Sanger sequencing of the first ~500 bp of the 16S rRNA gene is frequently used to identify bacterial pathogens that have ambiguous biochemical profiles or proteomic mass spectra. When diversity does not occur within that region, genus-level and/or species-level identification may not be possible, and a longer sequence or alternative target may be required to distinguish between genera/species. In this study, we evaluated a clinically relevant end-to-end solution for long-read (~1,500 nt) 16S rRNA next-generation sequencing by Oxford Nanopore Technologies (ONT) compared to a ~500 nt Sanger sequencing approach for the identification of 153 bacterial clinical isolates. Sequencing data were analyzed using the IDNS software from SmartGene and its proprietary 16S Centroid reference database (Centroid database) SmartGene software and the Centroid database. The agreement of the two platforms on species- and genus-level identification was determined, and discrepancies were resolved by whole-genome sequencing. ONT had a higher taxonomic resolution at the genus level (P < 0.01). When genus-level identification was achieved by both methods, concordance to the best matching genus was 100%. When species-level identification was achieved by both methods, concordance to the best matching species was 91%. The costs per test were ~$25.30 (when multiplexing 24 samples/run) and $74 for ONT and Sanger sequencing, respectively. The hands-on time spent performing sequencing was similar for both methods, but the turnaround time of ONT was significantly shorter than that of Sanger sequencing.IMPORTANCEThis study adds to existing literature by describing a validated end-to-end solution of 16S rRNA gene Oxford Nanopore sequencing for bacterial isolate identification, including sequencing run time evaluation, automated analysis (SmartGene 16S Identification App) and interpretation of results, that can be incorporated into clinical and public health laboratories with a simple and cost-effective workflow.

The human gut microbiome is increasingly recognized as important to health and disease, influencing immune function, metabolism, mental health, and chronic illnesses. Two widely used, cost-effective, and fast approaches for analyzing gut microbial communities are shallow shotgun metagenomic sequencing (SSMS) and full-length 16S rDNA sequencing. This study compares these methods across 43 stool samples, revealing notable differences in taxonomic and species-level detection. At the genus level, Bacteroides was most abundant in both methods, with Faecalibacterium showing similar trends but Prevotella was more abundant in full-length 16S rDNA. Genera such as Alistipes and Akkermansia were more frequently detected by full-length 16S rDNA, whereas Eubacterium and Roseburia were more prevalent in SSMS. At the species level, Faecalibacterium prausnitzii, a key indicator of gut health, was abundant across both datasets, while Bacteroides vulgatus was more frequently detected by SSMS. Species within Parabacteroides and Bacteroides were primarily detected by 16S rDNA, contrasting with higher SSMS detection of Prevotella copri and Oscillibacter valericigenes. LEfSe analysis identified 18 species (9 species in each method) with significantly different detection between methods, underscoring the impact of methodological choice on microbial diversity and abundance. Differences in classification databases, such as Ribosomal Database Project (RDP) for 16S rDNA and Kraken2 for SSMS, further highlight the influence of database selection on outcomes. These findings emphasize the importance of carefully selecting sequencing methods and bioinformatics tools in microbiome research, as each approach demonstrates unique strengths and limitations in capturing microbial diversity and relative abundances.

Mycobacterium riyadhense is a newly discovered, slow-growing nontuberculous mycobacterium with an emerging global distribution. We report a case of multifocal osteolytic lesions as the first sign of infection in a previously healthy 39-year-old female. M. riyadhense was detected in this case using next-generation metagenomic sequencing after it failed to be identified with conventional methods. The patient received 12 months of therapy with isoniazid, rifampin, and ethambutol, with the addition of moxifloxacin and clarithromycin in the first four months, and had a full return to health with no detectable disease at the last follow-up.

