PHF6 mutation occurred in 12% of UCEC patients, and PHF6 mutation predicted favourable clinical outcome in UCEC patients. Depletion of PHF6 effectively inhibited HEC‐1‐A and KLE cell proliferation in vitro and decreased HEC‐1‐A cell growth in vivo. Furthermore, high PHF6 level indicated a subtype of UCECs characterized by low immune infiltration, such as CD3+ T‐cell infiltration. While knockdown of PHF6 in endometrial carcinoma cells increased T‐cell migration by promoting IL32 production and secretion. Taken together, our findings suggested that PHF6 might play an oncogenic role in UCEC patients. Thus, PHF6 could be a potential biomarker in predicting the prognosis of UCEC patients. Depletion of PHF6 may be a novel therapeutic strategy for UCEC patients.Uterine corpus endometrial carcinoma (UCEC) is the most common cancer of the female reproductive tract. The overall survival of advanced and recurrent UCEC patients is still unfavourable nowadays. It is urgent to find a predictive biomarker and block tumorgenesis at an early stage. Plant homeodomain finger protein 6 (PHF6) is a key player in epigenetic regulation, and its alterations lead to various diseases, including tumours. Here, we found that PHF6 expression was upregulated in UCEC tissues compared with normal tissues. The UCEC patients with high PHF6 expression had poor survival than UCEC patients with low PHF6 expression.  For further verification, we performed OS and Relapse free survival (RFS) analyses based on PHF6 expression levels by Kaplan–Meier Plotter  between PHF6‐high and PHF6‐low groups. Gene ontology (GO) was used to explore potential functions of PHF6 in UCEC. The package in R to complete the above research is cluster Profiler. Finally, with the DEGs above, we utilized Cytoscape to capture some immune‐related GO terms in the form of a Netplot.2.4For the purpose of exploring the correlation between PHF6 expression and immune cells, we analysed the immune score and stromal score of each tumour sample in UCEC by the package ESTIMATE and CIBERSORT. Moreover, we divided the samples into PHF6‐high and PHF6‐low groups according to the expression level to observe the differences in the abundance of 22 immune fractions with the R package CIBERSORT. The Wilcoxon rank‐sum test was used to capture the differences.2.5The tissue microarray (TMA) was purchased from Shanghai Outdo Biotech Co., Ltd. The tissues for immunohistochemical analysis were fixed with formalin, followed by embedment in paraffin. After being dewaxed in xylene and ethyl alcohol, antigen retrieval and being rinsed with PBS, we incubated the slides with anti‐CD3 antibody , anti‐CD4 antibody , anti‐CD8 antibody , anti‐CD19 antibody , anti‐CD33 antibody , anti‐CDK4 antibody  and anti‐PHF6 antibody  overnight at four degrees Celsius and then at indoor temperature for half an hour, and then incubated with an anti‐rabbit/mouse IgG H&L antibody  at indoor temperature for 1 h and then incubated with a DAB Substrate Kit (1:20) for 10 min, counterstaining with haematoxylin for 15 min, differentiation fluid for 15 s and return blue liquid for 1 min. The slide was rehydrated with graded concentrations of ethanol, cleared with xylene, dried and sealed.The staining intensity of whole tumour tissue was estimated for the abundance of PHF6 and CDK4. We divided the staining intensity of whole tumour tissue into three levels, represented by 0–3, respectively. 0–1 symbolized the most weakly stained samples, while 3 represented the strongest intensity. For CD3/CD4/CD8/CD19/CD33 staining, the percentage of cells with strong intensity (brown staining) of membrane staining was only evaluated. Likewise, the degree of positive expression was classified into two groups with the division of samples with 40% positive expression, symbolized by 1 and 2, respectively. PHF6, CD3 and CDK4 staining was independently evaluated for immunoreactivity by two pathologists Tables .2.6l‐Glutamine. Trypsin/EDTA (0.25%) was used for dissociation. The two different miRNA oligos corresponding to PHF6 and the scrambled negative‐control miRNA were purchased from Invitrogen. The siRNA was transfected into HEC‐1‐A and KLE cells by Lipofectamine™ 2000 . The PHF6 mRNA was detected by real‐time PCR 24 h after transfection with siRNA. The PHF6 protein was detected by Western blot 48 hours after transfection with siRNA. The siRNA oligos for PHF6 KD‐1: GGTCAGTTTCTCATGAAGATA. The siRNA oligos for PHF6 KD‐2: GACAAAATGGAGACATTGATA.HEC‐1‐A and KLE cells were obtained from the American Type Culture Collection (ATCC). The cells were identified by STR profiling. HEC‐1‐A and KLE cells were cultured in high glucose DMEM (Corning) supplemented with 10% FBS, 1% penicillin/streptomycin and 1% 2.7PCR primers were designed to amplify the full length of CDK4 with the addition of a 5′ BamHI and a 3′ KpnI site. Primers were as follows: Forward, 5′‐CACACTGGACTAGTGGATCCCGCCACCATGGCTACCTCTCGATATGAG‐3′; Reverse, 5′‐AGTCACTTAAGCTTGGTACCTCCGGATTACCTTCATCCTTATG‐3′. Using standard molecular biology techniques, the PCR product was generated and ligated into GV208 vector (Genechem).2.85) were mixed with 100 μl matrigel and implanted subcutaneously into 8‐week‐old female NOD/SCID/IL‐2Rγnull (NSG) mice. Tumour volume was measured every 3 days. To investigate the role of PHF6 KD HEC‐1‐A cells in the infiltration of T cells, the T cells (1 × 106) were injected into NSG mice 1 week after implantation with PHF6 KD HEC‐1‐A cells (5 × 105). The mice were sacrificed 3 weeks after implantation with HEC‐1‐A cells. The animal experiments were approved by the Institutional Animal Care and Use Committee of Capital Medical University (Permit Number: CMU07802161).To investigate the role of PHF6 in the proliferation of HEC‐1‐A cells, PHF6 KD HEC‐1‐A cells (5 × 102.93 PHF6 KD and control cancer cells were seeded per well in 96‐well plates. Cells were plated in five wells per time point. The wells were incubated with 10 μl CCK‐8 (Sigma‐Aldrich) for 2 h in 37°C cell culture incubator. Then, the absorbance was measured at 450 nm by Paradigm Detection Platform (BECKMAN). The growth curve of cancer cells was plotted in Graphpad Prism 5.0.We harvested the cells 48 h after transfection with siRNA. Then, we evaluated the growth ability of PHF6 KD cells and control cells. 3 × 102.10Cells were fixed using the Cytofix Fixation/Permeabilization Kit (BD Biosciences) according to the manufacturer's instructions. Cells were stained with Ki67 antibody at room temperature for 30 min. Prior to analysis, cells were incubated with 1.62 μM Hochest 33342 (Invitrogen) at room temperature. Cells were analysed using a FACS Canto flow cytometer (BD Biosciences). Data were analysed with FlowJo software (Tree Star).2.11PHF6 KD and control cancer cells were used for analysis. The apoptosis of cells was examined using the Annexin‐V/PI Apoptosis Detection Kit (BD Biosciences) according to manufacturer's instructions.2.125 T cells were seeded into the top of the transwell chambers, and PHF6 KD or control cancer cells were seeded in the lower chambers. After 6 h of incubation, cells in the upper membrane surface were removed with cotton swab. Cells on the lower membrane surface were stained with 0.1% Crystal Violet for 30 min at room temperature. The cells in the lower chambers were counted by Countess II (Invitrogen).T‐cell migration assay was performed using 8‐μm pore size cell culture insert in a 24‐well plate . T cells were sorted from healthy donors. 1 × 102.135 T cells were seeded into the top of the transwell chambers, and PHF6 KD or control cancer cells were seeded in the lower chambers. After 6 h of incubation, cells in the upper membrane surface were removed with cotton swab. Cells on the lower membrane surface were stained with 0.1% Crystal Violet for 30 min at room temperature. The cells in the lower chambers were counted by Countess II (Invitrogen).For the IL32 neutralization experiment, IL32 antibody  was added to the culture system at 10 ng/ml. 1 × 102.14Total RNA was extracted from cells using TRIzol Reagent  according to manufacturer's instructions. cDNA was synthesized according to manufacturer's instructions of Revert Aid First SYBR Green PCR Kit (Thermo Fisher Scientific) with one μg RNA. PCR reactions were run with the CFX96 real‐time PCR detection system  according to the protocol of 2× SYBR Green qPCR Master Mix (Bimake). Primers used for PCR were as follows: human PHF6 F‐TGCTTTGGTGTCCTCACATTCT, R‐GCGAAGGTTTCTCTCGGATCT; human GAPDH F‐TTAAAAGCAGCCCTGG, R‐GACAGTCAGCCGCATCTTCT. GAPDH was used as a control. human CDK4 F‐CCATAGGCACCGACACCAAT, R‐GCGTGAGGGTCTCCCTTG; human CDK7 F‐TAACGCTTTGCCCGAGACTT; R‐ATTCGTGTTGTCCTGGGA.2.15The cells were harvested 48 h after transfection with siRNA. Total protein was extracted using RIPA with 1% PMSF (Solarbio). 