Camille Moriot

François Lesueur

Nicolas Stouls

Fabrice Valois

How to build socio-organizational information from remote IP addresses to enrich security analysis?

Keywords: IP qualification, Social Labels, Wikidata, Security analysis

There is a constant threat of having our computing systems under attack. Information regarding the origins of the traffic we receive can be valuable. Currently, the AS-number and the localization are the most commonly used IP-related information to characterize an attack.

In this paper, we propose expanding knowledge about a remote IP's owner to improve defensive reaction effectiveness and obtain in-depth analyzes of attacker profiles. We introduce the enrichment with socioorganizational information (such as organization type, activity field, etc.) about the entities owning the IP in addition to infrastructural information. This integration is driven by combining RDAP and Wikidata. We demonstrate that this proposal is promising.

Introduction

IT systems are regularly rocked by attacks. In recent years, it has become an important means of pressure on competing companies, governmental organizations, and individuals. But unfortunately, attacks can bring down a system and all the social structures behind it.

Consequences can be, at the enterprise level, financial costs and failures of operational services. Attacks on public services also have serious consequences: numerous attacks took place against hospitals during the 2020 health crisis 1 and led to slow operations in the establishments. Given the consequences, and frequency of attacks 2,3 developing efficient solutions appears as a necessity.

In-depth analysis of attacks, including the attacker's infrastructure, improves counterattack mechanisms as they provide precious description and awareness. Unfortunately, attacks keep evolving, and attackers keep circumventing protection mechanisms, resulting in a need to develop or readjust defense mechanisms constantly. Thus, automated tools are needed to keep up with the evolution. We claim that new approaches are mandatory to deal with the complexity of attack strategies and the increased frequency of attacks. We propose to characterize misbehaving host sending attack traffic and accordingly characterize the attackers in terms of social organization and technical capabilities.

In this paper, we present a new and original methodology that increases the knowledge about IP addresses used by attackers by assigning organizational labels to these addresses. We introduce an algorithm collecting the following labels: the type of structure (e.g., IT companies, universities), the field of work (e.g., web service hosting) and the human size (e.g., number of employees). Our approach aims to know the socio-organizational characteristics of the attacking host rather than the attack to establish the best security policies. We don't focus on the source of the attack but on the last host of an attack.

The following section provides a state of the art highlighting the context of our work, as well as some useful existing tools to analyze threats. Then, in section 3, we present a new and original methodology that increases the knowledge about the hosts' IP addresses by assigning socio-organizational labels. Finally, we provide promising results of our characterization algorithm in section 4.

State of the art

Defense mechanisms against attacks can be described by the following steps: detect, analyze and react. The analysis phase is crucial as it helps to develop countermeasures. Different aspects of an attack can be inspected, such as the attack mechanism or the origin of the attack. This paper focuses on analyzing the last host relaying the attack.

Why being focused on the source side, and how to track the last host? An in-depth study of the sources can be beneficial in two cases. First, it helps to identify the attack's origin and, if possible, to deploy solutions directly at the level of the last known host. Second, adding knowledge could differentiate legitimate traffic's sources from illegitimate ones during an attack. For instance, the attacker's infrastructure characterization is part of the widely-used Mitre ATT&CK matrix [START_REF] Blake | MITRE ATT&CK®: Design and Philosophy[END_REF].

What intelligence can be gathered about the misbehaving hosts? How do we know if a source of traffic is legitimate? Threat intelligence (TI) can be broken down into several categories [START_REF] Tounsi | A survey on technical threat intelligence in the age of sophisticated cyber attacks[END_REF] such as strategic TI, operational TI, tactical TI and technical TI. Operational intelligence designates the information that can be gathered by devices and used to analyze attacks. Nowadays, information such as localization tags is used to increase knowledge about traffic's senders automatically. Such information can identify abnormal behaviors. Multiple localization databases such as Maxmind's Geolite4 database or IP2Location database5 can be used to obtain such information. Different geotracking databases have been compared in [START_REF] Livadariu | On the Accuracy of Country-Level IP Geolocation[END_REF] showing that such geo-tracking tools tend to have the same result regarding the country of origin of an IP address. Crawlers can also be used to get information about hosts. Net crawlers such as Censys [START_REF] Durumeric | A Search Engine Backed by Internet-Wide Scanning[END_REF], Shodan6 or Onyphe 7 yield knowledge regarding open ports and services behind IP addresses. Net crawlers use a pro-active approach, meaning that the knowledge is gained from active measurements. They also provide information about the accessibility of IP addresses from the Internet and can be used to detect vulnerabilities [START_REF] Vlajic | IoT as a Land of Opportunity for DDoS Hackers[END_REF][START_REF] Bodenheim | Evaluation of the ability of the Shodan search engine to identify Internet-facing industrial control devices[END_REF]. Unfortunately, crawlers focus only on technical and factual information without proposing any interpretation of the entity behind.

As a result of threat intelligence, it is possible to obtain information about how trustworthy sources are and help to prevent attacks. Available blocklists8 are a first step to knowing if a source of traffic can be trusted. In the case of an IP address belonging to such databases, it can be dropped or filtered. This information is enriched only after attempted attacks have taken place. Moreover, due to the fact that IP addresses can be shared, side effects can impact legitimate users [START_REF] Ramanathan | Quantifying the Impact of Blocklisting in the Age of Address Reuse[END_REF]. To avoid these drawbacks, it is better to try to obtain more information about the sources before blocking its traffic. However, new blocklists using localization and political actualities [START_REF] Sainani | IP Reputation Scoring with Geo-Contextual Feature Augmentation[END_REF] tend to have better results as they integrate context into the analysis.

Why is there a need to integrate social components in attack analysis and mitigation? All the previous tools mainly focus on the infrastructural part of the network, whereas they have social, political, or economics dimensions [START_REF] Gandhi | Dimensions of Cyber-Attacks: Cultural, Social, Economic, and Political[END_REF]. In some cases, attackers can abuse specific infrastructure, and there is a need to evaluate phenomena based on who owns the IP addresses. In [START_REF] Mi | Resident Evil: Understanding Residential IP Proxy as a Dark Service[END_REF], the authors observed a new phenomenon that consists of using residential IP addresses through proxies to bypass security rules that can be encountered in other types of networks. With this work, we can see the need to explore social and organizational network features. To identify residential traffic, they used Whois answers and DNS features for the corresponding network.

The information regarding the ownership of an address or a net block are held by the Regional Internet Registry (known as RIR) and can be retrieved from those RIR. The RDAP [START_REF] Nikkel | Registration Data Access Protocol (RDAP) for digital forensic investigators[END_REF] protocol, progressively replacing Whois, allows to easily retrieve that information in JSON format. Observing the nature of the traffic as well as its organization also makes it possible to identify characteristics specific to attacks. For example, this allows in the context of certain attacks to determine whether certain structures can be abused to generate attacks. In [START_REF] Fukushi | A Large-scale Analysis of Cloud Service Abuse[END_REF], the authors conducted an evaluation of the presence of cloud IP addresses within different malicious activities. To do so, they studied IP addresses belonging to four of the biggest cloud service provider. They studied the apparition of those IP addresses in 39 blocklists. In this case, they concluded that cloud IPs could be blocklisted for another 30 days after participating in malicious activities. This result again proves the need for a tool characterizing IP addresses without blocking them.

All the previous work and tools demonstrate the need for efficient characterization of the traffic sources. This characterization needs to be not only technical but also focusing on social and organizational aspects as it was demonstrated that in many cases, attack phenomena could not be dissociated from a social context. Our work presents an original methodology characterizing misbehaving hosts in attacks based on organizational features. As we introduced in the previous sections, countering attacks requires knowing the attackers in depth. Existing tools have been used and developed to produce technical knowledge regarding devices sending malicious traffic. We claim that social characterization must enrich technical analysis to integrate context information into our analysis.

In order to conduct in-depth analyses, we propose to qualify the sources by the following labels: type of entity, domain of activity and human size. We argue that studying the type of organization owning the IP address is relevant to identify if the threats only come from a certain kind of entity (e.g. enterprise, ISP, university network) and to characterize different groups of attackers. The entity's domain of activity is also relevant to trace back some threats linked to the activity, such as, for example, hosting devices.

