Comprehensive evaluation of shotgun metagenomics, amplicon sequencing, and harmonization of these platforms for epidemiological studies

Comparative analysis of human gut bacterial microbiota between shallow shotgun metagenomic sequencing and full-length 16S rDNA amplicon sequencing

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.

Shining Light on Oral Biofilm Fluorescence In Situ Hybridization (FISH): Probing the Accuracy of In Situ Biogeography Studies

The oral biofilm has been instrumental in advancing microbial research and enhancing our understanding of oral health and disease. Recent developments in next-generation sequencing have provided detailed insights into the microbial composition of the oral microbiome, enabling species-level analyses of biofilm interactions. Fluorescence in situ hybridization (FISH) has been especially valuable for studying the spatial organization of these microbes, revealing intricate arrangements such as "corncob" structures that highlight close bacterial interactions. As more genetic sequence data become available, the specificity and accuracy of existing FISH probes used in biogeographical studies require reevaluation. This study examines the performance of commonly used species-specific FISH probes, designed to differentiate oral microbes within in situ oral biofilms, when applied in vitro to an expanded set of bacterial strains. Our findings reveal that the specificity of several FISH probes is compromised, with cross-species hybridization being more common than previously assumed. Notably, we demonstrate that biogeographical associations within in situ oral biofilms, particularly involving Streptococcus and Corynebacterium, may need to be reassessed to align with the latest metagenomic data.

Refining microbiome diversity analysis by concatenating and integrating dual 16S rRNA amplicon reads

Understanding the role of human gut microbiota in health and disease requires insights into its taxonomic composition and functional capabilities. This study evaluates whether concatenating paired-end reads enhances data output for gut microbiome analysis compared to the merging approach across various regions of the 16S rRNA gene. We assessed this approach in both mock communities and Korean cohorts with or without ulcerative colitis. Our results indicate that using the direct joining method for the V1-V3 or V6-V8 regions improves taxonomic resolution compared to merging paired-end reads (ME) in post-sequencing data. While predicting microbial function based on 16S rRNA sequencing has inherent limitations, integrating sequencing reads from both the V1-V3 and V6-V8 regions enhanced functional predictions. This was confirmed by whole metagenome sequencing (WMS) of Korean cohorts, where our approach improved taxa detection that was lost using the ME method. Thus, we propose that the integrated dual 16S rRNA sequencing technique serves as a valuable tool for microbiome research by bridging the gap between amplicon sequencing and WMS.

Advancing Gut Microbiome Research: The Shift from Metagenomics to Multi-Omics and Future Perspectives

The gut microbiome, a dynamic and integral component of human health, has co-evolved with its host, playing essential roles in metabolism, immunity, and disease prevention. Traditional microbiome studies, primarily focused on microbial composition, have provided limited insights into the functional and mechanistic interactions between microbiota and their host. The advent of multi-omics technologies has transformed microbiome research by integrating genomics, transcriptomics, proteomics, and metabolomics, offering a comprehensive, systems-level understanding of microbial ecology and host-microbiome interactions. These advances have propelled innovations in personalized medicine, enabling more precise diagnostics and targeted therapeutic strategies. This review highlights recent breakthroughs in microbiome research, demonstrating how these approaches have elucidated microbial functions and their implications for health and disease. Additionally, it underscores the necessity of standardizing multi-omics methodologies, conducting large-scale cohort studies, and developing novel platforms for mechanistic studies, which are critical steps toward translating microbiome research into clinical applications and advancing precision medicine.

Challenges in capturing the mycobiome from shotgun metagenome data: lack of software and databases

BACKGROUND

The mycobiome, representing the fungal component of microbial communities, is increasingly acknowledged as an integral part of the gut microbiome. However, research in this area remains relatively limited. The characterization of mycobiome taxa from metagenomic data is heavily reliant on the quality of the software and databases. In this study, we evaluated the feasibility of mycobiome profiling using existing bioinformatics tools on simulated fungal metagenomic data.

RESULTS

We identified seven tools claiming to perform taxonomic assignment of fungal shotgun metagenomic sequences. One of these was outdated and required substantial modifications of the code to be functional and was thus excluded. To evaluate the accuracy of identification and relative abundance of the remaining tools (Kraken2, MetaPhlAn4, EukDetect, FunOMIC, MiCoP, and HumanMycobiomeScan), we constructed 18 mock communities of varying species richness and abundance levels. The mock communities comprised up to 165 fungal species belonging to the phyla Ascomycota and Basidiomycota, commonly found in gut microbiomes. Of the tools, FunOMIC and HumanMycobiomeScan needed source code modifications to run. Notably, only one species, Candida orthopsilosis, was consistently identified by all tools across all communities where it was included. Increasing community richness improved precision of Kraken2 and the relative abundance accuracy of all tools on species, genus, and family levels. MetaPhlAn4 accurately identified all genera present in the communities and FunOMIC identified most species. The top three tools for overall accuracy in both identification and relative abundance estimation were EukDetect, MiCoP, and FunOMIC, respectively. Adding 90% and 99% bacterial background did not significantly impact these tools' performance. Among the whole genome reference tools (Kraken2, HMS, and MiCoP), MiCoP exhibited the highest accuracy when the same reference database was used.

