Rapid bacterial genome sequencing: methods and applications in clinical microbiology

Early identification of the nosocomial spread of vancomycin-resistant Enterococcus faecium by Fourier-transform infrared spectroscopy and performance comparison with PFGE and WGS

Early detection of disseminating vancomycin-resistant Enterococcus faecium (VREfm) in ICU wards is crucial for outbreak identification and the implementation of prompt infection control measures. Genotypic methods like pulsed-field gel electrophoresis (PFGE) and whole-genome sequencing (WGS) are costly and time-consuming, hindering rapid response due to batch dependency. Fourier-transform infrared spectroscopy (FT-IR) offers potential for real-time outbreak detection and reliable strain typing. We utilized FT-IR to identify clonal VREfm dissemination and compared its performance to PFGE and WGS. Between February through October 2023, an unusually high number of VREfm were recovered at a tertiary hospital in Barcelona. Isolates were examined for antimicrobial susceptibility, carriage of vanA/vanB genes and clonality was also studied using FT-IR, PFGE, and WGS. Routine FT-IR inspections revealed recurring VREfm clustering during the outbreak's initial weeks. In total, 104 isolates were recovered from 75 patients and from multiple wards. However, only one isolate was recovered from an environmental sample, suggesting the absence of environmental reservoirs. An ST80 vancomycin-resistant (vanA) E. faecium strain was the main strain responsible for the outbreak, although a few additional VREfm strains were also identified, all belonging to CC17. PFGE and cgMLST (WGS) yielded identical clustering results to FT-IR, and WGS confirmed vanA/vanB gene carriage in all VREfm isolates. Infection control measures led to a rapid decline in VREfm isolates, with no isolates detected in November. FT-IR spectroscopy offers rapid turnaround times, sensitivity, and reproducibility, comparable to standard typing methods. It proved as an effective tool for monitoring VREfm dissemination and early outbreak detection.

Universal Digital High-Resolution Melt Analysis for the Diagnosis of Bacteremia

Fast and accurate diagnosis of bloodstream infection is necessary to inform treatment decisions for septic patients, who face hourly increases in mortality risk. Blood culture remains the gold standard test but typically requires approximately 15 hours to detect the presence of a pathogen. We, therefore, assessed the potential for universal digital high-resolution melt (U-dHRM) analysis to accomplish faster broad-based bacterial detection, load quantification, and species-level identification directly from whole blood. Analytical validation studies demonstrated strong agreement between U-dHRM load measurement and quantitative blood culture, indicating that U-dHRM detection is highly specific to intact organisms. In a pilot clinical study of 17 whole blood samples from pediatric patients undergoing simultaneous blood culture testing, U-dHRM achieved 100% concordance when compared with blood culture and 88% concordance when compared with clinical adjudication. Moreover, U-dHRM identified the causative pathogen to the species level in all cases where the organism was represented in the melt curve database. These results were achieved with a 1-mL sample input and sample-to-answer time of 6 hours. Overall, this pilot study suggests that U-dHRM may be a promising method to address the challenges of quickly and accurately diagnosing a bloodstream infection.

Whole-Genome Sequencing and Phenotypic Analysis of Streptococcus equi subsp. zooepidemicus Sequence Type 147 Isolated from China

Streptococcus equi subsp. zooepidemicus (S. zooepidemicus) is one of the important zoonotic and opportunistic pathogens. In recent years, there has been growing evidence that supports the potential role of S. zooepidemicus in severe diseases in horses and other animals, including humans. Furthermore, the clinical isolation and drug resistance rates of S. zooepidemicus have been increasing yearly, leading to interest in its in-depth genomic analysis. In order to deepen the understanding of the S. zooepidemicus characteristics and genomic features, we investigated the genomic islands, mobile genetic elements, virulence and resistance genes, and phenotype of S. zooepidemicus strain ZHZ 211 (ST147), isolated from an equine farm in China. We obtained a 2.18 Mb, high-quality chromosome and found eight genomic islands. According to a comparative genomic investigation with other reference strains, ZHZ 211 has more virulence factors, like an iron uptake system, adherence, exoenzymes, and antiphagocytosis. More interestingly, ZHZ 211 has acquired a mobile genetic element (MGE), prophage Ph01, which was found to be in the chromosome of this strain and included two hyaluronidase (hyl) genes, important virulence factors of the strain. Moreover, two transposons and two virulence (virD4) genes were found to be located in the same genome island of ZHZ 211. In vitro phenotypic results showed that ZHZ 211 grows faster and is resistant to clarithromycin, enrofloxacin, and sulfonamides. The higher biofilm-forming capabilities of ZHZ 211 may provide a competitive advantage for survival in its niche. The results expand our understanding of the genomic, pathogenicity, and resistance characterization of Streptococcus zooepidemicus and facilitate further exploration of its molecular pathogenic mechanism.

Genome sequencing and protein modeling unraveled the 2AP biosynthesis in Bacillus cereus DB25

2-Acetyl-1-pyrroline (2AP) is an important and major flavor aroma compound responsible for the fragrance of basmati rice, cheese, wine, and several other food products. Biosynthesis of 2AP in aromatic rice and a few other plant species is associated with a recessive Betaine aldehyde dehydrogenase 2 (BADH2) gene. However, the literature is scant on the relationship between the functional BADH2 gene and 2AP biosynthesis in prokaryotic systems. Therefore, in the present study, we aimed to explore the functionality of the BADH2 gene for 2AP biosynthesis in 2AP synthesizing rice rhizobacterial isolate Bacillus cereus DB25 isolated from the rhizosphere of basmati rice (Oryza sativa L.). Full-length BcBADH2 sequence was obtained through whole genome sequencing (WGS) and further confirmed through traditional PCR and Sanger sequencing. Then the functionality of the BcBADH2 gene was evaluated in-silico through bioinformatics analysis and protein docking studies and further experimentally validated through enzyme assay. The sequencing and bioinformatics analysis results revealed a full-length 1485 bp BcBADH2 coding sequence without any deletion or premature stop codons. Full-length BcBADH2 was found to encode a fully functional protein of 54.08 kDa with pI of 5.22 and showed the presence of the conserved amino acids responsible for enzyme activity. The docking studies confirmed a good affinity between the protein and its substrate whereas the presence of BcBADH2 enzyme activity confirmed the functionality of BADH2 enzyme in B. cereus DB25. In conclusion, the findings of the present study suggest that B. cereus DB25 is able to synthesize 2AP despite a functional BADH2 gene and there may be a different molecular mechanism responsible for 2AP biosynthesis in bacterial systems, unlike that found in aromatic rice and other eukaryotic plant species.

Genomic investigation unveils high-risk ESBL producing Enterobacteriaceae within a rural environmental water body

Antimicrobial resistance is regarded as a global threat to public health, animals, and the environment, emerging in response to extensive utilization of antimicrobials. The determinants of antimicrobial resistance are transported to susceptible bacterial populations through genetic recombination or through gene transfer, mediated by bacteriophages, plasmids, transposons, and insertion sequences. To determine the penetration of antimicrobial resistance into the bacterial population of the Thiruvandarkoil Lake, a water body located in the rural settings of Puducherry, India, culture-based microbiological and genomic approaches were used. Resistant bacterial isolates obtained from microbiological screening were subjected to whole genome sequencing and the genetic determinants of antimicrobial resistance were identified using in silico genomic tools. Cephalosporin-resistant isolates were found to produce extended spectrum beta lactamases, encoded by blaVEB-6 (in Proteus mirabilis PS01), blaSHV-12 and ompK36 mutation (in Klebsiella quasipneumoniae PS02) and blaSHV-12, blaACT-16, blaCTX-M and blaNDM-1 in (Enterobacter hormaechei PS03). Genes encoding heavy metal resistance, virulence and resistance to detergents were also detected in these resistant isolates. Among ESBL-producing organisms, one mcr-9-positive Enterobacter hormaechei was also identified in this study. To our knowledge, this is the first report of mcr-9 carrying bacterium in the environment in India. This study seeks the immediate attention of policy makers, researchers, government officials and environmental activists in India, to develop surveillance programs to monitor the dissemination of antimicrobial resistance in the environment.

Complete genome sequencing and comparative genomic analysis of three donkey Streptococcus equi subsp. equi isolates

Introduction

Streptococcus equi subspecies equi (S. equi) is the causative agent of strangles, which is one of the most common and highly contagious respiratory infectious illnesses in horses. Streptococcus equi (S. equi) is a horse-specific pathogen that originated from the closely related zoonotic pathogen Streptococcus equi subspecies zooepidemicus (S. zooepidemicus). Despite decades of research, the movement of genetic material across host-restricted diseases remains a mystery.

Methods

Three S. equi donkey isolates (HTP133, HTP232, and HT1112) were recently isolated from a strangles epidemic on donkey farms in China's Xinjiang Province. In this study, we performed a comprehensive comparative analysis of these isolates using whole genome sequencing and compared them to the published genomic sequences of equine strain S. equi 4047 to uncover evidence of genetic events that shaped the evolution of these donkey S. equi isolates' genomes.

