Predictive computational phenotyping and biomarker discovery using reference-free genome comparisons

The antibiotic susceptibility data for the Clostridium difficile genomes. (XLSX 20.2 kb

Disease-associated genotypes of the commensal skin bacterium Staphylococcus epidermidis

Some of the most common infectious diseases are caused by bacteria that naturally colonise humans asymptomatically. Combating these opportunistic pathogens requires an understanding of the traits that differentiate infecting strains from harmless relatives. Staphylococcus epidermidis is carried asymptomatically on the skin and mucous membranes of virtually all humans but is a major cause of nosocomial infection associated with invasive procedures. Here we address the underlying evolutionary mechanisms of opportunistic pathogenicity by combining pangenome-wide association studies and laboratory microbiology to compare S. epidermidis from bloodstream and wound infections and asymptomatic carriage. We identify 61 genes containing infection-associated genetic elements (k-mers) that correlate with in vitro variation in known pathogenicity traits (biofilm formation, cell toxicity, interleukin-8 production, methicillin resistance). Horizontal gene transfer spreads these elements, allowing divergent clones to cause infection. Finally, Random Forest model prediction of disease status (carriage vs. infection) identifies pathogenicity elements in 415 S. epidermidis isolates with 80% accuracy, demonstrating the potential for identifying risk genotypes pre-operatively.Peer reviewe

Swine blood transcriptomics: Application and advancement

Improving swine feed efficiency (FE) by selection for low residual feed intake (RFI) is of practical interest. However, whether selection for low RFI compromises a pig’s immune response is not clear. In addition, current RFI-based selection for improving feed efficiency was expensive and time-consuming. Seeking alternative tools to facilitate selection, such as predictive biomarkers for RFI, is of great interest. The objectives of this thesis are as follows: (1) to investigate whether selection for low RFI compromise a pig’s immune response; (2) to develop candidate biomarkers applicable at early growth stage for predicting RFI at late growth stage; (3) to improve the annotation of the porcine blood transcriptome. In Chapter 2, pigs of two lines divergently selected for RFI were injected with lipopolysaccharide (LPS). Transcriptomes of peripheral blood at baseline and multi-time points post injection were profiled by RNA-seq. LPS injection induced systemic inflammatory response in both RFI lines. However, no significant differences were detected in dynamics of body temperature, blood cell count and cytokine levels during the time course. Only a very small number of differentially expressed genes (DEGs) were detected between the lines over all time points, though ~ 50% of blood genes were differentially expressed post LPS injection compared to baseline for each line. The two lines were largely similar in most biological pathways and processes studied. Minor differences included a slightly lower level of inflammatory response in the low- versus high-RFI animals. Cross-species comparison showed that humans and pigs responded to LPS stimulation similarly at both the gene and pathway levels, though pigs are more tolerant to LPS than humans. In Chapter 3, post-weaning blood transcriptomic differences between the two lines were studied by RNA-seq. DEGs between the lines significantly overlapped gene sets associated with human diseases, such as eating disorders, hyperphagia and mitochondrial disease. Genes functioning in the mitochondrion and proteasome, and signaling had lower and higher expression in the low-RFI group relative to the high-RFI group, respectively. Expression levels of five differentially expressed genes between the two groups were significantly associated with individual animal’s RFI values. These five genes were candidate biomarkers for predicting RFI. Given limitations of current annotation of the porcine reference genome, a high-quality annotated transcriptome of porcine peripheral blood was built in the last study via a hybrid assembly strategy with a large amount of blood RNA-seq data from studies mentioned above and public databases. Taken together, this work provides evidence that selection for low RFI did not significantly compromise pigs’ immune response to systemic inflammation, offers a few candidate biomarkers for predicting RFI to facilitate RFI-based selection, and significantly advances the structural and functional annotation of porcine blood transcriptome

Combination of Whole Genome Sequencing and Metagenomics for Microbiological Diagnostics

