Nuclear, mitochondrial and cytoplasmic signal transducer and activator of transcription 3 (STAT3) regulates many cellular processes, e.g., the transcription or opening of mitochondrial permeability transition pore, and its activity depends on the phosphorylation of Tyr705 and/or Ser727 sites. In the heterogeneous network of cardiac cells, STAT3 promotes cardiac muscle differentiation, vascular element formation and extracellular matrix homeostasis. Overwhelming evidence suggests that STAT3 is beneficial for the heart, plays a role in the prevention of age-related and postpartum heart failure, protects the heart against cardiotoxic doxorubicin or ischaemia/reperfusion injury, and is involved in many cardioprotective strategies . Ischaemic heart disease is still the leading cause of death worldwide, and many cardiovascular risk factors contribute to the development of the disease. This review focuses on the effects of various cardiovascular risk factors  on cardiac STAT3 under non-ischaemic baseline conditions, and in settings of ischaemia/reperfusion injury with or without cardioprotective strategies. Signal transducer and activator of transcription 3 (STAT3) has a central role in transmitting extracellular signals from the plasma membrane to the nucleus and mitochondria, where it influences transcription and mitochondrial function, thereby regulating diverse biological processes.The protein encoded by mammalian STAT3 gene contains six functional domains: N-terminal, coiled-coil, DNA-binding, linker, SH2 and C-terminal transactivation domains . There aA wide variety of extracellular polypeptide ligands like interleukin-(IL)-6-family, leukaemia inhibitory factor, oncostatin M, ciliary neurotrophic factor, and cardiotrophin acts on plasma membrane receptors and activate STAT3, predominantly via the glycoprotein-130-Janus kinase (gp-130-JAK) axis . Ligand Direct activation occurs once gp-130 is phosphorylated by JAK and provides binding sites for the SH2 domain of STAT3. After recruitment of STAT3, it is phosphorylated and activated by JAKs on Tyr705. Subsequently, STAT3 dissociates from the receptor complex, forms homo- or heterodimers with other STAT proteins, and translocates into the nucleus, where it binds to consensus DNA sequences ) and finally initiates transcription of target genes [The gp-130-JAK axis simultaneously activates ERK1/2, which in turn phosphorylates the Ser727 site of STAT3 monomers and/or Tyr705-phosphorylated STAT3 dimers to influence the dimer’s transcriptional activity. Phosphorylation of STAT3 monomers on Ser727 leads to translocation into the mitochondria without dimerization . In the Besides classic ligands, STAT3 signalling is activated or modulated by other endogenous or exogenous peptides including insulin, leptin, angiotensin II, erythropoietin, granulocyte stimulating factor, etc.STAT3 is expressed in different cell types of the heart such as cardiomyocytes, cardiac fibroblasts, endothelial cells, smooth muscle cells, inflammatory cells and cardiac neurons. In the heterogeneous network of cardiac cells STAT3 regulates cell-to-cell communication . For inIschaemic heart disease is still a major cause of death and disability worldwide; therefore, cardioprotective strategies and revealing of signalling are needed. Ischaemic conditioning is a manoeuvre for protecting the heart against the detrimental effects of ischaemia/reperfusion injury by means of the application of brief non-harmful ischaemia/reperfusion cycles to elicit endogenous cardioprotective mechanisms. When the conditioning method is applied before, under or after the prolonged lethal ischaemia, it is called ischaemic preconditioning, perconditioning or postconditioning, respectively. Research into the underlying molecular mechanisms of cardioprotection results in investigation of pharmacological agents that mimic the cardioprotective effect of ischaemic conditioning .Several studies have reported that ischaemia enhances STAT3 phosphorylation ,13,14, aMany cardiovascular risk factors contribute to the development of ischaemic heart disease and interfere with the cardioprotective effect of conditioning manoeuvres . TherefDiabetes mellitus is a condition characterized by an increased blood glucose level. It is one of the most prevalent metabolic diseases worldwide and is also associated with increased mortality. In 2017, an estimated 425 million people suffered from diabetes, a number that is expected to increase by 48% within three decades according to the International Diabetes Federation . DiabetiBased on a literature review, the effect of diabetes on non-ischaemic baseline expression, phosphorylation or activation of cardiac STAT3 protein seems to be rather controversial. Several publications demonstrated a significant decrease in cardiac STAT3 phosphorylation and/or activation in various experimental models of diabetes . The firIn contrast to the results above, a few studies found that the non-ischaemic baseline level of phosphorylated STAT3 significantly increased in diabetic hearts . For insRecently some potential cardioprotective agents have been suggested to attenuate STAT3 dysregulation in diabetes . The impIn summary, the findings regarding the effect of diabetes on cardiac STAT3 phosphorylation, expression and activation are inconsistent. The reasons for these controversies are unclear and may include substantial differences in the method of induction, type, severity, and duration of diabetes as well as differences in the method of detection of STAT3 phosphorylation and expression.2) [The definition of obesity among adults is based on a standard cutoff point of body mass index  . Obesity2) . Obesity2) ,40,41. I2) . Obese i2) . ObesityThe adipose tissue produces various adipokines such as leptin, which is involved in the regulation of appetite. Obesity is associated with elevated circulating leptin levels and hypothalamic leptin resistance . ClinicaA preclinical study proved that cardiac ciliary neurotrophic factor (CNTF) ameliorated left ventricular hypertrophy in leptin-deficient ob/ob and leptin-resistant db/db mice via the STAT3 and ERK1/2 signalling pathway by activation of CNTF receptor that is structurally similar to the leptin receptor . Altogether, the majority  of studies showed no change or increase in cardiac STAT3 phosphorylation, expression and/or activation in animal models of experimental obesity and/or hyperlipidemia. Based on the literature data, it seems that facilitated cardiac STAT3 signalling might contribute to activation of hypertrophic and surviving pathways in obesity. According to the 2017 High Blood Pressure Clinical Practice Guideline, systolic blood pressure ≥130 and/or diastolic blood pressure ≥80 mmHg is considered as hypertension . ObservaLeft ventricular hypertrophy is a secondary consequence of hypertension and independently predicts future cardiovascular events ,64. TherChronic kidney disease (CKD) is a clinical syndrome defined as persistent deterioration of kidney function . The preCardiac STAT3 phosphorylation increased in a rat model of doxorubicin-induced CKD and cardiac hypertrophy  Table 4Table 4. Although alteration of cardiac STAT3 has been reported in CKD, whether this is due to direct effects or due to secondary effects via cardiovascular complications of CKD  is very difficult to distinguish.The aging human population is an epidemiological burden. It is estimated that more than 2 billion individuals will be over the age of 60 by 2050 worldwide . The preThe first evidence demonstrating a crucial role for cardiac STAT3 in aging was reported by Jacoby et al., showing the development of cardiac dysfunction and fibrosis with advancing age in mice with a cardiomyocyte-restricted deletion of STAT3 . Later, In contrast, some studies described no alteration or increase of cardiac STAT3 in association with advanced age. Phosphorylation was unchanged in 14-month-old mice  or 20‒24Although age-related STAT3 dysregulation in human hearts has not been reported, research on blood samples from two independent cohort studies showed that STAT3 was positively associated with age . Regarding possible modulation of STAT3 dysregulation in aging, Castello et al. have reported that alternate-day fasting restored the decline in STAT3 activation in elderly rats to young values and protected the heart against age-related hypertrophy . Taken together, it is controversial whether STAT3 expression or activation is affected by aging or not, and further research is needed to elucidate the phenomenon. Nevertheless, fasting seems to be effective for the restoration of aging-associated STAT3 dysregulation.Smoking has well-known detrimental effects on health. Both active and passive (secondhand) smoking are predominant risk factors for coronary heart disease . We et aAlcohol consumption is a widespread social habit. Although several studies showed potential benefits of moderate alcohol consumption on coronary heart diseases , heavy aPatients at risk of cardiovascular disease are often treated with various medications that may also contribute to alteration of cardiac molecular signalling. Administration of the lipid-lowering drug simvastatin (10 mg/kg single daily dose) for five days in male Wistar rats increased cardiac p-STAT3 without affecting t-STAT3 . ControvThe effect of various cardiovascular risk factors on cardiac STAT3 signalling under non-ischaemic baseline conditions remains inconclusive  due to lDiabetes is a well-known risk factor for the development of ischaemic heart disease. Moreover, clinical studies showed that diabetes mellitus increased the susceptibility of the myocardium to ischaemia/reperfusion injury and that the long-term outcome of ischaemic heart disease is worsened by diabetes. However, the effect of diabetes on the susceptibility of the myocardium to acute ischaemia/reperfusion injury is controversial in animal models. In experimental models of both type I or type II diabetes, infarct size was demonstrated to be significantly larger, unchanged, or significantly smaller in diabetic