Regarding the attribution of labels to an IP address, we introduce, in this section, an algorithm retrieving organizational information from Wikidata starting from partial data given by RDAP, illustrated in figure 1.

Using Wikidata to retrieve social information

Wikidata9 is a collaborative platform owned by Wikimedia that serves as a support to Wikipedia. Wikidata is a database that contains items identified by Qids retrievable through an API using a SPARQL query. Each item is described by a list of labels. The database not only contains information about entities, but as an encyclopedia, general knowledge about various things. Especially, it contains the following labels relevant to our work: instance of the item (e.g. enterprise, business, telephone company, internet service provider, university), industry, employees, number of subscribers, autonomous system number and IPv4 routing prefix. Wikidata items may have an IPv4 routing prefix field. This can, when we try to qualify an IP address, allow us to easily identify the corresponding item. We notice that almost 25% of the IPv4 space is covered in Wikidata. In the rest of the cases, we need to find the name of the owning entity to perform a text search query to obtain the page corresponding to the entity sought.

Obtaining the entity name from RDAP

In order to perform the Wikidata query, we need to know some information about the entity that owns the IP address: the entity name, country, their domain name and the AS-Number. To do this, we use RDAP, which contains the information provided by the owners to the RIRs. RDAP returns a JSON with multiple fields, and obviously, the field called entity, which aims to designate the owner of the IP address, is a very interesting one. Unfortunately, a lot of information contained in this field has been made anonymous in Europe according to the GDPR10 or are identified by IDs. But, in most cases, there are many information regarding the entity hidden in different fields such as the remarks one for example. Information can be an email address with a domain named after a company or a postal address with the name of the entity. Thus, to extract and identify the correct information, we implement a text analysis algorithm identifying the most relevant sub-strings of a text based on their frequency. In this step, we also use a translator to normalize all RDAP entries in English. Note that our text analysis does not use ontology or deep learning. We consider the most frequent sub-strings containing the most frequent words in the RDAP entries. We do our comparisons by using the Jaro distance between words (composed of letters) or between fields (composed of words). At the end of this step, we also pinpoint potential discriminatory factors such as the country code, the domain name or an AS number. All this information also comes from RDAP.

Discriminating Wikidata's results

As represented on figure 1, after identifying key information representing the entity of the owner, we make a text research of the name via the Wikidata API to identify the corresponding resource. Note that this research returns all pages with the entity name within their title, therefore we need to analyze these results to identify the correct result associated with the entity. Many entities have names having several meanings or have changed their name during their existence. To this end, we inspect the following fields: item label, also known as and subsidiaries. The previous fields are used to refine our analysis as we noticed that RIR's database are not always up to date, especially for rebranded names. We aggregate results from the different queries and then define multiple criteria to filter the results. First of all, we identify Wikidata item with the maximum amount of words in common with the target text string. We also filter our results based on the expected fields we are searching in. Indeed, we noticed that entities have specific fields used to describe them, and if these fields are not present, we are less likely to identify the correct Qid. This particularly helps us to differentiate homographs: for instance, Orange is a french ISP, but it is also a color and the name of a city in the south of France. Finally, we use ASN, domain name, and country to discriminate the results. In Wikidata, there is an ASN field that may be specified for some entities. We also compare the domain address from the website field in Wikidata to the domains found within RDAP. The country may also help us discriminate results. Each of these factors is associated with a score, and the best ranked Wikidata item is then considered the one related to the entity. However, if the global score or the score in some Figure 2: Wikidata comparison with RDAP results criterion is too low, we consider that there is no result. These thresholds are defined experimentally.

Evaluation and discussion

In this section we evaluate the correctness of the results by evaluating the quality of the data contained in Wikidata and the results of our algorithm.

Evaluation of the data contained in Wikidata

As Wikidata is a collaboratively edited database, we want to ensure the quality of the information in it. Therefore, to verify the veracity of the IP information, we compare them to what we obtain with RDAP as a reference.

To this end, we extract all items in Wikidata having the IPv4 routing prefix. We remove all ranges assigned to RIR's items from this dataset. After this, we pick the first IP address of each range, and for each IP, we use RDAP to get the information stocked in the different RIR databases. From RDAP, we obtain a JSON with multiple fields. We inspect all the fields and try to match the label of the item (e.g., the name of the organization), as well as the alternative names within the fields. We obtained a match for 1208 out of 2212 ranges 2. Regarding the rest of the data, we evaluate by trying to find a partial match. After refining our criteria, we found another 697 matches. For the remaining data, we evaluated the results manually. As a result, we obtain a total 93,8% of coherent associations between RDAP and Wikidata. Our evaluation script is available online11 .

To conclude, the evaluation of the data contained in Wikidata validates the shortcut introduced in section 3.1 as we have excellent confidence in Wikidata.

Evaluation of our characterization

In this subsection, we evaluate the capacity of our algorithm to find the correct Wikidata Qid from an RDAP result. To this end, we create a dataset containing the association of an IP range with the corresponding Wikidata Qid. This available 11 dataset is an extract of the previous dataset and contains 1055 entries belonging to part of 1208 for which we had a perfect match with RDAP. With this dataset, we evaluate the algorithm described in section 3.2 and 3.3. We use the first IP address of the range given by Wikidata as input for RDAP and the expected correct Qid from the Wikidata block (see fig. 1).

In 53% of the cases, we directly obtain a correct result. In another 7% of the cases, the correct result is in a shortlist of three entries which could be shown to an analyst to help him, and in 2% of the cases, the resulting page was the parent company of the one we were looking for (which is still quite good). Finally, in 20% of cases, no results are returned as the confidence score was too low. More than 80% of our results are thus sound (either good, quite good, or explicitly unfound). There are currently a bit less than 20% of wrong characterizations. A large part of these errors come from university networks, which are often managed by a third party that we could identify from the data contained in RDAP but not the university itself.

Conclusions

In this paper, we claim that a new approach is needed to cope with the increase of attacks and their complexity. We highlight the necessity to investigate in-depth all the information related to the source or the relay of an attack. We introduce a new and original methodology to enrich security investigations, by characterizing infrastructures and owners of IP addresses thanks to organizational labels. We develop an algorithm assigning social labels to IP addresses. In particular, we want to obtain labels describing an organization owning an IP address by the type of organization, its domain of activity and its human size. Our results are promising and from our evaluation, we underline the following key points. First, the information contained in RDAP is not necessarily up-to-date because of the dynamic of companies (e.g., name change or merge/acquisition). This leads to difficulties to identify correctly an entity. However, thanks to the data available in different fields of the databases we use, it is often possible to identify and to characterize the correct entity. Second, the European anonymization of entity information (GDPR), which is also applied to RDAP, increases the complexity to correctly identify the owner of the IP range.

Since our algorithm affecting social label to an entity owning IP addresses is based on public data, RDAP entry and Wikidata, while our code is open source 12 , our results are transparent and reproducible.

To extend our approach, we identify two complementary directions for our next work. First, we will enhance our contribution using a semantic approach or a deep reinforcement learning one. Second, because of the limitations of RDAP, we will propose to collect organizational labels using web crawlers or network topology.