30–90 μg protein was separated using 10% SDS–PAGE and then transferred to PVDF membranes . PVDF membranes were blocked with milk for 1 h at indoor temperature and incubated with primary antibody overnight at four degrees Celsius. Then, PVDF membranes were incubated in secondary antibody (1:2000) (Zsbio) at indoor temperature for 1 h. Finally, ECL detection method (Bio‐Rad) was used for imaging analysis. Antibody list: PHF6‐antibody , CDK4‐antibody  and Actin‐antibody .2.165 cells in 200 μl of water were frozen and thawed three times and centrifuged at 3000g  for 5 min, and the liquid supernatant was collected for IL32 and IL12 test. In addition, the culture medium was collected for IL32 and IL12 tests. The ELISA was read on a SynergyH 4 Hybrid Reader at 450 nm.The ELISA was performed using the human IL32 and the human IL12 ELISA Kit according to the manufacturer's protocols . 5 × 102.17p < 0.05 was regarded as statistically significant By log‐rank tests, we compared survival curves between PHF6‐high and PHF6‐low groups. Pearson analysis was applied to estimate the connection between the PHF6 expression levels and immune cells. Student's two‐tailed t‐test and one‐way anova are used in Figures p‐value <0.05 as statistically significant .Statistical analysis and graphical visualization were completed by R software Version 4.1.1). We explored the expression of PHF6 between tumours and normal tissues through Wilcoxon rank‐sum test, and .1.1. We 33.1https://xenabrowser.net/) analysis system, which included 10,237 patients , indicating that PHF6 might involve in the carcinogenesis of UCECs  and DFI (p = 0.031) compared with patients without PHF6 mutations , high‐PHF6 + PHF6 mutation (H + mutation), low‐PHF6 + PHF6 non‐mutation (L + non‐mutation) and high‐PHF6 + PHF6 non‐mutation (H + non‐mutation) for further analysis of overall survival (OS). Interestingly, we found that the UCEC patients with the low‐PHF6 + PHF6 mutation had the best survival, while the high‐PHF6 + PHF6 wild‐type group had the worst survival in four groups (p = 0.004). The survival status of the high‐PHF6 + PHF6 mutation group was much better than low‐PHF6 + PHF6 wild‐type group , clinical stage (n = 544), histological grade (n = 544) and tumour status (n = 523). We found that the mRNA expression of PHF6 was related to the histological grade (p = 0.0004)  Table .3.2p < 0.001), while PHF6 KD‐2 did not change the apoptosis of cells . In KLE, PHF6 KD‐1 and PHF6 KD‐2 did not change the apoptosis of cells   was validated by Western blot and real‐time PCR Figure . In grow) Figure . It suggTo determine the effect of PHF6 KD in endometrial carcinoma cells in vivo, we implanted PHF6 KD HEC‐1‐A cells subcutaneously into the immunodeficient NOD SCID gamma mice (NSG). Knockdown of PHF6 significantly decreased tumour growth rate in vivo Figure , and theFurthermore, we over‐expressed CDK4 (CDK4 OE) in PHF6 KD HEC‐1‐A cells and detected cell proliferation Figure . We foun3.3+ and TNF‐α+ T cells are active T cells in peripheral blood and bone marrow. To investigate whether PHF6 KD endometrial carcinoma cells could influence the activity of T cells, we analysed the IFN‐γ and TNF‐α expression in T cells when coculture with PHF6 KD or control endometrial carcinoma cells by flow cytometry. We found that the percentage of IFN‐γ+ and TNF‐α+ cells was increased in T cells when cocultured with PHF6 KD or control endometrial carcinoma cells, compared with T cells alone. However, the IFN‐γ and TNF‐α expression was similar in T cells cocultured with PHF6 KD and T cells cocultured with control endometrial carcinoma cells. It suggested that PHF6 did not influence the activation of T cells. To investigate the effect of PHF6 on T cells infiltration into cancer stroma, T cells and endometrial carcinoma cells (HEC‐1‐A and KLE cells) were cocultured using the double‐chamber. We observed the increased migration of T cells when cocultured with endometrial carcinoma cells, as compared to T cells alone in the lower chambers .To further investigate whether PHF6 participates in immune infiltration in UCEC patients, we compared the profiles of infiltrated immune cells between 269 high‐PHF6 and 269 low‐PHF6 UCEC patients from TCGA. We found that the infiltration levels of immunocytes that inhibited tumour progression, such as CD8s Figure . The infs Figure . These rn Figure . Howevers Figure , indicatt Figure . This wap‐value <0.05, which contain 170 upregulated genes and 509 downregulated genes , innate immune response activating cell surface receptor signalling pathway (p = 3.4E−02), positive regulation of interleukin‐1 production (p = 1.4E−02) and antimicrobial humoral response (p = 1.8E−02) | > 1.0 and adjusted s Figure . Gene On) Figure . In addi) Figure . It has ) Figure . The proay p = 3.−02, posi) Figure , suggest) Figure , we adde) Figure . Then, w) Figure , suggest4Cancer is caused by a progressive series of genetic and epigenetic alterations that transform normal cells into malignant cells.PHF6 is involved in chemical chromatin modifications and plays a critical role in the tight regulation of gene expression during tissue homeostasis and development. Therefore, its alterations result in various diseases, such as tumours.PHF6 is a highly conserved epigenetic transcriptional regulator that is important for embryonic development. It contains two imperfect PHD‐like zinc finger domains, two nuclear localization signals as well as a nucleolar localization sequence. PHF6 functions in chromatin‐mediated regulation of the gene expression. It directly binds with double‐stranded DNA via its atypical PHD2 domain.PHF6 mutations had favourable survival compared with UCEC patients with WT PHF6, indicating that targeting PHF6 might be a potential therapeutic strategy for UCEC patients. The role of PHF6 in UCEC was validated by molecular and cell biology experiments. Depletion of PHF6 effectively inhibited the proliferation of endometrial carcinoma cells, which suggested that PHF6 might be a candidate therapeutic target for UCEC patients.Here, we displayed the expression landscape of PHF6 in tumour samples and normal samples of 33 cancers. We found that the expression of PHF6 was significantly higher in tumour samples than in normal samples in 15 cancers. A high expression level of PHF6 was associated with an unfavourable prognosis in UCEC, indicating that PHF6 might promote the initiation and progression of UCEC. Furthermore, UCEC patients with + T cells, and IL32 involved in the differentiation and infiltration of CD8+ T cells.+/CD8+ T cells and Tregs in PHF6‐high UCECs probably was due to the lower level of IL32 in PHF6‐high UCEC patients  in uterine corpus endometrial carcinoma patients.Cancer immunotherapy involves artificial increasing the cytotoxic capacity of immune cells and stimulation of the immune cells to treat tumour cells.Investigation of the biological mechanisms and functions of oncogenes and tumour suppressors involved in tumorigenesis is an effective approach to achieve personal and precise treatment for tumour patients. Our work provides new insights into the underlying molecular mechanisms and functions of the epigenetic gene PHF6 in tumorigenesis. PHF6 might be a candidate prediction biomarker that may serve as an additional indication for immunotherapies and other anticancer strategies in UCEC. Prospective studies are needed to investigate the correlation between PHF6 expression and immunotherapy response. It is essential to better understand the underlying mechanisms of PHF6 in the tumour immune microenvironment in UCEC.Xiaomin Wang: Conceptualization (lead); data curation (lead); formal analysis (lead); funding acquisition ; investigation (lead); writing – original draft (lead). Aizhong Fang: Data curation ; formal analysis ; methodology ; software . Yichen Peng: Data curation ; validation ; writing – original draft . Jianyu Yu: Methodology ; validation ; visualization . Chunna Yu: Data curation ; methodology . Jinxuan Xie: Data curation ; formal analysis ; methodology ; software . Yi Zheng: Data curation ; resources . Lairong Song: Project administration ; supervision ; visualization . Parker Li: Writing – original draft ; writing – review and editing . Jia Li: Methodology ; project administration ; supervision ; validation ; visualization . Xun Kang: Methodology ; resources ; visualization . Yi Lin: Project administration (lead); supervision ; visualization ; writing – review and editing . Wenbin Li: Funding acquisition ; supervision (lead); writing – review and editing .This work was supported by funds from Beijing Postdoctoral Science Foundation (2021‐ZZ‐019 to X. Wang); the National Natural Science Foundation of China (82070169 to X. Wang & 81972338 to W. Li).The authors declare no conflict of interest.Appendix S1Click here for additional data file.Appendix S2Click here for additional data file.Appendix S3Click here for additional data file.Appendix S4Click here for additional data file.Appendix S5Click here for additional data file.Appendix S6Click here for additional data file.


