AbstractThe One Health approach, recognizing the interconnectedness of human, animal, and environmental health, has gained significance amid emerging zoonotic diseases and antibiotic resistance concerns. This paper aims to demonstrate the utility of a collaborative tool, the SIEGA, for monitoring infectious diseases across domains, fostering a comprehensive understanding of disease dynamics and risk factors, highlighting the pivotal role of One Health surveillance systems.

摘要在新出现的人畜共患疾病和抗生素耐药性问题中，认识到人类，动物和环境健康相互关联的“一个健康”方法具有重要意义。本文旨在展示协作工具SIEGA的实用性，用于跨领域监测传染病，促进对疾病动态和风险因素的全面了解，突出一个健康监测系统的关键作用。

Raw whole-genome sequencing is processed through different species-specific open software that additionally reports the presence of genes associated to anti-microbial resistances and virulence. The SIEGA application is a Laboratory Information Management System, that allows customizing reports, detect transmission chains, and promptly alert on alarming genetic similarities.

。SIEGA应用程序是一个实验室信息管理系统，它允许定制报告，检测传播链，并及时提醒惊人的遗传相似性。

The SIEGA initiative has successfully accumulated a comprehensive collection of more than 1900 bacterial genomes, including Salmonella enterica, Listeria monocytogenes, Campylobacter jejuni, Escherichia coli, Yersinia enterocolitica and Legionella pneumophila, showcasing its potential in monitoring pathogen transmission, resistance patterns, and virulence factors.

SIEGA倡议已成功收集了1900多种细菌基因组，包括肠道沙门氏菌、单核细胞增生李斯特菌、空肠弯曲菌、大肠杆菌、小肠结肠炎耶尔森菌和嗜肺军团菌，展示了其在监测病原体传播、耐药模式和毒力因子方面的潜力。

SIEGA enables customizable reports and prompt detection of transmission chains, highlighting its contribution to enhancing vigilance and response capabilities. Here we show the potential of genomics in One Health surveillance when supported by an appropriate bioinformatic tool. By facilitating precise disease control strategies and antimicrobial resistance management, SIEGA enhances global health security and reduces the burden of infectious diseases.

SIEGA能够定制报告并迅速检测传输链，突出了其对提高警惕性和响应能力的贡献。在这里，我们展示了在适当的生物信息学工具的支持下，基因组学在一次健康监测中的潜力。通过促进精确的疾病控制策略和抗菌药物耐药性管理，SIEGA增强了全球健康安全并减轻了传染病的负担。

The integration of health data from humans, animals, and the environment, coupled with advanced genomics, underscores the .

来自人类、动物和环境的健康数据与先进的基因组学相结合，突显了这一点。

IntroductionThe One Health approach emphasizes the interconnectedness of human, animal, and environmental health, and has gained relevance in recent years due to the emergence of zoonotic diseases and increasing antibiotic resistance1. The importance of One Health in epidemiological surveillance lies in its capacity to monitor and control infectious diseases across different domains, enabling a comprehensive understanding of disease dynamics and risk factors2.

引言“一个健康”的方法强调了人类，动物和环境健康的相互联系，近年来由于人畜共患疾病的出现和抗生素耐药性的增加而变得越来越重要1。One Health在流行病学监测中的重要性在于它能够监测和控制不同领域的传染病，从而能够全面了解疾病动态和风险因素2。

By integrating human, animal, and environmental health data, One Health surveillance systems facilitate early detection and response to health threats, thereby enhancing global health security and reducing the burden of disease3. Furthermore, this integrated approach fosters multidisciplinary collaborations among stakeholders to develop and implement coordinated strategies aimed at disease prevention and control4.

通过整合人类，动物和环境健康数据，One健康监测系统有助于早期发现和应对健康威胁，从而增强全球健康安全并减轻疾病负担3。此外，这种综合方法促进了利益相关者之间的多学科合作，以制定和实施旨在预防和控制疾病的协调战略4。

Recently, the conventional process of serotyping through serology has been undergoing a gradual transformation, with molecular typing methods such as multi-locus sequence-based typing (MLST)5 increasingly complementing or replacing traditional methods. However, these techniques do not possess the necessary discriminatory power to differentiate between closely related strains6, which limits its application in many epidemiological studies.