rats compared to nondiabetic controls ,90,91,92In contrast to discrepancies regarding the effect of diabetes on infarct size, post-ischaemic phosphorylation/activation of cardiac STAT3 was clearly downregulated in experimental models of diabetes in all investigations . In an aThe same trend was observed in in vitro models of diabetes or acute hyperglycaemia. In H9c2 cells subjected to high glucose conditions (25 mM glucose), the post-ischaemic STAT3 phosphorylation and activation (at Ser727 and Tyr705 sites) were significantly lower ,32. SimiIn summary, post-ischaemic STAT3 phosphorylation and/or activation are significantly decreased due to diabetes in all studies irrespective of the applied models, which may contribute to increased susceptibility to myocardial ischaemia/reperfusion injury in diabetes.Obese people are more prone to developing coronary artery disease . Leptin The hypertrophic and fibrotic myocardium is more sensitive to ischaemia; therefore, acute myocardial infarction is a common cause of cardiovascular morbidity and mortality in CKD patients . To dateAging aggravates myocardial ischaemia/reperfusion injury in humans  and rodeIt is well known that the risk of cardiovascular diseases is higher in males compared to females. The molecular and cellular basis of the cardiovascular gender difference has been reviewed elsewhere . ScientiWang et al. demonstrated that in hearts isolated from male wild-type C57BL/6J mice and subjected to ex vivo global ischaemia/reperfusion, the STAT activation was lower compared to hearts from female mice  Table 7Table 7. Depression is common in patients with coronary heart disease and is associated with increased cardiovascular mortality . DepressAdministration of the lipid-lowering drug simvastatin (10 mg/kg single daily dose) for five days in male Wistar rats did not affect ischaemia/reperfusion-induced STAT3 phosphorylation . l-glutamate [In the literature, studies exist reporting both preserved as well as impaired cardioprotection by ischaemic or pharmacological conditionings . Neverthlutamate . In contlutamate .Several studies have demonstrated that the presence of diabetes attenuated phosphorylation and/or activation of STAT3 in hearts undergoing ischaemia/reperfusion with ischaemic , pharmacRecently, some promising agents have been suggested to prevent STAT3 dysregulation in diabetes. In diabetic mice, rapamycin  Table 8Table 8 aIn summary, the majority of articles revealed that diabetes attenuates ischaemic or pharmacological conditioning-induced cardiac STAT3 activation after ischaemia/reperfusion. This could be related to the loss of cardioprotection and increased infarct size in diabetes. Interestingly, there are some potential therapeutic agents like the antioxidant N-acetylcysteine and allopurinol or rapamycin that can restore the STAT3 phosphorylation and so can contribute to attenuated myocardial damage.There is a shortage of studies investigating the effect of obesity on ischaemic or pharmacological conditioning-induced cardiac STAT3 activation after ischaemia/reperfusion. Nevertheless, an experimental study demonstrated that cardiac STAT3 activation was increased in rabbit hearts subjected to two cycles of preconditioning with 5 min ischaemia/10 min reperfusion followed by 30 min ischaemia and 10 min reperfusion as compared to the ischaemia/reperfusion control group . In thisThere are only limited data on the possible interaction of CKD with cardioprotective strategies. Byrne et al. reported that ischaemic conditioning still reduces infarct size after four weeks of subtotal nephrectomy in male Wistar rats . This stSeveral clinical ,118 and Depression is common in patients with coronary heart disease and is associated with increased cardiovascular mortality . EndotheCertain pharmacological agents used for preventing ischaemic heart disease  have been reported to interfere with the cardioprotective effect of conditionings ,124,125.Here, we reviewed current knowledge regarding the effect of various cardiovascular risk factors on STAT3 in the heart under non-ischaemic baseline conditions, and in settings of ischaemia/reperfusion injury with or without cardioprotective strategies . Our conclusions may be somewhat limited as, in the case of most risk factors, only a few studies have focused primarily on the alteration of cardiac STAT3 due to a specific risk factor . Therefore, it is difficult to find strong evidence in those areas, and further studies are urged to investigate the effect of these risk factors on myocardial STAT3 signalling.Nevertheless, in this review we highlighted that under non-ischaemic baseline conditions the STAT3 phosphorylation in response to risk factors is inconsistent  . The reaMore interestingly, most of the findings indicate that certain risk factors  attenuate the activation of cardiac STAT3 in settings of ischaemia/reperfusion, which may contribute to a worsening of the ischaemic tolerance of the heart. Moreover, based on our review of the literature, it seems that risk factors including diabetes, aging and depression decrease, which likely plays a role in the loss of cardioprotection . These rIn summary, there is still no consensus in this research field and further focused studies are needed to elucidate the role of cardiovascular risk factors in dysregulation of myocardial STAT3 under different physiological and pathophysiological conditions. Further testing of the therapeutic potential of STAT3 activation in cardiac ischaemia/reperfusion in the presence of various cardiovascular risk factors should also be straightforward.


Cystoseira spp., which are a key component of the Mediterranean-Atlantic seaweed forests that are undergoing population declines. The C. tamariscifolia complex, composed of C. tamariscifolia s.s., C. amentacea and C. mediterranea, have indistinct boundaries and natural hybridization is suspected. Our aims are to (1) infer the genetic structure and diversity of these species throughout their distribution ranges using microsatellite markers to identify ancient versus recent geographical populations, contact zones and reproductive barriers, and (2) hindcast past distributions using niche models to investigate the influence of past range shifts on genetic divergence at multiple spatial scales. Results supported a single, morphologically plastic species the genetic structure of which was incongruent with a priori species assignments. The low diversity and low singularity in northern European populations suggest recent colonization after the LGM. The southern Iberian genetic hotspot most likely results from the role of this area as a climatic refugium or a secondary contact zone between differentiated populations or both. We hypothesize that life-history traits  and prior colonization effects, rather than reproductive barriers, might explain the observed genetic discontinuities.Climate-driven range-shifts create evolutionary opportunities for allopatric divergence and subsequent contact, leading to genetic structuration and hybrid zones. We investigate how these processes influenced the evolution of a complex of three closely related  Range expansions and contractions, and resulting patterns of refugial isolation, colonization and secondary contact have similarly structured the genetic and evolutionary traits of many marine species4. Populations isolated in climate refugia for long periods develop a genetic signature including more unique alleles5. On the contrary, outside stable areas, extinction/recolonization dynamics and associated bottlenecks typically result in eroded gene-pools, characterized by lower regional genetic diversity and originality6. High allelic richness can also arise by secondary contact between formerly vicariant, differentiated lineages7. Differentiating between refugia or admixture as causes of genetic hotspots is possible by analysing the global distribution of private alleles along ranges. Predictions that can be tested include: (i) secondary contact (admixture) zones with high allelic richness and low singularity; (ii) leading edges with lower diversity and differentiation; (iii) refugia with high and unique genetic diversity; and (iv) trailing edges with low diversity and high differentiation.Climate changes during the Quaternary glaciations (c. 2.6 Myr to the present) caused periodic fluctuations in sea-level, sea ice and sea-surface temperature that have resulted in radical species range-shifts, that left genetic signatures in many European species10. In such complexes, shallow genetic divergence, incomplete isolation and partially overlapping ranges and morphological traits can be particularly challenging to tackle. The very speciose brown algal genus Cystoseira is particularly interesting in this respect as it contains several complexes of closely related genetic entities with difficult taxonomic resolution11 that might hypothetically be influenced by hybrid zones with introgressive effects.The effects of range shifts on creating ancient vicariant refugia and more recent admixture zones are particularly interesting to investigate in complexes of closely related species that are still partially reproductively compatibleCystoseira forests are among the most important foundation species in the Mediterranean and warm-temperate northeast Atlantic. They create complex habitats and are essential for biodiversity and ecosystem functioning14. Over the last decades, Mediterranean Cystoseira populations have suffered a general decline due to anthropogenic pressures16 that is likely to intensify under climatic change19. Therefore, most of these species have been protected , and additional reforestation and protection measures have been recommended20. In the Mediterranean Sea, about two-thirds of ca. 28 species are considered to be endemic21, and new species are regularly added22. The genus is considered to be in the process of active speciation in the Mediterranean, but patterns and drivers of diversification remain poorly understood, partly due to morphological plasticity25 and shallow