Cornus florida L. from distinct ecological regions using genotype‐by‐sequencing markers and LC‐MS‐based untargeted metabolite profiling. We tested the hypothesis that higher genetic diversity in C. florida yielded higher chemical diversity and less disease susceptibility (screening hypothesis), and we also determined whether genetically similar subpopulations were similar in chemical composition. Most importantly, we identified metabolites that were associated with candidate loci or were predictive biomarkers of healthy or diseased plants after controlling for environment. Subpopulation clustering patterns based on genetic or chemical distances were largely congruent. While differences in genetic diversity were small among subpopulations, we did observe notable similarities in patterns between subpopulation averages of rarefied‐allelic and chemical richness. More specifically, we found that the most abundant compound of a correlated group of putative terpenoid glycosides and derivatives was correlated with tree health when considering chemodiversity. Random forest biomarker and genomewide association tests suggested that this putative iridoid glucoside and other closely associated chemical features were correlated to SNPs under selection.Understanding intraspecific relationships between genetic and functional diversity is a major goal in the field of evolutionary biology and is important for conserving biodiversity. Linking intraspecific molecular patterns of plants to ecological pressures and trait variation remains difficult due to environment‐driven plasticity. Next‐generation sequencing, untargeted liquid chromatography–mass spectrometry (LC‐MS) profiling, and interdisciplinary approaches integrating population genomics, metabolomics, and community ecology permit novel strategies to tackle this problem. We analyzed six natural populations of the disease‐threatened  In addition, we have applied exploratory analyses to winnow an untargeted list of metabolites down to a select group of potential antimicrobial compounds—closely resembling compounds previously observed in dogwoods  to North America . Five microliters of leaf extract was injected onto an Agilent ZORBAX Eclipse Plus C18 column . Metabolites were separated using a binary gradient of solvent A (0.1% formic acid in water) and solvent B (0.1% formic acid in acetonitrile) at a flow rate of 0.6/ml/min. The elution gradient started with a one‐min hold at 2% B, followed by ramping up to 45% B over 16 min, and then was increased to 90% B in one min and held at 90% B for 2.5 min. The acquisition of mass spectra was performed in negative mode for a m/z range from 100 to 1,600, with the following parameters: drying gas temperature, 300°C; drying gas flow rate, 7.0 L/min; nebulizer pressure, 40 psi; sheath gas temperature, 350°C; sheath gas flow rate, 10.0 L/min; Vcap, 3,500 V; nozzle voltage, 500 V; fragmentor, 150 V; skimmer, 65 V; OctopoleRFPeak, 750 V.2.4Raw data files obtained from LC‐MS experiments were converted to the mzData format using Agilent Masshunter software, grouped into directories by population, and then uploaded to the XCMS Online platform  variable into logistic models characterizing plant health and disease (see section C. florida draft genome (Dogwood Genome Project (NSF ID: 1444567), and the draft genome was used as an additional resource to predict candidate gene function by inspecting BLAST hit annotations surrounding SNPs of interest.We used genotype by sequencing  for 377 metabolites. We conducted DAPC both by defining groups by collection sites and by allowing the program clustering algorithm to find optimal cluster number (K) without priors. Discriminant analyses of principal components does not require assumptions on a population genetic model  in contrast to programs like STRUCTURE  to identify collection sites that clustered together, according to SNP or metabolite abundance data. Using the R package adegenet Jombart, , we perfC. florida. Genetic diversity estimates from Pais et al.  R matrix from intersample Euclidean distances following the approach of Houshyani et al.  tests and a logistic mixed model predicting disease states based on abundance data of each metabolite. Random forests tests were previously compared to partial least squares discriminant analysis (PLS‐DA), principal component discriminant analysis (DAPC), and support vector machines (SVM) for their ability to correctly assign samples to biologically based classes using metabolic data  no covariates present; (2) the Bio14 covariate present; (3) the Tcol covariate present; or (4) both covariates present  to the 377 metabolite dataset. Gaussian graphical modeling utilizes partial full‐order correlation coefficients to test for correlation between two metabolites while removing other metabolite effects. Justifications and additional considerations of GGM are further discussed in Appendix 2.11To assess whether chemodiversity of a putative terpenoid derivative network was related to the odds of being healthy versus diseased, we employed logistic mixed modeling  consistently genotyped from two Hiseq libraries in Pais et al. , we sele3.2As shown in Pais et al.  and corrSubpopulation clustering patterns based on metabolic distances from discriminant analysis of principal components largely followed clustering patterns based on genetic distances Figure . The onl3.3To understand the connections between genetic polymorphism and metabolite variation, we performed GWA analyses on all available SNPs and metabolites. As each SNP–metabolite association analysis was an independent test not biased by correlations among chemical features, each of the 2,785 chemical features of 377 metabolites was included. With and without the most important environmental covariates controlled for in GWA models, we identified 975 unique chemical features significantly associated with 347 unique SNPs. Overlapping chemical feature and SNP results among the various GWA tests (different covariates specified) are presented in Figure We performed GGM to formulate hypotheses of metabolite–metabolite associations based on partial correlation. This analysis, combined with the GWA results, revealed SNP–metabolite connections for a putative group of terpenoid glucosides Figure a. We not3.4We identified 39 metabolites that were informative biomarkers for predicting plant disease status using RF (12), DAPC (8), PLS‐DA (12), SVM (11), or logistic mixed modeling 6) . The Berger‐Parker (BP) dominance index  to chemodiversity indices  were significant for the majority of diversity indices  reported less sesquiterpene lactone diversity (herbivory deterrent) in populations further away from the equator, which was explained as a result of selection for lower toxicity due to fewer herbivore–plant interactions in areas far from the equator  of clear ecological importance was identified to guide future study. Additional investigation of chemical diversity with increased sampling across the species range may provide more details on the relationship among genetics, metabolites, and dogwood anthracnose in C. florida, which in turn may shed light on forest diseases in general.Our study demonstrates untargeted metabolite profiling is a useful approach for understanding biodiversity in a new dimension. Secondary metabolites preserved in dried leaves of None declared.With the support of Qiuyun Xiang and Xu Li, Andrew Pais conducted all collections, experiments, and analyses under the advice of the co‐authors and members of his dissertation committee. Xu Li supervised the metabolite profiling experiments and data analysis. This manuscript was drafted by Andrew as part of his PhD Dissertation and was edited by other co‐authors.https://doi.org/10.5061/dryad.15066), including (1) untransformed matrix of all 2,785 chemical features exported from XCMS; (2) matrices of all environmental‐functional‐genetic trait data used in multivariate models; and (3) Appendix Uploaded datasets for R analyses available on Dryad ( Click here for additional data file.


Niviventer confucianus) via analysis of molecular variance and FST along three mountain slopes and tested the isolation‐by‐environment hypothesis using Mantel test and redundancy analysis. We found a consistent phenotypic divergence and marked genetic structure along elevational gradients; however, the species showed mixed patterns of size and skull shape trends across mountain zones. Individuals living at lower altitudes differed greatly in both phenotype and genotype from those living at high elevations, while middle‐elevation individuals showed more intermediate forms. The ecological parameters associated with phenotypic divergence along elevation gradients are partly related to species' ecological and evolutionary constraints. Fossorial and solitary animals are mainly affected by climatic factors, while terrestrial and more gregarious species are influenced by biotic and abiotic parameters. A novel finding of our study is that predator richness emerged as an important factor associated with the intraspecific diversification of the mammalian skull along elevational gradients, a previously overlooked parameter. Population genetic structure was mainly driven by environmental heterogeneity along mountain slopes, with no or a week spatial effect, fitting the isolation‐by‐environment scenario. Our study highlights the strong and multifaceted effects of heterogeneous steep habitats and ecologically based divergent selective forces in small mammal populations.Species distributed along mountain slopes, facing contrasting habitats in short geographic scale, are of particular interest to test how ecologically based divergent selection promotes phenotypic and genetic disparities as well as to assess isolation‐by‐environment mechanisms. Here, we conduct the first broad comparative study of phenotypic variation along elevational gradients, integrating a large array of ecological predictors and disentangling population genetic driver processes. The skull form of nine ecologically distinct species distributed over a large altitudinal range (100–4200 m) was compared to assess whether phenotypic divergence is a common phenomenon in small mammals and whether it shows parallel patterns. We also investigated the relative contribution of biotic (competition and predation) and abiotic parameters on phenotypic divergence via mixed models. Finally, we assessed the population genetic structure of a rodent species ( AnticipDivergent natural selection, briefly defined as selection arising from uneven ecological conditions that promote phenotypic and genetic shifts among populations, is a key source of biological diversity  distributed over a wide altitudinal range and with distinct ecological attributes to assess whether phenotypic divergence is a common phenomenon in small mammals and whether it shows consistent parallel patterns. We chose skull morphology (size and shape) as a surrogate of overall morphological diversity as it is associated with the main sensory systems related to environmental perception and reflects changes in the dietary and ecological interactions across populations . Because of the large home range and generalist habits of our focal species, we expected weak or absent genetic structure along mountain slopes.22.1Soriculus) or along altitudinal gradients with similar climatic conditions. In the last case, to minimize putative effects of distinct evolutionary history, we selected individuals from closely genetic lineages  distributed over a large altitudinal range and representing distinct ecological niches were selected to assess phenotypic variation (size and shape of the skull) across elevational gradients Table . All spegeomorph package version 3.0.5 . Based on the geographic coordinates of each individual, we extracted the altitude information via the raster R package  at a 30‐s (~1 km2) spatial resolution , we used the database of sampling collections of the mammal group of the Institute of Zoology of the Chinese Academy of Science. This unique database includes specimens of small mammals systematically collected from several mountains in China. Detailed descriptions of the sampling protocols can be found in Wu et al.  and Wen To evaluate the influence of predation risk on phenotypic variation, we compiled a list of predators of small mammals  in China  as a model due to its wide elevational range , broad climatic tolerance, and relatively high dispersal capability  from 160 individuals comprising six populations covering an altitudinal range of 2,000 m . The delimitation of zones combined climatic and vegetation attributes and was based on Tang and Ohsawa , Zhong, geomorph R package with 10,000 permutations. In addition, we carried out pairwise comparisons between mean shapes and performed Tukey's honestly significant difference (HSD) test to assess the significance of morphological differences between pairs of elevation zones. We also explored size and shape trends across species to determine whether there was a consistent overall pattern. The relationship between size (centroid size) and altitude was visualized with scatterplots highlighting trends in each elevation zone. Skull shape deformation between zones was assessed via between‐group principal component analysis  on skull phenotypes by creating a set of competing models ranked by the second‐order corrected Akaike information criterion (AICc) via the  Barton,  and testSummarizing shape information in univariate terms might lead to a loss of information  or nonrandom (IBE) mechanisms, we performed two complementary tests . We found a mixed pattern of size trends across elevation zones. Rodents exhibited a marked decrease in size as altitude increased at low elevations, while shrews and moles were not affected or exhibited a weak positive correlation. Both groups presented contrasting patterns at middle elevations, and five of the seven species presented positive trends in size at high elevations  differed significantly among populations. Similarly, AMOVA revealed significant population genetic structure . A large part of the genetic variation was attributed to variance within populations (59.9%–96.1%), whereas differences among elevation zones were low to moderate, albeit not significant  between animals inhabiting low  and high altitudes . Comparisons between populations from low and middle  and middle and high elevations  revealed generally moderate‐to‐low genetic differentiation  or a weak effect on the landscape genetic structure of N. confucianus  pattern across the nine species. This finding is consistent with that of previous studies on animals along elevational gradients at intraspecific  shows a negative trend as elevation increases  presents a positive trend  were mainly associated with seasonal temperature and precipitation, while forest dwellers tended to be affected by multiple parameters.The ecological parameters associated with phenotypic divergence along elevational gradients are expected to be tied to species ecological and evolutionary traits  drives phenotypic divergence at each elevation zone. Answering this question could provide us with a better understanding of the dynamic ecological interactions along altitudinal gradients , fitting the IBE scenario.None declared.AF conceived the idea; AF, ZW, and QY designed the research; ZW, JC, DG, LX, and QY conducted the field sampling; AF, ZW, JC, and DG curated the data; and AF analyzed the data and led the writing. Click here for additional data file.


