An increasing body of evidence supports an essential role for endoplasmic reticulum stress (ERS) in colorectal cancer (CRC). In this study, we developed an ERS‐related genes (ERSRGs) model to aid in the prognostic evaluation and treatment of CRC patients.The training set and validation set data were extracted from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), respectively. ERSRGs were obtained from the GeneCards database. A prognostic risk scoring model was constructed using the least absolute shrinkage and selection operator (LASSO) along with univariate Cox regression analysis. To further predict the probability of survival for patients at 1, 2, and 3 years, a nomogram was devised. The advantages of the prognostic risk score model in screening patients’ sensitive to chemotherapy and immunotherapy were analyzed by drug sensitivity analysis and immune correlation analysis. Finally, hub genes associated with poor prognosis in the risk model were screened by Protein–protein interaction (PPI) network and their expression was validated using clinical specimens.A risk model for overall survival (OS) was developed using 16 ERSRGs associated with prognosis. Through analyses, we demonstrated a high degree of reliability for the prognostic risk scoring model. The constructed nomograms performed well in predicting patient survival over 1, 3, and 5 years. The calibration curve and decision curve analysis (DCA) supported a high degree of accuracy for the model. Patients in the low‐risk group had a lower IC50 for the common chemotherapy drug, 5‐FU, and responded better to immunotherapy. hub poor prognostic genes were validated in CRC clinical specimens.We have identified and validated a new ERS prognostic marker that can accurately predict the survival status of CRC patients for clinicians and better provide personalized treatment plans. This study found that the endoplasmic reticulum stress risk score model can accurately predict the prognosis of CRC patients. The endoplasmic reticulum stress risk score model can also provide individualized plans for patient treatment. We also provided clinical samples to validate the expression of prognostic key genes in the model. Therefore, we analyzed the expression and correlation of ERS‐related genes (ERSRGs) in CRC and constructed a prognostic risk score model. Our prognostic model performed well in predicting patients' prognosis conveniently and was able to differentiate between patients’ sensitive to chemotherapy and immunotherapy. In addition, by combining clinically relevant information, we also constructed nomograms. Taken together, we provide a new approach for the stratification of prognostic risk and navigation of clinical treatment options for CRC.22.1Genetic and clinically relevant information in the training set was extracted from The Cancer Genome Atlas (TCGA) established by American Cancer Center and no data has been eliminated, including 568 CRC and 44 normal specimens. All data for the validation set are from the GSE40967 and GSE17538 datasets in the Gene Expression Omnibus (GEO). Finally, 856 ERSRGs were screened from the online website GeneCards.2.2Analysis of differentially expressed ERSRGs samples was done using the “limma”, “pheatmap”, and “BiocManager” packages in R software. . Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of differentially expressed genes (DEGs) was conducted by R software.2.3p < 0.05). Visualization of mutations and associations of prognosis‐related genes in CRC samples by “maftools” in R software. Copy number variant (CNV) information for CRC was obtained from the UCSC Xena database. CNV frequencies were then calculated for prognosis‐related genes and the results were visualized via bivariate histograms and circle plots. The best prognostic risk score model was established using “glmnet” and “survivor” in R software. The calculation formula is as follows: risk score =∑1iCoefi*ExpGenei, where “Coef” and “ExpGene” were generated by least absolute shrinkage and selection operator (LASSO) Cox regression analysis. The Kaplan–Meier plot and log‐rank test combined with LASSO Cox regression were used to evaluate survival differences between the high‐ and low‐risk groups. The time‐dependent ROC curve was plotted using “survivalROC” in the R software, and the predictive accuracy of the model was assessed by the area under the curve (AUC). Finally, validation is performed in GSE40967.The training and validation set of this model are using CRC samples from the TCGA sample and the GSE40967 cohort, respectively. Screening for prognosis‐related genes from DEGs was done with univariate Cox regression analysis  was used to assess the clinical utility of the model.2.5The “pRRophetic” in the R software was applied to predict the IC50 of different drugs in each sample. Immunoinfiltration analysis was performed using “CIBERSORT” in the R software. The tumor cellularity was calculated by the “estimation” and ESTIMATE algorithms in the R software. Microenvironment scores were calculated for each sample and then differences in stroma and immune cells were predicted based on risk model files.2.6p < 0.05). GO and KEGG enrichment analysis of DEGs was performed using the “clusterProfiler” in R software to identify gene key functions. The protein–protein interaction (PPI) network of DEGs is calculated by website STRING . Import the PPI network data into Cytoscape software (version: 3.9.1), visualize it, and obtain the hub gene through a plug‐in. Finally, the pivotal genes associated with poor prognosis were obtained using “limma,” “survivor,” and “survminer” in the R software.DEGs were obtained from the RNA‐seq data of genes and risk files by “limma” in the R software . Next, reverse transcription was performed. Finally, quantitative real‐time polymerase chain reaction (qRT‐PCR) was performed using SYBR‐Green mixture (Vazyme) and gene‐specific primers (Sangon Biotech). Glyceraldehyde‐3‐phosphate dehydrogenase was used as a standard reference Table . Relativ2.8p < 0.05). ThWilcoxon rank‐sum test was used to compare the difference between the high‐risk and low‐risk groups. The Kruskal–Wallis test was used to compare more than two groups. Cox regression analysis was performed to determine the predictive factors affecting overall survival (OS). OS is the time from grouping to death due to any reason. For subjects who have been lost before death, the last follow‐up time is usually calculated as the time of death.Statistical analyses of the data in this study were performed using R version 4.2.0 software   and low‐risk group (n = 270) in the training set were divided according to the median value of the risk score. The validation set was divided in the same way, and principal component analysis (PCA) showed that the model was effective in dividing the CRC sample into high‐risk and low‐risk groups  Figure . The mut) Figure . In addi) Figure . Given t) Figure . The ris) Figure . The higs Figure .3.3Risk curves were generated from the samples in the dataset, where risk scores were visualized for both groups of patients Figure . The ana3.4p < 0.001)  . The results showed that the OS of patients in the high‐risk group was lower, and the AUC value of ROC performed well  Figure . To chec) Figure . The AUCl Figure . Through3.5In order to verify the superiority of the ER stress prognostic risk scoring model, the models in previous CRC studies were compared with our model through ROC. The first is a model for studying lipid metabolism, which is called Yang signature according to the name of the researcher.3.6p < 0.001).Since different clinicopathological features have different effects on disease prognosis, the distribution of clinicopathological features in the risk score model was also explored. The results suggest that risk scores are most strongly associated with tumor progression. Although risk scores did not differ significantly with age or gender Figure , they di3.7p < 0.05)  Figure . Figure 3.8p < 0.001)  Figure . Mutant ) Figure . In addi) Figure .3.9p < 0.001), immune score (p < 0.05) and stromal score (p < 0.001) were significantly different between the two groups, indicating that ERS profoundly affects immune and stromal cell infiltration , dendritic cells resting (p < 0.01), dendritic cells activated (p < 0.001), and plasma cells (p < 0.001) of low level of infiltration  validate the results of the training set and shows that the predictive power of our risk scoring model is reliable. However, in the validation set GSE40967, although our model performed well in predicting OS, it performed poorly in predicting 1‐, 3‐, and 5‐year survival (AUC = 0.61). In the validation set GSE17538, the 1‐, 3‐ and 5‐year survivals performed well (AUC = 0.77). This may be due to other reasons such as geography, race, data collection methods and sample testing methods. Therefore, in order to further verify the prediction performance of the model, we will further verify the data collected by ourselves and the data set GSE32323. Compared with the models of other studies, the prognostic model we constructed has the best predictive performance. In addition, nomograms combining risk scores and clinicopathological features were used to explore the potential of the model. The results show that the prediction ability of the nomograms constructed by us has been further improved. The performance of our established nomograms was confirmed by calibration plots and DCA analysis. Compared with other studies, the DCA decision curve we introduced can help clinicians.We also investigated the effect of ERS on CRC chemotherapy. Not only was the high‐risk group more resistant to the chemotherapeutic agent 5‐FU, but the risk score was positively correlated with resistance to 5‐FU. Meanwhile, patients with high‐risk scores had lower PFS. In conclusion, our risk score model can provide prediction of 5‐FU efficacy for patients. Gene mutations have been reported to cause poor prognosis due to chemotherapy resistance in tumor patients, and this study also analyzed common gene mutations in CRC.ERS affects a variety of immune processes, including the production of pro‐inflammatory cytokines, regulation of immune cells, antigen presentation, monitoring of cellular status, and maintenance of immune tolerance.In order to further explore the role of ERSRGs in CRC, we analyzed the DEGs of two subgroups with significant differences. SPP1, COMP, SERPINE1, and COL11A1 were found to be essential. SPP1 is a secreted glycoprotein that affects the adhesion, proliferation, differentiation, migration, and survival of a variety of cells.There are some limitations to this study. First, our data are from public datasets with incomplete relevant clinical information, and therefore more multicentre clinical specimens would be beneficial in understanding the role of ERS in CRC. Second, it is not clear why immune checkpoint‐related gene expression is high in the high‐risk group, but the response to immunotherapy against immune checkpoints is poor; more in‐depth studies will be required to reveal the reasons for this. Finally, further basic experimentation based upon this work would be critical in determining the specific mechanism of action of ERS in CRC.5This study provides a new and accurate protocol for predicting the prognosis of CRC patients. In view of the considerable prognostic ability of our model and its accurate prediction of treatment outcomes, our risk score model can be used as a new tool for patients to choose treatment. Given the importance of ERS in CRC, a more in‐depth study of its specific mechanism of action is warranted.Jingbo Geng: Data curation ; software ; visualization ; writing – original draft . Yunkai Guo: Formal analysis ; software . Mingjie Xie: Methodology ; supervision (supporting); validation (supporting). Zhipeng Li: Investigation ; methodology ; software ; supervision ; validation . Peng Wang: Methodology (supporting); software (supporting). Donghui Zhu: Data curation (supporting); methodology (supporting); software (supporting); visualization (supporting). Jing Li: Funding acquisition ; project administration ; writing – original draft ; writing – review and editing . Xiaopeng Cui: Conceptualization (lead); resources (lead).The authors declare that we do not have any possible conflict of interest.Funding for this study was obtained from the Nantong Science and Technology Foundation grant , the Project of Health Committee of Nantong , and by Nantong University Pre‐Research Project (17ZY36).This study was approved by the Ethics Committee of Nantong University Hospital (project number: 2022‐L090). All patients signed an informed consent form.Figure S1.Click here for additional data file.Figure S2.Click here for additional data file.Figure S3.Click here for additional data file.Figure S4.Click here for additional data file.Table S1.Click here for additional data file.Supplementary Table S2.Click here for additional data file.Supplementary Table S3.Click here for additional data file.Supplementary Table S4.Click here for additional data file.


