Approximate reasoning with fuzzy-syllogistic systems

The well known Aristotelian syllogistic system consists of 256 moods. We have found earlier that 136 moods are distinct in terms of equal truth ratios that range in τ=[0,1]. The truth ratio of a particular mood is calculated by relating the number of true and false syllogistic cases the mood matches. A mood with truth ratio is a fuzzy-syllogistic mood. The introduction of (n-1) fuzzy existential quantifiers extends the system to fuzzy-syllogistic systems nS, 1<n, of which every fuzzy-syllogistic mood can be interpreted as a vague inference with a generic truth ratio that is determined by its syllogistic structure. We experimentally introduce the logic of a fuzzy-syllogistic ontology reasoner that is based on the fuzzy-syllogistic systems nS. We further introduce a new concept, the relative truth ratio rτ=[0,1] that is calculated based on the cardinalities of the syllogistic cases

Draft genome sequence of extended-spectrum-β-lactamase-producing Escherichia coli strain CCUG 62462, isolated from a urine sample

The draft genome sequence has been determined for an extended-spectrum-β-lactamase (ESBL)-producing (blaCTX-M-15) Escherichia coli strain (CCUG 62462), composed of 119 contigs and a total size of 5.27 Mb. This E. coli is serotype O25b and sequence type 131, a pandemic clonal group, causing worldwide antimicrobial-resistant infections

Novel strain of Pseudoruminococcus massiliensis possesses traits important in gut adaptation and host-microbe interactions

Fecal microbiota transplantation (FMT) is an efficient treatment for recurrent Clostridioides difficile infection and currently investigated as a treatment for other intestinal and systemic diseases. Better understanding of the species potentially transferred in FMT is needed. We isolated from a healthy fecal donor a novel strain E10-96H of Pseudoruminococcus massiliensis, a recently described strictly anaerobic species currently represented only by the type strain. The whole genome sequence of E10-96H had over 98% similarity with the type strain. E10-96H carries 20 glycoside hydrolase encoding genes, degrades starch in vitro and thus may contribute to fiber degradation, cross-feeding of other species and butyrate production in the intestinal ecosystem. The strain carries pilus-like structures, harbors pilin genes in its genome and adheres to enterocytes in vitro but does not provoke a proinflammatory response. P. massiliensis seems to have commensal behavior with the host epithelium, and its role in intestinal ecology should be studied further.Peer reviewe

Dynamics of the normal gut microbiota: A longitudinal one-year population study in Sweden

Temporal dynamics of the gut microbiota potentially limit the identification of microbial features associated with health status. Here, we used whole-genome metagenomic and 16S rRNA gene sequencing to characterize the intra- and inter-individual variations of gut microbiota composition and functional potential of a disease-free Swedish population (n = 75) over one year. We found that 23% of the total compositional variance was explained by intra-individual variation. The degree of intra-individual compositional variability was negatively associated with the abundance of Faecalibacterium prausnitzii (a butyrate producer) and two Bifidobacterium species. By contrast, the abundance of facultative anaerobes and aerotolerant bacteria such as Escherichia coli and Lactobacillus acidophilus varied extensively, independent of compositional stability. The contribution of intra-individual variance to the total variance was greater for functional pathways than for microbial species. Thus, reliable quantification of microbial features requires repeated samples to address the issue of intra-individual variations of the gut microbiota

Discovery of Species-unique Peptide Biomarkers of Bacterial Pathogens by Tandem Mass Spectrometry-based Proteotyping