Panthera leo), listed as a vulnerable species on the IUCN Red List of Threatened Species (Appendix II of CITES), is mainly impacted by indiscriminate killing and prey base depletion. Additionally, habitat loss by land degradation and conversion has led to the isolation of some subpopulations, potentially decreasing gene flow and increasing inbreeding depression risks. Genetic drift resulting from weakened connectivity between strongholds can affect the genetic health of the species. In the present study, we investigated the evolutionary history of the species at different spatiotemporal scales. Therefore, the mitochondrial cytochrome b gene (N = 128), 11 microsatellites (N = 103) and 9,103 SNPs (N = 66) were investigated in the present study, including a large sampling from Tanzania, which hosts the largest lion population among all African lion range countries. Our results add support that the species is structured into two lineages at the continental scale , underlining the importance of reviewing the taxonomic status of the African lion. Moreover, SNPs led to the identification of three lion clusters in Tanzania, whose geographical distributions are in the northern, southern and western regions. Furthermore, Tanzanian lion populations were shown to display good levels of genetic diversity with limited signs of inbreeding. However, their population sizes seem to have gradually decreased in recent decades. The highlighted Tanzanian African lion population genetic differentiation appears to have resulted from the combined effects of anthropogenic pressure and environmental/climatic factors, as further discussed.The African lion ( Panthera leo leo for the Asian and West-Central African lion; P. l. melanochaita for the East-Southern African lion [2, which represents 8% of its ancestral distribution range [The African continent still hosts a uniquely diversified megafaunal community . This mecan lion –7, suppocan lion . Howevercan lion . The liocan lion . The popcan lion –13. The can lion . The loncan lion . Followion range ,13,14,16Habitat loss, climate change, armed conflicts, illegal trade of lion body parts , diseases and indiscriminate killing, primarily as a result of retaliatory or pre-emptive killing to protect human lives and livestock (‘human-lion conflicts’) are the main challenges threatening the species –20, as uGenetic tools can help gain further insight into the impact of population isolation on the long-term survival chances of threatened species. They can notably contribute to identifying management units or enable estimation of different demographic parameters, such as genetic differentiation, effective population size or inbreeding depression risks, which could help to draw up effective conservation practices for isolated and threatened populations. Indeed, isolated populations are more prone to inbreeding depression and extinction since low genetic diversity may lead to reduced fitness, while lowering the adaptive capacities of individuals to environmental change –30. The b gene, (ii) 11 microsatellites, and (iii) 9,103 single-nucleotide polymorphisms (SNPs), with the latter being obtained through the Genotyping-By-Sequencing (GBS) approach (Next-Generation Sequencing technology).The objectives of the present study were therefore: I) to review the phylogeographical relationship and taxonomic status of African lion populations at the continental scale, and II) to study the genetic structure and diversity of the lion in Tanzania, the country with the largest lion population throughout its range. Tanzania currently includes five LCUs, almost all belonging to class I. This country is therefore of prime interest for lion conservation. In the present study, the following molecular markers were used: (i) a 1014 bp fragment of the mitochondrial cytochrome ™ Picogreen dsDNA Assay Kit (Fisher Scientific) and processed on a FilterMax™ F3 Multi-Mode Microplate Reader . To fulfill the GBS technic criteria (Cornell Core Facility genotyping guidelines), samples displaying less than 20 ng/μl DNA were concentrated using Amicon Ultra 0.5 mL Centrifugal Filters (Merck Millipore), according to the manufacturer’s instructions.The present study was conducted within the framework of the protocol agreement between the Wildlife Division of the Ministry of Natural Resources and Tourism of Tanzania and the IGF Foundation, in partnership with the Tanzania Wildlife Research Institute (TAWIRI). Our collection of samples from West-Central Africa and Tanzania was compiled by the Wildlife Division of Tanzania and the IGF Foundation under the auspices of the François Sommer Foundation , and was supplemented with three samples from a breeding farm from South Africa provided by Mario Melletti  who had the required permits from the relevant national authorities. The samples were collected from dead animals, which were legally hunted following the rules of the Wildlife Division of the Ministry of Natural Resources and Tourism of Tanzania as well as the IGF foundation. Sampling was carried out in six countries, with larger scale sampling in Tanzanian protected areas: 74 from Tanzania, 20 from Burkina Faso, 1 from Benin, 3 from Congo, 3 from South Africa and 4 from the Central African Republic. A total of 105 tissue and hair samples, all from adult male lions  were collected and stored in 96% ethanol. Sample details and locations are reported in b (cytb) gene of the samples collected between 2005 and 2012  was amplified using forward L14724 (5’-CGAAGCTTGATATGAAAAACCATCGTTG) and reverse H15915 (5’-AACTGCAGTCATCTCCGGTTTACAAGAC) primers, targeting a 1140 bp fragment [cytb sequences from P. leo referenced on GenBank  [5’-ACATTCGAAATCACACCCCCTT; PCytb-R1: 5’-ATCTTTGATTGTATAGTATGGA) and the second, a 515 bp fragment . The PCR reaction was carried out in a final volume of 25.5 μl, with each reaction containing 2.5 μl of DNA, 0.2μl of GoTaq DNA Polymerase (Promega), 5 μl of 5X GoTaq Reaction Buffer, 0.9 μl of each primer diluted at 10 μM, 0.8 μl dNTP at 10 μM, 0.7 μl BSA, 0.5 μl MgCl2 and 14 μl of Milli-Q water. Amplification was performed on a Thermal VWR UnoCycler with an initial activation step at 95°C for 15 min, followed by 35 denaturation cycles at 94°C for 40 s, annealing at 50°C for 45 s, and elongation at 72°C for 45 s, with a final extension at 72°C for 10 min. PCRs were resolved on an agarose gel and the positive products were sent to Macrogen Inc. for sequencing (both directions). Sequence Navigator v1.0.1 (Perkin-Elmer Applied Biosystem) and Chromaspro v1.7.5 (Technelysium Pty Ltd) software packages were used for electropherogram visualization, sequence correction, and primer trimming, whenever necessary, before alignment with ClustalW implemented in Bioedit v7.1.11 [The cytochrome fragment . These sC495058) ,37. The  v7.1.11 . The datet al. [https://doi.org/10.5061/dryad.ff265). Four multiplex sets were designed based on size limitations and the amplification specificity (™ formamide and 0.3 μl of GeneScan™-500 LIZ size standard (Applied Biosystem). Length variation determination was performed using GeneMapper v4.0 (Applied Biosystems).Eleven microsatellites, as presented in the study of Dubach et al.  addressiet al. ). All thcificity . The PCRPreparation of the GBS library: Since the generated microsatellite database did not provide sufficient resolution to answer our study question , 50 μl of DNA of each of the 73 selected samples were prepared. The GBS library was constructed using the PstI (CTGCAG) restriction enzyme. After digestion, adapters were ligated: barcode-containing adapters and common adapters. Following ligation, the samples were pooled, as described in Elshire et al. [http://www.biotech.cornell.edu/brc/genomic-diversity-facility).tion see  section,e et al. ), were re et al.  and the DNA sequence alignment, SNP discovery and filtering: The raw Illumina DNA sequence  data were checked with FastQC [http://www.maizegenetics.net/). The first step involved read trimming at 64 bp. Reads with unrecognized barcodes, present in less than 10 copies and less than 20 bp in size were discarded, as well as reads including a nucleotide position with a Phred quality score lower than the threshold of 20 . Moreover, reads over-represented in the database (more than 3 times the average sequencing depth) were also discarded. To determine copy numbers and genomic coordinates, sequence reads were aligned to the closely related Felis catus species reference genome using Bowtie v2.2.7 [h FastQC  and proch FastQC   [Micro-Checker v2.2.3 was used to estimate the proportion of null alleles at each locus within our microsatellite dataset, as well as the stutter errors . The maret al.  includinet al. . Tests fr batch) . Fisher’P-values, and applying the Markov chain method  [P-value significance was sequentially Bonferroni-adjusted [D' estimator. If a locus pair had an r2 value > 0.8 in multiple populations, the locus that was genotyped in the fewest individuals would have been removed.Likewise for the SNP database, Arlequin v3.5 software was used to test genotypic distributions for conformance to HWE for each lineage and each cluster delineated with Structure v2.3.4 software see  section,n steps) . Loci woadjusted . Genotypadjusted . To predcytb sequences, as well as compiled database from previous studies, are presented in supplementary K from 1 to 5 and K from 1 to 10 were tested, with 10 iterations for each K, for each microsatellite and SNP dataset, respectively. The Markov chain convergence was checked between each 10 iterations for each K. The results and visual output of the 10 iterations for each K value were summarized using the web application CLUMPAK [http://clumpak.tau.ac.il/index.html). The optimal number of clusters was assessed based on correction as defined by Evanno et al. [vs East-Southern axis structure  and a neighbor-joining (NJ) tree (IBS distance matrix) were also performed using Tassel v5.2.15 on the SNP dataset . The priST/(1- FST) to the logarithm of the geographical distance  using the Isolation by Distance Web Service (IBDWS v3.23) (http://ibdws.sdsu.edu/~ibdws/) on the SNP dataset with 10,000 randomizations [r) was calculated among pairs of individuals (Multiple Dclass option). The autocorrelation coefficient and pairwise r values were divided into 18 distance classes (50 km each). These classes were chosen arbitrarily since the home range size of the African lion varies considerably between seasons and across study areas (e.g. 20–45 km2 in Manyara NP and Ngorongoro Crater (Tanzania) [2 in arid ecosystems such as Etosha NP (Namibia) [r values for each distance class was obtained by 9999 permutations and the confidence intervals for r were estimated by 9999 bootstraps with replacement, and plotted in a correlogram [r was no longer significant and the intercept crossed the x-axis.Finally, isolation by distance (IBD) patterns were determined by comparing pairwise Fizations . We thenizations . As thisanzania) –61, to mNamibia) ). The seNamibia) . AdditioNamibia) . A null relogram . The extF-statistics , allelic richness (AR) and heterozygosities  were investigated based on our microsatellite dataset, using Genetix v4.05 software [NA) and private alleles (PA) per population were estimated using Genepop v4.2 [cytb genetic diversities analyses and results can be found in supplementary document S7 (section S7.2).software . The numpop v4.2 . FurtherHE) and observed (HO) heterozygosities, the fixation indices and the inbreeding coefficient (FIS), using GenAlEx v6.502 and Arlequin v3.5 [FST [FST heatmap was reconstructed with RStudio v3.3.1 software using the heatmap.plus package v2.18.0  [For the following analyses, summary statistics were estimated only for lineages  and clusters  including more than 5 individuals, on both the complete SNP database and a reduced subset of 3,097 SNPs (maximum of 9% missing data accepted). The genetic diversity of each group was assessed by calculating the expected  .FCT), the variance among populations within clusters (FSC) and the variance among populations relative to the total variance (FST) . The populations and groups for the AMOVA analysis were defined according to the clustering results obtained with Structure v2.3.4 [The hierarchical distribution of genetic variance among and within populations was assessed using an analysis of molecular variance (AMOVA) performed with Arlequin v3.5 on the complete SNP dataset . The AMOe v2.3.4 . Finallye v2.3.4 , computie v2.3.4 . Estimatcytb gene. Three samples  failed at providing positive amplification results. Six other samples  could only be partly amplified using the internal primers, and were therefore discarded from the following analyses. Once aligned, the total overlapping fragment size was of 1014 bp. Seventeen haplotypes were identified, including one new haplotype . Samples TAZ57 from Tanzania and CON1 from Congo failed at providing genotypes. Micro-Checker v2.2.3 allowed us to identify the presence of null alleles among 5 microsatellites, which were corrected accordingly [p-value > 0.05). Moreover, no linkage disequilibrium (LD) was observed among the microsatellite markers used .The 11 microsatellites genotype database included 73 samples from Tanzania, 3 from South Africa, 2 from Congo, 4 from the Central African Republic (CAR), 20 from Burkina Faso and 1 from Benin . The total number of polymorphic sites identified from these 176,029 reads aligning at one position was of 66,033. This number decreased to 23,138 SNPs when filtering for missing data. Moreover, by retaining only one polymorphic site per read, the number decreased to 9,114 SNPs. Finally, the 11 outlier SNPs identified with BayeScan v2.1 were also discarded from the database . The TS/TV ratio was of 2.83, while ratios substantially less than 2 can be indicative of sequencing errors.Concerning the single-nucleotide-polymorphism (SNP) identification pipeline, among the 73 samples initially included , 66 passed the filtering criteria and were kept for the following analyses. The first filtering steps performed with Tassel v5.2.15 allowed the selection of 270,814 reads out of the 206,203,194, that were both correctly barcoded and of good quality (Phred score). Of these reads, 65% aligned to the All populations identified with the Structure v2.3.4 were shown to be in HWE. Some SNPs were found to be in LD within one of the Tanzanian populations. These were not removed because the same SNPs were not in disequilibrium within the other identified populations.Continental scale: All molecular markers led to the identification of a partitioning into two supported lineages at the continental scale. The lineages were separated by 7 mutational steps on the minimum spanning network  and SNP  datasets. The results were interpreted using the o et al. . The higor K = 2 . Based oor K = 2 . Also, tor K = 2 , while ior K = 2 . Howeveror K = 2 .Tanzanian scale: At the Tanzania country scale, clustering analyses (Structure and FCA) based on microsatellites did not identify a finer-scale structure (K = 1) ( (K = 1) . Based o (K = 1)  and on t (K = 1) . These pr  =  -0.071, p   =  0.66) among our sampling locations. Spatial autocorrelation showed a pattern of decreasing relatedness with increasing distance. Autocorrelation decreased to a level not significantly different from 0 to about 750 km  pattern in Tanzania was not found based on our database of 3,097 SNPs  and GST (0.089) were highlighted between the two main lineages. The analysis of molecular variance indicated that most of the variation occurred within lineages (85.6%), as compared to those observed among lineages (14.4%). Inbreeding coefficient estimates showed more pronounced homozygote excess within the West-Central lineage (FIS = 0.138) as compared to the East-Southern lineage (FIS = 0.064) (AR = 7.27—PA = 32) as compared to the West-Central lineage (AR = 5.09—PA = 8), but this may be an artefact associated with the different sampling sizes for each lineage (FST (0.271) between both main lineages was recorded. According to the previous results, the number of private alleles based on the SNP dataset was also higher for the East-Southern lineage (PA = 790) as compared to the West-Central lineage (PA = 182) (= 0.064) . Likewis lineage . Based oA = 182) .FST estimates, all were significant   than among lineages (18.35%), with the lowest variation occurring among clusters within both lineages (6.31%) . Regardi < 0.05) . Both thentation  indicateTanzanian scale: Lower FST were found among Tanzanian clusters, with the highest value observed between Cluster 1 (South region of Tanzania) and the other two (FIS) estimates indicated more pronounced homozygote excess in Cluster 2 (North region of Tanzania—FIS = 0.210) compared to the two other identified clusters (Heq) was investigated using Bottleneck. The results indicated that all three Tanzanian clusters showed significant (p < 0.05) heterozygosity excess under all mutation models (IAM and SMM), as well as a mode-shift.ther two . The obsclusters . Finallycytb gene, microsatellites and SNPs) supported the existence of two main lineages  vs East-Southern Africa) for the African lion (Panthera leo leo), as highlighted by previous studies [cytb haplotype in Tanzania, while the geographical distribution of four other haplotypes was extended to newly sampled areas. This main continental-scale division is a pattern that has also been observed in many other savanna mammals and corresponds to common evolutionary responses to environmental changes that drove their genetic differentiation over time [FST of the cytb gene (supplementary document S7), is usually observed between subspecies [At the continental scale, all molecular markers ( studies –7. The mver time . High dibspecies . Our resbspecies ).While based on the present microsatellite set, no finer scale structure within Tanzania (insufficient resolution) could be identified, SNPs enabled the identification of three clusters, geographically distributed across the country among the South (Cluster 1), North (Cluster 2) and Western (Cluster 3) regions. These specific microsatellites are therefore not recommended for fine scale studies. The higher number of SNP markers used in the present study in comparison to microsatellites seems to be the best explanation for the observed differences in our results, especially considering the present sampling number and coverage. However, previous studies revealed that four to twelve times more SNPs are needed for population structure inference to match the statistical power of one microsatellite . FollowiFST estimates (Hipotragus niger), with long-term isolation of distinct lineages in Western and Southern (i.e. Selous GR) Tanzania, each on either side of the Eastern Arc Mountains ([http://gfc.ucdavis.edu/profiles/rst/tza.html)). For example, the Kilombero valley between Clusters 1 and 3 is characterized by large cash crop plantations, with many villages and roads. Several studies have highlighted that carnivores tend to avoid regions with high human activity, even though avoidance is not total [FST indices (FST = 0.085) and the lowest between Clusters 1 and 3 (FST = 0.046), suggesting relatively recent differentiation as further discussed hereunder.The population structure highlighted on the basis of SNPs did not seem to result from an isolation-by-distance process . Therefore, the differentiation was probably linked to the combined effects of both anthropogenic pressure and environmental/climatic factors. Indeed, the presence of the Eastern Arc Mountains chain associated with the land-use pattern may represent a major biogeographical barrier to lion dispersal. This chain of mountains runs from northeastern to southwestern Tanzania and is geographically situated between Cluster 1 and the two other identified clusters , while Cstimates . A similuntains  located in Southern Tanzania, indicating a low risk of inbreeding depression. A recent census of the lion population in a sample of 1,300,000 ha in the Selous GR , which is devoid of human and cattle populations, confirmed that the lion density was still substantial; the Selous GR has therefore been proposed as an important lion stronghold in Tanzania . An intermediate FIS was obtained for Cluster 3 (FIS = 0.124), with the highest value recorded for Cluster 2 (FIS = 0.210) located in the North of the country. While the lion density in the Selous GR was shown to be homogenous throughout the studied area (4.2 lions/100 km2 (2.6–5.2)- Wildlife Division, unpublished data), the ecosystem was more heterogeneous in the Northern region, with lion densities markedly fluctuating between sites . Given that the Western African lion population has undergone a more serious population size decrease recently (see Inbreeding depression risks were investigated in each of the three identified clusters based on the iewed in ). The inntly see , these shttp://data.worldbank.org/), indicating that human pressure on wildlife habitats is not expected to decrease in the short-term. The present results may directly impact the management practices of this emblematic species in Tanzania, a major lion stronghold. In order to maintain historical levels of genetic variability in the long term, genetic admixture among recently-diverged clusters could be necessary through the establishment of ecological corridors or the translocation of individuals. Nevertheless, species-specific constraints could lead to failure of such programs. In the case of translocations, for example, the species-specific social