Dysfunction of iron metabolism, especially in substantia nigra (SN), is widely acknowledged in Parkinson's disease (PD), but the genetic influence on iron deposition remains largely unknown. Thus, in this study, we aimed to investigate potential genetic impacts on iron deposition in PD.Seventy‐four subjects, including 38 patients with PD and 36 age‐matched normal controls, participated in this study. Imaging genetic association analysis was used to identify the specific influence of single nucleotide polymorphism (SNP) on iron‐related quantitative traits (QT). Genetic effects on iron deposition at the disease level, SNP level, and their interactive effect were highlighted.Four strong SNP–QT associations were detected: rs602201‐susceptibility of bilateral SN, rs198440‐susceptibility of left SN, and rs7895403‐susceptibility of left caudate head. Detailed analyses showed that: (1) significant iron deposition was exclusively found in bilateral SN in PD; (2) altered polymorphisms of the A allele/A− genotype of rs602201 and G allele/G− genotype of rs198440 and rs7895403 were more frequently observed in PD; (3) for rs602201, among all subjects, A− genotype carriers showed significantly increased iron content than TT genotype in bilateral SN; for rs198440 and rs7895403, G− carriers showed increased iron content than AA genotype in left SN and left caudate head, respectively; and (4) rs602201 exhibited significant SNP‐by‐disease interaction in bilateral SN.This study shows that rs602201 and rs198440 have a stimulative impact on nigral iron deposition in PD, which provides improved understanding of iron‐related pathogenesis in PD, and specifically, that vulnerability to iron deposition in SN is genetic‐based. The dysfunction of iron metabolism, especially in substantia nigra (SN), in Parkinson's disease (PD) has been widely acknowledged, but the genetic influence on iron deposition remains largely unknown. Through the exploration of the relationship between genetics and iron metabolism in SN, this study discovers rs602201 and rs198440 have a stimulative impact on nigral iron deposition in PD. A newly developed MRI technology, quantitative susceptibility mapping (QSM), overcomes the non‐locality of tissue susceptibility in the brain, leading to a more precise measurement of brain iron with little effects of regional field inhomogeneity.Imaging genetics is a transdisciplinary field that examines associations between genetic variations, such as single nucleotide polymorphisms (SNPs), and imaging phenotypes ). Association studies combining neurobiological imaging are closer to the underlying neurobiology of the disease, and are therefore more likely to identify disease‐related genes.In the present study, we performed a preliminary examination of the associations between genes and iron deposition to determine the influence of genetics on iron‐related neurobiological traits in patients with PD. This approach can delineate the mechanism of nigral biased iron deposition from a genetic viewpoint. Furthermore, detailed analyses of significant SNPs with corresponding QTs were further examined to illustrate the genetic effects on iron deposition at the disease level, SNP level, and their interactive effect.22.1This study included 74 subjects: 38 patients with PD and 36 age‐matched normal controls (NC). All subjects were unrelated individuals. A diagnosis of PD was made by an experienced neurologist based upon the UK Parkinson's Disease Society Brain Bank criteria.2.2Subjects were scanned on a 3.0 Tesla MRI machine  equipped with an eight‐channel head coil. During MRI scanning, the subjects' heads were fixed with foam pads, and earplugs were provided to reduce noise.2; matrix = 416 × 384; slice thickness = 2 mm; slice gap = 0 mm; 64 continuous axial slices.ESWAN images were acquired using a Gradient Recalled Echo sequence: repetition time = 33.7 ms; 1st echo time/spacing/8th echo time = 4.556 ms/3.648 ms/30.092 ms; flip angle = 20°; field of view = 240 × 240 mm2.3https://people.eecs.berkeley.edu/~chunlei.liu/software.html) as follows: (1) the raw phase was unwrapped using Laplacian‐based phase unwrapping, and the normalized phase was calculated;QSM images were produced using the Susceptibility Tensor Imaging (STI) Suite V3.0 software package  as follows: 1) the native QSM image was registered to a newly constructed QSM template derived from a cohort of aging brains using ANTs‐SyN co‐registration algorithms; the nati2.4Genomic DNA of all subjects was extracted from peripheral blood samples using standard procedures and examined using an Illumina analyzer. Polymerase chain reaction and direct sequencing were used to sequence genes related to PD or parkinsonism symptoms . SNPs were excluded from this analysis that did not satisfy the following quality criteria: (1) call rate per SNP ≥90% (1 variant was excluded); (2) minor allele frequency ≥5% ; and (3) Hardy–Weinberg equilibrium test of p ≤ 10−6 (755 variants were excluded). Moreover, SNP QC was conducted among NC subjects only. As for subject level monitoring, the criteria were as follows: (1) call rate per subject ≥90% (no subject was excluded); and (2) identity check with PI‐HAT >0.5, which is a measure of subject relatedness (no subject was excluded). As a result, 4586 SNPs and all subjects remained for further analyses . A relatively less stringent threshold  was used to identify additional SNP and imaging QT associations, as well as guide the detailed analyses (see below). A heat map was used to visualize the association results.From parkinsonism‐related genes, 34,452 SNPs were acquired. Quality control (QC) and association analyses were conducted using the PLINK software package v1.90 . The detailed analyses included three steps: (1) allele/genotype frequency distribution calculation; (2) main effect of disease status and main effect of SNP estimation; and (3) SNP‐by‐disease interaction exploration.2.7t‐test, Pearson chi‐square test, or nonparametric test. p < 0.05 was regarded as statistically significant.Statistical analyses were performed using Statistical Product and Service Solutions (SPSS version 25.0). Demographics were obtained of subjects that passed the genotype QC procedure. Normal distribution of data was checked using the Kolmogorov–Smirnov test. Differences of age, sex, and education between groups were analyzed as appropriate using two‐sample p < 0.05 regarded as statistically significant.For the detailed analyses, the chi‐square test was used to calculate genetic distribution differences between groups at the allele and genotype level. The main effect of disease status and main effect of SNP on susceptibility of subcortical regions were analyzed using the general linear model, with the influence of age, sex, and education regressed out. Bonferroni correction was performed for multi‐comparisons. Furthermore, the SNP‐by‐disease interaction was explored, with 33.1p = 0.096), whereas sex and education were statistically different . Sex, education, and age were included as covariates in further analyses.After QC of the genotyping data, 4586 SNPs and all subjects were included in the analysis. Table 3.2p < 10−5 are shown in Figure MPPE1) with susceptibility of left SN, rs602201 (MPPE1) with susceptibility of right SN, rs198440  with susceptibility of left SN, and rs7895403  with susceptibility of left CN head. Specifically, the association between MPPE1 SNP rs602201 and magnetic susceptibility of right SN showed the most significant result with p < 10−6. For further examination, a Manhattan plot and quantile–quantile (Q–Q) plot were used to visualize the most significant association between a SNP and its corresponding imaging phenotype, that is, rs602201‐R_SN . Similar results were obtained for the altered G allele and G− genotype (GA + GG genotype) for rs198440 (both p < 0.001), and rs7895403 .Additional detailed analyses were used to further illustrate the importance of identifying imaging genetic associations for PD. First, we calculated genetic distribution differences between PD and NC at the allele and genotype level for rs602201, rs198440, and rs7895403. The results are shown in Table p < 0.001 for left SN and p = 0.001 for right SN; Figure p = 0.426). After evaluating the difference of magnetic susceptibility between groups of different disease status, we next estimated the SNP main effect. For rs602201, among all subjects, A− genotype carriers showed significantly increased magnetic susceptibility in bilateral SN (both p < 0.001) compared with TT genotype subjects. This indicates a higher iron content in bilateral SN in A− carriers , which suggests a possible SNP‐by‐disease interactive effect for rs602201 on nigral iron deposition. In subsequent analysis, as we speculated, a SNP‐by‐disease interactive effect was associated with bilateral nigral iron deposition . Focusing on the genetic effect within each group, we found that for rs198440, a higher iron content of G− > AA was only found among patients with PD (p = 0.034) and not in NC subjects (p = 0.199). For rs7895403, both PD and NC groups showed a higher iron content in G− carriers . However, both SNPs showed no SNP‐by‐disease interactive effect .Similar to previous analysis, when including all subjects, G− carriers showed increased iron content compared with the AA genotype for rs198440 on left SN and rs7895403 on left CN head (both 4MPPE1) and rs198440 (DAGLA), were associated with nigral iron deposition; and altered genotype carriers have significantly increased iron content compared with wild‐type genotype subjects; (2) the altered allele and genotype for both rs602201 and rs198440 were more frequently observed in PD patients; (3) for both rs602201 and rs198440, only PD patients have the pattern that carriers with altered polymorphisms exhibit increased iron content compared with wild‐type genotype subjects; furthermore, rs602201 exhibited a significant SNP‐by‐disease interaction in bilateral SN, indicating that PD patients with the rs602201 A− genotype may be particularly vulnerable to iron deposition in bilateral SN; and (4) the variant rs7895403 (OPTN) was associated with iron deposition in the left CN, but no intergroup difference of iron deposition within this nucleus was observed.Using imaging genetic association analysis, we find some preliminary findings: (1) two variants, rs602201 (4.1MPPE1) and rs198440 (DAGLA), were associated with nigral iron deposition in PD, with the altered genotype carriers showing significantly increased iron content compared with wild‐type genotype subjects at the whole subject range. This suggests a close relationship between these two SNPs and nigral iron deposition. Furthermore, altered polymorphisms of the A allele/A− genotype for rs602201 and G allele/G− genotype for rs198440 were more frequently observed in PD, indicating that these two SNPs might exert a pathological contribution to nigral iron metabolism in PD. The MPPE1 gene encodes a protein that is a member of the calcineurin‐like phosphoesterase superfamily.MPPE1 could impair physiological regulation of the dopaminergic system, which to some extent may give rise to PD development. Besides, MPPE1 protein is a metal‐dependent phosphoesterase containing metal binding and active sites, including iron binding sites, that is widely expressed in the brain, especially the SN.MPPE1 gene may exert a pivotal influence on nigral iron deposition in PD. As for the DAGLA gene, it encodes a protein that is a diacylglycerol lipase, which is a synthesis enzyme of the endocannabinoid system (ECS).DAGLA via an iron‐interactive ECS is associated with PD pathogenesis. Therefore, our findings indicate that these two SNPs have a pathological contribution to nigral iron metabolism, which may exert a hazardous effect on PD pathology.Two variants, rs602201 (MPPE1) may be a plausible biological candidate trait for nigral iron deposition. Thus, its altered polymorphism may leave carriers susceptible to PD pathology. We did not find a SNP‐by‐disease interaction for rs198440, which may suggest that the effect of PD heterogeneity is unavoidable for detecting association between SNP rs198440 and nigral iron accumulation. Therefore, these findings not only reveal the important role of SNP rs602201 in specifically modulating pathogenic iron deposition in SN in PD, but suggest the genetic effect of certain SNPs  may be diverse from disease status.Focusing on the genetic effect within each group, only patients with PD showed increased iron content in altered polymorphism carriers compared with wild‐type genotype subjects for both rs602201 and rs198440. This indicates that these two SNPs have specific stimulative effects on nigral iron deposition in PD. However, in a subsequent analysis, a SNP‐by‐disease interactive effect was only observed for rs602201, reinforcing the notion that rs602201 has a pathogenic effect on nigral iron deposition in PD. Therefore, we speculate that PD patients with the rs602201 A− genotype may be particularly vulnerable to nigral iron deposition. The SNP rs602201 (4.2OPTN) was associated with left CN iron deposition, and when all subjects were included, G− carriers showed increased iron content compared with the AA genotype. This indicates its genetic effect on iron deposition among the general population. OPTN protein is an autophagy receptor that is involved in mitophagy.The variant rs7895403  and rs198440 (DAGLA), that have a significant stimulative impact on nigral iron deposition in PD. However, only the rs602201 (MPPE1) polymorphism has a specific interactive effect on bilateral SN, indicating that PD patients with rs602201 polymorphism may be particularly vulnerable to nigral iron deposition. In contrast, the variant rs7895403 (OPTN) was only found to have a general genetic effect on iron deposition in CN among the whole population. Altogether, these findings provide a deeper understanding of iron‐related pathogenesis in PD, specifically that iron metabolism involves genetic interactions and vulnerability to iron deposition in the SN is genetic‐based.This study discovered two variants, rs602201 (All of the coauthors listed meet the criteria for authorship. JJW was involved with data acquisition, study concept and design, data analysis and interpretation, manuscript drafting and revising. XJG, TG, and CZ were involved with data acquisition, data analysis and interpretation. XQB, XCL, LYG, MX, QQG, and PYH were involved with data acquisition. ZS and BRZ were involved with PD patients' recruitment. XJX was responsible for data interpretation, manuscript revision, funding obtaining and study supervision. XJG and MMZ was responsible for study concept, manuscript revision, funding obtaining and study supervision. All authors read and approved the final manuscript.The authors reported no biomedical financial interests or potential conflicts of interest.Informed consent was obtained from all individual participants included in the study.