Results

Whole genome sequencing indicated that both strains were closely related, with comparable gene compositions and a high rate of shared core genomes (1788-2004). Our comparative genomic study indicated that the genome structure is substantially conserved across three donkey strains; however, there are several rearrangements and inversions when compared to the horse isolate S. equi 4047. The virulence factors conveyed by genomic islands and prophages, in particular, played a key role in shaping the pathogenic capacity and genetic diversity of these S. equi strains. Furthermore, we discovered that the HT133 isolate had a strong colonization ability and increased motility; the HT1112 isolates had a significantly higher ability for antimicrobial resistance and biofilm formation, and the HT232 isolate gained pathogenic specialization by acquiring a bacteriophage encoding hyaluronate lyase.

Discussion

In summary, our findings show that genetic exchange across S. equi strains influences the development of the donkey S. equi genome, offering important genetic insights for future epidemiological studies of S. equi infection.

Current and Future Technologies for the Detection of Antibiotic-Resistant Bacteria

Antibiotic resistance is a global public health concern, posing a significant threat to the effectiveness of antibiotics in treating bacterial infections. The accurate and timely detection of antibiotic-resistant bacteria is crucial for implementing appropriate treatment strategies and preventing the spread of resistant strains. This manuscript provides an overview of the current and emerging technologies used for the detection of antibiotic-resistant bacteria. We discuss traditional culture-based methods, molecular techniques, and innovative approaches, highlighting their advantages, limitations, and potential future applications. By understanding the strengths and limitations of these technologies, researchers and healthcare professionals can make informed decisions in combating antibiotic resistance and improving patient outcomes.

Research progress of single-cell sequencing in tuberculosis

Tuberculosis is a major infectious disease caused by Mycobacterium tuberculosis infection. The pathogenesis and immune mechanism of tuberculosis are not clear, and it is urgent to find new drugs, diagnosis, and treatment targets. A useful tool in the quest to reveal the enigmas related to Mycobacterium tuberculosis infection and disease is the single-cell sequencing technique. By clarifying cell heterogeneity, identifying pathogenic cell groups, and finding key gene targets, the map at the single cell level enables people to better understand the cell diversity of complex organisms and the immune state of hosts during infection. Here, we briefly reviewed the development of single-cell sequencing, and emphasized the different applications and limitations of various technologies. Single-cell sequencing has been widely used in the study of the pathogenesis and immune response of tuberculosis. We review these works summarizing the most influential findings. Combined with the multi-molecular level and multi-dimensional analysis, we aim to deeply understand the blank and potential future development of the research on Mycobacterium tuberculosis infection using single-cell sequencing technology.

Characterization and Pathogenicity of Flavobacterium psychrophilum Isolated from Rainbow Trout (Oncorhynchus mykiss) in Korea

Flavobacterium psychrophilum is the causative agent of bacterial cold-water disease in salmonids and rainbow trout fry syndrome. This pathogen has attained a global presence and can spread both horizontally and vertically. However, it was not documented in Korea before September 2018. In this study, the objectives were to characterize Flavobacterium psychrophilum strain FPRT1, isolated from diseased rainbow trout genotypically and phenotypically. We also conducted various investigations to better understand its impact and assess potential control measures. We acquired fifty rainbow trout (approximately 70 g in weight) and transferred them to a laboratory aquarium. During the initial acclimation period, we observed mortality and examined affected fish for clinical signs. We isolated the bacterium from the spleen of infected rainbow trout using tryptone yeast extract salts agar supplemented with glucose, naming this FPRT1. Antibiotic susceptibility testing was carried out, and from the result, we selected enrofloxacin to administer to the trout orally to reduce mortality. To evaluate pathogenicity, we exposed the trout to FPRT1 at different water temperatures (8, 15, and 22 °C). Genomic analysis was conducted to identify the serotype and relatedness of FPRT1 to European strains. Affected fish displayed clinical signs, such as ulcerative lesions in the mandible, anemia with pale gills, exophthalmia, and increased mucus secretion. Internal symptoms included pale liver and enlarged spleen. FPRT1 was susceptible to erythromycin, enrofloxacin, florfenicol, oxytetracycline, and gentamicin, but resistant to oxolinic acid and sulfamethoxazole/trimethoprim. Oral administration of enrofloxacin resulted in a decrease in mortality from 28% to 6%. Pathogenicity tests revealed varying mortality rates due to FPRT1 at different temperatures. The highest rates were observed at 8 °C (ranging from 43% to 100%) for both intraperitoneal and intramuscular injections, and lower rates occurred at 22 °C (ranging from 0% to 30%), with intramuscular injections displaying higher susceptibility. Genomic analysis identified FPRT1 as serotype 2 and indicated its close genetic relationship with European strains based on the core genome and dispensable genome. The substantial genomic similarity between our strain and European strains suggests the possibility of bacterial spread through the importation of fertilized eggs from Europe. In conclusion, this study highlights the introduction of the previously undocumented pathogen (F. psychrophilum) into Korean rainbow trout populations. The detection of this pathogen and its pathogenicity assessment is not only important for understanding its impact on local aquaculture but also for establishing surveillance and control measures to prevent further transmission and outbreaks in the region.

IVDR: Analysis of the Social, Economic, and Practical Consequences of the Application of an Ordinance of the In Vitro Diagnostic Ordinance in Switzerland

IVDR regulation represents a major challenge for diagnostic microbiology laboratories. IVDR complicates a broad range of aspects and poses a risk given the high diversity of pathogens (including rare but highly virulent microbes) and the large variety of samples submitted for analysis. The regular emergence of new pathogens (including Echovirus E-11, Adenovirus 41, Monkeypox virus, Alongshan virus, and Enterovirus D68, as recent examples in Europe in the post SARS-CoV-2 era) is another factor that makes IVDR regulation risky, because its detrimental effect on production of in-house tests will negatively impact knowledge and expertise in the development of new diagnostic tests. Moreover, such regulations negatively impact the availability of diagnostic tests, especially for neglected pathogens, and has a detrimental effect on the overall costs of the tests. The increased regulatory burden of IVDR may thereby pose an important risk for public health. Taken together, it will have a negative impact on the financial balance of diagnostic microbiology laboratories (especially small ones). The already-high standards of quality management of all ISO-accredited and Swissmedic-authorized laboratories render IVDR law of little value, at least in Switzerland, while tremendously increasing the regulatory burden and associated costs. Eventually, patients will need to pay for diagnostic assays outside of the framework of their insurance in order to obtain a proper diagnostic assessment, which may result in social inequity. Thus, based on the risk assessment outlined above, the coordinated commission for clinical microbiology proposes adjusting the IvDO ordinance by (i) introducing an obligation to be ISO 15189 accredited and (ii) not implementing the IvDO 2028 milestone.

Universal digital high resolution melt analysis for the diagnosis of bacteremia

Fast and accurate diagnosis of bloodstream infection is necessary to inform treatment decisions for septic patients, who face hourly increases in mortality risk. Blood culture remains the gold standard test but typically requires ∼15 hours to detect the presence of a pathogen. Here, we assess the potential for universal digital high-resolution melt (U-dHRM) analysis to accomplish faster broad-based bacterial detection, load quantification, and species-level identification directly from whole blood. Analytical validation studies demonstrated strong agreement between U-dHRM load measurement and quantitative blood culture, indicating that U-dHRM detection is highly specific to intact organisms. In a pilot clinical study of 21 whole blood samples from pediatric patients undergoing simultaneous blood culture testing, U-dHRM achieved 100% concordance when compared with blood culture and 90.5% concordance when compared with clinical adjudication. Moreover, U-dHRM identified the causative pathogen to the species level in all cases where the organism was represented in the melt curve database. These results were achieved with a 1 mL sample input and sample-to-answer time of 6 hrs. Overall, this pilot study suggests that U-dHRM may be a promising method to address the challenges of quickly and accurately diagnosing a bloodstream infection.

Universal digital high resolution melt analysis for the diagnosis of bacteremia

April Aralar, Tyler Goshia, Nanda Ramchandar, Shelley M. Lawrence, Aparajita Karmakar, Ankit Sharma, Mridu Sinha, David Pride, Peiting Kuo, Khrissa Lecrone, Megan Chiu, Karen Mestan, Eniko Sajti, Michelle Vanderpool, Sarah Lazar, Melanie Crabtree, Yordanos Tesfai, Stephanie I. Fraley.

