Italy displays a high level of agrobiodiversity due to its diversified pedoclimatic zones. The Administrative Region of Campania includes several and divergent biomes, occurring close to each other. In fact, the distance between a sea level environment and that of high mountains can be less than 20 km. These environmental conditions allow the cultivation of many different crops and vegetables, represented by diverse ecotypes and varieties that are well adapted to the distribution range where they have been selected and grown. Efforts to maintain and further increase biodiversity in farming systems require a better understanding of the existing diversity created by traditional farming practices. The aim of our study was to identify and molecularly characterize several ecotypes belonging to five horticultural species commonly cultivated in Campania. In particular, we analysed five ecotypes of maize, two of garlic, four of onion, one of escarole, and two of courgette by means of simple sequence repeat (SSR) markers in order to evaluate their level of genetic biodiversity. The results reveal, for the first time, the high genetic biodiversity of horticultural ecotypes of the Campania Region. This feature is very important to improve the quality and productivity of agroecosystems. In agroecosystems, genetic diversity is widely regarded as pivotal in sustaining and preserving ecosystem functioning and services . TraditiCrop biodiversity has been influenced by a number of genetic bottlenecks occurring during domestication and through modern plant breeding . Neverthe.g., urban or industrial), impacted , or lands that can be subjected to conservation potential. The natural and seminatural lands that are unprotected  can sustain numerous native species and associated habitats.Italy, and in particular southern Italy, displays a broad crop biodiversity, related to its heterogeneity at both the landscape and social/cultural level . HoweverCampania stands out for its extreme landscape heterogeneity and exceptional geomorphological and pedogenetic traits. Thanks to its highly fertile soils, it was called “Campania Felix” during the Roman Empire and crops cultivated on the volcanic soils near Vesuvio were regarded by Goethe as being unparalleled . LandscaIn order to protect its local horticultural ecotypes, the Administrative Region of Campania has launched a project called “Agrigenet,” a network for the protection and management of agrofood genetic resources. The project includes a biodiversity study of the regional ecotypes and their conservation both in situ, by keeper farmers, and ex situ through the establishment of a germplasm bank.Zea mays L.), four of onion (Allium cepa L.), two of garlic (Allium sativum L.), one of escarole (Cichorium endivia L.), and two ecotypes of courgette (Cucurbita pepo L.) were molecularly characterized using a set of highly polymorphic microsatellite  markers.Several studies have been conducted to evaluate the biodiversity in horticultural products in different countries and regions of the world –16, but The different accessions of diverse ecotypes of maize, onion, garlic, escarole, and courgette were provided by the Agricultural Research Council  and by the National Research Institute for Food and Nutrition . They were selected on the basis of the farmers' local knowledge. In origin, the institution obtained the ecotypes from the keeper farmers, but no information is available regarding the contribution of the farmers to these collections. The details of the furnished accessions are given in μl containing 20 ng DNA, 1.2–4.0 pmol of each primer, 10x PCR buffer, 1.25 mM dNTPs, and 0.5 units of Taq polymerase. Amplifications were performed using the following PCR amplification conditions: 94°C for 2 minutes followed by 30 cycles of 94°C for 30 seconds, X°C for 1 minute . Quality and quantity were assessed by DNA electrophoresis in 1.0% agarose gel with reference standards and by evaluating the 260/280 ratio with a Picodrop Microliter UV/Vis Spectrophotometer . A total of 35 SSR primer pairs  were used for PCR amplifications. The primer sequences are given in ith allele [Polymorphism Information Content (PIC) values or expected heterozygosity scores for SSR  markers were calculated using the formula: Hj = 1 − ∑pi2, where pi is the frequency for the h allele .Ho) and expected (He) heterozygosity are considered.The most used and informative indices for population genetics studies are the observed and expected heterozygosity. It can also refer to the fraction of loci within individuals that are heterozygous. Typically, the observed  was calculated on a single-locus basis, where ln is the natural logarithm and The genetic similarity matrices of all accessions were elaborated using Jaccard's similarity coefficient (JSC) . A clustK = 1 to K = 10. Each run consisted of 100,000 MCMC (Markov Chain Monte Carlo) permutations with a burn-period of 10,000, assuming no a priori information on population affiliation, the admixture, and correlated allele frequencies methods. The optimal population structure was estimated using the method of Evanno et al. [K value.Structure version 2.2 software  was usedo et al.  with 20 Accessions of six maize ecotypes were analysed to estimate genetic biodiversity and population structure. The SSR matrix was elaborated to perform a cluster analysis and obtain the dendrogram, as shown in e.g., subgroup 1A includes the MBA samples and even some SNB and SB individuals).The dendrogram displays two macrogroups (1 and 2) connected with a node at JSC = 0.25. Each macrogroup is subdivided in two clusters (A and B) including samples of different ecotypes. In fact, in this analysis, samples of the same ecotype do not fall in well-separated groups. Each subgroup  includes not only many samples of the same ecotype but also individuals belonging to different ecotypes  is close to zero, suggesting a random recombination among ecotypes and samples. The F value is greater than zero in the case of locus 6 or 5 relative to ecotypes MBA and SR. The high biodiversity is also confirmed by the PIC  and by the Shannon Index (I). In fact, the I values range from 0.00 to 1.28; the highest I value is observed for locus Phi029 in the case of MBA, the lowest for loci Nc130 and Phi014 in the case of SNR, and for locus Bnlg118 in the case of SR  includes the ecotypes Mais Bianco di Acerra (MBA), Spiga Napoletana Rossa (SNR), and Spiga Bianca (SB), whilst the samples belonging to Spiga Rossa (SR) and Spiga Napoletana Bianca (SNB) were grouped in the green cluster. However, the red cluster includes some admixed ecotypes, with a probability of belonging to the green cluster ranging from 20% to 80% as in the case of samples SB3 and SB9, respectively.At Febbrarese (F), Marzatica (M), Ramata di Montoro (R), and Vatolla (V), were analysed using seven microsatellite loci. The He, F, and PIC values suggest that the studied onion ecotypes are not genetically stable. In fact, the positive values of F close to 1 indicate phenomena of intersection or null alleles, while negative values tending to −1 suggest an excess of heterozygosity  in this region.Four onion ecotypes, zygosity , probablTo estimate the genetic similarity among samples, an UPGMA analysis was performed on molecular data .Febbrarese, Marzatica, and Ramata di Montoro). A similar result was obtained using a Bayesian statistical approach. The most probable K values are 6 and 8. At both K values, the obtained clusters appear admixed, and none of the samples can be clearly attributed to a specific cluster. In fact, they show the same probability of belonging to diverse clusters (data not shown).The JSC value is very low among different ecotypes, and thus the genetic biodiversity is very high; moreover, the ecotypes are distributed in admixed clusters. Clusters 1, 2, and 3 comprise many samples belonging to three different ecotypes  due to the fact that Ho is close to zero; F values indicate inbreeding events. F values for locus 4 are close to 0, as usually observed in the case of random reproduction; on the contrary, negative values for F, due to extreme heterozygosity and then to gene flow, are observed for loci B42 and EU01H08  and San Pasquale (S), respectively. However, this is not valid for some loci. For instance, in the case of Cilentano, loci 4991 and 5800 show higher values as do loci 1906, 4991, and 5800 for San Pasquale  to positive .The biodiversity indices for the two ecotypes of courgette are shown in Pasquale . In agreThe UPGMA dendrogram reveals three clusters : clusterThe population structure analysis performed on these molecular data shows that the optimal population numbers (K) are 2 and 3 .K = 2, samples are well separated into two clusters (green and red). However, samples C9 and C10, belonging to Cilentano, fall within the cluster including all accessions of San Pasquale. For K = 3 (data not shown), the resulting clusters are well separated. Samples C1 to C8 of Cilentano are allocated to the red cluster, while all other samples are distributed in two groups: green and blue. The San Pasquale samples C10, S1, S1, and S9 are separate from the others (blue cluster). Moreover, some samples belonging to San Pasquale (green cluster) show a 20% probability of being assigned to the blue cluster .For The Mediterranean Basin is one of the rarest terrestrial biomes, covering a mere 2% of Earth's land surface . Its smaBecause of its peculiar pedoclimatic conditions, the Mediterranean region has also been the cradle of the most important horticultural crops. In fact, many endemic species persist only on small remnants of their natural habitat separated by intensively cultivated agricultural land, thereby contributing to ecotype development. These ecotypes have an economical, ecological, and biological role. In this work, we estimated the genetic biodiversity of several ecotypes belonging to five different horticultural species: maize, onion, garlic, escarole, and courgette, occurring in the Campania Region . The final objective is to preserve the horticultural Campania ecotypes by means of in situ and/or ex situ conservation strategies. When the project started, the investigated ecotypes were not genetically characterized, although some of that, and in particular onion, was of interest from organoleptic and economic point of view. Therefore, no program of conservation in situ or ex situ was in progress. For this reason, the data reported in this manuscript are very useful to begin a program of conservation in situ, through the keeper farmers, and, ex situ, maintaining the genetic resources in the Universities and/or Research Centres.Maize has a strategic importance for agricultural and food of all industrialized countries. In addition to human consumption, its grain is also used for raising cattle . In thisHo and He mean values of the analysed loci were similar; moreover, in many cases, the F value is close to zero, suggesting a random recombination among different ecotypes. An average of six alleles for each locus were found, in agreement with the data reported for the same analysed loci by Ranatunga et al. [Spiga Rossa and Spogna Bianca, while in the case of ecotypes Bianco di Acerra, Spiga Bianca, and Spiga Napoletana Bianca, the PIC we obtained is higher. In the case of loci phi024 and phi029, the PIC values are lower than those reported by the same authors for inbred lines, suggesting a lower genetic variability in these loci relative to the inbred lines analysed in other studies. Although the genetic biodiversity of maize has been the object of several studies, this is the first one addressing the Campania Region ecotypes. A biodiversity study was conducted by Legesse et al. [K values higher than two. This result was confirmed by the graphical representation of the genetic similarity. In the similarity dendrogram, in fact, the samples were separated in two main groups, suggesting that the six maize ecotypes were not stabilized lines. The intraecotype genetic biodiversity was greater than the interecotype one. Some ecotypes, which might seem synonymies because of their names, were, on the contrary, clearly separated. In particular, samples belonging to ecotype Mais Bianco di