions of the 16S ribosomal RNA (rRNA) genes of each sample was performed. A set of 6-nucleotide barcodes was added to the universal forward primer 338F (5′-ACTCCTACGGGAGGCAGCA-3′) and the reverse primer 806R (5′-GGACTACHVGGGTWTCTAAT-3′)  were used in combination to quantify the PCR products according to the manufacturer's instructions. The amplified products of all the samples were pooled to a final concentration of 100 nmol/L in equimolar ratios and then loaded onto the Illumina MiSeq high-throughput sequencing platform for sequencing.Lactobacillus. After observing and recording the colony morphology, suspicious colonies were picked from the medium of optimal dilution and inoculated into the corresponding broth medium at 37°C for 24 h. Cell morphologies were observed under a light microscope by Gram staining to determine whether the pure bacteria were pure.Ten milliliters of each fermented vegetable sample was mixed with 90 mL of sterile NaCl solution , homogenized into a homogenous suspension, and then subjected to a 10-fold serial dilution. Next, 100 μl of each dilution sample was spread onto MRS  agar medium using a spreading plate and incubated at 37°C under anaerobic conditions for 2 days to isolate DNA was extracted from isolated single strains according to the method provided by Andrighetto et al. . PCR ampThe sequencing results were aligned using SeqMan software, and the obtained sequences were then compared with those in the NCBI database to determine sequence homology. We constructed a phylogenetic tree with the MEGA programme using the adjacency distance method and bootstrap analyses based on 1,000 random re-samplings to determine the confidence values of each branch.The original sequences were trimmed to remove the low-quality sequences. The sliding window method was used to filter the sequences according to quality scores. After filtering, more than 85.3% of the sequences were retained. The QIIME  values to control for multiple tests .Nucleotide sequence accession numbers: The sequence data reported in this paper have been deposited in the NCBI database (Accession Numbers: JZ and YS designed the study. JC and CM collected samples. QP and DH processed and sequenced samples. JZ and SJ analyzed data. QP and SJ wrote the paper.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Genetics in Macedonia—Following the international trends Macedonia gained its independence as one of the six Yugoslav republics in 1992, and today, most of its citizens perceive the aspiration for further development in the European integration processes.Macedonia is a small European country situated in the central part of the Balkan Peninsula and it borders other countries in the peninsula—Serbia, Bulgaria, Greece, Albania, and Kosovo Figure . It coveUnder the latest estimation of December 2015, the total population of the country is 2 071 278 inhabitants  suggest a diversity in relation to the neighboring nations  and consecutively, at the University Clinic for Gynaecology and Obstetrics . The first karyotyping was performed in 1972 at the University Clinic for Children's Diseases, while the prenatal analyses were introduced at the University Clinic for Gynaecology and Obstetrics . Along with the laboratory tests, a genetic counseling service is organized at both clinics  and later has been translated to the human genome. The first laboratory for genetic analysis of hemoglobinopathies was established within the University Clinic for Children's Diseases in 1973, which soon grew into a reference center for hemoglobinopathies in Yugoslavia and the Balkans. Based on population studies carried out on the territory of former Yugoslavia, many new types of hemoglobin (some of them unique) have been discovered and published Efremov, . The ResInstitute of Immunobiology and Human Genetics—involved in testing of immunogenetic polymorphisms, population studies, testing of genes associated with thrombophilia, familial Mediterranean fever, hereditary hemochromatosis, cystic fibrosis, and other frequent genetic disorders like lactose intolerance or celiac disease.Faculty of Pharmacy, Center for Biomolecular Pharmaceutical Analyses—exploring genomic variants responsible for efficacy/toxicity of therapeutic agents and detection of genetic changes in leukemia, colon, thyroid, breast cancer, etc.Molecular laboratory at the Institute of Biology, performing HPV genotypingGenetic laboratory for forensic medicine—determination of paternity, forensic analysis of human remains, population studies on mt‐ and Y chromosome DNA.Molecular laboratory at the Institute of Pathology, analyzing oncologic samples.Certain genetic services are offered by a number of private laboratories, partnering with many European renowned genetic laboratories , primarily intended to confirm less common or rare genetic disorders. In addition, whole genome and exome sequencing are being introduced and are available at present. However, due to the limited healthcare budget, they are used mainly by patients who can independently finance them. Table 6Genetic services in Macedonia are an integral part of the overall healthcare system. Most of the genetic services in the public healthcare are covered by the Health Insurance Fund and the users participate with 10% of the total cost of healthcare service. Implementation of new genetic tests in the public health institutions is constantly discussed among the Health Insurance Fund, professionals, patients, and their NGO‐s. However, the Fund still does not cover the costs of genome or exome sequencing which is available only by self‐financing so far. Since 2009, a rare diseases program exists within the Ministry of Health, funding for molecular detection of rare diseases, primarily aimed for conditions that have a possibility for orphan drug therapy or are suitable for prenatal diagnostics.7Generally, patients with genetic disorders are referred in two clinics in Macedonia:University Clinic for Gynaecology and Obstetrics—Risk Pregnancy Department, covering procedures for screening and genetic testing of fetuses. A genetic laboratory works within the Clinic, primarily focused on prenatal karyotyping.University Clinic for Children's Diseases—Department of Endocrinology and Genetics, where all children with syndromic or genetic diseases are transferred for diagnosis and consultation. Dysmorphic evaluation of syndromic disorders is performed using LDDB, Possum, and since recently the Face2Gene program.The genetic laboratory within the later clinic covers cytogenetic testing for conventional karyotyping and FISH testing for microdeletions and subtelomeric deletions, as well as molecular analysis for diagnosis of congenital adrenal hyperplasia, SRY gene analysis, etc. Oncogenetic examinations are also performed, primarily karyotyping for blood and bone marrow disorders, as well as FISH preparations that cover MYC proto‐oncogene, and fusion probes whenever needed at the Oncology Departments for children and adults. Neonatal screening operates within the laboratory; since 2006 all newborns in the country are screened for hypothyroidism as part of the National Program for Mothers and Children's Care. As for the remaining metabolic diseases , a selective screening for forty conditions is available, with a plan to be included into a program for all neonates in near future.A genetic counseling service for patients and families with genetic disorders is organized within both institutions. Genetic counseling follows world and European norms and recommendations for counseling high‐risk patients and families.8Knowledge accumulation in the field of human genetics has penetrated into every cell of the basic and clinical medicine, and has imposed the need for specific in‐depth education of medical students and related professionals in this field.Informal education in genetics of professionals involved in a range of related disciplines started like elsewhere in the world—with targeted training of the existing staff members—pediatricians, gynecologists, biologists, pharmacists, etc. Initially, undergraduate education in genetics was included in the study of biological sciences at the Faculty of Biology in Skopje. Specialists in various areas—pediatrics, gynecology, and oncology—had an opportunity to complete subspecialization in genetics.Comparing the curricula of universities around the world, an initiative to organize formal education in this field of genetics was started. The Cathedra of Human Genetics introduced theoretical and practical teaching for medical students since 2005, although it was officially constituted in 2011, according to the Rulebook of work at the Medical Faculty, accepted at session of the Teaching‐Scientific Council , Erasmus and Tempus (European Union program for support of education and training), Italian National Research Centre “Adriano Buzzati,” and Institute for Human Genetics in Lueven (Belgium) lead some of these projects.In addition, researchers from Macedonia have presented their work at a range of international and domestic congresses and expert meetings in the field of genetics. More than 100 papers have been published in international and domestic journals covering the field.10Modern genetics is a science with rapid development. In recent years, a comprehensive new knowledge about the pathogenic mechanisms of many genetic diseases and potential new treatments has come to light. Despite the limited healthcare and scientific resources, Macedonia manages to follow the new trends in genetic diagnostics and to modernize the legislation related to the genetic examinations and research. Considering that medical genetics penetrates all fields of medicine and can modify the approach toward certain diseases from its root, the healthcare professionals along with affected patients, families, and authorities in the country make efforts for a comprehensive coverage of the genetic diseases and conditions.


