Motivation The interplay between microbial communities and treatment outcomes represents a promising area in pharmacomicrobiomics. Identifying microbial biomarkers that differentiate toxicity levels could inform personalized cancer strategies. However, biomarker identification is strongly influenced by methodological choices in differential abundance analysis (DAA), and most studies focus on individual outcomes despite toxicity being inherently multifactorial. In this study, we defined a multi-dimensional toxicity variable integrating clinical symptoms and treatment modifications to stratify colorectal cancer patients. We then evaluated six widely used DAA methods (ALDEx2, ANCOM-BC, DESeq2, LEfSe, LinDA, and ZicoSeq) to assess how analytical variability affects the detection of microbiome signatures associated with chemotherapy-related toxicity. Analyses were performed under different preprocessing and multiple-testing correction strategies, and consistency was further examined using an independent validation dataset.Results Substantial variability was observed across methods, with limited overlap in detected taxa but moderate concordance in effect-size rankings. ANCOM-BC showed the most consistent overall performance across analytical scenarios, although trade-offs remained between taxa detection, ranking, and direction of association. Despite this variability, a subset of taxa was consistently identified across methods, including Parvimonas, Eubacterium ventriosum group, and Ruminococcus in the low-toxicity group, and members of the Lachnospiraceae family, such as Fusicatenibacter, Lachnospira, and the Lachnospiraceae NK4A136 group, in the severe-toxicity group. Analyses in the external validation dataset supported the reproducibility of methodological patterns, despite differences in cohort composition and sequencing strategy. These findings highlight the methodological dependence of microbiome biomarker discovery and the potential of pre-treatment microbial signatures to stratify toxicity risk. View collectively, our results support a context-dependent approach to DAA method selection in clinical microbiome studies.Availability and implementation The data supporting this study are available at NCBI SRA database (PRJNA911189) and NCBI SRA database (PRJNA893853).

Colorectal cancer (CRC) is the second leading cause of cancer-related deaths, accounting for more than 900,000 deaths a year worldwide. Microbial dysbiosis, including the presence of oral bacteria in the gut, has been linked to CRC. Some mechanisms by which specific microorganisms potentially drive tumorigenesis have been described, but there is a lack of studies elucidating whole microbiota activity in the tumor and their implication for the development of the disease. Here, the metatranscriptomic data of tumor and control tissue-associated microbiota (n = 18 pairs), as well as from subgingival sulcus (n = 15) of CRC patients, was analyzed. We confirmed that Fusobacterium nucleatum was more active in the tumor tissue than in the control gut mucosa. In addition, the activity of this species was positively correlated with other oral bacteria in the tumors, including Parvimonas micra, Peptostreptococcus stomatis, and Granulicatella adiacens, along with gut bacteria like Hungatella hathewayi, suggesting a potential relationship among them. Regarding bacterial gene expression, a change in the functional profile was observed, including a higher expression of genes associated with carbon metabolism in control in contrast to an increase of amino acid-related genes in tumor. Furthermore, genes implicated in the biosynthesis and transport of lipopolysaccharide were increased in tumors. Interestingly, a significantly higher expression in tumor than control tissue of potential virulence factors from F. nucleatum was found, supporting their relevance in niche colonization and tumorigenesis. Correlation analysis of the bacterial activity with the host transcriptional profile showed significant correlations of the Fusobacterium-Peptostreptotoccus-Hungatella cluster with human genes involved in inflammation and metastasis, confirming the association of this microbial consortium with tumor development. For the first time, the gene expression profiles of oral bacteria in the gut and the oral cavity were compared. The cluster of co-active bacteria identified in tumors was partially found in the oral samples, suggesting a stable interaction and potential synergy. Although there were thousands of differentially expressed genes between subgingival sulcus and tumor tissue, the expression of key virulence factors was not significantly different. In short, this study discovered new traits about tumor microbial-associated composition and activity and its connection with the oral composition that would be essential to unravel the translocation, colonization, and tumorigenesis of the CRC.