Bacterial infections often lead to severe health consequences owing to their ability to infiltrate multiple anatomical sites, including the bloodstream, respiratory tract, and digestive tract, posing substantial diagnostic and therapeutic challenges. Consequently, a rapid and versatile detection method capable of identifying a broad spectrum of bacterial pathogens is urgently required to facilitate precise antibiotic prescriptions. Addressing this need, we introduce MiND-DMF (Multibacterial Infection Nucleic Acid Detection on a Digital Microfluidic Platform), a cost-effective digital microfluidic platform tailored for multiplexed bacterial detection. This system integrates DNA extraction, recombinase polymerase amplification (RPA), and CRISPR-based detection technologies, enabling the efficient identification of six common infectious bacteria. Operating at a constant temperature of 37 °C, MiND-DMF completes the entire diagnostic process in just 55 min and is compatible with human reference genes. In spiked samples, the platform demonstrated a detection limit of 100 CFU/mL, highlighting its exceptional sensitivity and quantification capability. In clinical evaluations, MiND-DMF exhibited outstanding performance, achieving 100% sensitivity and 98%-100% specificity compared to conventional PCR methods across 50 samples derived from diverse tissue sources. This robust platform demonstrates strong anti-interference capabilities, making it suitable for analyzing various tissue fluids including blood, alveolar lavage fluid, urine, nasal secretions, appendiceal pus, and ear pus. The versatility and precision of MiND-DMF support the monitoring of hospital-acquired bacterial infection origins, empowering physicians to prescribe targeted antibiotics and enhancing overall infection prevention and control strategies. By accurately detecting bacteria from multiple sources, MiND-DMF can play a pivotal role in improving patient outcomes and public health.

This review article highlights the surveillance of bacterial, viral, and parasitic diseases in donkey populations in China. Key findings highlight significant threats from Equine herpesviruses (EHV-8 and EHV-1), which cause encephalitis, abortion, and respiratory distress. Several parasitic infections including Giardia duodenalis, Cryptosporidium spp., Enterocytozoon bieneusi, and Toxoplasma gondii present important zoonotic concerns across multiple regions of China. Additionally, this review synthesizes current knowledge on donkey microbiota across various body sites and examines their functional significance in health and disease. The complex relationship between the microbiota and host health represents a critical area of research in donkeys. Recent molecular advancements have enhanced our understanding of the diverse microbial ecosystems inhabiting different body sites in donkeys and their profound impact on health outcomes. As single-stomach herbivores, donkeys possess complex microbial communities throughout their digestive tracts that are essential for intestinal homeostasis and nutritional processing. Significant variations in microbiota composition exist across different intestinal segments, with the hindgut displaying greater richness and diversity compared to the foregut. Beyond the digestive system, distinct microbial profiles have been characterized across various body sites including the skin, oral cavity, reproductive tract, and body secretions such as milk. The health implications of donkey microbiota extend to critical areas including nutrition, immune function, and disease susceptibility. Research demonstrates how dietary interventions, environmental stressors, and physiological states significantly alter microbial communities, correlating with changes in inflammatory markers, antioxidant responses, and metabolic functions. Additionally, specific microbial signatures associated with conditions like endometritis and respiratory disease suggest the potential for microbiota-based diagnostics and therapeutics. The identification of antibiotic-resistant strains of Proteus mirabilis and Klebsiella pneumoniae in donkey meat highlights food safety concerns requiring enhanced monitoring systems and standardized safety protocols. These findings provide a foundation for improved donkey healthcare management, including targeted disease surveillance, microbiota-based interventions, and protective measures for those working with donkeys or consuming donkey-derived products.