structure  could seriously complicate the inclusion of translocated individuals into new prides [With the increasing human encroachment in landscapes, wildlife habitat fragmentation may become a serious issue for the long-term survival of wild species that barely co-exist with humans. In 2015, Tanzania had an annual human population growth rate of 3.1%  to specific environments. Nowadays, some morphological differences have been recorded between some Tanzanian regions. For example, it was observed that lions occupying woodlands displayed shorter manes compared to those living in plains [FST indices with the other clusters, it also displayed the lowest FIS value, while it is geographically connected through corridors to Niassa (Mozambique) and Mikumi NP (Tanzania). Moreover, a recent study demonstrated that maintaining a population size of at least 50 to 100 prides in a continuous ecosystem should avoid inbreeding depression within lions [The uninterrupted past connectivity between lion populations (panmixia) was also expected to be associated with a n plains . Indeed,n plains , lions sn plains , showingn plains ,87, and in lions . Based oet al. [Nevertheless, all three clusters have undergone recent demographic contraction, as supported by the census records. It would therefore be very important to avoid any further fragmentation within any of the identified clusters. Indeed, as underlined by Dolrenry et al. , lions het al. . Furtheret al. .The present results should also be taken into account when delimiting the LCUs: the 5 current LCUs defined for Tanzania in 2006 do not exactly correspond to the 3 identified clusters based on molecular markers. While a revision may be of interest, it is clear that for the conservation of the species, continuous monitoring on the largest possible sample would generate accurate information on the genetic health of Tanzanian lion populations and allow action to be rapidly taken whenever necessary .The present study supported assumptions that both ancient (over thousands of years) and recent (over the last century) population fragmentation has had an impact on the current genetic structure of the African lion, leading to the identification of two lineages at a continental scale (distinct management units or even subspecies), and of three genetic clusters in Tanzania. The results highlighted low levels of genetic differentiation between each Tanzanian cluster, as well as high genetic diversity and low inbreeding depression risks for each of them. Since human pressure between the three identified clusters is expected to increase in the near future, it is necessary to initiate appropriate management practices to ensure long-term conservation of African mammal diversity. In order to mitigate further genetic erosion, this should always be done while considering the environmental, behavioral, genetic and conservation related features of the concerned species.S1 Document(DOCX)Click here for additional data file.S1 FigThe tree was constructed with the maximum likelihood (ML) method using PhyML v3.0. Bootstrap support (above 800) are indicated on the branches. Orange: West-Central lineage, blue: East-Southern lineage.(TIF)Click here for additional data file.S2 FigK = 2), while (B) reports the results for the analysis conducted at the Tanzanian country scale (K = 3).(A) refers to the analysis conducted at the continental scale, including all samples ((TIF)Click here for additional data file.S3 FigYellow: Burkina Faso, CAR, Benin and Congo samples , blue: Tanzanian and South African samples (East-Southern lineage).(TIF)Click here for additional data file.S4 Figr.Dashed lines represent the 95% upper (U) and lower (L) bounds of the null distribution assuming no spatial structure. Error bars represent the 95% confidence intervals around (TIF)Click here for additional data file.S5 Figcytb haplotype distribution, as displayed on the present figure. Three distinct mitochondrial haplotypes Click here for additional data file.S6 FigSample colors were attributed according to the STRUCTURE assignment posterior probabilities . BUR: Bu(TIF)Click here for additional data file.S7 Fig(A) Probability of successive partitions of the data into an increasing number of clusters obtained at the continental scale. (B) Probability of successive partitions of the data into an increasing number of clusters obtained at the Tanzanian scale. (C) Population structure of the lion populations from Tanzania into a partitioning for the modal solution K = 1 to K = 5. Each individual is represented by a thin vertical line divided into K coloured segments representing the probability of membership of this individual to the K clusters.(TIF)Click here for additional data file.S1 TableThe table summarizes the sample origin , the number of samples collected at each locality, and gives a reference ID to (DOCX)Click here for additional data file.S2 Tableet al. [Felis catus species. The two last columns report the expected allele sizes and heterozygosities.The primers were initially described by Menotti-Raymond et al.  for the (DOCX)Click here for additional data file.S3 Tablecytb gene. (?) indicates an uncertain sample origin, e.g. samples collected in zoos. CAR stands for Central African Republic, DRC for Democratic Republic of Congo.Numbers marked in bold represent the newly sequenced (DOCX)Click here for additional data file.


Reticulitermes flavipes, currently inhabits previously glaciated regions of the northeastern U.S., as well as the unglaciated southern Appalachian Mountains and surrounding areas. We hypothesized that Pleistocene climatic fluctuations have influenced the distribution of R. flavipes, and thus the evolutionary history of the species. We estimated contemporary and historical geographic distributions of R. flavipes by constructing Species Distribution Models (SDM). We also inferred the evolutionary and demographic history of the species using mitochondrial (cytochrome oxidase I and II) and nuclear  DNA sequence data. To do this, genetic populations were delineated using Bayesian spatial‐genetic clustering, competing hypotheses about population divergence were assessed using approximate Bayesian computation (ABC), and changes in population size were estimated using Bayesian skyline plots. SDMs identified areas in the north with suitable habitat during the transition from the Last Interglacial to the Last Glacial Maximum, as well as an expanding distribution from the mid‐Holocene to the present. Genetic analyses identified three geographically cohesive populations, corresponding with northern, central, and southern portions of the study region. Based on ABC analyses, divergence between the Northern and Southern populations was the oldest, estimated to have occurred 64.80 thousand years ago (kya), which corresponds with the timing of available habitat in the north. The Central and Northern populations diverged in the mid‐Holocene, 8.63 kya, after which the Central population continued to expand. Accordingly, phylogeographic patterns of R. flavipes in the southern Appalachians appear to have been strongly influenced by glacial‐interglacial climate change.The eastern subterranean termite, https://doi.org/10.5061/dryad.5hr7f31.This article has been awarded Open Materials, Open Data Badges. All materials and data are publicly accessible via the Open Science Framework at  The influence of climate change  in generating phylogeographic structure is also widely recognized Hewitt, ,2004. InIn contrast to landscapes that were repeatedly covered by ice sheet advances throughout the Pleistocene, those in temperate or tropical regions that remained unglaciated potentially contained numerous refugia Byrne, . Indeed,The southern Appalachian Mountains represent some of the oldest uplands in North America  to be assessed using SDMs and genetic data. Given the reliance of this species on dead‐wood microhabitats, our expectation was that during the Pleistocene and earlier, R. flavipes closely tracked the changing distributions of forest habitats, and was strongly impacted by climatic fluctuations. Indeed, ecologically specialized low‐mobility forest insects may be particularly well‐suited for reconstructing past climatic impacts on montane forest landscapes, in part owing to their short generation times and ability to persist in habitat patches too small to support more mobile vertebrates . Based on this, we generated competing hypotheses about drivers of genetic divergence, and then tested these via analyses of DNA sequence data using coalescent simulations. In addition to the effects of historical climatic conditions, we also considered the influence, if any, of contemporary climatic conditions and dispersal‐based spatial structure on genetic variation in R. flavipes.In this study, we investigated the genetic consequences of glacial‐interglacial climate change on 22.1R. flavipes in order to identify potential refugia and generate expectations about directionality of range contractions or expansions, including distributional shifts;Step 1—Model present‐day and historical climate‐based distributions of Step 2—Infer the number of distinct populations using spatial‐genetic clustering, and cross‐validate via principal component analysis, and phylogenetic reconstruction; characterize genetic variation within and differentiation among populations, and; estimate the amount of genetic variation explained by dispersal  and environment (contemporary climatic conditions);Step 3—Test alternative phylogeographic hypotheses to determine whether expansion out of refugia, distributional shifts, or vicariance was the underlying historical process generating the observed patterns of genetic variation within and among populations; estimate values of parameters included in the best‐fit phylogeographic hypothesis; and assess evidence for changes in effective population size over time.To address the aims of this study, we used the following workflow:2.2Reticulitermes termites were collected between 2012 and 2014 from locations in the southern Appalachian Mountains. Since it is not possible to reliably distinguish among several co‐distributed species on the basis of morphology when only members of the worker caste are collected , R. malletei (n = 1) and R. nelsonae (n = 1).s Figure . From eas Figure : R. virgExtraction of genomic DNA was performed using a DNeasy tissue kit  following the manufacturer's recommendations. Portions of the mitochondrial cytochrome c oxidase subunit I (COI) and II (COII) genes, and an intronic portion of the nuclear endo‐beta‐1,4‐glucanase (EB14G) gene, were amplified via polymerase chain reaction using primers (Supporting Information Table We concatenated COI and COII and refer to this sequence (COI + COII) as the mitochondrial