DNA and whole‐genome sequence data and analyze variation across a sympatric zone between the European and the Siberian chiffchaff (Phylloscopus collybita abietinus/tristis) to study how gene exchange between the lineages affects trait variation. Our results show that chiffchaff within the sympatric region show more extensive trait variation than allopatric birds, with a large proportion of individuals exhibiting intermediate phenotypic characters. The genomic differentiation between the subspecies is lower in sympatry than in allopatry and sympatric birds have a mix of genetic ancestry indicating extensive ongoing and past gene flow. Patterns of phenotypic and genetic variation also vary between regions within the hybrid zone, potentially reflecting differences in population densities, age of secondary contact, or differences in mate recognition or mate preference. The genomic data support the presence of two distinct genetic clades corresponding to allopatric abietinus and tristis and that genetic admixture is the force underlying trait variation in the sympatric region—the previously described subspecies (“fulvescens”) from the region is therefore not likely a distinct taxon. In addition, we conclude that subspecies identification based on appearance is uncertain as an individual with an apparently distinct phenotype can have a considerable proportion of the genome composed of mixed alleles, or even a major part of the genome introgressed from the other subspecies. Our results provide insights into the dynamics of admixture across subspecies boundaries and have implications for understanding speciation processes and for the identification of specific chiffchaff individuals based on phenotypic characters.Characterizing patterns of evolution of genetic and phenotypic divergence between incipient species is essential to understand how evolution of reproductive isolation proceeds. Hybrid zones are excellent for studying such processes, as they provide opportunities to assess trait variation in individuals with mixed genetic background and to quantify gene flow across different genomic regions. Here, we combine plumage, song, mt The distribution range of the Iberian chiffchaff (P. ibericus) spans the Iberian Peninsula and the Pyrenees and southwestern France. The mountain chiffchaff (P. sindianus) breeds in high‐altitude habitats in the Caucasus Mountains (P. s. lorenzii) and in parts of eastern–central Asia (P. s. sindianus). The common chiffchaff (P. collybita) has traditionally been separated into six different subspecies: collybita , brevirostris (northern Asia Minor), caucasicus (lowlands in Caucasus), menzbieri (Kopet Dagh and southwestern Transcaspia), abietinus , and tristis .The chiffchaff superspecies complex contains four distinct species and a set of subspecies widely distributed within the Palearctic region Figure . The CanP. c. tristis) and the “European” common chiffchaff (P. c. abietinus) – two subspecies with differences in habitat preference, body size, plumage characteristics, song type, and migration routes/wintering quarters  to those observed in abietinus and tristis  and northernmost  parts of the sympatric zone.In some areas of the superspecies distribution range, chiffchaff species and/or subspecies occur in sympatry. The largest such zone was identified already in the late nineteenth century Sushkin, . This re22.1abitinus if they had bright yellow stripes on the breast and yellow and green tones in overall coloration and as tristis if they showed absence of any yellow hues in coloration except on the underwing  than in abietinus (2.8–3.3 notes per second), and the almost nonoverlapping frequency ranges of notes . To designate recorded birds to a specific song type, we calculated the percentage of ascending elements in three randomly selected three‐second long intervals of songs of each recorded male  and measured the frequency range and note rate using Syrinx 2.3 .For 138 birds, we could record the song and make high‐quality sonograms for quantification of acoustic patterns  and tristis (n = 37) and within both the northern  and the southern  part of the sympatric zone  locus by restriction analysis. We analyzed the same 389‐bp‐long region that has previously been published for the chiffchaff . Within this region of CytB, five SNPs have previously been found to be diagnostic between allopatric European and Siberian chiffchaff  and prepared standard insert size (380 bp) libraries for 100 bp paired‐end sequencing on the Illumina HiSeq2000 platform. Library preparation and sequencing was performed by the SNP&SEQ Technology Platform in Uppsala. The sequencing yield was in total 147 Gb and varied between 2.7 and 5.1 Gb for each sample. The range for individual samples was 1.6–4.3 Gb after filtering out bases with Q‐scores <30 using ConDeTri . We applied the methods in the package GATK  and save these in vcf format files. The SNP calling procedure resulted in 4.9 million high‐quality SNPs that could be used for downstream analysis . Two further filtering steps were performed to generate the final data sets used for analysis. We initially applied stringent criteria and selected a set of SNPs, in which we had high‐quality SNP score information from at least 9 of the 10 individuals from each population, respectively. After this filtering step, only 18,014 SNPs were retained—this is from here on referred to as the “high‐stringency data set”. This high‐stringency data set was subsequently used to quantify and visualize patterns of differentiation between populations but did not contain more than 50 fixed differences  between abietinus and tristis. To obtain a larger set of polymorphisms for assessment of rates of fixed, private, and shared polymorphisms between and within abietinus and tristis, respectively, and for analysis of genomic composition of birds sampled in the sympatric region, we selected sites that were present in at least six different individuals in both allopatric abietinus and allopatric tristis. After that filtering step, 1,233,236 high‐quality SNPs were retained—from here on referred to the “low‐stringency data set”. It should be noted that the relative frequency of fixed differences did not change for the lower stringency data set . All genotypes have been submitted to datadryad.org and are available under doi: 10.5061/dryad.g74v0.Processing of vcf files was performed in the package PyVCF in the Python environment (FST) were calculated for both allopatric and sympatric population comparisons  using the high‐stringency data set  and the package PopGenome (http://popgenome.weebly.com) in the R environment  allopatric abietinus, (2) allopatric tristis, (3) southern sympatric samples (sample id:s S1‐S10), and (4) northern sympatric samples (sample id:s N1‐N10), respectively) was visualized by using a principal component analysis of genetic variation as implemented in the package smartPCA (EigenSoft) for R , and graphs were constructed using the ggplot2 library in R. In addition, we applied a population assignment analysis based on allele frequencies across the high‐stringency SNPs as implemented in the program STRUCTURE 2.3.4  and evaluated the optimal K as described in Evanno et al.  to calculate the number of fixed substitutions between allopatric populations, as well as private and shared polymorphisms within populations using the larger set of SNPs . Fixation indices —63% in the northern and 40% in the southern sympatric region.Within the allopatric regions, all birds were scored as typical CytB gene for three positions (SNPs) using restriction analysis in 193 individuals of allopatric abietinus (n = 12) and tristis (n = 37) and birds from both the northern (n = 53) and the southern part (n = 91) of the sympatric zone. Allopatric birds all had distinct mtDNA haplotypes (a single haplotype in Europe and a single haplotype in Siberia). Both in the northern and in the southern sympatric regions, we observed a mixture of these haplotypes, along with a previously identified third haplotype  that differs from the diagnostic tristis haplotype found in allopatry at a single nucleotide position  than in the eastern part  . In the northern sympatric region, only three of the 53 males had the abietinus mtDNA haplotype, and a quantification of frequency differences across the zone had no power. A detailed summary of the counts of mitochondrial haplotypes, morphotypes, and dialects in the sympatric region is presented in Table From our data and previous analyses of morphological and acoustic characteristics , while the opposite was found to be rare . Second, all birds except one (97.5%) with intermediate morphotype had tristis mtDNA type. Third, 15 of 43 males with the tristis mtDNA sang the distinct tristis song while three males performed pure abietinus song, and as many as 25 were scored as mixed singers. Of 10 males with abietinus mtDNA and morphotype, three were scored as mixed singers, seven were singing pure abietinus song type, and none was found to perform tristis song type. Thus, of 53 males with known mtDNA type and scored dialect, as many as 28 (53%) performed mixed song and three males (5.6%) performed typical abietinus song, despite having tristis mtDNA.Our combination of quantification of song and morphology together with mtDNA haplotyping and characterization of nuclear SNPs allow us to characterize each male chiffchaff in the sample from the sympatric zone using a combination of characteristics: morphotype (plumage characteristics), acoustics/song type, and genetic data . In summary, we find a pronounced mosaic in the distribution of characteristics Table . Some exn = 3,555) between allopatric abietinus and tristis. This is summarized in Figure tristis morphotype, a majority of the nuclear SNPs were homozygous for diagnostic tristis alleles, while there was much more variation for birds with the abietinus morphotype, and there was a strong association between the proportion of diagnostic SNPs and the mtDNA haplotype. However, morphotype, mtDNA type, and genomic composition did not always match. For example, sample N1 had typical abietinus morphotype, but a very large fraction of tristis diagnostic SNPs and a tristis mtDNA haplotype, and sample S4 had typical tristis morphotype and was homozygous for >95% of diagnostic tristis alleles but actually had abietinus mtDNA  and abietinus mtDNA type. Of the 12 individuals with mixed song type, six had tristis morphotype and six had abietinus morphotype. A majority of these individuals (10/12) had a combination with a high fraction of tristis diagnostic SNPs and tristis mtDNA, while one bird (S6) had abietinus mtDNA and a large proportion of abietinus diagnostic SNPs. Another bird (S4 again) had abietinus mtDNA but >95% of diagnostic tristis nuclear SNP alleles  than between allopatric populations (0.3%), and the proportion of shared polymorphisms was higher between sympatric (60.9%) than between allopatric populations (48.2%)  for all nuclear SNPs. A summary of these estimates is presented in Table FST = 0.062) than between sympatric (FST = 0.006) abietinus and tristis. We also compared populations sampled in different parts within the sympatric zone. We observed a slightly lower level of genetic differentiation between sympatric abietinus and tristis populations within the southern region (FST = 0.013) than within the northern region (FST = 0.016).To quantify the proportion of shared genetic variation in allopatric and sympatric regions, we calculated the percentage of fixed, private, and shared polymorphisms (low‐stringency data set) between both allopatric and sympatric populations. For samples from the sympatric region, we grouped the data based on morphotype, regardless of from what part of the area in the sympatric zone the sample originated. We found that the percentage of fixed differences between representatives of the tions 48.