UACG: Up-to-Date Archaeal Core Genes and Software for Phylogenomic Tree Reconstruction

In the post-genomic era, phylogenomics is a powerful and routinely-used tool to discover evolutionary relationships between microorganisms. Inferring phylogenomic trees by concatenating core gene sequences into a supermatrix is the standard method. The previously released up-to-date bacterial core gene (UBCG) tool provides a pipeline to infer phylogenomic trees using single-copy core genes for the Bacteria domain. In this study, we established up-to-date archaeal core gene (UACG), comprising 128 genes suitable for inferring archaeal phylogenomic trees. To test the gene set, we selected the Haloarcula genus and scrutinized its phylogeny. The phylogeny inferred using the UACG tool was consistent with the orthoANIu dendrogram, whereas the 16S rRNA gene phylogeny showed high intragenomic heterogeneity resulting in phylogenetic discrepancies. The software tool using the UACG set is available at https://www.ezbiocloud.net/tools/uacg .

eXtreme gradient boosting-based classification of bacterial mixtures in water and milk using wireless microscopic imaging of quorum sensing peptide-conjugated particles

Numerous bacteria can cause water- and foodborne diseases and are often found in bacterial mixtures, making their detection challenging. Specific bioreceptors or selective growth media are necessary for most bacterial detection methods. In this work, we collectively used five quorum sensing-based peptides identified from bacterial biofilms to identify 10 different bacterial species (Bacillus subtilis, Campylobacter jejuni, Enterococcus faecium, Escherichia coli, Legionella pneumophila, Listeria monocytogenes, Pseudomonas aeruginosa, Salmonella Typhimurium, Staphylococcus aureus, Vibrio parahaemolyticus) and their mixtures in water and milk. Four different machine learning classification methods were used: k-nearest neighbors (k-NN), decision tree (DT), support vector machine (SVM), and eXtreme Gradient Boosting (XGBoost). Peptides were crosslinked to submicron particles, and peptide-bacteria interactions on paper microfluidic chips caused the particle aggregation. A wireless, pocket fluorescence microscope (interfaced with a smartphone) counted such particle aggregations. XGBoost showed the best accuracy of 83.75% in identifying bacterial species from water samples using 320 different datasets and 91.67% from milk samples using 140 different datasets (5 peptide features per dataset). Each peptide's contribution to correct classification was evaluated. The results were concentration-dependent, allowing the identification of a dominant species from bacterial mixtures. Using XGBoost and the previous milk database, we tested 14 blind samples of various bacterial mixtures in milk samples, with an accuracy of 81.55% to predict the dominant species. The entire process could be completed within a half hour. The demonstrated system can provide a handheld, low-cost, easy-to-operate tool for potential hygiene spot-checks, public health, or personal healthcare.

Insight into the dynamics of drinking water resistome in China

Antibiotic resistance (AR) is a serious environmental hazard of the current age. Antibiotic resistance genes (ARGs) are the fundamental entities that spread AR in the environment. ARGs are likely to be transferred from the non-pathogenic to pathogenic microbes that might ultimately be responsible for the AR in humans and other organisms. Drinking water (DW) is the primary interaction route between ARGs and humans. Being the highest producer and consumer of antibiotics China poses a potential threat to developing superbugs and ARGs dissemination. Herein, we comprehensively seek to review the ARGs from dominant DW sources in China. Furthermore, the origin and influencing factors of the ARGs to the DW in China have been evaluated. Commonly used methods, both classical and modern, are being compiled. In addition, the risk posed and mitigation strategies of DW ARGs in China have been outlined. Overall, we believe this review would contribute to the assessment of ARGs in DW of China and their dissemination to humans and other animals and ultimately help the policymakers and scientists in the field to counteract this problem on an emergency basis.

Overview and update on cytomegalovirus-associated anterior uveitis and glaucoma

Cytomegalovirus anterior uveitis is the most common ocular inflammatory disease caused by cytomegalovirus infection. It mainly occurs in middle-aged males with competent immunologic function, and the incidence is higher in Asia. The clinical manifestations vary from Posner-Schlossman syndrome and corneal endotheliitis to Fuchs uveitis syndrome, and are often accompanied by intraocular hypertension. Secondary glaucoma is a potentially blinding ocular complication with a pathogenesis that includes complicated immunological factors, intraocular inflammation, different types of angle abnormalities, and the administration of steroids, which may result in physical discomfort and visual impairment. Diagnostic tests, such as the polymerase chain reaction, optical coherence tomography, ocular microscopy, and confocal microscopy, might help in identifying anterior uveitis caused by other viruses. Combinations of antiviral medications and anti-inflammatory agents are effective treatments. If pharmacological therapy cannot reduce intraocular pressure or slow the progression of glaucomatous optic neuropathy, surgical intervention is required as a last resort.

Comparative Genomic Analysis of Fusobacterium necrophorum Provides Insights into Conserved Virulence Genes

Fusobacterium necrophorum is a Gram-negative, filamentous anaerobe prevalent in the mucosal flora of animals and humans. It causes necrotic infections in cattle, resulting in a substantial economic impact on the cattle industry. Although infection severity and management differ within F. necrophorum species, little is known about F. necrophorum speciation and the genetic virulence determinants between strains. To characterize the clinical isolates, we performed whole-genome sequencing of four bovine isolates (8L1, 212, B17, and SM1216) and one human isolate (MK12). To determine the phylogenetic relationship and evolution pattern and investigate the presence of antimicrobial resistance genes (ARGs) and potential virulence genes of F. necrophorum, we also performed comparative genomics with publicly available Fusobacterium genomes. Using up-to-date bacterial core gene (UBCG) set analysis, we uncovered distinct Fusobacterium species and F. necrophorum subspecies clades. Pangenome analyses revealed a high level of diversity among Fusobacterium strains down to species levels. The output also identified 14 and 26 genes specific to F. necrophorum subsp. necrophorum and F. necrophorum subsp. funduliforme, respectively, which could be essential for bacterial survival under different environmental conditions. ClonalFrameML-based recombination analysis suggested that extensive recombination among accessory genes led to species divergence. Furthermore, the only strain of F. necrophorum with ARGs was F. necrophorum subsp. funduliforme B35, with acquired macrolide and tetracycline resistance genes. Our custom search revealed common virulence genes, including toxins, adhesion proteins, outer membrane proteins, cell envelope, type IV secretion system, ABC (ATP-binding cassette) transporters, and transporter proteins. A focused study on these genes could help identify major virulence genes and inform effective vaccination strategies against fusobacterial infections. IMPORTANCE Fusobacterium necrophorum is an anaerobic bacterium that causes liver abscesses in cattle with an annual incidence rate of 10% to 20%, resulting in a substantial economic impact on the cattle industry. The lack of definite biochemical tests makes it difficult to distinguish F. necrophorum subspecies phenotypically, where genomic characterization plays a significant role. However, due to the lack of a good reference genome for comparison, F. necrophorum subspecies-level identification represents a significant challenge. To overcome this challenge, we used comparative genomics to validate clinical test strains for subspecies-level identification. The findings of our study help predict specific clades of previously uncharacterized strains of F. necrophorum. Our study identifies both general and subspecies-specific virulence genes through a custom search-based analysis. The virulence genes identified in this study can be the focus of future studies aimed at evaluating their potential as vaccine targets to prevent fusobacterial infections in cattle.

MALDI Mass Spectrometry Imaging: A Potential Game-Changer in a Modern Microbiology

Nowadays, matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF MS) is routinely implemented as the reference method for the swift and straightforward identification of microorganisms. However, this method is not flawless and there is a need to upgrade the current methodology in order to free the routine lab from incubation time and shift from a culture-dependent to an even faster independent culture system. Over the last two decades, mass spectrometry imaging (MSI) gained tremendous popularity in life sciences, including microbiology, due to its ability to simultaneously detect biomolecules, as well as their spatial distribution, in complex samples. Through this literature review, we summarize the latest applications of MALDI-MSI in microbiology. In addition, we discuss the challenges and avenues of exploration for applying MSI to solve current MALDI-TOF MS limits in routine and research laboratories.

Clinical Metagenomic Next-Generation Sequencing for Diagnosis of Secondary Glaucoma in Patients With Cytomegalovirus-Induced Corneal Endotheliitis

Glaucoma is the second leading cause of blindness globally. Growing scientific evidence indicated that inflammation of the trabecular meshwork induced by corneal endotheliitis could lead to secondary glaucoma. Cytomegalovirus (CMV) has been identified as the most common herpes virus in corneal endotheliitis patients. Early detection is critical in preventing endothelial cell loss, and patient management should vary based on different pathological factors. However, routine culture and real-time polymerase chain reaction (qPCR) have difficult in distinguishing whether CMV, Varicella Zoster Virus (VZV) or Herpes Simplex Virus (HSV) causes endothiliitis. This may result in inappropriate treatment, which may prolong or aggravate the status of disease. We compared the sensitivity and specificity of qPCR and Metagenomic Next-Generation Sequencing (mNGS) in the aqueous humor of patients with suspected CMV endotheliitis in this study. Our results showed that four out of 11 (36.4%) of our patients were positive for CMV by qPCR, whereas mNGS had a 100% detection rate of CMV. Our findings implied that mNGS could be a useful diagnostic tool for CMV-induced endotheliitis.