Figure 1 :

 1 Figure 1: RDAP to Wikidata algorithm

https://dev.maxmind.com/geoip/geolite2-free-geolocation-data

https://lite.ip2location.com/

https://www.shodan.io/

https://www.onyphe.io/

https://www.iblocklist.com/

https://www.wikidata.org/

https://en.wikipedia.org/wiki/General_Data_Protection_Regulation

https://gitlab.inria.fr/cmoriot/lcn_2022

https://gitlab.inria.fr/cmoriot/ipseen


Gregory R Young

Gaël Toubon

Marie-José Butel

Jean-Christophe Rozé

Patricia Lepage

Johanne Delannoy

Pierre-Yves Ancel

Marie-Aline Charles

Julio Aires

Very Preterm Children Gut Microbiota Comparison at the Neonatal Period of 1 Month and 3.5 Years of Life

Keywords: prematurity, gut microbiota, DOHaD, children, enterotypes

Prematurity is a risk factor for dysbiosis of the gut microbiota due to particular birth conditions and frequent prolonged hospitalization of neonates. Although gut microbiota colonization after birth and its establishment during the hospitalization period have been studied in preterm infants, data on gut microbiota following discharge, particularly during early childhood, are scarce. The present study investigated the relationship between gut microbiota at 1 month after birth (hospitalization period) and 3.5 years of age in 159 preterm children belonging to the French EPIFLORE prospective observational cohort study. Analysis using bacterial 16S rRNA gene sequencing showed that the gut microbiota of preterm neonates at 1 month was highly variable and characterized by six distinct enterotypes. In contrast, the gut microbiota of the same children at 3.5 years of age showed less variability, with only two discrete enterotypes. An absence of association between enterotypes at 1 month and 3.5 years of age was observed. While the alpha diversity of gut microbiota significantly increased between 1 month and 3.5 years of age, for both alpha and beta diversities, there was no correlation between the 1-month and 3.5-years time points. Comparison at 3.5 years between children born either preterm (n = 159) or full-term (n = 200) showed no differences in terms of enterotypes, but preterm children harbored a lower Shannon diversity index and a different overall composition of microbiota than full-term children. This study suggests that the characteristics of the early gut microbiota of preterm children are not predictive of the microbial community composition at 3.5 years of age. However, the impact of gestational age is still noticeable on the gut microbiota up to 3.5 years of age.

INTRODUCTION

The human microbiota has become a research topic of utmost interest in recent years, with several studies highlighting its potential impact on physiology and, as a result, its possible effects on health and disease [START_REF] Fan | Gut microbiota in human metabolic health and disease[END_REF][START_REF] Sarkar | The Association between Early-Life Gut Microbiota and Long-Term Health and Diseases[END_REF]. Recently, substantial efforts have focused on studying infant gut microbiota with the emergence of the concept of Developmental Origins of Health and Disease (DOHaD) [START_REF] Butel | The developing gut microbiota and its consequences for health[END_REF] and the critical first 1.000 days of life window hypothesis [START_REF] Wopereis | The first thousand days -intestinal microbiology of early life: establishing a symbiosis[END_REF][START_REF] Robertson | The human microbiome and child growth -first 1000 days and beyond[END_REF]. Prematurity, accounting for approximately 10% of births worldwide [START_REF] Blencowe | National, regional, and worldwide estimates of preterm birth rates in the year 2010 with time trends since 1990 for selected countries: a systematic analysis and implications[END_REF][START_REF] Chawanpaiboon | Global, regional, and national estimates of levels of preterm birth in 2014: a systematic review and modelling analysis[END_REF], is a risk factor for early gut dysbiosis, which is suggested to be associated with necrotizing enterocolitis [START_REF] Rozé | Nutritional strategies and gut microbiota composition as risk factors for necrotizing enterocolitis in very-preterm infants[END_REF], growth delay [START_REF] Tirone | Gut and lung microbiota in preterm infants: immunological modulation and implication in neonatal outcomes[END_REF], cognitive impairment [START_REF] Rozé | Assessment of neonatal intensive care unit practices and preterm newborn gut microbiota and 2-year neurodevelopmental outcomes[END_REF], and brain damage [START_REF] Seki | Aberrant gut-microbiota-immune-brain axis development in premature neonates with brain damage[END_REF]. In very preterm neonates, gut microbiota is characterized by high inter-individual variability [START_REF] Yee | Longitudinal microbiome composition and stability correlate with increased weight and length of very-low-birth-weight infants[END_REF][START_REF] Rozé | Assessment of neonatal intensive care unit practices and preterm newborn gut microbiota and 2-year neurodevelopmental outcomes[END_REF] that can be attributed to varying birth conditions and extended exposure to the neonatal intensive care unit (NICU) environment and practices. Indeed, preterm infants are more often born via cesarean section and are frequently administered broad-spectrum antibiotics. Furthermore, gestational age influences the dynamics of the intestinal bacterial establishment (La [START_REF] Rosa | Patterned progression of bacterial populations in the premature infant gut[END_REF]. The early establishment of preterm gut microbiota is characterized by a dominance of opportunistic and potentially pathogenic bacteria, such as Enterobacter, Enterococcus, and Staphylococcus, and a delay in colonization by Bifidobacterium and Bacteroides [START_REF] Arboleya | Establishment and development of intestinal microbiota in preterm neonates[END_REF][START_REF] Barrett | The individual-specific and diverse nature of the preterm infant microbiota[END_REF][START_REF] Rosa | Patterned progression of bacterial populations in the premature infant gut[END_REF][START_REF] Hill | Evolution of gut microbiota composition from birth to 24 weeks in the INFANTMET Cohort[END_REF][START_REF] Korpela | Intestinal microbiota development and gestational age in preterm neonates[END_REF]. Several studies have highlighted the influence of the NICU environment on the establishment of preterm neonate gut microbiota [START_REF] Hartz | Potential NICU environmental influences on the neonate's microbiome: a systematic review[END_REF][START_REF] Moles | Preterm infant gut colonization in the neonatal ICU and complete restoration 2 years later[END_REF][START_REF] D'agata | Effects of early life NICU stress on the developing gut microbiome[END_REF][START_REF] Tauchi | Gut microbiota development of preterm infants hospitalised in intensive care units[END_REF][START_REF] Yap | Neonatal Intensive Care Unit (NICU) exposures exert a sustained influence on the progression of gut microbiota and metabolome in the first year of life[END_REF], notably during hospitalization [START_REF] Rozé | Assessment of neonatal intensive care unit practices and preterm newborn gut microbiota and 2-year neurodevelopmental outcomes[END_REF], and the existence of overlap of microbial taxa between NICU surfaces and the gut of preterm neonates [START_REF] Brooks | Microbes in the neonatal intensive care unit resemble those found in the gut of premature infants[END_REF]. Thus, while the infant gut microbiota establishment during the first weeks and months of life is documented, only a few studies have reported on the late development of gut microbiota in preterm children. Additionally, the relationship between gut microbiota and early-life neonatal factors, such as birth mode, breastfeeding, and antibiotic treatments after NICU discharge and during early childhood of preterm children, has been rarely studied, although it has been emphasized as a research priority [START_REF] Groer | Development of the preterm infant gut microbiome: a research priority[END_REF]. Indeed, to date, only a few studies with small cohorts have explored the development of gut microbiota in preterm children after 1 year of age [START_REF] Gómez | Bacteriological and immunological profiling of meconium and fecal samples from preterm infants: a two-year follow-up study[END_REF][START_REF] Fouhy | Perinatal factors affect the gut microbiota up to four years after birth[END_REF][START_REF] Yee | Longitudinal microbiome composition and stability correlate with increased weight and length of very-low-birth-weight infants[END_REF]. These studies report an increase in alpha diversity over time and lower inter-individual variability in the gut microbiota between NICU discharge and early childhood. Furthermore, it has been suggested that gestational age at birth results in distinct microbial profiles extending up to 4 years of age [START_REF] Fouhy | Perinatal factors affect the gut microbiota up to four years after birth[END_REF]. To the best of our knowledge, these data constitute the only available longitudinal evidence describing the gut microbiota of preterm children during early childhood. Therefore, given the paucity of data regarding this subject, there is a need to add substantial evidence related to prematurity and gut microbiota during early childhood, particularly in large cohorts.