The evolutionary adaptation of pathogens to biological materials has led to an upsurge in drug-resistant superbugs that significantly threaten public health. Treating most infections is an uphill task, especially those associated with multi-drug-resistant pathogens, biofilm formation, persister cells, and pathogens that have acquired robust colonization and immune evasion mechanisms. Innovative diagnostic solutions are crucial for identifying and understanding these pathogens, initiating efficient treatment regimens, and curtailing their spread. While next-generation sequencing has proven invaluable in diagnosis over the years, the most glaring drawbacks must be addressed quickly. Many promising pathogen-associated and host biomarkers hold promise, but their sensitivity and specificity remain questionable. The integration of CRISPR-Cas9 enrichment with nanopore sequencing shows promise in rapid bacterial diagnosis from blood samples. Moreover, machine learning and artificial intelligence are proving indispensable in diagnosing pathogens. However, despite renewed efforts from all quarters to improve diagnosis, accelerated bacterial diagnosis, especially in Africa, remains a mystery to this day. In this review, we discuss current and emerging diagnostic approaches, pinpointing the limitations and challenges associated with each technique and their potential to help address drug-resistant bacterial threats. We further critically delve into the need for accelerated diagnosis in low- and middle-income countries, which harbor more infectious disease threats. Overall, this review provides an up-to-date overview of the diagnostic approaches needed for a prompt response to imminent or possible bacterial infectious disease outbreaks.

As the first line of defense against external pathogens, the skin and its resident microbiota are responsible for protection and eubiosis. Innovations in DNA sequencing have significantly increased our knowledge of the skin microbiome. However, current characterizations do not discriminate between DNA from live cells and remnant DNA from dead organisms (relic DNA), resulting in a combined readout of all microorganisms that were and are currently present on the skin rather than the actual living population of the microbiome. Additionally, most methods lack the capability for absolute quantification of the microbial load on the skin, complicating the extrapolation of clinically relevant information.

Here, we integrated relic-DNA depletion with shotgun metagenomics and bacterial load determination to quantify live bacterial cell abundances across different skin sites. Though we discovered up to 90% of microbial DNA from the skin to be relic DNA, we saw no significant effect of this on the relative abundances of taxa determined by shotgun sequencing. Relic-DNA depletion prior to sequencing strengthened underlying patterns between microbiomes across volunteers and reduced intraindividual similarity. We determined the absolute abundance and the fraction of population alive for several common skin taxa across body sites and found taxa-specific differential abundance of live bacteria across regions to be different from estimates generated by total DNA (live + dead) sequencing.

Our results reveal the significant bias relic DNA has on the quantification of low biomass samples like the skin. The reduced intraindividual similarity across samples following relic-DNA depletion highlights the bias introduced by traditional (total DNA) sequencing in diversity comparisons across samples. The divergent levels of cell viability measured across different skin sites, along with the inconsistencies in taxa differential abundance determined by total vs live cell DNA sequencing, suggest an important hypothesis for certain sites being susceptible to pathogen infection. Overall, our study demonstrates a characterization of the skin microbiome that overcomes relic-DNA bias to provide a baseline for live microbiota that will further improve mechanistic studies of infection, disease progression, and the design of therapies for the skin. Video Abstract.

Currently, diagnosis of bacterial infections is based on culture, possibly followed by the amplification and sequencing (Sanger method) of the 16S rDNA - encoding gene when cultures are negative. Clinical metagenomics (CMg), i.e. the sequencing of a sample's entire nucleic acids, may allow for the identification of bacteria not detected by conventional methods. Here, we tested the performance of CMg compared to 16S rDNA sequencing (Sanger) in 50 patients with suspected bacterial infection but negative cultures.

This is a prospective cohort study. Fifty patients (73 samples) with negative culture and a 16S rDNA sequencing demand (Sanger) were recruited from two sites. On the same samples, CMg (Illumina NextSeq) was also performed and compared to 16S rDNA Sanger sequencing. Bacteria were identified using MetaPhlAn4.

Among the 73 samples, 20 (27 %, 17 patients) had a clinically relevant 16S rDNA Sanger sequencing result (used for patient management) while 11 (15 %, 9 patients) were considered contaminants. At the patient level, the sensitivity of CMg was 70 % (12/17) compared to 16S rDNA. In samples negative for 16S rDNA Sanger sequencing (n = 53), CMg identified clinically-relevant bacteria in 10 samples (19 %, 10 patients) with 14 additional bacteria.

