Carbapenem-resistant Acinetobacter Baumannii Ventilator-Associated Pneumonia in Critically Ill Patients: Potential Inference with Respiratory Tract Microbiota Dysbiosis

Background: Carbapenem-resistant Acinetobacter baumannii (CRAB) is a common cause of ventilator-associated pneumonia (VAP) in intensive care unit (ICU) patients, but infection and colonization are dicult to distinguish which aggravates the abuse of antimicrobial drugs and further accelerates the evolution of drug resistance. We sought to provide new clues for the diagnosis, pathogenesis and treatment of CRAB VAP based on lower respiratory tract (LRT) microbiota. Methods: A prospective study was conducted on patients with mechanical ventilation from July 2018 to December 2019 in a tertiary hospital. Multi-genomics studies (16S rRNA amplicon, metagenomics and whole-genome sequencing [WGS]) of endotracheal deep aspirate (ETA) were performed. Results: Fifty-two ICU patients were enrolled, including 24 with CRAB VAP (CRAB-I), 22 with CRAB colonization (CRAB-C), and six CRAB-negative patients (infection-free) (CRAB-N). Diversity of pulmonary microbiota was signicantly lower in CRAB-I than in CRAB-C or CRAB-N (mean Shannon index, 1.79 vs. 2.73 vs. 4.81, P<0.05). Abundances of 11 key genera differed between the groups. Acinetobacter was most abundant in CRAB-I (76.19%), moderately abundant in CRAB-C (59.14%), and least abundant in CRAB-N (11.25%), but its interactions with other genera exhibited the opposite pattern. Metagenomics and WGS analysis showed that virulence genes were more abundant in CRAB-I than in CRAB-C. Multilocus sequence typing (MLST) of 46 CRAB isolates revealed that the main types were ST208 (30.43%) and ST938 (15.22%), with no difference between CRAB-I and CRAB-C. Conclusions: LRT microbiota dysbiosis including elevated relative abundance of Acinetobacter and reduced bacterial interactions, and virulence enrichment may lead to CRAB VAP. DMNC: VFs: Virulence factors.


Introduction
Acinetobacter baumannii (AB) is an environmental microorganism that can contaminate the surface of hospital equipment and colonise skin, wounds, and other parts of patients [1]. VAP is a frequent complication in ICUs and is associated with prolonged mechanical ventilation, longer ICU stay, and poorer outcomes [2]. AB is frequently isolated from the respiratory tract in patients with tracheal intubation, which is considered to be a high-risk factor for VAP [3][4]. Yin et al. found that the prevalence of AB was high (31.7%) among VAP pathogens in 15 teaching hospitals in China from 2007 to 2016 [5]. Due to the heavy use of broad-spectrum antibacterial agents in ICU patients, most AB strains were multi-drug resistant or even pan-drug resistant. A survey of hospital-acquired pneumonia (HAP) and VAP conducted from 2007 to 2013 revealed that multidrug-resistant AB (MDRAB) increased yearly and ranked rst in some ICU bacterial lists [6].
The carbapenem-resistant AB (CRAB) genome encodes various drug-resistance genes and virulence factors (VFs), including e ux pumps, iron acquisition systems, secretion systems, phospholipases, and capsular polysaccharides, which help the bacterium survive antibiotic treatment and colonise in the environment [7][8]. Without effective therapy, the mortality of patients with nosocomial CRAB infection remains high [5,9]. Accordingly, the World Health Organization (WHO) has designated CRAB as a pathogen that poses a major threat to human health and that should be urgently targeted by new antibiotics [10].
A key step in containing CRAB is the rational use of antibiotics based on accurate diagnosis of infection. However, the challenge of differentiating CRAB colonisation and infection could lead clinicians to prescribe excessive broad-spectrum antibiotics, potentially promoting the occurrence of drug-resistant bacteria and their spread in hospitals. Therefore, clinical infection control requires reliable methods for distinguishing CRAB colonisation from CRAB infection. Reduction in LRT microbiota diversity or elevated abundance of certain strains may lead to infections [11][12]. For example, LRT microbiota diversity was lower in 13 infected patients than in healthy control patients, and pathogenic bacteria in four subjects was consistent with the dominant ora identi ed by 16S rRNA analysis [12]. Through 16S rRNA analysis of 263 samples, Emonet et al. found that the low relative abundance of species in oropharyngeal secretions during intubation was strongly associated with subsequent VAP [13].
However, many aspects of the relationship between respiratory microecology and infection remain unknown, and current research is focused mainly on chronic lung diseases [14][15]. Budden et al. reviewed recent advances in understanding the composition of the lung microbiome and found that bacteria, viruses, and fungi from the respiratory tract produce structural ligands and metabolites that interact with the host and alter the development and progression of chronic respiratory diseases [15]. Hakansson et al. suggested that a complex interplay between the host, environment, and properties of the colonising microorganisms determines disease development and severity [16]. Moreover, Roquilly et al. proposed that the diversity of the microbiome and mucosal immunity are associated with HAP [17].
Metagenomic analysis of microbiome structure and function will aid in understanding the pathogenesis and regulatory networks of AB during infection [14]. This is the rst study to analyse and compare the characteristics of LRT microbiota of CRAB-negative, CRAB-colonised, and CRAB-infected patients using 16S rRNA, metagenomics, and whole-genome sequencing (WGS). We investigated whether VAP patients are associated with unique LRT microbiota to explore the pathogenesis of VAP at the level of the microbiota and provide an improved basis for clinical decision-making.

