Whole-genome sequence-informed MALDI-TOF MS diagnostics reveal importance of Klebsiella oxytoca group in invasive infections: a retrospective clinical study

Background: Klebsiella spp. are opportunistic pathogens which can cause severe infections, are often multi-drug resistant and are a common cause of hospital-acquired infections. Multiple new Klebsiella species have recently been described, yet their clinical impact and antibiotic resistance profiles are largely unknown. We aimed to explore Klebsiella group- and species-specific clinical impact, antimicrobial resistance (AMR) and virulence. Methods: We analysed whole-genome sequence data of a diverse selection of Klebsiella spp. isolates and identified resistance and virulence factors. Using the genomes of 3594 Klebsiella isolates, we predicted the masses of 56 ribosomal subunit proteins and identified species-specific marker masses. We then re-analysed over 22,000 Matrix- Assisted Laser Desorption Ionization - Time Of Flight (MALDI-TOF) mass spectra routinely acquired at eight healthcare institutions in four countries looking for these species-specific markers. Analyses of clinical and microbiological endpoints from a subset of 957 patients with infections from Klebsiella species were performed using generalized linear mixed-effects models. Results: Our comparative genomic analysis shows group- and species-specific trends in accessory genome composition. With the identified species-specific marker masses, eight Klebsiella species can be distinguished using MALDI-TOF MS. We identified K. pneumoniae (71.2%; n = 12,523), K. quasipneumoniae (3.3%; n = 575), K. variicola (9.8%; n = 1717), “K. quasivariicola” (0.3%; n = 52), K. oxytoca (8.2%; n = 1445), K. michiganensis (4.8%; n = 836), K. grimontii (2.4%; n = 425) and K. huaxensis (0.1%; n = 12). Isolates belonging to the K. oxytoca group, which includes the species K. oxytoca, K. michiganensis and K. grimontii, were less often resistant to 4th-generation cephalosporins than isolates of the K. pneumoniae group, which includes the species K. pneumoniae, K. quasipneumoniae, K. variicola and “K. quasivariicola” (odds ratio = 0.17, p \u3c 0.001, 95% confidence interval [0.09,0.28]). Within the K. pneumoniae group, isolates identified as K. pneumoniae were more often resistant to 4th-generation cephalosporins than K. variicola isolates (odds ratio = 2.61, p = 0.003, 95% confidence interval [1.38,5.06]). K. oxytoca group isolates were found to be more likely associated with invasive infection to primary sterile sites than K. pneumoniae group isolates (odds ratio = 2.39, p = 0.0044, 95% confidence interval [1.05,5.53]). Conclusions: Currently misdiagnosed Klebsiella spp. can be distinguished using a ribosomal marker-based approach for MALDI-TOF MS. Klebsiella groups and species differed in AMR profiles, and in their association with invasive infection, highlighting the importance for species identification to enable effective treatment options

Functional characterization and phenotypic monitoring of human hematopoietic stem cell expansion and differentiation of monocytes and macrophages by whole-cell mass spectrometry

The different facets of macrophages allow them to play distinct roles in tissue homeostasis, tissue repair and in response to infections. Individuals displaying dysregulated macrophage functions are proposed to be prone to inflammatory disorders or infections. However, this being a cause or a consequence of the pathology remains often unclear. In this context, we isolated and expanded CD34+ HSCs from healthy blood donors and derived them into CD14+ myeloid progenitors which were further enriched and differentiated into macrophages. Aiming for a comprehensive phenotypic profiling, we generated whole-cell mass spectrometry (WCMS) fingerprints of cell samples collected along the different stages of the differentiation process to build a predictive model using a linear discriminant analysis based on principal components. Through the capacity of the model to accurately predict sample's identity of a validation set, we demonstrate that WCMS profiles obtained from bona fide blood monocytes and respectively derived macrophages mirror profiles obtained from equivalent HSC derivatives. Finally, HSC-derived macrophage functionalities were assessed by quantifying cytokine and chemokine responses to a TLR agonist in a 34-plex luminex assay and by measuring their capacity to phagocytise mycobacteria. These functional read-outs could not discriminate blood monocytes-derived from HSC-derived macrophages. To conclude, we propose that this method opens new avenues to distinguish the impact of human genetics on the dysregulated biological properties of macrophages in pathological conditions

Diagnostic challenges within the Bacillus cereus-group: finding the beast without teeth

The Bacillus cereus-group (B. cereus sensu lato) includes common, usually avirulent species, often considered contaminants of patient samples in routine microbiological diagnostics, as well as the highly virulent B. anthracis. Here we describe 16 isolates from 15 patients, identified as B. cereus-group using a MALDI-TOF MS standard database. Whole genome sequencing (WGS) analysis identified five of the isolates as B. anthracis species not carrying the typical virulence plasmids pXO1 and pXO2, four isolates as B. paranthracis, three as B. cereus sensu stricto, two as B. thuringiensis, one as B. mobilis, and one isolate represents a previously undefined species of Bacillus (B. basilensis sp. nov.). More detailed analysis using alternative MALDI-TOF MS databases, biochemical phenotyping, and diagnostic PCRs, gave further conflicting species results. These cases highlight the difficulties in identifying avirulent B. anthracis within the B. cereus-group using standard methods. WGS and alternative MALDI-TOF MS databases offer more accurate species identification, but so far are not routinely applied. We discuss the diagnostic resolution and discrepancies of various identification methods