To evaluate the effect of coconut oil on the oral bacteriome and inflammatory response in patients with periodontitis by integrating next-generation sequencing analyses of pathogenic bacterial shifts and quantification of inflammatory markers, thereby assessing its potential as a natural adjunct to standard nonsurgical periodontal therapy. A triple-blind clinical trial was conducted with 30 participants diagnosed with periodontitis, randomized into 3 groups: (1) coconut oil, (2) chlorhexidine and (3) placebo. Saliva and gingival crevicular fluid (GCF) samples were collected before treatment, one month after treatment, and one month post-non-surgical periodontal therapy. Bacterial DNA was extracted, and the V3-V4 region of the 16 S rRNA gene was PCR-amplified and sequenced using Illumina MiSeq technologies. Inflammatory biomarkers, including Interleukin-6 (IL-6) and tumor necrosis factor-alpha (TNF-α), were quantified from GCF samples. Coconut oil treatment significantly reduced pathogenic bacterial families such as Spirochaetaceae and Tannerellaceae while promoting beneficial bacteria such as Streptococcaceae. At the genus and species levels, coconut oil reduced pathogens such as Tannerella forsythia and Treponema denticola along with increase in beneficial bacteria such as Streptococcus. The subgingival microbial dysbiosis index improved significantly in both coconut oil and chlorhexidine groups. Furthermore, the coconut oil demonstrated a reduction in IL-6 and TNF-α levels, indicating decreased local inflammation. Coconut oil treatment significantly modulated the oral microbiome and reduced inflammatory markers in patients with periodontitis, suggesting its potential as a natural and effective adjunct in periodontal therapy. This study highlights coconut oil’s potential as a natural adjunct in periodontal therapy, effectively reducing pathogenic bacteria and inflammatory markers (IL-6, TNF-α). It offers a safe alternative to chlorhexidine, promoting microbiome balance and improved periodontal health.

Discovery of disease-related bacterial biomarkers could be a useful approach for early prevention or diagnosis of various afflictions, such as colorectal cancer. This typically involves analyzing small regions of the 16S rRNA gene (e.g. V3V4) through short-read technologies like Illumina, obtaining genus-level results. However, recent developments in third-generation sequencing, such as Oxford Nanopore Technologies (ONT)'s new R10.4.1 chemistry and its improved basecalling models, are beginning to allow for a more complete and accessible species-level analysis through full-length 16S rRNA gene sequencing (spanning regions V1-V9). Thus, the goal of this study was to compare and evaluate both approaches, using colorectal cancer biomarker discovery as a representative case. This was achieved through the analysis of feces from 123 subjects, comparing both methods (Illumina-V3V4 with DADA2 and QIIME2 vs. ONT-V1V9 with Emu), multiple Dorado basecalling models (fast, hac and sup) and multiple databases (SILVA vs. Emu's Default database). Basecalling models broadly resulted in similar taxonomic output, but had significantly higher observed species and different taxonomic identification the lower the basecalling quality (p-value<0.05). Database choice with Emu influenced the identified species greatly, with Emu's Default database obtaining significantly higher diversity and identified species than SILVA (p-value<0.05). However, it overconfidently classified at times what should be an unknown species as the closest match due to its database structure. Bacterial abundance between Illumina-V3V4 and ONT-V1V9 at the genus level correlated well (R2≥0.8). Nanopore sequencing identified more specific bacterial biomarkers for colorectal cancer than those obtained with Illumina, such as Parvimonas micra, Fusobacterium nucleatum, Peptostreptococcus stomatis, Peptostreptococcus anaerobius, Gemella morbillorum, Clostridium perfringens, Bacteroides fragilis and Sutterella wadsworthensis. Prediction of colorectal cancer through manual feature selection and machine learning resulted in an AUC of 0.87 with 14 species or 0.82 with just 4 species (P. micra, F. nucleatum, B. fragilis and Agathobaculum butyriciproducens). Full 16S rRNA V1V9 sequencing through Oxford Nanopore and its new R10.4.1 chemistry achieved accurate species-level bacterial identification, facilitating the discovery of more precise disease-related biomarkers and increasing the taxonomic fidelity of future microbiome analyses.

The dynamics of SARS-CoV-2 transmission are influenced by a variety of factors, including social restrictions and the emergence of distinct variants. In this study, we delve into the origins and dissemination of the Alpha, Delta, and Omicron variants of concern in Galicia, northwest Spain. For this, we leveraged genomic data collected by the EPICOVIGAL Consortium and from the GISAID database, along with mobility information from other Spanish regions and foreign countries. Our analysis indicates that initial introductions during the Alpha phase were predominantly from other Spanish regions and France. However, as the pandemic progressed, introductions from Portugal and the USA became increasingly significant. Notably, Galicia's major coastal cities emerged as critical hubs for viral transmission, highlighting their role in sustaining and spreading the virus. This research emphasizes the critical role of regional connectivity in the spread of SARS-CoV-2 and offers essential insights for enhancing public health strategies and surveillance measures.