DNA (mtDNA) locus; we refer to EB14G as the nuclear DNA (nDNA) locus. For the latter, heterozygous sites were scored using the “Find Heterozygotes” plugin in Geneious. For a site to be considered heterozygous, we required that height of the secondary peak was at least 50% of the primary peak . Allele haplotypes were inferred using PHASE v.2.1.1 , the presence of R. flavipes at an additional 45 locations (surveyed from 2015 to 2016) was confirmed using Garrick et al.'s  versus unsuitable (≤0.2). The occurrence probability threshold was chosen based on the True Skill Statistic  ranging from 2–20, with 10 replicate runs each. We also examined evidence for geographically cohesive genetic groups by representing the variance in mtDNA sequences using Principal Components Analysis (PCA), performed with the “prcomp” function in R.To determine the number of geographically cohesive genetic groups of 2.4.2Reticulitermes taxa. Based on point estimates obtained using approximate Bayesian computation . BEAST was run for 50 million Markov chain Monte Carlo generations, with samples saved every 2,500 generations, after discarding the first 5 million generations as burn‐in. We used Tracer 1.6 . Because ABC analyses can suffer when a large number of candidate models are simultaneously considered . In a second set of analyses within the first tier, we assessed scenarios that involved distributional shifts . We performed two rounds of modeling: (a) a preliminary round with broad priors, and (b) the final round with narrower priors (Supporting Information Table S4.5). Briefly, all competing scenarios had two divergence events: any two of TN, TC or TS, , the prior range for the more recent event encompassed the MH and the LGM whereas priors for the older event ranged from the LGM to the LIG assuming a 1‐year generation time for R. flavipes. Full details of ABC priors on Ne and T parameters are given in Supporting Information Appendix −9 to 5.0 × 10−7, a broad range encompassing the Brower , and divergence times (e Brower  and Luche Brower  rates (sD (one‐sample), and FST  and private segregating sites (one‐sample), mean (one‐ and two‐sample) and variance of pairwise differences (one‐sample), mean and variance of numbers of the rarest nucleotide at segregating sites (one‐sample), Tajima's  D . The significance (α = 0.05) of each test was determined using 100,000 simulations.For each of the three Fu & Li,  from thed Rozas'  R2 statWe also examined evidence for changes in Ne over time in each cluster by analyzing the combined mtDNA plus (unphased) nDNA sequence data using Extended Bayesian Skyline Plots  cycle; (c) the eastern portion of West Virginia and areas around western North Carolina had suitable habitat from the LIG to the present; and (d) the amount of suitable habitat increased since the beginning of the Holocene.Distributional shift and stability maps Figure  showed t3.3The BAPS analysis identified three genetic clusters, each with largely separate geographic distributions Figure a. Hereinπ = 0.016; mean number of nucleotide differences, K = 17.50; Table π = 0.010, and K = 11.44, and, although there were 19 haplotypes in the Central cluster, these diversity values were lowest  whereas Northern versus Central differentiation was lowest  of the genetic variation. Environmental factors accounted for 5.2% (p = 0.012) of the variation. The interaction between the two explained an additional 12.4% of the genetic variation. After removing the effect of spatial structure, the factors with significant contribution to genetic variation were “temperature range” and “wet‐season precipitation”  the refuge‐based scenario with the highest posterior probability was the hypothesis that postulated the Northern region was the source from which the Southern cluster diverged first, followed by the Central cluster , whereas no size changes were detected in the Northern , or the Southern  clusters. The EBSP assessments of changes in Ne over time also showed evidence of growth of the Central cluster, initiated in the last 10,000 years , rather than a single refugium repeatedly serving as a reservoir of genetic diversity, whereby successive glacial‐interglacial cycles reinforce the same genetic signatures of contraction and expansion.This study provides new insights into how Pleistocene climatic fluctuations impacted the geographic distribution of  Hewitt, . While t Hewitt, , relativ Hewitt, . Indeed,4.1R. flavipes shifted from the East Coast and the Gulf Coast northward toward the former southern edge of the Laurentide ice sheet . While this was a global cooling period, locally in the southeastern U.S., this period was characterized by a warmer and wetter climate, reflecting the trapping of heat in the western subtropical gyre due to reduced Atlantic meridional overturning circulation  that is syntopic with R. flavipes consists of five distinct genetic groups .Although our ABC analyses identified distributional shift as the best‐fit scenario, it did not receive unambiguously superior support relative to the next‐best scenario, and the estimated error in scenario choice was large (Supporting Information Table None declared.Both authors conceived the study, and collected and curated samples from across the southern Appalachians; C.H. collected DNA sequence data, performed the analyses, and wrote the first draft; both authors edited subsequent drafts. Click here for additional data file. Click here for additional data file. Click here for additional data file.


Intermittent hypoxia (IH) is the predominant pathophysiological disturbance in obstructive sleep apnea (OSA), known to be independently associated with cardiovascular diseases. However, the effect of IH on cardiac fibrosis and molecular events involved in this process are unclear. Here, we tested IH in angiotensin II (Ang II)-induced cardiac fibrosis and signaling linked to fibroblast activation. IH triggered cardiac fibrosis and aggravated Ang II-induced cardiac dysfunction in mice. Plasma thrombospondin-1 (TSP1) content was upregulated in both IH-exposed mice and OSA patients. Moreover, both in vivo and in vitro results showed IH-induced cardiac fibroblast activation and increased TSP1 expression in cardiac fibroblasts. Mechanistically, phosphorylation of STAT3 at Tyr705 mediated the IH-induced TSP1 expression and fibroblast activation. Finally, STAT3 inhibitor S3I-201 or AAV9 carrying a periostin promoter driving the expression of shRNA targeting Stat3 significantly attenuated the synergistic effects of IH and Ang II on cardiac fibrosis in mice. This work suggests a potential therapeutic strategy for OSA-related fibrotic heart disease. The prevalence of sleep apnea is high in patients with cardiovascular disease, which could be caused by multiple risk factors including respiratory instability, obesity and upper airway dysfunction . Sleep aCardiac fibrosis is characterized by excessive deposition of extracellular matrix proteins by cardiac fibroblasts (CFs), which respond to pathological stress and environmental stimuli by transforming into myofibroblasts. Activated CFs express elevated levels of various proinflammatory and profibrotic factors causing fibroblast proliferation, migration and scar formation . The perThbs1 gene), which is a matricellular glycoprotein and can be secreted by various cell types, to remove its latency-associated propeptide , which is highly regulated at the level of activation . The actopeptide . Myocardopeptide , and bloopeptide . HoweverAs a member of the signal transducer and activator of transcription (STAT) protein family, STAT3 was originally identified as an interleukin-6–activated transcription factor. It can also be phosphorylated by receptor-associated Janus kinase (JAK) in response to growth factor and hemodynamic stress, thus acting as a regulator in fundamental cellular processes including inflammation, cell growth, proliferation, differentiation, migration, and apoptosis . EmerginIn the present study, we investigated the effect of IH exposure on cardiac fibrosis in response to angiotensin II (Ang II) in mice and the potential underlying mechanism. TSP1 expression induced by IH in CFs, mediated by phosphorylation of STAT3 at Tyr705, was involved in CF activation and cardiac fibrosis. Pharmacological or genetic inhibition of STAT3 restrained IH-induced CF activation and cardiac fibrosis and ameliorated IH-induced cardiac dysfunction.Most respiratory events of patients with OSA result in desaturation–reoxygenation sequences that cause IH . To inveTo investigate the effect of IH associated with cardiac function after injury, we used the mouse model of Ang II-induced cardiac hypertrophy to represent cardiac fibrotic responses. Consistent with previous studies , Ang II CFs are now recognized for their fundamental contributions to the heart’s response to various forms of injury . To inveTo test whether TSP1 plays an important role in the progression of cardiac fibrosis induced by IH, we next detected TSP1 expression in mice after IH exposure. As compared with normoxia, IH exposure significantly increased plasma TSP1 content and mRNA level in the mouse left ventricle, especially in Ang II-infused mice . The proTo identify the potential relevance of TSP1 to OSA, we measured the concentration of TSP1 in plasma samples from 21 patients with OSA and 21 healthy subjects. TSP1 content was significantly increased in patients with OSA as compared with healthy individuals . TogetheThbs1 significantly increased by IH as compared with normoxia in mPCFs  with or without IH exposure. As compared with normoxia, IH increased α-SMA expression in PCFs and led to an activated phenotype of fibroblast cells . In addiin mPCFs . In addiin mPCFs . Furtherin mPCFs . TSP1 dein mPCFs  induced TSP1 was reported to be transcriptionally regulated by STAT3 in astrocytes , and emeWe next explored the role of STAT3 in IH-induced TSP1 expression and CF activation. Knockdown of STAT3 with siRNA almost completely abolished IH-induced TSP1 expression both in mRNA and protein level . SimultaAs we found that STAT3-induced TSP1 contributed to IH-induced CF activation, we injected S3I-201 into IH-exposed mice with or without Ang II infusion for 28 days . First, To further investigate whether S3I-201 could reverse the damage already induced by IH, we treated mice with S3I-201 for 2 weeks after exposure to IH and Ang II for 28 days . The ratIn the last decades, we have extensive evidence for a causal link between OSA and cardiovascular disease . Here, wTSP1 belongs to the thrombospondin family, a conserved family of extracellular, oligomeric, multidomain, calcium-binding glycoproteins that can be secreted by various cell types . IncreasThbs1 continuously from 2 to 8 hr. However, IH-induced TSP1 