As one of the most prevalent heritable cardiovascular diseases, dilated cardiomyopathy (DCM) induces cardiac insufficiency and dysfunction. Although genetic mutation has been identified one of the causes of DCM, the usage of genetic biomarkers such as RNAs for DCM early diagnosis is still being overlooked. In addition, the alternation of RNAs could reflect the progression of the diseases, as an indicator for the prognosis of patients. Therefore, it is beneficial to develop genetic based diagnostic tool for DCM. RNAs are often unstable within circulatory system, leading to the infeasibility for clinical application. Recently discovered exosomal miRNAs have the stability that is then need for diagnostic purpose. Hence, fully understanding of the exosomal miRNA within DCM patients is vital for clinical translation. In this study, we employed the next generation sequencing based on the plasma exosomal miRNAs to comprehensively characterize the miRNAs expression in plasma exosomes from DCM patients exhibiting chronic heart failure (CHF) compared to healthy individuals. A complex landscape of differential miRNAs and target genes in DCM with CHF patients were identified. More importantly, we discovered that 92 differentially expressed miRNAs in DCM patients undergoing CHF were correlated with several enriched pathways, including oxytocin signalling pathway, circadian entrainment, hippo signalling pathway‐multiple species, ras signalling pathway and morphine addiction. This study reveals the miRNA expression profiles in plasma exosomes in DCM patients with CHF, and further reveal their potential roles in the pathogenesis of it, presenting a new direction for clinical diagnosis and management of DCM patients with CHF. Notably, DCM is often correlated with a raised risk of serious arrhythmia, suggesting the pathological involvement of the cardiac conducting system. As the disease progresses, diastolic dysfunction and impaired right ventricular function will develop, ultimately resulting in HF and premature death. Epidemiological surveys revealed that, in the United States, the prevalence of DCM was 36 cases per 100,000 (i.e. 1:2500). Ten thousand people die from DCM each year and 46,000 are hospitalized as a result.DCM's pathogenesis has not been well understood. Familial and genetic predisposition are commonly regard as contributors. Previous studies indicate that several genes may contribute to the initiation, progression and pathology of DCM, including titin (TTN), beta‐myosin heavy chain (MYH7), Cardiac troponin T(TNNT2), Desmoplakin (DSP), lamin A/C (LMNA), type V voltage‐gated cardiac Na channel gene (SCN5A), RNA‐binding motif protein 20 (RBM20) et al.Advances in molecular biology over the past decades have revealed that non‐coding RNA (ncRNA) that was once considered as ‘junk’ RNA plays vital roles in regulating distinct cellular processes. Among the ncRNA, miRNAs have been widely studied and better understood. miRNA, which are single‐stranded, conserved RNAs, comprised of approximately 18 ~ 25 nucleotides. It exists in both the supernatant and extracellular vesicles (EVs),Hence, we tested the expression profiling of plasma exosomal miRNAs of DCM with CHF patients. By comparative analysis, we identified the differentially expressed exosomal miRNAs. Target gene prediction and functional enrichment analysis were performed via bioinformatics methods, in order to reveal the underlying pathological changes of this disease. The result from this study may provide novel insights for the diagnosis and therapy of DCM with CHF patients.22.1Approval of this research was obtained from the institutional review board for the First Affiliated Hospital of Zhengzhou University (2020‐KY‐142). The DCM with CHF patients were recruited from the First Affiliated Hospital of Zhengzhou University. DCM was diagnosed according to the diagnostic criteria proposed by the American Heart Association expert consensus panel and the European Society of Cardiology,The inclusion criteria for the DCM with HF patients were as follows: (1) left ventricular ejection fraction (LVEF) is equal or lesser than 45% and had heart function grades ranging from II to IV; (2) patients with a history of congestive HF more than 6 months; and (3) informed consent was signed by all participates. The exclusion criteria were as follows: (1) congenital heart disease; (2) CHF induced by other aetiologies, such as coronary heart disease, hypertension and rheumatological disease; (3) patients were complicated with severe liver, kidney and other important organ dysfunction; (4) the history of malignancy; and (5) other connective tissue diseases. The healthy controls all had no any history of cardiovascular disease.2.2g for 15 min at room temperature. Subsequently, the upper plasma fraction, with an ideal volume of 2.5 mL per sample, was promptly frozen at −80°C to maintain the stability and integrity of the plasma biomolecules.Participants diagnosed with DCN and HF, as well as healthy controls were recruited for the study. Venous blood samples, collected using Na‐EDTA tubes to prevent coagulation, amounted to a minimum volume of 6 mL per participant. The blood samples were subjected to centrifugation at 2000 × 2.3Plasma exosomes were extracted using Exoquick reagent  in accordance with the manufacturer's protocol. Briefly, 250 μL of plasma sample was supplemented with 36 μL of ExoQuick Exosome Precipitation Solution. The mixed solution was set for 30 min at 4°C, followed by centrifugation at 1500 × g for 30 min. After the centrifugation, the supernatant was discarded before additional centrifugation (1500 × g for 5 min). Finally, 100 μL of sterile phosphate‐buffered saline (PBS) was add to resuspend exosome pellet. The solution was stored at −80 °C.2.4The transmission electron microscopy (TEM) was used to characterize the morphology character of isolated exosomes. Briefly, PBS was used to dilute extracted exosomes, before being dripped onto a copper mesh grid coated in holey carbon. The solution was set for 10 min, before the excess solution was blotted away with filter paper. The sample was further soaked in 3% of glutaraldehyde for 5 min, prior to the wash process with 10 times washing using de‐ionized water (2 min each time). Afterwards, 4% uranylacetate solution was dropped on the samples for 10 min, followed by 1% methylcellulose fixation for 5 min. Finally, the sample was air‐dried at room temperature for 30 minters and examined by TEM later.2.5The concentration and diameter distribution of extracted exosomes were determined by Flow NanoAnalyzer (FL Sciences).2.6The extracted exosomes were lysed with precooled RIPA lysis buffer  on ice to isolate protein. Equal volumes of protein samples (20 μg) were electrophoresis on a 12.5% sodium dodecyl sulphate‐polyacrylamide gel electrophoresis (SDS‐PAGE) gel, and then, the bands transferred onto a 0.45 μm polyvinylidene fluoride membranes . To block the membrane, 5% skimmed milk was applied at room temperature for 2 h, before the overnight incubation with primary antibodies against CD81 , CD63 , Calnexin  at 4°C, after that, incubated with horseradish peroxidase‐conjugated anti‐rabbit secondary antibodies  for 2 h at room temperature. Finally, Amersham Imager 600  and Image J analysis software were used to inspect and analyse protein bands.2.7The total RNA that containing miRNA in plasma exosomes was extracted by exoRNeasy  according to the manufacturer's instructions, and the purity and quantity of RNA were assessed by Bioanalyzer 2100 . The concentration of RNA is greater than 20 ng/μL as well as its RIN number >7.0 that can meet subsequent experimentation.2.8Small RNA library was constructed by using TruSeq Small RNA Sample Prep Kits  in accordance with the manufacturer's protocol. Specifically, 3′ and 5′ adapters were ligated with purified RNAs and then synthesized cDNA by using reverse transcription‐polymerase chain reaction (RT‐PCR). cDNA was further purified through High Sensitivity DNA Chip Kit . Amplification was carried out by quantitative polymerase chain reaction (qPCR) to establish a MicroRNA library. The single‐end sequencing on an Illumina Hiseq2500 was performed at the LC‐BIO  according to the vendor's recommended protocol.http://www.girinst.org/repbase), common RNA families, including tRNA, rRNA, snRNA and snoRNA (http://rfam.sanger.ac.uk/), and sequence length <18 nucleotide (nt) or >26 nt. Based on miRBase 22.0 (http://www.mirbase.org/), the unique sequences with the length of 18 ~ 26 nt were mapped to miRNA sequences, to recognize confirmed miRNAs and pre‐miRNAs (including novel 3p‐ and 5p‐derived miRNAs). Also, after the procedure, data normalization was applied based on the method described previously.http://rna.tbi.univie.ac.at/cgi‐bin/RNAWebSuite/RNAfold.cgi), in order to confirm the results of putative miRNAs in Homo sapiens. Raw sequencing data from this study are uploaded to the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/).The raw data were analysed via using ACGT101‐miR  to remove adapter dimers, low complexities, junk, repeats  in an external population. Internal reference gene U6 was employed. qRT‐PCR assays were performed in triplicate with the following conditions: (1) 95°C for 30 s, (2) 40 cycles of 95°C for 10 s and 60°C for 30 s, (3) 95°C for 15 s, 60°C for 60 s, 95°C for 15 s. Human abdominal aortic wall tissues were homogenized using TissueLyser II (QIAGEN) according to the manufacture's instruction. Total RNA was isolated using TRIzol® reagent  from homogenized tissues. High‐Capacity cDNA Reverse Transcription Kit  was used to generate cDNA. The relative mRNA levels of target genes were quantified using the iTaq UniverSYBR Green SMX 5000  with an ABI PRISM 7900 Sequence Detector system  following the manufacturer's instructions. The ΔCT (Ct mRNA‐Ct GAPDH) method was used to calculate mRNA relative expression. The relative quantification values for miRNAs were calculated by the 2 − ΔΔCt method.2.10www.targetscan.org) and miRDB version 3.3amirdb.org/miRDB/) were used to predict miRNA targets. Overrepresentation gene set analysis (ORA) of miRNA targets was performed by computing overlaps with annotated Hallmark gene sets in The Molecular Signatures Database . As previous studies,http://www.geneontology.org/) and Kyoto Encyclopedia Genes and Genomes (KEGG) Pathway Analysis (http://www.genome.jp/kegg/) were