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Enregistrement W4405766091 · doi:10.1097/cm9.0000000000003424

Fecal siderophore genes are potential biomarkers for ulcerative colitis

2024· article· en· W4405766091 sur OpenAlexaboutno aff
Jingshuang Yan, Rongrong Ren, Zhengpeng Li, Wanyue Dan, Xinyi Ma, Xiaohan Zhang, Xiaoyan Chi, Lihua Peng, Yunsheng Yang

Notice bibliographique

RevueChinese Medical Journal · 2024
Typearticle
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueGut microbiota and health
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésAerobactinUlcerative colitisMicrobiologyInflammatory bowel diseaseBiologyEnterobactinShigellaGut floraVirulenceMicrobiomeEscherichia coliDiseaseImmunologySalmonellaEnterobacteriaceaeMedicineBacteriaBioinformaticsGeneGeneticsPathology

Résumé

récupéré en direct d'OpenAlex

Ulcerative colitis (UC) and Crohn’s disease (CD) are chronic inflammatory bowel diseases (IBDs) with largely unclear etiologies and complex pathogeneses. The pathogenesis of UC has been linked to an imbalance in the gut microbiota, including in the prevalence of Enterobacteriaceae, especially pathogenic Escherichia coli (E. coli).[1] The UC diagnosis and assessment primarily rely on colonoscopy and mucosal biopsy pathology, which are limited by their invasiveness, constraints on medical resources, and potential risks and complications. Several biomarkers, such as serum C-reactive protein (CRP), fecal calprotectin, and fecal lactoferrin, are currently recommended for assessing UC disease activity. However, these markers are primarily inflammation-related factors produced by the host, not specific to IBD, and are influenced by other inflammatory states. Microbiome-related biomarkers are used as direct indicators of the gut microecosystem and have considerable potential for disease assessment and therapeutic guidance in IBD. However, current analyses of gut microbiota, such as 16S rRNA or metagenomic sequencing, are often time-consuming and costly. Siderophore, a low-molecular-weight protein secreted extracellularly to chelate ferric iron, is a crucial virulence factor in iron acquisition of bacteria and fungi. The presence of siderophores can indirectly reflect bacterial abundance and activity.[2] Gram-negative pathogens (E. coli, Klebsiella, Shigella, Salmonella, and Yersinia) have four common types of siderophores, including enterobactin, salmochelin, aerobactin, and yersiniabactin.[3] Here, we aimed to explore the relationships between positivity rates and copy numbers of siderophore genes and clinical characteristics of UC to evaluate their potential as noninvasive biomarkers for assessing UC disease activity. This single-center cohort study involved patients with UC who visited the First Medical Center of the People’s Liberation Army General Hospital from 2017 to 2023. The inclusion criteria were: age, 18–75 years; active UC (Mayo score ≥3); and no history of abdominal surgery or colorectal cancer. Clinical data were retrieved from medical records. The clinical phenotypes and disease activity were determined using the Montreal classification and Mayo score. The healthy control (HC) group consisted of fecal microbiota transplantation (FMT) candidate donors. All participants provided written informed consent. The study was approved by the ethics committee of the Chinese People’s Liberation Army General Hospital (S2016-129-01, S2016-130-01, S2021-602-01). Polymerase chain reaction (PCR) and absolute quantitative real-time PCR provided the positivity rates and copy numbers of siderophore genes. The copy number was calculated as copies/ng DNA = (DNA content [ng] × 6.023 × 1023) (template length × 660). The default template length was set to the base number of the E. coli genome (4700 kb). Standard curves were generated using a 10-fold serial dilution of DNA from the reference strains or plasmids [Supplementary Methods and Supplementary Figure 1, https://links.lww.com/CM9/C258]. Measurement data represent the median and interquartile range, and counting data with numbers and percentages. Differences between groups were assessed using the chi-squared, Mann–Whitney U, and Pearson correlation tests. Receiver