protein expression in PCFs peaked at 2 hr and decreased from 4 to 8 hr. The concentration of TSP1 in culture medium was still increased from 4 to 8 hr, which suggests that IH might promote TSP1 secretion as well. However, the mechanism underlying TSP1 secretion needs further elucidation.TGFβ, perhaps the most extensively studied mediator of fibroblast activation and having the greatest role in pathological fibrosis , can be STAT3, as a multifaceted molecule, plays a central role in organic fibrogenesis and cardiac hypertrophy . HypoxicIn summary, our study demonstrated that IH upregulated the expression of TSP1 in both mice and humans, and TGFβ pathway activation induced by JAK2/STAT3/TSP1 signaling played a vital role in IH-induced fibroblast activation and cardiac fibrosis. Pharmacological or genetic inhibition of STAT3 might be a potential therapeutic strategy in managing several diverse yet intertwined human pathologies related to IH.2 concentration was decreased to 4–6% approximately every 60 s. IH mice were exposed to IH for 8 hr/day in the light time for 28 days. In the pathologic cardiac fibrosis model, mice were subcutaneously implanted with an osmotic minipump  containing Ang II (1 mg/kg/day) in saline (0.9% w/v) or an identical volume of saline. After the surgery, all mice were exposed to IH for 28 days. In rescue experiments, S3I-201 (5 mg/kg) was given by intraperitoneal injection every 2 days during or after the IH exposure.Male C57BL/6J mice at 8–10 weeks of age were used. All mice were housed in a controlled environment  and maintained on a standard chow diet with free access to water. Intermittent hypoxia (IH) was induced as described previously by using an automated system to control ambient oxygen concentration . BrieflyGTCACACAGATGAACTTGGTCTTCAGGT and GCATCAATCCTGTGGTATA. The periostin AAV9-periostin promoter-shStat3 or AAV9-periostin promoter-shScramble (1.5 × 1011 v.g) was injected into the tail vein of mice. At 1 week post-injection, mice were exposed to IH with or without Ang II infusion.The AAV used in this study was constructed as previously reported . BrieflyTrans-thoracic echocardiography was performed on all mice by using a Vevo 2100 system with a MS400 linear array transducer  as previously reported . BrieflyTissues were fixed in 10% neutral-buffered formalin for 24 hr at room temperature and embedded in paraffin. Hearts were sectioned at 5 μm for staining. Collagen deposition was stained with Masson’s trichrome  and Sirius red  according to the manufacturer’s instructions. Images of sections were captured under an Olympus inverted microscope  and fibrotic areas were semi-quantitatively determined by using ImageJ 1.52.For immunofluorescence staining, harvested hearts were fixed and embedded in OCT  and sectioned at 5 μm. Then, sections were stained with primary antibodies for TSP1, Collagen I, periostin, α-SMA and vimentin overnight at 4°C. Alex 488-conjugated goat anti-rabbit and Alex 594-conjugated goat anti-mouse antibodies were used as secondary antibodies. Nuclei were stained with DAPI. TUNEL staining kit  was used for analyzing apoptotic cells in cardiac tissue. Images were acquired under an Olympus inverted microscope . Fluorescent intensity was quantified by using ImageJ. Colocalization of α-SMA and vimentin was quantified by using the Image J with colocalization plugin. Briefly, the plugin initially generates an 8-bit image with only the colocalized points, then it combines the three 8-bit images in an RGB image. Two points are considered as colocalized if their respective intensities are strictly higher than the threshold of their channels.RNA was extracted by using the Eastep Super Total RNA Extraction Kit . cDNA was synthesized by using the iScript cDNA Synthesis Kit . Quantitative RT-PCR was performed with gene-specific primers shown in Proteins were isolated from snap-frozen heart tissue and cultured cardiac fibroblasts (CFs), which were extracted in RIPA solution with a protease inhibitor cocktail (#4693132001) and PhosSTOP . Proteins were quantified by using the BCA Protein Assay Kit . Then, 20 μg each protein was separated on SDS-PAGE and electrotransferred onto PVDF membranes, blocked with TBST containing 5% bovine serum albumin, and blots on membranes incubated with antigen and antibody complexes were detected by an ECL protocol with horseradish peroxidase-conjugated IgG as secondary antibodies. Immunoblots were quantified by using ImageJ 1.52.2 and grown to 70–80% confluence. Cells at passages 2 to 3 were used in experiments. Cells exposed to IH were maintained in a hypoxia chamber  for the indicated times.Isolation of mouse primary CFs (mPCFs) was performed as previously reported . BrieflyFlow cytometry was performed as previously reported . DigesteMouse primary CFs were fixed with 4% paraformaldehyde and permeabilized with 0.25% Triton X-100. Then cells were stained with antibodies against α-SMA (1:100), STAT3 (1:100) or Ki67 (1:100). Alexa Fluor antibodies (1:200) were used as secondary antibodies. Stress fibers were stained with rhodamine phalloidin . Nuclei were stained with DAPI. Images were acquired under an Olympus laser scanning microscope .The collagen contraction assay was performed as previously reported . Mouse pThbs1 (LV-shThbs1) and non-specific shRNA (LV-shCtrl) were constructed by Shanghai Genechem Co. (Shanghai). CFs were infected with lentivirus at multiplicity of infection (MOI) 10, and no detectable cellular toxicity was observed.Lentiviruses carrying short hairpin (shRNA) for The secretion of TSP1 in mice or human plasma was quantified by using an ELISA kit  according to the manufacturer’s instructions.Mouse primary CFs were seeded on plates and cultured to 80% confluence, then transfected with Stat3 siRNA (#sc-29494) or control siRNA  by using Lipofectamine RNAi MAX transfection reagent  for 24 hr.TGGCTTCCTCTGTGGTCTCT-3’, and reverse, 5’-GTCAAGGTCATGGGATGGTC-3’.ChIP was performed as previously reported  with Simt test or one-way or two-way ANOVA with Bonferroni multiple comparison post-test, as appropriate. The criterion for statistical significance was p<0.05.Sample sizes were designed with adequate power according to the literature and our previous studies. Data are presented as mean ± standard error of the mean (SEM). Statistical analysis involved use of GraphPad Prism 7 v7.04 with two-tailed, unpaired Student’s  In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Acceptance summary:The paper, which is much improved after extensive revision in response to reviewers' comments, provides a detailed dissection of the impact of intermittent hypoxia (IH), as found in sleep apnea, on fibotic signalling through activation of JAK2, STAT3 and TSP1, leading to expression of SMA, Col1 and periostin reflecting fibroblast activation. A nice feature of this study is that the relatively mild effects of short term IH alone, significantly aggravate the pro-fibrotic effects of Ang II, allowing greater dynamic range for demonstration of its molecular and functional impact on the heart. Significantly, inhibition of STAT3 signaling reverses this impact, pointing to a possible new therapeutic approach for sleep apnea. The study effectively combines in vivo and in vitro evidence for molecular mechanism.Decision letter after peer review:eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Jonathan Cooper as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Barry Fine (Reviewer #2). The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.Thank you for submitting your article "Intermittent hypoxia mediated by TSP1 dependent on STAT3 induces cardiac fibroblast activation and cardiac fibrosis" for consideration by Summary:The study proposes a novel mechanism for the induction of cardiac fibrosis in patients suffering obstructive sleep apnea, which involves body-wide exposure to intermittent hypoxia (IH). A mouse model was established of IH in the context of a pro-fibrotic stimulus provided by Angiotenin II (Ang II). Cardiac functional outputs were explored in the various treatment groups comparing normoxia and IH. Developed fibrosis (sirius red staining) and induction of signaling pathways  were also explored. Through a series of in vivo and in vitro studies, the authors find that IH enhances the cardiac fibrosis, HW/BW changes and compromised cardiac outputs induced by Ang II. IH alone had weaker and non-significant effects. They define a pathway for the IH/Ang II interaction involving activation of the transcription factor STAT3 (through phosphorylation at position Y705), leading to induction of TSP1 (an inducer of latent TGFβ) and TGFβ/Smad2/3 signaling pathway, the main known inducer of cardiac fibrosis in different injury situations. siRNA and chemical inhibitors of STAT3 and TSP1 were used in vivo and/or in vivo to confirm the pathway links. Inhibition of STAT3 using S3I-201 significantly restored cardiac ejection fraction and reduced scar size, accompanied by changes in the proportion SMA+ fibroblasts, STAT3 activating phosphorylation, TSP1 protein levels.Essential revisions:eLife. In particular, the characterisation of fibrosis using just SMA staining or gene expression in vitro and in vivo is insufficient (SMA is a poor marker of myofibroblasts), and the rapid induction of TSP1 and SMA in vitro within 2 hours begs the question of the validity of the mechanism of activation of fibrosis promoted. We would specifically like to see data on the mechanism of activation of STAT3 and via which JAKs if possible, stronger evidence for STAT3 activation in vivo, and determination that TSP1 is a direct target of STAT3. Strengthening the assessment of fibrosis and accompanying inflammation could be achieved by analysis of collagen and other ECM components directly, and inflammatory markers. Use of genetic markers such as Postn-Cre would be desirable but not essential. Also, many of the other questions and comments should be addressed, although we don't consider human data to be an essential revision.The paper is extensive and novel, and the data are generally clean with a clear message. However the reviewers have identified a number of significant issues that need to be addressed before publication in 1) The concept of Stat3-TSP1-TGFβ signaling pathway in cardiac fibrosis activation is interesting. However, the traditional signaling involved in ischemia/hypoxia mediated cellular signaling often leads to cellular injury and death. This study offers a potential opportunity to illustrate if Stat3 activation in cardiac fibroblasts is a specific and unique signaling event for IH vs. ischemia/hypoxia injury. It is also important to investigate the molecular mechanism involved in Stat3 activation under IH and Ang II stimulation. Which JAK molecule is involved? How was Stat3 