This study aims to compare and characterize the gut microbiota of 159 preterm children from the EPIPAGE2 French nationwide cohort at two time points: at 1 month of life (NICU hospitalization) and at 3.5 years of age, when the child's gut microbiota supposedly stabilizes toward a mature adult-like gut microbiota [START_REF] Stewart | Temporal development of the gut microbiome in early childhood from the TEDDY study[END_REF]. Additionally, we investigated factors previously reported to influence early gut microbiota development, such as gestational age [START_REF] Groer | Development of the preterm infant gut microbiome: a research priority[END_REF][START_REF] Gritz | The human neonatal gut microbiome: a brief review[END_REF][START_REF] Chernikova | The premature infant gut microbiome during the first 6 weeks of life differs based on gestational maturity at birth[END_REF], birth mode [START_REF] Dominguez-Bello | Delivery mode shapes the acquisition and structure of the initial microbiota across multiple body habitats in newborns[END_REF][START_REF] Portela | Maternal obesity, environmental factors, cesarean delivery and breastfeeding as determinants of overweight and obesity in children: results from a cohort[END_REF][START_REF] Stewart | Temporal development of the gut microbiome in early childhood from the TEDDY study[END_REF], birth weight [START_REF] Groer | Development of the preterm infant gut microbiome: a research priority[END_REF][START_REF] Unger | Gut microbiota of the very-low-birth-weight infant[END_REF], sex [START_REF] Martin | Early-life events, including mode of delivery and type of feeding, siblings and gender, shape the developing gut microbiota Suchodolski[END_REF], antibiotics [START_REF] Gibson | Antibiotics and the developing infant gut microbiota and resistome[END_REF][START_REF] Tamburini | The microbiome in early life: implications for health outcomes[END_REF], breastfeeding [START_REF] Portela | Maternal obesity, environmental factors, cesarean delivery and breastfeeding as determinants of overweight and obesity in children: results from a cohort[END_REF][START_REF] Lemas | Exploring the contribution of maternal antibiotics and breastfeeding to development of the infant microbiome and pediatric obesity[END_REF][START_REF] Pannaraj | Association between breast milk bacterial communities and establishment and development of the infant gut microbiome[END_REF][START_REF] Stewart | Temporal development of the gut microbiome in early childhood from the TEDDY study[END_REF], skin-to-skin contact [START_REF] Hartz | Potential NICU environmental influences on the neonate's microbiome: a systematic review[END_REF], and NICU strategies [START_REF] Rozé | Assessment of neonatal intensive care unit practices and preterm newborn gut microbiota and 2-year neurodevelopmental outcomes[END_REF]. This study provides informative data concerning prematurity and the gut microbiota in early childhood based on a large and welldocumented French multicentric cohort of preterm children.

MATERIALS AND METHODS

EPIFLORE Study and ELFE Cohort

The preterm children included in this study were part of the EPIFLORE study [START_REF] Ancel | EPIPAGE 2: a preterm birth cohort in France in 2011[END_REF], an ancillary study of the EPIPAGE2 French national birth cohort that was launched in 2011 and included preterm neonates born between 24 and 31 weeks of gestation [START_REF] Lorthe | Cohort Profile: The Etude Epidémiologique sur les Petits Ages Gestationnels-2 (EPIPAGE-2) preterm birth cohort[END_REF]. The aim of EPIFLORE was to investigate the development of the intestinal microbiota and its relationship with early-life factors that influence its establishment and subsequent health outcomes in early childhood among very preterm children (<32 weeks of gestational age). The EPIFLORE study included clinical data and stool samples collected from 24 French NICUs [START_REF] Rozé | Assessment of neonatal intensive care unit practices and preterm newborn gut microbiota and 2-year neurodevelopmental outcomes[END_REF]. In the present study, full-term children who were born in 2011 from the ELFE French national birth cohort [START_REF] Charles | Cohort Profile: The French national cohort of children (ELFE): birth to 5 years[END_REF] were included as controls to investigate the differences between the gut microbiota of preterm and full-term children at 3.5 years of age. Full-term singletons born after 37 weeks of gestation with fecal samples available at 3.5 years of age and at least 2 years of subsequent follow-up were randomly selected from the ELFE cohort.

Participants and Sample Collection

The EPIFLORE study included 729 neonates, and stool samples were collected from 574 preterm neonates during hospitalization at a median age of 23 days (interquartile range [IQR], 22-26 days), which is referred to as 1-month sampling. An additional stool sample was obtained for 208 preterm children at a median age of 42.6 months (IQR, 42.2-43.4 months), referred to as 3.5 years of age sampling. In the current study, 159 preterm children with fecal samples collected at both time points were included. Additionally, stool samples of 200 full-term children from the ELFE cohort were collected at 3.5 years of age (median age,42.1 months;IQR, during the same period and using the same methodology as for preterm children (Supplementary Figure 1).

DNA Extraction, Sequencing, and Data Processing

The total fecal DNA was extracted according to the International Human Microbiome Standards operating procedure [START_REF] Dore | HMS_SOP_07_V1: standard operating procedure for fecal samples DNA extraction, protocol H. International Human Microbiome Standards[END_REF], as previously reported [START_REF] Rozé | Assessment of neonatal intensive care unit practices and preterm newborn gut microbiota and 2-year neurodevelopmental outcomes[END_REF]. We included negative controls that went through the same process as samples, from DNA extraction to sequencing. Sequencing was performed on the GeT-PlaGe platform of Génopole (Toulouse Midi-Pyrénées, France) using Illumina Miseq technology (V3, 2 × 250 bp) targeting the V3-V4 primers (V3fwd: ACGGRAGGCAGCAG, V4rev: AGGATTAGATACCCTGGTA) regions of the 16S bacterial rRNA gene. Raw sequences were analyzed using the pipeline "Find Rapidly OTU with Galaxy Solution" (FROGS) version 3.2 (September 2021) from the Galaxy software framework [START_REF] Escudié | Frogs: find, rapidly, otus with galaxy solution[END_REF]. Sequences were checked for quality using FastQC (threshold 20-38). After trimming barcodes, the sequences were filtered (length of the reads ranged from 435 to 459 bp) and clustered into operational taxonomic units (OTUs) using the swarm clustering method implemented in the FROGS version 3.2 pipeline. OTUs representing less than 0.005% (788,210 reads) of all the sequences, the majority corresponding to singleton OTUs, were discarded [START_REF] Bokulich | Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing[END_REF]. Taxonomic affiliation was assigned to the OTUs using the Silva 138.1 pintail100 database (September 2021) yielding 99% of the sequences affiliated with ≥99% identity and 100% coverage. A total of 11,526,400 reads (median, 22,619 reads per sample) were obtained from 16S rRNA gene sequencing. To adjust for the influence of uneven sampling depth, each sample was rarefied to the minimum sampling depth of the dataset (5,201 reads, Supplementary Figure 2), and rarefied data were used for all downstream analyses unless stated otherwise.

Microbiota Analysis: Diversity and Composition

The Chao1 richness estimate (richness) and Shannon index (diversity) were calculated to investigate the patterns of microbial community diversity in the gut microbiota of preterm and full-term children. For beta diversity analysis, we computed dissimilarity matrices using Bray-Curtis and Unifrac distances. The Bray-Curtis dissimilarity distance reflects community composition considering the abundance of taxa, while the Unifrac distance considers the phylogenetic relationships among members of the bacterial communities. Gut microbiota inter-individual variability was visualized using principal coordinate analysis (PCoA/MDS) based on dissimilarity matrices. Stratification of the cohort based on gut microbial composition was performed according to the previously reported enterotyping guidelines [START_REF] Arumugam | Enterotypes of the human gut microbiome[END_REF]. Briefly, the relative abundances of the classified genera were used to produce a Jensen-Shannon divergence (JSD) distance matrix between samples. Based on the obtained distance matrix, samples were clustered using Partitioning Around Medoids (PAM) algorithm. The optimal number of clusters was chosen by maximizing the Calinski-Harabasz index and was cross-validated with the silhouette index and prediction strength. The results of PAM clustering were visualized on PCoA biplots, and driver genera vectors were assessed using the "envfit" function. The vectors show the direction in the ordination space toward which genera change most rapidly and to which they have maximal correlations within the ordination configuration. The genus with the highest relative abundance in each group was considered the main contributor to each enterotype.

Neonatal Characteristics and Neonatal Intensive Care Unit Strategies

In each NICU, data on children and mothers during the perinatal and neonatal periods were collected prospectively during hospitalization until discharge. Available clinical data included gestational age, birth mode, birth weight, sex, antibiotics, breastfeeding, skin-to-skin contact, and NICU strategies. The NICU strategies were previously characterized in the EPIPAGE2 study regarding early extubation or no intubation, use of sedation, direct breastfeeding, skin-to-skin practice, speed of progression of enteral feeding, and duration of primary and secondary antibiotic therapy [START_REF] Rozé | Assessment of neonatal intensive care unit practices and preterm newborn gut microbiota and 2-year neurodevelopmental outcomes[END_REF]. All these practices concern the early period of hospitalization before the 1month stool sampling period. The methods to characterize these strategies were applied as previously described [START_REF] Rozé | Assessment of neonatal intensive care unit practices and preterm newborn gut microbiota and 2-year neurodevelopmental outcomes[END_REF].