Acerra were grouped in different clusters from those formed by the ecotypes Spiga Napoletana Bianca and Spiga Bianca, which in turn were divided into two subgroups. However, a basal cluster can be observed, in which samples belonging to different ecotypes were allocated. Our results confirm that the maize ecotypes are not stabilized lines and represent an important source of genetic biodiversity useful for further breeding programmes.In this study, we investigated six ecotypes of maize through seven SSR markers. The a et al.  and Pejia et al. ; on the a et al.  observeda et al. . In thata et al.  althougha et al.  analyseda et al.  for ecote et al.  to improOnions are the second most produced horticulture crop after tomatoes, with about 3'642'000 ha grown annually worldwide and a production of 53.6 Mt. (2017). Their large distribution is probably due to the versatile culinary uses as the raw food or in different manners of cooking . In addition, onions are among the healthier vegetables due to the high levels of bioactive compounds, such as phenolic acids, flavonoids, thiosulfinates, and anthocyanins.The flowers are proterandrous, the male flower matures before the female one; moreover, fertilization is allogamous, favoured by pollinating insects.He, Fixation Index, and PIC values highlighted that the onion ecotypes analysed were not genetically stabilized. In fact, the F values suggest phenomena of out-crossings, null alleles, or an excess of heterozygosity probably due to the selection techniques designed to enhance the heterosis phenomenon [Ho values are greater than those of He. However, present results are in line with those reported by Baldwin et al. [Febbrarese). The Dest values also support this hypothesis; in fact, they range from a minimum of 0 at locus 2, to a maximum 0.55 at locus 4. Onion often undergoes self-pollination phenomena; in fact, the ecotypes showed a significant inbreeding depression, even higher than that reported by Vigouroux et al. [The enomenon . Selectienomenon , may be enomenon . Onion rn et al. . For mosx et al.  for cornx et al.  using twx et al. .Schiacciato and Tondo di Torella, suggest that they are genetically identical. Therefore, a case of synonymy is possible; moreover, garlic flowers as well as seeds are sterile. Its propagation and production is obtained by means of vegetative reproduction by division and planting of cloves [In the case of garlic, all the biodiversity indices and the population structure analyses of the two ecotypes, f cloves . Garlic f cloves  and althf cloves . Our stuf cloves , who anaf cloves , confirmRiccia Schiana ecotype, revealed that there is a discrete genetic variability among the investigated specimens, probably favoured by its reproductive mechanism. The Ho mean value was very low, reflecting the high homozygosity of the investigated loci; the Shannon Index suggested that the intraecotype biodiversity was limited. The similarity dendrogram showed that the analysed specimens constituted a fairly homogeneous group with high similarity values (about 85%), with the exception of samples RS1, RS9, and RS10. Genetic variability is affected by a number of factors including out-crossing mechanisms, environment, and natural or artificial selection [Cichorium [Escarole is diploid (2n = 18) and allogamous ; its floelection . In factelection  reportedelection . Italy iichorium . Therefoichorium .Cilentano and San Pasquale have low Ho values for all analysed loci, in line with the He, although San Pasquale showed the highest heterozygosity values in the case of loci 1 and 7. The I and PIC values confirmed that the two ecotypes are not characterized by a high genetic biodiversity, probably because of an ancestral selection aimed at the genetic and phenotypic stabilization of the lines [I was about one-third. The dendrogram revealed that courgette ecotypes had a high genetic similarity, especially in the case of Cilentano. Barzegar et al. [C. pepo by means of microsatellites, reported similarity values with a maximum equal to 0.5 on a scale from 0 to 1. The number of alleles per locus obtained in our study varies from a minimum of one to a maximum of five, in agreement with the data obtained by Paris et al. [C. pepo.Courgette is cultivated all over the world as different varieties and ecotypes well adapted to diverse climates and environments . Zucchinhe lines . In facthe lines , while Ir et al. , estimats et al. . Yu et as et al.  reporteds et al.  about ths et al.  who showCucurbita rootstock accessions. In conclusion, the molecular analyses conducted on courgette ecotypes of Campania highlight a good stabilization aimed at obtaining homogeneous lines [Breeding of compatible, multidisease-resistant ecotypes with tolerance to abiotic stresses is crucial for the sustainable production of courgette. Knowledge on the genetic biodiversity present in ecotypes is of fundamental importance, not only for the genetic improvement but also for cultivar identification and protection. SSR markers have been extensively used for commercial cultivar discrimination and assessment of genetic biodiversity of commercial cultivars . Kong etus lines .The results reported here reveal, for the first time, that a high genetic biodiversity is present in Italian horticultural crops, in particular those grown in the Campania Region. The ancient traditions of this region have allowed maintaining several agricultural ecotypes with a high genetic biodiversity. This feature is very important in view of climate change and for sustaining improved quality and yield in order to produce agricultural products of excellence with a very important social and economic role. At the present, also on the basis of these results, the Campania Region has planned a series of initiatives to preserve the agrobiodiversity, and some of them are carrying out. The main aim of the study is to ensure that biodiversity can contribute to the development and enhancement of rural areas, especially those where a high crop biodiversity is present and has to be maintained. In this sense, the regional programs foresee the recovery and enhancement of the ecotypes of autochthonous species, as well as safeguard the environment, in the broader perspective of protecting the typical local agriproducts. Starting from these and other results, the Campania region has adopted a model based mainly on the Regional repertoire of the main endangered genetic resources, by means of keeper farmers, germplasm banks, conservation, and safety network.


DNA damage-induced apoptosis suppressor (DDIAS) regulates cancer cell survival. Here we investigated the involvement of DDIAS in IL-6–mediated signaling to understand the mechanism underlying the role of DDIAS in lung cancer malignancy. We showed that DDIAS promotes tyrosine phosphorylation of signal transducer and activator of transcription 3 (STAT3), which is constitutively activated in malignant cancers. Interestingly, siRNA protein tyrosine phosphatase (PTP) library screening revealed protein tyrosine phosphatase receptor mu (PTPRM) as a novel STAT3 PTP. PTPRM knockdown rescued the DDIAS-knockdown-mediated decrease in STAT3 Y705 phosphorylation in the presence of IL-6. However, PTPRM overexpression decreased STAT3 Y705 phosphorylation. Moreover, endogenous PTPRM interacted with endogenous STAT3 for dephosphorylation at Y705 following IL-6 treatment. As expected, PTPRM bound to wild-type STAT3 but not the STAT3 Y705F mutant. PTPRM dephosphorylated STAT3 in the absence of DDIAS, suggesting that DDIAS hampers PTPRM/STAT3 interaction. In fact, DDIAS bound to the STAT3 transactivation domain (TAD), which competes with PTPRM to recruit STAT3 for dephosphorylation. Thus we show that DDIAS prevents PTPRM/STAT3 binding and blocks STAT3 Y705 dephosphorylation, thereby sustaining STAT3 activation in lung cancer. DDIAS expression strongly correlates with STAT3 phosphorylation in human lung cancer cell lines and tissues. Thus DDIAS may be considered as a potential biomarker and therapeutic target in malignant lung cancer cells with aberrant STAT3 activation. IL-6 is released from tumor cells and immune cells and is involved in tumor initiation, progression, and metastasis via activation of multiple intracellular signaling pathways, including the Janus kinase (JAK)/signal transducer and activator of transcription (STAT), phosphoinositide-3 kinase/AKT, RAS/mitogen-activated protein kinase (MAPK), MEK/extracellular signal-regulated kinase 5 (ERK5), and p38/JNK pathways5. Binding of IL-6 to IL-6R activates the JAK tyrosine kinases to induce phosphorylation of STAT3 at Y705, thereby activating it. This activated STAT3 plays crucial roles in proliferation, survival, metastasis, and self-renewal of cancer cells8. In lung cancer cells, STAT3 is activated by tyrosine kinases such as epidermal growth factor (EGF) receptor, JAK2, Src, and IL-6R10. Phosphorylated STAT3 molecules form homodimers, which enter the nucleus and transcribe target genes, such as survivin, Bcl-2, Mcl-1, c-Myc, cyclin D1, slug, and matrix metalloproteinase-2, involved in tumorigenesis, survival, and metastasis14. Aberrant activation of STAT3 is associated with malignant cancers with poor clinical prognosis, and STAT3 inhibition causes cancer cell death, suggesting STAT3 as a potential target for cancer therapy15.Lung cancer is a leading cause of cancer mortality worldwide. A variety of factors, including genetic alterations and tumor environment, contribute to lung cancer development and drug resistance. High interleukin-6 (IL-6) expression is common in patients with lung cancer and is indicative of poor prognosis5R motif17. These enzymes are divided into classical PTPs and dual-specificity phosphatases (DSPs). The classical PTPs include 21 receptor-type PTPs (RPTPs) or 16 non-transmembrane proteins (NT-PTPs). The intracellular region of RPTPs contains the PTP domain with phosphatase activity, whereas the extracellular region shows functions similar to those of cell adhesion molecules and is involved in cell–cell and cell–matrix contact16. The NT-PTPs contain catalytic domains with phosphatase activity and non-catalytic domains that regulate subcellular distribution by restricting access to their particular substrates16. STAT3 inactivation occurs via dephosphorylation by PTPs such as SH2-containing phosphatase 2 (SHP2), T cell PTP (TC-PTP), and low molecular weight-DSP220. Recent studies reported that STAT3 is dephosphorylated by RPTPs, including PTPRT, PTPRD, and PTPRK in colorectal cancer, glioblastoma multiforme, and nasal-type natural killer/T cell lymphoma, respectively23. PTPRT and PTPRD are tumor suppressors and are frequently inactivated or mutated in human cancers24.Phosphatases (protein tyrosine phosphatases (PTPs)) control the Y705 phosphorylation-mediated activation of STAT3. PTPs are composed of approximately 280 amino acids with a conserved HCX26. SOCS3 also negatively regulates JAK-STAT3, p44/P42 MAPK, and p53 in prostate and hepatocellular cancer29. STAT3-interacting proteins, including chromatin remodeling proteins such as BRG1 and oncogenic transcription factors such as nuclear factor kB (NF-kB) or nuclear factor of activated T cells 1 (NFATc1), have been reported to function as either positive or negative regulators in many types of cancers30. Of the positive regulators, annexin A2 binds to STAT3 C-terminal domain containing transactivation domain (TAD) and enhances STAT3 activity, thereby promoting epithelial-to-mesenchymal transition in breast cancer31. In contrast, GdX (X-linked gene in G6PD cluster at Xq28) is a negative regulator of STAT3 and promotes STAT3 dephosphorylation by stabilizing the interaction between STAT3 and TC45 (the nuclear isoform of TC-PTP), a nuclear phosphatase of STAT332.In addition to PTPs, various negative regulators of STAT3 signaling in cancer cells are known. Protein inhibitor of activated STAT (PIAS1) and suppressor of cytokine-signaling proteins 3 (SOCS3) negatively regulate STAT3 in cancer. PIAS1 can suppress JAK-STAT3 signaling and bind to activated STAT3 dimers to prevent DNA binding for transcription36. DDIAS expression is activated by the transcription factor NFATc1 and is induced by ERK5/myocyte enhancer