selected to study among other possible signaling pathways?2) Cellular specificity of the Stat3 signaling in cardiac fibrosis should be better demonstrated. Stat3 mediated signaling has a significant impact on cardiomyocyte hypertrophy and survival. Much of the in vivo characterization, including Stat3 measurement or inhibitor treatment, can potentially be contributed by myocytes rather than fibroblasts. Their interaction could also be an important contributor to the hypertrophic remodeling and contractile dysfunction. Therefore, authors should recognize the limitation of these data and their in vivo experimental approaches. They should carefully determine the myocyte morphology, apoptotic status and function in conjunction with fibrosis analysis. It is also important to recognize that the IH effect is global to all tissues. Overall, the lack of fibroblast specific manipulation of Stat3 or TSP1 in vivo appears to be a significant limitation of the current study.3) In characterization of the relevance of TSP1 induction among OSA patients, there was no data demonstrating potential spectrum of cardiac function among the OSA patients (their Echo parameters) which could be a major support for the functional relevance of the elevated plasma TSP1 in the cardiovascular complications among OSA patients.4) The use of human serum samples is quite intriguing and hypothesis generating and unfortunately not very well developed. Clinical relevance and human applicability is always suspect in terms of mouse models so it is quite refreshing that it appears there is some signal of TSP1 in human serum – though this is not well fleshed out by further experiments. The paper, specifically Figures 1 and 2, are very reliant on finding a phenotype that reaches significance thresholds by overlaying with a model of hypertrophy using angiotensin II. The interaction between OSA and hypertension and heart failure is a very hot topic and I think that the authors here really missed a significant opportunity to make a clinical leap. They showed conclusively an additive interaction between IH and angiotensin. What would have really elevated this paper into clinical relevance is treating their mouse model somewhere with an ARB to see if they can mitigate either some of the signaling or mouse phenotype. That could have then been further investigated in their human samples, specifically the TSP1 levels stratified by whether the patient is on an ACEi or ARB. This could have been a real nidus for a clinical trial in terms of treating OSA induced hypertension with RAAS blockade.5) The authors use a published intermittent hypoxia model but do not show any indication that the hearts are hypoxic. Based on the mechanism proposed, it is important to distinguish between a direct impact of hypoxia on cardiac fibroblasts (CF) as implied from in vitro data, from myriad other mechanisms that may impact cardiac function including kidney dysfunction and inflammation, to name just two. Showing hypoxia with a hypoxia probe such as pimonidazole is important.6) The authors have superficially evaluated the impact of IH and Ang II by evaluating only α-SMA expression as a readout of myofibroblast activity. The authors do not evaluate collagen or other ECM genes and do not evaluate the contribution of CF proliferation to the phenotype. Do CF proliferate ? Are the CF secreting ECM or other inflammatory cytokines? Are CF contractile? What is the impact on inflammation (TSP1 has been shown to induce inflammation in other heart disease models)? There are also a number of genetic markers of fibroblasts that might complement this study or allow the authors to isolate fibroblast populations by flow cytometry .7) The authors also rely heavily on co-localization of SMA and vimentin and TSP1 and vimentin by Immunofluorescence. How is co-localization defined (the methods only state fluorescence intensity was quantified by image J)? How is a secreted factor (TSP1) co-localized with an intracellular CF marker? There seems to be considerable background expression of TSP1, perhaps in cardiomyocytes, and very little seems to be specific for CF.8) The authors have not shown the mechanism whereby IH induces Stat3 dependent phosphorylation and activity, nor have they demonstrated Stat3 occupancy of a relevant binding site that regulates Tsp1 gene expression with IH. The authors have also not shown Stat3 nuclear accumulation in IH treated mice in vivo, either by immunostaining or nuclear-cytoplasmic fractionation and Western. Many of the observed results thus could be true-true and unrelated.9) Figure 3D shows a very rapid induction of Tsp1 and SMA protein following IH (2 hrs). This is extremely rapid to see SMA induction even by exogenous TGF-β administration, which typically works on the order of days. Furthermore, the finding that TSP1 and SMA are both induced at 2 hrs of IH seems to argue against a linear IH-Stat3-Tsp1-SMA/myofibroblast model as proposed. It seems equally plausible that (If SMA is indeed induced within 2 hrs) that Stat3 or another mechanism acts independently of TSP1 on myofibroblast activation.10) The authors treat mice with a Stat3 inhibitor and show it can block fibrosis and functional decline. Can this drug reverse damage already developed by IH?11) Figure 2B refers to a significant induction of Tsp1 mRNA by IH alone, which is not true . This is surprising, considering the rapid and robust induction of TSP1 protein shown after only 2 hrs of IH in Figure 3D. What is the cause of this discrepancy? Perhaps looking at more acute timepoints would be useful in the mouse model. Essential revisions:The paper is extensive and novel, and the data are generally clean with a clear message. However the reviewers have identified a number of significant issues that need to be addressed before publication in eLife. In particular, the characterisation of fibrosis using just SMA staining or gene expression in vitro and in vivo is insufficient (SMA is a poor marker of myofibroblasts), and the rapid induction of TSP1 and SMA in vitro within 2 hours begs the question of the validity of the mechanism of activation of fibrosis promoted. We would specifically like to see data on the mechanism of activation of STAT3 and via which JAKs if possible, stronger evidence for STAT3 activation in vivo, and determination that TSP1 is a direct target of STAT3. Strengthening the assessment of fibrosis and accompanying inflammation could be achieved by analysis of collagen and other ECM components directly, and inflammatory markers. Use of genetic markers such as Postn-Cre would be desirable but not essential. Also, many of the other questions and comments should be addressed, although we don't consider human data to be an essential revision.We deeply appreciate the editor and reviewers for the comments and suggestions to improve the scientific merit of the manuscript. These comments are valuable for us and we have performed several experiments to address all the concerns. We have added more than 30 novel figure panels that represent new experiments or contain additional experiments. In the revised manuscript, we included more reparative myofibroblast markers (collagen I and periostin). We prolonged the treatment time of IH and looked at more acute timepoints for primary cardiac fibroblast (CF) activation in vitro. Upstream signaling of STAT3 was assessed and we found that JAK2 phosphorylation mediated IH-induced STAT3 activation. Evidence of IH-induced STAT3 activation was also observed in vivo. ChIP assay proved that TSP1 was a direct target of STAT3 in CFs. Assessment of fibrosis and accompanying inflammation were also strengthened with new experiments. Moreover, we determined the effect of AAV9-periostin promoter-shStat3 upon IH-induced fibrosis and further confirmed the vital role of fibroblast STAT3 in the process. Below please find our response to each of these comments.1) The concept of Stat3-TSP1-TGFβ signaling pathway in cardiac fibrosis activation is interesting. However, the traditional signaling involved in ischemia/hypoxia mediated cellular signaling often leads to cellular injury and death. This study offers a potential opportunity to illustrate if Stat3 activation in cardiac fibroblasts is a specific and unique signaling event for IH vs. ischemia/hypoxia injury. It is also important to investigate the molecular mechanism involved in Stat3 activation under IH and Ang II stimulation. Which JAK molecule is involved? How was Stat3 selected to study among other possible signaling pathways?We thank the reviewer for recognizing the novelty of our study and the valuable comments. To address the reviewer’s concern about the upstream of STAT3, we detected phosphorylation of JAK1-3 under IH. As shown in Figure 4A-B, we found that IH induced phosphorylation of JAK2 at Tyr1008 site time-dependently without altering the phosphorylation of JAK1 or JAK3. In addition, a JAK2 inhibitor, TG101209, significantly abolished IH-induced STAT3 activation . These results indicated that JAK2 is involved in IH-induced STAT3 activation.TSP1 has been reported to be transcriptionally regulated by STAT3 in astrocytes . Also, STAT3 has been found involved in other fibrosis diseases including skin fibrosis  and liver fibrosis . These studies led us to hypothesize that STAT3 might mediate IH-induced TSP1 expression and cardiac fibrosis. We have cited these studies and added a brief introduction: “TSP1 was reported to be transcriptionally regulated by STAT3 in astrocytes, and emerging evidence has indicated that STAT3 is involved in fibrosis diseases. Here, we hypothesized that STAT3 might play a vital role in IH-induced TSP1 expression and cardiac fibrosis.”2) Cellular specificity of the Stat3 signaling in cardiac fibrosis should be better demonstrated. Stat3 mediated signaling has a significant impact on cardiomyocyte hypertrophy and survival. Much of the in vivo characterization, including Stat3 measurement or inhibitor treatment, can potentially be contributed by myocytes rather than fibroblasts. Their interaction could also be an important contributor to the hypertrophic remodeling and contractile dysfunction. Therefore, authors should recognize the limitation of these data and their in vivo experimental approaches. They should carefully determine the myocyte morphology, apoptotic status and function in conjunction with fibrosis analysis. It is also important to recognize that the IH effect is global to all tissues. Overall, the lack of fibroblast specific manipulation of Stat3 or TSP1 in vivo appears to be a significant limitation of the current study.We agree. Indeed, both STAT3 inhibitor treatment and IH challenge are global to all tissues, although we have demonstrated their roles in vitro. As the reviewer suggested, we assessed effects of IH on cardiomyocyte morphology and apoptotic status  in the revised manuscript. Nuclear translocation of STAT3 induced by IH was also observed in CFs in vivo . In addition, we performed new in vivo experiments with AAV9-periostin promoter-shStat3 to knockdown STAT3 in fibroblasts. In line with the STAT3 inhibitor experiments, STAT3 knockdown in CFs significantly attenuated IH and Ang II-induced cardiac fibrosis and improved cardiac function . The new added experiments together with other results further demonstrated that CF STAT3 might mediate IH-induced cardiac fibrosis.3) In characterization of the relevance of TSP1 induction among OSA patients, there was no data demonstrating potential spectrum of cardiac function among the OSA patients (their Echo parameters) which could be a major support for the functional relevance of the elevated plasma TSP1 in the cardiovascular complications among OSA patients.We thank the reviewer for the valuable comments. It is indeed helpful to analyze whether the plasma TSP1 level is associated with cardiac function of OSA patients. Unfortunately, we did not find echocardiography records for the patients and healthy subjects. Since the plasma concentration of NT-proBNP is raised in cardiac impairment (Clin Endocrinol (Oxf). 