The incidence of colorectal cancer (CRC) has increased worldwide, and early diagnosis is crucial to reduce mortality rates. Therefore, new noninvasive biomarkers for CRC are required. Recent studies have revealed an imbalance in the oral and gut microbiomes of patients with CRC, as well as impaired gut vascular barrier function. In the present study, the microbiomes of saliva, crevicular fluid, feces, and non-neoplastic and tumor intestinal tissue samples of 93 CRC patients and 30 healthy individuals without digestive disorders (non-CRC) were analyzed by 16S rRNA metabarcoding procedures. The data revealed that Parvimonas, Fusobacterium, and Bacteroides fragilis were significantly over-represented in stool samples of CRC patients, whereas Faecalibacterium and Blautia were significantly over-abundant in the non-CRC group. Moreover, the tumor samples were enriched in well-known periodontal anaerobes, including Fusobacterium, Parvimonas, Peptostreptococcus, Porphyromonas, and Prevotella. Co-occurrence patterns of these oral microorganisms were observed in the subgingival pocket and in the tumor tissues of CRC patients, where they also correlated with other gut microbes, such as Hungatella. This study provides new evidence that oral pathobionts, normally located in subgingival pockets, can migrate to the colon and probably aggregate with aerobic bacteria, forming synergistic consortia. Furthermore, we suggest that the group composed of Fusobacterium, Parvimonas, Bacteroides, and Faecalibacterium could be used to design an excellent noninvasive fecal test for the early diagnosis of CRC. The combination of these four genera would significantly improve the reliability of a discriminatory test with respect to others that use a single species as a unique CRC biomarker.

Treated sewage contains a large diversity of pathogens that can be transmitted to the environment and, directly or indirectly, infect humans through water use (i.e., consumption, bathing, or irrigation). In urban environments, wastewater normally flows into wastewater treatment plants (WWTPs), where it is subjected to different processes in order to eliminate the greatest amount of waste. However, there are inequalities among European countries concerning wastewater management. In this context, we evaluate the potential of freshwater mussels to improve water quality (i.e., reduce bacterial abundance) in rivers receiving primary, secondary, or tertiary sewage-treated effluents. Additionally, because freshwater mussels are declining at a global scale and empty niches are progressively occupied by non-native counterparts, we evaluate if depauperate communities and the Asian clams, Corbicula genus, can provide equivalent ecosystem services (i.e., water quality improvement by biofiltration) formerly provided by diverse native communities. For this, an analysis of the bacterial biodiversity of the samples filtered by the different bivalve communities was carried out. The experimental approach was performed by metabarcoding the 16S rRNA gene using Illumina technologies. According to the results obtained, secondary treatment processes were effective in reducing the bacterial diversity. Furthermore, the waters filtered by the bivalves presented a lower bacterial abundance for certain genera. Biofiltration differs, however, among species, with Corbicula reducing a large number of taxa much more efficiently than native freshwater mussels in both diverse and depauperated communities. These results are likely related to Corbicula being a generalist species in front of native mussels, which may be more selective. Considering it is not possible to eradicate Corbicula from European rivers, its filtering capacity should be considered when managing freshwater ecosystems.

Wastewater treatment plants (WWTPs) are recognized as important niches of antibiotic-resistant bacteria that can be easily spread to the environment. In this study, we collected wastewater samples from the WWTP of A Coruña (NW Spain) from April 2020 to February 2022 to evaluate the presence of Gram-negative bacteria harboring carbapenemase genes. Bacteria isolated from wastewater were classified and their antimicrobial profiles were determined. In total, 252 Gram-negative bacteria carrying various carbapenemase genes were described. Whole-genome sequencing was conducted on 55 selected carbapenemase producing isolates using Oxford Nanopore technology. This study revealed the presence of a significant population of bacteria carrying carbapenemase genes in WWTP, which constitutes a public health problem due to their risk of dissemination to the environment. This emphasizes the usefulness of WWTP monitoring for combating antibiotic resistance. Data revealed the presence of different types of sequences harboring carbapenemase genes, such as blaKPC-2, blaGES-5, blaGES-6, blaIMP-11, blaIMP-28, blaOXA-24, blaOXA-48, blaOXA-58, blaOXA-217, and blaVIM-2. Importantly, the presence of the blaKPC-2 gene in wastewater, several months before any clinical case was detected in University Hospital of A Coruña, suggests that wastewater-based epidemiology can be used as an early warning system for the surveillance of antibiotic-resistant bacteria.