genetic divergences11. The recent development of high-resolution microsatellite markers27 is finally allowing the analysis of evolutionary processes at the species/population interface for some Cystoseira lineages. This may shed new lights on the role of climate-driven range-shifts and of putative introgressive processes in the evolution of this genus.C. tamariscifolia complex: C. amentacea (C. Agardh) Bory , C. mediterranea Sauvageau, and C. tamariscifolia (Hudson) Papenfuss25. The complex belongs to the mtDNA clade V of Draisma et al.11, which also includes other ecologically important species such as C. brachycarpa J. Agardh, C. crinita Duby and C. zosteroides (Turner) C. Agardh. The three putative species differ in their general distributions, but overlap in some areas such as the Alboran Sea and Sicily30. C. tamariscifolia is distributed throughout the Atlantic from the British Isles to Mauritania (including the Macaronesia Islands), and in some Mediterranean areas under Atlantic influence . The other two species are mainly restricted to the western Mediterranean, the Adriatic, and the Aegean Seas. Given the geological history of the Mediterranean Sea, Atlantic C. tamariscifolia has been hypothesized to be the ancestor of the other two species, which would have evolved independently from one another25. The extent to which these putative species are genetically distinct and reproductively isolated has never been investigated genetically, but natural ongoing hybridization has been suspected based on morphologically intermediate individuals31.This study focuses on three closely related species, herein collectively referred to as the C. tamariscifolia complex form discrete genetic pools and how their divergence may have been shaped by past range shifts and hybridization at contact zones. To address this question, we analyse the genetic structure and diversity of this species complex throughout its distribution and integrate it with modelled past range shifts during climate extremes (the LGM) using SDMs. We use these results to identify genetic signatures of past range shifts and possible hybridization at contact zones between the three species. To further distinguish the alternative hypotheses of distinct species that admix when in contact versus a single species with morphotypes that vary among habitats, we analyse in detail a zone in the Iberian Peninsula containing parapatric populations of C. amentacea and C. tamariscifolia.This study asks whether these species within the 21, Marine Forest (http://www.marineforests.com/) and the Global Biodiversity Information Facility (http://www.gbif.org/). A resolution of 5 arc minutes (~ 9.2 km) was used to georeference species data and climatic variables. We obtained 259 occurrence records for C. amentacea (which collapsed into 171 unique cells), 154 for C. mediterranea (80 cells), and 580 for C. tamariscifolia (315 cells) (see Supplementary S1.1 in Supporting information). Because the reproductive isolation between the three morphospecies is uncertain, the occurrences of these species were also pooled  to develop models including all entities, based on the hypothesis of a single “species complex” rather than three species. The coastline to 20 m depth was inferred from the 30 arc-seconds General Bathymetric Chart of the Oceans (http://www.gebco.net/). For the LGM coastline, a mean sea-level change of −116 m was considered.Georeferenced occurrences were compiled from our sampling surveys, plus 88 available publications and three databases: Algaebase32 and tested with Pearson’s correlation coefficient (r < |0.7|). Variables for the LGM were obtained from the Coupled Model Intercomparison Project Phase 5 . The uncertainty produced by the variability among the ocean general circulation models33 was reduced by using a multi-model ensemble with five models from the CMIP5 LGM experiment: CCSM4, CNRM-CM5, NASA-GISS-E2-R, IPSL-CM5A-LR, and MIROC-ESM.Maximum and minimum sea surface temperature (SST) under present conditions were obtained from the Bio-ORACLE datasetCystoseira species individually, and for the species complex as a whole, were obtained using an ensemble approach. “biomod2”34 was used to perform six presence-absence algorithms: generalized additive model, flexible discriminant analysis, generalized boosting model, generalized linear model, randomForest and multiple adaptive regression splines. Two sets of pseudo-absences were used, extracted at random with the same number of pseudo-absences as presence cells. We performed 10 iterations for each model and sets of pseudo-absences. In each iteration, data were split into a calibration (70%) and a validation set (30%). Thus, 120 models were computed for each species (2 pseudo-absence sets × 6 modelling techniques × 10 iterations). Model accuracy was assessed using the area under the receiver operating characteristic (ROC) curve (AUC), ROC-derived sensitivity (presences correctly predicted) and specificity (absences correctly predicted)35, and the true skill statistic (TSS36) considering a threshold that optimized ROC and TSS scores34. Two ensembles with the models that obtained TSS >0.7 through two different algorithms were computed: the mean of the probabilities and the average of the binary predictions. Finally, a consensus prediction averaging the two ensembles for each species was obtained. Ensembles were hindcasted to the LGM. The uncertainty in past projections was assessed computing a clamping mask, identifying the different values for each variable between the training range and the past climate. We also estimated the relative importance of each variable by the correlation between the full model and a model rearranged without one variable. After three iterations, the mean for each variable was calculated to obtain an importance value from 0 to 1 (the highest importance)34. Finally, the spatial overlap among the hindcasts for the LGM based on the three species was computed after reclassifying the probabilities into binary predictions according to the TSS-derived threshold of each ensemble. All analyses were performed in R37.The predictions for the present and the LGM for the three C. tamariscifolia and the Mediterranean C. amentacea var. stricta , occurred close to the surface  forming a dense meadow, while individuals of C. tamariscifolia, identified as those with a single cauloid, were scattered at depths between 1 and 2 m  (see Supplementary S2).Sampling aimed to cover most of the known distribution range of the three species studied, with a special focus on the Atlantic-Mediterranean transition zone, a contact zone between the mostly Atlantic ta Table . Some coAt each site throughout the geographical distribution, 30 to 48 individuals of each species were sampled. The minimum distance between sampled individuals was 0.5 m. A small clean piece of the apical branch was collected per individual, preserved in silica gel. Between two and four voucher specimens from most of the sampling sites were lodged in the herbarium of the University of Algarve (see Supplementary S1.4).et al.38. Six microsatellite loci were amplified with fluorescently labelled primers as in Engelen et al.27. Fragment sizes were determined using an ABI PRISM 3130xl (Applied Biosystems) with GeneScan-500 (LIZ) as size standard. Alleles were manually scored using STRand39.DNA was isolated from 5–10 mg of dry tissue as in Zardi 40. Nei’s unbiased gene diversity (expected heterozygosity) was obtained using GENALEX 6.50241. For comparisons between regions, data were standardised to a sample size of 23 individuals . The analyses were done for a minimum of 1 site per region since one of the clusters was just one location. One-way ANOVAs were performed to assess differences in the means of private alleles and allelic richness between regions, followed by Tukey tests for post hoc comparisons.The number of alleles and private alleles was standardised by resampling for the smallest sample size  using HP-rare 1.1IS) within each population were estimated with Genetix42 and tested with 10 000 permutations. MICRO-CHECKER v.2.2.3 software43 was used to check for scoring errors because of stuttering, large allele dropout and to estimate null allele frequencies. When evidence of null alleles was significant, a correction for null alleles was applied.The analyses of departures from Hardy–Weinberg equilibrium (HWE) were estimated using GENALEX 6.502. The inbreeding coefficients  from 1 to 31. The model was run 14 times for each K to check consistency. The number of clusters was estimated by ΔK. A correspondence analysis based on individual allelic composition was performed using GENETIX42. All individuals with missing data at one or more loci were excluded . Finally, to assess isolation by distance (IBD), Mantel tests based on Jost’s D against the shortest sea distance were implemented in the R package vegan46. ANCOVA was performed to assess isolation by distance between clusters. To ensure independence between samples, we only considered populations separated by 150 to 700 km for the ANCOVA.Genetic structure was inferred using STRUCTURE v2.4C. amentacea (Pi) and C. tamariscifolia (Ps) in parapatry (in “El Playazo”), STRUCTURE was used (as described above) to assign individuals to genetic groups and to detect putative admixed individuals. In this case, two genetic groups were considered (i.e. two taxa assumed). The mean of the posterior distribution of each individual admixture coefficient, qx(i), represents the proportion of the ith individual’s genotype drawn from cluster “x”. The concordance between the morphological and genetic classification was evaluated using the weighted kappa coefficient47, following the scale proposed by Monserud and Leemans48. Subsequently, STRUCTURE and kappa analysis were repeated (keeping K = 2) including the closest localities sampled where C. tamariscifolia (He) and C. amentacea (GV and Cq) occurred alone in the contact area to assess whether the individuals would group according to site or morphological entity.To assess reproductive