Statistical Analysis

For the comparative analysis of alpha diversity and pairwise beta diversity between enterotypes and time points, Wilcoxon ranksum tests and Kruskal-Wallis tests were performed. Hypothesis testing was carried out using permutational multivariate analysis of variance (PERMANOVA) based on 999 permutations to analyze the influence of neonatal factors on the gut microbiota and to compare the gut microbiota between preterm and full-term children. For all PERMANOVA analyses, Benjamini-Hochberg FDR (false discovery rate) correction was used to adjust for multiple testing. We implemented a differential abundance testing analysis in order to assess significant changes in abundance at the genus level between samples at 1 month and 3.5 years in the preterm population. As these methods can produce heterogeneous results [START_REF] Nearing | Microbiome differential abundance methods produce disturbingly different results across 38 datasets[END_REF][START_REF] Wallen | Comparison study of sixteen differential abundance testing methods using two large Parkinson disease gut microbiome datasets[END_REF], we used two different methods: Analysis of Compositions of Microbiomes with Bias Correction (ANCOM-BC) [START_REF] Lin | Analysis of compositions of microbiomes with bias correction[END_REF] and ANOVA-Like Differential Expression tool for compositional data (ALDEx2) [START_REF] Fernandes | Unifying the analysis of high-throughput sequencing datasets: characterizing RNA-seq, 16S rRNA gene sequencing and selective growth experiments by compositional data analysis[END_REF]. Differences in abundance were based on non-rarefied counts for both ANCOM-BC and ALDex2, as both methods used their own normalization methods, and only genera with an abundance of 0.1 in at least 1% of the samples were used. We considered only concordant results between the two methods. P-values were corrected using the Benjamini-Hochberg method. Correlations of alpha-and beta-diversity community structures between 1 month and 3.5 years of age samples were assessed as follows: pairwise Spearman rank correlation test for alphadiversity indexes; Mantel test for beta-diversity matrices using the Spearman rank correlation method based on 999 permutations. The association between enterotypes at both ages was tested using Fisher's exact test. To analyze the association between microbiota enterotypes found at 3.5 years of age and both neonatal characteristics and NICU strategies (neonates hospitalized at day 7), we used the chi-squared tests for categorical variables and student tests for continuous variables in univariate analyses.

Mixed-effects logistic regression with a random hospital intercept to consider the correlation between newborns in the same NICU was performed in multivariate analyses. All analyses were performed using the R software version 4.0.4 (R Foundation) and the packages phyloseq (v1.34.0), vegan (v2.6-0), cluster (v2.1.2), clusterSim (v0.49-2), fpc (v 2.2-9), ade4 (v1.7-16), ggplot2 (v 3. 3.5), andlme4 (v1.1-27.1).

RESULTS

Enrolled Children Population

Our analyzed sample of preterm children (n = 159) was compared with the total population enrolled in the EPIFLORE study (n = 570), without stool samples available at the two time points, that is, 1 month and 3.5 years of age (Supplementary Table 1). We found that the characteristics of our study population did not significantly differ from the total population enrolled in the EPIFLORE study except in terms of the mother's age, the rate of mothers born outside France, and maternal level of education (Supplementary Table 1).

Preterm Gut Microbiota at 1 Month of Age

Among the 159 stool samples analyzed at 1 month, 18 were non-amplifiable. The 141 neonates for whom sequencing data were obtained were partitioned into five bacterial enterotypes similar to those previously described by [START_REF] Rozé | Assessment of neonatal intensive care unit practices and preterm newborn gut microbiota and 2-year neurodevelopmental outcomes[END_REF].

In the present study, enterotypes 1-5 were characterized by a dominance of Enterobacter (n = 69), Clostridium sensu stricto 1 (n = 18), Escherichia (n = 25), Enterococcus (n = 18), and Staphylococcus (n = 11), respectively (Figures 1A,B). At the OTU level, enterotypes 1-5 were characterized by the dominance of OTU_1_E. hormaechei, OTU_13_C. neonatale, OTU_2_E. coli, OTU_4_E. faecalis, and OTU_19_S. caprae, respectively (Supplementary Figure 3). Enterotype 2 (dominance of Clostridium sensu stricto 1) and enterotype 3 (dominance of E. coli) were associated with greater gestational age (Supplementary Figure 4). Enterotype 3 was considered to be the more mature microbiota and defined as the reference enterotype at 1 month, as previously reported [START_REF] Rozé | Assessment of neonatal intensive care unit practices and preterm newborn gut microbiota and 2-year neurodevelopmental outcomes[END_REF]. Concerning the 18 non-amplifiable samples, they represented the enterotype 6 gathering infants with a low bacterial load [START_REF] Rozé | Assessment of neonatal intensive care unit practices and preterm newborn gut microbiota and 2-year neurodevelopmental outcomes[END_REF].

Community complexities of the five different enterotypes were compared at the genus level according to alpha (Chao 1 richness and Shannon index) and beta (Bray-Curtis and Unifrac distances) diversities (Figure 1C). Significant differences were observed in alpha diversity across all enterotypes (Shannon diversity index, p = 0.003). Compared to reference enterotype 3, enterotypes 4 and 5 showed a significantly lower alpha diversity (Shannon index, p < 0.05 and p < 0.001, respectively). However, pairwise beta-diversity comparisons between enterotypes showed significant overall differences across groups (Bray-Curtis and Unifrac distance dissimilarities, p < 10 -4 for both). Higher distance dissimilarities among infants belonging to enterotypes 1 and 2 were observed when compared to the reference enterotype 3 (Bray-Curtis, p < 0.0001 for both enterotypes), suggesting higher inter-individual variation. Enterotypes 4 and 5 were characterized by lower pairwise distance dissimilarities within groups (Bray-Curtis, [p < 0.0001], Unifrac [p = 0.007] for Ent4, and Bray-Curtis [p < 0.0001] for Ent5). At the OTU level, significant differences were observed in alpha diversity across all enterotypes (Chao1 and Shannon diversity index, p = 0.005 and p = 0.008, respectively). Differences at the OTU level are further presented in the Supplementary Figure 3.

Preterm Gut Microbiota at 3.5 Years of Age At 3.5 years of age, the gut microbiota of the 159 preterm children was characterized by the presence of six different phyla as follows: Actinobacteriota, Bacteroidota, Desulfobacterota, Firmicuteota, Fusobacteriota, and Proteobacteriota, with Bacteroidota and Firmicuteota being the most abundant phyla (Supplementary Figure 5). Concerning the stratification of the gut microbiota, it is optimally separated into two distinct enterotypes (Figure 2A). Clustering based on the genus taxonomic level showed that Prevotella had the strongest positive correlation and Bacteroides had the strongest negative correlation with the PCoA 1 dimension (Figure 2A). The enterotypes were enriched in either Prevotella (P_type) (n = 31) or Bacteroides (B_type) (n = 128) (Figure 2B). Interestingly, Prevotella was depleted in most children with B_type stools, with a median abundance of 0.02% (IQR, 0.02-0.06%), whereas Bacteroides remained the second most abundant genus in children with P_type stools, with a median abundance of 14% (IQR,(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19). At the OTU level, the B_type enterotype was characterized by the dominance of OTU_3_B. vulgatus, and the P_type enterotype was characterized by the dominance of OTU_30_P. copri (Supplementary Figure 6).

When comparing the microbiota of both P_type and B_type enterotypes according to alpha and beta diversities, the P_type enterotype showed greater richness (Chao1 index, p = 0.031, genus level) (Figure 2C). At the OTU level, no difference in richness was observed between the two enterotypes (Supplementary Figures 6A,C).

The Bray-Curtis distance pairwise dissimilarities between P_type and B_type enterotypes based on the genus taxonomic profile showed that B_type and P_type children shared a similar bacterial community within each enterotype (Figure 2D). The dissimilarity, indicated by the heatmap color and boxplot of pairwise dissimilarities, was significantly higher in the P_type enterotype than in the B_type enterotype, suggesting that the P_type community was more heterogeneous among children (Figure 2D). In contrast, at the OTU level, the P_type community was less heterogeneous and was characterized by lower pairwise dissimilarity distances (Supplementary Figure 6).