Finding the Peak in the Mass-Stack: Rapid and Accurate Detection of Virulence and Resistance in Clinical Routine Diagnostics with MALDI-TOF Mass Spectrometry

Infectious diseases are amongst the most common causes of morbidity and death. Moreover, the global increase of antimicrobial resistance (AMR) threatens to undo earlier achievements in modern medicine. Accurate and fast bacterial identification in clinical diagnostics is key, as it forms the basis of a tailored treatment. The most commonly used tool for bacterial species identification in clinical routine diagnostics is Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry (MALDI-TOF MS). MALDI-TOF MS is fast, accurate, and costeffective. However, three key problems remain: (i) not all clinically relevant bacterial species can reliably be identified using MALDI-TOF MS; (ii) virulent strains within a species are not routinely identified; and (iii) the time from sample collection until an AMR profile is available still requires 48-72 hours. How can these critical aspects in diagnostics be overcome? And what would be the potential impact for the patient? In this thesis, I aimed to increase the resolution of bacterial identification by MALDI-TOF MS in clinical routine diagnostics with a focus on the genus Klebsiella (Chapter I) and the species E. coli (Chapter II) and to predict AMR from MALDI-TOF mass spectra, using machine learning approaches (Chapter V). In the first part of my thesis (Chapter I), I established a ribosomal marker-based approach to distinguish the species within the genus Klebsiella with MALDI-TOF MS. Next, I applied this to a large, international dataset of mass spectra (n=33,160) and AMR profiles (n=7,876) to identify species-specific trends in AMR profiles. Further, I linked the species classification to clinical outcomes compiled from a single healthcare centre (n=957 clinical cases). I found that strains of the K. oxytoca complex to be significantly more likely causes invasive infections than strains of the K. pneumoniae complex. To anticipate the course of an infection, it is necessary to know which bacterial strains have the potential to cause life-threatening diseases, such as sepsis. In the second project of my thesis (Chapter II), I, therefore, isolated over 1,000 E. coli strains from urinary tract- and bloodstream infections and compiled patient characteristics and outcomes of the respective clinical cases (n=831). Applying a bacterial genome wide association study (bGWAS), I substantiated papGII as an important, patient-independent bacterial factor for causing invasive infections (bacteraemia). However, I could not identify MALDI-TOF MS peaks specific for E. coli strains carrying this virulence factor and rapid sequence amplification-based diagnostics might be more suitable and faster. MALDI-TOF mass spectral quality (MSQ) is crucial for accurate species identification. While analysing MALDI-TOF mass spectra from different healthcare centres, I observed differing numbers of detected ribosomal marker masses. MSQ is currently not precisely defined nor regularly assessed in diagnostic laboratories. I, therefore, sought to identify mass spectral features, which can be used to precisely describe MSQ and identify simple protocols yielding the highest MSQ for varying bacterial strains (Chapter III). Further, I identified a large heterogeneity of MSQ from mass spectra acquired in 36 international diagnostic laboratories, mainly driven by a few particularly well or poorly performing MALDI-TOF MS devices and likely linked to sample preparation practices (Chapter IV). Applying the simple protocols identified in Chapter III improved MSQ for previously poorly performing devices/laboratories. The resolution of MALDI-TOF MS based bacterial identification can be improved with a high MSQ in clinical routine diagnostics. A standardised MSQ will likely benefit direct phenotype prediction by supervised classification algorithms (i.e. machine learning). To assess the potential of machine learning based analysis approaches to predict AMR from MALDI-TOF mass spectra, we compiled an extensive dataset of over 300,000 routinely acquired MALDI-TOF mass spectra with matching AMR data from four healthcare centres (Chapter V). We yielded accurate predictions for important species and clinically relevant antibiotic drugs: for Ceftriaxone (as an indicator for ESBL) for E. coli and K. pneumoniae, we yield an area under the receiver operating characteristic curve (AUROC) of 0.74 for both and for Oxacillin (as an indicator for MRSA) for S. aureus with an AUROC of 0.80. The classification was most accurate if the classifiers were trained on spectra of a single species acquired at the same centre and in close temporal proximity to the test set. Overall, my thesis showed (i) the potential of MALDI-TOF MS to identify bacteria with higher resolution, (ii) that AMR can accurately be predicted from MALDI-TOF mass spectra and (iii) high MSQ is essential to translate these advances into clinical routine diagnostics

Topological and kernel-based microbial phenotype prediction from MALDI-TOF mass spectra