isolation between C. amentacea and C. mediterranea were mainly explained by the minimum SST, while for C. tamariscifolia maximum and minimum SST had similar importance  distributions. For the Mediterranean endemic C. amentacea and C. mediterranea, the models predicted high probabilities of occurrence along the Atlantic coasts of the Iberian Peninsula and the Azores, with the North Atlantic African coast also suitable for C. mediterranea (Supplementary S1.2).All distribution prediction models had good accuracy Table . The curce Table . The modC. tamariscifolia  to 11.47 (CB) . However, K = 3 did not retrieve the three species, as individuals grouped more based on their geographical location rather than separating species  North Atlantic (NAtl), including northern Portugal and all sites northwards, (2) Central Mediterranean and Saharan (CMed&Sah), the former including the southern Italy populations and the latter including the Canary and Saharan populations, and (3) Iberian (Ibe) including Iberian sites southwards from northern Portugal and the NW Mediterranean. For K = 7, the “CMed&Sah” cluster was divided into Central Mediterranean (CMed) and Saharan (Sah) populations; and the “Ibe” cluster was divided into four subclusters: Atlantic-Mediterranean transition (A-M trans), Southeastern Iberia (SEibe), Northeastern Iberia (NEibe), and Gulf of Lion (GL)  populations  clustered with Central Mediterranean populations (data not shown).The estimated number of genetic clusters was ies Fig. . For K =thwards,  Central The correspondence analysis explained a small part of total variability  and SE Iberia (from GV to SP) Fig. . In contJost’s D Genetic distance was significantly correlated with the shortest sea distance across all sampling sites Fig. , as wellC. amentacea and C. tamariscifolia . The kappa coefficient (0.545) indicated moderate agreement between genetic and morphological identification (Monserud and Leemans 1992). However, when other nearby localities  of C. amentacea and C. tamariscifolia were considered, the kappa coefficient decreased (kappa = 0.238) indicating poor agreement between morphological and genetic features. In the global analyses of the entire complex, these two parapatric populations were not genetically distinguishable  the LGM and continue afterwards as a consequence of the expansion of refugial populations into a more continuous distribution area. If they were not strongly reproductively isolated, then widespread admixture and introgression would be expected. This scenario cannot be ruled out as the cause for the observed genetic patterns, although there is little evidence for admixture and introgression of distinct genetic groups co-occurring together. On the contrary, the clustering of individuals in multiple geographical regions suggests that the recovered genetic structure is not primarily defined by morphological species assignments and that each regional genetic group is a distinct lineage with a common ancestor.The North Atlantic cluster showed the lowest genetic diversity and singularity of all the regions considered. Such a pattern is often a consequence of consecutive bottlenecks due to founder events, and its high differentiation suggests that the separation of this group is not recent (see Supplementary S3.2). The most likely hypothesis for such a present scenario is a past postglacial colonization from a genetically distinct group, such as northern Atlantic Iberia, where the models indicate that an ancient refugial population could have persisted.30, and potentially hybridize31. However, the high differentiation there also suggests it could have been a stable LGM climate refugium. This hypothesis is supported by the C. tamariscifolia and C. mediterranea models  hosted the highest genetic diversity. This follows expectations as this region is considered an important contact area where different morpho-entities meetels Fig. , but notels Fig. . Thus, t49 where ancient divergent populations are presently undergoing bottlenecks50. The models for the whole complex predicted a post-LGM retreat in the southernmost Atlantic distribution edge, supporting this hypothesis. In the Mediterranean, models suggested important range changes since the LGM and a current and past range edge location of Sicilian populations. Thus, the relatively low allelic richness found in Saharan and Mediterranean populations should be the consequence of genetic erosion associated with their marginal and unstable character. In contrast, the clusters Northeast Iberia and Gulf of Lion (one population only) showed similar allelic richness and number of unique alleles as Saharan and Central Mediterranean populations. However, the different models do not support long-term persistence there. Thus, the high number of private alleles could have their origin in other regions nearby that might presently be extinct or unsampled. Future studies of the genetic diversity and differentiation of extant Libyan populations will be key to better understand the phylogeography of this complex since according to the models these should be the most stable populations in the Mediterranean Sea.Saharan and Central Mediterranean populations, in comparison with other regions, showed many unique alleles and low allelic richness, a common genetic signature in rear-edgesC. amentacea in the Gulf of Lion51 and Sicily52, and other fucoid algae53. Beyond the historical processes described above, patterns of genetic differentiation within and among populations are also determined by mating systems, dispersal processes and meta-population dynamics acting at different spatio-temporal scales52. The dispersal potential of eggs and embryos of the genus Cystoseira is low due to their negative buoyancy and because as gametes are released the eggs remain attached to the alga until settlement takes place later already as an embryo54. This promotes selfing, bi-parental inbreeding and spatial clustering of related individuals. Long-distance dispersal may, however, take place via detached and drifting fertile specimens55, a particularly relevant means for colonization of new habitats. Moreover, although settling embryos attach readily, wave action-mediated translocation of embryos on scales of hundreds of meters is conceivable52.Overall, high genetic structure was observed even at small spatial scales. Heterozygote deficits and high rates of inbreeding were also pervasive, especially in the Mediterranean Sea. This is expected considering the reproductive biology of these species as selfing hermaphrodites and it agrees with previous findings for populations of 57, also known as priority colonization effects. Mediterranean populations of these Cystoseira spp. are restricted to the first centimetres of depth forming dense canopies and continuous belts throughout long stretches of coasts55. The saturation of available habitat is likely to favour local inbreeding and reduce the genetic impact of rare long-distance immigrants, i.e., contributing to the maintenance of differentiated gene-pools despite some migration and gene flow. By contrast, North Atlantic populations are usually composed of scattered individuals or patches appearing over wider areas and depth ranges58. The more important role of water motion and greater habitat diversity in the Atlantic, due to the interaction between wave exposure, tide and geomorphology59, should support a seascape with more frequent gaps favouring immigration and recolonization events. Furthermore, floating aerocysts that favour dispersal are more abundant and frequent in Atlantic (C. tamariscifolia) than in Mediterranean specimens (C. mediterranea and C. amentacea) of these Cystoseira species30. However, the hypothesis that Northern Atlantic populations could have originated from just one or a few Iberian refugia, while the Mediterranean could have been recolonised from different glacial refugia from Eastern Mediterranean, Iberian Peninsula and Northern Africa, might also partially explain these differences in genetic structure and similarity.The higher genetic differentiation observed between geographically close populations, especially in the Mediterranean cluster, suggests important density barrier effectsC. tamariscifolia and C. amentacea, showed significant but low genetic differentiation, suggesting extensive admixture. The detection of hybridization between these entities was previously suggested based on intermediate morphological traits31. Since these parapatric populations of C. tamariscifolia and C. amentacea are more similar to each other than to other allopatric conspecific populations, there is no evidence to support their retention as distinct species. The poor match observed between morphological and genetic classification at such a local scale is best explained by the distinct environmental conditions experienced (affecting morphology) and by the lower connectivity between littoral and sublittoral subpopulations, rather than by reproductive barriers. It is therefore concluded that C. amentacea and C. tamariscifolia along the Alboran Sea should be considered a single entity, likely C. tamariscifolia. Hence, differences in morphotypes  between different environmental conditions  and canopy features  seem to be responses to the environment and may have poor taxonomic value. We hypothesize that intertidal stands are more exposed to surf and that their typical caespitose habit is a consequence of recurrent break of cauloids and primary branches, whereas more protected sublittoral stations allow the development of a main axis. Accordingly, previous studies on other fucoids identified marked morphological variations  along depth, population density or wave exposure gradients61, as well as low connectivity between littoral and sublittoral specimens62.Our local-scale analyses revealed that when in parapatry  the morphological entities C. mediterranea from NE Iberia and C. amentacea from Marseille (“GL” cluster). These populations, separated by a relatively short geographical distance, are genetically isolated indicating