Preterm and Full-Term Children Gut Microbiota Comparison at 3.5 Years of Age

To investigate the differences between the gut microbiota of preterm and full-term children at 3.5 years of age, we included 200 full-term children (ELFE cohort). Full-term children were significantly different in terms of gestational age, delivery mode, birth weight, the rate of mothers born outside of France, and the level of mother's education (Supplementary Table 2). Firmicuteota and Bacteroidota were the main phyla identified in the gut microbiota of full-term children. Moreover, the top 10 genera found in preterm children (except for the genus Blautia) also characterized the gut microbiota of full-term children (Figure 3A). Enterotyping at the genus taxonomic level showed that the gut microbiota in full-term and preterm children was optimally clustered into two discrete B_type (n = 293) and P_type (n = 66) enterotypes, with the two largest predictors being Bacteroides and Prevotella, respectively (Figure 3B). Interestingly, full-term children showed the same patterns as those seen in preterm children, where Prevotella was depleted from the majority of B_type children [median abundance of 0.04% (IQR, 0.02-0.08%)], whereas Bacteroides remained the second most abundant genus in P_type children (median abundance of 14% [IQR, 9-19%]) (Figure 3A). No differences in enterotype proportion distribution were observed between preterm and full-term children (p = 1.000; Figure 3C).

Analysis of alpha diversity showed that the gut microbiota of preterm children was characterized by a significantly lower Shannon diversity index than that of full-term children at the genus (p = 0.012) (Figure 3D) and OTU levels (p = 0.037). Overall, there were no differences in richness assessed using the Chao1 index at both the genus and OTU levels.

Using both Bray-Curtis and Unifrac distances at the genus level, the PERMANOVA suggested differences in the microbial community between preterm and full-term children (p = 0.012 and p = 0.006, FDR-corrected, respectively, Supplementary Figure 7).

Preterm Gut Microbiota Comparison at 1 Month and 3.5 Years of Age

Figures 1B, 2B present the top 10 abundant genera at 1 month and 3.5 years of age, respectively, showing that the major genera present in the gut microbiota of preterm children changed between the two ages. In addition to Bifidobacterium and Bacteroides, the major genera initially found at 1 month (Enterobacter, Clostridium sensu stricto, Escherichia, Enterococcus, and Staphylococcus) were replaced by other genera (Agathobacter, Faecalibacterium, Roseburia, Prevotella, and Ruminococcus). Except for Bifidobacterium and Prevotella, all genera cited above were significantly differentially abundant between the two time points (Supplementary Table 3). Based on the genus composition, the alpha diversity (Chao1 richness and Shannon index) of the gut microbiota significantly increased between both ages (Figure 4A). Pairwise dissimilarities based on genus composition within each age group showed that the gut microbiota of children at 1 month of age harbored higher beta diversity (Bray-Curtis and Unifrac) than at 3.5 years of age (p < 0.0001 for both; Figure 4A). Investigation of the relationship between gut microbiota at 1 month and 3.5 years of age in preterm children showed the absence of correlations of diversities between the two age groups either according to alpha diversity (rho = -0.054, p = 0.521 [Chao1 index]; rho = 0.063, p = 0.458 [Shannon index]) or beta diversity (r = 0.008, p = 0.410 [Bray-Curtis]; r = 0.030, p = 0.176 [Unifrac]). Comparison of the proportions between the two 3.5-year and the six 1-month enterotypes showed that enterotypes 3, 4, and 5 tended to harbor the smallest proportion of P_type, but overall, the association was not significant (Fisher's exact test, p = 0.490) (Figure 4B). Compared to the reference enterotype 3, the gut microbiota of children originally clustered into enterotype 2 presented a higher alpha diversity at 3.5 years of age, but no overall differences were observed (Shannon index, p = 0.355; Figure 4C). No overall differences were noted at the OTU level (p = 0.173).

Preterm Gut Microbiota at 3.5 Years of Age and Neonatal Factor Relations

Since we found that enterotypes at 3.5 years of age explained most of the variance in the gut microbiota (PERMANOVA analysis; Supplementary Table 4), we further explored the association between neonatal characteristics and enterotypes. Higher birth weight was significantly associated with the P_type enterotype in the univariate analysis (Table 1). In the multivariate model adjusted for maternal age, education level, and country of birth, we included covariates known to influence early gut microbiota development, such as gestational age, birth mode, birth weight, sex, antibiotics, breastfeeding, skin-toskin contact, and NICU strategies. The results showed that birth weight was no longer associated with enterotypes, and no other neonatal factors were associated with enterotypes (Supplementary Table 5). Concerning the NICU strategies, we observed a tendency between the practice of a low volume of enteral nutrition and an increased probability of belonging to the P_type enterotype (OR: 5.3, 95% confidence interval [0.9-30.8]) (Supplementary Table 5).

DISCUSSION

This study showed that the gut microbiota of preterm and full-term children at 3.5 years of age is characterized by two enterotypes dominated by either Bacteroides or Prevotella. Interestingly, we found that prematurity still imprints the gut microbiota of children, as the microbiota of preterm children showed lower diversity and different community composition than that seen in full-term children's microbiota. Additionally, the gut microbiota at 3.5 years of age was not related to that at 1 month in preterm children.

We confirmed that the gut microbiota of preterm neonates at 1 month of age is highly variable and clustered into six distinct enterotypes. Preterm neonate enterotypes were driven by a specific genus (i.e., Enterobacter, Clostridium sensu stricto 1, Escherichia, Enterococcus, and Staphylococcus), except for the sixth enterotype, which was characterized by a low bacterial load, leading to the absence of DNA amplification. These findings are in agreement with those of previous studies indicating that the early intestinal microbiota composition in preterm neonates is characterized by a strong dominance of only a few taxa [START_REF] Stewart | Temporal bacterial and metabolic development of the preterm gut reveals specific signatures in health and disease[END_REF][START_REF] Korpela | Intestinal microbiota development and gestational age in preterm neonates[END_REF]. In a previous study, one of four genera, Bifidobacterium, Enterobacter, Staphylococcus, or Enterococcus, represented > 50% of the reads in a given sample [START_REF] Korpela | Intestinal microbiota development and gestational age in preterm neonates[END_REF]. The authors associated Staphylococcus and Enterococcus dominance with younger postmenstrual age. In our cohort, Staphylococcus-and Enterococcus-driven enterotypes were also characterized by a lower gestational age (Supplementary Figure 4). Additionally, we found a dominance of Clostridium sensu stricto 1 and Escherichia, and an absence of a Bifidobacterium-driven enterotype, even though Bifidobacterium was among the top 10 dominant genera at 1 month. [START_REF] Korpela | Intestinal microbiota development and gestational age in preterm neonates[END_REF] reported an association between postnatal age and Bifidobacterium colonization. As their study was conducted with stool samples collected up to 60 days after birth, the lack of a Bifidobacterium-driven enterotype in our study could be explained by the fact that in our cohort, stool samples were collected at a median age of 23 days (IQR, 22-26 days), which is too early for this genus to reach a state of dominance. In a study by [START_REF] Stewart | Temporal bacterial and metabolic development of the preterm gut reveals specific signatures in health and disease[END_REF], the gut microbiota of preterm neonates, examined using stool samples collected from birth to 100 days of life, was grouped into six clusters characterized by the dominance of Klebsiella, Staphylococcus, Enterococcus, Escherichia, and Bifidobacterium, or dominance of both Klebsiella and Enterococcus.