Wastewater-based epidemiology (WBE) has become an effective tool in the surveillance of infectious diseases such as COVID-19. In this work, we performed a brief study of monitoring the SARS-CoV-2 viral load in wastewater from six nursing homes located in the metropolitan area of A Coruña (Spain) between December 2020 and March 2021. The main objective was to detect SARS-CoV-2 outbreaks among residents and study the efficacy of the vaccination campaign. SARS-CoV-2 viral load (RNA copies per L of wastewater) was determined by reverse-transcription quantitative PCR (RT-qPCR) using the quantification cycle (Cq) values for the nucleocapsid (N) gene. Our results showed that the increase in viral load preceded the increase in clinical cases, favoring an early warning system that detects COVID-19 outbreaks in advance, making it possible to contain and stop the transmission of the virus among residents. In addition, the efficacy of the new COVID-19 vaccines was evidenced, since after the vaccination campaign in nursing homes in A Coruña, it was observed that many residents did not present any symptoms of the disease, although they excreted high amounts of virus in their feces. WBE is a cost-effective strategy that should be implemented in all cities to prevent new emerging diseases or future pandemic threats.

Pharmaceutical active compounds (PhACs) are emerging contaminants that pose a growing concern due to their ubiquitous presence and harmful impact on aquatic ecosystems. Among PhACs, the anti-inflammatory ibuprofen (IBU) and the antibiotic oxytetracycline (OTC) are two of the most used compounds whose presence has been reported in different aquatic environments worldwide. However, there is still scarce information about the cellular and molecular alterations provoked by IBU and OTC on aquatic photosynthetic microorganisms as microalgae, even more if we refer to their potential combined toxicity. To test the cyto- and genotoxicity provoked by IBU, OTC and their binary combination on Chlamydomonas reinhardtii, a flow cytometric panel was performed after 24 h of single and co-exposure to both contaminants. Assayed parameters were cell vitality, metabolic activity, intracellular ROS levels, and other programmed cell death (PCD)-related biomarkers as cytoplasmic and mitochondrial membrane potentials and caspase-like and endonuclease activities. In addition, a nuclear DNA fragmentation analysis by comet assay was carried out. For most of the parameters analysed (vitality, metabolic activity, cytoplasmic and mitochondrial membrane potentials, and DNA fragmentation) the most severe damages were observed in the cultures exposed to the binary mixture (IBU+OTC), showing a joint cyto- and genotoxicity effect. Both PhACs and their mixture caused a remarkable decrease in cell proliferation and metabolic activity and markedly increased intracellular ROS levels, parallel to a noticeable depolarization of cytoplasmic and mitochondrial membranes. Moreover, a strong increase in both caspase and endonuclease activities as well as a PCD-related loss of nuclear DNA integrity was observed in all treatments. Results analysis showed that the PhACs caused cell death on this non-target organism, involving mitochondrial membrane depolarization, enhanced ROS production and activation of PCD process. Thus, PCD should be an applicable toxicological target for unraveling the harmful effects of co-exposure to PhACs in aquatic organisms as microalgae.

Wastewater-based epidemiology has been widely used as a cost-effective method for tracking the COVID-19 pandemic at the community level. Here we describe COVIDBENS, a wastewater surveillance program running from June 2020 to March 2022 in the wastewater treatment plant of Bens in A Coruña (Spain). The main goal of this work was to provide an effective early warning tool based in wastewater epidemiology to help in decision-making at both the social and public health levels. RT-qPCR procedures and Illumina sequencing were used to weekly monitor the viral load and to detect SARS-CoV-2 mutations in wastewater, respectively. In addition, own statistical models were applied to estimate the real number of infected people and the frequency of each emerging variant circulating in the community, which considerable improved the surveillance strategy. Our analysis detected 6 viral load waves in A Coruña with concentrations between 103 and 106 SARS-CoV-2 RNA copies/L. Our system was able to anticipate community outbreaks during the pandemic with 8–36 days in advance with respect to clinical reports and, to detect the emergence of new SARS-CoV-2 variants in A Coruña such as Alpha (B.1.1.7), Delta (B.1.617.2), and Omicron (B.1.1.529 and BA.2) in wastewater with 42, 30, and 27 days, respectively, before the health system did. Data generated here helped local authorities and health managers to give a faster and more efficient response to the pandemic situation, and also allowed important industrial companies to adapt their production to each situation. The wastewater-based epidemiology program developed in our metropolitan area of A Coruña (Spain) during the SARS-CoV-2 pandemic served as a powerful early warning system combining statistical models with mutations and viral load monitoring in wastewater over time.