done to predict the function of these target genes. Finally, Metascape (http://metascape.org) were used for the pathway and process enrichment analyses.TargetScan Human 7.0 database2.11http://www.cytoscape.org/) was used to visualize the PPI network. As a commonly‐used plugin, CytoHubba calculates the degree of each node in Cytoscape. Nodes with a higher degree or connectivity indicated the highly interacting proteins or genes in the network. In our study, nodes with a degree >5 were recognized as hub genes in the network.Protein–protein interaction (PPI) network analysis was done using The Search Tool for the Retrieval of Interacting Genes (STRING) database.2.12https://www.mirnet.ca) is an easy‐to‐use platform for the visualization and analysis of miRNA‐centric network, designing to help unravel microRNA functions through consolidating existing knowledge with users' data.The miRNet database  with p and q samples , .where 2.14https://umap‐learn.readthedocs.io/en/latest/) was applied for dimensional reduction of internal transcriptomics data based on DE miRNAs. To test the discrimibility of identified hub miRNAs, we performed principal coordinates analysis (PCoA) and analysis of similarities (ANOSIM) on them. Bray–Curtis dissimilarity matrix was calculated by beta_diversity.py, and Bray–Curtis diversity was calculated using the R package Vegan with the function vegdist.The uniform manifold approximation and projection (UMAP) using the umap‐learn package , including 61 that were upregulated in DCM with HF patients and 24 that were downregulated | more than 0.5, we determined 92 DE‐miRNAs for subsequent analysis, including 48 upregulated and 44 downregulated miRNAs  biological process, (ii) cellar component and (iii) molecular function. Taken as a whole, the 75 targets of DE‐miRNAs remarkably enriched in asymmetric synapse, neuron to neuron synapse and postsynaptic density and others. Figure , and the3.6p‐value <0.05). In order to explore the vital mechanistic pathways of the targeted genes of DE‐miRNAs, we identified the top 15 statistically remarkably enriched pathways based on KEGG pathway results , metal ion transport (GO:0030001) and metal ion transport (GO:0000902). The top 20 clusters enrichment terms were showed in Figure 3.8The 1176 matched genes of DE‐miRNAs through TargetScan and miRanda were imported into the STRING database. Excluding the isolated targeted genes without interaction, there was a total of 156 target genes were mapped into the PPI network , which comprised with 156 nodes and 263 edges. We also used Cytoscape software to visualize the interactions between the genes Figure . Accordihttps://www.wikipathways.org/), we found these DE‐miRNAs were mainly involved in the PDGF pathway (WP2526), leptin signalling (WP2034) and T‐cell receptor (TCR) signalling pathway (WP69). BioCarta database analysis (https://cgap.nci.nih.gov/Pathways/BioCarta_Pathways) showed DE‐miRNAs and their target hub genes played a major role in the VEGF, hypoxia and angiogenesis pathways. Furthermore, BioPlanet database annotation confirmed they were significantly associated with the activation of signalling mediated by EGFR, VEGF and VEGFR.The specific roles of DE‐miRNAs and their target hub genes were further investigated using the comprehensive biological annotation analysis Figure . Based o3.9Aiming to understand the molecular mechanisms of previously identified exosomal DE‐miRNAs in DCM accompanied with CHF, miRNet was used to recognize miRNAs targeting hub genes. As a result, a miRNA–mRNA regulatory network with 55 nodes (32 miRNAs and 23 hub genes) and 59 edges was constructed Figure .Notably, we found that 9 DE‐miRNAs which contemporaneously targeted two or more hub genes, including hsa‐miR‐185‐5p, bta‐miR‐150, mdo‐miR‐150‐5p_R‐1_1ss22GA, hsa‐miR‐150‐5p, hsa‐miR‐423‐5p, hsa‐miR‐6741‐5p_R+1, hsa‐miR‐1304‐3p_1ss13CA, hsa‐miR‐10a‐5p_R‐1 and hsa‐miR‐3138_L‐5R+2. Additionally, it is also intriguing that the hub gene SMAD2 have the highest overlapping target number with DE‐miRNAs .3.10The top 10 miRNAs that were significantly dysregulated between DCM and healthy individuals in silico were identified as hub miRNAs . We next validated the performance of top hub miRNAs in an external population  based on qRT‐PCR.Our analysis demonstrated a significant upregulation of hub miRNAs in the DCM samples, which further supported the results in silico Figure . We then4In the present study, a specific miRNA signature of plasma exosomes was identified in DCM accompanied with CHF patients. We screened 92 DE‐miRNAs related to DCM accompanied with CHF using miRNA sequencing. Forty‐eight upregulated and 44 downregulated miRNAs were identified, and 75 targets of these DE‐miRNAs were confirmed. To predict the function of dysregulation miRNAs in DCM accompanied with CHF, GO and KEGG analyses were further performed. Some enriched pathways were enunciated. Particularly, oxytocin signalling pathway, circadian entrainment and hippo signalling pathway‐multiple species were listed as the top three.In addition to its distinguished role played in lactation and parturition, oxytocin signalling pathway also involved in many other physiological functions and pathological processes, such as the maintenance of blood supply to the cortex, the regulation of the growth of the neocortex and the modulation of the autonomic nervous system via the vagal pathway to name a few. Additionally, it also demonstrated anti‐atherogenic, anti‐oxidant, anti‐inflammatory andanti‐dyslipidaemic effects.Circadian entrainment is endogenous oscillations, widely found in biological species, that possess the ability of entraining to the 24 h light–dark cycle. Studies have revealed that circadian dysregulation is closely associated with increased risk of cardiovascular diseases, including hypertension, atherosclerosis, stroke and myocardial infarction.Hippo signalling pathway is a functionally and evolutionarily conserved regulator of organ size and growth with essential roles in cell differentiation, proliferation and apoptosis. Transgenic and gene knockout mouse models have revealed that the Hippo signalling pathway is participated in cardiomyocyte proliferation, heart development, hypertrophy, apoptosis and cardiac regeneration.Nine dysregulated exosomal miRNAs related to DCM with CHF were further screened out, among which hsa‐miR‐185‐5p, hsa‐miR‐423‐5p, hsa‐miR‐6741‐5p_R+1, hsa‐miR‐3138_L‐5R+2 and hsa‐miR‐1304‐3p_1ss13CA were upregulated, bta‐miR‐150, mdo‐miR‐150‐5p_R‐1_1ss22GA, hsa‐miR‐150‐5p and hsa‐miR‐10a‐5p_R‐1 were downregulated. Some of these dysregulated miRNAs have been reported. For example, consistent with our current study, miR‐423‐5p is increased in the plasma of patients of heart failure caused by DCM, and the expression level of miR‐423‐5p in the plasma is positively related to the level of N‐terminal pro‐brain natriuretic peptide (NT‐proBNP).In summary, the identified 9 miRNAs have been found to play important roles in the pathogenesis of DCM through a variety of mechanisms, including regulation of fibrosis, hypertrophy, inflammation, oxidative stress, angiogenesis and mitochondrial function. Further studies are needed to elucidate the exact mechanisms by which these miRNAs are involved in DCM, which may contribute to the development of novel diagnostic and therapeutic strategies for this debilitating disease.Several limitations are existed in this study. Firstly, the study sample was relatively small, and the patients who were included were strictly DCM with CHF subjects. Secondly, limited evidence indicates the circulating miRNAs secretion into the extracellular space were from the heart directly. Hence, additional experiments on human heart samples are valuable to further confirm these results and bioinformatic predictions. Moreover, the related mechanism about how differentially expressed miRNAs participate in the pathological process of DCM requires further exploration.In conclusion, our presented study identified the differentially expressed exosomal miRNAs in DCM with CHF patients and revealed the underlying pathological changes of DCM with CHF, which may also provide novel diagnosis and therapeutic methods of DCM.Li Zhang: Conceptualization ; data curation ; formal analysis ; funding acquisition ; investigation ; methodology ; project administration ; resources ; software ; supervision ; validation ; visualization ; writing – original draft ; writing – review and editing . Ge Zhang: Validation ; visualization ; writing – original draft ; writing – review and editing . Yongzheng Lu: Validation ; visualization ; writing – original draft ; writing – review and editing . JIamin Gao: Validation ; visualization ; writing – original draft ; writing – review and editing . Zhen Qin: Validation ; visualization ; writing – original draft ; writing – review and editing . Shuai Xu: Validation ; visualization ; writing – original draft ; writing – review and editing . Zeyu Wang: Validation ; visualization ; writing – original draft ; writing – review and editing . Yanyan Xu: Validation ; visualization ; writing – original draft ; writing – review and editing . Yu Yang: Validation ; visualization ; writing – original draft ; writing – review and editing . Jinying Zhang: Conceptualization ; data curation ; formal analysis ; funding acquisition ; investigation ; methodology ; project administration ; resources ; software ; supervision ; validation ; visualization ; writing – original draft ; writing – review and editing . Junnan Tang: Conceptualization (lead); data curation (lead); formal analysis (lead); funding acquisition (lead); investigation (lead); methodology (lead); project administration (lead); resources (lead); software (lead); supervision (lead); validation (lead); visualization (lead); writing – original draft (lead); writing – review and editing (lead).This work was supported by the National Natural Science Foundation of China , the Henan Thousand Talents Program (No. ZYQR201912131), Excellent Youth Science Foundation of Henan Province (No. 202300410362) and Central Plains Youth Top Talent, Advanced funds (No. 2021‐CCA‐ACCESS‐125).The authors declare no conflicts of interest.Figure S1Click here for additional data file.Table S1Click here for additional data file.Table S2Click here for additional data file.Table S3Click here for additional data file.