% . To test the hypothesis of presence of a third subspecies (fulvescens), we modeled the presence of different numbers of discrete clades within the investigated region (K = 1 to K = 7) and evaluated the number of clades with highest likelihood , and there was no support for the presence of a distinct clade that differs from abietinus and tristis, again supporting the hypothesis that birds in the sympatric region have a genomic composition with admixed abietinus and tristis alleles  and clustering analysis (STRUCTURE) of the high‐stringency SNP data. Figure s Figure . The STRs Figure  analysiss Figure , K = 2. s Figure .4abietinus and tristis. By combining data on morphology and acoustics with mtDNA haplotype information and a set of genome‐wide SNPs, we analyzed patterns of phenotypic and genetic diversity and differentiation within allopatric and sympatric regions. Below we discuss these patterns of variation in light of species history and identification issues.In this study, a large number of male chiffchaff were sampled across the distribution ranges of subspecies abietinus and within tristis and each subspecies was carrying a distinct mtDNA haplotype. In the sympatric region, however, sampled individuals showed considerable variation in both traits and we found a mixture of the two distinct mtDNA haplotypes . This supports previous studies indicating presence of mtDNA introgression between the two subspecies , while samples from the sympatric zone have a genetic set‐up built up by intermixed contributions from the two subspecies. When assessing the fractions of shared and fixed polymorphisms and estimating the global genetic differentiation (FST), we observed a substantially lower level of fixed differences (higher genetic differentiation) and higher fraction of shared polymorphisms (lower genetic differentiation) between subspecies in sympatry than in allopatry, again supporting a scenario with admixture and allele sharing in the region where subspecies ranges overlap, although this should be taken as circumstantial rather than hard evidence, as the grouping of individuals in the sympatric region was based on morphotype alone.In allopatric regions, we observed limited variation in morphotype and song type across individuals within tristis sample showed clear differentiation to all other tristis samples. This sample originates from the Chukotka region in far eastern Siberia and is hence considerably geographically separated from all other tristis. In addition, the tristis‐2 mtDNA haplotype was not found in allopatric tristis. These observations indicate that there is genetic structure also within the tristis subspecies, but extended sampling is obviously necessary to quantify the degree of potential differentiation between tristis populations and how that relates to observed “intrasubspecific” variation in phenotypic traits such as song and plumage color  than in abietinus . That indicates a slightly larger effective population size in tristis than in abietinus and is in agreement with the larger distribution range but could also be a consequence of that current tristis populations expanded from multiple glacial refugia  in these particular individuals. It could either indicate that i) these samples are representatives for multi‐generation recurrent backcrossings into one of the parental subspecies, and/or ii) that our selection of diagnostic SNPs contains a minor proportion of markers that in fact are shared or private). However, six birds that were scored as tristis based on plumage characters and mtDNA performed the mixed song type, and there were also several examples where the appearance and song did not match the genetic set‐up. The most evident cases include five birds with typical abietinus morphotype that actually had the tristis mtDNA, and a majority of diagnostic tristis nuclear SNPs that also performed mixed song and one bird, also a mixed singer, that had the typical tristis morphotype and almost 100% tristis diagnostic nuclear SNPs but abietinus mtDNA. This is in line with previous analysis of correlations between mtDNA and phenotype in chiffchaff sampled across ringing stations in Netherlands where a large proportion of birds identified as abietinus in hand actually had a typical tristis mtDNA type  compared to abietinus and tristis  form represents individuals with a mixed genetic set‐up from both tristis and abietinus rather than a distinct taxon and that the most plausible explanation for the occurrence of “fulvescens” type birds east of the immediate contact zone is that these represent individuals with introgressed abietinus alleles on a tristis genomic background . This scenario could easily be envisioned if hybrids and backcrosses preferentially interbreed with “pure” tristis generating a more diluted transition zone east of the immediate contact zone.On a related note, it might be of interest to use our results to speculate in more detail around the taxonomic status of chiffchaff populations, inhabiting the 1,500 km long region from the Arkhangelsk region in the north to the southern Ural Mountains in the south and also the region covering parts of the west Siberian plain. Based on spearheading naturalist work during the nineteenth century  indicating that potential prezygotic barriers related to both vocalizations and morphology are still permeable. Our analysis of genomic composition of birds within the sympatric zone showed that several individuals express diagnostic plumage characters and perform diagnostic song despite harboring the foreign mtDNA type and/or a considerable proportion of foreign nuclear alleles which indicates that the genetic elements underlying morphological and vocalization differences occur in a restricted portion of the genome. It is too early to speculate if these regions also contain genes of importance for reproductive isolation. However, investigating the genome‐wide pattern of differentiation for a much larger set of nuclear markers and investigating the genomic admixture proportions in birds with intermediate phenotypes will give a chance to identify regions that show elevated differentiation and assess if those have appeared as a result of restricted gene flow in particular genomic regions or as a consequence of adaptation to different habitats during separation , as these could carry a fraction of abietinus alleles that have spread through the tristis population eastwards by recurrent backcrossings. As our sampling was restricted to only include males, we are unfortunately not able to compare to the geographic distribution of mtDNA haplotypes and correlation between mtDNA and genomic composition in females. As many avian taxa have female‐biased dispersal  forest while the habitat is more fragmented in the southern zone with a patchy distribution of mixed and boreal forest. Abietinus is breeding in higher densities in mixed forest while tristis is more common in boreal (coniferous) forest and the relative density of tristis is higher in the northern zone , all individuals with tristis mtDNA also have the largest proportion of diagnostic tristis nuclear SNPs. This could potentially be a result of low fitness of hybrid females from the reciprocal cross , or that these hybrid females do not mate with abietinus males, but the sample size is limited, and extended sampling and mtDNA and nuclear genome analyses would be needed before this can be verified.In many avian study systems, there is clear evidence for male‐biased gene flow as a result of reduced fitness in female hybrids due to exposure of recessive incompatibility alleles on the Z‐chromosome  and Siberian (tristis) chiffchaff. Our results indicate that the overall genetic differentiation between subspecies is low despite considerable phenotypic divergence. A large proportion of birds in the hybrid zone exhibit intermediate phenotypic characters and a mix of genetic ancestry, indicating extensive ongoing and past gene flow. Patterns of phenotypic and genetic variation vary between the northern and the southern part of the hybrid zone, probably reflecting differences in population densities and/or relative age of secondary contact. The data indicate that birds showing intermediate characters very likely represent individuals resulting from recurrent backcrossings and introgression of (predominantly) abietinus alleles into a tristis genomic background, and the previously described subspecies “fulvescens” is therefore not to be considered a distinct taxon. The data also point to the difficulties in identification of specific individuals based on phenotypic characters alone.In this study, we combine genetic data with morphology and vocalizations across a hybrid zone between European  and P. c. tristis (yellow)Figure S2 An illustration of the different steps in the generation and analysis of nuclear DNAClick here for additional data file.