operating characteristic (ROC) curves were plotted, and the area under the curve (AUC) was calculated. Statistical significance was set at P <0.05. Statistical analyses were performed using SPSS 21.0 (IBM SPSS, Armonk, NY, USA), and figures were created using GraphPad Prism 7.0 (GraphPad Software, San Diego, CA, USA) and Adobe Illustrator CC 22.0 (Adobe, San Jose, CA, USA). We enrolled 166 patients with UC and 168 healthy adults who were FMT candidate donors as HCs. The baseline clinical characteristics of the 166 UC patients are presented in Supplementary Table 1, https://links.lww.com/CM9/C258. According to the Montreal classification, there were 112 cases (67.5%) of extensive UC, 46 (27.7%) of left-sided UC, and 8 (4.8%) of proctitis. Based on the Mayo clinical score, 46 patients were in the severe, 81 in the moderate, and 39 in the mild active stages. According to the Mayo Endoscopic Score, 116 patients were in the severe, 45 in the moderate, and 5 in the mild active stages. The UC group had a higher prevalence of all siderophore genes than the HC group [Supplementary Table 2, https://links.lww.com/CM9/C258], especially for the salmochelin (iroB 30.7% vs. 13.3%, P <0.01; iroN 38.0% vs. 21.8%, P <0.01) and aerobactin genes (iucA 57.8% vs. 42.4%, P <0.01; iutA 59.0% vs. 44.2%, P <0.01). The total copy number of siderophore genes was also higher in the UC group than in the HC group (1182.49 copies vs. 176.44 copies, P <0.01); moreover, the UC group had higher copy numbers of all eight siderophore genes compared to the HC group [Supplementary Table 3, https://links.lww.com/CM9/C258]. We used the total copy number of siderophore genes to distinguish UC patients from HCs, with the area under the receiver operating characteristic (AUROC) of 0.701 (95% confidence interval [CI], 0.644–0.757, P <0.001). A threshold of 2877.68 copies/ng yielded a specificity of 90.3% and a sensitivity of 33.1% [Figure 1A].Figure 1: (A) ROC curves of siderophore gene copy number for distinguishing active UC from HCs. (B) ROC curves of siderophore gene copy number, CRP, and ESR. AUROC: CRP: C-reactive protein; ESR: Erythrocyte sedimentation rate; HC: Healthy control; ROC: Receiver operating characteristic; UC: Ulcerative colitis.The total copy number of siderophore genes was significantly higher in patients with severe active UC than in those with moderate (3111.03 copies/ng vs. 1183.54 copies/ng DNA, P = 0.013) and mild (3111.03 copies/ng vs. 672.60 copies/ng DNA, P = 0.005) active UC [Supplementary Figure 2, Supplementary Table 4, https://links.lww.com/CM9/C258]. Patients with severe endoscopic activity exhibited an increased total copy number of fecal siderophore genes, higher than that in patients with mild-to-moderate endoscopic activity (1318.02 copies vs. 723.10 copies, P = 0.044) [Supplementary Figure 3, Supplementary Table 5, https://links.lww.com/CM9/C258]. There were no significant differences in the total copy number between different sexes, disease extents, and medication administration. The ROC curves indicated that a threshold of 10,298.63 copies/ng DNA diagnosed severe active UC, with a specificity of 92.5% and a sensitivity of 43.5%. In contrast, a CRP cut-off value of 0.8 mg/dL showed a sensitivity and specificity of 61.4% and 82.2%, respectively, in diagnosing severe active UC, and ESR (threshold 30 mm/h) presented a sensitivity of 36.4% and a specificity of 86.3%, respectively [Figure 1B]. When combining total fecal siderophore gene copy number and serum CRP in a parallel test, the sensitivity and specificity were 89.1% and 75.8%, respectively, with a positive predictive value (PPV) and a negative predictive value (NPV) of 58.6% and 94.8%, respectively. When total fecal siderophore gene copy number was combined with a parallel fecal immunochemical test (FIT), the sensitivity, specificity, PPV, and NPV for severe UC were 45.7%, 94.2%, 75.0%, and 81.9%, respectively [Supplementary Table 6, https://links.lww.com/CM9/C258]. Regarding the detection of severe endoscopic activity, the threshold of 10,298.63 copies/ng achieved a sensitivity and specificity of 22.4% and 94.0%, respectively. When tested in parallel with serum CRP, a specificity of 88.0%, sensitivity of 55.2%, PPV of 91.4%, and NPV of 45.8% were obtained. In terms of siderophores as disease biomarkers, it has been reported that fungal siderophores have potential diagnostic value in invasive aspergillosis.