Evaluation of Fourier Transform Infrared Spectroscopy as a First-Line Typing Tool for the Identification of Extended-Spectrum β-Lactamase-Producing Klebsiella pneumoniae Outbreaks in the Hospital Setting

Early detection of pathogen cross-transmission events and environmental reservoirs is needed to control derived nosocomial outbreaks. Whole-genome sequencing (WGS) is considered the gold standard for outbreak confirmation, but, in most cases, it is time-consuming and has elevated costs. Consequently, the timely incorporation of WGS results to conventional epidemiology (CE) investigations for rapid outbreak detection is scarce. Fourier transform infrared spectroscopy (FTIR) is a rapid technique that establishes similarity among bacteria based on the comparison of infrared light absorption patterns of bacterial polysaccharides and has been used as a typing tool in recent studies. The aim of the present study was to evaluate the performance of the FTIR as a first-line typing tool for the identification of extended-spectrum β-lactamase-producing Klebsiella pneumoniae (ESBL-Kp) outbreaks in the hospital setting in comparison with CE investigations using WGS as the gold standard method. Sixty-three isolates of ESBL-Kp collected from 2018 to 2021 and classified according to CE were typed by both FTIR and WGS. Concordance was measured using the Adjusted Rand index (AR) and the Adjusted Wallace coefficient (AW) for both CE and FTIR clustering considering WGS as the reference method. Both AR and AW were significantly higher for FTIR clustering than CE clustering (0.475 vs. 0.134, p = 0.01, and 0.521 vs. 0.134, p = 0.009, respectively). Accordingly, FTIR inferred more true clustering relationships than CE (38/42 vs. 24/42, p = 0.001). However, a similar proportion of genomic singletons was detected by both FTIR and CE (13/21 vs. 12/21, p = 1). This study demonstrates the utility of the FTIR method as a quick, low-cost, first-line tool for the detection of ESBL-Kp outbreaks, while WGS analyses are being performed for outbreak confirmation and isolate characterization. Thus, clinical microbiology laboratories would benefit from integrating the FTIR method into CE investigations for infection control measures in the hospital setting.

A Review of Next Generation Sequencing Methods and its Applications in Laboratory Diagnosis

Next-generation sequencing (NGS) is a new technology used to detect the sequence of DNA and RNA and to detect mutations or variations of significance. NGS generates large quantities of sequence data within a short time duration. The various types of sequencing includes Sanger Sequencing, Pyrosequencing, Sequencing by Synthesis (Illumina), Ligation (SoLID), Single molecule Fluorescent Sequencing (Helicos), Single molecule Real time Sequencing (Pacbio), Semiconductor sequencing (Ion torrent technology), Nanopore sequencing and fourth generation sequencing. These methods of sequencing have been modified and improved over the years such that it has become cost effective and accessible to diagnostic laboratories. Management of Outbreaks, rapid identification of bacteria, molecular case finding, taxonomy, detection of the zoonotic agents and guiding prevention strategies in HIV outbreaks are just a few of the many applications of Next Generation sequencing in clinical microbiology.

Enabling genomic island prediction and comparison in multiple genomes to investigate bacterial evolution and outbreaks

Outbreaks of virulent and/or drug-resistant bacteria have a significant impact on human health and major economic consequences. Genomic islands (GIs; defined as clusters of genes of probable horizontal origin) are of high interest because they disproportionately encode virulence factors, some antimicrobial-resistance (AMR) genes, and other adaptations of medical or environmental interest. While microbial genome sequencing has become rapid and inexpensive, current computational methods for GI analysis are not amenable for rapid, accurate, user-friendly and scalable comparative analysis of sets of related genomes. To help fill this gap, we have developed IslandCompare, an open-source computational pipeline for GI prediction and comparison across several to hundreds of bacterial genomes. A dynamic and interactive visualization strategy displays a bacterial core-genome phylogeny, with bacterial genomes linearly displayed at the phylogenetic tree leaves. Genomes are overlaid with GI predictions and AMR determinants from the Comprehensive Antibiotic Resistance Database (CARD), and regions of similarity between the genomes are also displayed. GI predictions are performed using Sigi-HMM and IslandPath-DIMOB, the two most precise GI prediction tools based on nucleotide composition biases, as well as a novel blast-based consistency step to improve cross-genome prediction consistency. GIs across genomes sharing sequence similarity are grouped into clusters, further aiding comparative analysis and visualization of acquisition and loss of mobile GIs in specific sub-clades. IslandCompare is an open-source software that is containerized for local use, plus available via a user-friendly, web-based interface to allow direct use by bioinformaticians, biologists and clinicians (at https://islandcompare.ca).

Four decades of experience of prosthetic valve endocarditis reflect a high variety of diverse pathogens

Prosthetic valve endocarditis (PVE) remains a serious condition with a high mortality rate. Precise identification of the PVE-associated pathogen/s and their virulence is essential for successful therapy, and patient survival. The commonly described PVE-associated pathogens are staphylococci, streptococci and enterococci, with Staphylococcus aureus being the most frequently diagnosed species. Furthermore, multi-drug resistance pathogens are increasing in prevalence, and continue to pose new challenges mandating a personalized approach. Blood cultures in combination with echocardiography are the most common methods to diagnose PVE, often being the only indication, it exists. In many cases, the diagnostic strategy recommended in the clinical guidelines does not identify the precise microbial agent and to frequently, false negative blood cultures are reported. Despite the fact that blood culture findings are not always a good indicator of the actual PVE agent in the valve tissue, only a minority of re-operated prostheses are subjected to microbiological diagnostic evaluation. In this review, we focus on the diversity and the complete spectrum of PVE-associated bacterial, fungal and viral pathogens in blood, and prosthetic heart valve, their possible virulence potential, and their challenges in making a microbial diagnosis. We are curious to understand if the unacceptable high mortality of PVE is associated with the high number of negative microbial findings in connection with a possible PVE. Herein, we discuss the possibilities and limits of the diagnostic methods conventionally used and make recommendations for enhanced pathogen identification. We also show possible virulence factors of the most common PVE-associated pathogens and their clinical effects. Based on blood culture, molecular biological diagnostics, and specific valve examination, better derivations for the antibiotic therapy as well as possible preventive intervention can be established in the future.

Genome wide association study of Escherichia coli bloodstream infection isolates identifies genetic determinants for the portal of entry but not fatal outcome

Escherichia coli is an important cause of bloodstream infections (BSI), which is of concern given its high mortality and increasing worldwide prevalence. Finding bacterial genetic variants that might contribute to patient death is of interest to better understand infection progression and implement diagnostic methods that specifically look for those factors. E. coli samples isolated from patients with BSI are an ideal dataset to systematically search for those variants, as long as the influence of host factors such as comorbidities are taken into account. Here we performed a genome-wide association study (GWAS) using data from 912 patients with E. coli BSI from hospitals in Paris, France. We looked for associations between bacterial genetic variants and three patient outcomes (death at 28 days, septic shock and admission to intensive care unit), as well as two portals of entry (urinary and digestive tract), using various clinical variables from each patient to account for host factors. We did not find any association between genetic variants and patient outcomes, potentially confirming the strong influence of host factors in influencing the course of BSI; we however found a strong association between the papGII operon and entrance of E. coli through the urinary tract, which demonstrates the power of bacterial GWAS when applied to actual clinical data. Despite the lack of associations between E. coli genetic variants and patient outcomes, we estimate that increasing the sample size by one order of magnitude could lead to the discovery of some putative causal variants. Given the wide adoption of bacterial genome sequencing of clinical isolates, such sample sizes may be soon available.

“Omics” Technologies - What Have They Told Us About Uropathogenic Escherichia coli Fitness and Virulence During Urinary Tract Infection?

Uropathogenic Escherichia coli (UPEC) is the main etiological agent of urinary tract infection (UTI), a widespread infectious disease of great impact on human health. This is further emphasized by the rapidly increase in antimicrobial resistance in UPEC, which compromises UTI treatment. UPEC biology is highly complex since uropathogens must adopt extracellular and intracellular lifestyles and adapt to different niches in the host. In this context, the implementation of forefront ‘omics’ technologies has provided substantial insight into the understanding of UPEC pathogenesis, which has opened the doors for new therapeutics and prophylactics discovery programs. Thus, ‘omics’ technologies applied to studies of UPEC during UTI, or in models of UTI, have revealed extensive lists of factors that are important for the ability of UPEC to cause disease. The multitude of large ‘omics’ datasets that have been generated calls for scrutinized analysis of specific factors that may be of interest for further development of novel treatment strategies. In this review, we describe main UPEC determinants involved in UTI as estimated by ‘omics’ studies, and we compare prediction of factors across the different ‘omics’ technologies, with a focus on those that have been confirmed to be relevant under UTI-related conditions. We also discuss current challenges and future perspectives regarding analysis of data to provide an overview and better understanding of UPEC mechanisms involved in pathogenesis which should assist in the selection of target sites for future prophylaxis and treatment.