Study design and patient enrolment
This prospective study was conducted from July 2018 to December 2019 in the adult ICUs of the First A liated Hospital, College of Medicine of Zhejiang University, China. The patient inclusion criteria were as follows: (1) patient was mechanically ventilated and hospitalised in the ICU; (2) Acute Physiology and Chronic Health Evaluation (APACHE) II score was greater than 12; and (3) collection of endotracheal deep aspirate (ETA) specimens was possible. Exclusion criteria were as follows: (1) age < 18 years; (2) comorbidity of chronic lung disease or lung cancer; (3) hospital stay < 24 hours; and (4) co-infection with other bacteria. VAP was de ned by the criteria of the Centers for Disease Control and Prevention (CDC) of the United States based on clinical, laboratory, radiological, and microbiological data [18]. Patients meeting VAP criteria with positive culture for CRAB ETA were assigned to the CRAB VAP group (CRAB-I).
Respiratory tract CRAB colonisation (CRAB-C) was de ned as CRAB-positive culture from ETA without VAP. Control patients (CRAB-N) were patients with neither CRAB VAP nor CRAB colonisation. Approval was obtained from the ethical board of the hospital (reference number: 2016-458-1).

Patient sampling and bacterial isolation
The aspirate samples were transported to the microbiology laboratory within two hours. ETAs were inoculated with a calibrated loop (0.001 ml). A gram-stained smear was prepared for all specimens and examined microscopically. MacConkey agar [HiMedia Laboratory Pvt. Ltd. India] and incubated aerobically at 37°C overnight. A. baumannii was identi ed by matrix-assisted laser desorption/ionisation time-of-ight (MALDI-TOF) analysis using a VITEK MS instrument (bioMérieux, US). The con rmed A. baumannii from clinical and sputum samples were stored at − 80°C for further study. The resistance of A.  [19]. Resistance to Tigecycline (TGC) and Polymyxin (POL) was examined by the broth microdilution method and evaluated according to the European Committee on Antimicrobial Susceptibility Testing (EUCAST) [20]. Escherichia coli ATCC25922 was used as a quality control strain. CRAB was de ned as AB strains that were non-susceptible to imipenem or meropenem.