CMg was not 100 % sensitive when compared to 16S, supporting that it may not be a suitable replacement. However, CMg did find additional bacteria in samples negative for 16S rDNA Sanger. CMg could therefore be positioned as a complementary to 16S rDNA Sanger sequencing.

Biosolids, derived from wastewater treatment processes, are valuable resources for soil amendment in agriculture due to their nutrient-rich composition. However, various contaminants of concern (CEC) such as pharmaceuticals, per-and poly-fluoroalkyl substances, endocrine disruptive chemicals, surfactants, pathogens, nanoplastics, and microplastics, are also reported in biosolids. The use of biosolids for agriculture may introduce these CEC into the soil, which raises concerns about their environmental and human health impacts. Moreover, the presence of pathogens (Escherichia coli, Salmonella sp., Shigella, Giardia, Rotavirus, etc.) even after treatment calls for microbial profiling of biosolids, especially in developing countries. Multi-omics approaches can be used as powerful tools for characterizing microbial communities and highlighting metabolic pathways. Moreover, these approaches also help in predicting the ecological and agronomic effects of biosolids application in agricultural soils. This review discusses the advantages and challenges of using biosolids in agriculture, considering the range of different CEC reported in biosolids. Moreover, the current legislation for the use of biosolids in agriculture is also presented, highlighting the limitations with respect to guidelines for emerging contaminants in biosolids. Furthermore, the role of the multi-omics approach in biosolids management, focusing on genomics, transcriptomics, proteomics, and metabolomics is also assessed. Multi-omics also allows for real-time monitoring, ensuring continuous optimization of biosolids towards changing environmental conditions. This dynamic approach not only enhances the safe use, but also enhances the sustainability of waste management practices, minimizing the negative effects. Finally, the future research directions for integrating the multi-omics approach into biosolid management practices are also suggested. The need for updating the legislative framework, continued innovation to promote sustainable and robust agricultural systems, bringing the process closer to the principles of a circular bioeconomy is also empahasized.

Effective treatment of infectious diseases requires prompt and accurate bacterial identification and tailored antimicrobial treatments. Traditional culture methods are considered the gold standard, but their effectiveness diminishes for fastidious and hard-to-grow microorganisms. In recent years, molecular diagnostic tools such as 16S rRNA gene next-generation sequencing (16S NGS) have gained popularity in the field. We analysed data from samples submitted for 16S NGS between July 2022 and July 2023 at the Department of Advanced Translational Microbiology in Trieste, Italy. The study included samples submitted for both culture-based identification and 16S NGS. Conventional media were used for culture, and bacterial identification was performed using MALDI-TOF mass spectrometry. The V3 region of the 16S rRNA gene was sequenced using the Ion PGM platform. Among the 123 samples submitted, drainage fluids (38%) and blood (23%) were the most common, with requests predominantly from the Infectious Diseases (31.7%) and Orthopedic (21.13%) Units. In samples collected from patients with confirmed infections, 16S NGS demonstrated diagnostic utility in over 60% of cases, either by confirming culture results in 21% or providing enhanced detection in 40% of instances. Among the 71 patients who had received antibiotic therapies before sampling (mean 2.3 prior antibiotic days), pre-sampling antibiotic consumption did not significantly affect the sensitivity of 16S NGS. In routine microbiology laboratories, combining 16S NGS with culture method enhances the sensitivity of microbiological diagnostics, even when sampling is conducted during antibiotic therapy.

The application of high throughput technologies has enabled unravelling of unique differences between healthy mares and mares with endometritis at transcriptomic and proteomic levels. However, differences in the uterine microbiome are yet to be investigated.

The present study was aimed at evaluating the differences in uterine microbiome between healthy mares and mares with endometritis.