Toward accurate diagnosis and surveillance of bacterial infections using enhanced strain-level metagenomic next-generation sequencing of infected body fluids

Metagenomic next-generation sequencing (mNGS) enables comprehensive pathogen detection and has become increasingly popular in clinical diagnosis. The distinct pathogenic traits between strains require mNGS to achieve a strain-level resolution, but an equivocal concept of 'strain' as well as the low pathogen loads in most clinical specimens hinders such strain awareness. Here we introduce a metagenomic intra-species typing (MIST) tool (https://github.com/pandafengye/MIST), which hierarchically organizes reference genomes based on average nucleotide identity (ANI) and performs maximum likelihood estimation to infer the strain-level compositional abundance. In silico analysis using synthetic datasets showed that MIST accurately predicted the strain composition at a 99.9% average nucleotide identity (ANI) resolution with a merely 0.001× sequencing depth. When applying MIST on 359 culture-positive and 359 culture-negative real-world specimens of infected body fluids, we found the presence of multiple-strain reached considerable frequencies (30.39%-93.22%), which were otherwise underestimated by current diagnostic techniques due to their limited resolution. Several high-risk clones were identified to be prevalent across samples, including Acinetobacter baumannii sequence type (ST)208/ST195, Staphylococcus aureus ST22/ST398 and Klebsiella pneumoniae ST11/ST15, indicating potential outbreak events occurring in the clinical settings. Interestingly, contaminations caused by the engineered Escherichia coli strain K-12 and BL21 throughout the mNGS datasets were also identified by MIST instead of the statistical decontamination approach. Our study systemically characterized the infected body fluids at the strain level for the first time. Extension of mNGS testing to the strain level can greatly benefit clinical diagnosis of bacterial infections, including the identification of multi-strain infection, decontamination and infection control surveillance.

Allele-specific assembly of a eukaryotic genome corrects apparent frameshifts and reveals a lack of nonsense-mediated mRNA decay

To date, most reference genomes represent a mosaic consensus sequence in which the homologous chromosomes are collapsed into one sequence. This approach produces sequence artefacts and impedes analyses of allele-specific mechanisms. Here, we report an allele-specific genome assembly of the diploid parasite Trypanosoma brucei and reveal allelic variants affecting gene expression. Using long-read sequencing and chromosome conformation capture data, we could assign 99.5% of all heterozygote variants to a specific homologous chromosome and build a 66 Mb long allele-specific genome assembly. The phasing of haplotypes allowed us to resolve hundreds of artefacts present in the previous mosaic consensus assembly. In addition, it revealed allelic recombination events, visible as regions of low allelic heterozygosity, enabling the lineage tracing of T. brucei isolates. Interestingly, analyses of transcriptome and translatome data of genes with allele-specific premature termination codons point to the absence of a nonsense-mediated decay mechanism in trypanosomes. Taken together, this study delivers a reference quality allele-specific genome assembly of T. brucei and demonstrates the importance of such assemblies for the study of gene expression control. We expect the new genome assembly will increase the awareness of allele-specific phenomena and provide a platform to investigate them.

Comparative Analysis of Machine Learning Algorithms on Surface Enhanced Raman Spectra of Clinical Staphylococcus Species

Raman spectroscopy (RS) is a widely used analytical technique based on the detection of molecular vibrations in a defined system, which generates Raman spectra that contain unique and highly resolved fingerprints of the system. However, the low intensity of normal Raman scattering effect greatly hinders its application. Recently, the newly emerged surface enhanced Raman spectroscopy (SERS) technique overcomes the problem by mixing metal nanoparticles such as gold and silver with samples, which greatly enhances signal intensity of Raman effects by orders of magnitudes when compared with regular RS. In clinical and research laboratories, SERS provides a great potential for fast, sensitive, label-free, and non-destructive microbial detection and identification with the assistance of appropriate machine learning (ML) algorithms. However, choosing an appropriate algorithm for a specific group of bacterial species remains challenging, because with the large volumes of data generated during SERS analysis not all algorithms could achieve a relatively high accuracy. In this study, we compared three unsupervised machine learning methods and 10 supervised machine learning methods, respectively, on 2,752 SERS spectra from 117 Staphylococcus strains belonging to nine clinically important Staphylococcus species in order to test the capacity of different machine learning methods for bacterial rapid differentiation and accurate prediction. According to the results, density-based spatial clustering of applications with noise (DBSCAN) showed the best clustering capacity (Rand index 0.9733) while convolutional neural network (CNN) topped all other supervised machine learning methods as the best model for predicting Staphylococcus species via SERS spectra (ACC 98.21%, AUC 99.93%). Taken together, this study shows that machine learning methods are capable of distinguishing closely related Staphylococcus species and therefore have great application potentials for bacterial pathogen diagnosis in clinical settings.

From Sequence Data to Patient Result: A Solution for HIV Drug Resistance Genotyping With Exatype, End to End Software for Pol-HIV-1 Sanger Based Sequence Analysis and Patient HIV Drug Resistance Result Generation

Introduction:

With the rapid scale-up of antiretroviral therapy (ART) to treat HIV infection, there are ongoing concerns regarding probable emergence and transmission of HIV drug resistance (HIVDR) mutations. This scale-up has to lead to an increased need for routine HIVDR testing to inform the clinical decision on a regimen switch. Although the majority of wet laboratory processes are standardized, slow, labor-intensive data transfer and subjective manual sequence interpretation steps are still required to finalize and release patient results. We thus set out to validate the applicability of a software package to generate HIVDR patient results from raw sequence data independently.

Methods:

We assessed the performance characteristics of Hyrax Bioscience’s Exatype (a sequence data to patient result, fully automated sequence analysis software, which consolidates RECall, MEGA X and the Stanford HIV database) against the standard method (RECall and Stanford database). Exatype is a web-based HIV Drug resistance bioinformatic pipeline available at sanger.exatype.com. To validate the exatype, we used a test set of 135 remnant HIV viral load samples at the National HIV Reference Laboratory (NHRL).

Result:

We analyzed, and successfully generated results of 126 sequences out of 135 specimens by both Standard and Exatype software. Result production using Exatype required minimal hands-on time in comparison to the Standard (6 computation-hours using the standard method versus 1.5 Exatype computation-hours). Concordance between the 2 systems was 99.8% for 311,227 bases compared. 99.7% of the 0.2% discordant bases, were attributed to nucleotide mixtures as a result of the sequence editing in Recall. Both methods identified similar (99.1%) critical antiretroviral resistance-associated mutations resulting in a 99.2% concordance of resistance susceptibility interpretations. The Base-calling comparison between the 2 methods had Cohen’s kappa (0.97 to 0.99), implying an almost perfect agreement with minimal base calling variation. On a predefined dataset, RECall editing displayed the highest probability to score mixtures accurately 1 vs. 0.71 and the lowest chance to inaccurately assign mixtures to pure nucleotides (0.002–0.0008). This advantage is attributable to the manual sequence editing in RECall.

Conclusion:

The reduction in hands-on time needed is a benefit when using the Exatype HIV DR sequence analysis platform and result generation tool. There is a minimal difference in base calling between Exatype and standard methods. Although the discrepancy has minimal impact on drug resistance interpretation, allowance of sequence editing in Exatype as RECall can significantly improve its performance.

Antibiotic resistance: Time of synthesis in a post-genomic age

Antibiotic resistance has been highlighted by international organizations, including World Health Organization, World Bank and United Nations, as one of the most relevant global health problems. Classical approaches to study this problem have focused in infected humans, mainly at hospitals. Nevertheless, antibiotic resistance can expand through different ecosystems and geographical allocations, hence constituting a One-Health, Global-Health problem, requiring specific integrative analytic tools. Antibiotic resistance evolution and transmission are multilayer, hierarchically organized processes with several elements (from genes to the whole microbiome) involved. However, their study has been traditionally gene-centric, each element independently studied. The development of robust-economically affordable whole genome sequencing approaches, as well as other -omic techniques as transcriptomics and proteomics, is changing this panorama. These technologies allow the description of a system, either a cell or a microbiome as a whole, overcoming the problems associated with gene-centric approaches. We are currently at the time of combining the information derived from -omic studies to have a more holistic view of the evolution and spread of antibiotic resistance. This synthesis process requires the accurate integration of -omic information into computational models that serve to analyse the causes and the consequences of acquiring AR, fed by curated databases capable of identifying the elements involved in the acquisition of resistance. In this review, we analyse the capacities and drawbacks of the tools that are currently in use for the global analysis of AR, aiming to identify the more useful targets for effective corrective interventions.