factor 2B pathway34. Moreover, DDIAS protein stability is negatively regulated by the E3 U-box ubiquitin ligase carboxyl terminus of heat shock protein 70-interacting protein35. Furthermore, DDIAS acts as a cofactor for DNA polymerase–primase complex by associating with DNA polymerase to promote tumorigenesis in HCC37. DDIAS protects cancer cells from DNA-damaging reagents and tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) and positively regulates cancer cell invasion by stabilizing β-catenin38. Hence, DDIAS is suggested as a potential therapeutic target for malignant lung cancer36. Although IL-6 is implicated in the malignancy of lung cancers, the association of DDIAS in IL-6–mediated signaling has not been investigated. Therefore, using DDIAS-knockdown experiments, we investigated the mechanisms underlying the role of DDIAS in the IL-6–mediated pathway.DNA damage-induced apoptosis suppressor (DDIAS), which is abundantly expressed in lung cancer and hepatocellular carcinoma (HCC), regulates the survival of cancer cells via multiple signaling pathwaysIn this study, we demonstrated that DDIAS positively regulated IL-6–mediated STAT3 activation. We also showed that DDIAS physically associated with STAT3 to prevent recruitment of STAT3 to protein tyrosine phosphatase receptor mu (PTPRM), a novel phosphatase for STAT3, thereby sustaining STAT3 Y705 phosphorylation. Our findings suggest a novel pathway for DDIAS-mediated aberrant activation of STAT3 in malignant lung cancer cells.TM antibody array in NCI-H1703 cells. Decreased pSTAT3 (Y705) levels were observed in DDIAS-depleted cells in the presence of IL-6 -qPCR and western blot analysis revealed that DDIAS did not affect the expression of PTPRM as well as the known STAT3 phosphatases PTPRT, SHP1, and TC-PTP Fig. . Next, wTo further understand the mechanism underlying DDIAS-mediated regulation of STAT3 phosphorylation, we assessed whether DDIAS directly interacts with STAT3. Co-immunoprecipitation assay clearly showed that Flag-DDIAS interacted with HA-STAT3 in HEK293T cells Fig. . FurtherWe observed that PTPRM overexpression inhibited cell growth in a dose-dependent manner Fig. . Then we4. STAT3 activity is regulated via posttranslational modifications, such as phosphorylation, acetylation, oxidation, and ubiquitination42. Of these, IL-6–mediated phosphorylation mainly controls STAT3 activity, which is reversibly regulated by protein tyrosine kinases or PTPs43. The known STAT3-binding phosphatases are receptor-type PTPs such as PTPRK, PTPRD, and PTPRK and non-receptor PTPs, including SHP1, SHP2, PTP-MEG2, and TC-PTP. These PTPs are frequently mutated or inactivated in lung cancer, colorectal cancer, glioblastoma, head and neck squamous cell carcinomas, and renal cell carcinoma46, suggesting that their inactivation contributes to the malignancy of human cancers.IL-6 and STAT3 are implicated in the malignancy of lung cancers, and many studies have attempted to target IL-6/IL-6 receptor for drug development17. PTPRM facilitates hemophilic cell–cell interaction through the extracellular region independent of phosphatase activity in the intracellular domains47. Indeed, PTPRM has been shown to inhibit cancer cell proliferation, migration, and invasion50. PTPRM expression is downregulated by genetic and epigenetic alterations such as loss of heterozygosity and hypermethylation in breast cancer, colorectal cancer, and acute lymphoblastic leukemia51. Moreover, PTPRM is downregulated by miRNA-221/-222 in glioblastoma, suggesting its tumor-suppressor function52.In this study, we found that DDIAS positively regulated IL-6–mediated STAT3 activation. Using siRNA PTP library screening, we first identified PTPRM as a novel PTP that suppresses STAT3 Y705 phosphorylation in NCI-H1703 cells. Furthermore, we found that PTPRK or PTPRT also recovered the STAT3 dephosphorylation induced by DDIAS knockdown Fig. , Fig. S444. However, it is believed that the D2 domain of RPTP is catalytically inactive and functions as a substrate-binding domain that modifies the RPTP structure53. It has been shown that PTPRK directly binds to STAT3 and dephosphorylates STAT3 Y7051. In contrast, PTPRM dephosphorylates catenin p120ctn by binding to the N-terminus of catenin p120ctn, which is important for tyrosine phosphorylation56. Our data show that PTPRM interacted with the STAT3 TAD, which contains the Y705  domains, mediate the interaction of PTP with its substrates through SH domains, which function as phospho-tyrosine-binding domains. While receptor-type PTPs such as PTPRK and PTPRT have two intracellular PTP domains (D1 and D2), mutants of the D1 and D2 domain show decreased PTP activityWe found that DDIAS expression is closely correlated with STAT3 Y705 phosphorylation in NSCLC cell lines and that PTPRM knockdown recovered the reduced STAT3 Y705 phosphorylation in DDIAS-knockdown cells. However, PTPRM expression was variable and showed no correlation with STAT Y705 phosphorylation in lung cancer cell lines and human tissues Fig. ; this suPreviously, we showed that DDIAS promotes proliferation and invasion of cancer cells and is involved in suppression of DNA-damaging-induced apoptosis and TRAIL-mediated extrinsic apoptosis in cancer cells. In this study, we demonstrated that DDIAS promoted IL-6–mediated STAT3 activation in lung cancer cells by competing with PTPRM. DDIAS knockdown caused growth inhibition of lung cancer cells through STAT3 inactivation by PTPRM, suggesting DDIAS as a potential therapeutic target in aberrant STAT3-activated lung cancer. Moreover, IHC analysis demonstrated correlation between the DDIAS expression level and STAT3 phosphorylation in lung cancer patient tissues Fig. . TherefoWe newly identified the DDIAS/PTPRM/STAT3 system in lung cancer cells with aberrant STAT3 expression. We expect multiple, intertwined crosstalks and interactions between DDIAS/PTPRM and other STAT3 regulation system in lung cancer cells. However, we do not have conclusive evidence regarding it. The ability of PTP to compete with DDIAS for STAT3 inactivation depends on its specificity to STAT3, its structural similarity to PTPRM, and its intracellular availability. PTPRM belongs to the same family as PTPRK and PTPRT. Although PTPRT has specificity to STAT3, it is less abundant than PTPRM in the cell. Non-receptor-type PTP, such as SHP1, SHP2, and TC-PTP, are not specific to STAT3. Even though TC-PTP is present at a high level, the dephosphorylation efficiency of STAT3 by TC-PTP is expected to be lower than that by PTPRM. Interestingly, DUSP11 and DUSP12 seem to affect STAT3 inactivation through MAP inhibition.Using siRNA PTP library screening, we identified PTPRM as an activator of STAT3 Y705 phosphorylation in DDIAS-knockdown cells. However, the PTP siRNA library does not contain all the known PTPs; therefore, it is possible that we may have missed a PTP involved in STAT3 Y705 dephosphorylation. Moreover, a PTP could have been excluded as a false negative in our siRNA screening system, although the experiment using validated siRNA was performed under stringent conditions. Nevertheless, we demonstrated that DDIAS binds to STAT3 and prevents STAT3 recruitment to PTPRM for Y705 dephosphorylation. We further plan to screen drugs inhibiting the interaction between DDIAS and STAT3 and investigate the effectiveness of this drug in combination with the currently available treatment for lung cancer.In summary, we provide novel insights into the crucial role of DDIAS in STAT3 signaling in malignant lung cancer. We confirmed that DDIAS is involved in STAT3 activation in malignant lung cancer. We report that PTPRM is a novel STAT3 tyrosine phosphatase and that it negatively regulates STAT3 activation by dephosphorylating Y705 of STAT3 in the presence of IL-6. We also demonstrate that DDIAS promotes STAT3 phosphorylation by blocking PTPRM recruitment of STAT3 via competitive binding with STAT3.35. 4×M67 pTATA TK-Luc (#8688) and Flag-STAT3 Y705F (#24984) were obtained from Addgene . Myc-DDK-tagged PTPRM (RC226398) was obtained from OriGene . Full-length and deletion mutants of STAT3-tagged HA were subcloned from the Flag-STAT3 expression vector (Addgene). STAT3 gene was mutated using the QuickChange Site-Directed Mutagenesis Kit . The constructs were verified using sequence analysis.EGF, IL-6, and human phospho-kinase array were purchased from R&D Systems . Other chemicals were purchased from Sigma-Aldrich . The following antibodies were used: anti-DDIAS (HPA038540) from Atlas Antibodies ; anti-SHP1 (sc-7289), anti-TC-PTP (sc-373835), anti-HA (sc-805), and anti-Myc (sc-40) from Santa Cruz Biotechnology ; anti-STAT3 , anti-HA (3724), anti-pSTAT3 , anti-pSTAT3 , anti-pJAK1 , anti-JAK1 (3344), anti-pJAK2 , and anti-JAK2 (3230) from Cell Signaling Technology ; anti-PTPRM (MAB4446) from R&D Systems; anti-GAPDH (LF-P-A0212) from AbFrontier ; and anti-Flag (F1804) from Sigma-Aldrich. Full-length and deletion mutants of Flag-DDIAS were cloned as previously described57.Human embryonic kidney 293T (HEK293T), Calu-3, and human NSCLC cell lines NCI-H23, NCI-H1703, NCI-H1793, NCI-H2009, NCI-H358, NCI-H460, NCI-H1299, NCI-H1437, HCC15, HCC827, and HCC2279 were purchased from Korean Cell Line Bank  or KRIBB Cell Line Bank . Calu-3 and HEK293T cells were cultured in Dulbecco’s modified Eagle’s medium and all NSCLC cell lines in RPMI-1640 supplemented with 10% fetal bovine serum and penicillin/streptomycin . Cells were transfected with the indicated plasmids using TurboFect  according to the manufacturer’s instructions and siRNAs (20–40 nM) by electroporation  according to the manufacturer’s instructions. Cell viability was determined using the sulforhodamine B (SRB) assay, as previously describedAccuTarget™ Genome-wide validated siRNA Library and siRNAs used in this study were purchased from Bioneer Corporation . The target sequences were as follows: siDDIAS: 5′- CAGAAGAGAUCUGCAUGUU-3′, siDUSP11: 5′-CUAUUCACACAGGAGGUAU-3′, siDUSP12: 5′-CAUUCAUGGCAGAUUGUUU-3′, siPTPRM (#1): 5′-CGAGCUAUAAAAUUGGACA-3′, siPTPRM (#2): 5′-CUGGUUACAGGGCAUUGAU-3′, siPTPRK: 5′-GCCCAGACUAAGAACAUCAAU-3′, siPTPRN2: 5′-AGUAUCCGAUUCGCCAUCA-3′, siPTPRZ1: 5′-GACAUGGGAGUACCAGAGU-3′, or Scrambled: 5′-CCUACGCCACCAAUUUCGU-3′.33.Cells were co-transfected with 300 ng of 4×M67 pTATA TK-Luc and 10 ng of pRL-TK using 1 μl of TurboFect per well in 24-well plates, serum starved for 24 h, and treated with 20 ng/ml of IL-6 for 6 h. Luciferase assay was performed as previously described34. The primers for DUSP11 (P318718), DUSP12 (P266589), PTPRK (P109949), PTPRM (P153012), PTPRN2 (P103059), PTPRZ1 (P254869), PTPRT (P315908), SHP1 (P277701), TC-PTP (P205396), and STAT3 (P229000) were obtained from Bioneer . Primers used for DDIAS and glyceraldehyde 3-phosphate dehydrogenase (GAPDH) were as described previously33. All reactions were performed in triplicate and normalized to GAPDH as the internal control.Total RNA isolation, RT, and qPCR were performed as described previously5) were seeded in a culture insert in serum-free media and treated with 20 ng/ml IL-6 into the upper chamber. Complete medium was applied to the lower chamber. Cells were allowed to migrate or invade the layer for 12 or 24 h, respectively. Migrating/invading cells were stained with 0.4% SRB and visualized under a microscope. The experiment was repeated three times.These assays were performed in 24-well Transwell plates  with 8-μm pore inserts coated with or without Matrigel (Invitrogen). Cells .Immunofluorescence staining was performed as described previously33. Tissues were incubated with anti-pSTAT3 (Y705), anti-DDIAS, or anti-PTPRM antibodies overnight. The images were visualized using an Olympus BX51 microscope equipped with a DP71 digital camera and DP-B software . pSTAT3, DDIAS, and PTPRM positivity was defined as 3+, 2+, 1+, or 0 by IHC. Tissues stained were divided into two high (3+ and 2+) and low (1+ and 0) groups. Scoring of the human tissue array was performed by two independent observers (J.-Y.I. and K.-W.L.), showing high consistency between scores for both pSTAT3 and DDIAS.Human tissue arrays (LC485) were obtained from US Biomax, Inc. . IHC staining was performed as previously described36. For endogenous binding, lysates were incubated with anti-STAT3 or anti-DDIAS antibody and subjected to western blot analysis with specific antibodies. Signals were detected using an enhanced chemiluminescence kit . The densitometry numbers indicated were measured using Image J (NIH).Immunoprecipitation and western blotting were performed as described previouslyPTP activity was measured using a Tyrosine Phosphatase Assay System  according to the manufacturer’s protocol. NCI-H1703 cells were transfected with siDDIAS for 72 h and lysed with storage buffer. Spin columns were used to remove endogenous free phosphate from the cell lysates. Tyrosine phosphatase activity was measured using the EMax plate reader system  at 600 nm.t test. A value of p < 0.05 was accepted as significant.Data were obtained from at least three independent experiments with biological triplicates and presented as mean ± SEM. Statistical analyses were performed using two-tailed Student’s Supplementary Figure S1. DDIAS knockdown suppresses IL-6-induced STAT3 phosphorylationSupplementary Figure S2. siRNA screen for protein tyrosine phosphatases (PTPs) on STAT3 dephosphorylation by DDIAS knockdownSupplementary Figure S3. Overexpression of PTPRM inhibited lung cancer cell growthSupplementary Figure S4. The effect of PTPRT silencing on STAT3 dephosphorylation by DDIAS knockdownSupplementary Figure S5. The level of RPTPs in NSCLCSupplementary Figure S6. Staining of DDIAS, PTPRM, pSTAT3 (Y705) in human lung cancer tissue array