Phoenicopterus roseus, a long-lived colonial bird showing a homogeneous genetic structure of neutral markers at the scale of the Mediterranean region, to test the prediction that higher MHC allelic diversity with no population structure should occur in large panmictic populations of long-distance dispersing birds than in other resident species.Selective pressure from pathogens is thought to shape the allelic diversity of major histocompatibility complex (MHC) genes in vertebrates. In particular, both local adaptation to pathogens and gene flow are thought to explain a large part of the intraspecific variation observed in MHC allelic diversity. To date, however, evidence that adaptation to locally prevalent pathogens maintains MHC variation is limited to species with limited dispersal and, hence, reduced gene flow. On the one hand high gene flow can disrupt local adaptation in species with high dispersal rates, on the other hand such species are much more likely to experience spatial variation in pathogen pressure, suggesting that there may be intense pathogen mediated selection pressure operating across breeding sites in panmictic species. Such pathogen mediated selection pressure operating across breeding sites should therefore be sufficient to maintain high MHC diversity in high dispersing species in the absence of local adaptation mechanisms. We used the Greater Flamingo, We assessed the level of allelic diversity at the MHC Class IIB exon 2 from 116 individuals born in four different breeding colonies of Greater Flamingo in the Mediterranean region. We found one of the highest allelic diversity  of any non-passerine avian species investigated so far relative to the number of individuals and loci genotyped. There was no evidence of population structure between the four major Mediterranean breeding colonies.Our results suggest that local adaptation at MHC Class IIB in Greater Flamingos is constrained by high gene flow and high MHC diversity appears to be maintained by population wide pathogen-mediated selection rather than local pathogen-mediated selection. Further understanding of how pathogens vary across space and time will be crucial to further elucidate the mechanisms maintaining MHC diversity in species with large panmictic populations and high dispersal rates.The online version of this article (doi:10.1186/s12862-017-0905-3) contains supplementary material, which is available to authorized users. Genetic diversity is an essential component of the adaptive potential of populations , 2. The The maintenance of MHC polymorphism is generally attributed to powerful pathogen-mediated balancing selection, with gene conversion and recombination, maternal-foetal interactions and sexual selection also thought to play a role , 5. SincLong-lived and dispersing species with large geographical distributions, often associated with panmixia at neutral markers, face a particular challenge: individuals are likely to encounter during their life heterogeneous environmental conditions, each associated with distinct pathogen communities . Indeed,To investigate MHC diversity and population structure in a long-lived colonial species with high gene flow, we used data from Gillingham et al. , which gSamples were collected between 1998 and 2009, at four breeding colonies across the Mediterranean basin breeding range  all sequences that had a 1–2 base pair difference to one of the validated alleles within the sample were assumed to be artefacts; 5) all sequences that had more than a 2 base pair difference from validated alleles and were also validated as alleles if they were classified as alleles in other individuals; 6) the remaining sequences that had more than a 2 base pair difference from a validated allele were validated as alleles only if their frequency exceeded any artefact; 7) we assumed that chimeras were always present together with the true alleles from which they were formed and in lower frequency. Accordingly chimeras were easily identified when reads were aligned within individuals in SESAME and were discarded if in lower frequency than source validated alleles. Gillingham et al. [Details of MHC Class IIB exon 2 genotyping can be found in Gillingham et al.  which gen et al.  with Fim et al.  reportedm et al. . Of the m et al.  for detam et al.  found a Rho  devised by Ronfort et al. [Rho statistic is independent of both the ploidy level and the amount of within-individual diversity. This statistic has also recently been demonstrated by simulation to be less biased when assumptions of diploid inheritance are violated than Nei’s GST [ST [D [Rho statistic in conjunction with traditional Fst [First, we estimated nucleotide diversity and number of segregating sites using DnaSP version 5 . Second,t et al.  for genoei’s GST , G″ST [3 GST [ST  and JostT [ST [D , 32. We onal Fst  to measuonal Fst . We carronal Fst  (i.e. diSnn) which measures how often the “nearest neighbours” of sequences (i.e. the most similar sequence(s) of each sequence) are within the same geographical locality. Sequence similarity is determined as the number of base differences per sequence. For a dataset of sequences collected from n individuals , Snn is estimated as:When using markers that are highly diverse such as the MHC, many alleles may only be sampled once, even when sample sizes are fairly large, and, in extreme cases, analyses based solely on allele frequencies might be largely ineffective , 40. TheX is the number of nearest neighbour belonging to the same geographical locality divided by the total number of nearest neighbour for sequence j. Hence, if Xj is equal to 1, then all nearest sequences belonged to individuals sampled within the same geographical locality; and, conversely if Xj is equal to 0, all nearest sequences were from a different geographical locality. In a pairwise comparison between populations, a Snn that is significantly greater than 0.5 therefore indicates evidence of some population structure, as does a global Snn which is significantly greater than 0.25 when simultaneously comparing four populations. The Snn statistic was estimated using the PopGenome package [where,  package  in R and package . We carr package .The second statistic that we used which does not solely rely on allele frequency was the average percentage difference (APD) which has previously been used in studies investigating MHC population structure , 44. We k, N is the number of pairwise comparisons, Dab is the number of alleles not shared between individual a and individual b, Aa and Ab are the total number of unique alleles in individuals a and b. APD therefore measures within-population genetic variation. We tested whether between breeding colonies APD was significantly different from within breeding colonies APD by permutation using 100,000 reshuffling. APD calculations were performed using R [where for population  using R .dist), and (iii) the mean amino acid allele divergence within an individual over sites found to be under positive selection by Gillingham et al. [distSelect). We obtained the proportion of base pair differences between sequences required to compute AAdist and AAdistSelect using MEGA 7 [dist and AAdistSelect between breeding colonies we used a GLM model with a binomial distribution  and a logit link function. All GLMs were fitted in R [Finally, we estimated MHC heterozygosity with three metrics that did not require the assignment of MHC alleles to loci (since this is not possible in our dataset ): (i) thm et al.   and the number of alleles reported. We also calculated the proportion of alleles per individual and the proportion of alleles per individual and loci. We excluded passerines from our study for the following reasons: (1) the evolution and structure of passerine MHC is completely different from other avian species , with paudogenes . Howeverce versa .Table 2Nn > 116) except for the Eurasian coot .As reported in Gillingham et al. , we deteot Table . When raFst or Rho was investigated . Similar results were found when investigating alleles called from sequences of sites under positive selection  and when investigating sequences from amino acid sequences .There was either little or no differentiation  between breeding colonies regardless of whether ed Table  and b anSnn did not significantly deviate from the predicted value for a panmictic population of 0.25 when analysing full length sequences  and sequences known to be under positive selection . Similarly pairwise comparison between breeding colonies never significantly deviated from the predicted panmictic value of 0.5 (Table p = 0.590) and a similar result was found when analysing APD from amino acid sequences (p = 0.493). The mean number of alleles per individual , mean AAdist  and mean AAdistSelect  were not significantly different between breeding colonies.Further supporting no population structure at the MHC Class IIB exon 2, global .5 Table . AverageTo the best of our knowledge, the MHC allelic diversity in greater flamingos is one of the highest reported so far (this study & ) relativFulica atra, [Falco naumanni [Passerines frequently appear to also present a very high diversity of MHC alleles –50 but tca atra,   which showed distinct MHC differentiation between breeding colonies, despite close proximity between sites (80–260 km) [Theory predicts that local adaptation and divergent selection between populations should be favoured when gene flow is at an intermediate level . Such in–260 km) . However–260 km) –62. It i–260 km) . Indeed,–260 km)  demonstren sites 0–260 km The lack of locally adapted alleles may be explained by gene swamping, whereby specific local polymorphism is lost because gene flow is larger than selection . FurtherAlternatively, the high dispersal rates of flamingos may act to facilitate the dispersal of parasites themselves, and, hence, homogenise parasite communities across the Mediterranean. Furthermore it has been suggested that saline wetlands may harbour fewer parasites than freshwater wetlands . For exaRegardless of whether parasites vary across time and space in flamingos, the very high diversity of MHC Class IIB alleles observed in this study suggests strong pathogen-mediated balancing selection at the scale of the whole population. Given the lack of local MHC population structure, MHC diversity appears to be maintained by overdominant heterozygote advantage and/or population wide frequency dependent selection rather than local fluctuating selection and/or local variation in frequency dependent selection. Further understanding of how pathogens vary across space and time will be crucial to further elucidate the mechanisms maintaining MHC diversity in large panmictic populations with high dispersal rates .