Heart failure with preserved ejection fraction (HFpEF) is a highly prevalent but still poorly understood clinical entity. Its current pathophysiological understanding supports a critical role of comorbidities and their chronic effect on cardiac function and structure. Importantly, despite the replication of some HFpEF phenotypic features, to this day, experimental models have failed to bring new effective therapies to the clinical setting. Thus, the direct investigation of HFpEF human myocardial samples may unveil key, and possibly human-specific, pathophysiological mechanisms. This study employed quantitative proteomic analysis by advanced mass spectrometry (SWATH–MS) to investigate signaling pathways and pathophysiological mechanisms in HFpEF. Protein-expression profiles were analyzed in human left ventricular myocardial samples of HFpEF patients and compared with a mixed control group. Functional analysis revealed several proteins that correlate with HFpEF, including those associated with mitochondrial dysfunction, oxidative stress, and inflammation. Despite the known disease heterogeneity, proteomic profiles could indicate a reduced mitochondrial oxidative phosphorylation and fatty-acid oxidation capacity in HFpEF patients with diabetes. The proteomic characterization described in this work provides new insights. Furthermore, it fosters further questions related to HFpEF cellular pathophysiology, paving the way for additional studies focused on developing novel therapies and diagnosis strategies for HFpEF patients. Despite its high prevalence, surmounting health-costs, and increasing death toll, heart failure (HF) with preserved ejection fraction (HFpEF) remains one of the major unmet challenges in current healthcare. Whereas improvements have been made for HF with reduced ejection fraction (HFrEF), few advances have been made for patients with HFpEF, with just one pharmacological group (SGLT2i) having been recently shown to improve clinical outcomes . The maiThe current consensus regarding the key pathophysiological aspects of HFpEF links comorbidity-driven chronic low-grade inflammation, endothelial dysfunction, alterations in fluid balance, and multiple organ dysfunctions to functional and structural changes  that, overall, impair cardiac reserve despite preserved ejection fraction and gross systolic function indexes ,3,4,5.There is a clear need to understand the protein-mediated mechanisms of human HFpEF. Despite mimicking some HFpEF phenotypic features, to this day, HfpEF experimental models have failed to bring new effective therapies to the clinical setting. In this regard, the evaluation of human myocardial biopsies ,7,8 and Herein, we aimed to characterize the proteome of human HFpEF left ventricle myocardial samples. These samples were thoroughly characterized by untargeted mass-spectrometry-based quantitative proteomic analysis (SWATH–MS) to investigate signaling pathways and mechanisms associated with HFpEF and linked to comorbidities. The knowledge generated here will contribute to unveiling human myocardial signatures of HFpEF and its correlation with known comorbidities, potentially improving experimental modeling, diagnosis, and therapeutics. All procedures were performed according to the Declaration of Helsinki and were approved by the local ethics committee and data-protection authority . Myocardial samples were collected between 2010 and 2018 from (i) HFpEF patients, during LV myocardial biopsy during coronary artery bypass grafting surgery (n = 11) or due to non-confirmed suspicion of infiltrative or inflammatory cardiomyopathy (n = 5), and (ii) a mixed control group, composed of explanted donor hearts (n = 5) and aortic stenosis patients submitted to aortic valve replacement surgery (n = 7). Sample collection during aortic valve replacement was carried out at the interventricular septum, as part of the Morrow procedure. Sample collection during coronary artery bypass grafting was carried out during target exposition for distal graft anastomosis. Patients were diagnosed according to diagnostic guidelines contemporary to the date of procedures. The echocardiography protocol followed the 2016 ESC HF Guidelines recommendations  and look® Homogenizer. Then, samples were transferred to a new tube and incubated at 55 °C for 10 min. Subsequently, precipitation and wash steps were performed to purify proteins. Extracted protein was quantified by Bradford protein assay kit (ThermoScientific). Samples were kept at −80 °C until further analysis.Proteins were extracted using the Norgen kit (Norgen Biotek), following the manufacturer’s protocol. Briefly, frozen LV myocardium samples were homogenized in the presence of ceramic beads (ø = 2.8 mm) and kit lysis buffer (200 µL or 300 µL if sample ≥ 5 mg) using a PreCellysProteins were subjected to electrophoresis and digested with trypsin, as described previously ,17. Each sample (2.5 μg) was used for information-dependent acquisition (IDA) analysis by NanoLC–MS using TripleTOF 6600 (ABSciex), as described previously ,17. The http://proteomecentral.proteomexchange.org, accessed on 12 May 2022) via the PRIDE partner repository with the dataset identifier PXD033876.For quantitative analysis, 2.5 μg of each sample was subjected to 3 SWATH runs, as previously described ,17. A toSWATH log2 normalized data from 3 different MS runs were adjusted for batch effects with the parametric empirical Bayes ComBAT framework, using surrogate variable analysis (sva) R package version 3.42.0 . Normalit-test using GraphPad Prism6 . The resulting p-values and fold changes indicated up- and downregulated proteins. Differentially expressed proteins were defined as those that showed a fold-change greater than 1.5 (upregulated) or lower than 0.67 (downregulated) and p-values lower than 0.05. Principal component analysis and hierarchical clustering heatmap analysis were performed using Perseus software environment [To identify differentially expressed proteins, quantitative data from SWATH was analyzed using student’s ironment . p-value ≤ 0.05) and the respective fold change. A statistically significant representation of biological functions and canonical pathways was identified based on the IPA p-value, displayed as −log . The IPA p-value is a probability score calculated by considering the total number of proteins known to be associated with a given function or pathway, and their representation in the experimental dataset. The prediction of inhibition and the activation of biological functions and canonical pathways was based on the IPA z-score, a statistical measure of the match between the expected relationship direction and the observed protein expression, resulting in the activation (z ≥ 2) or inhibition (z ≤ −2) of the respective pathway. Pathway analysis was performed using Ingenuity Pathway Analysis software  by uploading the quantified protein list . Data were analyzed by One Way ANOVA Tukey test or Student’s http://proteomecentral.proteomexchange.org, accessed on 12 May 2022).Proteomic analysis of 27 human ventricular myocardium samples, including 16 samples from patients with HFpEF and 11 samples from patients without HFpEF, resulted in the identification and quantification of a total of 1043 proteins. In addition, relative abundances were further normalized between analytical batches, leaving a total of 1015 analysis-ready proteins. A full list of raw and normalized protein quantification data can be accessed in Principal component analysis (PCA) and hierarchical clustering revealed high heterogeneity between samples, with no distinct clustering between HFpEF and non-HFpEF groups, with two HFpEF samples (HFpEF S09 and HFpEF S10) presenting a more distinct proteomic profile . Such diWhen investigating the data of those two samples more deeply, we could observe higher levels of myosins  and troponins  in HFpEF S09 , suggestp-value ≤ 0.05), 9 proteins presented significantly higher expression levels , and COMT and EHD1 proteins also presented a tendency for higher expression levels  when comparing both sample groups , in which there is a triggering event such as acute myocardial infarction, HFpEF pathophysiology mechanisms are believed to be mainly caused by inflammation arising from cardiovascular comorbidities ,5,20. SuThe upregulation of peroxiredoxin-like 2A  was also identified as being upregulated in HFpEF samples. LAMA4 is part of laminin 8 and 9, which are major constituents of heart-basement membranes . IncreasOther proteins found to be upregulated in HFpEF include PRKACA (involved in contraction of cardiac muscle via phosphorylation of RyR2 channel) ; actin AOn the other hand, serum amyloid A-1 protein (SAA1), a circulatory acute-phase protein produced in response to inflammation, was detected as down-regulated in HFpEF myocardium samples. Biopsy samples had different degrees of blood contamination; therefore, the presence of circulatory proteins, such as SAA1, might be artificially differentially expressed between groups. The down-regulation of SAA1 was therefore devalued in our analysis.p-value ≤ 0.05) when comparing both sample groups , part of a multienzyme complex that catalyzes the oxidative decarboxylation of α-ketoglutarate to succinyl-coenzyme A, acting as a rate-limiting enzyme of the tricarboxylic acid cycle, was also identified as downregulated in HFpEF+DM samples (FC = 0.37), indicating the dysregulation of overall cellular metabolism in the myocardium of HFpEF+DM patients . The inhFatty acids β oxidation also occurs in mitochondria and is a major energy source to sustain the energy requirements of the adult heart muscle ,44. In HInterestingly, several proteins involved in cardiac muscle contraction were also identified as downregulated, including myosins MYH2 and MYH7; titin (TTN); and cardiac actin ACTC1, CAPZA2, and MYOM3.Two proteins identified as up-regulated in HFpEF + DM, DCD-2 (FC = 1.97) and GLRX (FC = 1.81) have been previously reported to have increased levels in the serum of diabetic patients ,48.We are aware that the study presents some limitations. Due to the inherent low availability of human heart ventricle biopsies, a small cohort of samples was analyzed, impacting the strength/statistical power of the findings. The groups analyzed were not perfectly matched in terms of clinical characteristics, age, cardiovascular comorbidities, the anatomical site of the myocardial sample collection, and contamination with blood. Therefore, we