Clinical data
Clinical data including demographic variables, length of ICU stay, length of hospital stay, comorbidities, previous invasive procedures (central line insertion, intubation, continuous renal replacement therapy, and surgery under general an aesthesia), as well as lengths and types of antibiotic treatments and severity of illness (Acute Physiology and Chronic Health Evaluation (APACHE II)), were recorded [21]. Data on the levels of serum markers for liver and renal function (e.g. bilirubin, alanine transaminase (ALT), aspartate transaminase (AST), gamma glutamyl transpeptidase (γ -GT), urea, and creatinine) as well as those of blood biomarkers of infection other than core temperature (e.g. white blood cell (WBC) and CRP) were also collected.
16S rRNA amplicon and data analysis Total genomic DNA was extracted from samples using the CTAB method. PCR ampli cation was performed, puri ed amplicons were pooled, and paired-end sequencing was carried out on an Illumina NovaSeq PE250 platform. After demultiplexing and trimming of the barcode and primer sequence using FLASH (V1.2.7) [22], the paired-end raw read data of each sample was acquired. Subsequently, quality control was carried out using Qiime [23][24] and effective tags were obtained for analysis. OTUs were clustered with a 97% similarity cut-off using Usearch (Version 7.0) [25]. OTU sequences were taxonomically annotated using the Mothur (ref) method and SILVA database (ref) [26]. The abundance matrices at the levels of phylum, class, order, family, and genus were constructed for each sample. MUSCLE (Version 3.8.31) software was used for rapid multiple sequence alignment, and the phylogenetic relationships of all OTU representative sequences were obtained [27]. Finally, the data of each sample was normalised, and the sample with the least amount of data was used as the standard. Alpha diversity (Shannon and Simpson index) within a sample and Beta diversity (Bray-Curtis dissimilarity matrix) across samples were calculated using the phyloseq R package (version 1.32.0). PCoA and NMDS analysis based on Bray-Curtis dissimilarity was performed using the Vegan R package (version 2.5.6) and visualised using the ggplot2 R package (version 3.3.2). ANOSIM was used to determine statistical signi cance. A complete list of sample names and accession numbers is provided in Table S1.

Metagenomics sequencing and analysis
Following fragmentation of microbial DNA, metagenomic sequencing was performed on an Illumina NovaSeq 6000. After paired-end Illumina sequencing, we employed a previously reported bioinformatics pipeline to detect and pro le the airway microbiome (ref). The low-quality sequences were ltered out or trimmed using PRINSEQ-lite (Version 0.19.3). And then de novo assembly was generated using SPAdes genome assembler (Version 3.11.1) [28] and coding sequences were predicted using MetaGeneMark (Version 3.38). Taxonomy assignments of both the clean reads and coding sequences were performed by Kaiju classi er (Version 1.7.2) with the National Center for Biotechnology Information Refseq database [29]. The functions of coding sequences were obtained using DIAMOND software (Version 0.9.30) with Kyoto Encyclopedia of Gene and Genomes (KEGG) database [30][31]. Furthermore, virulence genes in metagenomics sequences were identi ed by comparing the coding sequences against the Virulence Factor Database using DIAMOND software [32]. The correlation coe cient between bacteria and virulence genes was generated using Python-based SparCC tool with SparCC correlation method and visualized using Cytoscape (Version 3.8.0). We performed multivariate linear regressions with feature selection, using the lasso penalized maximum likelihood technique in the "glmnet" R package (Version 4.0.2). A complete list of sample names and accession numbers is provided in Table S1.

WGS and analysis
The genomic DNA of 46 isolates was extracted using a Qiagen DNA puri cation kit (Qiagen, Hilden, Germany) and sequenced on an Illumina HiSeq 4000-PE150. For each isolate, de novo assembly of reads was performed using SPAdes genome assembler. The assembled genome sequences were annotated using Prokka (version 1.14.6) [33]. GenBank les produced from Prokka were converted to GFF format, and then subjected to Roary (version 3.11.2) [34] to obtain core genome sequences. A maximumlikelihood phylogenetic tree was constructed using RaxML software (version 8.2.12) with 1000 bootstraps replicates [35]. Average nucleotide identity (ANI) was calculated by using a pyani (version 0.2.10). ANI values above 95% between genomes of these isolates denote the same species [36]. MLST analysis were determined according to the Institute Pasteur scheme (MLST-IP) and Oxford Database (MLST-OD) [37]. Clonal complexes were assigned by eBURST and were de ned as single locus [38]. CC was de ned as a group of STs sharing at least ve or more identical loci among the seven housekeeping genes tested by goeBURST (goeburst.phyloviz.net). CCs were named according to the number of the predicted founder ST. Antimicrobial resistance genes (ARG) and AB virulence genes were identi ed by comparing genome assemblies against the ResFinder antibiotic resistance gene database using the Abricate software (version 0.8) and against the Virulence Factor Database using the DIAMOND software (version 0.9.30), respectively [39]. We determined the capsular polysaccharide (KL) and lipooligosaccharide outer core (OCL) synthesis of the A. baumannii using Kaptive software (version 0.5.1) [40]. A complete list of strain names and accession numbers is provided in Table S1.