Whole genome sequencing (WGS) provides the highest resolution for genome-based species identification and can provide insight into the antimicrobial resistance and virulence potential of a single microbiological isolate during the diagnostic process. In contrast, metagenomic sequencing allows the analysis of DNA segments from multiple microorganisms within a community, either using an amplicon- or shotgun-based approach. However, WGS and shotgun metagenomic data are rarely combined, although such an approach may generate additive or synergistic information, critical for, e.g., patient management, infection control, and pathogen surveillance. To produce a combined workflow with actionable outputs, we need to understand the pre-to-post analytical process of both technologies. This will require specific databases storing interlinked sequencing and metadata, and also involves customized bioinformatic analytical pipelines. This review article will provide an overview of the critical steps and potential clinical application of combining WGS and metagenomics together for microbiological diagnosis

Resources for the analysis of bacterial and microbial genomic data with a focus on antibiotic resistance

Antibiotics are drugs which inhibit the growth of bacterial cells. Their discovery was one of the most significant achievements in medicine: it allowed the development of successful treatment options for severe bacterial infections, which has helped to significantly increase our life expectancy. However, bacteria have the ability to adapt to changing environmental conditions through genetic modifications, and can, therefore, become resistant to an antibiotic. Extensive use of antibiotics promotes the development of antibiotic resistance and, since some genetic factors can be exchanged between the cells, emergence of new resistance mechanisms and their spread have become a serious global problem. Counteractive measures have been initiated, focusing on the different factors contributing to the antibiotic resistance crisis. These include the study of bacterial isolates and complete microbial communities using whole-genome sequencing (WGS) data. In both cases, there are specific challenges and requirements for different analytical approaches. The goal of the present thesis was the implementation of multiple resources which should facilitate further microbiological studies, with a focus on bacteria and antibiotic resistance. The main project, GEAR-base, included an analysis of WGS and resistance data of around eleven thousand bacterial clinical isolates covering the main human pathogens and antibiotics from different drug classes. The dataset consisted of WGS data, antibiotic susceptibility profiles and meta-information, along with additional taxonomic characterization of a sample subset. The analysis of this isolate collection allowed for the identification of bacterial species demonstrating increasing resistance rates, to construct species pan-genomes from the de novo assembled genomes, and to link gene presence or absence to the available antibiotic resistance profiles. The generated data and results were made available through the online resource GEAR-base. This resource provides access to the resistance information and genomic data, and implements functionality to compare submitted genes or genomes to the data included in the resource. In microbial community studies, the metagenome obtained through WGS is analyzed to determine its taxonomic composition. For this task, genomic sequences are clustered, or binned, to represent sequences belonging to specific organisms or closely-related organism groups. BusyBee Web was developed to provide an automatic binning pipeline using frequencies of k-mers (subsequences of length k) and bootstrapped supervised clustering. It also includes further data annotation, such as taxonomic classification of the input sequences, presence of know resistance factors, and bin quality. Plasmids, extra-chromosomal DNA molecules found in some bacteria, play an important role in antibiotic resistance spread. As the classification of sequences from WGS data as chromosomal or plasmid-derived is challenging, demonstrated by evaluating four tools implementing three different approaches, having a reference dataset to detect the plasmids which are already known is therefore desirable. To this end, an online resource for