最近，通过血清学进行血清分型的常规过程正在逐渐转变，分子分型方法如多位点序列分型（MLST）5越来越多地补充或替代传统方法。然而，这些技术不具备必要的歧视能力来区分密切相关的菌株6，这限制了其在许多流行病学研究中的应用。

In recent years, High-throughput sequencing (HTS) technologies have revolutionized the field, enabling rapid and cost-effective analyses of complete genomes7. Whole-genome sequences (WGS) offer an unparalleled level of discrimination among genetically related isolates, allowing for the exploration of compelling questions such as accurate phylogenetic and phylogenomic analyses8, as well as the examination of serotype- or subtype-determining genes9.

近年来，高通量测序（HTS）技术彻底改变了该领域，能够对完整基因组进行快速且经济高效的分析7。全基因组序列（WGS）在遗传相关分离株之间提供了无与伦比的区分水平，可以探索令人信服的问题，例如准确的系统发育和系统基因组分析8，以及检测血清型或亚型决定基因9。

Thus, the use of sequen.

因此，使用sequen。

Table 1 SIEGA features and description.Full size tableThe entire SIEGA data management system has been designed to free users of the need to have in-house expertise in genomic data management and resources to store such data. Simultaneously, the system offers a centralized database of all genomes sampled, allowing optimal exploitation of the results and the correct implementation of one health surveillance.

表1 SIEGA特征和描述。全尺寸表整个SIEGA数据管理系统的设计目的是让用户无需在基因组数据管理方面拥有内部专业知识和存储此类数据的资源。同时，该系统提供了所有采样基因组的集中数据库，可以对结果进行最佳利用，并正确实施一次健康监测。

It provides a convenient user permission structure to allows data sharing and collaborative work (if desired), detailed quality control for the standard pipelines used for data processing and accurate data traceability. Beyond facilitating collaborative work, user permission also helps with data privacy in samples of human origin.

它提供了一个方便的用户权限结构，允许数据共享和协作工作（如果需要），对用于数据处理的标准管道进行详细的质量控制，并实现准确的数据可追溯性。除了促进协作工作之外，用户权限还有助于保护人类来源样本中的数据隐私。

In any case, metadata does not include any field with personal identification and, ultimately, it is user responsibility not including any information that would reveal the origin of the sample. In general, data is treated in an open data philosophy where possible, according to FAIR principles (findable, accessible, interoperable and reusable)28.Detailed reports on each sample, that include MLST, cgMLST, serotyping outcomes, antimicrobial resistance and virulence genes, plasmids and a phylogenetic tree with the most related samples are provided.

无论如何，元数据不包括任何带有个人标识的字段，最终，用户有责任不包括任何可能揭示样本来源的信息。一般来说，根据公平原则（可查找，可访问，可互操作和可重复使用），在可能的情况下，数据以开放数据哲学进行处理28。提供了每个样本的详细报告，包括MLST，cgMLST，血清分型结果，抗微生物药敏性和毒力基因，质粒和具有最相关样本的系统发育树。

Additionally, customized phylogenetic analysis can be carried out in an easy and intuitive way. Finally, one of the most interesting features is the automatic alert system. SIEGA can be configured to automatically send a warning when a new sample is introduced that meets some criteria defined by the user, based on genetic distance, serotype, presence of antimicrobial or virulence genes, etc.

此外，可以以简单直观的方式进行定制的系统发育分析。最后，最有趣的功能之一是自动警报系统。SIEGA可以配置为在引入符合用户定义的某些标准的新样本时，根据遗传距离，血清型，抗菌或毒力基因的存在等，自动发出警告。

(see Table 1 and Supplementary results for details).Advantages of SIEGAThe overarching goal of the SIEGA init.