Statistical analysis
The Linear Discriminant Analysis Effect Size (LEfSe) program was used to identify taxa that differed consistently between sample types [41]. Network X, built on correlation coe cients obtained using the Python-based SparCC tool with the SparCC correlation method and visualised using Cytoscape (version 3.8.0), was used to explore and visualise the associations between microbial communities [42]. Normally distributed continuous variables were expressed as means ± standard deviation (SD) and compared using Student's t-test, whereas non-normally distributed continuous variables were expressed as median and interquartile range (IQR) and compared using Mann-Whitney U-tests. Categorical variables were compared by the χ 2 test or two-tailed Fisher's exact test, as appropriate.

Accession numbers
All sequencing data during the current study are available in the Sequence Read Archive (SRA). Metagenomics data and the 16S rRNA gene data are under BioProject PRJNA 681291, and the WGS data under BioProject PRJNA 679997.

Results
Characteristics of the study population A total of 101 patients were screened. According to the inclusion criteria, 64 patients were enrolled, and 52 patients completed the entire study protocol (Fig. 1): 24 with CRAB-I, 22 with CRAB-C, and six with CRAB-N. The CRAB-I and CRAB-C patients did not differ in terms of age, sex, or severity indices (APACHE II scores), but C-reactive protein (CRP) and 30 day mortality were higher in CRAB-I (Table S2). All CRAB isolates were highly resistant to all antibiotics except amikacin, polymyxin, and tetracycline (Fig. S1).
Comparison of microbiota of ETA Overview 16S rRNA sequencing from a total of 52 ETA specimens revealed that the average Operation taxonomic units (OTU) numbers for CRAB-N, CRAB-C, and CRAB-I were 1427, 2422, and 2248, respectively. Rarefaction curves of numbers of observed OTUs per sample and group indicated that almost all OTUs present in each group were detected (Fig. S2). Three samples failed to sequence. The LRT microbiome was examined in the remaining 49 ETA specimens by shotgun metagenomic sequencing (Table S1). The 16S rRNA sequencing data identi ed 597 genera, of which 249 (41.2%) were also identi ed in the metagenomic data (Fig. S3, Fig. S4). All of the alpha diversity indices in the metagenomic analysis were higher than those in the 16S amplicon analysis (Fig. S5). CRAB-positive patients had reduced microbiota diversity Relative to CRAB-N, the diversity of pulmonary microbiota in the CRAB-C group was signi cantly reduced, and a further reduction was observed in the CRAB-I group (Shannon index in CRAB-N, -C, and -I: 4.80 ± 1.47, 2.73 ± 1.24, and 1.79 ± 0.95; Simpson index: 0.90 ± 0.08, 0.60 ± 0.23, and 0.40 ± 0.20, respectively; P < 0.05 for all biodiversity parameters; Fig. 2A and 2B). Moreover, principal co-ordinate analysis (PCoA) and non-metric multidimensional scaling (NMDS) analysis of the Bray-Curtis dissimilarity metric revealed that the composition of the microbiota between the three groups were quite different (P = 0.001 and Stress = 0.113; respectively; Fig. 2C and 2D). Analysis of similarity (ANOSIM) comparative analysis revealed that the differences between the three groups were higher than those within each group, indicating that the microbial community structure of the LRT microbiota in the three groups was signi cantly distinct (Table S3).

Bacterial taxonomic characters in the three groups
To obtain a global view of LRT microbiota in the study subjects, we compared the taxa at the phylum and genus levels between the three groups by 16S rRNA amplicon analysis. Overall, Proteobacteria (43.57%), Firmicutes (2.58%), and Bacteroidetes (2.42%) were the dominant phyla in the 52 ETA specimens (Fig. 3A). The relative abundance of Proteobacteria was signi cantly higher in the CRAB-I and CRAB-C groups than in the CRAB-N group (91.77% vs. 84.50% vs. 49.25%, P < 0.05), and Firmicutes exhibited the opposite pattern (2.23% vs. 4.38% vs. 15.67%, P < 0.05) (Fig. 3B). At the genus level, the bacterial composition of the CRAB-positive groups differed from that of the negative group. Acinetobacter was the predominant genus in CRAB-I and CRAB-C patients (76.19% vs. 59.14%), followed by Klebsiella (5.80% vs. 8.01%) and Pseudomonas (2.84% vs. 6.88%). By contrast, in the CRAB-N group, the top three genera were Acinetobacter (11.24%), Haemophilus (8.67%), and Pseudomonas (9.68%) ( Fig. 3C and 3D).
LEfSe comparison identi ed 11 signi cant biomarker genera (Fig. 3E) with discrimination value in the three groups. Further pairwise comparison revealed two signi cant biomarker genera (Acinetobacter and Nocardia) between the CRAB-C and CRAB-I groups (Fig. 3F), and the CRAB-I group had 14 signi cant biomarker genera with the CRAB-N group: Acinetobacter was enriched in the CRAB-I group and 13 other genera, including Haemophilus, Bacteroides, and Streptococcus, were enriched in the CRAB-N group (Fig. 3G). In addition, ve key genera (Acinetobacter,unidenti ed_Corynebacteriacea, Nesseria, Nordella, and Streptococcus) differentiated between the CRAB-C and CRAB-I groups (Fig. 3H).