Genetic diversity is needed for species’ adaptation to changing selective pressures and is particularly important in regions with rapid environmental change such as the Baltic Sea. Conservation measures should consider maintaining large gene pools to maximize species’ adaptive potential for long-term survival. In this study, we explored concerns regarding genetic variation in international and national policies that governs biodiversity and evaluated if and how such policy is put into practice in management plans governing Baltic Sea Marine Protected Areas (MPAs) in Sweden, Finland, Estonia, and Germany. We performed qualitative and quantitative textual analysis of 240 documents and found that agreed international and national policies on genetic biodiversity are not reflected in management plans for Baltic Sea MPAs. Management plans in all countries are largely void of goals and strategies for genetic biodiversity, which can partly be explained by a general lack of conservation genetics in policies directed toward aquatic environments.The online version of this article (doi:10.1007/s13280-016-0776-7) contains supplementary material, which is available to authorized users. Implementation of the common international policy framework is thus incorporated into many different national contexts. In this study, we focus on national implementation in Sweden, Finland, Estonia, and Germany.In this paper, we investigate With respect to regional management, we focus on marine protected areas (MPAs) because they constitute an important tool for biodiversity conservation in the marine environment  is global and overriding, whereas the Convention on the Protection of the Marine Environment of the Baltic Sea Area  focus directly on the Baltic Sea. Similarly, the EU Habitats Directive (Directive 92/43/EEC), the EU Birds Directive (Directive 2009/147/EC), the EU Marine Strategy Framework Directive , and the EU Water Framework Directive  have a regional focus and should, with respect to biodiversity, reflect implementation of the CBD within the EU.We first identified key international agreements and regulations that apply to the Baltic Sea including its biodiversity: two conventions and four EU directives Fig. . The ConWe reviewed how concerns regarding gene level biodiversity are formulated in these six main documents, as well as in a total of 49 identified follow-up agreements and guidelines Fig. ; Table 1Next, we addressed how these policies were implemented at the national level by reviewing national policy documents and the national reports to the institutions of the identified international agreements, including the secretariats of international conventions and to the Commission of the European Union (EU). We were able to focus on a subset of four countries and chose Sweden, Finland, Estonia, and Germany because (i) together they cover a large part of the Baltic Sea coastline (c. ¾), thus conservation practices within these countries have a large influence over the Baltic area, (ii) they represent regional variation by including northern, eastern, as well as central European countries, and (iii) they represent early, moderate, and late memberships of the EU .www.cbd.int, October 2014), (iv) national implementation plans for the HELCOM Baltic Sea Action Plan , and (v) country specific technical assessment reports generated by the EU Commission to monitor MSFD and WFD implementation .We chose to include six documents per country reflecting examples of national implementation of the international policies. We focused on a subset for which we were able to obtain comparable documents from all four countries. The selected documents include (i) national strategies for conservation of biodiversity that could be obtained from government official webpages or via email from government ministry officials, (ii) national biodiversity and action plans reflecting national implementation of the CBD and EU directives, (iii) the fifth national reports to the CBD (ii and iii obtained from S1) we decided to focus on HELCOM MPAs that represent regional implementation of the Helsinki Convention  in the Baltic Sea in the four countries. There are several types of MPAs in the Baltic Sea, both international and national ones, and after becoming aware of considerable complexity with respect to the management structure  or as hits per page in document . The difference disappears, however, when the follow-up documents of the Water Framework Directive (WFD) are removed from the analysis. This indicates that low occurrence of genetic search terms in WFD documents explains the difference among follow-up documents grouped after main international agreement.When we grouped our sample of 49 follow-up documents after the six main conventions/directives, we found a statistically significant difference in the occurrence of genetic search words among the six groups of documents. This difference is observed regardless of whether we measure hits per words in document . This difference remains also when removing the WFD follow-up documents when measuring hits per page  but not when measuring per mille hits per word . Thus, the low occurrence of genetic diversity in documents focusing on the aquatic environment is not explained fully by low occurrence in WFD documents. Rather, other aquatic follow-up documents (MSFD and Helsinki Convention) appear to have low mentioning of genetics in comparison to the broader focused ones  versus those with a broader focus , we found a strongly significant statistically lower frequency of genetic search words in the group of aquatic documents for both types of measurements  mentions genetic diversity as one of several indicators to be used in determination of environmental status. The Helsinki Convention, the WFD, and the Birds Directive do not mention genetic variation as a conservation goal.www.cbd.int/sp/targets/; Table In 20 of the 49 follow-up documents, concerns regarding genetic variation are mentioned, and in 15 of these documents, conservation goals for genetic diversity are expressed . In later HELCOM documents, genetic diversity is mentioned as an important conservation goal and MPAs are described as important means for reaching this goal  where the Target 9 goal for 2020 says: “70 % of the genetic diversity of crops including their wild relatives and other socio-economically valuable plant species conserved.”Area protection is the most common strategy to conserve genetic diversity; “[e]stablish a system of protected areas or areas where special measures need to be taken to conserve biological diversity [genetic resources included]…” . The Natura 2000 network and “their linear and continuous structures…” are of vital importance for “…the migration, dispersal and genetic exchange of wild species…” . Other strategies include legislation, policies, research, inventories, databases, gene banks, breeding and re-stocking, habitat restoration, technology and information exchange : “releases of reared salmon should be carefully monitored and their genetic or other impact on wild salmon evaluated by scientists”  states that contracting parties “shall, as far as possible and as appropriate…” “[i]dentify components of biological diversity important for its conservation and sustainable use…” and “[m]onitor, through sampling and other techniques, the components of biological diversity…” identifying biological diversity as variation of ecosystems, species, and genes. An explicit call for genetic monitoring in the Baltic Sea concerns protection of wild salmon , but not fully so when measuring hits per word in documents . The highest frequency occurs in Finnish documents and the lowest in Estonian ones (average number of hits per page over the six documents is 0.69 for Finland vs. 0.16 for Estonia). Also, we observe a difference between types of documents when we compare reporting documents relating to the CBD and the EU Habitats and Birds Directives versus those with a marine/aquatic focus  that occur both when measuring per mille hits per words in documents  and hits per page in documents . Here, average number of hits per word over documents and countries are 1.83 for CBD-, Habitats-, and Birds-Directive-related ones versus 0.12 for the marine policy-related documents, whereas average number of hits per page gives 0.82 versus 0.06 for the same comparison  suggesting that there is a difference with respect to how often the countries include genetic terms in CBD/EU Habitats and Birds Directives versus Helsinki Convention/EU Marine Strategy Framework Directive, and Water Framework Directive reporting documents; the highest frequency of genetics in marine/aquatic documents occurs in Sweden (28 hits in the three documents), and the lowest in Germany .Further, there is a significant interaction between country and type of document , and the Water Framework Directive  for instance through area protection (Germany), more research and compiling existing information (Sweden), by avoiding spread of alien species and GMOs , and by creating and maintaining gene banks and other ex situ programs .S1). In all four countries, the 64 HELCOM MPAs overlap with other types of protection including Natura 2000, and national protection measures such as national parks or nature reserves. Overall, the 64 HELCOM MPAs include other types of protected areas with 1–35 such areas (average = 3) per HELCOM MPA. Management responsibility varies among countries and rests with regional, County Administrative Boards (Sweden), regional authorities (Finland), federal states (Germany), and the National Environmental Board (Estonia).There are a total of 64 HELCOM MPAs in the Baltic Sea countries we investigated; 20 Swedish ones, 33 Finnish, 7 Estonian, and 4 German . The management plans have typically not been developed for the HELCOM MPA but for other types of protection that apply to the whole or parts of the same area .S1). In all four countries, other Natura 2000 and other types of protected areas exist in the Baltic Sea in addition to those included in the 64 HELCOM MPAs. Thus, we regard the analyzed management plans as a sample from a pool of all plans for protected areas in the Baltic Sea for our quantitative statistical assessments.For 8 of the 45 HELCOM MPAs that have management plans the plans only cover part of the HELCOM MPA area .Genetic concerns are rarely expressed in the management plans. In total we find 72 hits referring to genetic variation occurring in 37 out of 161 management plans  representing 17 of the 64 HELCOM MPA areas. The frequency of plans that include genetics is thus 0.23, 0.14, 0.15, and 1.0 for Sweden, Finland, Estonia, and Germany, respectively, and close to a statistically significant difference among countries . The difference still holds when ignoring the 11 newly established Finnish HELCOM MPAs that all lack plans , but not when removing Finland (which has considerably less hits than the other countries) altogether from the analysis. Thus, genetic concerns are significantly much less frequent for the Finnish HELCOM MPAs than for those of the other countries.The frequency of HELCOM MPAs with hits is 0.55, 0.06, 0.28, and 0.50 for Sweden, Finland, Estonia, and Germany, respectively, and is statistically different among countries (Pearson’s S1). A total of 13 species are mentioned as having such genetic concerns, and of these 9 species are typically land or freshwater living , marsh angelica, natterjack toad, little grapefern, northern crested newt, bluntleaf sandwort, or Siberian primrose; Tables S1) whereas only four are species whose primary habitat is the marine Baltic . Almost half of the Swedish hits (14 out of 31) refer to concern for genetic isolation of the Siberian primrose in Haparanda Archipelago.The genetic hits in management plans typically refer to concern for small population size and/or lack of gene flow in particular species; 33 of the 72 hits refer to such cases . Thus, genetic diversity appears to be considered much less than species diversity in Baltic Sea MPA management.Genetic diversity is the focus of the present paper but for the purpose of discussion on how this level of biodiversity compares to the extent to which the species level of biodiversity is considered in the Baltic Sea MPA management plans we analyzed a subset of plans for species diversity. We searched all the 14 Finnish MPAs plans and 20 out of 132 Swedish plans for words relating to species and species diversity. These 34 plans represented the 17 Finnish and 19 Swedish HELCOM MPAs for which management plans are available. We found a total of 2714 hits reflecting species diversity in these 34 plans as compared to 30 hits for genetic search terms in the same plans. All of the 34 plans included species diversity hits (15–501 hits per plan) as compared to 10 of them containing hits for genetic search words (0–13 hits per plan); the difference is highly significant  as compared to documents with a broader focus (CBD and the EU Habitats Directive).We conducted quantitative and qualitative textual analyses of 240 documents to investigate if and how concerns regarding genetic biodiversity expressed in international policies governing biological diversity in the Baltic Sea is transferred into national policy in Sweden, Finland, Estonia, and Germany. We then analyzed the extent to which expressed concerns, goals, and targets are further implemented in management plans of Baltic Sea MPAs in these four countries. Key findings are as follows:3.International policy clearly express that genetic biodiversity should be conserved, strategies for such conservation should be formulated, and monitoring programs should be developed.4.National policies in all four countries are in line with international intentions. Quantitatively, Finnish documents have the highest occurrence of our genetic search words, whereas qualitatively Swedish documents are strongest including most far-reaching intentions for monitoring genetic biodiversity of wild animals and plants.5.Area protection is expressed as a frequent, explicit measure to conserve genetic biodiversity both at the international and the national level.6.Genetic diversity is mentioned much less than species diversity in Baltic Sea MPA management plans.Other results include the following:The fact that the international policy that focus on aquatic environments in the Baltic Sea region is weak with respect to genetic diversity, and do not incorporate CBD conservation goals in the main documents, can to some extent explain the lack of conservation genetic concerns in MPA management plans. Such lag of conservation genetics in aquatic environments as compared to terrestrial ones was highlighted twenty years ago . This finding supports the notion that implementation of conservation policy for genetic biodiversity lags behind.The lack of explicit genetic goals for MPAs in the Baltic Sea is unfortunate considering the particular importance of genetic diversity in this area  are largely void of goals, concerns, strategies, or other mentioning of genetic biodiversity. This is in spite of area protection being expressed as an explicit measure to conserve genetic biodiversity in both international policy and in national implementation documents of these four countries. Thus, outspoken international goals of MPAs to function to conserve genetic diversity and to support gene flow among species appear not to be implemented at the regional level.S1). It is important that these examples are spread and discussed among managers involved with MPA design and planning. Also, finding ways to bridge current gaps between conservation genetics researchers, policy makers, and managers  as compared to documents with a broader focus (CBD and the EU Habitats Directive). Other factors most likely also affect the situation and a better understanding of why implementing conservation genetic principles lags behind in marine environments is needed. Such factors could include lack of resources among regional policymakers and managers. We are addressing those issues for the Baltic Sea area in forthcoming studies that include interviews with managers and knowledge communication studies . Several good examples of explicit goals and strategies for genetic conservation can be found in a few of the management plans for HELCOM MPAs Tables , S1. It Supplementary material 1 (PDF 489 kb)Below is the link to the electronic supplementary material.