[3] Isolated E. coli in inflamed mucosal sites of patients with UC had a higher prevalence of siderophore genes, such as iroN (72.7%), fyuA (68.2%), and iucC (68.2%).[4] However, the applicability of bacterial siderophores as disease biomarkers, particularly in UC, remains underexplored. In the current study, we first reported that the positivity rates and copy numbers of eight genes associated with four siderophores (enterobactin, salmochelin, aerobactin, and yersiniabactin) were significantly higher in the feces of patients with active UC than in those of HCs. A total siderophore gene copy number had high specificity in differentiating patients with active UC and HC. Gut dysbiosis predates the onset of the disease, and studies have shown that a panel of serum antibodies, including anti-E. coli outer membrane porin C and anti-flagellins antibodies, can predict CD diagnosis years before.[5] Our work represents an important foundation for future work on the preclinical phase of UC, especially in early detection and diagnosis. In clinical practice, noninvasive biomarkers have gained considerable attention owing to their extensive application in disease monitoring and therapeutic efficacy assessment. The diagnostic efficacy of fecal calprotectin and lactoferrin has been investigated in differentiating patients with active and inactive UC. However, there are few studies on biomarkers for distinguishing mild-to-moderate from severe active UC, especially in endoscopic severe patients. We found that severe active UC had a higher total siderophore gene copy number than moderate and mild active UC. Notably, when tested in parallel with serum CRP, total siderophore gene copy number had both high specificity and sensitivity in diagnosing patients with severe active UC, both in clinical and endoscopic scores. A high specificity reduces misdiagnosis rates, and the frequency of colonoscopy in mild-to-moderate patients. Our study has several limitations. First, the patient sample size was relatively limited. Future research should verify the relationship between the total copy number of siderophore genes and UC disease activity in a larger cohort, and develop predictive models for assessing disease activity. Long-term monitoring is also necessary to evaluate the diagnostic efficacy during the course of the disease. Second, further analysis by combining metagenomic sequencing and gene detection could more comprehensively reflect microbiota changes. Third, comparative studies with other fecal biomarkers, like fecal calprotectin, should be carried out to fully evaluate the value of siderophore gene copy number as a UC biomarker. In conclusion, this is a pioneering study to report higher siderophore gene prevalence and total copy number in the feces of patients with active UC than in those of HCs, particularly in severe active UC. These bacterial biomarkers performed well in the diagnosis and assessment of UC and provided a new noninvasive quantitative tool for UC severity clinical evaluation. Siderophore gene copy number constitutes a specific and direct parameter for clinicians to use in disease management. Combined with existing biomarkers, this approach can aid in the formulation of personalized treatment plans and reduce reliance on invasive diagnostic methods for determining clinical and endoscopic disease activity in UC. Acknowledgments We express our deepest appreciation to Professor Kaichun Wu (Xijing Hospital, Xi’an, China) for his kind donation of E. coli strain LF82. Funding None. Conflicts of interest None.

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Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,647
Score d'incertitude au seuil0,475

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,008
Tête enseignante GPT0,298
Écart entre enseignants0,290 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2024
Routes d'admission1
Résumé présentoui

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