Identifying potential microbial interactions by Correlation Network Analysis
Overall, the microbial co-occurrence network constructed from CRAB-I had a lower complexity index than those of CRAB-C and CRAB-N at the genus level (4239.37 vs. 4257.25 vs. 7459.00) (Fig. 4). The total number of negative microbial interactions, as indicated by the number of edges between the nodes, was highest in CRAB-N, moderate in CRAB-C, and lowest in CRAB-I (CRAB-N: n = 2356, 2189 positive and 167 negative; CRAB-C: n = 1436, 1411 positive and 25 negative; CRAB-I: n = 1533, 1516 positive and 17 negative). Notably, in the CRAB-I group, Acinetobacter was abundant and signi cantly negatively correlated with four genera (Klebsiella, Pseudomonas, unidenti ed_Erysipelotrichaceae, and Oscillibacter), whereas in the CRAB-C group, it was negatively correlated with ve other members of the microbiota (Limnobacter, Brevundimonas, Dialister, Barnesiella, and Bilophila), and in the CRAB-N group, it had six negative connections. Thus, the number of negative interactions decreased much more between CRAB-N and CRAB-I than the number of positive interactions.

Comparison of functional pro les of microbiota
LEfSe analysis using a logarithmic LDA score cut-off of 2.5 identi ed 46 and 55 different pathways in CRAB-C and CRAB-I, respectively, relative to CRAB-N, of which 40 and 45, respectively, increased. These pathways included fatty acid degradation, oxidative phosphorylation, nicotinate and nicotinamide metabolism, transport, porphyrin and chlorophyll metabolism, benzoate degradation, and bio lm formation in Vibrio cholerae ( Fig. 5A and 5B). Moreover, signalling proteins (KEGG pathway ko99995, Fig. 5C) were more active in CRAB-C than in CRAB-I.

Comparison of VFs in the microbiome
In the three groups of patients, we detected a total of 1,628 virulence genes, divided into 69 functional groups. The top 10 most abundant functional virulence groups in each group accounted for 26.5% of all factors, and the composition of the "toxicity" functional group differed between the three groups of patients (Fig. 6A). These factors tended to be involved in iron uptake, siderophore biosynthesis, immune evasion, and bio lm formation. Ninety-ve virulence genes (relative abundance > 0.1% for each) differed in abundance between CRAB-N and CRAB-C, and 105 differed between CRAB-N and CRAB-I (Table S4). Eleven genes, including AB57_ 0984, AB57_ 0990, AB57_ 0992, and mymA, were more abundant in CRAB-I than in CRAB-C ( Fig. 6B and Table S4). Virulence gene networks constructed from CRAB-I had a higher complexity index than those from CRAB-C and CRAB-N patients (56206.99 vs. 44722.75 vs. 11052.12) (Fig. 6C). More virulence genes associated with Acinetobacter were detected in CRAB-I than in CRAB-C and CRAB-N, with functions including immune evasion, iron uptake, and VFDB-unclassi ed (Fig. 6D). Consistent with this association, the levels of VFs and the relative abundances of genera (Acinetobacter and Methylorubrum) or species exhibited a strong and signi cant positive correlation (R2 = 0.529, P = 1.1e-06; R2 = 0.755, P = 7.6e-12, Pearson's correlation; Fig. 7A and 7B), indicating that differences in the abundance of VFs were driven by differences in the species present in each group of patients.