cannot exclude that other confounding factors and parameters besides HFpEF diagnosis and comorbidities (DM) may be hampering stronger results. More than the variability introduced by tissue-sample harvesting, the HFpEF broad range of phenotypical variations might hamper uncovering, whose proteins and signaling pathways are differentially expressed in these patients, leading to a lower statistical power of the analysis performed in this work. The co-occurrence of DM further increases the heterogeneity of the HFpEF population.Overall, we have identified the increased expression of several proteins associated with protection against oxidative stress and the negative regulation of inflammation in HFpEF, which might constitute a reaction of the myocardium to the inflammatory environment and oxidative imbalance . In addition, within HFpEF, the tissue samples of patients with DM presented protein signatures that correlated with oxidative phosphorylation, fatty acid β oxidation, mitochondrial function, and contractility impairments.From these analyses, the deleterious impact that HFpEF and DM have on mitochondrial function and metabolism is clear: there is a need to develop drugs targeting the mitochondria. Despite the inherent disadvantage of human myocardial biopsies scarcity, leading to lower biological replicates and the small statistical power of studies using these types of samples, we strongly believe that investigating proteomic profiles and signaling pathways directly in human tissue samples brings us one step closer to the disease phenotype, providing new relevant insights into human HFpEF and DM biology at the myocardium cellular level. Machine and statistical-learning models applied to omics datasets are being increasingly explored to classify patient cohorts and predict pathologies and associated risks in a broad range of diseases, including HFpEF . The dat


Heart failure with preserved ejection fraction (HFpEF) is one of the most complex and most prevalent cardiometabolic diseases in aging population. Age, obesity, diabetes, and hypertension are the main comorbidities of HFpEF. Microvascular dysfunction and vascular remodeling play a major role in its development. Among the many mechanisms involved in this process, vascular stiffening has been described as one the most prevalent during HFpEF, leading to ventricular-vascular uncoupling and mismatches in aged HFpEF patients. Aged blood vessels display an increased number of senescent endothelial cells (ECs) and vascular smooth muscle cells (VSMCs). This is consistent with the fact that EC and cardiomyocyte cell senescence has been reported during HFpEF. Autophagy plays a major role in VSMCs physiology, regulating phenotypic switch between contractile and synthetic phenotypes. It has also been described that autophagy can regulate arterial stiffening and EC and VSMC senescence. Many studies now support the notion that targeting autophagy would help with the treatment of many cardiovascular and metabolic diseases. In this review, we discuss the mechanisms involved in autophagy-mediated vascular senescence and whether this could be a driver in the development and progression of HFpEF. Cardiovascular diseases (CVDs) are the leading cause of death worldwide, resulting in 17.9 million deaths in 2019 . In the HFpEF is associated with a poor quality of life, crucial healthcare resource utilization, high rates of hospitalization, and mortality that are similar to patients with HFrEF . IncidenHFpEF is considered a clinical syndrome rather than a discrete disease . TherefoIn this review, we go through available data regarding vascular and microvascular dysfunction related to autophagy and cell senescence, to propose a potential role of these processes in the development and progression of HFpEF.The microvasculature is comprised of arterioles, capillaries and venules; microcirculation through these vessels allows the delivery of oxygen and nutrients to meet the energetic demands of local tissues, mainly through regulation of vascular tone, structural microvascular adaptations such as angiogenesis, and the regulation of hemostasis, inflammation and vascular permeability , 20. TheRecently, using cell therapy, de Couto et al.,  administTaken together, the evidence presented above strengthens the previously proposed role of microvascular dysfunction in HFpEF pathogenesis Figure 1Arterial intima-media thickness and stiffness have been identified as important risk factors for HF . The assvia reactive hyperemia (RH), it was reported that HFpEF patients had a reduction in brachial artery diameter in response to RH, compared to aged-matched controls . Because in both cells are described the occurrence of phenotypic features commonly observed in senescent cells , and chaVSMCs are the key component of the medial layer in arteries, with an important role in contraction and regulation of blood pressure and vascular tone . Aging-dIntimal thickening is also associated with the formation of atherosclerotic lesions . InteresROS and angiotensin (Ang) II are well-known inducers of senescence in VSMC , 93. Angin vitro, and that altered RNA metabolism could be a key feature to ensure VSMC senescence detection  microvascular and macrovascular dysfunction are present in HFpEF and play an important role in its pathophysiology, 2) aging, one of the most prevalent comorbidities of HFpEF involves both vascular senescence and vascular autophagy impairment, 3) autophagy modulates cells senescence in both VSMC and ECs and 4) both autophagy and cell senescence in the heart muscle and endothelium are most likely to be participating in the development and progression of HFpEF.It is currently believed that chronic low-grade inflammation is one of the main drivers of HFpEF, resulting in a decreased NO bioavailability, an increase in proinflammatory cytokine levels and oxidative stress . ROS andThe inhibition of autophagy leads to cell senescence in both VSMCs and ECs , 149. OnIn this review, we propose a potential role for vascular autophagy and senescence in the development and progression of HFpEF, focusing on both VSMCs and ECs. Diabetes, obesity, aging and hypertension, main risk factors for HFpEF, triggers chronic inflammation and ROS, that impairs autophagy and triggers cell senescence. The presented data support the idea that both autophagy and cell senescence, processes that are strongly related to one another, might be important components of HFpEF pathophysiology Figure 2The authors confirm contribution to the paper as follows: review conception and design: FS-O, MT, FP, IN-S, and MC; critical analysis of the literature: FS-O, MT, FP, JM-B, IN-S, JR, MV, and MC; draft manuscript preparation: FS-O, MT, FP, JM-B, IN-S, JR, MV, SL, PC, and MC; SL and MC reviewed the final version of the manuscript. All authors approved the final version of the manuscript.The authors received funding from the Agencia Nacional de Investigación y Desarrollo (ANID), Chile: FONDAP 15130011, Fondecyt 1180157, Fondecyt 1220392, and Postdoctoral fellowship 3210496.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


We compare Covid-related working papers in economics to non-Covid-related working papers in four dimensions. Based on five well-known working papers series and data from the RePEc website, we find that Covid papers mainly cover topics in macroeconomics and health, they are written by larger teams than non-Covid papers, are more often downloaded and they receive more citations relative to non-Covid papers. Covid-19 constitutes a dramatic exogenous shock affecting almost all areas of life. It immediately led to an explosion in research, particularly in medicine  codes were of particular interest since the outbreak of Covid-19. We also investigate two metrics after the working paper has become available, namely the number of citations and downloads of the working papers. Fraser et al.  found thIn order to assess the impact of Covid-19, we use data from five (major) working paper series in economics obtained by scraping the RePEc website. We provide some descriptive statistics and regression-based evidence to answer how the different metrics differ for Covid-related research relative to non-Covid research. We focus on working papers as they are a rapid communication channel and they are quite common and have long standing tradition in economics.While we focus on some aspects of the publication process from a producer perspective of research, there have also been important changes to the reviewing process and how research is conducted. The wealth of new papers and impacts on the everyday live has affected the reviewing process. For example, Kodvanj et al.  and AvivNBER  Working PapersCEPR (Centre for European Policy Research) Discussion PapersIZA (Institut für die Zukunft der Arbeit) Discussion PapersCESifo (Center for Economic Studies) Working PapersMPRA (Munich Personl RePEc Archive) Working PapersThe first four series are network based, meaning that the authors need to be part of the respective network while anyone can make working papers available in the last one. This choice is driven by the number of yearly published papers, reputation and influence . We focus on the 20 main categories represented by the letters.In a first step, we compare the topics of the different papers based on the Journal of Economic Literature (JEL) codes. In economics, the assignment of publications to sub-fields has a long history. Early classification attempts by the American Economic Association go back to the beginning of the 20th century when ten major categories were defined in the American Economic Review. These categories were developed to arrange publications to their subject matter and have subsequently been revised several times and transferred to the EconLit system, including JEL codes. The majority of economics journals ask authors to provide JEL codes for their papers. Cherrier  providesFor each JEL category, we add up all the JEL codes listed in the papers and divide their sum by the total number of papers. As many papers have multiple codes (2.14 on average), the fractions do not add up to one. For the pre-crisis period we take the average of JEL distribution over the period 2015-2019 in order to avoid to average out short-run developments. Comparing the pre-crisis (column two) period with all papers from the crisis period (column three) there are only minor differences.Conversely, it appears that