Low-volume lavage (LVL) samples were collected from the uterus of 30 mares classified into healthy (n = 15) and endometritis (n = 15) based on their reproductive history, intrauterine fluid accumulation, gross appearance of LVL samples, endometrial cytology and bacterial culture. The samples were subjected to 16S rRNA sequencing.

Notable differences in the uterine microbiome were observed between healthy mares and mares with endometritis at various taxonomic levels. In healthy mares, the most abundant phylum, class, order and family were Firmicutes, Bacilli, Bacillales and Paenibacillaceae, respectively. In contrast, the most abundant corresponding taxonomic levels in mares with endometritis were Proteobacteria, Gammaproteobacteria, Enterobacterales and Enterobacteriaceae, respectively. At the genus level, Brevibacillus and Paenibacillus were more abundant in healthy mares, whereas Escherichia, Salmonella and Klebsiella were more abundant in mares with endometritis. In healthy mares, Brevibacillus brevis was the most abundant species, followed by Brevibacillus choshinensis and Paenibacillus sp JDR-2. However, in mares with endometritis, Escherichia coli was the most abundant species, followed by Salmonella enterica and Klebsiella pneumoniae.

These results confirmed the previously reported presence of a uterine microbiome in healthy mares and helped unravel some alterations that occur in mares with endometritis. The findings can potentially help formulate new approaches to prevent or treat equine endometritis.

Infective endocarditis (IE) remains a dangerous disease and continues to have a high mortality rate. Unfortunately, despite continuous improvements in diagnostic methods, in many cases, blood cultures remain negative, and the pathogen causing endocarditis is unknown. This makes targeted therapy and the selection of appropriate antibiotics impossible. Therefore, we present what methods can be used to identify the pathogen in infective endocarditis. These are mainly molecular methods, including PCR and MGS, as well as imaging methods using radiotracers, which offer more possibilities for diagnosing IE. However, they are still not widely used in the diagnosis of IE. The article summarizes in which cases we should choose them and what we are most hopeful about in further research into the diagnosis of IE. In addition, registered clinical trials that are currently underway for the diagnosis of IE are also presented.

Metagenomic next-generation sequencing (mNGS) allows untargeted identification of a broad range of pathogens, including rare or novel microorganisms. Despite the recognition of mNGS as a valuable diagnostic tool for infections, the most relevant indications for this innovative strategy remain poorly defined. We aimed to assess the determinants of positivity and clinical utility of mNGS.

In this observational study, we prospectively performed short-read shotgun metagenomics analysis as a second-line test (in cases of negative first-line test or when the symptoms were not fully explained by initial positive results) or as a first-line test in life-threatening situations requiring urgent non-targeted pathogen identification at the Necker-Enfants Malades Hospital (Paris, France). All sample types, clinical indications, and patient populations were included. Samples were accompanied by a mandatory form completed by the senior clinician or pathologist, on which the clinical level of suspected infection (defined as high or low) was indicated. We assessed the variables (gender, age, immune status, initial suspicion of infection, indication, and sample type) associated with mNGS pathogen detection using odds ratios (ORs) from multivariate logistic regression. Additional investigations were carried out using specific PCR or culture techniques, to confirm positive mNGS results, or when infectious suspicion was particularly high despite a negative mNGS result.

Between Oct 29, 2019, and Nov 7, 2022, we analysed 742 samples collected from 523 patients. The initial suspicion of infection was either high (n=470, 63%) or low (n=272, 37%). Causative or possibly causative pathogens were detected in 117 (25%) samples from patients with high initial suspicion of infection, versus nine (3%) samples analysed to rule out infection (OR 9·1, 95% CI 4·6-20·4; p<0·0001). We showed that mNGS had higher odds of detecting a causative or possibly causative pathogenic virus on CNS biopsies than CSF samples (4·1, 1·7-10·7; p=0·0025) and in samples from immunodeficient compared with immunocompetent individuals (2·4, 1·4-4·1; p=0·0013). Concordance with conventional confirmatory tests results was 103 (97%) of 106, when mNGS detected causative or possibly causative pathogens. Altogether, among 231 samples investigated by both mNGS and subsequent specific tests, discordant results were found in 69 (30%) samples, of which 58 (84%) were mNGS positive and specific tests negative, and 11 (16%) mNGS negative and specific tests positive.