complete bacterial plasmids (PLSDB) was implemented. In summary, the herein described online resources represent valuable datasets and/or tools for the analysis of microbial genomic data and, especially, bacterial pathogens and antibiotic resistance.Antibiotika sind Medikamente, die das Wachstum von Bakterienzellen hemmen. Ihre Entdeckung war eine der bedeutendsten Leistungen der Medizin: Es erlaubte die Entwicklung von erfolgreichen Behandlungsmöglichkeiten von schwerwiegenden bakteriellen Infektionen, was geholfen hat, unsere Lebenserwartung zu erhöhen. Allerdings sind Bakterien in der Lage sich den wechselnden Umweltbedingungen anzupassen und können dadurch resistent gegen ein Antibiotikum werden. Der extensive Gebrauch von Antibiotika fördert die Entwicklung von Antibiotikaresistenzen und, da einige genetische Faktoren zwischen den Zellen ausgetauscht werden können, sind das Auftauchen von neuen Resistenzmechanismen und deren Verbreitung zu einem seriösen globalen Problem geworden. Gegenmaßnahmen wurden ergriffen, die sich auf die verschiedenen Faktoren fokussieren, die zur Antibiotikaresistenzkrise beitragen. Diese umfassen Studien von bakteriellen Isolaten und ganzen Mikrobengemeinschaften mithilfe von Gesamt-Genom-Sequenzierung (GGS). In beiden Fällen gibt es spezifische Herausforderungen und Bedürfnisse für verschiedene analytische Methoden. Das Ziel dieser Dissertation war die Implementierung von mehreren Ressourcen, die weitere mikrobielle Studien erleichtern sollen und einen Fokus auf Bakterien und Antibiotikaresistenz haben. Das Hauptprojekt, GEAR-base, beinhaltete eine Analyse von GGS- und Resistenzdaten von ungefähr elftausend klinischen Bakterienisolaten und umfasste die wichtigen menschlichen Pathogene und Antibiotika aus verschiedenen Medikamentenklassen. Neben den GGS-Daten, Empfindlichkeitsprofilen für die Antibiotika und Metainformation, beinhaltete der Datensatz zusätzliche taxonomische Charakterisierung von einer Teilmenge der Proben. Die Analyse dieser Sammlung an Isolaten erlaubte die Identifizierung von Spezies mit ansteigenden Resistenzraten, die Konstruktion von den Spezies-Pan-Genomen aus den de novo assemblierten Genomen und die Verknüpfung vom Vorhandensein oder Fehlen von Genen mit den Antibiotikaresistenzprofilen. Die generierten Daten und Ergebnisse wurden durch die Online-Ressource GEAR-base bereitgestellt. Diese Ressource bietet Zugang zur Resistenzinformation und den gesammelten genomischen Daten und implementiert Funktionen zum Vergleich von hochgeladenen Genen oder Genomen zu den Daten, die in der Ressource enthalten sind. In den Studien von Mikrobengemeinschaften wird das durch GGS erhaltene Metagenom analysiert, um seine taxonomische Zusammensetzung zu bestimmen. Dafür werden die genomischen Sequenzen in sogenannte Bins gruppiert (Binning), die die Zugehörigkeit von den Sequenzen zu bestimmten Organismen oder zu Gruppen von nah verwandten Organismen repräsentieren. BusyBee Web wurde entwickelt, um eine automatische Binning-Pipeline anzubieten, die die Häufigkeitsprofile von k-meren (Teilsequenzen der Länge k) und eine auf dem Bootstrap-Verfahren basierte Methode für die Gruppierung der Sequenzen nutzt. Zusätzlich wird eine Annotation der Daten durchgeführt, wie die taxonomische Klassifizierung der hochgeladenen Sequenzen, das Vorhandensein von bekannten Resistenzfaktoren und die Qualität der Bins. Plasmide, DNA-Moleküle, die zusätzlich zum Chromosom in einigen Bakterien vorhanden sind, spielen eine wichtige Rolle in der Verbreitung von Antibiotikaresistenzen. Die Klassifizierung von Sequenzen aus der GGS als von einem Chromosom oder einem Plasmid stammend ist herausfordernd, wie es in einer Evaluation von vier Tools, die drei verschiedene Ansätze implementieren, demonstriert wurde. Deshalb ist das Vorhandensein von einem Referenzdatensatz, um schon bekannte Plasmide zu detektieren, sehr wünschenswert. Zu diesem Zweck wurde eine Online-Ressource von vollständigen bakteriellen Plasmiden implementiert (PLSDB). Die hier beschriebenen Online-Ressourcen stellen nützliche Datensätze und/oder Werkzeuge dar, die für die Analyse von mikrobiellen genomischen Daten, insbesondere von bakteriellen Pathogenen und Antibiotikaresistenzen, eingesetzt werden können

Fluoroquinolone resistance in the environment and the human gut – Analysis of bacterial DNA sequences to explore the underlying genetic mechanisms

