Achromobacter spp. in Cystic Fibrosis Patients: A Genomic-Based Approach to Unravel Microbe-Host Adaptation

Bacteria belonging to the genus Achromobacter are widely distributed in natural environments and have been recognized as emerging nosocomial pathogens for their contribution to a wide range of human infections. Achromobacter spp. can establish chronic infections associated with inflammation, produce biofilm, resist common disinfectants, readily acquire antibiotic resistance and outcompete resident microbiota. In particular, cystic fibrosis (CF) patients with lung disease are the most frequently colonized and infected by Achromobacter species usually developing persistent respiratory tract infections. In the last five years the number of publications regarding these pathogens has doubled in comparison to the preceding five-year period and their whole genome sequencing data availability has seen a steep increase, underlining both the growing research interest for these microorganisms as well as their emergence in the clinical setting. Nonetheless, many clinical aspects and pathogenic mechanisms still remain to be elucidated. The main focus of this thesis has been to unravel underlying key processes and to investigate the adaptive mechanisms exploited by these microorganisms during lung infection in CF patients. This has been pursued by analysing both genomic and phenotypic data of 103 Achromobacter spp. clinical isolates from 40 CF patients followed at the CF centres in Verona (Italy), Rome (Italy), and Copenhagen (Denmark). The work presented in this thesis provides new knowledge on the onset of Achromobacter spp. infections and their adaptation to the CF lung environment. With further genomic and phenotypic studies it will be possible to translate these results into the clinical setting, leading to better predictions of the infection course and improvement of treatment strategies to the benefit of CF patients

The interplay between microbiota and human complex traits

Microorganisms have been one of the most influential drivers propelling some of the greatest environmental and evolutionary changes in the landscape and biology of the entire planet [...]

The BioVRPi project: a valuable and sustainable alternative for genomic analysis

Since 2012, the Raspberry Pi Foundation has started developing pocket-sized and low-cost devices, originally meant to teach computer science in developing Countries. Its growing interest and constant improvement led Raspberry Pi devices to find different applications and to suit the needs of various research areas. In the previous years, different researchers already reported applications of Raspberry Pi devices in bioinformatics, such as basic train- ing and proteomics. In the beginning of 2021, we gave birth to BioVRPi, a project which aims to develop and offer a low-cost and stable bioinformatic environment for students and re- searchers involved in the genomics and transcriptomics fields. We evaluated performances and software compatibilities of different scenarios, focusing on Genome-Wide Association Studies for complex traits in Homo sapiens, transcriptomic analyses on RNA-seq data from Strongyloides stercoralis samples and alignment of small organisms, such as SARS-CoV-2 (virus), Escherichia Coli (bacterium) and Caenorhabditis elegans (nematode). Results from both the bioinformatic and benchmarking analyses showed that Raspberry Pi devices are capable of accomplishing different bioinformatic tasks in terms of results and performances. Moreover, they proved to be a valuable low-cost and sustainable alternative, in accordance with the United Nation 2030 Agenda, to answer the needs and the challenges of the current socio-economic situation

Resistome, mobilome and virulome analysis of Shewanellaalgae and Vibrio spp. strains isolated in italian aquaculture centers

Antimicrobial resistance is a major public health concern restricted not only to healthcare settings but also to veterinary and environmental ones. In this study, we analyzed, by whole genome sequencing (WGS) the resistome, mobilome and virulome of 12 multidrug-resistant (MDR) marine strains belonging to Shewanellaceae and Vibrionaceae families collected at aquaculture centers in Italy. The results evidenced the presence of several resistance mechanisms including enzyme and efflux pump systems conferring resistance to beta-lactams, quinolones, tetracyclines, macrolides, polymyxins, chloramphenicol, fosfomycin, erythromycin, detergents and heavy metals. Mobilome analysis did not find circular elements but class I integrons, integrative and conjugative element (ICE) associated modules, prophages and different insertion sequence (IS) family transposases. These mobile genetic elements (MGEs) are usually present in other aquatic bacteria but also in Enterobacteriaceae suggesting their transferability among autochthonous and allochthonous bacteria of the resilient microbiota. Regarding the presence of virulence factors, hemolytic activity was detected both in the Shewanella algae and in Vibrio spp. strains. To conclude, these data indicate the role as a reservoir of resistance and virulence genes in the environment of the aquatic microbiota present in the examined Italian fish farms that potentially might be transferred to bacteria of medical interest