The present work describes a statistical model to account for sequencing information of SARS-CoV-2 variants in wastewater samples. The model expresses the joint probability distribution of the number of genomic reads corresponding to mutations and non-mutations in every locus in terms of the variant proportions and the joint mutation distribution within every variant. Since the variant joint mutation distribution can be estimated using GISAID data, the only unknown parameters in the model are the variant proportions. These are estimated using maximum likelihood. The method is applied to monitor the evolution of variant proportions using genomic data coming from wastewater samples collected in A Coruna (NW Spain) in the period May 2021 - March 2022. Although the procedure is applied assuming independence among the number of reads along the genome, it is also extended to account for Markovian dependence of counts along loci in the aggregated information coming from wastewater samples.

In the day-to-day work of bioinformatics, the use of integrated software packages, which encompass a wide range of tools, enables the development of pipelines for omics data analysis. Within the various existing pipelines, we focus on the analysis of the 16S rRNA gene as it allows for the study of diversity and taxonomy of prokaryotic microorganisms such as Bacteria and Archaea. However, these pipelines often involve a sequence of multiple tools that require intermediate steps before further processing can proceed, as in the case between Cutadapt and DADA2. In fact, in a typical pipeline, the values for DADA2 input arguments ’trunc-len-f’ and ’trunc-len-r’ are extracted from the output of Cutadapt. The best approach for selecting optimal values (aka the trimming positions) is graphically visualizing Cutadapt output and manually selecting the most accurate trimming position length. Therefore, we propose the automation of this specific intermediate step between Cutadapt and DADA2 tools, by selecting values displayed in the graphs that meet the filtering criteria. This automation has been incorporated into a custom pipeline for the analysis of the microbiome in 16S paired-end samples from colorectal cancer patients, and could potentially serve as a standardization approach in these processes

Oral and intestinal samples from a cohort of 93 colorectal cancer (CRC) patients and 30 healthy controls (non‐CRC) were collected for microbiome analysis. Saliva (28 non‐CRC and 94 CRC), feces (30 non‐CRC and 97 CRC), subgingival fluid (20 CRC), and tumor tissue samples (20 CRC) were used for 16S metabarcoding and/or RNA sequencing (RNAseq) approaches. A differential analysis of the abundance, performed with the ANCOM‐BC package, adjusting the P ‐values by the Holm‐Bonferroni method, revealed that Parvimonas was significantly over‐represented in feces from CRC patients ( P ‐value < 0.001) compared to healthy controls. A total of 11 Parvimonas micra isolates were obtained from the oral cavity and adenocarcinoma of CRC patients. Genome analysis identified a pair of isolates from the same patient that shared 99.2% identity, demonstrating that P. micra can translocate from the subgingival cavity to the gut. The data suggest that P. micra could migrate in a synergistic consortium with other periodontal bacteria. Metatranscriptomics confirmed that oral bacteria were more active in tumor than in non‐neoplastic tissues. We suggest that P. micra could be considered as a CRC biomarker detected in non‐invasive samples such as feces.

In the last decades, personalized medicine has been increasing its presence in different fields of medicine, including ophthalmology. A new factor that can help us direct medicine towards the challenge of personalized treatments is the microbiome. The gut microbiome plays an important role in controlling immune response, and dysbiosis has been associated with immune-mediated diseases such as non-infectious uveitis (NIU). In this review, we gather the published evidence, both in the pre-clinical and clinical studies, that support the possible role of intestinal dysbiosis in the pathogenesis of NIU, as well as the modulation of the gut microbiota as a new possible therapeutic target. We describe the different mechanisms that have been proposed to involve dysbiosis in the causality of NIU, as well as the potential pharmacological tools that could be used to modify the microbiome (dietary supplementation, antibiotics, fecal microbiota transplantation, immunomodulators, or biologic drugs) and, consequently, in the control of the NIU. Furthermore, there is increasing scientific evidence suggesting that the treatment with anti-TNF not only restores the composition of the gut microbiota but also that the study of the composition of the gut microbiome will help predict the response of each patient to anti-TNF treatment.