Research suggests that perception and imagination engage neuronal representations in the same visual areas. However, the underlying mechanisms that differentiate sensory perception from imagination remain unclear. Here, we examine the directed coupling (effective connectivity) between fronto-parietal and visual areas during perception and imagery. We found an increase in bottom-up coupling during perception relative to baseline and an increase in top-down coupling during both perception and imagery, with a much stronger increase during imagery. Modulation of the coupling from frontal to early visual areas was common to both perception and imagery. Furthermore, we show that the experienced vividness during imagery was selectively associated with increases in top-down connectivity to early visual cortex. These results highlight the importance of top-down processing in internally as well as externally driven visual experience. In 1980, Kosslyn proposed that imagery uses the same ‘visual buffer’ as perception to represent visual content. In line with this idea, neuroimaging has shown that visual areas have similar neural representations of imagined and perceived objects, with higher overlap in late visual areas3. The overlap in early visual areas depends on the exact imagery task5 and the experienced vividness of the imagery7.Visual experience can be caused by external events in the outside world, like the appearance of an object, or by internal signals generating visual images in our mind’s eye. Localisation of the neural structures that represent the content of visual imagery is an important step in the process of understanding the underlying mechanisms that generate visual images9. Whereas during perception, activation of visual representations is ultimately caused by bottom-up influences from the retina, these exogenous influences are absent during visual imagery. How visual areas are activated in the absence of stimulus bound, bottom-up input, remains an open question. Recent work using measures of effective (directed) connectivity during imagery suggests that top-down projections from fronto-parietal areas to visual areas are involved in visual imagery11.Developing a detailed understanding of the mechanisms by which our brains generate visual experience calls for the elucidation of dynamic top-down and bottom-up connectivity within and between the neural structures involved14. The predictive coding account of perception proposes that visual experience is a product of the reciprocal exchange of bottom-up and top-down influences throughout the neuronal hierarchy16. From this perspective, the question arises to what extent recurrent exchange differs during perception and imagery. Here, we investigated this aspect of distributed neuronal processing by examining how effective connectivity changes during these two forms of visual experience.There is a large body of research showing that top-down influences also play an important role in perceptionWe hypothesized distinct context sensitive patterns of top-down and bottom-up influences during imagery compared to perception. We used dynamic causal modelling (DCM) to characterise the effective connectivity that best explains the BOLD (Blood Oxygen-Level Dependent) response during visual perception and imagery. Based on hierarchical predictive coding, we hypothesized an increase in bottom-up coupling, relative to baseline, during perception but not imagination and an increase in top-down coupling during visual experience; i.e., both perception and imagery.7. Three participants were excluded; two due to insufficient data caused by scanner problems and one due to not completing the task. Twenty-six participants  were included in the reported analyses. The study was approved by and in accordance with the guidelines of the local ethics committee (CMO Arnhem-Nijmegen).Twenty-nine healthy adult volunteers with normal or corrected to normal vision gave written informed consent and participated in the experiment. An initial analysis of these data is already published in Dijkstra, Bosch, and van Gerven17. In each trial, participants were shown two successive images, followed by a cue indicating which of the two they should subsequently imagine. The stimulus set consisted of six images obtained from the World Wide Web: two faces (Barack Obama and Emma Watson), two letters (‘D’ and ‘I’) and two kinds of fruit (banana and apple). During imagery, a frame was presented within which subjects were asked to imagine the cued stimulus as vividly as possible, while maintaining fixation on a cross presented in the centre of the screen. It has been shown that neural activity during visual imagery is influenced by the experienced vividness of visual imagery18. Therefore, we asked participants to indicate their experienced vividness of visual imagery on each trial on a scale from one to four, where one was low vividness and four was high vividness. Previous research has shown that such a subjective imagery rating shows high test-retest reliability and correlates with objective measures of imagery vividness19.The experimental paradigm is depicted in Fig. The experiment comprised nine blocks, each consisting of twenty trials. Hence, each stimulus was perceived 60 times and imagined 30 times over the course of the whole experiment, resulting in a total scanning time of approximately 1.5 hours per participant.7. This indicated that any effect of vividness could be explained by stimulus category. To control for contributions of stimulus category, we regressed out the category effect by mean-centring the vividness scores per category. Finally, the visual cues, the presentation of the vividness instruction screen and the button presses were modelled with separate regressors, along with the six movement (nuisance) regressors.The fMRI acquisition and pre-processing details are described in the Supplementary Material. All analyses were performed using SPM12. To identify our regions of interest, prior to the connectivity analyses, we inverted a general linear model (GLM), with data and regressors concatenated over runs. As our focus was on establishing domain general mechanisms of imagery and perception, we collapsed over stimulus categories, thereby simplifying the subsequent DCM analysis. Our regressors of interest modelled the perception events, the imagery events, and the parametric modulation of imagery vividness. Inspection of the design orthogonality revealed an absolute cosine angle of 0.13 between the perception and imagery regressor, indicating that the experimental design enabled the imagery response to be disentangled from the perception response. Analysis of behavioural data (vividness ratings during scanning) demonstrated an effect of stimulus category on vividness, with letters being experienced as more vivid than fruit and fruit as more vivid than faces21. To allow for variation between participants in the exact location of the effect, we selected the ROIs per participant as an 8 mm sphere centred on the subject-specific maximum within a 16 mm sphere centred on the group maximum, in space. This approach ensures that the subject- specific ROIs were close to the average group activity, while allowing for slight variation in functional anatomy between participants.We selected regions of interest (ROI) for our connectivity model based on the results from our GLM analysis and prior knowledge from the literatureIn each subject, time series were extracted from every voxel in each of the ROIs. The data were subsequently adjusted based on an F-contrast that retained the experimental effects of interest  and regressed out task-unrelated variance caused by sources that were not of interest, for example, head movement. The first principal component (eigenvariate) of each region’s adjusted data was then used as the time series for subsequent DCM analysis.22. DCM uses the following bilinear state equation to infer effective connectivity parameters:z describes neuronal activity resulting from the interplay of three different influences. First, the A matrix represents endogenous or fixed connectivity during baseline, in the absence of external stimulation. Second, the elements in B j represent the changes in connectivity due to experimental influences uj. Finally, the matrix C denotes the direct influence of each experimental input uj. The neuronal model is coupled to a biophysically plausible model of neurovascular coupling and the BOLD response, which together generate the predicted BOLD time series. The slice timing model within DCM was set to its default value of 0.5 TR23. The model is then inverted to find the neural and haemodynamic parameters which offer the best trade-off between model fit and complexity )24.In order to quantify the effects of perception, imagery and vividness on effective connectivity between the ROIs, we used dynamic causal modelling (DCM)i,j of a connection from area i to area j, reflects the rate of change (in Hz) in the neuronal activity of area j that is caused by a change in the firing rate of area i. In this context, a positive parameter refers to an increase in the firing rate of area j caused by area i, which can be interpreted as an excitatory influence. A negative parameter refers to a decrease in the firing rate of area j caused by area i, which can be interpreted as an inhibitory influence. Note that this does not relate to specific excitatory or inhibitory neurons or their axonal projections; rather it is the net influence of one brain region on another. Similarly, a parameter-estimate Bi,j,k of an experimental influence k on the connection from area i to j, reflects the increase (in Hz) in the coupling from area i to area j caused by the experimental manipulation k.A parameter-estimate Astrength of influence would be different. In other words, our experimental questions were about the relative strength of coupling under different experimental manipulations, instead of the presence or absence of context sensitive changes in coupling. In other words, our aim was to test quantitative hypotheses about model parameters rather than qualitative aspects of model structure. To test hypotheses about changes in connectivity at the group level, we used hierarchical Bayesian modelling and averaging. This entails evaluating a large number of connectivity models