Major determinants of pathogen detection by mNGS are immune status and initial level of suspicion of infection. These findings will contribute, along with future studies, to refining the positioning of mNGS in diagnostic and treatment decision-making algorithms.

Necker-Enfants Malades Hospital and Institut Pasteur.

For the French translation of the abstract see Supplementary Materials section.

Shotgun and targeted metagenomic sequencing have been shown in separate studies to be potentially useful for culture-free pathogen identification in blood and/or plasma of patients with infective endocarditis (IE). However, the 2 approaches have not been directly compared. The aim of this study was to compare shotgun metagenomic sequencing with targeted metagenomic sequencing (tMGS) for organism identification in blood or plasma of patients with IE.

Patients with possible or definite IE were prospectively enrolled from October 2020 to July 2021. Shotgun metagenomic sequencing was performed with the Karius test, which uses microbial cell-free DNA (mcfDNA) sequencing to detect, identify, and quantitate DNA-based pathogens in plasma. tMGS was performed using a 16S ribosomal RNA (rRNA) polymerase chain reaction assay targeting the V1 to V3 regions of the 16S rRNA gene. Results were compared using the McNemar test of paired proportions.

Samples from 34 patients were investigated. The Karius test was positive in 24/34 (71%), including 3/6 (50%) with blood culture-negative endocarditis (BCNE), which was not significantly different from the positivity rate of tMGS (P = .41). Results of the Karius test were concordant with tMGS in 75% of cases. The Karius test detected 2 cases of methicillin-resistant Staphylococcus aureus among the 7 S. aureus detections, in accordance with results of phenotypic susceptibility testing. The combination of blood cultures, the Karius test, and tMGS found a potential causative pathogen in 33/34 (97%), including 5/6 with BCNE.

The Karius test and tMGS yielded comparable detection rates; however, beyond organism identification, the Karius test generated potentially useful antibiotic resistance data.

A 43-year-old woman presented bilateral anterior granulomatous uveitis associated with bilateral disc edema and bilateral peripheral retinochoroidal lesions. Intravenous corticosteroids after negative investigations for infectious causes did not prevent spreading of the lesions and retinal atrophy. A diagnostic vitrectomy with vitreous analysis, including pan-genomic, next-generation sequencing showed a positive result for rhinovirus HRV B91, and the cytological analysis was suggestive of infection. Intravenous immunoglobulins associated with pegylated interferon-alpha strongly slowed the progression of the lesions and led to scarred and atrophic aspect in both eyes after 6 months. [Ophthalmic Surg Lasers Imaging Retina 2023;54:720-722.].

Shotgun metagenomics (SMg) sequencing has gained a considerable interest, as it enables the detection of any microorganisms through a single analysis. Due to the limitations of standard microbiological approaches, the microbial documentation of liver abscesses (LA), which is crucial for their medical management, can be difficult. Here we aimed to compare the performance of SMg with standard approaches for the microbiological documentation of LA.

In this retrospective study conducted at two centers, we compared the results of standard microbiology with metagenomics analysis of consecutive LA samples. For samples tested positive for Klebsiella pneumoniae, we compared the analysis of virulence and resistance genes using metagenomics data to whole-genome sequencing of corresponding isolates obtained in culture.

Out of the 62 samples included, standard approaches and SMg yielded documentation in 80.6% and 96.8%, respectively. In 37.1% (23/62) of cases, both methods showed identical results, whereas in 43.5% (27/62) of cases, the samples were positive by both methods, but SMg found additional species in 88.9% (24/27), mostly anaerobes. When the standard approaches were negative, the SMg was able to detect microorganisms in 80.0% of cases (8/10). Overall, SMg identified significantly more microorganisms than culture (414 vs.105; p<0.05). K. pneumoniae genome analysis was able to detect resistance and virulence genes with a level of sensitivity depending on the depth of sequencing.