Whole-genome analysis of CRAB
The average nucleotide identity (ANI) of the 46 CRAB strains was > 95%, indicating that they belonged to the same species (Table S1, Fig. S6A). MLST of 46 CRAB was dominated by ST208 (30.43%), followed by ST938 (15.22%). eBURST analysis revealed that seven ST types (87.5%) clustered in the same clonal complexes (CCs) (CC92) (Fig. S6B). The KL types were mainly KL9, KL2, KL93, and KL7, and all strains belonged to the OCL1 type (Fig. 8). We observed no statistically signi cant difference in ST or KL type between the CRAB-I and CRAB-C groups (P = 0.478 and 0.444; respectively).
All CRAB isolates harboured bla ADC−25 , bla OXA−23 , and bla OXA−66 . All isolates harboured more than one oxacillinase gene, and 28 (60.8%) harboured the class A β-lactamase gene bla TEM . The number of resistance genes did not differ signi cantly between the two groups (Fig. 8). Annotation and analysis of virulence genes showed that strains from CRAB-I had more virulence genes than those from CRAB-C ( Fig. 8), and chi-square tests (Table 1) revealed that AB57_0990, Lpxl, and ABZJ_00085 were more abundant in CRAB-I, consistent with the metagenomics analysis (Fig. 6B).