Evaluation of a New Culture-Based AtbFinder Test-System Employing a Novel Nutrient Medium for the Selection of Optimal Antibiotics for Critically Ill Patients with Polymicrobial Infections within 4 h

Here, we describe the validation of a new phenotypic culture-based AtbFinder method for rapid selection of antibiotics in vitro using specimens with mono- and polybacterial infections. AtbFinder, which can be applied to any type of non-blood tissue, does not require isolation of pure bacterial cultures. The method uses a novel TGV medium that allows more rapid bacterial growth of Gram-positive and Gram-negative monoisolates compared with that achieved with conventional laboratory media, demonstrating overall sensitivity, specificity, PPV, NPV values of 99.6%, 98.1%, 98.5%, and 99.4%, respectively, after 4 h. For polymicrobial infections, AtbFinder utilized a novel paradigm of the population response to antibiotics, enabling bacterial growth in the form of a mixed microbial community and selecting antibiotics targeting not only the principal pathogen, but also those bacteria that support their growth. TGV medium allowed culturing of a more diverse set of bacteria from polymicrobial biospecimens, compared with that achieved with the standard media, and enabled, within 4 h, accurate selection of the antibiotics that completely eliminated all cultivatable bacteria from clinical samples. In conclusion, the AtbFinder system may be a valuable tool in improving antibiotic selection, and enabling targeted empirical therapy and accurate antibiotic replacement, which is especially important in high-risk patients.

Genotypic Characterization of Clinical Klebsiella spp. Isolates Collected From Patients With Suspected Community-Onset Sepsis, Sweden

Klebsiella is a genus of Gram-negative bacteria known to be opportunistic pathogens that may cause a variety of infections in humans. Highly drug-resistant Klebsiella species, especially K. pneumoniae, have emerged rapidly and are becoming a major concern in clinical management. Although K. pneumoniae is considered the most important pathogen within the genus, the true clinical significance of the other species is likely underrecognized due to the inability of conventional microbiological methods to distinguish between the species leading to high rates of misidentification. Bacterial whole-genome sequencing (WGS) enables precise species identification and characterization that other technologies do not allow. Herein, we have characterized the diversity and traits of Klebsiella spp. in community-onset infections by WGS of clinical isolates (n = 105) collected during a prospective sepsis study in Sweden. The sequencing revealed that 32 of the 82 isolates (39.0%) initially identified as K. pneumoniae with routine microbiological methods based on cultures followed by matrix-assisted laser desorption-time of flight mass spectrometry (MALDI-TOF MS) had been misidentified. Of these, 23 were identified as Klebsiella variicola and nine as other members of the K. pneumoniae complex. Comparisons of the number of resistance genes showed that significantly fewer resistance genes were detected in Klebsiella oxytoca compared to K. pneumoniae and K. variicola (both values of p < 0.001). Moreover, a high proportion of the isolates within the K. pneumoniae complex were predicted to be genotypically multidrug-resistant (MDR; 79/84, 94.0%) in contrast to K. oxytoca (3/16, 18.8%) and Klebsiella michiganensis (0/4, 0.0%). All isolates predicted as genotypically MDR were found to harbor the combination of β-lactam, fosfomycin, and quinolone resistance markers. Multi-locus sequence typing (MLST) revealed a high diversity of sequence types among the Klebsiella spp. with ST14 (10.0%) and ST5429 (10.0%) as the most prevalent ones for K. pneumoniae, ST146 for K. variicola (12.0%), and ST176 for K. oxytoca (25.0%). In conclusion, the results from this study highlight the importance of using high-resolution genotypic methods for identification and characterization of clinical Klebsiella spp. isolates. Our findings indicate that infections caused by other members of the K. pneumoniae complex than K. pneumoniae are a more common clinical problem than previously described, mainly due to high rates of misidentifications.

Gastric Cancer Microbiome

Identifying a microbiome pattern in gastric cancer (GC) is hugely debatable due to the variation resulting from the diversity of the studied populations, clinical scenarios, and metagenomic approach. H. pylori remains the main microorganism impacting gastric carcinogenesis and seems necessary for the initial steps of the process. Nevertheless, an additional non-H. pylori microbiome pattern is also described, mainly at the final steps of the carcinogenesis. Unfortunately, most of the presented results are not reproducible, and there are no consensual candidates to share the H. pylori protagonists. Limitations to reach a consistent interpretation of metagenomic data include contamination along every step of the process, which might cause relevant misinterpretations. In addition, the functional consequences of an altered microbiome might be addressed. Aiming to minimize methodological bias and limitations due to small sample size and the lack of standardization of bioinformatics assessment and interpretation, we carried out a comprehensive analysis of the publicly available metagenomic data from various conditions relevant to gastric carcinogenesis. Mainly, instead of just analyzing the results of each available publication, a new approach was launched, allowing the comprehensive analysis of the total sample amount, aiming to produce a reliable interpretation due to using a significant number of samples, from different origins, in a standard protocol. Among the main results, Helicobacter and Prevotella figured in the "top 6" genera of every group. Helicobacter was the first one in chronic gastritis (CG), gastric cancer (GC), and adjacent (ADJ) groups, while Prevotella was the leader among healthy control (HC) samples. Groups of bacteria are differently abundant in each clinical situation, and bacterial metabolic pathways also diverge along the carcinogenesis cascade. This information may support future microbiome interventions aiming to face the carcinogenesis process and/or reduce GC risk.

Benchmarking Long-Read Assemblers for Genomic Analyses of Bacterial Pathogens Using Oxford Nanopore Sequencing

Oxford Nanopore sequencing can be used to achieve complete bacterial genomes. However, the error rates of Oxford Nanopore long reads are greater compared to Illumina short reads. Long-read assemblers using a variety of assembly algorithms have been developed to overcome this deficiency, which have not been benchmarked for genomic analyses of bacterial pathogens using Oxford Nanopore long reads. In this study, long-read assemblers, namely Canu, Flye, Miniasm/Racon, Raven, Redbean, and Shasta, were thus benchmarked using Oxford Nanopore long reads of bacterial pathogens. Ten species were tested for mediocre- and low-quality simulated reads, and 10 species were tested for real reads. Raven was the most robust assembler, obtaining complete and accurate genomes. All Miniasm/Racon and Raven assemblies of mediocre-quality reads provided accurate antimicrobial resistance (AMR) profiles, while the Raven assembly of Klebsiella variicola with low-quality reads was the only assembly with an accurate AMR profile among all assemblers and species. All assemblers functioned well for predicting virulence genes using mediocre-quality and real reads, whereas only the Raven assemblies of low-quality reads had accurate numbers of virulence genes. Regarding multilocus sequence typing (MLST), Miniasm/Racon was the most effective assembler for mediocre-quality reads, while only the Raven assemblies of Escherichia coli O157:H7 and K. variicola with low-quality reads showed positive MLST results. Miniasm/Racon and Raven were the best performers for MLST using real reads. The Miniasm/Racon and Raven assemblies showed accurate phylogenetic inference. For the pan-genome analyses, Raven was the strongest assembler for simulated reads, whereas Miniasm/Racon and Raven performed the best for real reads. Overall, the most robust and accurate assembler was Raven, closely followed by Miniasm/Racon.

Remote near infrared identification of pathogens with multiplexed nanosensors

Infectious diseases are worldwide a major cause of morbidity and mortality. Fast and specific detection of pathogens such as bacteria is needed to combat these diseases. Optimal methods would be non-invasive and without extensive sample-taking/processing. Here, we developed a set of near infrared (NIR) fluorescent nanosensors and used them for remote fingerprinting of clinically important bacteria. The nanosensors are based on single-walled carbon nanotubes (SWCNTs) that fluoresce in the NIR optical tissue transparency window, which offers ultra-low background and high tissue penetration. They are chemically tailored to detect released metabolites as well as specific virulence factors (lipopolysaccharides, siderophores, DNases, proteases) and integrated into functional hydrogel arrays with 9 different sensors. These hydrogels are exposed to clinical isolates of 6 important bacteria (Staphylococcus aureus, Escherichia coli,…) and remote (≥25 cm) NIR imaging allows to identify and distinguish bacteria. Sensors are also spectrally encoded (900 nm, 1000 nm, 1250 nm) to differentiate the two major pathogens P. aeruginosa as well as S. aureus and penetrate tissue (>5 mm). This type of multiplexing with NIR fluorescent nanosensors enables remote detection and differentiation of important pathogens and the potential for smart surfaces.

Performance and Application of 16S rRNA Gene Cycle Sequencing for Routine Identification of Bacteria in the Clinical Microbiology Laboratory

This review provides a state-of-the-art description of the performance of Sanger cycle sequencing of the 16S rRNA gene for routine identification of bacteria in the clinical microbiology laboratory. A detailed description of the technology and current methodology is outlined with a major focus on proper data analyses and interpretation of sequences. The remainder of the article is focused on a comprehensive evaluation of the application of this method for identification of bacterial pathogens based on analyses of 16S multialignment sequences. In particular, the existing limitations of similarity within 16S for genus- and species-level differentiation of clinically relevant pathogens and the lack of sequence data currently available in public databases is highlighted. A multiyear experience is described of a large regional clinical microbiology service with direct 16S broad-range PCR followed by cycle sequencing for direct detection of pathogens in appropriate clinical samples. The ability of proteomics (matrix-assisted desorption ionization-time of flight) versus 16S sequencing for bacterial identification and genotyping is compared. Finally, the potential for whole-genome analysis by next-generation sequencing (NGS) to replace 16S sequencing for routine diagnostic use is presented for several applications, including the barriers that must be overcome to fully implement newer genomic methods in clinical microbiology. A future challenge for large clinical, reference, and research laboratories, as well as for industry, will be the translation of vast amounts of accrued NGS microbial data into convenient algorithm testing schemes for various applications (i.e., microbial identification, genotyping, and metagenomics and microbiome analyses) so that clinically relevant information can be reported to physicians in a format that is understood and actionable. These challenges will not be faced by clinical microbiologists alone but by every scientist involved in a domain where natural diversity of genes and gene sequences plays a critical role in disease, health, pathogenicity, epidemiology, and other aspects of life-forms. Overcoming these challenges will require global multidisciplinary efforts across fields that do not normally interact with the clinical arena to make vast amounts of sequencing data clinically interpretable and actionable at the bedside.