and taking a weighted average of the connectivity parameters, weighted by the evidence of each model25. This approach, called Bayesian Model Averaging (BMA), accommodates uncertainty about the underlying model structure on the parameter estimates.Typically, in DCM, a few models are specified, each of which represents a hypothesis about the connectivity architecture of the system being studied. These models differ in the presence or absence of an influence of experimental manipulations on certain connections. However, in the current context we expected almost all connections to be influenced by perception and imagery, but that the 25.Estimating the parameters of all plausible DCMs can be a time consuming process and novel ways have been developed to make BMA more computationally efficient. Instead of estimating every possible model separately, it is possible to only estimate a full or parent model, containing all the parameters of interest and use the posterior estimates of this full or parent model to derive the posterior estimates (and model evidence) of reduced models, in which one or more parameters are systematically removed. This is called Bayesian model reduction (BMR) and gives similar results to the standard approach, but is computationally much more efficient27. In short, the PEB scheme uses BMR to invert a hierarchical (Bayesian) model of between subject effects on within-subject parameters. This involves specifying a general linear model of between-subject effects. Here, we simply tested for nontrivial group means of condition-specific changes in connectivity . Further details on how we used PEB in the current analysis can be found in the Supplementary Material.The BMA scheme described above could be used with classical inference methods such as t-tests to evaluate whether a parameter is non-zero at the group level. However, this ignores uncertainty at the within subject level . To circumvent this issue, we used a parametric empirical Bayesian (PEB) approach with DCMuj, here modelled as boxcar regressors .In DCM, the activity in the network is driven by direct experimental inputs ors Fig. . We assuors Fig.  as drivi28, suggests strong connectivity among all our ROIs29. Thus, we have no a priori reasons to constrain our model. Therefore, we modelled both imagery and perception as potentially influencing all connections. We also had no reason to constrain which connections could be influenced by imagery vividness: more vivid imagery could be caused by an increase in top-down processing from either parietal or frontal areas to early or late visual areas, or by a decrease in bottom-up processing in the same pathways. Furthermore, since most effects of vividness have been found in early visual areas30, this could also be due to an increase in excitability within this area as a result of decreased self-inhibition31. Therefore, we modelled vividness on all connections and on the self-connection of OCC. We used the analysis scheme described above with PEB and BMR to estimate differential (group average) changes in directed connectivity within the DCM.The main aim of this study was to investigate changes in top-down and bottom-up connectivity between visual perception and imagery. Research on connectivity during visual imagery has been surprisingly scarce. Functional connectivity studies of working memory maintenance, which is assumed to show a substantial overlap with the neural mechanisms of visual imageryThe data that support the findings of this study are available from the corresponding author upon reasonable request.The mean vividness rating over trials, averaged over participants, was 2.97 (SD = 0.52). All participants showed variation in their vividness ratings over the course of the experiment . On average, 8.79% of trials were rated as vividness = 1, 20.85% as vividness = 2, 35.10% as vividness = 3 and 35.28% as vividness = 4.32.The standard SPM (GLM whole brain) analysis showed that both perception and imagery activated early visual, ventral stream and parietal areas Fig. . FurtherBased on these regionally specific effects, we defined four ROIs: early visual cortex (OCC), late visual cortex/fusiform gyrus (FG), intraparietal sulcus (IPS) and inferior frontal gyrus (IFG). OCC, FG and IPS regions were based on the conjunction between the main effect of imagery and the main effect of perception and IFG was selected based on the main effect of imagery Table .Table 1Ra priori, we specified the driving input during perception on OCC and during imagery on both IFG and OCC, for the subsequent DCM analysis of quantitative changes in effective connectivity.To identify where imagery exerts the driving input Fig. , we consWe next performed a group-level analysis on the baseline connectivity and its modulation by perception, imagery and imagery vividness (modelled as a parametric regressor). As before, we estimated a group level PEB model and pruned any connections not contributing to the model evidence. Significance in this context is defined as a posterior probability (Pp) of at least 0.95, based on the (family) comparison of models in which the parameter was switched on, versus models in which it was switched off. The results from the BMR on all connections are shown in Fig. The effects of perception and imagery were modelled as changes in connectivity relative to baseline coupling. Perception had an influence on most connections throughout the network; one clear example is the coupling from OCC to IPS Fig. . During The main objective of this study was to characterise differences between perception and imagery in terms of their effect on the strength of top-down and bottom-up coupling. To quantify this differential effect, we pooled over bottom-up and top-down connections. We defined bottom-up (forward) coupling as outgoing connections from OCC and incoming connections to IFG, whereas top-down (backward) coupling was defined as incoming connections to OCC and outgoing connections from IFG. The posterior distribution over this contrast  comparing the strength of bottom-up Fig.  and top-We found stronger bottom-up coupling during perception as compared to imagery (Posterior probability (Pp) = 1.0). Furthermore, whilst bottom-up coupling was clearly stronger during perception than baseline (Pp = 1.0), this was not the case during imagery (Pp = 0.48). In contrast, during both perception (Pp = 0.87) and imagery (Pp = 1.0) the top-down coupling was stronger than baseline. Note that the net top-down coupling during perception was close to zero because it was a mixture of excitatory and inhibitory connections Fig. . FurtherThe aim of the current study was to characterise bottom-up and top-down coupling during perception and imagery by examining changes in effective connectivity. We found that during perception there was an increase in both bottom-up and top-down coupling relative to baseline. In contrast, during imagery, there was only an increase in top-down coupling – and this increase was much stronger than during perception. These findings illustrate that distinct dynamic top-down and bottom-up mechanisms underlie visual experience during perception and imagery. We show that during perception, an interplay between top-down and bottom-up influences is responsible for visual experience. In contrast, during imagery, where bottom-up drive is absent, there is an increase of top-down coupling that accompanies the visual experience.33. Top-down connections from the IFG are important for selective attention during encoding35 as well as for the maintenance of visual information during the delay period37. Our results support the idea that this coupling reflects a general top-down mechanism responsible for enhancing the relevant visual representations in early visual areas: both in the presence as well as in the absence of bottom-up sensory input, subserving perception and imagery, respectively. In this context, the increase in strength during imagery could reflect the increase in general attentional load associated with  generating a visual experience in the absence of sensory input.Examination of the connectivity parameters shows that the observed increase in top-down coupling during perception was a mixture of inhibitory and excitatory influences. The most prominent excitatory connection was apparent from inferior frontal gyrus (IFG) to early visual areas (OCC); this was also the connection that showed the strongest effect during visual imagery. Coupling from IFG to OCC has been proposed as a common top-down mechanism for different cognitive processes during visual working memory39. Within this framework, perceptual synthesis arises from an interplay between inhibitory top-down predictions and excitatory bottom-up prediction errors, iteratively leading to an accurate representation of the outside world41.The largest inhibitory top-down modulation during perception was an increase in the inhibitory connection from IPS to OCC. We also observed a strong increase in the excitatory bottom-up connection from OCC to IPS. Findings of increased inhibitory top-down influence together with an increase in excitatory bottom-up influence supports a predictive coding view of perception that proposes reciprocal top-down and bottom-up influences during perceptionWithin hierarchical predictive coding, imagination  is thought to reflect the generation of sensory predictions in the absence of precise sensory constraints. In biological implementations of predictive coding, the precision of sensory constraints  is encoded by the gain of bottom-up prediction errors. This formulation of perception and imagination fits very comfortably with our results. Our findings are consistent with an increase in the precision of ascending prediction errors during perception – that induce descending predictions – but not imagination; where sensory precision is reflected in the strength of bottom-up effective connectivity at low levels of the visual hierarchy.11. However, we did find a significant modulation of this connection by the experienced vividness of the imagery, with more vivid imagery being associated with more excitatory influences from IPS to OCC. During baseline and during perception, coupling from IPS to early visual areas was inhibitory. This means that an increase in imagery vividness was associated with a relative decrease in