microeconomics and economic development saw a clear dip in their share for Covid papers, even if the overall share (column three) was not much affected. One explanation for this finding could be that researchers in these fields did not switch to writing Covid-related papers to the same extent as other subject fields like macroeconomics and health.Looking at the development of JEL codes over time from February 2020 to August 2021 there are no obvious trends in the JEL distribution, i.e. the chosen topics, defined by the assigned JEL codes, remained fairly the same across time.As the share of health paper has increased considerably, we want to take a closer look at these papers. We collected all corresponding co-JEL codes of a paper, i.e. codes that are assigned in addition to the JEL code I for health. We compare the distribution before and after the outbreak of the pandemic. In Table Next, we compare the number of authors that each paper has. We can think of two opposing forces regarding the number of authors. On one hand, the lock-downs and travel restrictions make it harder to collaborate with other authors. This could in turn lower the number of authors for each paper as evidenced in Homolak et al. , Aviv-ReIn order to check whether these difference are statistically significant after controlling for working paper and topic fixed effects in the form of dummy variables we run three regression models. The first is an ordinary least squares (OLS) regression. As we deal with count data we also consider a Poison regression model and a negative binomial regression (NBREG) model. The variable of interest is a dummy variable which takes a value of one in case of a Covid-related paper and zero otherwise. The top panel of Table Next, we address how many downloads articles receive depending on whether they were Covid-related or not. As shown in Table For downloads, the log OLS and negative binomial regressions are of particular importance as the distribution is very skewed. The first two columns of Table 4As the lower panel in Table Last but not least, we assess whether the citations of the Covid-related papers are higher or lower than non-Covid papers. Table Table Our results are robust to excluding highly cited papers based on the 95% percentile as shown in the lower panel of Table We have shown that around 15% of economic working papers have been Covid-related between January 2020 and August 2021. This share has decreased since the peak in spring 2020 but there is still a steady flow of Covid papers. When comparing the Covid papers to non-Covid papers made available as working papers at the same time, we find that Covid papers are written by larger teams, are downloaded more often and receive more citations. This somewhat contrasts with general science where articles are written by fewer authors. Further research might be able to determine the exact causes for this divergent pattern.One important limitation of this paper is that we only looked at the data available on RePEc. While the analysis about JEL codes and the number of authors does not require any additional assumption, the analysis about downloads and citations does. Specifically, there are many places where one can download a working paper including the working paper series website. As a result, we only have a subset of the downloads and need to assume that our sample is representative. Similarly to the downloads, RePEc is somewhat of a closed system regarding citations. Citations are only tracked for papers within RePEc. If for example a medical journal cites an article available on RePEc, this is not counted towards the citation count of the article. As with downloads, we thus need to assume that our data set is representative.Some potential extensions for future research of our work could include a more granular split into topics using several digits of the JEL code instead of the one digit used here. How the pandemic affected male vs. female researchers differently and last but not least, where the working papers end up. Baumann and Wohlrabe  have fouIn the future one could repeat our analysis for articles finally published in a journal. As noted before, the publication process is quite slow in economics. We found only two articles according to our search criteria that were published in the so-called top five journals in economics (Bornmann et al.,


Crohn’s disease (CD), a subtype of inflammatory bowel disease (IBD), causes chronic gastrointestinal tract inflammation. Thirty percent of patients do not respond to anti-tumor necrosis factor (TNF) therapy. Sialylation is involved in the pathogenesis of IBD. We aimed to identify potential biomarkers for diagnosing CD and predicting anti-TNF medication outcomes in CD. Three potential biomarkers  were screened using bioinformatics analysis and machine learning based on sialylation-related genes. Moreover, the combined model of SERPINB2, TFPI2, and SLC9B2 showed excellent diagnostic value in both the training and validation cohorts. Importantly, a Sial-score was constructed based on the expression of SERPINB2, TFPI2, and SLC9B2. The Sial-low group showed a lower level of immune infiltration than the Sial-high group. Anti-TNF therapy was effective for 94.4% of patients in the Sial-low group but only 15.8% in the Sial-high group. The Sial-score had an outstanding ability to predict and distinguish between responders and non-responders. Our comprehensive analysis indicates that SERPINB2, TFPI2, and SLC9B2 play essential roles in pathogenesis and anti-TNF therapy resistance in CD. Furthermore, it may provide novel concepts for customizing treatment for individual patients with CD. Crohn’s disease (CD) is a significant type of inflammatory bowel disease (IBD) that is characterized by a chronic inflammatory condition that can affect any area of the gastrointestinal tract . ApproxiE. coli, which exacerbates the pro-inflammatory response by intestinal dendritic cells , least absolute shrinkage and selection operator (LASSO) regression, random forest (RF), and support vector machine recursive feature elimination (SVM-RFE) were applied to further screen biomarkers for anti-TNF therapy response. Moreover, a scoring system, the Sial-score, was established to predict the response to anti-TNF therapy in patients with CD before or after treatment.Using the search terms “Crohn’s Disease and anti-TNF” or “infliximab”, gene expression cohorts for CD were retrieved from the Gene Expression Omnibus database. The following cohorts were obtained: GSE16879  , GSE1117p-value <0.05 and | log2 (fold-change)| > 1.0. Sangerbox3.0 (http://vip.sangerbox.com/) was used to perform a consensus clustering algorithm using the R package ConsensusClusterPlus . The gene signatures of the immune cells are listed in LASSO regression was applied to select potential biomarkers using the glmnet R package . BinomiaThe expression of potential biomarkers was used to develop a scoring system based on principal component analysis (PCA) to predict the response to anti-TNF medication. The Sial-score was calculated as follows: Sial-score = ∑PC1i, where i is the potential biomarker.Differential expression analyses were performed using the limma R package in the GSE16879 cohort to identify sialyation-related DEGs between the CD and normal samples. A volcano plot of the DEGs is presented in n = 20) and 17.6% (n = 16), respectively  and 94.4% (n = 18) of the patients, respectively. Further analysis of the two groups with immune cell infiltration showed that the Sial-high group was enriched in immune cell infiltration (Patients with CD in GSE16879 were divided into Sial-high and Sial-low groups based on the Sial-score. To explore the characteristics of the Sial-high and Sial-low groups, we performed GSEA using the REACTOME pathway database. As shown in ltration . Figure Anti-TNF treatment helps patients with CD have better clinical outcomes, mucosal healing rates, and quality of life, although 10%–40% of individuals predominantly have no response . PathophRecently, To screen biomarkers for predicting the outcome of anti-TNF medication, we identified two modules related to the anti-TNF response using WGCNA. Considering the unique characteristics of the two subtypes, DEGs between the two clusters were identified and overlapped with the two modules. Furthermore, SERPINB2, TFPI2, and SLC9B2 were selected as biomarkers of anti-TNF response using LASSO, RF, and SVM-RFE algorithms. Our results also indicated that the diagnostic model combining the expression of SERPINB2, TFPI2, and SLC9B2 showed excellent performance in both the training and validation cohorts.+/H+ exchangers and present in epithelial cells of the small intestine (SERPINB2, also known as plasminogen activator inhibitor 2, is highly expressed in peripheral blood from CD patients . Wei et ntestine . SLC9B2 ntestine .To improve the clinical significance of our study, we constructed a Sial-score system based on SERPINB2, TFPI2, and SLC9B2 expression. The Sial-score has an outstanding ability to predict and classify the response to anti-TNF therapy in patients with CD before or after treatment. Importantly, only 15.8% of patients responded to anti-TNF therapy in the Sial-high group. However, 94.4% of patients with a low Sial-score responded to anti-TNF therapy. Moreover, the Sial-high group was significantly enriched in immune-related pathways and showed a high level of immune cell infiltration. This indicated that immune cell infiltration plays a role in anti-TNF resistance. Martin et al. created a cell module named GIMATS, composed of IgG plasma cells, inflammatory mononuclear phagocytes, activated T cells, and stromal cells. Cell module scores differed between non-responsive and responsive patients with CD. Monocyte-derived macrophages dominated lesions enriched in the GIMATS module . The faiHowever, our study has some limitations. First, these datasets lacked data regarding important clinical variables such as disease activity and duration, previous bowel resection, and smoking. Second, we used retrospective data from public databases for our research. Future prospective studies are needed to confirm our findings.In summary, a valid diagnostic model and scoring system for predicting anti-TNF therapy response was constructed based on the expression levels of SERPINB2, TFPI2, and SLC9B2. Our findings may aid auxiliary diagnoses and provide personalized treatment strategies for patients with CD.