（详见表1和补充结果）。SIEGA的优势SIEGA init的总体目标。

Salmonella entericaThe SIEGA encompasses a dataset comprising 670 whole genome sequences of Salmonella enterica, which were sequenced from June 2020 to July 2023 using samples collected between 2013 and 2023. Within this dataset, 42.54% (285) of the samples were sourced from clinical origins, 34.63% from the food-related sector, and 21.34% from livestock sources.

肠炎沙门氏菌SIEGA包含一个包含670个肠炎沙门氏菌全基因组序列的数据集，这些序列是从2020年6月至2023年7月使用2013年至2023年间收集的样本进行测序的。在这个数据集中，42.54%（285）的样本来自临床来源，34.63%来自食品相关部门，21.34%来自牲畜来源。

A total of 448 distinct Sequence Types (STs) were identified and categorized into clonal complexes (CCs). The prevailing ST, ST 309694 (corresponding to clonal complex ST-71), was encountered on 26 occasions. Additionally, 83 strains exhibited concurrence with more than one ST. 6 STs (ST-67337, ST-138467, ST-197094, ST-207307, ST-247937, and ST-320298) were found cross-wide clinical, food-related, and livestock-origin samples.

共鉴定出448种不同的序列类型（ST），并将其分类为克隆复合物（CC）。在26次中遇到了流行的ST，ST 309694（对应于克隆复合物ST-71）。此外，83株菌株与一种以上的ST.6 ST（ST-67337，ST-138467，ST-197094，ST-207307，ST-247937和ST-320298）表现出一致性，被发现跨临床，食品相关和家畜来源的样本。

Similarly, 18 STs were identified in both clinical and food samples, and 7 STs were identified in clinical and livestock-origin samples..

同样，在临床和食品样本中均鉴定出18种ST，在临床和家畜来源样本中鉴定出7种ST。。

Listeria monocytogenesThe SIEGA includes a dataset comprising 678 whole genome sequences of Listeria monocytogenes, which were sequenced from June 2019 to July 2023. Within this dataset, 69.61% (472) of the samples were sourced from clinical origins, including all the samples from Andalusia sequenced by the Neisseria, Listeria and Bordetella Unit of the National Centre for Microbiology in Spain while investigating the Listeriosis outbreak caused by contaminated stuffed pork in Spain in 201929, and 30.38% (206) from food origin.

单核细胞增生李斯特菌SIEGA包括一个数据集，其中包含678个单核细胞增生李斯特菌全基因组序列，这些序列于2019年6月至2023年7月测序。在这个数据集中，69.61%（472）的样本来自临床来源，包括来自安达卢西亚的所有样本，这些样本是由西班牙国家微生物中心的奈瑟菌，李斯特菌和博德特氏菌单位测序的，同时调查了201929年由西班牙受污染的填充猪肉引起的李斯特菌病爆发，以及来自食品来源的30.38%（206）。

A total of 248 distinct STs were identified and categorized into CCs. The prevailing ST, ST 29,514 (corresponding to clonal complex ST-388), was encountered on 210 occasions.Campylobacter sppThe SIEGA contains 276 whole genome sequences of Campylobacter, received between December 2020 and June 2023, corresponding to both C.

总共确定了248个不同的ST，并将其分类为CCs。。弯曲杆菌SIEGA包含276个弯曲杆菌的全基因组序列，在2020年12月至2023年6月期间收到，对应于C。

jejuni and C. coli. Most of the sequences have been obtained from human clinical strains from two reference hospitals in Cádiz and Seville. There are some STs, grouped into CCs, which have been detected with greater frequency in clinical samples. From ST-16294 (corresponding to the clonal complex ST-206) 12 sequences have been obtained, with the interest of being detected from 2020 to 2023 and in a scattered way, with 8 isolates in Cádiz and 4 in Seville.

空肠和大肠杆菌。大多数序列是从卡迪兹和塞维利亚两家参考医院的人类临床菌株中获得的。有一些ST分为CC，在临床样本中检测到的频率更高。从ST-16294（对应于克隆复合物ST-206）获得了12个序列，有兴趣从2020年到2023年以分散的方式检测到，在Cádiz有8个分离株，在塞维利亚有4个分离株。

Their identical virulome and resistome profiles have been recovered from these sequences, using the tools described, particularly ABRicate30 on VFDB31 and CARD32, databases. Another frequent STs have been ST-12550 (ST-573CC) and ST-18855 (ST-52CC)..