Nei’ genetic distance between the materials ranged from 1.04 to 25.02, with an average of 13.03. At the genetic distance of 25.02, the materials clustered into two major groups, consistent with the result of population structure analysis. However, more subgroups existed between 5.21 and 13.32. Although not all the materials from the same region were clustered in the same group, an obvious trend existed where the groups were related to regions to a great extent. Based on multiple indices, the genetic diversity of materials from Hainan was the lowest. However, there was not much difference between West Guangdong and Guangxi, although the former was slightly higher. Moderate genetic differentiation was observed in wild materials in South China. The genetic differentiation mainly occurred within population, with maximum differentiation in Guangxi, followed by West Guangdong and the minimum in Hainan. Nonetheless, there was an extensive geneflow between populations. The above results provided a direction for the conservation and breeding application of these materials.Castor bean is an important seed oil crop. Castor oil is a highly demanded oil for several industrial uses. Currently, castor bean varieties suffer from low productivity and high risk of insect pests and diseases. It is in urgent need to mine elite genes from wild materials for castor breeding. 29 pairs of polymorphic SRAP primers out of 361 pairs were used to analyse the genetic diversity of 473 wild castor materials from South China. 203 bands were amplified by the 29 pairs of primers, of which 169 bands were polymorphic, with a polymorphic percentage of 83.25%. With an average number of alleles per locus (A Ricinus communis L.; 2n = 20) [Castor 2n = 20)  belongs n = 20  b2n = 20) . It is s2n = 20)  partiallCastor, which is a non-edible oilseed crop is grown extensively in the tropical, subtropical and temperate regions of the world for its highly valued oil (Castor oil). The oil from castor is used as a raw material for many industrial products owing to its unique physical and chemical properties. Castor oil has numerous industrial and medicinal applications  like beiGenetic diversity describes the total number of characteristics in the genetic makeup of a species and provides a way for populations to adapt to fluctuating environments. For a breeding program to be effective, good knowledge of the extent and nature of genetic diversity within the crops is a necessity. The presence of genetically different landraces of a crop is a handy resource that can be used in the development of such a crop. Analysis of population structure and genetic diversity of germplasm is a very productive tool in providing information for association mapping, allele mining for novel traits and crop breeding .For the possibility of proper improvement of new cultivars with required characters that are suited to specific microclimates, there should be some knowledge about the extent of genetic diversity of the species .Many molecular markers like SSR (Simple Sequence Repeats), SNP (Single Nucleotide Polymorphism), EST-SSR (Expressed Sequence Tag-Simple Sequence Repeat), AFLP (Amplified Fragment Length Polymorphism), SCOT (Start Codon Targeted Polymorphism), RAPD (Random Amplified Polymorphic DNA), ISSR (Inter-Simple Sequence Repeat) and TRAP ((Target Region Amplification Polymorphism) have been used in assessing the genetic diversity of castor –26. NoneSRAP (Sequence Related Amplified Polymorphism) markers since their introduction  have nevThe procedure of SRAP primer design is relatively easy and fast since no prior sequence information is needed and the forward and reverse primer combinations are not limited, yielding more polymorphisms. Compared with other molecular marker methods, primers designed for SRAP markers are more reproducible, stable and less complicated  and haveCastor has been planted in China over 1,400 years, and it is mainly planted in northern China. Most popularised varieties were developed in the north but expressed premature senility, weak resistance to disease and poor tolerance to heat and moist in South China, while the abundant wild and semi-wild (domesticated ancestors but found in the wild) castor plants in South China lack effective improvement and utilisation. As a result, South China lacks adaptable varieties. The objective of this study is to access the genetic diversity of castor accessions from South China using SRAP markers to provide a basis for efficient protection, research, and utilisation of these resources.All the castor materials were provided by the tropical crop molecular breeding laboratory of Guangdong Ocean University. The sampling plan was formulated as follows: On the map, the collection area was divided into stripes 50–100 kilometers apart in the north-south direction, and several roads each going across a stripe were selected; we drove along these roads, searching and collecting; at the same time, we visited local people to look for the places where castor plants were distributed. At each collection point, we collected a few to a dozen plants, and 3–5 seeds were collected from each plant. The distance between the collection points was ~50 kilometers. Actually, the castor plants at each collection point were well isolated in space. After screening and sorting, 473 materials   were sel361 pairs of SRAP primers were prepared by randomly combining 19 forward primers, Me1-Me19, and 19 reverse primers, Em1-Em19. The SRAP primers were synthesised by Shanghai Bioengineering Company. A total of 29 pairs of polymorphic primers were selected to be scanned on the entire materials after being screened on the 23 selected materials. The 29 combinations were M1E3, M1E9, M2E2, M2E4, M2E5, M2E6, M2E7, M2E13, M3E7, M3E9, M4E1, M5E7, M6E4, M6E5, M6E6, M9E1, M9E8, M10E16, M10E17, M15E5, M15E12, M17E2, M17E3, M17E9, M17E11, M17E12, M18E10, M18E16, AND M19E1. The primer sequence is listed in Total genomic DNA was extracted from the young leaves following the improved cetyltrimethylammonium bromide method (CTAB).). The DNA extraction method is as follows:Fresh castor leaves were ground into powder using liquid nitrogen, and the grounded powder was filled to about 1/3 volume of 1.5ml Eppendorf tube;-1, pH 8.0 EDTA 50mmol L-1, 3% CTAB, NaCl 100 mmol L-1, 4% PVP, 2% β-mercaptoethanol) was added and to grounded leaf powder and thoroughly mixed; tubes were placed in a water bath at 65 °C for 20–30 min and were shaken at every 5–10 minute interval;800μL CTAB buffer  was added. Incubation was done at 37 ° C for 1 hour;The supernatant was taken, and 6μL of RnaseA enzyme  was added, and the solution was gently shaken to milky white (about 5 min) and was centrifuged at 10,000 rpm for 10 minutes at room temperature;-1 pH 5.2) and 120μL absolute ethanol (pre-cooled at -20 °C) was added, gently mixed, and left at room temperature for 2 min. It was centrifuged at 10,000rpm for 5 minutes. The solution was poured away leaving the DNA pellet settled at the bottom of the tube.The supernatant was taken, and 20μL NaAc 1.0 μL, 10×Buffer 2.0 μL, Mg2+ (25 mmoL·L-1) 2.5 μL, Taq Enzyme (4 U·μL-1) 0.4 μL, dNTPs (10 mmoL·L-1) 0.2 μL, SRAP Primers (2 μmoL·L-1) 1.0 μL+ 1.0 μL, and 11.9 μL ddH2O in a total reaction volume of 20 μL.These 29 SRAP primer pairs were used to screen all the 473 accessions for polymorphisms. The PCR reactions to analyse the SRAP markers consisted of template DNA , 14 ml 30% acrylamide (29 acrylamide: 1 bisacrylamide), 0.5 ml 10% (w/v) ammonium persulfate and 20 μL TEMED, was prepared to segregate the PCR products. After gel electrophoresis, the gel was silver stained to aid in visualizing of polymorphic bands.Every SRAP primer combination was given a score (‘1’ for the presence or ‘0’ for the absence of bands in each accession), and a binary matrix was generated. SPSS 18.0 was used for cluster analysis to obtain clusters of 473 wild castor materials from South China.p), average number of effective alleles per locus (Ae), mean expected heterozygosity per locus (He) were computed for each primer combination using POPGENE 1.32 [Genetic diversity parameters, the proportion of polymorphic loci (P%), average number of alleles per locus  in POPGENE; gene flow was estimated from GST, expressed as (Nm) = 0.5 (1- GST)/GST , 36, 37.Population structure was analyzed using the program STRUCTURE , and theThe extracted DNA was subjected to agarose gel electrophoresis to check the quality. The DNA bands were clear, bright and there was no tailing phenomenon. The extracted DNA was nearly transparent, flocculent and elastic indicating that the improved CTAB method could obtain higher quality and higher DNA concentrations to be used for PCR amplification.p) was 1.801 while the effective alleles per locus (Ae) was 1.713. The percentage of polymorphic loci (P) ranged from 50–100% with an average of 90.04%  and subgroup E (82) respectively; and 48 Hainan materials clustered separately into subgroup F at the genetic distance of 9.98 . Not allp). However, average number of effective allele per locus (Ae) and mean expected heterozygosity per locus (He) of West Guangdong and Guangxi were higher than Hainan, and those of West Guangdong were slightly higher than Guangxi  and average number of allele per locus , 168 materials from Guangxi (GX) and 60 from Hainan were used to construct phylogenetic trees  and West Guangdong (0.28). This showed that the materials in Guangxi differentiated more greatly. GX172 was the root of 96 accessions out of the 168 total accessions in Guangxi indicating 57.14% which is a sign of high ancestry. GX113, the immediate descendant of GX 172 also branched into 95 descendants. 