The pro-inflammatory responses and proapoptotic signaling pathways predominantly mediated through TNFR1, while neuroprotective and anti-apoptotic signals induced by TNFR2. In this review, we attempt to discuss the involvement of TNF receptors (TNFRs) and their signaling pathway in ocular tissues with focus on RGC and glial cells in glaucoma. This review also outlines the potential application TNFRs agonist and/or antagonists as neuroprotective strategy from a therapeutic standpoint. Taken together, a better understanding of the function of TNFRs may lead to the development of a treatment for glaucoma.Glaucoma is an irreversible sight-threatening disorder primarily due to elevated intraocular pressure (IOP), leading to retinal ganglion cell (RGC) death by apoptosis with subsequent loss of optic nerve fibers. A considerable amount of empirical evidence has shown the significant association between tumor necrosis factor cytokine  and glaucoma; however, the exact role of TNF in glaucoma progression remains unclear. Total inhibition of TNF against its receptors can cause side effects, although this is not the case when using selective inhibitors. In addition, TNF exerts its antithetic roles  Glaucoma, an ocular neurodegenerative disease, is a leading cause of irreversible blindness that has affected over 70 million people worldwide . The halThe axonal damage caused by injury to the optic nerve is thought to be the first stage of insult in glaucoma. However, direct impact on the optic nerve may ultimately result in RGCs death, which is exacerbated by the production of inflammatory cytokines from resident innate immune cells (glia) and the complement system in retina . Meanwhivia TNF receptor I (TNFR1) and TNF receptor II (TNFR2). Various cellular responses can elicit TNF expressions through these receptors. It depends on many factors, including the metabolic state and the adaptor proteins present in the cells  , 10. Sinhe cells . The dervia these two receptors. Collectively, a better understanding of the differential roles of TNFRs may lead to the development of a treatment for glaucoma.Divergent actions of TNF in neurodegenerative diseases have been long implicated. Because of the opposite natures, we reasoned that both an inhibition of TNFR1 signaling and selective activation of TNFR2 signaling, respectively, could shift the balance of endogenous TNF activity toward an overall neuroprotective/regenerative response in retinal neurons . Since TCentral nervous system (CNS) is considered to be an immune-privileged system. It comprises a network of vessels with a very specialized endothelial lining supported by macroglia, microglial, pericytes, and neurons. Together these cells form a substantial blood-brain barrier (BBB). The latter strictly regulates the immune cells entering the CNS and maintains homeostasis concomitantly. However, under inflammatory conditions, the BBB gets disrupted, and immune cells migrate into the CNS parenchyma. Retinal neurons, including RGCs, are part of the CNS. They share several similar properties with other CNS neurons, including the inability to regenerate in response to cellular damage. The immune-mediated inflammation in the retina is tightly regulated by the specialized intraocular environment with tight interconnected junctions in a similar fashion to BBB, referred to as the blood-retinal barrier (BRB) . This baAccording to Streilein, the ocular immune-privileged is attributed to the immunosuppressive and anti-inflammatory properties of the ocular cells, tissues, and fluids . For insRecent studies have focused on immunological changes occurring in glaucoma pathogenesis, and potential preventative therapies based along those lines have been proposed. The major targets of interest are cytokines; as in the aforementioned, the inflammatory mediators play an essential role in the early progression of glaucoma and may regulate RGC survival or death , 40. In via the primary members of TNFR superfamily and high-affinity receptors; TNFR1 (p55) and TNFR2 (p75), that are expressed on different cell types and secreted predominantly by various inflammatory cells , Parkinson’s disease (PD), encephalopathy, meningitis, multiple sclerosis (MS), neuromyelitis optica spectrum disorder (NMOSD), myelin oligodendrocyte glycoprotein (MOG) antibody-associated disease, neuropathy as well as myelosuppression , 89, 90.Interestingly, patients with rheumatoid arthritis (RA) and psoriasis were reported with a low risk of developing AD when treated with TNF blockers therapy  , which sIt has been reported that there is an increased case of demyelinating disorders and opportunistic infections of CNS following TNF blockade. However, neurodegenerative diseases like dementia may also benefit from anti-TNF therapy. One of them is thalidomide, a drug that can reach CNS and block TNF and related cytokines local generation by microglia responsible for neuroinflammation . Due to in vivo models of glaucoma  has been investigated to deactivate astrocyte e injury . More stFurthermore, several TNFα signaling-associated biomarkers, such as NF-κB and MAPKs aim to govern signals of cell death or survival. More research is needed to investigate the neuroprotective effects of anti-TNF/TNFRs strategies in targeting axonal injury . The keyTNF inhibitors suppress the elevated TNF-mRNA found in the iris and ciliary body experimental autoimmune anterior uveitis . TopicalIn clinical trials of ocular inflammatory diseases, promising findings were reported on individuals with several phenotype uveitis effectively treated with adalimumab and infliximab –117. ThiInvestigating the anti-TNF agents has been growing as the subject of interest in the treatment of neurodegenerative diseases; however, some have shown no beneficial effects. For example, Infliximab, administered intraperitoneally 15 minutes after alkali burn inflicted, is demonstrated to provide significant retinal and corneal protection . Howeverin vivo glaucoma models. Although several studies documented contradicting role of TNFR1 and TNFR2 in glaucoma, TNF and its receptor may still serve as attractive targets in glaucoma therapy. For example, RGCs loss induced by TNF is shown to be caused by TNFR2, instead of TNFR1, when TNFR2 knockout mice showed no RGCs degeneration while in TNFR1 knockout mice showed otherwise .The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Squalus suckleyi, we predicted (3) long reaction times . We used two-dimensional, high-speed videography analysis of mechano-acoustically stimulated neonate blacktip reef shark, Carcharhinus melanopterus (n=12), and sicklefin lemon shark, Negaprion acutidens (n=8). Both species performed a characteristic C-start double-bend response (i.e. two body bends), but single-bend responses were only observed in N. acutidens. As predicted, neonate sharks showed high manoeuvrability with high turning rates and tight turning radii (3–11% of body length) but low propulsive performance  when compared with similar-sized teleosts and S. suckleyi. Contrary to expectations, escape latencies were <20 ms in both species, suggesting that the neurophysiological system of sharks when reacting to a predatory attack may not be limited to long response times. These results provide a quantitative assessment of survival traits in neonate tropical sharks that will be crucial for future studies that consider the vulnerability of these sharks to predation.Accelerative manoeuvres, such as fast-starts, are crucial for fish to avoid predation. Escape responses are fast-starts that include fundamental survival traits for prey that experience high predation pressure. However, no previous study has assessed escape performance in neonate tropical sharks. We quantitatively evaluated vulnerability traits of neonate tropical sharks by testing predictions on their fast-start escape performance. We predicted (1) high manoeuvrability, given their high flexibility, but (2) low propulsive locomotion owing to the drag costs associated with pectoral fin extension during escape responses. Further, based on previous work on dogfish,  Summary: Tropical reef shark neonates show high manoeuvrability and short reaction times during antipredator escape responses compared with expectations based on previous work on teleost fishes and sharks. C’ shape (termed a C-start) as a result of a unilateral muscle contraction, which corresponds to stage 1 of the escape response  and sicklefin lemon (Negaprion acutidens) sharks use very shallow (<1 m), nearshore habitats likely for predator avoidance  can help overcome our limitations to understand how neonate sharks exploit nearshore habitats with high predation pressure. However, despite the extensive work on escape behaviours in teleosts, there is a lack of detailed kinematic studies on taxa that include large individuals, such as chondrichthyans but see . This isvoidance . In factvoidance . Nonethevoidance . These tSqualus suckleyi; originally reported as S. acanthias; see Pterophyllum eimikei), but are slower in speed and acceleration  are absent in adult elasmobranchs  and Dalatias licha  and N. acutidens  in Mo'orea, respectively. The high flexibility of the anterior part of the body  and high turning rates . Althougthe body  facilitathe body . Indeed,the body . Howeverthe body , contrarthe body . This drsuckleyi . With resuckleyi .C. melanopterus and N. acutidens to quantitatively evaluate their vulnerability traits. We used mechano-acoustic stimulation