either a reproductive barrier , a dispersal barrier, or density barrier effects. In this sense, the existence of well-developed C. mediterranea belts in northeast Iberia63 and C. amentacea in the eastern area of the Gulf of Lion55 could preclude the expansion of immigrant genes by density barrier effects. This, in combination with long sandy coast and the Rhône Estuary hindering the arrival of putative immigrants, could explain this break. It is remarkable that this C. amentacea population (Gulf of Lion) was among the most differentiated populations, and that C. mediterranea (in the NE Iberian cluster) was genetically closer to C. amentacea from southern Spain and Sicily. The type locality for C. amentacea var. stricta is Algiers (Algeria), which is geographically closer to the southern Iberian and Sicilian populations of this species than GL. Thus, the existence of marked genetic divergence between C. amentacea from the Gulf of Lion and South-Western Mediterranean populations is expected and could have important taxonomical implications.Of the seven secondary clusters identified, populations from the Gulf of Lion and Sahara were the most differentiated Fig. . In the C. tamariscifolia, C. amentacea and C. mediterranea  and (iii) the extensive gene flow between C. tamariscifolia and C. amentacea in the Alboran Sea all indicated that there is no support for three separate species within the C. tamariscifolia complex as currently accepted, and that the morphological characters traditionally used to distinguish among these species have poor taxonomic value. Specifically, our analyses of microsatellite data support the hypothesis that the C. tamariscifolia complex corresponds to a single, extremely polymorphic and highly structured species over the competing hypothesis that it corresponds to three well defined entities matching distinct gene pools and largely non-overlapping distributions. A single species also seems to be the most likely hypothesis considering previous studies pointing out the existence of intermediate morphologies that hamper accurate species determination31, so that identification often relies on the region of collection rather on the observation of specific and diagnostic morphological features. This view actually has some historical support. Accordingly, C. Agardh originally described C. tamariscifolia and C. amentacea as different varieties of the same specific entity64. If the three species are eventually considered a single taxonomic entity, C. tamariscifolia65 has nomenclatural priority and that name that should be retained. However, despite the obvious lack of support of microsatellite data for three separate species within the C. tamariscifolia complex, we do not consider this course of action at present as incomplete taxon sampling does not allow an unambiguous assignment, especially in the case of C. amentacea. In the case of C. mediterranea and C. tamariscifolia, populations close to the type localities were sampled , but not in the case of C. amentacea. Furthermore, different varieties of C. amentacea has been described based on material from different parts of the Mediterranean, such as Algier 66, or Cap Tenare 67, beside others varieties that have been suggested as synonyms from the Adriatic Sea  Giaccone; Cystoseria spicate Ercegovic)68, which make more difficult a final taxonomic assessment of this species complex. Furthermore, the Mediterranean taxa C. amentacea and C. mediterranea have longstanding and widespread use in ecological studies and their final synonymization with C. tamariscifolia s.s. would benefit from additional data before being fully adopted . In the future our key inference for a single entity could be verified using nuclear multi-gene approaches taking into account divergence with gene-flow69 and reticulation, and by experiments determining reproductive compatibility among populations assigned to different genetic clusters70. Such studies may provide new insights and help capture with a finer resolution the historical patterns of diversification within this ecologically important complex.In summary: (i) the general clustering of individuals by geographical region rather than by current taxonomical identity, (ii) the existence of genetic clusters or subclusters that include populations assigned to Supplementary info


Giardia duodenalis  is an enteric protozoan parasite of a wide range of mammalian hosts, including humans and various domestic and wild animals. There is considerable genetic variability in G. duodenalis and isolates of this parasite have been divided into eight genetic assemblages. Microsatellites markers can be used to discriminate isolates with a high level of sensitivity. This study was conducted to identify and characterize genomic microsatellites (simple sequence repeats—SSRs), sequences of one- to six-nucleotide motifs repeated in tandem, present in the available genomes of G. duodenalis and to develop new markers that can serve as a tool for detection and for characterizing the genetic diversity of this parasite.For each genetic assemblage, polymorphism levels for the microsatellite markers were evaluated. After performing the analysis using the MISA and SciRoKo software, 1,853 simple sequence repeats (SSRs) were identified. In all the genomes, trinucleotide repeats were the most common class followed by tetranucleotide. Many of the SSR loci are assemblage-specific, and 36 SSR loci shared among all the genomes were identified. Together with hypothetical proteins, variant-specific surface proteins represented nearly half of the annotated SSR loci. The results regarding the most common repeat among the SSRs led us to infer that positive selection occurred to avoid frameshift mutations. Additionally, based on inter- and intra-genetic assemblages polymorphism analyses, we unveiled previously undetected genetic variation, indicating that the microsatellite markers we developed are useful molecular tools for epidemiological inferences based on population genetics patterns and processes.G. duodenalis microsatellites markers that exhibited high polymerase chain reaction (PCR) amplification efficiency among the different genetic assemblages. Twenty of these markers presented nucleotide sequence polymorphisms and may be used as a genotyping tool. The monomorphic markers can be used for the detection of the parasite at the species and genetic assemblage level. These polymorphic markers revealed a genetic diversity that was previously undetectable, thus they can be considered valuable molecular tools for high resolution markers in future studies investigating Giardia and may also be used for epidemiological inferences based on populations genetics patterns and processes.There is increasing demand for the development of new molecular markers and for the characterization of pathogens at a higher resolution level. In this study, we present 60  Giardia duodenalis is a protozoan parasite that causes giardiasis, a disease that affects millions of people worldwide and a wide range of mammalian hosts. G. duodenalis presents considerable genetic variability and there is an increasing demand for the development of new molecular markers. This study aimed to identify and characterize genomic microsatellites of Giardia based on the different available genomes. After performing an analysis using MISA and SciRoKo software, 1,853 simple sequence repeats (SSRs) were identified and many of them were annotated as variant-specific surface proteins. We detected 20 polymorphic SSRs, including one shared among all available genomes. Because the markers are based on a simple PCR, they have a great potential for use in future studies of Giardia through PCR, genetic sequencing or genotyping by real-time PCR. Giardia duodenalis  is an enteric protozoan that parasitizes a wide range of mammalian hosts, including humans and various domestic and wild animals , p, pG. duo animals , 46] . ,53G. duoG. duodenalis genomes. The presence of the SSR loci in the different genomes suggests highly conserved regions and, therefore, these experimentally validated genetic markers can be used for the detection of the parasite. Moreover, for the shared microsatellites that were identified, the PIC values were zero or extremely low, which is also in accordance with the low variability of these loci. The comparisons made among the microsatellite loci developed in the present study indicated a greater extent of shared SSRs between genetic assemblages AI, AII and E, while simultaneously, both assemblages exhibited great differences from genetic assemblage B. These results corroborate findings obtained by constructing phylogenies of conserved genes; this revealed greater genetic similarity between genetic assemblages A and E than between either of these assemblages and genetic assemblage B [Notably, only a few of the detected microsatellite loci (36 of 1853) are shared among all available mblage B .Giardia genomes [Giardia assemblages [Regarding the functional annotation, at first, it is noted that a majority of the SSR loci were located within or close to coding regions (85.07%). This result is in accordance with the few and sparse intergenic regions reported in  genomes . When diemblages .Giardia. Each organism expresses only one VSP at a time, and RNAi is likely the mechanism underlying post-transcriptional regulation of VSP transcripts because all members of the RNAi machinery are present in the Giardia genomes [Giardia variant-specific surface protein (PF03302) was the most prevalent among the analyzed SSR loci, with a higher representation in isolate E.Polymorphisms were identified in several SSR loci that encode important proteins, such as kinases (different types), coiled-coil proteins, IF-1, HSP-90, and many hypothetical proteins  Tables.  