70 progenies branching from GX78 also confirmed 41.67% . LZ5, DHThe results output from STRUCTURE HARVESTER showed a clear maximum ΔK at K = 2  where alPOPGENE was used to analyze the genetic differentiation between the populations, with a GST value of 0.1075(<0.5), a moderate level of differentiation was observed. The Geneflow estimate result, Nm value, was 4.1543 (>4), showing an extensive geneflow existed between regions.p) of 1.801 and average number of effective allele per locus (Ae) of 1.713, the SRAP molecular marker showed its usefulness and effectiveness in identifying the genetic diversity of wild castor germplasm resources.With a polymorphic percentage of 83.25%, an average percentage of polymorphic loci of 90.04%, an average number of allele per locus  West Guangdong materials in group B, 82 (33.47%) West Guangdong materials in subgroup E, 111 (66.07%) Guangxi materials in subgroup A2, and 48 (80%) Hainan materials in subgroup F. This conclusion was different from the previous reports –19, 23 wThirdly, the genetic homogeneity and differentiation existed within and between regions. The genetic homogeneity resulted from the bottleneck effect of a population where the materials sharing ancestry in the same area were related closely. Geographic segregation easily exists, and hybridisation rate is low. The genetic homogeneity between regions may be due to the immigrants from each other and subsequent randomly mating within population. The genetic differentiation within a region mainly arose from gene mutation and crossing recombination especially crossing with immigration. The genetic differentiation between regions was mainly because of the founder effect of each population.Ae) and mean expected heterozygosity per locus (He), mainly because West Guangdong, is relatively flat and located between Guangxi and Hainan. There is frequent gene exchange as compared to the mountainous area Guangxi and completely isolated island Hainan. With the same average number of allele per locus (AP) and percentage of polymorphic loci (P), there was not much difference between materials from West Guangdong and Guangxi. However West Guangdong was slightly higher than Guangxi. The largest genetic differentiation existed in Guangxi subpopulation. With a much larger territory and diverse ambient conditions and much-reserved nature due to the presence of many mountains, more variation was produced compared to West Guangdong and Hainan. The genetic diversity and differentiation of Hainan subpopulation were the lowest. This was probably because Hainan is an isolated island, separated from mainland China with lower contamination of foreign genes, low heterogeneity between materials and the differentiation of genetic diversity mainly caused by gene mutation and recombination. However, Hainan’s unique climate has caused its phenotype to be more diverse. Therefore, we finally determined the genetic diversity as West Guangdong > Guangxi > Hainan. GST is the relative measure of genetic differentiation between samples, and it is an indication as to whether the greater variation is within or between the samples of interest [Nm value of our experiment (Nm = 4.1543) could describe the low heterozygosity of wild castor materials in South China.Fourthly, the genetic diversity of the three regions followed the descending order from West Guangdong, Guangxi, to Hainan. The subpopulation West Guangdong had the highest average number of effective allele per locus Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 TableFrequency Based Genetic Distances.(XLSX)Click here for additional data file.


Organisms are adapted to each other and the environment because there is an inbuilt striving toward security, stability, and equilibrium. A General Theory of Behavior connects imagery, affect, and action with the central executive system we call consciousness, a direct emergent property of cerebral activity. The General Theory is founded on the assumption that the primary motivation of all of consciousness and intentional behavior is psychological homeostasis. Psychological homeostasis is as important to the organization of mind and behavior as physiological homeostasis is to the organization of bodily systems. Consciousness processes quasi-perceptual images independently of the input to the retina and sensorium. Consciousness is the “I am” control center for integration and regulation of (my) thoughts, (my) feelings, and (my) actions with (my) conscious mental imagery as foundation stones. The fundamental, universal conscious desire for psychological homeostasis benefits from the degree of vividness of inner imagery. Imagery vividness, a combination of clarity and liveliness, is beneficial to imagining, remembering, thinking, predicting, planning, and acting. Assessment of vividness using introspective report is validated by objective means such as functional magnetic resonance imaging (fMRI). A significant body of work shows that vividness of visual imagery is determined by the similarity of neural responses in imagery to those occurring in perception of actual objects and performance of activities. I am conscious; therefore, I am. A General Theory of Behavior concerns the “I am” control center for (my) thoughts, (my) feelings, and (my) actions with (my) conscious mental imagery. Thus, 382 years after Descartes stated his “cogito, ergo sum” discovery, the evidence from cognitive neuroscience suggests the possibility of a more satisfying claim: “I am conscious, therefore, I am”. The certainty of human existence relies not on the ability to think, but on the richer endowment provided by consciousness. It is not too large a stretch to assert that consciousness brought humans their privilege of pole position in the food chain. Without consciousness, humans would not benefit from what, I claim, is the pre-eminent force of nature that drives evolution, homeostasis. Organisms are adapted to each other and the environment as conscious beings because they possess an inbuilt striving toward stability, security, and equilibrium. Arguably, human homeostatic activity in niche construction generates less variation in the source of selection than where there is no feedback from organisms’ activities to the environment .The General Theory connects imagery, affect, and action with consciousness and the primary motivation that is psychological homeostasis. It is suggested that homeostasis provides a unifying concept across biology and psychology. Psychological homeostasis is as important to the organization of mind and behavior as physiological homeostasis to the organization of bodily systems. According to the theory, psychological homeostasis is available to organisms with consciousness, a process that embraces a near infinite supply of quasi-perceptual-affective images independently of the retina and sensorium. Homeostatic striving for security, stability, and equilibrium is a precondition for well-being. One major form of behavioral homeostasis is niche construction which alters ecological processes, modifies natural selection, and contributes to inheritance through ecological legacies. To do full justice to this very broad topic within the confines of the special issue, this invited review article refers to previous publications as supporting material, with all of the detailed references, a more complete explanation of the ideas, and the ongoing state of the research. One major purpose of the review is to present a summary of the evidence that mental imagery plays an essential role in the control of behavior. Mental imagery is an essential component of a new General Theory of Behavior involving homeostasis. A second purpose of this review is to explore the executive function of consciousness in the organization and control of behavior.Central to the General Theory is the construct of mental image vividness, that combination of clarity and liveliness, so beneficial to consciousness and all its works: imagining, remembering, thinking, feeling, predicting, planning, pretending, dreaming, and acting. Introspective reports and objective indicators suggest the existence of wide individual differences in mental imagery vividness. Only in rare cases do is there evidence of absolutely no vividness at all. Vividness was studied under controlled conditions with standardized questionnaires, including the Vividness of Visual Imagery Questionnaire (VVIQ) ,3,4. An Philosophers and psychologists discussing the vividness construct and its measurement sometimes displayed the characteristics of a “streetlight effect”, the observational bias that occurs when searching for something and looking only where it is easiest, where there is light . SearchiWorking Principle:The voluntary behavior of conscious organisms is guided by a universal striving for equilibrium, a striving with purpose, desire, and intentionality.The General Theory assumes there is universal and constant striving for stability and equilibrium, a process termed “psychological homeostasis” . NeuroscPsychological homeostasis, as a process of consciousness, is intentional, purposeful, and driven by the desire for security, safety, and equilibrium. It is necessary to assume that the mind/body system as a whole can be studied using objective methods. For one hundred years, mental imagery, vividness, and consciousness remained under-investigated in psychology and neuroscience for being too “subjective”. The so-called “subjective” processes of vividness, mental imagery, and consciousness are amenable to objective study with tools and methods designed specifically for the purpose. This article indicates how these once “tabooed” areas became part of the scientific mainstream. A primary function of consciousness is the mental rehearsal of adaptive, goal-directed action through the experimental manipulation of perceptual-motor imagery. Every cycle of mental activity includes a goal, the means to reach it, and the consequences for the organism and objective world. The control system has a “meta level”, a “schema level”, and an “automatized level” in a hierarchical relationship. The meta level, which is a major function within “consciousness”, sets the goals for a project of activity. The schema level monitors and controls the flow of action, making moment-by-moment adjustments in light of the goal set for it by the meta level above. The automatized level carries out repetitive and routine, everyday tasks that require zero planning or attention from higher up. When one hammers a nail into a piece of wood, one checks if the nail straight, if it is going in properly, whether it hit an obstacle, and so on. In cooking a soup, one tastes and seasons it with salt and pepper, not too much and not too little. In driving on the highway, one keeps to one’s lane, attends to the road, monitors the other vehicles, and keeps to the speed limit, constantly vigilant for signs and warning signals, adjusting, adapting, and correcting. Conscious goal-setting lends purpose to our every action, striving for equilibrium with desire and intentionality.What exactly do I mean by “vividness”? It is necessary to be clear to avoid misunderstanding. I need search no further than the definition provided in an earlier publication: “a combination of clarity and liveliness. The more vivid an image, therefore, the closer it approximates an actual percept” . The twoA “life-like” image allows a person to experience it in a very real way. “Evoke” means to cause someone to sense or feel something. I appreciate this fact from personal experience. When I am watching a film of people lighting a camp fire, or smoking a cigarette, I actually can “smell” the smoke at the camp fire or the stench of a cigarette. These olfactory imaginings occur as if I am actually carrying out the activity, albeit on a lessor scale. Note that it is an activity, not a picture. Evoking a vivid image produces a life-like activity of “seeing”, “hearing”, “tasting”, “smelling”, “touching” or “feeling” something; mental imagery is a sensory-affective process that resembles, but is not identical to, perception with action.The Hate U Give or Kite Runner. There would also be fewer scientific discoveries—no Maxwell’s demon, Einstein’s elevator, or Schrödinger’s cat .The analysis of vividness risks entering a cul-de-sac when entertaining unhelpful metaphors such as the Platonic “picture” theory or the “descriptive”, propositional theory  are adduTo date, around 2000 studies have used the VVIQ or Vividness of Movement Imagery Questionnaire (VMIQ)  as a mea“Visual imagery refers to the ability to visualize, that is, the ability to form mental pictures, or to ‘see in the mind’s eye’. Marked individual differences are found in the strength and clarity of reported visual imagery and these differences are of considerable psychological interest.The aim of this test is to determine the vividness of your visual imagery. The items of the test will possibly bring certain images to your mind. You are asked to rate the vividness of each image by reference to the five-point scale given below. For example, if your image is ‘vague and dim’, then give it a rating of 4. After each item, write the appropriate number in the box provided. The first box is for an image obtained with your