Serum protein biomarkers are used to diagnose, monitor treatment response, and to differentiate various forms of chronic enteropathies (CE) in humans. The utility of liquid biopsy proteomic approaches has not been examined in cats.To explore the serum proteome in cats to identify markers differentiating healthy cats from cats with CE.Ten cats with CE with signs of gastrointestinal disease of at least 3 weeks duration, and biopsy‐confirmed diagnoses, with or without treatment and 19 healthy cats were included.Cross‐sectional, multicenter, exploratory study with cases recruited from 3 veterinary hospitals between May 2019 and November 2020. Serum samples were analyzed and evaluated using mass spectrometry‐based proteomic techniques.P < .02, ≥5‐fold change in abundance) differentially expressed between cats with CE and controls. Thrombospondin‐1 (THBS1) was identified with >50‐fold increase in abundance in cats with CE (P < 0.001) compared to healthy cats.Twenty‐six proteins were significantly (Damage to the gut lining released marker proteins of chronic inflammation that were detectable in serum samples of cats. This early‐stage exploratory study strongly supports THBS1 as a candidate biomarker for chronic inflammatory enteropathy in cats. CEchronic enteropathiesECMextracellular matrixFCEAIfeline chronic enteropathy activity indexFDRfalse discovery ratefPLIfeline pancreatic lipase immunoreactivityfTLIfeline trypsin‐like immunoreactivityIBDinflammatory bowel diseaseLGALlow‐grade alimentary lymphomaMMPmatrix metalloproteinaseMSmass spectrometryPARRpolymerase chain reaction to assess antigen receptor rearrangementsT4thyroxineTHBS1thrombospondin‐11Chronic enteropathy in cats is 1 of the most common disorders of cats and is characterized by persistent or recurrent signs of gastrointestinal disease such as weight loss, vomiting, inappetence, and diarrhea.Biomarkers have been investigated as potential diagnostic tests for CE with minimally invasive diagnostic methods.The aim of this exploratory study was to investigate changes in the serum proteomic profiles of cats with CE. This study provides useful preliminary data on the serum protein markers differentiating healthy cats from cats with CE. The ability to use a minimally invasive diagnostic method to assist in diagnosis of CE might reduce the need for more invasive diagnostics and anesthesia for pets with comorbidities and lead to reduced costs for pet owners.22.1Tritrichomonas foetus, Cryptosporidium species, panleukopenia virus, Clostriudium perfringens, Giardia species, Salmonella species, Toxoplasma gondii, Campylobacter jejuni, Campylobacter coli). Serum feline pancreatic lipase immunoreactivity (fPLI) and feline trypsin like immunoreactivity (fTLI) were measured in some cats. All the cats diagnosed with CE using endoscopic biopsies were scored using the feline chronic enteropathy activity index (FCEAI) which was calculated based on their clinical signs, clinicopathological, and endoscopic findings.Cases were recruited from 3 veterinary hospitals, including referral and primary care practices, between May 2019 and November 2020. Cats presented for evaluation of CE with clinical signs  of at least 3 weeks' duration, and that were eventually diagnosed with CE/LGAL, were prospectively recruited for this study. Cat information including signalment, diagnostic tests, diet, previous and current treatment, and comorbidities was recorded. Cats with comorbidities such as pancreatitis, cholangiohepatopathy, urinary tract disease, and endocrinopathies, and cats without intestinal biopsies were excluded from further assessment. Diagnoses of CE were made on the basis of clinical signs, histopathology and exclusion of other causes of gastrointestinal manifestations, including metabolic disease, infection, parasitic disease, hepatic, and renal disease. The following diagnostic tests were performed: a complete blood count, a serum biochemistry profile, serum total T4 concentration, serum concentrations of cobalamin, abdominal ultrasonography and fecal flotation and PCR  as per manufacturer's instruction. Samples of 100 μg total protein were mixed in 50 μL AMBIC buffer  and trypsin digested at 25°C for 16 hours in a 1:100 enzyme‐to‐protein ratio based on the calculated serum protein concentration.2.32O:CH3CN  at 10 μL min−1. After washing, the pre‐column was switched  into line with a virgin fritless nanocolumn (75 μm i.d × 20 cm) containing reverse phase C18 media . Peptides were eluted using a linear gradient of H2O:CH3CN  to H2O:CH3CN  at 250 nL/min over 120 min. The QExactive  mass spectrometer was run in DDA mode where a high voltage of 2000 V was applied to a low volume union and the column (45°C) positioned 0.5 cm from the heated capillary (275°C). A survey scan 350‐1750 m/z was acquired in the Orbitrap (resolution 70 000 at 200 m/z) with an accumulation target of 106 ions, lock mass enabled and up to the 10 most abundant ions  with charge states ≥+2 and ≤+6 sequentially isolated and fragmented. Samples were run as a single batch in a random order separated by a buffer injection H2O:CH3CN .Digested peptides were reconstituted in 10 μL 0.1% formic acid and separated by nano‐LC using an Ultimate 3000 HPLC and autosampler  and followed methods described previously.2.4www.matrixscience.com). All MS/MS spectra were searched against the Uniprot database  for protein identification with the following criteria: allowed 1 missed cleavage; variable modifications oxidation (M), deamidation (R), carbamidomethyl (C), and phosphorylation ; peptide tolerance, ±5 ppm; fragment tolerance, ±0.5 Da; peptide charge +2 to 4+; and enzyme specificity set to semi‐tryptic. A decoy database search was also performed. Scaffold Software  was used to record the protein profiles using spectral counting. Identifications were accepted if they could establish less than 5% false discovery rate (FDR) and contained at least 2 identified peptides per protein.Protein dataset‐peak lists were generated from raw files using Mascot Daemon v2.5.1  software and normal distribution confirmed by D'Agostino's K‐squared test.Descriptive statistics of cat information including age, sex, weight, FCEAI and cobalamin concentrations were analyzed using R software. Continuous data with non‐normal distribution including age, weight and protein concentrations was compared and analyzed using the Mann‐Whitney P values. Differences in fold change were considered statistically significant at P < .02, with a minimum fold change of ≥5.Changes in expression of protein (defined as fold change) were analyzed using Fisher's exact test with Benjamini‐Hochberg adjusted For bioinformatics analysis, the Gene Ontology concept was used to investigate molecular function, biological processes, and cellular components. The identified biological module groups were evaluated by their enrichment score and the significance of the module's enrichment was determined by AmiGo Panther analysis.https://string-db.org).Data were also analyzed with STRING version 11.5 database (33.1P = .38), sex status (P = .64), and body weight (P = .61) between the CE and clinically healthy cats. There was also no significant different in protein concentrations (P = .10) between the CE and control cats.Fifty‐five cats were initially included: 29 cats with chronic signs of gastrointestinal disease and 26 control cats. After exclusion of cats with comorbidities, and cats without intestinal biopsies, a total of 31 cats were included in our proteomic study. Based on clinical signs, physical examination findings, exclusion of other gastrointestinal diseases, and available histopathology results, 10 cases were classified as CE. Four cats had endoscopic biopsies collected by gastroduodenoscopy and colonoscopy and 6 had surgical biopsies. Endoscopic biopsies were taken from the stomach, duodenum, ileum, and colon while surgical biopsies include stomach, duodenum, jejunum, ileum, mesenteric lymph node liver, or a combination of these tissues. The median number of biopsy specimens examined per site was 4 . Six cases were classified as lymphoplasmacytic enteritis , 3 cases had lymphoplasmacytic and eosinophilic enteritis  and 1 case had moderate to severe mixed inflammation. Nineteen cats classified as healthy controls were enrolled in this study. All cases had physical examination and history evaluated and were free of clinical signs of CE. Gastrointestinal histology was not available in these cats because of ethical concerns. Characteristics of cats are summarized in Table Two additional cats were recruited and were classified as LGAL based on histopathology. One cat with LGAL had endoscopic biopsies via gastroduodenoscopy and colonoscopy and the other cat had surgical biopsies. Immunohistochemistry and PARR (polymerase chain reaction to assess antigen receptor rearrangements) were not performed. Both cats with LGAL were receiving prednisolone, and 1 was also receiving cyclophosphamide treatment.3.2P < .02) , and notably lower abundance in 2 cats with LGAL . Comparative statistics between cats with CE and LGAL were not performed due to the small sample size of cats with LGAL. A dot plot .Further evaluation of THBS1 abundances in individual cats better shows the significant increase in THBS1 in cats with CE  compared to controls  (4P < .02 and greater than |5| fold abundance change) in confirmed cats with CE compared with healthy controls. Differentially abundant proteins included several proteins involved in cellular processes, proteins with binding‐activities, modifying enzymes, and metabolic enzymes. Although further studies are required for validation, these preliminary data indicate the potential for using serum to identify disease related proteins from cats with CE. Proteins with the greatest abundance discrepancies between controls and cats with CE included coagulation factors, thrombospondin‐1, serpins, antithrombins, apolipoproteins, complement, hemopexin, and fibronectin. These proteins are involved in remodeling and regulation of the extracellular matrix, inflammation, and lipid metabolism; a common finding with human IBD.Proteomic analysis allows large scale detection and rapid identification of proteins of pathological significance.Among the 26 proteins reported here, thrombospondin‐1 (THBS1) is of particular interest in the pathology of CE. Thrombospondins are a family of secreted glycoproteins that are important in maintaining the extracellular matrix (ECM) structure and regulation of cellular phenotype.The data reported here show potential for THBS1 to be used as a diagnostic test in enteropathies in cats, where the diagnosis typically relies on invasive surgical or endoscopic intestinal biopsies. However, further investigation of this preliminary finding using western blot analysis, parallel reaction monitoring technique and data analytical validation are required to test the feasibility of using THBS1 in diagnosis of CE in cats. Additional cohort studies between cats with LGAL and CE, food responsive and immunosuppressant responsive enteropathies are warranted. THBS1 is a glucocorticoid responsive protein in humans and was elevated in human patients being treated with prednisolone and with Cushing's syndrome.P < .001) in cats with CE compared to controls in our analysis. Coagulation factor V is a protein of the coagulation system and acts as a non‐enzymatic cofactor for activated factor X to form the prothrombinase complex which contributes to generation of thrombin.A further finding in the study reported here was the involvement of coagulation factors in enteropathies. Coagulation factor V showed a significant difference with >100‐fold increase in relative abundance .Authors declare human ethics approval was not needed for this study.