genomes . SSRs we genomes . The PfaG. duodenalis samples, attesting usefulness of the microsatellite described in this study as a complementary method to characterize the genetic diversity of a Giardia populations. Additionally, the population genetic structure we observed in PCA analyses support these results as it is possible to clearly tell apart isolates from different assemblages . The results we obtained showed the differences among isolates that had been considered clonal by their unique MLG based on conventional markers ,35, nameThe data in this study reveal a highly distinct pattern of variation among the markers developed for genetic assemblages A and B, in both the number of alleles found and the polymorphism information content in the loci. Differences in the frequency of heterozygosity among these genetic assemblages, previously described in other studies ,34, wereMany SSR loci were detected in more than one region of the same genome. This phenomenon prevented the use of these loci in designing primers. Microsatellites frequently may be associated with repetitive regions, such as transposable elements or proteins of antigenic variation. The association between microsatellites and repetitive elements has been previously documented in vertebrates and plants. Dinucleotide motifs, especially CA/GT, are commonly associated with repetitive elements .G. duodenalis genomes presented low levels of allelic heterozygosity, which was particularly evident in the WB_A genome. Although higher in the GS_B genome, this rate/level may still be considered low. One hypothesis is that a heterozygous individual may have variability within the nucleus and that such variation is expressed through different alleles. We also cannot exclude the possibility that variation between different nuclei occurred through genetic exchange or lateral gene transfer [G. duodenalis has been considered an indicator of the clonal evolution of the parasite through purifying selection and gene conversion, whereas an excess of heterozygotes is considered evidence of hybridization or recent sexual events [In this study, we detected 20 new markers which were genotyped as polymorphic in the different genetic assemblage. The sequencing of certain transfer . The lacl events .Giardia genomes are small and highly compact [Most of the tested SSR loci were monomorphic according to the tested population. Considering that  compact , this reG. duodenalis. The developed primers (including the specific monomorphic markers) can be used for detection of G. duodenalis in clinical and environmental samples and to identify the major genetic assemblages of the species. Because the markers are based on a simple PCR, these primers may represent an accessible method for diagnostic sanitation companies, diagnostic institutions  and research institutions.In this study, we reported the development of microsatellite markers for Giardia and may also be used for epidemiological inferences based on populations genetics patterns and processes.The increasing demand for the development of new informative loci will define whether the new molecular markers made available in this research will be used for assessing allelic polymorphisms through PCR, genetic sequencing or genotyping by real-time PCR. Furthermore, the molecular markers developed in this study presented a considerable diversity in alleles and high PIC values. These polymorphic markers revealed a genetic diversity that was previously undetectable ,35, thusS1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.S6 Table(DOCX)Click here for additional data file.S7 Table(DOCX)Click here for additional data file.S8 Table(DOCX)Click here for additional data file.S9 Table(DOCX)Click here for additional data file.S10 Table(DOCX)Click here for additional data file.S11 Table(DOCX)Click here for additional data file.S12 Table(DOCX)Click here for additional data file.S13 TableThis table presents the results of each SSR loci in genetic assemblage A that were considered suitable for genotyping analysis. HE means expected heterozygosity Ho means observed heterozygosity PIC means polymorphic information content. The allelic variation represents the length variation of the alleles in the population. The number of alleles represents the number of alleles in the population.(DOCX)Click here for additional data file.S14 TableThis table presents the results of each SSR loci in genetic assemblage B that were considered suitable for genotyping analysis. HE means expected heterozygosity Ho means observed heterozygosity PIC means polymorphic information content. The allelic variation represents the length variation of the alleles in the population. The number of alleles represents the number of alleles in the population.(DOCX)Click here for additional data file.S15 TableThis table presents the results of each SSR loci in the shared SSR loci that were considered suitable for genotyping analysis. HE means expected heterozygosity Ho means observed heterozygosity PIC means polymorphic information content. The allelic variation represents the length variation of the alleles in the population. The number of alleles represents the number of alleles in the population.(DOCX)Click here for additional data file.S16 Table(DOCX)Click here for additional data file.S1 FigA—PCR products for the monomorphic marker GduA17 (205bp). B–PCR products for the polymorphic marker GduA20 (242bp) which three different alleles could be identified in the specimens from the same genetic assemblage. The numbering follows the number of the samples in the (TIFF)Click here for additional data file.S2 FigRed denotes for AI assemblage isolate whereas blue indicates AIV assemblage samples.(TIF)Click here for additional data file.


Transcription factor (TF) STAT3 contributes to pancreatic cancer progression through its regulatory roles in both tumor cells and the tumor microenvironment (TME). In this study, we performed a systematic analysis of all TFs in patient-derived gene expression datasets and confirmed STAT3 as a critical regulator in the pancreatic TME. Importantly, we developed a novel framework that is based on TF target gene expression to distinguish between environmental- and tumor-specific STAT3 activities in gene expression studies. Using this framework, our results novelly showed that compartment-specific STAT3 activities, but not STAT3 mRNA, have prognostications towards clinical values within pancreatic cancer datasets. In addition, high TME-derived STAT3 activity correlates with an immunosuppressive TME in pancreatic cancer, characterized by CD4 T cell and monocyte infiltration and high copy number variation burden. Where environmental-STAT3 seemed to play a dominant role at primary pancreatic sites, tumor-specific STAT3 seemed dominant at metastatic sites where its high activity persisted. In conclusion, by combining compartment-specific inference with other tumor characteristics, including copy number variation and immune-related gene expression, we demonstrate our method’s utility as a tool to generate novel hypotheses about TFs in tumor biology. The overall five-year survival rate of pancreatic cancer after diagnosis is approximately 9%, making it the cancer type with the worst prognosis1. Such a decimal survival rate is caused by many different factors, including a high proportion of late stage tumors at the time of diagnosis, poor resectability, and minimal durable response rates to conventional chemo- and/or radiotherapy2.Pancreatic cancer accounts for 3.2% of new cases but 7.5% of cancer deaths in the United States according to the 2019 Cancer statistics, estimated by the American Cancer Society6. To therapeutically modify the immune milieu of cancer tissues, immune checkpoint blockade therapies have been used. Such therapies have experienced progress in the treatment of melanoma and lung cancer9, but have had lackluster success in treating pancreatic cancer11. This is likely due to the highly immunosuppressive environment of pancreatic cancer, which is characterized by extensive fibrosis and chronic inflammation14.Recently, T-cell infiltration in the tumor environment has also been identified as a prognostic factor15. STAT3 is activated by a variety of extracellular stimuli, including interleukin (IL) −616, IL-10, and epidermal growth factor (EGF); it can also be activated by intracellular stimuli, such as SRC and ABL17. Its role in pancreatic cancer is complex due to the diversity of cells that express STAT3. For example, STAT3 activity inhibits the chemotaxis and activation of cytotoxic CD8 T cells in solid tumors19, mediates the differentiation of suppressive T regulatory (Treg) cells and enhances the expression of immune checkpoints CTLA-420 and PD-L122. STAT3 is also active in and required for the presence of suppressive myeloid cells, including the prevalent myeloid-derived suppressor cell (MDSC) population23 and profibrotic M2 macrophages25. In addition, cancer-associated fibroblasts (CAFs) use STAT3 activity to secrete cytokines that recruit additional immune cells and promote STAT3 activity in other cell types in the TME28. In turn, STAT3 is also active in tumor cells30. Importantly, STAT3 is required for the evolution of pancreatic neoplasia into pancreatic cancer in the presence of KRAS mutations33.Several components that contribute to this immunosuppressive environment have been identified, including transcription factor (TF) STAT3in vitro studies and animal models, which bear a resemblance to patient tumors but cannot fully recapitulate all pancreatic cancer features. In addition, the use of patient-based tissue arrays or immunohistochemistry often preclude the use of large sample sizes. Since TF expression generally does not correlate with activity35, the use of larger-scale patient-derived gene expression studies to investigate STAT3 has been limited. Models for TF activity inference from gene expression studies have been proposed39, but current models do not support a distinction between TME-derived and tumor-derived TF activity signals. Since STAT3 is active in several cell types in the TME as well as in tumor cells, being able to make a distinction between TME- and tumor-specific STAT3 activity is crucial. Therefore, we sought to develop a method that can distinguish between TF activities in the