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: 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 micrain 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. micrastrains obtained. Results: Several P. micraisolates 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.

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.