Mass spectrometry (MS) and proteomics offer comprehensive characterization and identification of microorganisms and discovery of protein biomarkers that are applicable for diagnostics of infectious diseases. The use of biomarkers for diagnostics is widely applied in the clinic and the use of peptide biomarkers is increasingly being investigated for applications in the clinical laboratory. Respiratory-tract infections are a predominant cause for medical treatment, although, clinical assessments and standard clinical laboratory protocols are time-consuming and often inadequate for reliable diagnoses. Novel methods, preferably applied directly to clinical samples, excluding cultivation steps, are needed to improve diagnostics of infectious diseases, provide adequate treatment and reduce the use of antibiotics and associated development of antibiotic resistance. This study applied nano-liquid chromatography (LC) coupled with tandem MS, with a bioinformatics pipeline and an in-house database of curated high-quality reference genome sequences to identify species-unique peptides as potential biomarkers for four bacterial pathogens commonly found in respiratory tract infections (RTIs): Staphylococcus aureus; Moraxella catarrhalis; Haemophilus influenzae and Streptococcus pneumoniae. The species-unique peptides were initially identified in pure cultures of bacterial reference strains, reflecting the genomic variation in the four species and, furthermore, in clinical respiratory tract samples, without prior cultivation, elucidating proteins expressed in clinical conditions of infection. For each of the four bacterial pathogens, the peptide biomarker candidates most predominantly found in clinical samples, are presented. Data are available via ProteomeXchange with identifier PXD014522. As proof-of-principle, the most promising species-unique peptides were applied in targeted tandem MS-analyses of clinical samples and their relevance for identifications of the pathogens, i.e. proteotyping, was validated, thus demonstrating their potential as peptide biomarker candidates for diagnostics of infectious diseases

Integration of molecular profiles in a longitudinal wellness profiling cohort

An important aspect of precision medicine is to probe the stability in molecular profiles among healthy individuals over time. Here, we sample a longitudinal wellness cohort with 100 healthy individuals and analyze blood molecular profiles including proteomics, transcriptomics, lipidomics, metabolomics, autoantibodies and immune cell profiling, complemented with gut microbiota composition and routine clinical chemistry. Overall, our results show high variation between individuals across different molecular readouts, while the intra-individual baseline variation is low. The analyses show that each individual has a unique and stable plasma protein profile throughout the study period and that many individuals also show distinct profiles with regards to the other omics datasets, with strong underlying connections between the blood proteome and the clinical chemistry parameters. In conclusion, the results support an individual-based definition of health and show that comprehensive omics profiling in a longitudinal manner is a path forward for precision medicine

The \u3ci\u3ePrevotella copri\u3c/i\u3e Complex Comprises Four Distinct Clades Underrepresented in Westernized Populations

Prevotella copri is a common human gut microbe that has been both positively and negatively associated with host health. In a cross-continent metaanalysis exploiting \u3e6,500 metagenomes, we obtained \u3e1,000 genomes and explored the genetic and population structure of P. copri. P. copri encompasses four distinct clades (\u3e10% inter-clade genetic divergence) that we propose constitute the P. copri complex, and all clades were confirmed by isolate sequencing. These clades are nearly ubiquitous and co-present in non-Westernized populations. Genomic analysis showed substantial functional diversity in the complex with notable differences in carbohydrate metabolism, suggesting that multi-generational dietary modifications may be driving reduced prevalence in Westernized populations. Analysis of ancient metagenomes highlighted patterns of P. copri presence consistent with modern non-Westernized populations and a clade delineation time pre-dating human migratory waves out of Africa. These findings reveal that P. copri exhibits a high diversity that is underrepresented in Western-lifestyle populations

Analysis of large-scale metagenomic data

The topic of this thesis is the analysis of large data sets of DNA sequence data produced from modern high-throughput DNA sequencing machines. Using such machines to sequence the genetic content of a microbial community produces a metagenome. This thesis comprises three research papers, all connected to the study of large metagenomic data sets. In the first paper, we developed a method for discovering fragments of fluoroquinolone antibiotic resistance genes in short fragments of DNA. The method uses hidden Markov models for identifying qnr genes in short DNA fragments. Cross-validation showed that our method for classifying short fragments has high statistical power even for fragments as short as 100 base pairs, a length commonly encountered in modern next-generation sequencing data.In the second paper, the putative qnr genes identified in the first paper were verified using wet-lab experiments. This was a follow-up study to validate the findings from the first paper. An expression system for qnr genes in Escherichia coli hosts was developed and used to evaluate the resistance phenotype of the novel gene candidates discovered in the first paper. In the third paper, we developed an easy-to-use high performance method for distributed gene quantification in metagenomic sequence data. It leverages high-performance computing resources to provide high throughput while maintaining sensitivity. This enables efficient and accurate gene quantification, suitable for use in comparative metagenomics.Next-generation DNA sequencing has had a big impact on molecular biology. As the size of the produced data sets increases, there is an equally increasing need for methods suited for the analysis of such data sets. This thesis presents several new methods that are well adapted to analysis of modern terabase-sized metagenomic data sets