To provide real‐world outcomes for the combination of etoposide and platinum as a first‐line treatment for advanced thymic neuroendocrine neoplasms (TNENs).Retrospective analysis was performed on patients with advanced TNENs confirmed by pathology who received etoposide combined with platinum as a first‐line chemotherapy in our institution between 2010 and 2022.p = 0.095) and OS (p = 0.061). Treatment‐related adverse events were observed in all 12 patients when evaluated for toxicity, manifesting as hematologic toxicity. Grade 3–4 bone marrow suppression occurred in six patients (50%). No treatment‐related deaths were recorded.A total of 16 patients were included in this study. Twelve patients (75%) received etoposide combined with cisplatin, and four patients (25%) received etoposide combined with carboplatin. Efficacy was evaluated in all patients, with an objective response rate of 31.3%. One patient achieved a complete response, four achieved a partial response, and in eight patients the disease remained stable; the disease control rate was 81.3%. The median progression‐free survival (PFS) was 7.2 months with a 95% confidence interval (CI) of 2.1–12.3 months. The median overall survival (OS) was 50.4 months with a 95% CI of 32.1–68.8 months. No significant difference in efficacy was observed between the treatment groups with regards to PFS (This retrospective analysis, conducted in a real‐life setting, suggests that the combination of etoposide and platinum has a promising anti‐tumor activity in advanced TNENs, with a clinically significant overall response rate. Rosai and Higa were the first to reported this type of tumor in 1972.Although most patients with this type of disease present with distant metastases at the time of presentation, TNENs appear to fare better than neuroendocrine tumors of pulmonary origin in terms of survival. TNEN has long been considered a mediastinal neuroendocrine carcinoma of the lungs, because this condition is pathologically consistent with an origin in the lungs. According to the literature, the overall 5‐year survival of this condition varies from 31% to 52.8%.Based on this background, our current research provided a key data relating to the efficacy and safety of etoposide plus platinum as a first‐line treatment for advanced TNENs in a real‐world setting.22.1We retrospectively recruited patients who had been diagnosed with TNENs at the Zhejiang Cancer Hospital between 2010 and 2022, and received etoposide in combination with platinum at advanced stages of disease. We extracted a range of information from electronic medical records, including basic patient information, pathology type, histological grade, chemotherapy regimen, efficacy, and toxicity. The enrollment criteria for this study were as follows: (1) patients with pathologically confirmed TNENs ; (2) patients with TNENs staged IVA or IVB according to the American Joint Commission on Cancer (AJCC) staging 8th edition Tumor‐Node‐Metastasis (TNM) staging or Masaoka‐Koga staging principles ; (3) received at least 2 cycles of first‐line chemotherapy involving etoposide plus platinum, with evaluable tumor lesions; (4) etoposide combined with platinum as an adjuvant chemotherapy regimen was considered as first‐line treatment if distant metastasis occurred during or within 6 months after the end of the last treatment. The exclusion criteria were as follows: (1) pathologically confirmed compound small cell carcinoma of thymic origin; (2) two or more primary site tumors, and (3) active disease requiring hormonal or hospital treatment.2.22 on day 1 to 3, and the dosage of cisplatin was 60–75 mg/m2 on day 1, q21d. Patients were treated with an EC regimen using etoposide 100 mg/m2 on days 1 to 3 with carboplatin (area under the curve of 5–6), q21d. All patients were evaluated for post‐treatment efficacy by enhanced computed tomography (CT) of the chest and abdomen, and magnetic resonance imaging (MRI) of the brain to measure the size of the lesions. Toxicity assessment was performed according to the Common Terminology Criteria for Adverse Events (CTCAE version 5.0).All patients in our study were treated according to the National Comprehensive Cancer Network (NCCN) guidelines for small cell lung cancer (SCLC). Etoposide plus cisplatin (EP) or carboplatin (EC) was used as first‐line chemotherapy in all advanced patients until the patient developed intolerable toxicity or received up to 4 to 6 cycles. The dosage of etoposide was 100–120 mg/m2.3All patients included in this study had received at least two circle treatments of etoposide in combination with platinum and had measurable lesions. Efficacy was assessed according to the version 1.1 of the Response Evaluation Criteria in Solid Tumors (RECIST) for patients who had received 2 cycles of treatment. Overall survival (OS) was defined as the time between the diagnosis of advanced disease and the time the patient died or last follow‐up. Progression‐free survival (PFS) was defined as the time between receiving first‐line chemotherapy and disease progression or last follow‐up. Efficacy was classified as a complete response (CR), a partial response (PR), stable disease (SD), and progress disease (PD). Objective response rate (ORR) was defined as the proportion of patients achieving CR and PR out of all evaluable patients. Disease control rate (DCR) was defined as the proportion of patients receiving CR, PR, and SD out of all patients evaluable for efficacy.2.4p‐values, and p‐values <0.05 were statistically different. All patients in this single sample study were included in the statistical analysis. The follow‐up rate was 100% and the median follow‐up time was 59.8  months. The last follow‐up visit was February 24, 2023.Data were analyzed by SPSS version 25.0 for Windows (SPSS Inc.). The median PFS and OS time were estimated by the Kaplan–Meier method. The test results of this study were reported using two‐sided 33.1Between the January 1, 2010 and the February 24, 2022, 16 patients were enrolled from our institution . All patients were eligible. None of the patients had a concurrent MEN‐1 or other disease of the endocrine system. However, two patients had a combination of superior vena cava syndrome, presenting with facial swelling, hoarseness, and shortness of breath. The baseline characteristics of the patients are shown in Table n = 11, 68.8%) showed a poorly differentiated histological type, two cases (12.5%) involved atypical carcinoid, and three cases (18.8%) involved well differentiated neuroendocrine carcinoma.The median age at diagnosis was 53 years (range: 13–77 years). All patients were of Chinese Han ethnicity, 12 were male and four were female; the male‐to‐female ratio was also 3:1. Most patients had an Eastern Cooperative Oncology Group (ECOG) performance status score of 1 (87.5%) and two patients (12.5%) had a score of 2. Seven patients (43.8%) had never smoked, and nine patients (56.3%) had a history of previous smoking. The pathological type of most patients in this study was neuroendocrine carcinoma (NEC). Half of the patients (n = 2) and complete excision (n = 4). Of them, five (31.3%) patients had undergone surgery prior to treatment, and one patient with mediastinal lymph node metastasis progressed after 2 cycles of EP neoadjuvant chemotherapy and underwent surgical excision. Ten (62.5%) patients received radiation therapy during treatment. More than half of the patients  had at least two distal organ metastases, and six patients (37.5%) had no more than two distant metastases. Furthermore, the most common distant metastatic organ was the lung or pleura, accounting for 68.8% of all patients. The other common metastases were liver , bone , adrenal glands , brain , and pancreas .In addition, the median tumor size was 5.8 cm (range: 3.5–20.0). The ki‐67 index were available for 12 patients and the median ki‐67 index was 70% (range: 5%–95%). All patients received a first‐line chemotherapy regimen of etoposide in combination with platinum. Twelve patients (75%) received chemotherapy with EP and four patients (25%) treated with EC. Six patients underwent surgical excision, including partial (3.2n = 3) and NEC were 7.2 months  and 17.4 months , respectively (p = 0.463). The mOS for patients with NEC and AC were 40.0 months  and 95.0 months , respectively (p = 0.187). However, most inevitably develop disease progression or drug resistance. Swimming plots of etoposide plus platinum for the first‐line treatment of advanced TNENs are shown in Figure n = 14, 87.5%) will inevitably develop disease progression or resistance. Finally, 11 patients (68.8%) had died and five patients (31.3%) remained alive at the end of the study.The observed therapeutic effects are shown in Table p = 0.095) and OS (p = 0.061). Notably, one patient presented with only right supraclavicular lymph node metastasis  with a pathological type of poorly differentiated NEC; this patient achieved complete remission after 6 cycles of chemotherapy with the EP regimen. The patient did not receive radiotherapy or surgery in addition to chemotherapy. At the end of follow‐up, the patient was still alive with a PFS of 119 months and an OS of nearly 10 years. In our research, 62% of patients survived more than 3 years, and 29% survived for more than 5 years. For patients who progressed after treatment with first‐line etoposide combined with platinum, the following strategies were applied: irinotecan combined with paclitaxel, temozolomide + capecitabine, irinotecan monotherapy, gemcitabine combined with oxaliplatin or docetaxel, anti‐angiogenesis targeted therapy (anlotinib or apatinib), and re–challenge treatment with first‐line chemotherapy regimens re‐challenge.There was no significant difference in efficacy between the treatment groups in terms of PFS (p = 0.403) and radiotherapy subgroups (p = 0.277). The median OS for patients receiving surgery resection or not receiving surgery resection was 63.0  months and 38.6  months, respectively. In the radiotherapy subgroup, the median OS for patients receiving radiotherapy or not receiving radiotherapy was 38.6 months  and 54.2  months, respectively. Univariate COX regression analysis associated with PFS and OS is shown in Table p‐values were generated in our study, and no risk factors were identified by multivariate analysis.In this study, six patients received surgery and ten patients received radiation therapy. Of these, three cases developed distant metastases after adjuvant radiotherapy after radical excision. Figure 3.3For all 12 evaluable patients, treatment‐related adverse events manifested as hematological toxicity. Grade 1–2 myelosuppression was identified in six patients (50%); three cases were in the EC group and three were in the EP group. The remaining six patients (50%) developed grade 3–4 myelosuppression. In addition, three patients had grade IV myelosuppression and two patients had grade III myelosuppression, respectively. Of concern, all patients with grade 3 and above hematologic toxicity were in the EP treatment group. However, after symptomatic management of all 12 patients, myelosuppression improved, and the original treatment regimen was continued or the treatment dose was reduced. No patient experienced treatment‐related death.4According to previous reports, TNENs accounts for 0.4% of all neuroendocrine tumors.In fact, there is considerable controversy relating to the full management of thymic tumors, including the tumor