eyes open and the second box is for an image obtained with your eyes closed. Before you turn to the items on the next page, familiarize yourself with the different categories on the rating scale. Throughout the test, refer to the rating scale when judging the vividness of each image. Try to do each item separately, independent of how you may have done other items.Complete all items for images obtained with the eyes open and then return to the beginning of the questionnaire and rate the image obtained for each item with your eyes closed. Try and give your ‘eyes closed’ rating independently of the ‘eyes open’ rating. The two ratings for a given item may not in all cases be the same.” .The five-point rating scale of the VVIQ is presented in The 16 items are arranged in blocks of four, in which each has a theme and at least one item in each cluster describes a visual image that includes movement . Each thFor a small minority of people, the capacity for visual imagery is unavailable. In the absence of mental imagery, consciousness consists of “unheard” words, “unheard” music, and “invisible” imagery. This minority needs to employ more generic, verbal methods to recall events, and to plan goals and future activity—compensatory strengths that remain under-investigated.A large body of psychological and neuroscientific research is consistent with the tenet that imagery is functionally equivalent to, but not identical to, perception. Research summarized in a later section indicates similar anatomical patterning of neural activity in the cerebral cortex. I am discussing a graded phenomenon in which “the ordinary course of thought involves an interaction at sensory input with the central processes…”  (p. 476)Another method to explore a person’s imagery experience is to provide simple color suggestions while the participant looks into the center of a circle drawn on paper—the Open Circle Test . When a Each image connected and overlapped with the previous one. The sequence could have been continued indefinitely but we stopped after four transitions. The dynamic images triggered by a simple color name show the defining feature of liveliness. This attribute is neglected in practically all analyses of imagery. Vividness, action, and affect are the foundation stones for the General Theory of Behavior and of consciousness itself.An extensive literature on mental “practice” refers to both imagery “rehearsal” and mental “simulation” ,17. ImagConverging evidence suggests that mental simulation of movement and actual movement share similar neurocognitive and learning processes, leading to considerable interest in imagery simulation of movement as a therapeutic tool in the rehabilitation of stroke patients, patients with Parkinson’s disease, and other neurological syndromes . ConscioA common neural basis exists for imitation, observational learning, and motor imagery. During mental simulation, the excitatory motor output generated for executing the action is inhibited. The autonomic system is also activated during motor imagery. The principal function of consciousness is to plan and make predictions about the consequences of actions. Simulation enables the imager to mentally try out a sequence of goals, schemata, and actions that minimize hazard, loss, and pain.The principal measures of vividness, the VVIQ and VR, are strongly associated with performance in different kinds of tasks: self-report, physiological motor, perceptual, cognitive, and memory ,5,24,25.Vividness of visual imagery correlates with fMRI activity in early visual cortex scores, demonstrating that higher visual cortex activity indexes more vivid imagery. Variations in imagery vividness depend on a large network of brain areas, including frontal, parietal, and visual areas. The more similar the neural response during imagery is to the neural response during perception, the more vivid or perception-like the imagery experience will be. From these findings, it can be concluded that an image is an idea with visual attributes. The more vivid the image, the more strongly a person will be aware of it. Upon reflection of the alternative actions available, it is possible to inhibit certain actions and implement others, or to keep actions “on hold” for the future. Thus, consciousness is able to facilitate successful striving toward goals, including the construction of new niches and environments, and thereby the effectiveness of type II homeostasis, providing a significant evolutionary advantage through niche construction and other adaptive mechanisms .The VVIQ is only minimally contaminated or not contaminated at all by extraneous variables such as social desirability.The VVIQ has acceptable levels of split-half and test-retest reliability.The VVIQ has acceptable validity coefficients with other verbal-report measures of imagery and with physiological, motor, memory, and cognitive tasks.The VVIQ has excellent criterion validity coefficients with perceptual tasks, exceeding those obtained with other self-report measures of imagery.The ability to produce vivid visual imagery is of most benefit in tasks that are heavily loaded with perceptual-motor components and is of considerably less benefit to memory tasks.A recent meta-analysis reported large effect size estimates (ESEs) for both VR and VVIQ measures, with larger ESEs for neuroscientific than behavioral-cognitive measures .The evidence on the VVIQ ,25 permiI summarize here two key findings.STUDY 1:Vividness of mental imagery: individual variability can be measured objectively  and type II homeostasis is the outwardly striving psychological homeostasis H[Ψ]. From birth to death, these two forms of homeostasis provide optimum levels of controllable equilibrium. The reset equilibrium function (REF) integrates the principle of homeostasis with our understanding of psychological processes and behavior. Systems theory with cyclical negative feedback loops is a central feature. Feedback loops in cybernetics and control theory mirror homeostasis within biology and neuroscience. Psychologists employ control theory as a conceptual tool for large areas of psychology . NotablyThis General Theory draws upon systems of homeostasis consisting of interconnected processes in continuous feedback loops that are updated with each reset of the REF. The REF extends the reach of homeostasis to a general control function which automatically restores psychological processes to equilibrium and stability. The REF is triggered when any of the processes within a system strays outside of its set point or range. The REF is innate, but it can only exist in conscious organisms which all have two kinds of homeostasis (types I and II). Non-conscious organisms are availed with only one type of homeostasis (Type I). Type II homeostasis exists in a system with any number of processes, each with its own set range, making a series of resets.Any set of processes, such as the four shown in The General Theory explains the relevance of the REF to numerous psychological functions including those where reset is a condition for change, e.g., affect, chronic stress, excessive behaviors such as smoking, drinking, gambling, and overeating, pain, sleep loss, and low subjective well-being. In all of these situations, the subject’s conscious acknowledgement that there is behavior in need of change is of primary importance. This acknowledgement is a necessary precondition for purposeful striving toward making that change.“The purpose of a brain is not to think, but to act” . The cenMental imagery principle: A mental image is a quasi-perceptual experience that includes action schemata, affect, and a goal.According to Frederic Bartlett 1932), schemata are much more than elementary reactions ready for use; “they are also arrangements of material, sensory at a low level, affective at a higher level, imaginal at a higher level yet, even ideational and conceptual” . The act, schematA system based on this principle is shown in Involuntary images, persistent, unpleasant memories, and repetitive habits are symptomatic of disorders, e.g., patients with posttraumatic stress disorder, anxiety disorders, depression, eating disorders, and psychosis frequently report repeated visual intrusions concerning real or imaginary events that can be extremely vivid, detailed, and with highly distressing content . HalluciAs noted, the General Theory proposes a cyclical system of objects, schemata, affective experience, and actions. The control system has both an executive level and a schema level. The executive level, which is what is normally referred to as “consciousness”, controls and monitors the schema level. This duality of levels enables moment-by-moment adjustments to goal-seeking behavior at the schema level. Goals are set at the executive level of consciousness. Goal-setting is guided by values and beliefs which inform actions, inhibit actions, or reflect on what action to take, as the situation requires.In competent performers, speech, decisions, routines, and many complex behaviors normally do not require conscious control to operate . AfferenConscious imagery is useful in the planning and organization of behavior through enabling the simulation of action sequences at the object level without energy expenditure or risk. The object level interfaces with the social level in the public domain of shared activities and object levels. The possible outcomes of alternative future actions can be appraised prior to a course of action. In this way, conscious mental imagery serves as a mental toolbox, producing its internal contents for the user to explore and manipulate in the selection and preparation of future physical and social activity.An internal clock controls physiological and behavioral processes of daily living in synchrony with regular changes in the environment. Over hundreds of millions of years in an environment that changes dramatically over every 24-h cycle, evolution produced universal rhythms throughout the plant and animal kingdoms such that each organism’s biochemistry, physiology, and behavior are organized in diurnal cycles . Many ciThe light-dark (LD) cycle is the most reliable of the external signals enabling entrainment and is referred to as a “zeitgeber” . LD information is perceived by mammals with retinal photoreceptors and conveyed directly to the suprachiasmatic nucleus (SCN) of the hypothalamus, where it entrains oscillators in what is regarded as the master clock of the organism . Other cAn entrainable circadian clock is present in the SCN during fetal development, and the maternal circadian system coordinates the phase of the fetal clock to environmental lighting conditions. Even before birth, the organism is entrained to the LD cycle . Having It was proposed that a common design principle applies to the clock in all organisms, from bacteria to humans, and that the circadian clock existed for at least 2.5 billion years. . The preSynchronicity at a neural level with 35–75-Hz oscillations in the cerebral cortex, hypothesized to be “the basis of” consciousness, form the binding that may be achieved by the synchronized oscillations of neuronal groups . It is s“Every person on the planet (barring illness) can tell good from bad, positive from negative, pleasure from displeasure” . Not onlEmerging evidence indicates similarities in the anatomical substrates of painful and pleasant sensations in the opioid and dopamine systems . The expIn the following section, the different modular systems described for imagery, timing, action, and inhibition are integrated into a single system for behavior control.As the executive controller of brain and behavior, consciousness occupies pole position, providing a significant evolutionary advantage to the organism. Critics claim that consciousness is an epiphenomenon and that its contents are formed “backstage” by non-conscious systems. Oakley and Halligan  stated tAccording to a personal narrative account , the expAnother argument against the view that consciousness controls action and thought is the empirical evidence that the outcome of a decision can be encoded in brain activity of prefrontal and parietal cortex seconds before it enters awareness. It is presumed