Rapid multiplex MinION nanopore sequencing workflow for Influenza A viruses

BACKGROUND

Due to the frequent reassortment and zoonotic potential of influenza A viruses, rapid gain of sequence information is crucial. Alongside established next-generation sequencing protocols, the MinION sequencing device (Oxford Nanopore Technologies) has become a serious competitor for routine whole-genome sequencing. Here, we established a novel, rapid and high-throughput MinION multiplexing workflow based on a universal RT-PCR.

METHODS

Twelve representative influenza A virus samples of multiple subtypes were universally amplified in a one-step RT-PCR and subsequently sequenced on the MinION instrument in conjunction with a barcoding library preparation kit from the rapid family and the MinIT performing live base-calling. The identical PCR products were sequenced on an IonTorrent platform and, after final consensus assembly, all data was compared for validation. To prove the practicability of the MinION-MinIT method in human and veterinary diagnostics, we sequenced recent and historical influenza strains for further benchmarking.

RESULTS

The MinION-MinIT combination generated over two million reads for twelve samples in a six-hour sequencing run, from which a total of 72% classified as quality screened, trimmed and mapped influenza reads to produce full genome sequences. Identities between the datasets of > 99.9% were achieved, with 100% coverage of all segments alongside a sufficient confidence and 4492fold mean depth. From RNA extraction to finished sequences, only 14 h were required.

CONCLUSIONS

Overall, we developed and validated a novel and rapid multiplex workflow for influenza A virus sequencing. This protocol suits both clinical and academic settings, aiding in real time diagnostics and passive surveillance.

Microbial genomic island discovery, visualization and analysis

Horizontal gene transfer (also called lateral gene transfer) is a major mechanism for microbial genome evolution, enabling rapid adaptation and survival in specific niches. Genomic islands (GIs), commonly defined as clusters of bacterial or archaeal genes of probable horizontal origin, are of particular medical, environmental and/or industrial interest, as they disproportionately encode virulence factors and some antimicrobial resistance genes and may harbor entire metabolic pathways that confer a specific adaptation (solvent resistance, symbiosis properties, etc). As large-scale analyses of microbial genomes increases, such as for genomic epidemiology investigations of infectious disease outbreaks in public health, there is increased appreciation of the need to accurately predict and track GIs. Over the past decade, numerous computational tools have been developed to tackle the challenges inherent in accurate GI prediction. We review here the main types of GI prediction methods and discuss their advantages and limitations for a routine analysis of microbial genomes in this era of rapid whole-genome sequencing. An assessment is provided of 20 GI prediction software methods that use sequence-composition bias to identify the GIs, using a reference GI data set from 104 genomes obtained using an independent comparative genomics approach. Finally, we present guidelines to assist researchers in effectively identifying these key genomic regions.

Whole-genome next-generation sequencing and phylogenetic characterization of viral haemorrhagic septicaemia virus in Korea

Whole-genome next-generation sequencing was used to investigate the local evolution of viral haemorrhagic septicaemia virus, a serious pathogen affecting economically important fish such as rainbow trout and turbot in Europe and olive flounder in Asia. Sequence analysis showed that all isolates were genotype IVa, but could be classified further into four subgroups (K1-K4). In addition, genomic regions encompassing the nucleoprotein, phosphoprotein, matrix protein and non-virion protein genes, as well as the seven non-coding regions, were relatively conserved, whereas glycoprotein and RNA-dependent RNA polymerase genes were variable in the coding region. Taken together, the data demonstrate that whole-genome next-generation sequencing may be useful for future surveillance, prevention and control strategies against viral haemorrhagic septicaemia.

Review of Clinically and Epidemiologically Relevant Coagulase-Negative Staphylococci in Africa

Coagulase-negative staphylococci (CoNS) have engendered substantial interest in recent years as pathogenic causes of infections in both human and veterinary medicine, especially in the immunocompromised, critically ill, long-term hospitalized and in those harboring invasive medical devices such as catheters. They have been implicated in infections such as urinary tract infections, bloodstream infections, and invasive device-related infections, and are responsible for substantial economic losses in livestock production. The advancement of diagnostic techniques has increased our understanding of their molecular mechanisms of pathogenicity, even though distinguishing between innocuousness and pathogenicity is still challenging. The incidence of CoNS varied across the continent in humans and animals (mainly cattle), ranging from 6% to 68% in suspected human infections and from 3% to 61.7% in suspected animal infections, distributed across different geographic locations. Furthermore, there were varying antibiotic resistance patterns observed in CoNS isolates, with high methicillin resistance in some cases, leading to crossresistance against many antibiotics. Staphylococcus epidermidis, Staphylococcus haemolyticus, and Staphylococcus xylosus were most commonly reported in studies herein reviewed, while the enterotoxin C gene, atl E gene, ica gene, and hemolysin virulence factors were linked with enhanced pathogenicity. Advancement in identification and typing methods, including whole genome sequencing, virulence screening, and the assessment of the immune status of subjects in studies will help to thoroughly assess the true pathogenic potential of isolated CoNS species in developing countries. Careful antibiotic stewardship guidelines should be followed due to the ability of CoNS to develop multidrug resistance.

Antibiotic resistance by high-level intrinsic suppression of a frameshift mutation in an essential gene

Frameshift mutations have been reported in rpoB, an essential gene encoding the beta-subunit of RNA polymerase, in rifampicin-resistant clinical isolates of Mycobacterium tuberculosis. These have never been experimentally validated, and no mechanisms of action have been proposed. We show that Escherichia coli with a +1-nt frameshift mutation centrally located in rpoB is viable and highly resistant to rifampicin. Spontaneous frameshifting occurs at a high rate on a heptanucleotide sequence downstream of the mutation, with production of active protein increased to 61–71% of wild-type level by a feedback mechanism that increases translation initiation. Accordingly, apparently lethal mutations can be viable and cause clinically relevant phenotypes, a finding that has broad significance for predictions of phenotype from genotype.

A fundamental feature of life is that ribosomes read the genetic code in messenger RNA (mRNA) as triplets of nucleotides in a single reading frame. Mutations that shift the reading frame generally cause gene inactivation and in essential genes cause loss of viability. Here we report and characterize a +1-nt frameshift mutation, centrally located in rpoB, an essential gene encoding the beta-subunit of RNA polymerase. Mutant Escherichia coli carrying this mutation are viable and highly resistant to rifampicin. Genetic and proteomic experiments reveal a very high rate (5%) of spontaneous frameshift suppression occurring on a heptanucleotide sequence downstream of the mutation. Production of active protein is stimulated to 61–71% of wild-type level by a feedback mechanism increasing translation initiation. The phenomenon described here could have broad significance for predictions of phenotype from genotype. Several frameshift mutations have been reported in rpoB in rifampicin-resistant clinical isolates of Mycobacterium tuberculosis (Mtb). These mutations have never been experimentally validated, and no mechanisms of action have been proposed. This work shows that frameshift mutations in rpoB can be a mutational mechanism generating antibiotic resistance. Our analysis further suggests that genetic elements supporting productive frameshifting could rapidly evolve de novo, even in essential genes.

Clinical metagenomics for infectious corneal ulcers: Rags to riches?

The emergence of clinical metagenomics as an unbiased, hypothesis-free approach to diagnostic testing is set to fundamentally alter the way infectious diseases are detected. Long envisioned as the solution to the limitations of culture-based conventional microbiology, next generation sequencing methods will soon mature, and our attention will inevitably turn to how they can be applied to areas of medicine which need it most urgently. In ophthalmology, the demand for this technology is particularly pressing for the care of infectious corneal ulcers, where current diagnostic tests may fail to identify a causative organism in over half of cases. However, the optimism found in the budding discourse surrounding clinical metagenomics belies the reality that clinicians and scientists will soon be inundated by oppressive volumes of sequencing data, much of which will be foreign and unfamiliar. Therefore, our success in translating clinical metagenomics is likely to hinge on how we make sense of these data, and understanding its implications for the interpretation and implementation of sequencing into routine clinical care. In this consortium-led review, we provide an outline of these data-related issues and how they may be used to inform technical workflows, with the hope that we may edge closer to realizing the potential of clinical metagenomics for this important unmet need.