and high-speed videography analysis of the startle responses to assess both non-locomotor and locomotor performance. We predicted performance based on the following hypotheses: compared with previous work on teleosts, neonate tropical sharks will show (1) high turning rates and tight turning radii, but (2) low propulsive performance  during fast-start escape responses, and (3) long escape latencies (>20 ms). We used indicative performance ranges for each variable reported in S. suckleyi , n=12] and sicklefin lemon sharks  were collected between November 2019 and January 2020 around Mo'orea island  in French Polynesia using monofilament gillnets (50×1.5 m with 5 cm mesh size) at dusk (17:00–20:00 h). Neonates were identified by their umbilical scar stage, which is used to estimate age classes (LT) and body mass (mb) were 56.9±1.1 cm  and 0.98±0.02 kg in blacktip reef sharks, and 66.0±1.4 cm (range: 61.2 to 71.8 cm) and 1.31±0.07 kg in sicklefin lemon sharks, respectively. The morning following capture (day 1), sharks were marked with passive integrated transponder (PIT) tags  inserted below the first dorsal fin for individual identification.Neonate blacktip reef sharks [ classes . Neonate classes . Total tSharks were maintained in the laboratory for 9 days in 1250 l circular tanks (∼1.5 m diameter) with flow-through filtered seawater and aeration, and covered with 60% shade cloth. Blacktip reef sharks were maintained in groups of three, and sicklefin lemon sharks were maintained in groups of two because of their larger size. The open-sided facilities provided a natural photoperiod. Feeding started at day 2 in captivity. We fed sharks every second day with fresh tuna at 3–5% of body mass . To contOn day 10, a single shark was randomly selected from the holding tank and transferred with a hand net to an adjacent 3.4 m diameter circular pool  using a GoPro Hero7 Black camera with wide field of view (see section below for details on lens distortion correction). Three sharks were tested per day. To control for a potential temporal signal, trials took place during three different periods of the day, such that one individual was tested during the morning (9:00–11:00 h), one at midday (12:00–14:00 h) and one in the afternoon (15:00–18:00 h).We used a sudden (mechano-acoustic) stimulus to elicit a startle response because it has been shown to elicit stronger responses compared with other types of stimuli . The stik1, k2, k3, k4) cannot be accessed. The curvature of straight lines in the distorted image was rectified in the resulting undistorted image. This correction reduces the magnification effect of the centre of the image by reducing radial distortion. Tangential distortion (or de-centring distortion) was negligible. Parallax effects, in which a moving object appears to move a shorter or longer distance than it actually does, were also negligible because (1) we restricted the vertical movement of the sharks (see above), (2) we placed the reference lengths at the same level of the sharks, and (3) our camera was at the same angle and distance with respect to the bottom of the test arena throughout the trials. Finally, we exported corrected image sequences from the undistorted videos at 240 frames s−1 for escape trials and 120 frames s−1 for spontaneous turns.We used a GoPro Hero7 Black camera with a wide field of view (FOV) and the following video parameters: (1) equivalent focal length: 15–30 mm, (2) aspect ratio: 4:3, (3) pixel resolution: 1280×960, (4) vertical FOV: 94.4 deg, (5) horizontal FOV: 122.6 deg and (6) diagonal FOV: 149.2 deg. Prior to analysis, we used Adobe Premier Pro v22.2.0 to correct for wide-angle lens distortion. The lens distortion algorithm calibrated for GoPro cameras applied a –31 curvature correction to each video. This parameter is already established by the software as it is already calibrated for the camera model/lens type. For this reason, calibration parameters . Using the reference lengths, a value of ∼0.3703 was applied for x-axis (forward displacement) and y-axis  calibration. Calibration of the z-axis  was not necessary, as water depth was sufficiently shallow to minimize shark movements in this plane. We digitized the x,y coordinates (tracking points) of the predetermined body landmarks. Distance between tracking points and instantaneous speed (between two successive frames) were obtained from the tracking software. For the escape responses, tracking was initiated one frame before the first detectable movement of the tip of the head and continued for 100 frames (i.e. 416 ms). For spontaneous turns, tracking started one frame before the first detectable movement of the head and finished with the end of the turn. Tracking coordinates were then imported to the R environment for further analysis (see below).We used the manual tracking plugin in ImageJ v2.0.0 for a two-dimensional video analysis frame by frame using the corrected image sequences. The corrected image resolution (1280×960 pixels) yielded ∼2.7 pixels cmResponsiveness was measured as the proportion of escape responses out of the total number of stimulations for each shark. Directionality was defined as the proportion of responses in which the body bent in a direction away from the stimulus (i.e. ‘away responses’ as opposed to ‘towards responses’) out of the total number of responses, and did not necessarily indicate the final escape trajectory . Escape TS1 and TS2), respectively. To define TS1 and TS2, we used the smoothed turning rate versus time curve (see below) and applied the function uniroot.all from the package stats (https://www.r-project.org/) to find the time points defining the stages. The first time point (T0), at the start of the tracking, was one frame before the first detectable movement of the head. Stage 1 started at T0 and finished with a reversal of turn direction, at T1, indicated by the smoothed turning rate curve crossing 0 deg s−1. Stage 2 started at T1 and finished with a stop or reversal of turning direction, at T2; that is, when the turning rate curve crossed 0 deg s−1 again. Total escape response duration (TEscape) was the sum of TS1 and TS2. The mean duration of all responses recorded, by species, was defined as TMean. Although variable, continuous swimming or coasting have been defined as the final stage (S3) in some fish species . Turning rate data paired with video inspections were used for stage 3 analysis.Stage 1 (S1) and stage 2 (S2) were defined by the turning of the anterior segment of the body . The rotation of this line was measured in degrees. Because stage 2 corresponds to a body bend opposite to that of stage 1, θS2 bears a negative sign.Angles were measured using the angle tool in ImageJ as the relative positions of the line projecting from the tip of the head to the COM between the start and end of each stage . Mean turning rate was obtained as the ratio between angle and duration for stage 1  in deg s1 ωMean; .RTurn) was calculated using the formula in Turning radius  of spontaneous turns during routine swimming for unstimulated individuals for both species.We also measured turning angle, duration and mean turning rate using image sequences (at 120 frames sTMean) to measure these variables to avoid any performance bias owing to differences in escape duration (Mean) was calculated as the length of the path of the COM in metres from T0 until TMean. Displacement  was measured as a straight line between the positions of the COM at T0 and at TMean (see −1) was obtained by dividing DMean by TMean. Average speed , maximum speed  and maximum acceleration  were calculated from the smoothed distance–time data obtained from ImageJ during the escape response . Velocity differed from speed (UAvg and UMax), as the former was based on how fast the shark displaced its body from point A (one frame before stimulation) to point B (TMean), and the latter was derived from the path of the COM throughout the escape sequence (TEscape).We measured two different distance variables: distance travelled and displacement. We used a fixed time (duration . The cumMean see C. Velocihttps://www.r-project.org/). For the analysis of turning rate, speed and acceleration, we used smoothed curves for each of these variables. We applied a locally estimated scatterplot smoothing (LOESS) to the raw data using the loess function. The degree of the polynomial (1 or 2) and the size of the neighbourhood (α) were optimized using a 5-fold cross-validation procedure specific to each escape response using the kfold function from the dismo package (https://CRAN.R-project.org/package=dismo). The root mean square error (RMSE) was obtained from the cross-validation results for each model combination with the function rmse from the package hydroGOF (https://CRAN.R-project.org/package=hydroGOF) such that the fit with the minimum RMSE was used for smoothing.All data processing and statistical analyses were performed in the R statistical environment , TS1 , TS2 , TEscape , ωS1  or ωS2  between stimulations in neonate blacktip reef sharks. This indicated no habituation effect or muscle fatigue over the consecutive stimulations  and included the stimulus number as a random effect . To investigate the relationship between turning performance and locomotor performance, we built LMMs with velocity (m s−1) as the dependent variable with either θS1,θS2, ωS1 or ωS2 as the explanatory (fixed effect) variable. Stimulus number was included as a random effect. Model validating