that this delay reflects the operation of a network of high-level control areas that begin to prepare an upcoming decision long before it enters awareness . We needThe authors of another Libet-style readiness potential study  suggesteSome critics claim that totally unconscious living beings could conceivably exhibit all of the adaptive behaviors of conscious beings, the so-called “zombie” option. They suggest that it is entirely conceivable and coherent to state that all structural and functional characteristics of living beings could be explained without any accompanying conscious experience. It is metaphysically possible that such unconscious zombies could be “in the dark” and be equally effective. This is the “zombie” hypothesis of conscious inessentialism in the philosophy of the mind . The zomWhy did consciousness evolve? I suggest the answer lies in the significant evolutionary advantages of an emergent, purposeful, and integrative process of psychological homeostasis to direct holistic control ,75. The “The long-standing assumption in the neurosciences that the subjective phenomena of conscious experiences do not exert any causal influence on the sequence of events in the physical brain process is directly challenged in this current view of the nature of mind and the mind-brain relationship. A theory of mind is suggested in which consciousness, interpreted to be a direct emergent property of cerebral activity, is conceived to be an integral component of the brain process that functions as an essential constituent of the action and exerts a directive holistic form of control over the flow pattern of cerebral excitation” (p. 532)my” experience , and type II, H[Ψ], serve identical stabilizing functions internally in the body and externally in socio-physical interactions, respectively. With Cannon, I hypothesize that “steady states in society as a whole and steady states in its members are closely linked” . H[Φ] anA much neglected topic in the history of psychology and today in consciousness studies is mental imagery. Yet, the evidence suggests that mental imagery is the basic building block of consciousness . In prot


Salix salviifolia, an Iberian endemic riparian tree, across the species latitudinal range. We applied spatial stream-network models that aptly integrate dendritic features  to quantify the impacts of multiple scale factors in determining genetic diversity. Based on the drift hypothesis, we expect that genetic diversity accumulates downstream in riparian ecosystems, but life history traits  and abiotic or anthropogenic factors  might alter expected patterns. Hydrological factors explained the downstream accumulation of genetic diversity at the intermediate scale that was likely mediated by hydrochory. The models also suggested upstream gene flow within basins that likely occurred through anemophilous and entomophilous pollen and seed dispersal. Higher thermicity and summer drought were related to higher population inbreeding and individual homozygosity, respectively, suggesting that increased aridity might disrupt the connectivity and mating patterns among and within riparian populations.Quantifying the genetic diversity of riparian trees is essential to understand their chances to survive hydroclimatic alterations and to maintain their role as foundation species modulating fluvial ecosystem processes. However, the application of suitable models that account for the specific dendritic structure of hydrographic networks is still incipient in the literature. We investigate the roles of ecological and spatial factors in driving the genetic diversity of  Rivers have been exposed to long-lasting human pressures worldwide, and they are threatened by climate change and a resurgence of damming plans in response to freshwater and energy demands3. On the one hand, river regulation alters peak flows and creates physical barriers to gene flow, which hinder the regeneration dynamics of riverine plant communities4. On the other hand, climate-driven changes, such as precipitation shifts, might decouple seed development and dispersal from the discharge regime to which they evolved5. Improving our capacity to understand and anticipate changes in these fragile ecosystems is of the utmost importance if we are to mitigate the expected pervasive environmental and societal consequences of hydroclimatic alterations3. However, the current approaches used to monitor functional responses of riparian forests to global change do not fully accommodate the dendritic structures of hydrographic networks, which hinders the accurate management of these threatened ecosystems. Here, we combined spatial stream-network (SSN) models with landscape genetics tools to quantify the role of ecological factors in determining the amount and distribution of genetic diversity harboured in riparian forests. By doing so, we tested hypotheses on the main drivers of gene flow and connectivity at the population, basin and regional scales, and we showed that this monitoring tool is suitable to design science-based conservation plans.Riparian trees are foundation species that support biodiversity and modulate key ecosystem functions through their interactions with flooding and sediment regimes in river channels and their floodplains6. Spatial models previously applied to investigate genetic patterns across dendritic structures, such as rivers, have provided interesting but limited information because (1) Euclidean geographic distances disregard the complexities of hydrographic networks within nested watersheds7; (2) proxies of the basin hierarchy  poorly capture the topological properties of dendritic networks8; and (3) models do not tackle the joint effects of environmental and spatial components together, providing an incomplete picture of the multiple factors that drive genetic patterns9. SSN models7 provide a timely opportunity to investigate the joint effects of environmental drivers and spatial properties on a hydrographic network in riparian forests, and these models provide a complete and validated methodological toolbox11. These models integrate a set of explanatory variables into a single geostatistical model that can accommodate different spatial autocorrelation structures and use spatial weights to capture the influence of branching, flow direction, and discharge7. SSN models have been successfully applied to detect spatial patterns of water chemistry12 and the distribution of vagile organisms13, but they have not yet been applied to investigate the distribution of the genetic diversity of riparian forest species across different hydrographic networks.Riparian forests are confined along dendritic hydrographic networks with typically directional water flow that disperses seeds and vegetative propagules downstream. Any attempt to investigate riparian genetic patterns requires accommodating the dendritic structure of hydrographic networks and its specific topology, connectivity and directionality within the hierarchical organization of riparian landscapesSalix salviifolia Brot. at various spatial scales. S. salviifolia is a foundation species that preferentially grows in intermediate to large order streams where natural flow, erosion and fluvial sedimentation processes create in-channel deposit bars14. In this species, gene flow occurs through pollen grains (transported by wind and insects), seeds (mobilized by wind and water) and vegetative propagules transported by water flow. Therefore, the disruption of the natural water flow and sediment regimes by river regulation threatens the dispersal ability and the genetic connectivity of these populations16. Furthermore, this taxon is endemic to the Western Iberian Peninsula, listed in the EU Habitats Directive, and it creates habitats that host IUCN Endangered Mediterranean fish species such as Anaecypris hispanica. The distribution of S. salviifolia encompasses a pronounced climatic gradient that spans from the southern Mediterranean edge to the Temperate ecoregion17. Hence, S. salviifolia is an ideal model species for studying the environmental and spatial drivers of genetic patterns in riparian species.In this study, we combined landscape genetic tools and SSN models to quantify the impacts of key ecological drivers on the distribution of the genetic diversity and structure of the riparian tree 18, which causes the accumulation of riparian plant propagules downstream unless there are other means of upstream dispersal  investigate the genetic diversity levels of riparian populations in basins along an environmental gradient; (ii) quantify the relative contributions of ecological and spatial factors; and (iii) assess the spatial extent at which dominant factors impact the genetic patterns across dendritic networks. Finally, we further discussed the potential applications of SSN models to monitor the responses of riparian ecosystems to global change drivers.Water flow is the main vector that mobilizes propagules for riparian species downstreamca. 15% of sites show signs of a deficit or excess of heterozygotes  (Table e). Therefore, larger and wetter drainage basins tended to correlate with increased levels of genetic diversity. In addition, Ho was significantly and positively correlated with altitude (ALT). Climatic covariates were retained for Fis and HL (homozygosity level). The thermicity index (BIOC.TH) had a positive effect on Fis and, at the individual-level, the summer ombrothermic index (BIOC.SO) significantly affected the HL, with higher HL levels at decreasing BIOC.SO values. This result entails that locations undergoing intense summer droughts tend to host individuals and populations with increased levels of homozygosity.The optimal sets of covariates were similar across the population-level response variables, and the hydrologic index (DA) was selected across the four estimators Ay) Table  and had e and HL, ; the tail-down for Fis and Ho  and the Euclidean for uHe (64.5% of the variation explained).The optimal covariance structure was a mixture of the possible structures, but with an overriding dendritic model (tail-up or tail-down) across most genetic estimators Table , Fig. 3.e, Fis, uHe, Ho), autocovariance models captured intermediate-scale spatial patterns with spatial ranges of 0.2–20 km. For Ae and Fis, the covariance mixture also captured large-scale patterns of variation (ranges >100 km) .The amount of genetic diversity and its spatial distribution within and among populations reflect complex interactions between intrinsic and extrinsic factors such as phenology, dispersal, topography and flood regimeSalix salviifolia displayed higher levels of genetic diversity (uHe) compared to other Salix species20, which is coherent with mating processes that favour outcrossing and suggests a relatively good genetic status in this species, at least under the current environmental conditions. Migration history might have modulated the current genetic patterns of these populations, as favourable microclimatic conditions within protected river valleys in the Iberian Peninsula offered refugia for tree species during glaciations24. The prevalence of private alleles may be interpreted as genetic evidence for persistence of S. salviifolia in the southern part of the peninsula during the last glacial period, as shown for other riparian species24.The Iberian endemic The movements of propagules or individuals that inhabit riparian habitats are confined to the river flow, either upstream, downstream or both. Our results showed that a large proportion of the residual genetic variation is spatially structured within the basin . Furthermore, by applying SSN models, we evidenced that the dendritic spatial structure accounted for majority (70–89%) of the variation observed within basins for most estimators of genetic diversity. Therefore, overlooking the dendritic structure of riparian habitats could lead to a misunderstanding of the main ecological drivers that underlie the biodiversity patterns within basins.18. In Salix, the potential sources of gene flow are the movement of pollen grains, seeds and vegetative propagules. Seeds are dispersed by wind  and water (mainly downstream with some possible upstream events caused by