Mobilome analysis of Achromobacter spp. isolates from chronic and occasional lung infection in cystic fibrosis patients

Achromobacter spp. is an opportunistic pathogen that can cause lung infections in patients with cystic fibrosis (CF). Although a variety of mobile genetic elements (MGEs) carrying antimicrobial resistance genes have been identified in clinical isolates, little is known about the contribution of Achromobacter spp. mobilome to its pathogenicity. To provide new insights, we performed bioinformatic analyses of 54 whole genome sequences and investigated the presence of phages, insertion sequences (ISs), and integrative and conjugative elements (ICEs). Most of the detected phages were previously described in other pathogens and carried type II toxin-antitoxin systems as well as other pathogenic genes. Interestingly, the partial sequence of phage Bcep176 was found in all the analyzed Achromobacter xylosoxidans genome sequences, suggesting the integration of this phage in an ancestor strain. A wide variety of IS was also identified either inside of or in proximity to pathogenicity islands. Finally, ICEs carrying pathogenic genes were found to be widespread among our isolates and seemed to be involved in transfer events within the CF lung. These results highlight the contribution of MGEs to the pathogenicity of Achromobacter species, their potential to become antimicrobial targets, and the need for further studies to better elucidate their clinical impact

Hypermutation as an Evolutionary Mechanism for Achromobacter xylosoxidans in Cystic Fibrosis Lung Infection

Achromobacter xylosoxidans can cause chronic infections in the lungs of patients with cystic fibrosis (CF) by adapting to the specific environment. The study of longitudinal isolates allows to investigate its within\u2010host evolution to unravel the adaptive mechanisms contributing to successful colonization. In this study, four clinical isolates longitudinally collected from two chronically infected patients underwent whole genome sequencing, de novo assembly and sequence analysis. Phenotypic assays were also performed. The isolates coming from one of the patients (patient A) presented a greater number of genetic variants, diverse integrative and conjugative elements, and different protease secretion. In the first of these isolates (strain A1), we also found a large deletion in the mutS gene, involved in DNA mismatch repair (MMR). In contrast, isolates from patient B showed a lower number of variants, only one integrative and mobilizable element, no phenotypic changes, and no mutations in the MMR system. These results suggest that in the two patients the establishment of a chronic infection was mediated by different adaptive mechanisms. While the strains isolated from patient B showed a longitudinal microevolution, strain A1 can be clearly classified as a hypermutator, confirming the occurrence and importance of this adaptive mechanism in A. xylosoxidans infection

Genomic characterization of Achromobacter species isolates from chronic and occasional lung infection in cystic fibrosis patients

Achromobacter species are increasingly being detected in cystic fibrosis (CF) patients, where they can establish chronic infections by adapting to the lower airway environment. To better understand the mechanisms contributing to a successful colonization by Achromobacter species, we sequenced the whole genome of 54 isolates from 26 patients with occasional and early/late chronic lung infection. We performed a phylogenetic analysis and compared virulence and resistance genes, genetic variants and mutations, and hypermutability mechanisms between chronic and occasional isolates. We identified five Achromobacter species as well as two non-affiliated genogroups (NGs). Among them were the frequently isolated Achromobacter xylosoxidans and four other species whose clinical importance is not yet clear: Achromobacter insuavis, Achromobacter dolens, Achromobacter insolitus and Achromobacter aegrifaciens. While A. insuavis and A. dolens were isolated only from chronically infected patients and A. aegrifaciens only from occasionally infected patients, the other species were found in both groups. Most of the occasional isolates lacked functional genes involved in invasiveness, chemotaxis, type 3 secretion system and anaerobic growth, whereas the great majority (>60%) of chronic isolates had these genomic features. Interestingly, almost all (n=22/23) late chronic isolates lacked functional genes involved in lipopolysaccharide production. Regarding antibiotic resistance, we observed a species-specific distribution of blaOXA genes, confirming what has been reported in the literature and additionally identifying blaOXA-2 in some A. insolitus isolates and observing no blaOXA genes in A. aegrifaciens or NGs. No significant difference in resistance genes was found between chronic and occasional isolates. The results of the mutator genes analysis showed that no occasional isolate had hypermutator characteristics, while 60% of early chronic (<1 year from first colonization) and 78% of late chronic (>1 year from first colonization) isolates were classified as hypermutators. Although all A. dolens, A. insuavis and NG isolates presented two different mutS genes, these seem to have a complementary rather than compensatory function. In conclusion, our results show that Achromobacter species can exhibit different adaptive mechanisms and some of these mechanisms might be more useful than others in establishing a chronic infection in CF patients, highlighting their importance for the clinical setting and the need for further studies on the less clinically characterized Achromobacter species