使用所描述的工具，特别是VFDB31和CARD32数据库上的ABRicate30，已经从这些序列中恢复了它们相同的病毒组和抗性谱。另一个常见的ST是ST-12550（ST-573CC）和ST-18855（ST-52CC）。。

Escherichia coliThe SIEGA includes 121 whole genome sequences of Escherichia coli, 44 of them downloaded from the EnteroBase website33 for reference and 77 of them collected between November 2021 and May 2023. To date, most of the sequences (72) have been obtained from food samples taken at retail level on behalf of the monitoring programme of anti-microbial resistance (AMR) according to the provisions of the Commission Implementing Decision (EU) 2020/172934 implemented in Andalusia.

大肠杆菌SIEGA包括121个大肠杆菌的全基因组序列，其中44个从EnteroBase网站33下载以供参考，其中77个在2021年11月至2023年5月期间收集。。

The human clinical strains come from two reference hospitals in Cádiz and Seville. There are STs, grouped into CCs, which have been detected with greater frequency in food samples. The prevailing ST, ST 169652 (corresponding to clonal complex ST-10) was encountered in 6 occasion all from food samples.

人类临床菌株来自卡迪兹和塞维利亚的两家参考医院。有一些ST被归类为CC，在食品样本中被检测到的频率更高。普遍存在的ST，ST 169652（对应于克隆复合物ST-10）在6次都是从食物样本中遇到的。

Additionally, other 4 strains grouped in ST142026 (CC 155) and 3 strains exhibited concurrence with ST 191979 (CC 162) or 60064 (CC 93). To the date, no shared CCs have been detected in the food and human clinical origin samples..

此外，ST142026（CC 155）中的其他4株菌株和3株菌株与ST 191979（CC 162）或60064（CC 93）一致。迄今为止，在食品和人类临床来源的样品中未检测到共有的CCs。。

Yersinia enterocoliticaThe SIEGA encompasses 23 whole genome sequences of Yersinia enterocolitica, received in the year 2022, sampled between February and November. To date 21 (91.3%) of the sequences have been obtained from clinical samples from one of the reference hospitals, the Hospital Virgen del Rocio in Seville, and 2 were obtained from food samples.

小肠结肠炎耶尔森菌SIEGA包含2022年收到的23个小肠结肠炎耶尔森菌全基因组序列，在2月至11月之间采样。迄今为止，有21个（91.3%）序列是从塞维利亚Virgen del Rocio医院的一家参考医院的临床样本中获得的，有2个是从食品样本中获得的。

There are some STs, grouped into CCs, which have been detected with greater frequency in these clinical samples. The prevailing ST, ST-1574 (corresponding to clonal complex ST-135), was encountered on 4 occasions. Additionally, 3 strains exhibited concurrence with ST-52 and 2 strains grouped into ST-1716 (corresponding also to clonal complex ST-135).

有一些ST分为CC，在这些临床样本中检测到的频率更高。4次遇到了流行的ST ST-1574（对应于克隆复合物ST-135）。此外，3株菌株与ST-52一致，2株分为ST-1716（也对应于克隆复合物ST-135）。

Figure 3 depicts the genetic relationships between all the Yersinia enterocolitica samples.Figure 3Y. enterocolitica GrapeTree representation, generated within the SIEGA application. Node labels represent ST (in some cases an ambiguous ST assignation occurred and more than one number is displayed) and node color correspond to the sampling month (a warm gradient has been used to better display the time scale).

图3描绘了所有小肠结肠炎耶尔森菌样品之间的遗传关系。图3Y。小肠结肠炎GrapeTree表示，在SIEGA应用程序中生成。节点标签表示ST（在某些情况下，发生了不明确的ST分配并且显示了多个数字），节点颜色对应于采样月份（使用了温暖的渐变来更好地显示时间尺度）。

Numbers in the branches correspond to the allelic distances among nodes. The GrapeTree representation provides an intuitive visualization of the temporal scale of sampling and the genetic similarities among the samples. Using different labels from the metadata and the results tables, it is possible to obtain visual representations of many aspects of the epidemiology of the selected samples.Full size image.