Discussion
CRAB has a high clinical prevalence and is a common pathogen in VAP, but a positive ETA culture alone cannot effectively distinguish between bacterial colonisation and infection, representing a longstanding clinical challenge in the management of severely ill patients. Therefore, we investigated the difference in LRT microecology between infected and colonised patients using multi-genomics methods, with the goal of clarifying the clinical management of CRAB infection.
In recent years, several studies have shown that changes in the LRT microbiome are related to the occurrence of lung diseases, but few studies have examined the relationship between respiratory microbiota and infection, and most of those focused on pulmonary tuberculosis and pulmonary brosis [13,43]. In this study, the 16S rRNA analysis of 52 patients revealed that the α and β diversity of the LRT microbiome was signi cantly lower in CRAB-I patients than in CRAB-C and CRAB-N patients (Fig. 2). The ETA microbiota in the CRAB-N group consisted mainly of Proteobacteria and Haemophilus, consistent with a previous report [44], and was more diverse than in the CRAB-C and CRAB-I groups. The microbiota of the latter two groups were mainly Proteobacteria and Acinetobacter, and the abundance of Acinetobacter in CRAB-I was as high as 76.19% (Fig. 3). Further LEfSe analysis (Fig. 3H) revealed that, in comparison with CRAB-C patients (who were enriched in unidenti ed_Corynebacteriacea, Nesseria, Nordella, and Streptococcus), CRAB-I patients had a higher abundance of Acinetobacter. The relative abundance of Acinetobacter increased in the order CRAB-N, CRAB-C, and CRAB-I; this trend was con rmed by Woo et al. [45]. Together, these results indicated that a dynamic evolution of pulmonary microbiota, including a decline in diversity and enrichment of Acinetobacter, occurs prior to the onset of CRAB VAP [46][47].
Network analysis revealed that the connections between bacteria were most abundant in CRAB-N, less abundant in CRAB-C, and least abundant in CRAB-I; in parallel, the number of genera negatively associated with Acinetobacter also decreased (6, 5, and 4 negative connections in CRAB-N, CRAB-C, and CRAB-I, respectively). In CRAB-I, only four genera (Klebsiella, Pseudomonas, unidenti ed_Erysipelotrichaceae, and Oscillibacter) were negatively correlated with Acinetobacter (Fig. 4). Zakharkina et al. [46] found that Acinetobacter, Pseudomonas, Staphylococcus, and Burkholderia were negatively correlated with the development of VAP; Wouter et al. [48] found that an increase in the abundance of Lactobacillus and Rothia strains was negatively correlated with the speci c microbial infection of VAP patients. These ndings suggest that disturbance of the respiratory microbiota relieves negative inhibition of CRAB and is therefore likely to promote infection of the host. However, this idea requires further validation.
Functional metagenomic studies of the respiratory tract microbiome are also valuable for detecting bacterial pathogenesis. Mice infected with Streptococcus pneumoniae and Haemophilus in uenzae could cause pulmonary in ammatory responses by activating the MAPK signal pathway [49]. In this study, KEGG functional analysis revealed that genes involved in 40 and 45 metabolic pathways (including oxidative phosphorylation, phenylalanine metabolism, fatty acid degradation) were more abundant in the CRAB-C and CRAB-I groups, respectively, than in the CRAB-N group; moreover, signalling protein pathways were more active in CRAB-C patients than in CRAB-I patients (Fig. 5). A previous review described how assessment of microbial function using metagenomics, metatranscriptomics, and metabolomics has identi ed metabolites produced by respiratory microbiota (especially fatty acids, sugars, and amino acids) that can in uence host immunity [15]. This also indicated that the change in bacterial pathogenicity from CRAB-N to CRAB-I may be associated with more active metabolism; this possibility is worthy of further study.
During progression from colonisation to infection, bacterial invasiveness and toxicity play a key role. Metagenomics analysis revealed that four major virulence gene clusters (iron uptake, siderophore, immune evasion, and bio lm formation) increased in abundance from the CRAB-N to the CRAB-I group (Fig. 6A). The number of virulence genes annotated and networks constructed was signi cantly higher in the CRAB-I group in the CRAB-C and CRAB-N groups (Fig. 6C). Wilcoxon tests showed that the abundance of virulence genes related to heme utilisation, such as AB57_ 0984, AB57_ 0990, AB57_ 0992, and mymA, was higher in the CRAB-I group than in the CRAB-C group (Table S4 and Fig. 6B). AB57_ 0984, a LysR family transcription regulator, is linked to elevated invasiveness [50]. AB57_ 0990 (a member of the TonB family) and the TonB system play key roles in the pathogenicity of AB [51]. Network diagram and tted curve analysis con rmed that these virulence genes were associated with Acinetobacter ( Fig. 6D and Fig. 7). In addition, WGS of strains from CRAB-C and CRAB-I patients revealed that more virulence genes such as Lpxl, ABZJ_00085, and AB57_0990 were present in the infection group (Table 1 and Fig. 8). The enrichment in virulence of CRAB indicated that enhancement of bacterial pathogenicity could be another key factor that promotes infection after perturbation of the microbiota.
In terms of patient clinical characteristics, we observed no signi cant differences in gender, age, or severity of disease at the time of enrolment between the three groups. The number of days of mechanical ventilation before collection was smaller in CRAB-N patients than in CRAB-positive patients, and mortality was higher in CRAB-I patients (Table S2). ST208 was the main type (30.43%), followed by ST938 and ST195, and bacterial MLST distribution did not differ signi cantly between the colonisation and infection group (Fig. 8). All isolates were highly drug-resistant, and bla oxa-23 was the major determinant of resistance [52].
Our results indicate that to draw conclusions about the importance of microbiota evolution, it will be necessary to perform consecutive observations of individual patients, spanning the period from colonisation to infection. In future studies, transcriptome and proteome analysis could be used to explore germ-host interactions and pathogenesis.      Co-occurring network of microbial communities in LRT samples from CRAB-N, CRAB-C and CRAB-I patients. Note: A co-occurring network containing strong (ρ > 0.6) and signi cant (FDR-adjusted P < 0.05) correlations was represented. Each node represents a genusa and the nodes are colored by phylum. The size of each node is proportional to the number of connections. The thickness of each edge is proportional to the ρ. Light blue lines represent negative correlations, and red lines represent positive correlations.

Figure 5
Functionally distinct and Linear discriminant analysis effect size (LEfSe) analysis based on KEGG pathway. (A) Linear discriminant analysis (LDA) scores indicate signi cant differences in the microbiota between the CRAB-C patients(blue) and CRAB-N controls(gray); (B) LDA scores indicate signi cant differences in the microbiota between the CRAB-I patients(red) and CRAB-N controls(gray); (C) LDA scores indicate signi cant differences in the microbiota between the CRAB-C patients(blue) and CRAB-I patients(red). LDA scores > 2.5. Abbreviations: CRAB, Carbapenem -resistant Acinetobacter baumannii; Page 23/25 LRT, Lower respiratory tract; VAP, Ventilator associated pneumonia; CRAB-N, LRT microbiota of patients with neither VAP nor CRAB LRT colonization; CRAB-C, LRT microbiota of patients with CRAB colonization but without VAP; CRAB-I, LRT microbiota of patients who developed CRAB VAP.