Pocket-sized genomics and transcriptomics analyses: a look at the newborn BioVRPi project

BioVRPi is a newborn project, started in January 2021, that focuses on Raspberry Pi (RPi) employment in bioinformatics, with particular regards on genomics. In the previous years, some research groups have already reported several examples of applications for RPi, including bioinformatic basic training and proteomics. Our project aims to develop and offer a low-cost, stable, and tested bioinformatic environment for students and researchers involved in genomics and transcriptomics fields. Raspberry Pi is a small single-board low-cost computer that was developed by the Raspberry Pi Foundation since 2012. Its original purpose aimed to facilitate computer science basic teaching in developing countries, but the growing worldwide interest has permitted its constant progress and development. Thanks to its features, RPi can suit several disciplines in need for computational supports and reach almost every, if not all, research group in the world. We tested RPi capabilities on real case studies, relatively to Genome-Wide Association Studies (GWAS) for complex traits in Homo sapiens data and in transcriptomic analyses (RNA-seq) on the Strongyloides stercoralis human parasite samples, using two RPi-4 devices equipped with different amount of RAM (8GB for genomics and 2 GB for transcriptome analyses, respectively), and running a 64-bit Operating System. The analyses leveraged on state-of-art bioinformatic toolset, such as Plink and Plink1.9, SAMtools, Bowtie 2, R, and different R packages, all compiled from source code. Moreover, the GWAS was run according to the golden standard protocols and results from the different platforms were compared. The results showed that RPi are effective devices that can efficiently handle whole GWAS and RNA-seq analyses. Benchmarking showed that the computational time taken by RPi was of the same order of magnitude when compared to the ones from a commonly used bioinformatic computer. At last, BioVRPi project shows how to implement new strategies for bioinformatic analyses, in order to provide a having-fun environment to learn and explore new alternatives in bioinformatic data analysis

Testing the performance of the imputation of MHC region in large datasets when using different reference panels

The major histocompatibility complex (MHC) contains a group of genes (~260 genes in ~4Mb) involved in several inflammatory disorders and immune response including the HLA-C gene. So far, the IPD-IMGT/HLA database reports more than 4000 different HLA-C alleles. Given the highly polymorphic nature of the gene, GWAS generally don’t study or study only a small subset of polymorphic sites of the region. Imputation procedures may help in gaining additional information on this region. However, the successful imputation of the MHC region would require a reference panel with detailed information. The main goal of this study is to investigate whether imputation procedures using appropriate reference panels may effectively increase the number of polymorphic sites of the MHC region for association with complex traits. We studied the MHC region imputation performances using 3 different reference panels (Michigan and TOPMed imputation servers): TOPMed-r2, 1000 Genomes (Phase3, v5), and the novel four-digit multi-ethnic HLA panel (v1, 2021). Here, 5 datasets with more than 1000 individuals each underwent imputation. We then focused on the imputation results of the MHC region that surround the HLA-C gene (hg19: 31234948-31241032). Imputation reported a different number of markers for the different reference panels: 482 in 1000G, 365 in TOPMed, and 1272 in HLA-panel. Of note, the HLA panels gave a higher number of imputed markers than the others. We then selected the 104 common markers imputed by all the 3 reference panels. Moreover, 162 markers were found only by 1000G panel, 194 by TOPMed, and 998 by the HLA-panel. The first preliminary comparisons showed a high concordance value for the genotype calling by the 3 different reference sets. The efficiency of the imputation was measured by the R-squared (R2) values stratifying the markers into 3 groups according to the minor allele frequency (MAF). The 104 common markers showed high R2 values (>0.96). As expected, in the other marker groups, the R2 mean values were lower for markers with MAF<0.1 (>0.65 in 1000G, 0.15-0.20 in TOPMed, >0.40 in HLA panel). In conclusion, imputation-based procedures with dedicated HLA panels can produce much more high-quality information than other general purpose reference panels for the MHC region

Analyzing BioRad-Illumina Single Cell RNA-Seq data with open source tools

Single cell RNA-Seq is a powerful technique that is becoming more popular since it enables to sequence the transcriptome of each cell within a population of different cell types in a single experiment. Currently, there are a few different technologies, like BioRad-Illumina ddSeq and 10X Chromium