分支中的数字对应于节点之间的等位基因距离。GrapeTree表示提供了采样时间尺度和样本之间遗传相似性的直观可视化。使用元数据和结果表中的不同标签，可以获得所选样本流行病学许多方面的视觉表示。全尺寸图像。

Legionella pneumophilaLegionella data stored in SIEGA, includes the comparative analyses of 58 Legionella pneumophila isolates during 2021–2023. Of these, 12 isolates corresponded to clinical isolates and 46 to environmental isolates. Clonal relation between the Isolates was determined by cgMLST. This scheme classified the isolates into 18 ST (sequence type).

嗜肺军团菌（Legionella pneumophilaLegionella）存储在SIEGA的数据包括2021-2023年间58株嗜肺军团菌分离株的比较分析。其中，12个分离株对应于临床分离株，46个对应于环境分离株。通过cgMLST确定分离株之间的克隆关系。该方案将分离株分为18 ST（序列型）。

The most abundant being ST 293 (20 isolates, 34.5%) and 180F (11 isolates, 18.9%). Four ST (293, 427, 489 and 180F) were present in both clinical and environmental isolates. In addition, we identified 3 STs (95, 98, and 524F) in clinical isolates that are not associated with environmental origin, suggesting that they derived from unrecognized sources.Analysis of antimicrobial resistanceIn the EU, the new legislation related to the harmonized monitoring and reporting of AMR from 202135 authorized whole genome sequencing as an alternative method to supplementary phenotypic testing of Salmonella and E.

最丰富的是ST 293（20个分离株，34.5%）和180F（11个分离株，18.9%）。临床和环境分离株中均存在四种ST（293427489和180F）。此外，我们在临床分离株中鉴定出3种与环境来源无关的ST（95,98和524F），表明它们来自无法识别的来源。。

coli in certain conditions. The SIEGA allows the monitoring of the presence of resistance genes in the different microorganisms facilitating the tracking of the dissemination or emergence of AMR throughout the food chain under a One Health approach. For example, the presence of AMR genes in the population of Salmonella included in the SIEGA can be analyzed, categorized by antimicrobial classes (Fig. 4).

大肠杆菌在某些条件下。SIEGA允许监测不同微生物中抗性基因的存在，有助于在单一健康方法下追踪AMR在整个食物链中的传播或出现。例如，可以分析SIEGA中沙门氏菌群体中AMR基因的存在，按抗菌类别分类（图4）。

This analysis reveals that 52.7% (347) exhibit resistance genes to only 1 group of antimicrobials, while 15% (99) carry resistance genes to two distinct classes of antimicrobials. In contrast, 32.2% (212) demonstrate resistance genes to 3 or more classes of antimicrobials. In a similar manner, this could be carried out with the other microorganisms hosted in the database, or further analysis could be conducted by delving.

该分析显示，52.7%（347）仅对1组抗菌剂表现出抗性基因，而15%（99）携带对两种不同类别抗菌剂的抗性基因。相比之下，32.2%（212）表现出对3类或更多类抗菌药物的抗性基因。以类似的方式，这可以用数据库中托管的其他微生物进行，或者可以通过深入研究进行进一步分析。

Data availability

数据可用性

The data that support the findings of this study are not openly available due to reasons of sensitivity and are available from the corresponding author upon reasonable request. Data are located in controlled access data storage at SIEGA application.