The present study showed that alpha diversity of the gut microbiota in preterm children increases with age and that the gut microbiota community is significantly different between 1 month and 3.5 years of age. The latter is characterized by two discrete enterotypes driven by two dominant genera, Bacteroides and Prevotella. Previous studies monitoring the gut microbiota of preterm children between the NICU stay and 2 years of age also showed a significant increase in alpha diversity with age ( [START_REF] Gómez | Bacteriological and immunological profiling of meconium and fecal samples from preterm infants: a two-year follow-up study[END_REF][START_REF] Yee | Longitudinal microbiome composition and stability correlate with increased weight and length of very-low-birth-weight infants[END_REF] and reported differential beta-diversity structures between NICU stay and 4 years of age [START_REF] Yee | Longitudinal microbiome composition and stability correlate with increased weight and length of very-low-birth-weight infants[END_REF]. We observed greater variability in the diversity of preterm children's gut microbiota at 1 month than at 3.5 years; this finding is in accordance with previous reports [START_REF] Barrett | The individual-specific and diverse nature of the preterm infant microbiota[END_REF][START_REF] Stewart | Temporal bacterial and metabolic development of the preterm gut reveals specific signatures in health and disease[END_REF][START_REF] Korpela | Intestinal microbiota development and gestational age in preterm neonates[END_REF]. This indicates that preterm neonates' early gut microbiota has greater inter-individual variability and lower stability but evolves and stabilizes over time. Furthermore, between 1 month and 3.5 years, 68 genera were significantly differentially abundant (25 negatively and 43 positively associated with the gut microbiota at 3.5 years). Indeed, in addition to Bifidobacterium and Bacteroides, the major genera shifted from organisms previously found to be abundant in preterm neonates during the NICU stay to common organisms found in adults, such as Faecalibacterium, Roseburia, and Ruminococcus [START_REF] Roswall | Developmental trajectory of the healthy human gut microbiota during the first 5 years of life[END_REF]. Altogether, these data support the fact that the intestinal microbiota of children born preterm evolves toward a richer and more diverse adult-like microbiota over time.

Analyses of correlations between preterm children's gut microbiota diversity during the NICU stay and early childhood showed no significant relationship. Children with higher alpha diversities at 1 month of age did not harbor higher alpha diversities at 3.5 years of age. Based on beta diversity, we found no correlation when examining community composition among close-together (i.e., lower dissimilarity distance) preterm children at 1 month and at 3.5 years of age, demonstrating that closetogether newborns do not conserve their proximity over time. Similarly, there was no association between preterm neonate enterotypes at 1 month and 3.5 years of age, suggesting that belonging to one of the six enterotypes at 1 month did not predispose to harbor a certain type of enterotype at 3.5 years. If clustering methods are sensitive to sample size [START_REF] Koren | A guide to enterotypes across the human body: meta-analysis of microbial community structures in human microbiome datasets[END_REF][START_REF] Henry | Clustering methods with qualitative data: a mixed-methods approach for prevention research with small samples[END_REF][START_REF] Preud'homme | Head-to-head comparison of clustering methods for heterogeneous data: a simulation-driven benchmark[END_REF], in the present study, we demonstrated good reproducibility of the clustering at 1 month based on our population of 141 EPIFLORE preterm neonates (i.e., 159 neonates excluding the 18 preterm neonates belonging to enterotype 6) compared to the previous clustering performed on 484 EPIFLORE preterm neonates [START_REF] Rozé | Assessment of neonatal intensive care unit practices and preterm newborn gut microbiota and 2-year neurodevelopmental outcomes[END_REF]. Because we identified the same clustering patterns based on smaller sample size, we can assume that the shift from six to two enterotypes that we observed is not an artifact of the smaller sample size of the present study.

In the present study, the analysis between preterm and fullterm children at 3.5 years of age showed that both populations shared similar microbiota profiles characterized by Bacteroidesand Prevotella-driven enterotypes. This clustering is consistent with the gut microbiota studies among full-term children [START_REF] Bergström | Establishment of intestinal microbiota during early life: a longitudinal, explorative study of a large cohort of danish infants[END_REF][START_REF] Nakayama | Diversity in gut bacterial community of school-age children in Asia[END_REF][START_REF] Méndez-Salazar | Altered gut microbiota and compositional changes in firmicutes and proteobacteria in mexican undernourished and obese children[END_REF][START_REF] Xiao | Deterministic transition of enterotypes shapes the infant gut microbiome at an early age[END_REF] and adults [START_REF] Roager | Microbial Enterotypes, Inferred by the Prevotella-to-Bacteroides Ratio, Remained Stable during a 6-month Randomized Controlled Diet Intervention with the New Nordic Diet[END_REF][START_REF] Levy | Longitudinal analysis reveals transition barriers between dominant ecological states in the gut microbiome[END_REF]. A comparison of the microbiota community complexities at 3.5 years of age indicated that preterm and full-term children shared a similar richness composition, but preterm children had a lower alpha diversity and a different beta diversity (Supplementary Figure 7). It is known that the early gut microbiota differs between full-term and preterm children because of differences in exposure to prenatal and postnatal determinants [START_REF] Groer | Development of the preterm infant gut microbiome: a research priority[END_REF][START_REF] Tauchi | Gut microbiota development of preterm infants hospitalised in intensive care units[END_REF][START_REF] Aguilar-Lopez | A systematic review of the factors influencing microbial colonization of the preterm infant gut[END_REF]. Nonetheless, little is known about how early differences in gut colonization and establishment evolve as preterm neonates grow older. Differences in gut microbiota diversity between preterm and full-term children are observed within the first 6 months of life and are either still present [START_REF] Vandenplas | Factors affecting early-life intestinal microbiota development[END_REF] or disappear [START_REF] Dahl | Preterm infants have distinct microbiomes not explained by mode of delivery, breastfeeding duration or antibiotic exposure[END_REF][START_REF] Yap | Neonatal Intensive Care Unit (NICU) exposures exert a sustained influence on the progression of gut microbiota and metabolome in the first year of life[END_REF] at 12 months of age. Recently, the transition of enterotypes shaping the gut microbiota of children at an early age revealed that gut microbiota maturity of the preterm children was delayed up to 12 months compared to that of full-term children [START_REF] Xiao | Deterministic transition of enterotypes shapes the infant gut microbiome at an early age[END_REF]. The present study indicates that at 3.5 years of age, the gut microbiota of the preterm children differs in terms of composition and is less diverse than that of full-term children's gut microbiota, suggesting that the transition recovery of the initially delayed gut microbiota colonization in preterm children still continues at 3.5 years of age. Our results are in accordance with those of [START_REF] Fouhy | Perinatal factors affect the gut microbiota up to four years after birth[END_REF], who reported that gestational age imprints gut microbiota up to 4 years of age. Over the course of 3.5 years, we observed the evolution of preterm children's gut microbiota toward an enterotype-like gut microbiota similar to those found in full-term children. Because there is currently a lack of data on the development of preterm children's gut microbiota during early childhood and its impact on later health outcomes, the present study provides another insight into our understanding of the impact of gestational age on gut microbiota development up to 3.5 years of age.

The neonatal factors, such as gestational age, delivery mode, breastfeeding, early antibiotic therapy medication, skin-to-skin practice, and NICU practices, are known to influence early gut microbiota colonization [START_REF] Rodríguez | The composition of the gut microbiota throughout life, with an emphasis on early life[END_REF][START_REF] Martin | Early-life events, including mode of delivery and type of feeding, siblings and gender, shape the developing gut microbiota Suchodolski[END_REF][START_REF] Tamburini | The microbiome in early life: implications for health outcomes[END_REF][START_REF] Kumbhare | Factors influencing the gut microbiome in children: from infancy to childhood[END_REF][START_REF] Rozé | Assessment of neonatal intensive care unit practices and preterm newborn gut microbiota and 2-year neurodevelopmental outcomes[END_REF][START_REF] Vandenplas | Factors affecting early-life intestinal microbiota development[END_REF]. However, little is known about the long-term impact of these factors on the gut microbiota. Therefore, we investigated the potential effects of these factors on the gut microbiota of preterm children aged 3.5 years. We found no effects on the gut microbiota composition or any significant associations with the enterotype prevalence in preterm children at 3.5 years of age, indicating that these factors may not persist up to this age. Nonetheless, we observed a tendency between a low enteral volume at 7 days and an increased probability of belonging to the Prevotella-driven enterotype; the odds ratio indicates a strong association, but the wide 95% confidence interval indicates poor precision, which may be due to the limited sample size. Previously, the EPIFLORE study reported that gestational age and birth mode were associated with enterotypes at 1 month of age. Furthermore, no assisted ventilation on day 1 was associated with a decreased risk of belonging to enterotypes 5 or 6, while sedation and low volume of enteral nutrition were associated with an increased risk [START_REF] Rozé | Assessment of neonatal intensive care unit practices and preterm newborn gut microbiota and 2-year neurodevelopmental outcomes[END_REF], [enterotypes 5 and 6 were the less mature enterotypes characterized by a smaller gestational age (Supplementary Figure 4)]. If NICU practices are associated with preterm neonates' gut microbiota during the NICU stay, some of these practices may remain weakly associated with the microbiota 3.5 years later.