1997 9;47(3):287-96.), we determined the NT-proBNP concentration in plasma of patients and analyzed the correlation of NT-proBNP and TSP1 concentrations, instead. As shown in 4) The use of human serum samples is quite intriguing and hypothesis generating and unfortunately not very well developed. Clinical relevance and human applicability is always suspect in terms of mouse models so it is quite refreshing that it appears there is some signal of TSP1 in human serum – though this is not well fleshed out by further experiments. The paper, specifically Figures 1 and 2, are very reliant on finding a phenotype that reaches significance thresholds by overlaying with a model of hypertrophy using angiotensin II. The interaction between OSA and hypertension and heart failure is a very hot topic and I think that the authors here really missed a significant opportunity to make a clinical leap. They showed conclusively an additive interaction between IH and angiotensin. What would have really elevated this paper into clinical relevance is treating their mouse model somewhere with an ARB to see if they can mitigate either some of the signaling or mouse phenotype. That could have then been further investigated in their human samples, specifically the TSP1 levels stratified by whether the patient is on an ACEi or ARB. This could have been a real nidus for a clinical trial in terms of treating OSA induced hypertension with RAAS blockade.We thank the reviewer for offering a professional clinical perspective. To investigate whether ACEi or ARB might have an effect on IH-induced TSP1 expression, we first performed in vitro experiments in primary CFs. As shown in 5) The authors use a published intermittent hypoxia model but do not show any indication that the hearts are hypoxic. Based on the mechanism proposed, it is important to distinguish between a direct impact of hypoxia on cardiac fibroblasts (CF) as implied from in vitro data, from myriad other mechanisms that may impact cardiac function including kidney dysfunction and inflammation, to name just two. Showing hypoxia with a hypoxia probe such as pimonidazole is important.According to the reviewer’s suggestion, we performed the experiments with pimonidazole. Mice were exposed to IH for 7 days. Pimonidazole was injected at 60 mg/kg body weight. Mice were sacrificed 1 hr after pimonidazole injection. Then heart tissue was fixed and embedded into OCT for immunofluorescence staining. The pimonidazole-positive area was significantly increased under stimulation of IH. Data are shown in Figure 1—figure supplement 1A in the revised manuscript.6) The authors have superficially evaluated the impact of IH and Ang II by evaluating only α-SMA expression as a readout of myofibroblast activity. The authors do not evaluate collagen or other ECM genes and do not evaluate the contribution of CF proliferation to the phenotype. Do CF proliferate ? Are the CF secreting ECM or other inflammatory cytokines? Are CF contractile? What is the impact on inflammation (TSP1 has been shown to induce inflammation in other heart disease models)? There are also a number of genetic markers of fibroblasts that might complement this study or allow the authors to isolate fibroblast populations by flow cytometry .We thank the reviewer for the valuable suggestions. Several new experiments were performed to address the concerns. First, we agree that α-SMA expression as a only readout of myofibroblast activity is insufficient. So, we observed the other two markers (collagen I and periostin) in most experiments for the revised manuscript . Second, for the in vitro analysis, we observed the expression of collagen I and periostin in CFs , CF proliferation , CF contraction  and expression of inflammatory cytokine . Finally, following the suggestion of the reviewer, we sorted CF populations by flow cytometry and further confirmed the TSP1 upregulation in CFs of IH-treated mice .7) The authors also rely heavily on co-localization of SMA and vimentin and TSP1 and vimentin by Immunofluorescence. How is co-localization defined (the methods only state fluorescence intensity was quantified by image J)? How is a secreted factor (TSP1) co-localized with an intracellular CF marker? There seems to be considerable background expression of TSP1, perhaps in cardiomyocytes, and very little seems to be specific for CF.We thank the reviewer for the comments. We quantified the colocalization of dual-color immunofluorescence images by using Image J with the colocalization plugin. Briefly, the plugin initially generates an 8-bit image with only the colocalized points, then it combines the three 8-bit images in an RGB image. Two points are considered colocalized if their respective intensities are strictly higher than the threshold of their channels. We detailed the quantification method in the subsection “Histology”.We thank the reviewer for pointing out that TSP1 acts as a secretion protein and its localization with vimentin might not be convincing. In the revised manuscript, we replaced the statistical analysis of colocalization with quantification of TSP1 intensity . In addition, to address the reviewer’s concern about the source of TSP1, we also sorted fibroblast populations by flow cytometry and further confirmed the TSP1 upregulation in CFs of IH-treated mice .8) The authors have not shown the mechanism whereby IH induces Stat3 dependent phosphorylation and activity, nor have they demonstrated Stat3 occupancy of a relevant binding site that regulates Tsp1 gene expression with IH. The authors have also not shown Stat3 nuclear accumulation in IH treated mice in vivo, either by immunostaining or nuclear-cytoplasmic fractionation and Western. Many of the observed results thus could be true-true and unrelated.We thank the reviewer for pointing out these important issues. To address the reviewer’s concerns about STAT3, we performed new experiments for the resubmitted manuscript. First, we detected the upstream factors of STAT3 and demonstrated that JAK2 but not JAK1 or JAK3 mediated IH-induced STAT3 phosphorylation. IH strongly induced phosphorylation of JAK2 at Tyr1008 but barely had an effect on phosphorylation of JAK 1 or JAK3 . Additionally, a JAK2 inhibitor, TG101209, blocked the IH-increased levels of p-JAK2 and p-STAT3 in vitro . These results are consistent with a previous report that JAK2-mediated STAT3 phosphorylation played a critical role in fibroblast activation in systemic sclerosis . Second, to investigate whether STAT3 could transcriptionally regulate the expression of TSP1 in CFs directly, we used ChIP assay and found that IH enhanced the enrichment of STAT3 at the TSP1 promoter in PCFs as compared with the control . Finally, we also observed STAT3 subcellular localization in vivo. In line with our in vitro results, IH markedly induced STAT3 nuclear accumulation in CFs . We believe that these new added results strongly strengthen our hypothesis.9) Figure 3D shows a very rapid induction of Tsp1 and SMA protein following IH (2 hrs). This is extremely rapid to see SMA induction even by exogenous TGF-β administration, which typically works on the order of days. Furthermore, the finding that TSP1 and SMA are both induced at 2 hrs of IH seems to argue against a linear IH-Stat3-Tsp1-SMA/myofibroblast model as proposed. It seems equally plausible that (If SMA is indeed induced within 2 hrs) that Stat3 or another mechanism acts independently of TSP1 on myofibroblast activation.We greatly appreciate the reviewer for asking a critical question. To investigate whether IH could really induce primary CF activation rapidly, we detected the expression of collagen I and periostin, which are more promising markers for myofibroblasts, in primary CFs after IH treatment for 2 hr. IH failed to alter their expression . Therefo10) The authors treat mice with a Stat3 inhibitor and show it can block fibrosis and functional decline. Can this drug reverse damage already developed by IH?The reviewer has pointed out an important question. To investigate whether the STAT3 inhibitor could reverse the damage already induced by IH, we treated mice with IH and Ang II for 4 weeks, then treated these mice with S3I-201 (5 mg/kg) for another 2 weeks. We detected the cardiac function index and fibrosis area. As shown in Figure 6—figure supplement 1A-E, the groups did not significantly differ in the indices, so STAT3 inhibition might not have beneficial effects on damage already induced. One of the possible reasons might be that although fibroblasts are the main effector cells in the pathogenesis of cardiac fibrosis, reversing fibrosis is an active process that may require co-operation of interstitial cell subsets, immune cells, vascular cells, and cardiomyocytes : 2283-2285.).11) Figure 2B refers to a significant induction of Tsp1 mRNA by IH alone, which is not true . This is surprising, considering the rapid and robust induction of TSP1 protein shown after only 2 hrs of IH in Figure 3D. What is the cause of this discrepancy? Perhaps looking at more acute timepoints would be useful in the mouse model.Thbs1 mRNA was significantly induced by IH alone, which was consistent with our in vitro results.Thanks for the suggestion. IH-induced TSP1 expression is a rapid process and we may have missed the peak time point to observe the change of TSP1 mRNA level. To test this possibility, we treated mice with IH and Ang II for 2 weeks and determined mRNA level of TSP1 in heart. As shown in Figure 2B,