由于敏感性的原因，支持本研究结果的数据无法公开获得，并且可以根据合理的要求从通讯作者那里获得。数据位于SIEGA应用程序的受控访问数据存储中。

AbbreviationsAMR:

缩写AMR：

Anti-microbial resistance

抗微生物抗性

CC:

抄送：

Clonal complexes

克隆复合体

cgMLST:

cgMLST：

Core genome multi-locus sequence typing

核心基因组多位点序列分型

CSV:

CSV：

Comma-separated values

逗号分隔值

ECDC:

ECDC：

European Centre for Disease Prevention and Control

欧洲疾病预防和控制中心

EFSA:

EFSA：

European Food Safety Authority

欧洲食品安全局

LIMS:

LIMS：

Laboratory Information Management System

实验室信息管理系统

MLST:

MLST：

Multi-locus sequence-based typing

基于多位点序列的分型

RASFF:

拉夫：

Rapid alert system for food and feed

食品和饲料快速预警系统

SIEGA:

锡加：

Sistema Integrado de Epidemiologia Genomica de Andalucia

安达卢西亚基因组流行病学综合系统

SNP:

单核苷酸多态性：

Single nucleotide polymorphism

单核苷酸多态性

ST:

ST公司：

Sequence types

序列类型

SVEA:

斯维阿：

Sistema de Vigilancia Epidemiológica de Andalucía

安达卢西亚流行病学监测系统

WGS:

WGS：

Whole genome sequencing

全基因组测序

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Download referencesAcknowledgementsThis work was supported by the Instituto de Salud Carlos III (ISCIII), co-funded with European Regional Development Funds (ERDF) (Grant IMP/00019), it has also been funded by Consejería de Salud y Consumo, Junta de Andalucía (Grants COVID-0012-2020, PIP-0087-2021), and by grant ELIXIR-CONVERGE—Connect and align ELIXIR Nodes to deliver sustainable FAIR lifescience data management services (AMD-871075-16), funded by EU – H2020.

下载参考文献致谢这项工作得到了萨卢德·卡洛斯三世研究所（ISCIII）的支持，该研究所与欧洲区域发展基金（ERDF）共同资助（Grant IMP/00019），它也得到了Consejería de Salud y Consumo，Junta de Andalucía（Grants COVID-0012-2020，PIP-0087-2021）的资助，以及Grant ELIXIR CONVERGE和align ELIXIR Nodes的资助，以提供可持续的公平生命科学数据管理服务（AMD-871075-16），由欧盟H2020资助。

CSCS was funded by a Juan de la Cierva Grant (FJC2021-046546-I) from Ministerio de Ciencia e Innovación.Author informationAuthors and AffiliationsAndalusian Platform for Computational Medicine, Andalusian Public Foundation Progress and Health-FPS, Seville, SpainCarlos S. Casimiro-Soriguer, Javier Pérez-Florido, Enrique A.

CSC由Ciencia e Innovación部长的Juan de la Cierva Grant（FJC2021-046546-I）资助。作者信息作者和附属机构安达卢西亚计算医学平台，安达卢西亚公共基金会进步与健康FPS，塞维利亚，斯帕因卡洛斯S.Casimiro Soriguer，哈维尔·佩雷斯·弗洛里多，恩里克A。

Robles, María Lara, Andrea Aguado & Joaquin DopazoInstitute of Biomedicine of Seville, IBiS, University Hospital Virgen del Rocío/CSIC/University of Seville, 41013, Seville, SpainCarlos S. Casimiro-Soriguer, Javier Pérez-Florido, José A. Lepe & Joaquin DopazoServicio de Microbiología. Hospital Universitario Puerta del Mar, 11009, Cádiz, SpainManuel A.

罗伯斯、玛丽亚·拉拉、安德里亚·阿瓜多和华金·多帕佐塞维利亚生物医学研究所、伊比斯、维珍德尔罗西奥大学医院/CSIC/塞维利亚大学，41013年，塞维利亚，西班牙。卡西米罗-索里格、哈维尔·佩雷斯-弗洛里多、何塞A。莱佩和华金·多帕佐微生物学服务。西班牙加的斯Puerta del Mar大学医院，11009年。

Rodríguez IglesiasServicio de Microbiología, Unidad Clínica Enfermedades Infecciosas, Microbiología y Medicina Preventiva, Hospital Universitario Virgen del Rocío, 41013, Sevilla, SpainJosé A. LepeCentro de Investigación Biomédica en Red en Enfermedades Infecciosas (CIBERINFEC), ISCIII, Madrid, SpainJosé A.