Fluoroquinolones (FQs) are synthetic, broad-spectrum antibiotics that target type II topoisomerases. High-level resistance is often caused by mutations in the target genes of FQs, especially in gyrA and parC. In contrast, plasmid-mediated resistance genes, such as qnr, often confer moderate levels of resistance. Several sites near Patancheru, India, have been previously shown to be severely contaminated with FQs. To study how environmental bacteria adapt to this extreme environment, we first used whole-genome sequencing (454) of a highly multi-drug resistant strain of Ochrobactrum intermedium. The strain was isolated from a wastewater treatment plant (WWTP) in Patancheru that treats industrial effluent from pharmaceutical production. The strain was considerably more resistant to tetracyclines, sulphonamides, and FQs than to other O. intermedium strains, and it had, accordingly, acquired a tetracycline efflux pump, a sulphonamide resistance gene, and mutations in the target genes for FQs. In the second study, sequencing (Illumina) was used to characterise horizontally transferrable resistance plasmids captured from bacterial communities sampled from a lake with a history of FQ pollution, near Patancheru. All transconjugants had acquired qnr genes and this is, to the best of our knowledge, the first time qnrVC1 has been described on a conjugative plasmid. Furthermore, the bacteria from the lake sediments were significantly more resistant to FQs and sulphonamides compared to bacteria from Indian and Swedish reference lakes. In the third study, the Escherichia communities inhabiting a stream in Patancheru receiving WWTP effluent with high levels of FQs were tested for resistance mutations in gyrA and parC using amplicon sequencing (454). A stream receiving municipal WWTP effluent in Sk\uf6vde, Sweden, and a remote highland lake were included as references. To our surprise, all communities showed high abundances of FQ resistance mutations, suggesting that these mutations are not associated with a fitness cost in the studied environments. The same method was utilised in the fourth study, on faecal samples collected from Swedish students before and after travel to India. The abundance of the amino acid substitution S83L in GyrA increased significantly, and the number of observed genotypes decreased after travel. This finding shows that international travel contributes to the spread of bacteria carrying chromosomal resistance mutations. Taken together, the development and spread of antibiotic resistance from antibiotic-polluted environments is a concern for everyone

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

Genome-Scale Metabolic Models and Machine Learning Reveal Genetic Determinants of Antibiotic Resistance in Escherichia coli and Unravel the Underlying Metabolic Adaptation Mechanisms

Antimicrobial resistance (AMR) is becoming one of the largest threats to public health worldwide, with the opportunistic pathogen Escherichia coli playing a major role in the AMR global health crisis. Unravelling the complex interplay between drug resistance and metabolic rewiring is key to understand the ability of bacteria to adapt to new treatments and to the development of new effective solutions to combat resistant infections. We developed a computational pipeline that combines machine learning with genome-scale metabolic models (GSMs) to elucidate the systemic relationships between genetic determinants of resistance and metabolism beyond annotated drug resistance genes. Our approach was used to identify genetic determinants of 12 AMR profiles for the opportunistic pathogenic bacterium E. coli. Then, to interpret the large number of identified genetic determinants, we applied a constraint-based approach using the GSM to predict the effects of genetic changes on growth, metabolite yields, and reaction fluxes. Our computational platform leads to multiple results. First, our approach corroborates 225 known AMR-conferring genes, 35 of which are known for the specific antibiotic. Second, integration with the GSM predicted 20 top-ranked genetic determinants (including accA, metK, fabD, fabG, murG, lptG, mraY, folP, and glmM) essential for growth, while a further 17 top-ranked genetic determinants linked AMR to auxotrophic behavior. Third, clusters of AMR-conferring genes affecting similar metabolic processes are revealed, which strongly suggested that metabolic adaptations in cell wall, energy, iron and nucleotide metabolism are associated with AMR. The computational solution can be used to study other human and animal pathogens.IMPORTANCE Escherichia coli is a major public health concern given its increasing level of antibiotic resistance worldwide and extraordinary capacity to acquire and spread resistance via horizontal gene transfer with surrounding species and via mutations in its existing genome. E. coli also exhibits a large amount of metabolic pathway redundancy, which promotes resistance via metabolic adaptability. In this study, we developed a computational approach that integrates machine learning with metabolic modeling to understand the correlation between AMR and metabolic adaptation mechanisms in this model bacterium. Using our approach, we identified AMR genetic determinants associated with cell wall modifications for increased permeability, virulence factor manipulation of host immunity, reduction of oxidative stress toxicity, and changes to energy metabolism. Unravelling the complex interplay between antibiotic resistance and metabolic rewiring may open new opportunities to understand the ability of E. coli, and potentially of other human and animal pathogens, to adapt to new treatments