Abstract Background : The carcinogenesis of colorectal cancer (CRC) is a multifactorial process involving both environmental and host factors, such as human genetics or the gut microbiome, which in CRC patients appears to be enriched in oral microorganisms. The aim of this work was to investigate the presence and activity of Parvimonas micra in CRC patients. To do that, samples collected from subgingival sulcus and neoplastic lesions were used for culturomics. Then, samples from different body locations (saliva, gingival crevicular fluid, feces, non-neoplastic colon mucosa, transition colon mucosa, adenocarcinoma, adenomas, metastatic and non-neoplastic liver samples) were used for 16S rRNA metabarcoding and metatranscriptomics. Whole genome sequencing was conducted for all P. micra strains obtained. Results: Several P. micra isolates from the oral cavity and adenocarcinoma tissue from CRC patients were obtained. The comparison of oral and tumoral P. micra genomes identified that a pair of clones (PM89KC) were 99.2% identical between locations in one CRC patient, suggesting that the same clone migrated from oral cavity to the gut. The 16S rRNA metabarcoding analysis of samples from this patient revealed that P. micra cohabits with other periodontal pathogens such as Fusobacterium, Prevotella or Dialister, both in the intestine, liver and the subgingival space, which suggests that bacterial translocation from the subgingival environment to the colon or liver could be more efficient if these microorganisms travel together forming a synergistic consortium. In this way, bacteria might be able to perform tasks that are impossible for single cells. In fact, RNA-seq of the adenocarcinoma tissue confirmed the activity of these bacteria in the neoplastic tissue samples and revealed that different oral species, including P. micra, were significantly more active in the tumor compared to non-neoplastic tissue from the same individuals. Conclusion: P. micra appears to be able to translocate from the subgingival sulcus to the gut, where oral bacteria adapt to the new niche and could have a relevant role in carcinogenesis. According to our findings, periodontal disease, which increases the levels of these pathogens and facilitates their dissemination, could represent a risk factor for CRC development and P. micra could be used as a non-invasive CRC biomarker.

Acinetobacter baumannii is a multidrug-resistant pathogen that represents a serious threat to global health. A. baumannii possesses a wide range of virulence factors that contribute to the bacterial pathogenicity. Among them, the siderophore acinetobactin is one of the most important, being essential for the development of the infection. In this study we performed an in-depth analysis of the acinetobactin cluster in the strain A. baumannii ATCC 17978. For this purpose, nineteen individual isogenic mutant strains were generated, and further phenotypical analysis were performed. Individual mutants lacking the biosynthetic genes entA, basG, basC, basD, and basB showed a significant loss in virulence, due to the disruption in the acinetobactin production. Similarly, the gene bauA, coding for the acinetobactin receptor, was also found to be crucial for the bacterial pathogenesis. In addition, the analysis of the ΔbasJ/ΔfbsB double mutant strain demonstrated the high level of genetic redundancy between siderophores where the role of specific genes of the acinetobactin cluster can be fulfilled by their fimsbactin redundant genes. Overall, this study highlights the essential role of entA, basG, basC, basD, basB and bauA in the pathogenicity of A. baumannii and provides potential therapeutic targets for the design of new antivirulence agents against this microorganism.

Background. Infections caused by multidrug-resistant pathogens such as Acinetobacter baumannii constitute a major health problem worldwide. In this study we present a global in vivo transcriptomic analysis of A. baumannii isolated from the lungs of mice with pneumonia infection. Methods. Mice were infected with A. baumannii ATCC 17978 and AbH12O-A2 strains and the total bacterial RNA were analyzed by RNA sequencing. Lists of differentially expressed genes were obtained and 14 of them were selected for gene deletion and further analysis. Results. Transcriptomic analysis revealed a specific gene expression profile in A. baumannii during lung infection with upregulation of genes involved in iron acquisition and host invasion. Mutant strains lacking feoA, mtnN, yfgC, basB, hisF, oatA, and bfnL showed a significant loss of virulence in murine pneumonia. A decrease in biofilm formation, adherence to human epithelial cells, and growth rate was observed in selected mutants. Conclusions. This study provides an insight into A. baumannii gene expression profile during murine pneumonia infection. Data revealed that 7 in vivo upregulated genes were involved in virulence and could be considered new therapeutic targets.