Overall, we showed that SMg had better performance in detecting and identifying microorganisms from LA samples and could help characterizing strain's resistome and virulome. Although still costly and requiring specific skills and expensive equipment, MGs methods are set to expand in the future.

The lower respiratory tract microbiome is widely studied to pinpoint microbial dysbiosis of diversity or abundance that is linked to a number of chronic respiratory illnesses. However, it is vital to clarify how the microbiome, through the release of microbial metabolites, impacts lung health and oncogenesis.

In order to discover the powerful correlations between microbial metabolites and disease, we collected, under electronic bronchoscopy examinations, samples of paired bronchoalveolar lavage fluids (BALFs) from tumor-burden lung segments and ipsilateral non-tumor sites from 28 lung cancer participants, further performing metagenomic sequencing, short-chain fatty acid (SCFA) metabolomics, and multiomics analysis to uncover the potential correlations of the microbiome and SCFAs in lung cancer.

In comparison to BALFs from normal lung segments of the same participant, those from lung cancer burden lung segments had slightly decreased microbial diversity in the lower respiratory tract. With 18 differentially prevalent microbial species, including the well-known carcinogens Campylobacter jejuni and Nesseria polysaccharea, the relative species abundance in the lower respiratory tract microbiome did not significantly differ between the two groups. Additionally, a collection of commonly recognized probiotic metabolites called short-chain fatty acids showed little significance in either group independently but revealed a strong predictive value when using an integrated model by machine learning. Multiomics also discovered particular species related to SCFAs, showing a positive correlation with Brachyspira hydrosenteriae and a negative one with Pseudomonas at the genus level, despite limited detection in lower airways. Of note, these distinct microbiota and metabolites corresponded with clinical traits that still required confirmation.

Further analysis of metagenome functional capacity revealed that genes encoding environmental information processing and metabolism pathways were enriched in the lower respiratory tract metagenomes of lung cancer patients, further supporting the oncogenesis function of various microbial species by different metabolites. These findings point to a potent relationship between particular components of the integrated microbiota-metabolites network and lung cancer, with implications for screening and diagnosis in clinical settings.

Microbiological diagnosis of intrauterine infections (IIU) still relies on bacteriological cultures or targeted DNA amplification lacking in sensitivity. Shotgun metagenomics (SMg) is an emerging unbiased molecular approach that makes it possible to sequence all the nucleic acids from any sample. It had never previously been used for IIU.

We here report the case of a patient with an unexplained IIU and fetal loss that could be documented by a combined SMg/microbiological approach, leading to the diagnosis of maternal brucellosis.

A 31-year-old woman presented with an undocumented IIU with fetal loss at 24 weeks of gestation. Culture-based work-up failed to identify the pathogen involved. Paraffin-embedded placenta sample was retrospectively analyzed by SMg. Brucella spp nucleic acids were detected, and subacute maternal brucellosis was confirmed by targeted PCR and serological testing.

This case provides grounds for further utilization of SMg for the microbiological diagnosis of unexplained obstetrical infections.