Resistome Identification from Whole Genome Sequencing Data of Norwegian Isolates

Masters in Applied and Commercial Biotechnology. Inland Norway University of Applied Sciences. Faculty of Applied Ecology, Agricultural sciences and BiotechnologyAntimicrobial resistance (AMR) is considered a potential threat to global health. Norway have had a low prevalence of resistant bacteria. But in the recent years there has been an increase in resistant bacteria including, Escherichia coli, Klebsiella pneumoniae and Acinetobacter baumannii. Traditionally, clinical microbiology has used culture-based techniques to determine antimicrobial susceptibility and resistance profiles, but now whole–genome sequencing for antibiotic susceptibility (WGS-AST) has emerged as a potential alternative. We aimed to investigate the prevalence of antimicrobial resistance genes and plasmids in WGS of 111 clinical Norwegian isolates of E. coli, K. pneumoniae, and A. baumannii, to identify correlations between phenotypic and genotypic resistance in the isolates, which are related to antibiotic resistance to β-lactam, aminoglycosides, fluoroquinolone, trimethoprim, tetracycline, and phenicol. The most occurring drug class was β-lactam antibiotic with TEM (38%) in E.coli, SHV (67%) in K. pneumoniae, and OXA (100%) and TEM (45%) gene families in A. baumannii. In silico detection of plasmids with Brooks et al database showed plasmid p2_000837 as prominent plasmid 12% E.coli isolates. There were four plasmids (pIB_NDM_1, p2_W5-6, pCHL5009T-102k-mcr3, pVir_020022) in 2% K. pneumoniae isolates which were also shared with E. coli. Only one plasmid (pHZ23-1-1) was confirmed in 9% of A. baumannii isolates. PLSDB detected Plasmid A and plasmid 4 with the maximum percentage in E.coli (10%) and K. pneumoniae isolates (4%). In E. coli and K. pneumoniae, the presence of incompatibility groups was observed; IncFIB (64% and 27%), Col156 (74% and 27%), IncFII (43% and 15%), while IncHI-1B(pNDM-MAR) (12%) were present only in K. pneumoniae . A total of 75 isolates had resistance to the tested β-lactam antibiotics, out of which 63 had the corresponding resistance genes (ampC, SHV, CTX-M, TEM, LEN, OXA). Only 11 E.coli and one K. pneumoniae isolates were found to have resistance genes and the plasmids on the same node to confirm plasmid mediated resistance. This study demonstrates the utility of WGS in defining resistance elements and highlights the diversity of resistance within the selected isolates to further the diagnostics and therapeutics for the treatment of the relevant infections

Alfalfa for a sustainable ovine farming system: Proposed research for a new feeding strategy based on alfalfa and ecological leftovers in drought conditions

In the past 10 years, the average demand for meat and milk across the world has significantly increased, especially in developing countries. Therefore, to support the production of animal-derived food products, a huge quantity of feed resources is needed. This paper does not present original research, but rather provides a conceptual strategy to improve primary production in a sustainable way, in relation to forthcoming issues linked to climate change. Increases in meat and milk production could be achieved by formulating balanced diets for ovines based on alfalfa integrated with local agricultural by-products. As the central component of the diet is alfalfa, one goal of the project is increasing the yield of alfalfa in a sustainable way via inoculating seeds with symbiotic rhizobia (i.e., Sinorhizobium meliloti). Seed inoculants are already present on the market but have not been optimized for arid soils. Furthermore, a part of the project is focused on the selection of elite symbiotic strains that show increased resistance to salt stress and competitiveness. The second component of the experimental diets is bio-waste, especially that obtained from olive oil manufacturing (i.e., pomace). The addition of agro-by-products allows us to use such waste as a resource for animal feeding, and possibly, to modulate rumen metabolism, thereby increasing the nutritional quality of milk and meat