tumor and TME compartment to better characterize the multifaceted role of STAT3 in pancreatic cancer using a collection of gene expression datasets.The aforementioned insights into the role of STAT3 in pancreatic cancer have mostly come from Our framework relies on the expression pattern of TF target genes to create compartment-specific TF profiles that can be used for TF activity inference. After validating STAT3 as a TME-expressed TF, we show that STAT3 activity is prognostic, whereas STAT3 mRNA is not. We also show that biological insights can be obtained utilizing TME- and tumor-specific STAT3 activity inferences. For example, environmental-STAT3 plays dominant roles in establishing or maintaining an immunosuppressive TME and is associated with tumor intrinsic and extrinsic factors, such as immune infiltration and copy number variation (CNV) burden. In addition, while environmental-STAT3 is most influential at the primary site, tumor-derived STAT3 seems to be dominant at metastatic sites where its activity persists. Thus, using our approach, we can distinguish between tumor- and TME-specific TF activity to obtain more insights into the role of TFs in disease using gene expression datasets.In this study, we developed a novel method that infers compartment-specific TF activity in gene expression datasets. We first performed a systematic analysis to investigate the differential expression of all human TFs; our analysis included 1164 human TFs expressed in pancreatic cancer and confirmed STAT3 as one of the TFs being more highly expressed in the tumor microenvironment than in cancer cells Fig. . Given tWe systematically investigated the expression patterns of TFs in pancreatic cancer - whether they were more specifically expressed in tumor cells or in microenvironmental non-tumor cells Fig. . Since nSecond, we compared the expression of TFs between pancreatic tumor tissues and cell lines. Specifically, we compared the expression ranks of TFs in TCGA pancreatic cancer samples with their ranks in pancreatic cell lines using the Student’s t-test. Pancreatic tumor samples are mixed tissues with both tumor and non-tumor cells, whereas cancer cell lines contain merely tumor cells. As such, TFs with a high and low t-statistic (tumor samples versus cell lines) are TME- and tumor-specific, respectively. At the significance level of p < 0.001, we identified zero tumor-specific TFs and 35 TME-specific TFs Fig. . Combini40. To corroborate the negative correlation between STAT3 expression and tumor purity observed in the TCGA dataset  according to STAT3 ChIP-seq data . The logic behind this adjustment is that a tumor with high tumor purity does not necessarily have to display high T-STAT3 activity. Without a purity adjustment, this patient would likely receive a high T-STAT3 score just because of high purity, not because of high T-STAT3 activity. Thus, each sample received three scores based on the expression of selected STAT3 target genes.To more precisely characterize the regulatory roles of STAT3 in tumor and TME cells, we devised a method to infer compartment-specific STAT3 activity, since previous studies have shown that the regulatory activities of TFs, rather than their mRNA expression levels, more correctly reflect their functionsata Fig. . Based oata Fig. . Since tata Fig. . Gene Se3 p < 1E- accordin3 p < 1E- accordinTo show that the inferred compartment-specific STAT3 scores indeed reflected regulatory activities in tumor cells or the TME, rather than capturing tumor purity, we examined their correlation with tumor purity. As shown, correlating the inferred STAT3 activities with tumor purity revealed that STAT3 activity inferences did not reflect tumor purity. On the contrary, although G-STAT3 activity had no correlation with purity  and -low (STAT3 activity score < 0) groups, we indeed confirmed that high STAT3 activities conferred poor prognosis, irrespective of tumor compartment , whereas stromal scores were negatively correlated with E-STAT3 when adjusted for by immune scores . This indicated that E-STAT3 activity likely originated from tumor-infiltrating immune cells.Based on the previously recognized coordination between STAT3 activity in cells of the TME and tumor cells, we were curious if we could assess this interaction with our compartment-specific framework. To investigate this, we first we attempted to uncover the source of E-STAT3 activity. Since STAT3 can be active in CAFs46. We found that T- and E-STAT3 activity were both most strongly associated with the monocyte profile , whereas E-STAT3 activity was also positively correlated with CD4 T cells  and negatively correlated with naïve B cells   Fig. . Since aets Fig. . Thus, tTo investigate these relations in more detail, we stratified samples into the aforementioned STAT3 groups . Three consistent patterns were observed across pancreatic cancer datasets. First, a T-STAT3-dominant pattern was observed for monocyte infiltration, where monocytes were enriched in T-STAT3 high samples, irrespective of E-STAT3 activity Figs.  and S5A.49. We thus evaluated if T-and E-STAT3 scores were associated with any of the identified subtypes. T-STAT3 activity was highly enriched in the squamous subtype reported by Bailey et al.48 and in the quasi-mesenchymal subtype reported by Collisson et al.49 with distinct tumor characteristics. By assessing intrinsic and extrinsic tumor characteristics of these groups, we observed three STAT3 patterns: a T-STAT3 dominant pattern, a E-STAT3 dominant pattern, and an E-STAT3 depleted pattern. Whereas the T-STAT3 dominant pattern was only associated with high monocyte infiltration, the E-STAT3 dominant pattern was characterized by high CD4 T cell infiltration, high CNV burden, and high HR deficiency burden. The E-STAT3-depleted pattern was associated with elevated B, CD8 T, and NK cell infiltration. Since these patterns were observed across datasets, this suggests a general pancreatic cancer characteristic which could be utilized to identify high-risk patients, i.e. patients with high T- and E-STAT3 activities.24. A similar mechanism might be present in a subset of pancreatic cancer patients with high T- and E-STAT3 activities. Although this hypothesis might not be sufficient to explain the extremely poor prognosis in this group of patients, it does point to a specific tumor composition in which T- and E-STAT3 are coordinated to provide tumor cells with a proliferation advantage compared to patients that do not display high T- and E-STAT3 activity.T-STAT3-high/E-STAT3-high samples had worst prognosis and the highest proliferation scores. It is likely that the combination of high E- and T-STAT3 activities confers tumor growth advantages compared to other STAT3 groups. This group seemed to have an immunosuppressive environment, characterized by the infiltration of monocytes and CD4 T cells, absense of cytotoxic immune cells, and relatively high CNV burden. Intriguingly, a macrophage–tumor cell feedback mechanism has been described in ovarian cancer, in which STAT3 activity in either macrophages or tumor cells can activate STAT3 activity in the other cell type55. Lastly, both Th2 cells and myeloid cells have been shown to propagate pancreatic cancer growth. Myeloid cells are immunosuppressive and secrete cytokines that prevent the activation of tumor-eliminating immune cells56, which is consistent with our observations of low CD8 T and NK cell infiltration in E-STAT3 high samples, but not in E-STAT3 low samples. Although CD4 Th2 cells are commonly involved in parasitic responses and allergy, their role in pancreatic cancer seems to be the exacerbation of fibrosis and prevention of collagen clearance55. However, experimental validation is necessary to confirm E-STAT3 activity in these cell types.The origin of E-STAT3 activity cannot be determined exactly, but we hypothesize that a combination of CD4 T cells, specifically Th2-polarized CD4 T cells, and myeloid cells contributes to the E-STAT3 signal. First, CD4 T cells and monocytes are most highly correlated with E-STAT3 activity. Second, Th2 cells and myeloid cells are abundant in the pancreatic TME in certain patients57. Co-targeting IL-6, which is major factor in activating STAT3, and PD-L1 was shown to inhibit growth in a murine model of pancreatic cancer58. Identifying which immune cells are major contributors to STAT3 activity might identify additional drugs that inhibit these cell types. In addition, clinical trials have recently been initiated to test the efficacy of STAT3 pathway inhibitors in pancreatic cancer . Although no preliminary results are available yet, inhibitors of IL-6 and the IL-6 receptor have been proven to be effective in preclinical models of KRAS-driven pancreatic cancer59. Thus, further stratifying the involvement of TFs in pancreatic cancer might reveal new treatment strategies.A better understanding of STAT3 activities is essential in identifying new therapeutic avenues. Due to the previous notion that STAT3 is aberrantly activated in tumor-infiltrating immune cells, STAT3 pathway inhibition has been suggested in immunotherapy combinationsAlthough we believe that our results add valuable insights into the role STAT3 in pancreatic cancer, we note a few limitations present in this study. First, our framework of TF inference relies on TF target genes, which indicates that if two TFs share a large number of common targets, the inferred TF activities will be correlated. The family of STAT TFs has a number of shared target genes and we cannot exclude the possibility that some of these shared target genes might have affected STAT3 activity by being transcribed by another STAT TF. Second, our STAT3 target profiles are in part based on pancreatic cancer cell lines, which might not fully reflect pancreatic cancer cells within a tumor environment. Third, even though we have narrowed down tumor and environment-specific genes using a tumor purity, we cannot exclude the possibility that genes specific to the tumor are expressed in the TME and vice versa.In conclusion, we