Rodríguez Iglesiasservicio de微生物学，Virgen del Rocio大学医院传染病、微生物学和预防医学诊所，41013年，塞维利亚，西班牙。传染病网络生物医学研究中心，ISCIII，马德里，西班牙。

Lepe & Federico GarcíaServicio de Microbiología. Hospital Universitario San Cecilio, 18016, Granada, SpainFederico GarcíaInstituto de Investigación Biosanitaria, Ibs.GRANADA, 18012, Granada, SpainFederico GarcíaGenomic Unit, Andalusian Molecular Biology and Regenerative Medicine Center (CABIMER), CSIC University of Seville Univers.

Lepe和Federico Garcia微生物学服务。圣塞西利奥大学医院，18016年，格拉纳达，Spainfederico Garcia生物卫生研究所，IBS。格拉纳达，18012年，格拉纳达，Spainfederico-Garciagenomic部门，安达卢西亚分子生物学和再生医学中心，塞维利亚大学。

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PubMed Google ScholarContributionsConceptualization, JD. and J.A.C.; methodology, C.S.C.S., J.P.F., M.L., A.A., M.P.A., E.A., V.E.J. and L.P.C.; software, E.A.R.; formal analysis, C.S.C.S. and J.P.F.; investigation, M.A.R.I., J.A.L., F.G., N.L., U.A., I.M.V.; data curation, C.S.C.S., J.P.F., M.L., A.A.; writing—original draft preparation, J.D., J.A.C., C.S.C.S., J.P.F.; writing—review and editing, J.D., J.A.C., C.S.C.S., J.P.F., J.A.L., M.A.R.I., C.M.L.; supervision, J.D., J.A.C.; funding acquisition, J.D., J.A.L., F.G., C.S.C.S.

PubMed谷歌学术贡献概念化，JD。和J.A.C。；方法论，C.S.C.S.，J.P.F.，M.L.，A.A.，M.P.A.，E.A.，V.E.J.和L.P.C。；软件，E.A.R。；形式分析，C.S.C.S.和J.P.F。；调查，M.A.R.I.，J.A.L.，F.G.，N.L.，U.A.，I.M.V。；数据管理，C.S.C.S.，J.P.F.，M.L.，A.A。；撰写原始草案准备，J.D.，J.A.C.，C.S.C.S.，J.P.F。；写作评论和编辑，J.D.，J.A.C.，C.S.C.S.，J.P.F.，J.A.L.，M.A.R.I.，C.M.L。；监督，J.D.，J.A.C。；资金收购，J.D.，J.A.L.，F.G.，C.S.C.S。

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Reprints and permissionsAbout this articleCite this articleCasimiro-Soriguer, C.S., Pérez-Florido, J., Robles, E.A. et al. The integrated genomic surveillance system of Andalusia (SIEGA) provides a One Health regional resource connected with the clinic.

转载和许可本文引用本文Casimiro Soriguer，C.S.，Pérez Florido，J.，Robles，E.A。等人。安达卢西亚（SIEGA）的综合基因组监测系统提供了与诊所相关的单一健康区域资源。

Sci Rep 14, 19200 (2024). https://doi.org/10.1038/s41598-024-70107-0Download citationReceived: 30 January 2024Accepted: 13 August 2024Published: 19 August 2024DOI: https://doi.org/10.1038/s41598-024-70107-0Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy to clipboard.

Sci Rep 141920（2024）。https://doi.org/10.1038/s41598-024-70107-0Download引文接收日期：2024年1月30日接受日期：2024年8月13日发布日期：2024年8月19日OI：https://doi.org/10.1038/s41598-024-70107-0Share本文与您共享以下链接的任何人都可以阅读此内容：获取可共享链接对不起，本文目前没有可共享的链接。复制到剪贴板。

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KeywordsOne healthSurveillanceWhole genome sequencingResistancesAMREpidemiology

关键词健康监测全基因组测序耐药性血液学

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