Computer Vision Approach for the Determination of Microbial Concentration and Growth Kinetics Using a Low Cost Sensor System

The measurement of microbial contamination is of primary importance in different fields, from environmental monitoring to food safety and clinical analysis. Today, almost all microbiology laboratories make microbial concentration measurements using the standard Plate Count Technique (PCT), a manual method that must be performed by trained personnel. Since manual PCT analysis can result in eye fatigue and errors, in particular when hundreds of samples are processed every day, automatic colony counters have been built and are commercially available. While quick and reliable, these instruments are generally expensive, thus, portable colony counters based on smartphones have been developed and are of low cost but also not accurate as the commercial benchtop instruments. In this paper, a novel computer vision sensor system is presented that can measure the microbial concentration of a sample under test and also estimate the microbial growth kinetics by monitoring the colonies grown on a Petri dish at regular time intervals. The proposed method has been in-house validated by performing PCT analysis in parallel under the same conditions and using these results as a reference. All the measurements have been carried out in a laboratory using benchtop instruments, however, such a system can also be realized as an embedded sensor system to be deployed for microbial analysis outside a laboratory environment.

mNGS in clinical microbiology laboratories: on the road to maturity

Metagenomic next-generation sequencing (mNGS) is increasingly being applied in clinical laboratories for unbiased culture-independent diagnosis. Whether it can be a next routine pathogen identification tool has become a topic of concern. We review the current implementation of this new technology for infectious disease diagnostics and discuss the feasibility of transforming mNGS into a routine diagnostic test. Since 2008, numerous studies from over 20 countries have revealed the practicality of mNGS in the work-up of undiagnosed infectious diseases. mNGS performs well in identifying rare, novel, difficult-to-detect and coinfected pathogens directly from clinical samples and presents great potential in resistance prediction by sequencing the antibiotic resistance genes, providing new diagnostic evidence that can be used to guide treatment options and improve antibiotic stewardship. Many physicians recognized mNGS as a last resort method to address clinical infection problems. Although several hurdles, such as workflow validation, quality control, method standardisation, and data interpretation, remain before mNGS can be implemented routinely in clinical laboratories, they are temporary and can be overcome by rapidly evolving technologies. With more validated workflows, lower cost and turnaround time, and simplified interpretation criteria, mNGS will be widely accepted in clinical practice. Overall, mNGS is transforming the landscape of clinical microbiology laboratories, and to ensure that it is properly utilised in clinical diagnosis, both physicians and microbiologists should have a thorough understanding of the power and limitations of this method.

Application of different DNA extraction procedures, library preparation protocols and sequencing platforms: impact on sequencing results

In this study three DNA extraction procedures, two library preparation protocols and two sequencing platforms were applied to analyse six bacterial cultures and their corresponding DNA obtained as part of a proficiency test. The impact of each variable on sequencing results was assessed using the following parameters: reads quality, assembly and alignment statistics; number of single nucleotide polymorphisms (SNPs), detected applying assembly- and alignment-based strategies; antimicrobial resistance genes (ARGs), identified on de novo assemblies of all sequenced genomes. The investigated nucleic acid extraction procedures, library preparation kits and sequencing platforms do not significantly affect de novo assembly statistics and number of SNPs and ARGs. The only exception was observed for two duplicates, which were associated to one PCR-based library preparation kit. Results from this comparative study can support researchers in the choice toward the available pre-sequencing and sequencing options, and might suggest further comparisons to be performed.

Model-Based Spectral Library Approach for Bacterial Identification via Membrane Glycolipids

By circumventing the need for a pure colony, MALDI-TOF mass spectrometry of bacterial membrane glycolipids (lipid A) has the potential to identify microbes more rapidly than protein-based methods. However, currently available bioinformatics algorithms (e.g., dot products) do not work well with glycolipid mass spectra such as those produced by lipid A, the membrane anchor of lipopolysaccharide. To address this issue, we propose a spectral library approach coupled with a machine learning technique to more accurately identify microbes. Here, we demonstrate the performance of the model-based spectral library approach for microbial identification using approximately a thousand mass spectra collected from multi-drug-resistant bacteria. At false discovery rates < 1%, our approach identified many more bacterial species than the existing approaches such as the Bruker Biotyper and characterized over 97% of their phenotypes accurately. As the diversity in our glycolipid mass spectral library increases, we anticipate that it will provide valuable information to more rapidly treat infected patients.

Integrated genomic epidemiology and phenotypic profiling of Clostridium difficile across intra-hospital and community populations in Colombia

Clostridium difficile, the causal agent of antibiotic-associated diarrhea, has a complex epidemiology poorly studied in Latin America. We performed a robust genomic and phenotypic profiling of 53 C. difficile clinical isolates established from diarrheal samples from either intrahospital (IH) or community (CO) populations in central Colombia. In vitro tests were conducted to evaluate the cytopathic effect, the minimum inhibitory concentration of ten antimicrobial agents, the sporulation efficiency and the colony forming ability. Eleven different sequence types (STs) were found, the majority present individually in each sample, however in three samples two different STs were isolated. Interestingly, CO patients were infected with STs associated with hypervirulent strains (ST-1 in Clade-2). Three coexistence events (two STs simultaneously detected in the same sample) were observed always involving ST-8 from Clade-1. A total of 2,502 genes were present in 99% of the isolates with 95% of identity or more, it represents a core genome of 28.6% of the 8,735 total genes identified in the set of genomes. A high cytopathic effect was observed for the isolates positive for the two main toxins but negative for binary toxin (TcdA+/TcdB+/CDT- toxin production type), found only in Clade-1. Molecular markers conferring resistance to fluoroquinolones (cdeA and gyrA) and to sulfonamides (folP) were the most frequent in the analyzed genomes. In addition, 15 other markers were found mostly in Clade-2 isolates. These results highlight the regional differences that C. difficile isolates display, being in this case the CO isolates the ones having a greater number of accessory genes and virulence-associated factors.

Improved genomic island predictions with IslandPath-DIMOB

Motivation

Genomic islands (GIs) are clusters of genes of probable horizontal origin that play a major role in bacterial and archaeal genome evolution and microbial adaptability. They are of high medical and industrial interest, due to their enrichment in virulence factors, some antimicrobial resistance genes and adaptive metabolic pathways. The development of more sensitive but precise prediction tools, using either sequence composition-based methods or comparative genomics, is needed as large-scale analyses of microbial genomes increase.

Results

IslandPath-DIMOB, a leading GI prediction tool in the IslandViewer webserver, has now been significantly improved by modifying both the decision algorithm to determine sequence composition biases, and the underlying database of HMM profiles for associated mobility genes. The accuracy of IslandPath-DIMOB and other major software has been assessed using a reference GI dataset predicted by comparative genomics, plus a manually curated dataset from literature review. Compared to the previous version (v0.2.0), this IslandPath-DIMOB v1.0.0 achieves 11.7% and 5.3% increase in recall and precision, respectively. IslandPath-DIMOB has the highest Matthews correlation coefficient among individual prediction methods tested, combining one of the highest recall measures (46.9%) at high precision (87.4%). The only method with higher recall had notably lower precision (55.1%). This new IslandPath-DIMOB v1.0.0 will facilitate more accurate studies of GIs, including their key roles in microbial adaptability of medical, environmental and industrial interest.

Availability and implementation

IslandPath-DIMOB v1.0.0 is freely available through the IslandViewer webserver {{http://www.pathogenomics.sfu.ca/islandviewer/}} and as standalone software {{https://github.com/brinkmanlab/islandpath/}} under the GNU-GPLv3.

Supplementary information

Supplementary data are available at Bioinformatics online.

IslandViewer 4: expanded prediction of genomic islands for larger-scale datasets

IslandViewer (http://www.pathogenomics.sfu.ca/islandviewer/) is a widely-used webserver for the prediction and interactive visualization of genomic islands (GIs, regions of probable horizontal origin) in bacterial and archaeal genomes. GIs disproportionately encode factors that enhance the adaptability and competitiveness of the microbe within a niche, including virulence factors and other medically or environmentally important adaptations. We report here the release of IslandViewer 4, with novel features to accommodate the needs of larger-scale microbial genomics analysis, while expanding GI predictions and improving its flexible visualization interface. A user management web interface as well as an HTTP API for batch analyses are now provided with a secured authentication to facilitate the submission of larger numbers of genomes and the retrieval of results. In addition, IslandViewer's integrated GI predictions from multiple methods have been improved and expanded by integrating the precise Islander method for pre-computed genomes, as well as an updated IslandPath-DIMOB for both pre-computed and user-supplied custom genome analysis. Finally, pre-computed predictions including virulence factors and antimicrobial resistance are now available for 6193 complete bacterial and archaeal strains publicly available in RefSeq. IslandViewer 4 provides key enhancements to facilitate the analysis of GIs and better understand their role in the evolution of successful environmental microbes and pathogens.