was done via visual inspection of residuals following We pooled all data for summary results but used linear mixed-effects models (LMM) for all regressions to account for the non-independence owing to the repeated measures in the data. This approach allowed us to also account for the unbalance structure of the data . We used the lme function from the nlme package  or in sicklefin lemon sharks . All individuals resumed similar space use following a startle, and space use was not significantly different between pre-startle states in either species .Individuals were deemed acclimated to the test arena after 2 h, as evidenced by undisturbed swimming. Sharks were startled three times, 20 min after each stimulation. Normal swimming activity resumed within the 20 min . Only one escape response, and only in blacktip reef sharks, was towards the weight, such that similar directionality (97% and 100%) was observed in blacktip reef sharks and sicklefin lemon sharks, respectively.We were able to successfully video record 32 responses from 12 blacktip reef sharks and 20 responses from seven sicklefin lemons sharks. Both species responded to the mechano-acoustic stimulus with a fast unilateral contraction bending the body to one side, followed by a body bend in the opposite direction . In sicklefin lemon sharks, the turning rate data of 11 escape responses showed that eight responses were double-bends and three of them were single-bends  and 15.9±3.18 ms in sicklefin lemon sharks  and 11.8±2.93 ms in sicklefin lemon sharks . We found no significant differences in mean  or minimum latencies  between blacktip reef sharks and sicklefin lemon sharks  in blacktip reef sharks had a much broader range than in sicklefin lemon sharks (S2) were smaller  at smaller turning angles (θS1) than escape responses (During their first body bend (stage 1), both species showed fast angular velocities  are summarized in TEscape) was comparable to that of S. suckleyi but longer compared with similar-sized teleost species  or TEscape  in blacktip reef sharks. Similarly, LT did not affect TS1  or TEscape =0.56, P=0.482, R2=−0.067) in sicklefin lemon sharks. TS1 was linearly correlated with the angle of turn during stage 1 (θS1) in both species  or ωS2  in blacktip reef sharks. Unfortunately, the lower sample size in sicklefin lemon sharks precluded the possibility to draw a reliable correlation. Peak acceleration occurred at the beginning of the first stage, and it was similar in both species . However, all single-bend responses occupied an area in the graph closer to that of double-bend escape responses and not to routine turns . Displacement and velocity were calculated at the mean TS1 (n=3) precluded the possibility to statistically test for differences between single-bends and double-bends in sicklefin lemon sharks. See Single-bend responses were only observed in three sicklefin lemon shark individuals and not in blacktip reef sharks. In all cases, stage 1 was followed by coasting and no stage 2 was visible in the turning rate data D. Singlene turns . Therefoesponses  and the mean TS1 . VelocitWe observed a consistent pattern in the motor characteristics that followed stage 2 in double-bend responses. This pattern (defined as stage 3) was observed and analysed in 31 and 5 responses in blacktip reef and sicklefin lemon sharks, respectively. In the remaining responses, we were unable to determine whether stage 3 was present as the sharks were in a blind spot for the camera during this section of the tracking. One or two tail beats were followed by coasting, in which the tail aligned with the midline during stage 3. This pattern was defined in the turning rate data by a positive signal followed by a negative-going signal of similar intensity A–C. The S. suckleyi , and tight turning radii (3–11% of body length). This is in line with a high turning manoeuvrability . We also showed that speeds (4.1–5.2 BL s−1) and accelerations (34.3–36.5 m s−2) were at the lower end for both species, when compared with published data in similar size species of teleosts, in line with hypothesis 2. Contrary to our expectations for shark reaction times , we reported short escape latencies (<20 ms). Finally, we emphasize the relevance of a third stage (stage 3), rarely described before  of their escape behaviour. We used neonates of two highly mobile tropical reef shark species and successfully obtained escape kinematic data under laboratory conditions. Both species engaged in fast-start escape responses typical of those observed previously in teleosts and in adult suckleyi . Specifid before , to char−1 expected values for 57.3 and 66.5 cm long individuals, respectively; versus 1148 and 972 deg s−1 observed in blacktip and sicklefin lemon sharks, respectively) based on the length-turning rate relationship in aquatic vertebrates  for double-bend responses was 2.43 (blacktip reef shark) and 2.06 (sicklefin lemon shark) times higher than that of fast responses reported for adult S. suckleyi of a similar length . Pectoral fins are likely an important control surface for shark escape responses. Indeed, S. tiburo reduced turning radius using the pectoral fin inside the body curvature as a pivot point, unlike juvenile sandbar Carcharhinus plumbeus and scalloped hammerhead Sphyrna lewini. This high manoeuvrability will be advantageous in structurally complex environments, such as coral reefs. Further, high manoeuvrability represents a competitive advantage for neonate sharks over larger predators , because turning performance decreases with body size (Another characteristic of high manoeuvrability was the tight turning radius observed in both species (3–11% of body length), equivalent to turning specialists ∼6%; , and in es ∼40%; . Tight tes ∼40%;  and can es ∼40%; . We obsesuckleyi  and in ua tiburo . Therefoody size . Taken tC. melanopterus and N. acutidens compared with S. suckleyi, such differences may be attributable to difference in temperature (29°C compared with 12°C for S. suckleyi). However, speed and acceleration in neonate sharks was in the lower end of the indicative range for these parameters in teleosts (see Esox lucius) can achieve both high manoeuvrability and acceleration. Hence, future studies may need to focus on addressing the root causes for the low propulsive performance observed so far in the escape responses of sharks when compared with teleosts.It is also possible that the extent to which certain mechanisms that increase turning performance used by the sharks  may cause a relatively low locomotor performance owing to the considerable drag costs associated . In factosts see , despiteosts see  and someThe high manoeuvrability during stage 1 may lead to a reduction in velocity in tropical neonate sharks as in many other teleosts . This trS2 values in both species, and single-bends (i.e. stage 2 absent) were reported in three cases in sicklefin lemon sharks. This plasticity in stage 2 might confer an energetic advantage to the prey when responding to a predatory threat under different contexts. For example, when the danger is not imminent, a low-energy response may be sufficient to avoid predation.Escape responses are energetically expensive and, therefore, overall escape performance must meet a compromise with energy expenditure. For example, escapes lacking stage 2 (i.e. single-bend responses) may be less energetically costly to undertake than double-bend responses . In factS1 or ωMean), which are driven by different muscle contraction speeds and/or neural pathways  dominated blacktip reef sharks , indicating that the level of threat is much higher in blacktip reef sharks than in sicklefin lemon sharks. Furthermore, there are reports of giant trevally  ramming and mortally injuring adult blacktip reef sharks , the lower responsiveness in sicklefin lemon sharks (64.6%), compared with blacktip reef sharks (100%), suggests that the level of perceived threat varies between them. In fact, behavioral trials showed that neonate sicklefin lemon sharks and grey reef sharks (S. acanthias (embryos) and Dalatias licha show a transient Mauthner apparatus (Short-latency responses may signify a competitive advantage to small neonate sharks that likely rely on rapidly moving away from an approaching, larger predator. Neonate sharks were fast to react to the stimulus; their escape latency was shorter than previous observations on adult sharks 67 ms;  and simipparatus . Future pparatus . Regardlpparatus . TherefoThis study improves our knowledge on escape behaviours by reporting, for the first time, detailed escape kinematics for neonate and tropical sharks. Laboratory studies impose logistical limitations on studying large taxa. Here, we successfully applied a common methodology to two shark species to understand behaviours that are important for fitness. We highlighted the main components of the escape response, such as high turning agility and fast reaction times, and their potential contribution toward survival. It is important to emphasize that future studies on the neuromuscular control of escape responses in sharks will be of particular value. Understanding how neonate sharks exploit coastal habitats where predation pressure is high, and how future environmental change will affect escape ability in sharks, is important. For instance, rising sea surface temperatures owing to anthropogenic climate change  are like10.1242/jexbio.243973_sup1Supplementary informationClick here for additional data file.