inhibitory top-down influences . This might reflect an imagery specific mechanism responsible for preserving early visual cortical activation. The extent to which this succeeds is then reflected in the experienced imagery vividness. On average, this effect may be too subtle to lead to a main effect of imagery.Surprisingly, we did not observe increased top-down coupling from IPS during imagery. This is in contrast with the results from other studies on effective connectivity during visual imagery18. Here, we extend previous findings by revealing a possible mechanism for this observation. Decreased inhibitory influences of IPS might allow activation in early visual cortex engendered by top-down signals from IFG to be better preserved. The increase in inhibitory influence from FG might in turn reflect top-down sharpening of OCC activity mediated by the selection of (or biased competition among) category representations in FG43.Finally, we found that imagery vividness was specifically related to the strength of top-down coupling to the early visual cortex. More vivid imagery was associated with a decrease in inhibition from IPS and an increase in inhibition from FG. The fact that only connections to early visual cortex were modulated by vividness is in line with the findings of previous studies that the vividness of visual imagery is specifically associated with early visual cortex activity47, which in turn projects to large parts of the cortex to initiate and guide orienting movements50. Therefore, it is possible that part of the sensory input entered the network via other cortical regions. This simplifying assumption should be kept in mind when interpreting our findings and future research should explore the effects of changing the locus of visual input during perception.It could be argued that the assumption we made to model sensory information as entering the network during perception via early visual cortex is too simplistic. Afferent neurons from the retina also project directly to the superior colliculusIn conclusion, our results reveal both differences and similarities in the neural mechanisms underlying perception and imagery. Modulation of bottom-up connections was specific for perception, while top-down coupling was increased during both perception and imagery, albeit much more during imagery. Coupling from frontal to early visual areas seems to represent a common mechanism during perception and imagery, whereas the role of top-down connections from parietal cortex is less clear: the inhibitory influence increases during perception and decreases with more vivid imagery.Supplementary material


Accumulated studies have provided controversial evidences of prognostic value for signal transducer and activator of transcription proteins 3 (STAT3) in cancers. To address this inconsistency, we performed a systematic analysis to determine whether STAT3 can serve as a prognostic marker in human cancers. STAT3 expression was assessed using Oncomine analysis. cBioPortal, Kaplan-Meier Plotter, and Prognoscan were performed to identify the prognostic roles of STAT3 in human cancers. The copy number alteration, mutation, interactive analysis, and visualize the altered networks were performed by cBioPortal. We found that STAT3 was more frequently overexpressed in lung, ovarian, gastric, blood and brain cancers than their normal tissues and its expression might be negatively related with the prognosis. In addition, STAT3 mutation mainly occurred in uterine cancer and existed in a hotspot in SH2 domain. Those findings suggest that STAT3 might serve as a diagnostic and therapeutic target for certain types of cancer, such as lung, ovarian, gastric, blood and brain cancers. However, future research is required to validate our findings and thus promote the clinical utility of STAT3 in those cancers prognosis evaluation. Cancer is one of the major causes threatening human health and life . DespiteSignal transducer and activator of transcription proteins 3 (STAT3), a member of STAT family, is well demonstrated to exerts an important effect on tumorigenesis and tumor-related inflammation . AberranTo explore the role of STAT3 in cancers, we compared the transcription levels of STAT3 in cancers with that in normal tissues, using Oncomine database and found that the mRNA expressions of STAT3 were significantly over-expressed in certain types of tumors and lower in others as compared to that of the normal sample. As show in Figure We conducted cDNA microarray analysis by using the Oncomine database to explore gene expression of STAT3 in cancer types. The Oncomine database was queried for STAT3 expression in cancer and normal tissues. Our analysis revealed that STAT3 was over-expressed in brain and cns, gastric, head and neck, melanoma, myeloma cancers, but was under-expressed in breast, leukemia, liver, lymphoma, and sarcoma cancers as compared to that in normal tissue Table  13–28].–28.13–28Using the comprehensive Kaplan-Meier survival analysis platform, we discovered that decreased mRNA expression of STAT3 is an unfavorable prognostic factor of overall survival for patients with breast adenocarcinoma , coiled-coil, DNA binding, linker, Src homology 2 (SH2), and transcriptional activation domain , 32. UndWhen compared with the high frequency of STAT3 genetic deletions, there were few, frequent STAT3 gene mutations in 87 studies examined using cBioPortal Web. As show in Figure Furthermore, we applied the OncoPrint from a query for alterations in CCND1, EGFR, IL6, JAK1, JAK3, MAPK1, MYC, SOCS3, SRC, and STAT3 genes. The percentages of alterations in these genes among NEPC varied from 10–53% for individual genes , the MYC gene was amplified predominantly in the NEPC type Figure , Table 4In order to discover whether each gene pair has a significant correlation, the portal performs a Fisher’s exact test. The mutual exclusivity panel analysis revealed that the co-occurrent alternations of STAT3 and CCND1, EGFR, IL6, JAK1, JAK3, MAPK1, MYC, SOCS3, SRC has statistically significant. Functional plotting of the corresponding mRNA level associated with the genetic status of STAT3 revealed that deletion of STAT3 was associated with increased mRNA expression Figure .The cBioPortal analysis program identified 12 types of human cancer with significant CNAs in the chosen genes’ signature . The STAT3 signature was created such as to represent the structures and functions of STAT3. The CNAs of specific structural components of the STAT3 in tumors may be potential targets to prevent metastatic spread. Network view of STAT3 and other chosen genes in neuroendocrine prostate cancer was presented in Figure STAT3 has been proved to participate in the generation and development of various cancers . MoreoveTo gain further insights into the role of a prognostic marker, we next investigated the association of STAT3 expression and OS in various cancers, the prognostic value of STAT3 mRNA expression was assessed using the Kaplan-Meier Plotter and PrognoScan. Overall, high levels of STAT3 gene expression result in low survival in ovarian, lung, blood, and brain cancer, however, which is not clear in breast cancer. Therefore, to assess the tumorigenic or tumor suppressor role of STAT3 in breast cancer, many previous studies have demonstrated that the protein expression was significantly up-regulated in breast cancer tissues compared with their matched normal breast tissues –41. FurtSomatically acquired genetic, epigenetic, transcriptomic, and proteomic alternations are the major four factors in tumorigenesis . The somcMyc and CyclinD1), prosurvival genes (Bcl-xl and Survivin) and invasive genes (VEG-f and Klf-8), leading to fast-growing tumors with highly metastatic capability [et al. recently demonstrated that BMX can promote cell proliferation through STAT3 signaling pathways in cervical cancer cells [et al. revealed that STAT3 could bind promoter region of TXNDC17 for regulating its expression and mediating Taxol resistance via enhancing autophagy in human colorectal cancer cells [Subsequently, we performed cBioPortal to interactive analysis and visualize the altered networks of STAT3. From the network analysis, we can discover more information about the mechanisms of interaction among the different genes . Figure pability . Yuanyaner cells , meanwhier cells . Along wIn the present study, we used portals to systemically analyze the expression and prognostic value of STAT3 in cancer development, which contributes to a better understanding of molecular etiology and epidemiology of cancer, and ultimately accelerates the transformation of genomic knowledge into clinical practice. Our finding demonstrates that STAT3 might serve as a diagnostic and therapeutic target for certain types of cancer, including lung, ovarian, gastric, blood and brain cancers. However, the deep mechanism of these results remains unclear, further researches need to be performed in the future.https://www.oncomine.org/resource/login.html), an online database consisting of previously published and open-access microarray data, was performed to identify the transcription level of STAT3 gene in various types of cancers [t-test to generate a p value. The parameters p-value < IE-4, fold change >2, and gene ranking in the top 10% were used to obtain the most significant STAT3 probes. Heat map was used to define the co-expression profiles of STAT3 gene in different types of cancers.Oncomine database  [The cBioPortal for Cancer genomics is an open-access resource  , 56, prohttp://kmplot.com/analysis/) is an online database of published microarray datasets that assess the effect of 54,675 genes on survival using 10,461 cancer samples  [p-value was also computed.Kaplan-Meier Plotter ( cancer) . We perfhttp://www.prognoscan.org/) is a comprehensive online platform for assessing potential tumor biomarkers and therapeutic targets. We used the PrognoScan platform to validate the prognostic value of STAT3 expression in patients with various types of cancers. The threshold was adjusted to cox p-value < 0.05.PrognoScan (http://www.string-db.org/), a database of known and predicted protein interacting, to determine interacting proteins using STAT3 as the query [We utilized the STRING analysis tool . Survival curves were plotted using the cBioPortal and Kaplan–Meier plots. All results are displayed with