Motivation Microbial species identification based on matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) has become a standard tool in clinical microbiology. The resulting MALDI-TOF mass spectra also harbour the potential to deliver prediction results for other phenotypes, such as antibiotic resistance. However, the development of machine learning algorithms specifically tailored to MALDI-TOF MS-based phenotype prediction is still in its infancy. Moreover, current spectral pre-processing typically involves a parameter-heavy chain of operations without analyzing their influence on the prediction results. In addition, classification algorithms lack quantification of uncertainty, which is indispensable for predictions potentially influencing patient treatment. Results We present a novel prediction method for antimicrobial resistance based on MALDI-TOF mass spectra. First, we compare the complex conventional pre-processing to a new approach that exploits topological information and requires only a single parameter, namely the number of peaks of a spectrum to keep. Second, we introduce PIKE, the peak information kernel, a similarity measure specifically tailored to MALDI-TOF mass spectra which, combined with a Gaussian process classifier, provides well-calibrated uncertainty estimates about predictions. We demonstrate the utility of our approach by predicting antibiotic resistance of three clinically highly relevant bacterial species. Our method consistently outperforms competitor approaches, while demonstrating improved performance and security by rejecting out-of-distribution samples, such as bacterial species that are not represented in the training data. Ultimately, our method could contribute to an earlier and precise antimicrobial treatment in clinical patient care.ISSN:1367-4803ISSN:1460-205

Cutibacterium modestum and 'Propionibacterium humerusii' represent the same species that is commonly misidentified as Cutibacterium acnes

Cutibacterium spp. play an increasing role in soft tissue and implant-associated infections. We isolated a novel Cutibacterium spp. from an implant and investigated this isolate using multiple identification approaches. Correct identification was hampered by inconsistent reference data. The isolate was characterised using conventional methods such as Gram stain, MALDI-TOF MS, and antimicrobial susceptibility testing against multiple antimicrobials. Partial 16S rRNA gene sequencing and whole genome sequencing were also performed. In addition, we summarised the available published sequence data and compared prior data to our strain. Conventional phenotypic identification of our isolate resulted in Cutibacterium spp. After analysis of 16S rRNA gene and genome sequences, our isolate was identified as C. modestum, a very recently described species. The 16S rRNA gene analysis was hampered by three incorrect nucleotides within the 16S rRNA gene reference sequence of C. modestum M12(T) (accession no. LC466959). We also clearly demonstrate that this novel species is identical to tentatively named "Propionibacterium humerusii". Retrospective data analysis indicates that C. modestum is a clinically important Cutibacterium species often misidentified as C. acnes. The isolation and identification of Cutibacterium spp. is still a challenge. The correct description of very recently named C. modestum and the availability of a correct 16S rRNA sequence of the type strain may help to clarify the taxonomical uncertainty concerning "P. humerusii". The novel C. modestum is an additional, clinically important species within the genus Cutibacterium and may represent a new member of the human skin microbiome

Bacterial genome-wide association study substantiates papGII of Escherichia coli as a major risk factor for urosepsis

Abstract Background Urinary tract infections (UTIs) are among the most common bacterial infections worldwide, often caused by uropathogenic Escherichia coli. Multiple bacterial virulence factors or patient characteristics have been linked separately to progressive, more invasive infections. In this study, we aim to identify pathogen- and patient-specific factors that drive the progression to urosepsis by jointly analysing bacterial and host characteristics. Methods We analysed 1076 E. coli strains isolated from 825 clinical cases with UTI and/or bacteraemia by whole-genome sequencing (Illumina). Sequence types (STs) were determined via srst2 and capsule loci via fastKaptive. We compared the isolates from urine and blood to confirm clonality. Furthermore, we performed a bacterial genome-wide association study (bGWAS) (pyseer) using bacteraemia as the primary clinical outcome. Clinical data were collected by an electronic patient chart review. We concurrently analysed the association of the most significant bGWAS hit and important patient characteristics with the clinical endpoint bacteraemia using a generalised linear model (GLM). Finally, we designed qPCR primers and probes to detect papGII-positive E. coli strains and prospectively screened E. coli from urine samples (n = 1657) at two healthcare centres. Results Our patient cohort had a median age of 75.3 years (range: 18.00–103.1) and was predominantly female (574/825, 69.6%). The bacterial phylogroups B2 (60.6%; 500/825) and D (16.6%; 137/825), which are associated with extraintestinal infections, represent the majority of the strains in our collection, many of which encode a polysaccharide capsule (63.4%; 525/825). The most frequently observed STs were ST131 (12.7%; 105/825), ST69 (11.0%; 91/825), and ST73 (10.2%; 84/825). Of interest, in 12.3% (13/106) of cases, the E. coli pairs in urine and blood were only distantly related. In line with previous bGWAS studies, we identified the gene papGII (p-value < 0.001), which encodes the adhesin subunit of the E. coli P-pilus, to be associated with ‘bacteraemia’ in our bGWAS. In our GLM, correcting for patient characteristics, papGII remained highly significant (odds ratio = 5.27, 95% confidence interval = [3.48, 7.97], p-value < 0.001). An independent cohort of cases which we screened for papGII-carrying E. coli at two healthcare centres further confirmed the increased relative frequency of papGII-positive strains causing invasive infection, compared to papGII-negative strains (p-value = 0.033, chi-squared test). Conclusions This study builds on previous work linking papGII with invasive infection by showing that it is a major risk factor for progression from UTI to bacteraemia that has diagnostic potential

Metabolic activities of intestinal bacteria

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