massive flooding)4. The dendritic structure of a basin imposes that the volume of water that flows through the river channel  increases downstream. Our models showed a positive correlation between river discharge and the genetic diversity level that also tended to increase downstream. In addition, the tail-up model explained significant proportions of Ae and uHe spatial variation. Both results suggest an important role of hydrochory in mobilizing propagules, as expected based on the drift hypothesis4. However, our models also noted a significant role of upstream processes that counteracted the dominant downstream movement. For example, the tail-down explained meaningful proportions of Ae, Fis and Ho, and the Euclidean model explained large proportion of uHe spatial variation. Upstream seed dispersal has been identified in other riparian species associated with zoochorous and human dispersal22. Salix spp. are wind and insect pollinated, and these vectors are reported to generate strong genetic patterns that follow dominant wind directions. In river valleys, topography and wind channelling constrain prevailing winds through the hydrographic network25, which is a phenomenon that can permit upstream gene movement, as reported in other Salicaceae species21. Despite the ability of Salicaceae to resprout from vegetative propagules26, the reduced presence of clones in our study discards the notion that they significantly contribute to natural S. salviifolia regeneration.The distribution of the genetic diversity across populations is strongly determined by dispersal and gene flow patternse, Fis, uHe, Ho) displayed patterns of spatial autocorrelation mostly at the intermediate scale  through flow-connected (tail-up) and flow-unconnected (tail-down) spatial relationships , which is likely associated with the interaction of the Salix life history with the formation and distribution of hydrogeomorphological landforms  where the species recruit and colonize28. Indeed, key biological and physical processes in riparian systems, such as metapopulation dynamics and disturbance regimes, are thought to operate at intermediate scales27.The population genetic diversity estimators  that included flow-unconnected relationships among populations. This result suggests that within hydrographic networks, even remote populations would eventually become connected, possibly integrating a long-term effect of successive pollen and seed dispersal events or through rare long-distance pollen-mediated dispersal events4, as in other Salicaceae species21.Interestingly, ASalix seedlings when the water level decreases after natural flooding events29. This finding is consistent with the significant correlation of summer drought with the HL, suggesting that increased aridity may constrain gene flow within basins as drought events become more extreme.The HL revealed a limited influence of distance among individuals where only 14.2% of the residual variation in the HL exhibited fine-scale patterns of autocorrelation (range = 11.9 m). The fine-scale spatial aggregation may be primarily due to spatially structured variables, such as microenvironmental heterogeneity. Indeed, fine-scale soil-moisture gradients within riparian habitats are critical for the survival of 13. Here, we have extended the application of SSN models to identify the ecological drivers that underlie population genetic diversity patterns within basins and quantify how the impacts of these drivers change across an environmental gradient. The integration of these models with increasingly available datasets that survey different community compositions based on environmental DNA can be used to map biodiversity hotspots and depict connectivity networks. This application would assist in decision making to prioritize the conservation of biodiversity hotspots and can be applied to draw mitigation and restoration measures to enhance gene flow among disconnected populations1. In addition, quantifying connectivity changes after adding or removing multiple barriers is a top concern of catchment planning30; thus, simulating alternative scenarios is a promising application of the stream-network approach for riverine species management. Simulation techniques can also be used to optimize sampling strategies for different purposes in stream networks or provide recommendations about sample sizes needed to achieve study objectives. These applications can significantly aid in the design of efficient monitoring strategies at relatively low costs10.Spatial stream networks have been previously applied to investigate biodiversity patterns in riparian insect communities31. Preserving their potential for future adaptation will enhance the resilience of riparian population networks with cascading effects on associated biological communities, ecosystem functions and services, contributing to ecologically successful river management.Overall, refining our capacity to describe, predict and simulate the amount and distribution of genetic diversity harboured by riparian populations of foundation species can improve adaptive management through cost-effective monitoring designs for conservationS. salviifolia and largely covers its latitudinal range and approximately one-third of its longitudinal range. We sampled 30 sites (each site representing a population) totalling 605 trees that were georeferenced with a submetre precision handheld GPS (Ashtech MobileMapper100). We sampled 15 or more individuals along the river reaches, and we collected as many individuals as possible (up to six) in low-density reaches to survey a similar sample area per site. We collected six healthy leaves per tree, and we stored them in paper bags containing silica gel until further work in the lab.In the summers of 2010–2012, we conducted field surveys in riparian forests in 24 river valleys located in the Western Iberian Peninsula, across eight independent catchment systems: Tua-Douro, Zêzere-Tagus, Aljezur, Seixe, Odiáxere, Arade, Quarteira, and Guadiana 33 (Supplementary Information −4) and scoring errors (2*10−3) by applying Microchecker 2.2.334. We also applied pedant 1.035 to duplicated samples to estimate the per-allele maximum likelihood allelic dropout  and false alleles . Our study species exhibits clonal reproduction; therefore, we used the R package Rclone36 to identify identical genotypes, estimate the probability that they have been generated by independent sexual reproduction events, and evaluate the discriminative power of our 12 polymorphic markers to identify unique multilocus genotypes , bioclimatic variables , altitude and hydrology would determine the spatial distribution of the genetic variation of S. salviifolia at different spatial scales. We calculated environmental variables based on the GPS coordinates of the populations and individuals. We used two bioclimatological indices: the thermicity index (BIOC.TH) depicts the thermal envelope where plant species thrive, while the summer ombrothermic index (BIOC.SO) estimates the intensity of summer drought37. Geographic and hydrological variables were inferred from a digital elevation model (DEM) downloaded from the Shuttle Radar Topography Mission (SRTM) 90 m Digital Elevation Database v4.138. We projected the Iberian Peninsula territory to the Lambert azimuthal equal-area projection, which guarantees equal pixel areas , and simultaneously interpolated it to 35 m resolution. The altitude (ALT) at each site was extracted from the produced DEM. Then, to estimate the potential discharge, we derived a hydrographic network and computed a hydrologic index (DA) consisting of the drainage area of each site that was weighted by the total annual precipitation (P) in its contributing area. This hydrologic index distinguishes catchments presenting similar dimensions but occurring in regions with different P, providing a surrogate for total discharge.Based on previous studies39 using the Functional Linkage of Water Basins and Streams (FLoWS) and the Spatial Tools for the Analysis of River Systems (STARS) geoprocessing toolboxes. We applied the FLoWS toolset40 to construct a landscape network, which is a spatial data structure that stores the topological relationships between nodes (stream confluences) and directed edges (stream segments). For the analyses at the individual level, we incorporated the position of each sampled tree into the landscape network. For the analyses at the population level, we used the position of the central tree to map each site. Based on the landscape network, we used the STARS toolset to generate41 (1) hydrologic distances, (2) weights for converging stream tributaries thought to have stronger influences downstream, and (3) the SSN objects that contain feature geometry, attribute data, and topological relationships of the dataset, which are intended for geostatistical modelling within the SSN R package42.We generated the spatial data necessary for geostatistical modelling in ArcGIS 9.243: (i) fixation measures ; and (ii) allelic differentiation measures (DJost). Given the controversy about the ability of Fst to quantify genetic structure45 when applying highly polymorphic genetic markers we opted for reporting four fixation measures (Supplementary Information 46. We tested for Hardy-Weinberg equilibrium (HWE) and linkage disequilibrium (LD) at each site by using GENEPOP47 and, for multiple tests, we applied the B-Y method48, which is a modified Bonferroni method recommended for conservation genetics studies49. We estimated the following population genetic diversity metrics by applying the R package gstudio50: observed and expected unbiased heterozygosity , mean number of alleles (AR), effective number of alleles (Ae), and mean number of private alleles (PA). We used INEST 2.051 to estimate the Fis per population while considering the frequency of null alleles. We calculated the homozygosity level (HL) for each individual as implemented in the adegenet R package v.3.2.2.52. HL works as a proxy of individual inbreeding and it provides insights on mating patterns at the population level that are expected to change across an environmental gradient, with increased HL levels expected in small populations and those poorly connected by water flow, partly due to genetic drift. Different components of the genetic diversity respond to the impact of ecological factors at a different pace; thus, allelic diversity typically changes fast after an ecological perturbation, whereas uHe and Ho show slow-paced changes53. Then, we evaluated the impacts of spatial and ecological factors on genetic diversity and structure by choosing a variety of estimators at the population level  and the individual level (HL).To gain a comprehensive depiction of the genetic structure observed per study region, we calculated two types of estimatorse, Ho, uHe, Fis, HL) with spatially explicit stream-network models by applying the R package SSN42.We first performed an exploratory analysis to check for multicollinearity among environmental variables by inspecting the variance inflation factor (VIF). We retained all variables because they showed VIF values <2, suggesting no or little multicollinearity among study variables. We then modelled five genetic diversity estimators  that better explained the observed genetic patterns. First, we fixed the covariance structure and focused on covariate selection through an exhaustive screening of the candidate models that resulted from every linear combination of covariates. In this stage, we applied maximum likelihood to estimate model parameters, and we used Akaike’s information criterion for covariate selection, which prevents over-fitting of the model54. Then, we fixed the selected covariates and compared every linear combination of tail-up, tail-down and Euclidean covariance structures, testing four different autocovariance functions for each model type: the spherical, exponential, Mariah and linear-with-sill functions for tail-down and tail-up models; and the spherical, exponential, Gaussian and Cauchy functions for the Euclidean model as recommended by Peterson & Ver Hoef7. Overall, we tested 125 models 13.We used a two-step model selection procedure as in Frieden SUPPLEMENTARY INFORMATION