have shown that we can distinguish between tumor and environmental STAT3 activity in gene expression studies and that this distinction leads to biological and clinical insights. Our analysis provides a framework by which to study tumor- and TME-specific TF activity levels and can be expanded to other TFs and cancer types.61, resulting in the inclusion of 150 patient samples (see Suppl Table 62. Five independent pancreatic cancer datasets were used in this study. Four pancreatic cancer microarray datasets were obtained from Gene Expression Omnibus (GEO) under accession numbers GSE1547163, GSE5749564, GSE7172952, GSE2873565. These datasets were provided as normalized expression at the probeset level, in which some genes might be represented by multiple probesets. We converted probeset expression into gene expression values. Specifically, for one-channel arrays, we selected the probeset with the highest hybridization intensity across all samples to represent gene expression. For two-channel arrays, the average expression values of all probesets were calculated to represent gene expression. Datasets from one-channel arrays were further median normalized for each gene to transform intensities into relative expression values. The last RNAseq pancreatic cancer dataset was obtained from the ICGC project at ww.dcc.icgc.org and only primary tumor specimens classified as pancreatic ductal carcinoma were included , generated by The Cancer Genome Atlas (TCGA) were downloaded though FireBrowse in June 2015 . Absolute expression values were log10 transformed. A number of samples was excluded based on previous reports indicating that some samples do not represent pancreatic cancerForty-four pancreatic cancer cell lines were used to compare tumor gene expression to primary pancreatic cancer gene expression . Second, we divided these targets into a tumor-specific and an environmental-specific target gene set, based on their positive and negative correlation with pancreatic cancer tumor purity, respectively. This resulted in 121 tumor- and 171 environmental-specific genes and 94 genes that did not show compartment-specificity. Third, three STAT3 profiles were created based on these groups; general-STAT3 (G-STAT3) containing all 386 STAT3 target genes, tumor STAT3 (T-STAT3) containing 121 genes, and environmental STAT3 (E-STAT3) containing 171 genes  project by using a computational method called Target Identification from Profiles (TIP)66. Each of the three STAT3 signatures was inputted into BASE along with a patient gene expression matrix. If the gene expression dataset was a one channel microarray and RNAseq dataset, we median normalized the data before inputted it into BASE. The BASE algorithm calculated STAT3 activity scores for each patient sample by ranking genes in descending order based on gene expression values. Two cumulative distributions were then generated by a foreground (f) and background (b) function. These functions are given by:w represents the affinity of STAT3 targets as determined by ChIP-seq, g is the expression value of gene j in the patient expression profile. The maximal deviation between these two distributions represents the preliminary STAT3 activity score. This score is adjusted for by 1000 iterations of random patient expression profiles. The resulting score constituted the STAT3 activity score, where a higher score represented greater STAT3 activity and lower scores lower activity.STAT3 activity scores were calculated by the (Binding Associated with Sorted Expression) BASE algorithmIn order to obtain compartment-specific scores, we adjusted T- and E-STAT3 activities for purity. We utilized all genes in the gene expression datasets to calculate G-STAT3 activity since this inference should be minimally affected by tumor purity. To calculate T-STAT3 activity scores, we only selected tumor-specific STAT3 target genes (T-STAT3) and reference genes (SCC > 0.1) from the gene expression dataset and used this as the patient gene expression input for BASE; to calculate E-STAT3 activity scores, we only selected microenvironment-specific STAT3 target genes (E-STAT3) and reference genes (SCC < −0.1). Since neither STAT3 target genes or reference genes are overlapped, the resulting sample-specificT-STAT3 and E-STAT3 activities are inherently independent of each other.http://software.broadinstitute.org/gsea/index.jsp. All pathways in the C2 database (version 6.2) were used for this analysis.Gene Set Enrichment Analysis (GSEA) was performed on ranked STAT3 profiles using the GSEA software (version 3.0) provided by the Broad Institute, available at http://bioinformatics.mdanderson.org/estimate/ , while tumor purity scores for other datasets were calculated using the R “estimate” package45. The infiltration of tumor and stromal cells were also calculated based on the R “estimate” package. Immune infiltration scores of specific immune cell types were calculated using our established framework described in46. In short, immune cell-specific weight profiles and a patient gene expression dataset were inputted into BASE to infer the infiltration of selected immune cells; naïve B cells, memory B cells, CD4 T cells, CD8 T cells, NK cells, and monocytes.Tumor purity scores of pancreatic cancer samples obtained from TCGA were obtained from: Ci and si represent the copy number and the size DNA segments n is the total number of segments in the genome and N is the size of the human genome. Tumor mutation burden (TMB) was represented by the number of non-silent mutations in a given PAAD sample.Genomic features of the TCGA PAAD dataset were calculated based on MAF files and DNA-sequencing profiles, downloaded from FireBrowse (gdac.broadinstitute.org/). Copy number variation data provided by TCGA was used to calculate total copy number variation (CNV) burden for each sample, which represented the deviation of total copy number compared to normal . For each DNA fragment, its copy number was divided by two to account for diploidy, log2 transformed and multiplied by the size of the DNA fragment to take into account the magnitude of copy number alteration. The CNV scores for all fragments were then summed and scaled by the length of the entire genome to generate the final CNV burden score. This can be represented by:The efficacy of patient-specific STAT3 activity scores in predicting overall survival were verified by Cox proportional hazards models using the “coxph” function from the R “survival” package. Using zero as a cutoff for STAT3 activity, patients were stratified into STAT3-high and STAT3-low groups. Cumulative incidence and the proportion of survival was calculated by log-rank tests using the R “survival” package. Kaplan-Meier curves were generated using the “survfit” function. The statistics calculated to present the difference between survival curves were generated by the “survdiff” function. Forest plots were generated by multivariate Cox regression using the “coxph” function. Variables included in this multivariate analysis were T-STAT3 activity, E-STAT3 activity, gender, age and tumor stage. Unless indicated otherwise, all correlations were performed using Spearman correlation. Conditional correlations were calculated using the “pcor.test” function from the R “ppcor” package. R version 3.4.1 was used for all analyses.Supplementary Figures and TablesAccompanying code


Saxifraga sinomontana (Saxifragaceae) is a widespread alpine species in the Qinghai–Tibetan Plateau and its flanking mountains. We developed a set of expressed sequence tag–simple sequence repeat (EST‐SSR) markers to investigate the genetic diversity and evolutionary history of the species.EST‐SSR markers based on transcriptome data of S. sinomontana. Nineteen of 50 loci (38%) were successfully amplified, 13 of which were polymorphic. These were tested on 71 individuals from four populations. Three to 18 alleles per locus were detected, and the levels of observed and expected heterozygosity ranged from 0.2817 to 0.9155 and 0.2585 to 0.8495, respectively. In addition, cross‐amplification was successful for all 13 loci in three congeneric species, S. tangutica, S. heleonastes, and S. congestiflora.We initially designed 50 EST‐SSR markers will be useful for studying the genetic diversity of S. sinomontana and disentangling the phylogenetic relationships of related species.These  Saxifraga L. (Saxifragaceae) consists of approximately 500 species that are mainly distributed in the mountainous regions of Europe and Asia . Reads were filtered by trimming adapters and removing ambiguous reads (N > 10%) and low‐quality reads  using in‐house perl scripts, yielding 90,311,228 clean reads. Clean reads were assembled into 176,110 transcripts using Trinity v2012‐10‐05 , and 2.5 μL of 10 mM dNTPs. The PCR profile included an initial pretreatment of 10 min at 94°C; followed by 35 cycles of 1 min denaturation at 94°C, 50 s at locus‐specific annealing temperatures (Table S. sinomontana with the same protocol described above (Table A), observed heterozygosity, and expected heterozygosity were calculated with POPGENE version 1.32 . The Dingri population had the lowest mean values (A = 2) among the four populations. Levels of observed and expected heterozygosity varied from 0.2817 to 0.9155 and 0.2585 to 0.8495, respectively, which indicates that genetic diversity is relatively high in this species. Additionally, a few loci showed significant deviations from Hardy–Weinberg equilibrium: three in the Aba population, two in the Chengduo population, and seven in the Dingri population (P < 0.05; Table Among the 13 polymorphic loci, lues A =  among thS. tangutica, S. heleonastes, and S. congestiflora, using the same PCR protocol as for S. sinomontana (Table All 13 EST‐SSR markers also amplified successfully in S. sinomontana and high cross‐species amplification success. Hence, these are valuable loci for investigating genetic diversity, population structure, and evolutionary history in S. sinomontana and throughout Saxifraga.The 13 EST‐SSR markers developed here showed high polymorphism in