Study Limitations and Strengths

The use of the 16S rRNA gene sequencing approach limits this study to the description of bacterial composition. Analysis using shotgun metagenomics would allow greater sequencing depth, thus providing additional information regarding microbiome functional profiles and meaning at the species level of the microbiome that can differ between preterm and full-term populations, despite similar bacterial composition. Microbiota analysis was only performed for two times, that is, at 1 month and 3.5 years of age, which does not take into account possible dynamic changes in microbiota between these two time points. Moreover, we studied the influence of prenatal factors on the gut microbiota at 3.5 years in a preterm population, but we did not take into account more contemporary factors occurring during childhood, such as the use of antibiotics or dietary habits that might influence the gut microbiota at 3.5 years. Finally, the number of children included may represent a limitation to definitely conclude about the association between the gut microbiota at 3.5 years of age and neonatal factors, but a tendency was observed, indicating a potential lack of statistical power.

In terms of major strengths, the present study is rare in the number of preterm children included within a multiyear follow-up and was performed on a large nationwide multicentric cohort, providing an accurate description of the neonatal characteristics of preterm children and characterization of the NICU strategies during hospitalization. Therefore, it allows a confident description of the preterm gut microbiota at both 1 month and 3.5 years of age, when the children's gut microbiota is supposed to mature toward an adultlike microbiota.

CONCLUSION

This study demonstrates that during the NICU stay, the gut microbiota of preterm neonates is characterized by high interindividual variation and low stability shaped by the NICU environment. However, NICU-shaped gut microbiota was not predictive of microbial community composition in the same child at 3.5 years of age. At 3.5 years of age, preterm children share gut microbiota similar to that of children born full-term and correspond to a mature adult-like microbiota in terms of enterotypes. However, the gut microbiota of preterm children was characterized by a lower Shannon diversity and different overall microbial composition compared to that seen in the gut microbiota of full-term children, indicating that prematurity still imprints the gut microbiota up to 3.5 years of age. These results must be replicated in other large-scale longitudinal cohorts to confirm the relationship between gestational age and other perinatal factors and the gut microbiota during early childhood.

FIGURE 1 |

 1 FIGURE 1 | Gut microbiota composition and diversity of the 141 preterm infants at 1 month after birth. (A) Clustering based on the genus taxonomic profiles. Biplot arrows indicate the top six genera that drive the sample to different locations on the plot. (B) The boxplots represent the relative abundance of the top 10 genera distributed among the five enterotypes. The sixth enterotype is not represented because it is constituted by infants without sample amplification (low bacterial load). The boxplots show the smallest and largest values, 25 and 75% quartiles, the median, and outliers. (C) Boxplots of alpha diversity (top) assessed by Chao1 and Shannon index, and pairwise beta diversity dissimilarities (bottom) assessed by Bray-Curtis and Unifrac distances within enterotypes based on genus taxonomic profiles (*p < 0.05, ****p < 0.001, ns = p > 0.05, Reference group = Ent3); Ent, Enterotype.

FIGURE 2 |

 2 FIGURE 2 | Gut microbiota composition and diversity of the 159 preterm children at 3.5 years of age. (A) Clustering based on the genus taxonomic profile. Biplot arrows indicate the top four genera that drive samples to different locations on the plot. (B) The boxplots represent the relative abundance of the top 10 genera distributed among the P_ and B_type enterotypes. The boxplots show the smallest and largest values, 25 and 75% quartiles, the median, and outliers. (C) Boxplot of alpha diversity assessed by Chao1 estimate based on the genus taxonomic profile. (D) Inter-individual dissimilarity of the genus community based on the Bray-Curtis distance and represented by the heat map and boxplot. Each cell in the heat map, ordered according to B_ and P_type enterotypes, represents the dissimilarity level as per the color scale beside the box plot (*p < 0.05, ****p < 0.0001).

FIGURE 3 |

 3 FIGURE 3 | Clustering of the gut microbiota of preterm (n = 159) and full-term (n = 200) children at 3.5 years of age. (A) The boxplots represent the relative abundance of the top 10 genera distributed between the B_ and P_types among the full-term children. The boxplots show the smallest and largest values, 25 and 75% quartiles, the median, and outliers. (B) Gut microbiota clustering of the 159 preterm and 200 full-term children at 3.5 years of age based on the genus taxonomic profile. Biplot arrows indicate the top four genera that drive samples to different locations on the plot. (C) Distribution of enterotypes among full-term and preterm children. The distribution is expressed as the proportion of each enterotype among each group of children. (D) Boxplot of alpha diversity assessed by Shannon index based on the genus taxonomic profile between preterm and full-term children (*p < 0.05). The boxplots show the smallest and largest values, 25 and 75% quartiles, the median, and outliers.

FIGURE 4 |

 4 FIGURE 4 | Comparison of the gut microbiota of the same preterm children at 1 month and 3.5 years of age. (A) Boxplots of alpha (top) and beta diversities (bottom) based on both Chao1 and Shannon index and both Bray-Curtis and Unifrac dissimilarity distances, respectively, at genus taxonomic level in the 141 preterm children with sequencing data at two time points. (B) Distribution of B_ and P_type enterotypes among the six enterotypes at 1 month in 159 preterm children with a fecal sample at two time points. The values are expressed as proportions of each enterotype at 3.5 years of age among each enterotype at 1 month. (C) Boxplots of Shannon diversity based on genus level at 3.5 years of age according to enterotype at 1 month (*p < 0.05, ns = p > 0.05; Reference group = Ent3, Ent, Enterotype). The boxplots show the smallest and largest values, 25 and 75% quartiles, the median, and outliers. ****p < 0.0001.

TABLE 1 |

 1 Univariate association between preterm children enterotypes at 3.5 years of age, neonatal characteristics, and NICU strategies.

	Variables

Frontiers in Microbiology | www.frontiersin.org

July 2022 | Volume 13 | Article 919317

ACKNOWLEDGMENTS

We thank the Genotoul Get-PlaGe sequencing platform and GenoToul bioinformatics facility (http://bioinfo.genotoul.fr) for providing computational resources.

FUNDING

This work was supported by l'Agence Nationale pour la Recherche under Grants (EPIFLORE: ANR-12-BSV3-0025 and ELFE: ANR-11-EQPX-0038), the Nestec Research Center (Constitution of the collection of stool samples during EPIPAGE2), and the Biostime Institute for Nutrition and Care-Geneva (BINC-Geneva) (16S rRNA gene sequencing at 3.5 years).

DATA AVAILABILITY STATEMENT

Personal data of children from the ELFE and EPIPAGE2 cohorts cannot be made publicly available for ethical reasons. They are available upon reasonable request from the authors under data-security conditions. The 16S rRNA gene reads are publicly available from the National Center for Biotechnology Information (nih.gov) Sequence Read Archive (SRA) under the Bioproject accession number: PRJNA798897, https://www.ncbi.nlm.nih.gov/bioproject/PRJNA798897.

ETHICS STATEMENT

This study was approved by the National Data Protection Authority (Commission Nationale de l'Informatique et des Libertes [CNIL]), by the national advisory committee on information processing in health research (Comité Consultatif sur le Traitement de l'Information en matière de Recherche dans le domaine de la Santé [CCTIRS]) and by the Committee for the Protection of People Participating in Biomedical Research (Comité de Protection des Personnes [CPP]). Recruitment and data collection occurred only after the families had received information and agreed to participate in these cohorts with informed consent.

AUTHOR CONTRIBUTIONS

J-CR, M-JB, M-AC, P-YA, and JA conceived and designed the study. M-JB, PL, GT, JD, and J-CR generated the data. JD and GT analyzed the data. GT drafted the manuscript. M-JB, J-CR,

SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmicb. 2022.919317/full#supplementary-material

Conflict of Interest:

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's Note: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.