staging system, the timing of surgery, the safety of postoperative adjuvant radiotherapy, the appropriate population for adjuvant chemotherapy, and the standard of care for advanced patients. With regards to the characteristics of the patients, our study was similar to those previously reported, with a median age at diagnosis of 53 years; this was similar to previous study that reported with a median age of diagnosis of 54 years, and a similar male to female ratio (3:1).A previous clinical trialIn addition, a retrospective studyConsidering these studies, significant tumor remission was observed in patients with unresectable locally advanced or advanced TCs, either with etoposide alone or in combination with other cytotoxic agents. However, previous studies did not specifically classify pathological subtypes of TCs. With an in‐depth understanding of the biology of thymic tumors, the subtypes of thymic carcinomas continue to be refined, and the treatment strategies for different prognostic subtypes of tumors are bound to differ. The above‐mentioned case report showed the better anti‐tumor activity for the EC regimen in advanced TNENs. Our study is the first to describe the efficacy of etoposide in combination with platinum in advanced TNENs, reporting data on ORR of 31.3%.TNENs are biologically different from neuroendocrine tumors of pulmonary origin, less aggressive than SCLC, and have a lower median age of diagnosis. Furthermore, these patients are less likely to develop brain metastases, not associated with smoking, and have a longer survival compared to SCLC.Multi‐center, data from prospective study may warrant additional attention to support the position of etoposide plus platinum for the first‐line treatment of advanced TNENs. Remarkably, immune checkpoint inhibitors (ICIs) have shown surprising anti‐tumor activity for extensive‐stage SCLC and the efficacy of ICIs in advanced TNENs is also worth investigating. Since thymic tumor cell lines are extremely rare, research in the experimental field also requires the technical dissemination of researchers to expand research and treatment options for this rare tumor.Unfortunately, the univariate COX regression analysis performed in this study did not yield significant survival‐related risk factors. This may be related to our study design and limited sample size. In addition, our research has several limitations that need to be considered. First, this study was a retrospective study carried out in a single center; thus, retrospective defects, such as recall bias and selection bias, may have existed. Second, this research included three patients with atypical carcinoid; thus, our study population was not exclusive to patients with neuroendocrine carcinoma. The prognosis between carcinoid tumors and neuroendocrine carcinoma may differ. Third, the limited sample size of this study may have influenced our conclusion.5This retrospective analysis, conducted in a real‐life setting, suggests that etoposide a combination with platinum has promising anti‐tumor activity for the treatment of advanced TNENs, with a clinically significant overall response rate.Yelan Guan: Conceptualization ; data curation ; formal analysis ; methodology ; software ; writing – original draft . Qifeng Yao: Conceptualization ; data curation ; formal analysis ; methodology ; writing – original draft . Yue Hao: Software ; writing – review and editing . Xiaohong Zeng: Software ; writing – review and editing . Wenxian Wang: Data curation . Xiadong Gu: Data curation . Jing Xiang: Data curation . Yan Sun: Supervision . Zhengbo Song: Conceptualization (lead); methodology (lead); supervision (lead); writing – review and editing (lead).None.The authors have no relevant interests to disclose.The study design was approved by the Ethics Committee of Zhejiang Cancer Hospital with the approval number IRB‐2022‐62. The Ethics Committee of Zhejiang Cancer Hospital approved the waiver of written informed consent for retrospective study.Figure S1Click here for additional data file.Table S1Click here for additional data file.


Vibrio natriegens is a Gram‐negative bacterium with an exceptional growth rate that has the potential to become a standard biotechnological host for laboratory and industrial bioproduction. Despite this burgeoning interest, the current lack of organism‐specific qualitative and quantitative computational tools has hampered the community's ability to rationally engineer this bacterium. In this study, we present the first genome‐scale metabolic model (GSMM) of V. natriegens. The GSMM (iLC858) was developed using an automated draft assembly and extensive manual curation and was validated by comparing predicted yields, central metabolic fluxes, viable carbon substrates, and essential genes with empirical data. Mass spectrometry‐based proteomics data confirmed the translation of at least 76% of the enzyme‐encoding genes predicted to be expressed by the model during aerobic growth in a minimal medium. iLC858 was subsequently used to carry out a metabolic comparison between the model organism Escherichia coli and V. natriegens, leading to an analysis of the model architecture of V. natriegens' respiratory and ATP‐generating system and the discovery of a role for a sodium‐dependent oxaloacetate decarboxylase pump. The proteomics data were further used to investigate additional halophilic adaptations of V. natriegens. Finally, iLC858 was utilized to create a Resource Balance Analysis model to study the allocation of carbon resources. Taken together, the models presented provide useful computational tools to guide metabolic engineering efforts in V. natriegens. Vibrio natriegens. This new model enabled the study of V. natriegens metabolism, halophilic adaptions, and resource analysis.iLC858 is the first constructed and validated genome‐scale metabolic model for the fast‐growing bacterium  Vibrio natriegens is a Gram‐negative bacterium that in recent years has been gaining attention as an academically and industrially promising host for synthetic biology and biotechnology applications due to its exceptionally fast growth rate. The V. natriegens genome consists of two circular chromosomes, like the majority of members of the genus Vibrio  or Escherichia coli (7 rRNA operons and 99 tRNA genes). The annotation also suggested that V. natriegens has a versatile metabolism equipping it with the ability to grow on a vast range of carbon substrates, to reduce nitrate, and to fix atmospheric nitrogen under anaerobic, nitrogen‐limited conditions   is a mathematical reconstruction containing all the known metabolites, enzymatic metabolic reactions, and the corresponding genes for a certain organism  and subsequently used to generate a draft model using the SEED server  of reactions in our model, not counting passive transmembrane transport reactions or metabolite sink reactions, are associated with at least one known coding sequence in the Vibrio vulnificus model VvuMBEL943 , the model will maximally utilize any provided sources of carbon to generate biomass. Hence, constraints on substrate uptake rate and secretion of metabolites must be imposed to generate realistic simulations.We first simulated aerobic growth on minimal medium by providing glucose as the sole carbon source in the V. natriegens secretes part of its substrate as acetate, indicating a mixed respiratory‐fermentative growth phenotype. This behavior is similar to the Crabtree effect observed in yeast under aerobic conditions, which is hypothesized to be a reflection of the fitness trade‐off between the efficiency gained by full respiration and the cost of the expression of the respiratory pathways .Even under aerobic conditions, rt et al  and Longl  pathway, and the glyoxylate shunt (GS). For this simulation, the same glucose uptake rate and acetate secretion rate as in the aerobic growth simulations were used. The predicted fluxes were represented as percentages of the glucose uptake rate, in order to enable comparison to Long's data. Comparing the fluxes predicted by the model's optimal solution and experimental fluxes yielded a high Pearson's correlation coefficient of 99.2%, suggesting high consistency between our model and V. natriegens' central metabolism.Pathways such as glycolysis, pentose phosphate pathway (PPP), and the tricarboxylic acid cycle (TCA) form the central motor driving metabolism in bacteria. Given the central role these pathways play in metabolism during growth, we investigated how well the predicted corresponding fluxes compared to experimentally determined central metabolic fluxes. We simulated aerobic growth on glucose minimal medium using iLC858, and compared the fluxes to data reported by Long et al , who useet al , producing boundaries on these fluxes allowing solutions yielding at least 98% of the optimal solution in terms of produced biomass. The results of this comparison, together with a schematic overview of experimentally determined fluxes through central metabolism, are shown in Fig et al .et al  carbon sources on which V. natriegens cannot grow, leading to an overall predictive accuracy of 83.8%.We experimentally assessed pply Fig . An overin silico predictions and experimental data, we suggest a few explanations for the discrepancies between prediction and experimentation in addition to what is discussed above. Firstly, it is important to notice that the experimental data quantify respiration and not growth, and some of these substrates, while potentially used for cellular respiration, may not be enough to allow biomass synthesis. Secondly, homology‐based functional annotation of substrate‐specific transport proteins is a notoriously complicated task , being significantly higher than the biomass yield reported for glucose  had associated genes of which the corresponding coded enzymes were entirely found in at least one of the proteomics samples. Moreover, 331 reactions (84.9%) had associated genes of which all enzymatic subunits except one were found in at least one of the proteomics samples.Flux sampling was used to determine reactions in the GSMM which are predicted to be able to produce fluxes of at least 10no et al . Across E. coli. Mapping reactions from one GSMM to another allows to identify reactions that are shared between or unique to one of the metabolic networks. Hence, we performed a reaction mapping of all cytoplasmatic reactions in ILC858 to the E. coli GSMM iML1515 and found 772 shared reactions out of 1,187 V. natriegens cytoplasmatic reactions , in addition to pumping out protons using terminal oxidases. The resulting proton motive force (PMF) and sodium motive force (SMF) are interchangeable through sodium‐proton antiporters and drive processes such as the rotation of a flagellum and ATP synthesis through oxidative phosphorylation. The central role of Na+‐NQR and the mechanisms related to proton and sodium gradients in V. cholerae have been previously described in detail   strain for this gene, and a reverted strain harboring the re‐inserted OAD gene. We found that only under high‐salinity conditions, the OAD knockout grew slightly slower and to a lower OD than the WT and OAD reversion strain, while at lower salinity, we observed no difference in growth  transporters for glycine‐betaine, another osmoprotectant used by E. coli is involved in the regulatory response to the accumulation of misfolded or unfolded proteins in the extra cytoplasmatic space .V. natriegens RBA model to