rRNA gene Sanger sequencing is being used for the identification of cultured pathogens. A new diagnostic approach is sequencing of uncultured samples by using the commercial DNA extraction and sequencing platform SepsiTest (ST). The goal was to analyze the clinical performance of ST with a focus on nongrowing pathogens and the impact on antibiotic therapy. A literature search used PubMed/Medline, Cochrane, Science Direct, and Google Scholar. Eligibility followed PRISMA-P criteria. Quality and risk of bias were assessed drawing on QUADAS-2 (quality assessment of diagnostic accuracy studies, revised) criteria. Meta-analyses were performed regarding accuracy metrics compared to standard references and the added value of ST in terms of extra found pathogens. We identified 25 studies on sepsis, infectious endocarditis, bacterial meningitis, joint infections, pyomyositis, and various diseases from routine diagnosis. Patients with suspected infections of purportedly sterile body sites originated from various hospital wards. The overall sensitivity (79%; 95% confidence interval [CI], 73 to 84%) and specificity (83%; 95% CI, 72 to 90%) were accompanied by large effect sizes. ST-related positivity was 32% (95% CI, 30 to 34%), which was significantly higher than the culture positivity (20%; 95% CI, 18 to 22%). The overall added value of ST was 14% (95% CI, 10 to 20%) for all samples. With 130 relevant taxa, ST uncovered high microbial richness. Four studies demonstrated changes of antibiotic treatment at 12% (95% CI, 9 to 15%) of all patients upon availability of ST results. ST appears to be an approach for the diagnosis of nongrowing pathogens. The potential clinical role of this agnostic molecular diagnostic tool is discussed regarding changes of antibiotic treatment in cases where culture stays negative.

Blood cultures have been the gold standard for identifying pathogens in infective endocarditis (IE). Blood culture-negative endocarditis (BCNE), however, occurs in 40% or more of IE cases with the bulk of them due to recent antibiotic exposure prior to obtaining blood cultures. Increasingly, molecular techniques are being used for pathogen identification in cases of BCNE and more recently has included metagenomic next-generation sequencing (mNGS). We therefore performed a literature search on August 31, 2022, that assessed the mNGS in IE and 13 publications were identified and included in a systematic review. Eight (61.5%) of them focused only on IE with mNGS performed on cardiac valve tissue in four studies, plasma in three studies and cardiac implantable electronic devices (CIED) in one study. Gram-positive cocci, including Staphylococcus aureus (n = 31, 8.9%), coagulase-negative staphylococci (n = 61, 17.6%), streptococci (n = 130, 37.5%), and Enterococcus faecalis (n = 23, 6.6%) were the predominant organisms identified by mNGS. Subsequent investigations are needed to further define the utility of mNGS in BCNE and its impact on patient outcomes. Despite some pitfalls, mNGS seems to be of value in pathogen identification in IE cases, particularly in those with BCNE. This study was registered and on the Open Science Framework platform.

Objectives: Smoking is the commonest cause of oral cavity squamous cell carcinoma (OC-SCC), but the etiology of OC-SCC in nonsmokers is unknown. Our primary goal was to use metagenomic shotgun sequencing (MSS) to define the taxonomic composition and functional potential of oral metagenome in nonsmokers with OC-SCC. Methods: We conducted a case-control study with 42 OC-SCC case and 45 control nonsmokers. MSS was performed on DNA extracted from mouthwash samples. Taxonomic analysis and pathway analysis were done using MetaPhlAn2 and HUMAnN2, respectively. Statistical difference was determined using the Mann-Whitney test controlling false discovery rate. Results: There was no significant difference in age, sex, race, or alcohol consumption between OC-SCC and control patients. There was a significant difference in beta diversity between OC-SCC and controls. At the phylum level, Bacteroidetes and Synergistetes were overly represented in OC-SCC while Actinobacteria and Firmicutes were overly represented in controls. At the genus level, Fusobacterium was overly represented in OC-SCC compared with controls, while Corynebacterium, Streptococcus, Actinomyces, Cryptobacterium, and Selenomonas were overly represented in controls. Bacterial pathway analysis identified overrepresentation in OC-SCC of pathways related to metabolism of flavin, biotin, thiamin, heme, sugars, fatty acids, peptidoglycans, and tRNA and overrepresentation of nucleotides and essential amino acids in controls. Conclusions: The oral microbiome in nonsmoker patients with OC-SCC is significantly different from that of nonsmoker control patients in taxonomic compositions and functional potentials. Our study's MSS findings matched with previous 16S-based methods in taxonomic differentiation but varied greatly in functional differentiation of microbiomes in OC-SCC and controls.

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