simulate aerobic growth on glucose. The model was calibrated by estimating apparent catalytic rates for all the enzymes detected in our proteomics dataset using RBApy scripts . When comparing the networks for their theoretical capacities to produce amino acids, we found that V. natriegens' Alanine dehydrogenase and L‐alanine:glycoxylate aminotransferase, two genes not found on the E. coli genome, provide it with a metabolic advantage over E. coli for the production of the amino acid glycine. We did not detect high levels of expression of these genes in our proteomics samples of growth on minimal medium but found evidence of high transcription of these genes on MOPS medium in a ribosomal profiling dataset , a unique enzyme whose central role has been studied in V. cholerae and which has no equivalent on the E. coli genome  was highlighted as another functional membrane pump interacting with V. natriegens' sodium gradient. The role of this pump was so far unknown. Simulations with the GSMM suggested that this pump might work as a gradient‐driven pyruvate carboxylation and, therefore, an alternative anaplerotic carboxylation. We detected expression of this pump in our proteomics data, in high levels in the same order of magnitude as the Na+‐NQR. Further experimental work with a knockout strain for this pump supported the functionality of the Na+‐OAD pump during aerobic growth. To our knowledge, this is the first demonstration of the functionality of this enzyme during aerobic growth in bacteria. These observations suggest that this enzyme plays a role during V. natriegens growth that has no equivalent in E. coli. Moreover, they may provide insights into the lifestyle of the important human pathogen V. cholerae, as it also possesses a homologous Na+‐OAD that is decoupled from the citrate fermentation operon  was annotated using RAST  for Phenotype MicroArrays (PMs) for Microbial Cells™ analysis on the OmniLog System®. Cells were grown on BioLog Universal Growth media with sheep's blood at 30°C overnight. They were then added into the wells of the plates, which were run according to a proprietary protocol for running Gram‐negative bacteria. The base test media for the metabolic test plates is proprietary, but it is a minimal media composed mostly of salt and buffers. For more information, see the BioLog website  and reference papers Bochner et al  with 10 g l−1 of carbon substrate, at 700 rpm. Fully grown cultures were centrifuged and dried to measure dry weight. Yield was then calculated as the final dry weight divided by the initial carbon substrate weight.For the determination of biomass yields, et al, in silico knockout strains, using biomass formation as an objective. Correspondingly, genes where knockouts lead to infeasible FBA, or a growth rate reduced below 90% of nonimpaired growth were classified as “essential.”Gene knockouts were simulated by applying the COBRApy built‐in knockout function to genes (Ebrahim V. natriegens OAD knockout strain (OAD KO) was generated using the MuGENT protocol  with the pMMB67EH‐‐tfox(Vc) AmpR plasmid was used as the base/wild‐type strain for genome modification.The et al, First, fusion PCR was used to generate a 5.2‐kb linear fragment in which the Cmp cassette was fused with 2‐kb flanking regions of the OAD gene. The Phusion Flash Master Mix (ThermoFisher) was used. As per manufacturer's instructions, the three fragments to be fused were first separately amplified. The cassette was amplified using the primers Cm4OAD‐F (aacatcctgcacctgtcaGAGCGATTGTGTAGGCTG) and Cm4OAD‐R (gcgagtatgttacccacaTGGGAATTAGCCATGGTCC), the upstream fragment was amplified using primers OADup‐F (CGTTGCTTCAGGTAACTACTAA) and OADup‐R (cagcctacacaatcgctcTGACAGGTGCAGGATGTT), and the downstream fragment was amplified using OADdn‐F (ggaccatggctaattcccaTGTGGGTAACATACTCGC) and OADdn‐R (GGATATAACGCTTCTGACATG). These were run as 20‐μl reactions, gel‐verified, and purified using a Zymo DNA Clean and Concentrate Kit as per manufacturer's instructions. To perform the fusion reaction, 15 μl of Master Mix, 2 μl of the Cmp cassette, and 1 μl of each of the flanking cassettes were added to a 30‐μl reaction . The following program was run: 98°C for 1 min, then 11 cycles of: 98°C for 1 s, 75°C for 1 s, Ta ‐ ramped down to 72?C at −0.1°C/s from 75°C, hold for 30 s at 72°C for 10 min. For the purification reaction, 5 μl of the above reaction was taken and added to 50 μl of Master Mix, 0.5 μM each of nested primers  in a 100 μl reaction. A 2 step PCR was run, as per the manufacturer's protocol. The purified band was gel verified and purified using the Zymo DNA Clean and Concentrate Kit, and then used in the MuGENT protocol  and OADdn‐R (GGATATAACGCTTCTGACATG) to amplify the OAD gene and up and downstream regions out of the V. natriegens genome. A second cassette was created by replacing the DNS gene in V. natriegens with the ampicillin resistance cassette. The cassette was created as following: primers for region upstream of DNS: Dns‐3 kb‐F (ctaacatggctaagcacctg) and DNS4AmpR‐R (tatttcctattgagacttaattgaactgagg); Primers for ampR cassette: AmpR‐F(tcaataggaaatattagacgtcaggtggcact) and AmpR‐R (aaagattagcgattgagtaaacttggtctgacagttacc); Primers for region downstream of DNS: DNS4AmpR‐F (ccaagtttactcaatcgctaatctttctgtttgagg) and DNS‐3 kb‐R (actggtaagccataacgacc). MUGENT with the addition of 100 ng of the DNS:Amp cassette and 250 ng of the OAD complement cassette was preformed, and cells were selected on ampicillin. The colonies that grew were then patched on Cmp, and those which did not grow on Cmp were confirmed for OAD in situ complementation and Amp insertion at DNS by colony PCR and sequencing. MUGENT was also performed on the V. natriegens RifR and RifR OAD KO strains with the DNS:AmpR cassette only to create appropriate controls.The confirmed OAD KO strain was the complemented V. natriegens strains were inoculated and grown overnight from glycerol stocks at 30°C in modified minimal medium , 6.78 g Na2HPO4, 3 g KH2PO4, 2 mM MgSO4, 0.1 mM CaCl2, 25 μM FeCl3, indicated amount of NaCl, and 0.4% glucose, pH 7.4). The following morning, cells were diluted 1:200 in the same media and dispensed in triplicate into the wells of a BioScreen C Analyzer 100‐well honeycomb plate , 150 μl per well. Growth was measured at OD600, with measurements taken every 15 min. for 24 h. All chemicals, including carbon sources, were from Sigma‐Aldrich  or Fisher Scientific .The indicated Vibrio natriegens (ATCC 14048) was grown in a modified M9 minimal medium  supplemented with glucose and NaCl as indicated below. Three different colonies of the expected morphology were used to inoculate each test tube containing modified M9 minimal medium with 0.4% (w/v) glucose and 0 mM NaCl and grown for 16 h at 36.5°C and 250 rpm in an orbital shaker. Based on the density of the overnight cultures, using a conversion factor of 1.75 × 108 CFU ml−1 OD600−1, the appropriate volumes containing ∼ 105 cells were transferred to flasks containing prewarmed modified M9 minimal medium with 1% (w/v) glucose and either 0, 300, 540, or 650 mM NaCl. The flasks were incubated at 36.5°C with continuous shaking and OD600 measurements were monitored, and samples were collected during early log, mid‐log, and stationery phases of growth. Aliquots for proteomic analysis (2 ml) were taken directly from the flasks at the specified growth points and pelleted at 5,000 g for 1 min. at 20°C.et al, Cell pellets were taken from −80°C storage and placed on ice. Lysis buffer (10% n‐propanol in 50 mM ammonium bicarbonate) was added and volumes adjusted based on optical density measurements . The LC–MS/MS system consisted of an Ultimate U3000 LC coupled with an Orbitrap Fusion Lumos mass spectrometer  via a Nanospray Flex Ion Source. The U3000 system was configured for nano‐flow separations using an autosampler, loading pump, and analytical pump. The autosampler loaded samples onto a trap column  via loading pump . The analytical pump (flow rate 300 nl/min) was used to elute peptides from the trap onto an analytical column  and into the mass spectrometer. Mass spectra were acquired on a Fusion Lumos Orbitrap mass spectrometer in data‐dependent mode with 3 s cycle times. A survey scan range of 400–1,600 Da was acquired on the Orbitrap detector (resolution 120 K). The maximum injection time was 50 ms and automatic gain control (AGC) target was set to standard. Ion intensities > 5.03 with predicted charges of 2+ to 7+ were fragmented using higher energy collisional dissociation (HCD). Dynamic exclusion was enabled for a duration of 30 s for subsequent MS/MS submissions. The MS/MS detector was set to Ion Trap rapid scan with maximum injection time 35 ms and AGC target set to standard.et al, V. natriegens ATCC 14048 predicted proteome and 190 sequences representing common contaminants, proteolytic enzymes, and mass standards . Variable modifications for the search included oxidation of methionine and deamidation of glutamine and asparagine. Proteolytic cleavage search parameters used trypsin with > 3 missed cleavages. The precursor ion tolerance was set to +50 ppm and fragment ion to +0.6 Da. Scaffold  was used to validate MS/MS based peptide and protein identifications. Following Scaffold delta‐mass correction, identifications were retained at >80.0% probability and > 95.0% probability at the peptide  via in‐house scripting . Rates for these processes were taken from Jahn et al  and 0.03 g g−1 ectoine production targets were taken from literature on Vibrio (Chien et al, et al, Macrocomponent production targets were set to match the macrocomponent production targets of the et al (et al (Finally, the model was calibrated estimating apparent catalytic rates for all the enzymes detected in our proteomics dataset. For this, we followed the procedure proposed by Jahn et al , which il (et al  was used−1, as reported by Hoffart et al (For RBA growth simulations, an acetate production rate was enforced at a value of 0.42 g gDWrt et al .The Python package scipy was used to perform one‐way ANOVAs between the 0 mM and 300 mM NaCl proteomics samples.Lucas Coppens: Data curation; formal analysis; investigation; visualization; methodology; writing – original draft. Tanya Tschirhart: Data curation; formal analysis; writing – review and editing. Dagmar H Leary: Data curation; formal analysis. Sophie M Colston: Data curation; formal analysis. Jaimee R Compton: Data curation; formal analysis. William Judson Hervey: Data curation; formal analysis. Karl L Dana: Data curation; formal analysis. Gary J Vora: Resources; methodology; writing – review and editing. Sergio Bordel: Data curation; formal analysis; investigation; methodology. Rodrigo Ledesma‐Amaro: Conceptualization; resources; data curation; formal analysis; supervision; funding acquisition; investigation; writing – original draft; project administration; writing – review and editing.The authors declare that they have no conflict of interest.Appendix S1Click here for additional data file.Dataset EV1Click here for additional data file.Dataset EV2Click here for additional data file.Dataset EV3Click here for additional data file.Dataset EV4Click here for additional data file.Dataset EV5Click here for additional data file.Dataset EV6Click here for additional data file.Dataset EV7Click here for additional data file.Dataset EV8Click here for additional data file.Dataset EV9Click here for additional data file.Dataset EV10Click here for additional data file.