CONCLUSION

Our survey of mycobiome-specific software revealed a very limited selection of such tools and their poor robustness due to error-prone software, along with a significant lack of comprehensive databases enabling characterization of the mycobiome. None of the implemented tools fully agreed on the mock community profiles. FunOMIC recognized most of the species, but EukDetect and MiCoP provided predictions that were closest to the correct compositions. The bacterial background did not impact these tools' performance. Video Abstract.

Examining the healthy human microbiome concept

Human microbiomes are essential to health throughout the lifespan and are increasingly recognized and studied for their roles in metabolic, immunological and neurological processes. Although the full complexity of these microbial communities is not fully understood, their clinical and industrial exploitation is well advanced and expanding, needing greater oversight guided by a consensus from the research community. One of the most controversial issues in microbiome research is the definition of a 'healthy' human microbiome. This concept is complicated by the microbial variability over different spatial and temporal scales along with the challenge of applying a unified definition to the spectrum of healthy microbiome configurations. In this Perspective, we examine the progress made and the key gaps that remain to be addressed to fully harness the benefits of the human microbiome. We propose a road map to expand our knowledge of the microbiome-health relationship, incorporating epidemiological approaches informed by the unique ecological characteristics of these communities.

Characterization of Lung Microbiome in Subclinical Pneumonic Thai Pigs Using 16S rRNA Gene Sequencing

Bacterial respiratory disease is one of the major concerns in the modern pig industry. To address the limitations of culture-based methods, 16S rRNA sequencing was employed to characterize the pig lung microbiome to gain a better understanding of microbial physiology and their population genetics. A batch of 510 slaughtered pigs from a farm located in Lampang province, Thailand, was selected. Individual pig weight was recorded. A total of 24 lungs (10 normal and 14 pneumonic lungs) were sampled for gross lesion examination and lung microbial communities were investigated. Poor growth performance and weight uniformity were denoted in this batch. Several pathogenic bacteria were detected in both normal and pneumonic lungs. Microbial diversity was decreased in the pneumonic group. PCoA and NMDS analysis showed a clear separation between the groups. Stenotrophomonas spp. (42.12%) was the dominant genus identified in normal lungs, while Mycoplasma hyopneumoniae (71.97%) was the most abundant in pneumonic lungs, correlating with the commonly observed consolidation lesions. The slaughterhouse serves as a key checkpoint for gathering comprehensive information on pig respiratory health, and lung is representative of the lower respiratory tract for microbiomics. Monitoring of lung lesions should be implemented routinely to gain a better understanding of regional pig respiratory health.

Integrating the milk microbiome signatures in mastitis: milk-omics and functional implications

Mammalian milk contains a variety of complex bioactive and nutritional components and microorganisms. These microorganisms have diverse compositions and functional roles that impact host health and disease pathophysiology, especially mastitis. The advent and use of high throughput omics technologies, including metagenomics, metatranscriptomics, metaproteomics, metametabolomics, as well as culturomics in milk microbiome studies suggest strong relationships between host phenotype and milk microbiome signatures in mastitis. While single omics studies have undoubtedly contributed to our current understanding of milk microbiome and mastitis, they often provide limited information, targeting only a single biological viewpoint which is insufficient to provide system-wide information necessary for elucidating the biological footprints and molecular mechanisms driving mastitis and milk microbiome dysbiosis. Therefore, integrating a multi-omics approach in milk microbiome research could generate new knowledge, improve the current understanding of the functional and structural signatures of the milk ecosystem, and provide insights for sustainable mastitis control and microbiome management.

Metagenomic analyses of gut microbiome composition and function with age in a wild bird; little change, except increased transposase gene abundance

Studies on wild animals, mostly undertaken using 16S metabarcoding, have yielded ambiguous evidence regarding changes in the gut microbiome (GM) with age and senescence. Furthermore, variation in GM function has rarely been studied in such wild populations, despite GM metabolic characteristics potentially being associated with host senescent declines. Here, we used 7 years of repeated sampling of individuals and shotgun metagenomic sequencing to investigate taxonomic and functional changes in the GM of Seychelles warblers (Acrocephalus sechellensis) with age. Our results suggest that taxonomic GM species richness declines with age and in the terminal year, with this terminal decline occurring consistently across all ages. Taxonomic and functional GM composition also shifted with host age. However, the changes we identified occurred linearly with age (or even mainly during early years prior to the onset of senescence in this species) with little evidence of accelerated change in later life or during their terminal year. Therefore, the results suggest that changes in the GM with age are not linked to senescence. Interestingly, we found a significant increase in the abundance of a group of transposase genes with age, which may accumulate passively or due to increased transposition induced as a result of stressors that arise with age. These findings reveal taxonomic and functional GM changes with age, but not senescence, in a wild vertebrate and provide a blueprint for future wild functional GM studies linked to age and senescence.

Metagenome-validated combined amplicon sequencing and text mining-based annotations for simultaneous profiling of bacteria and fungi: vaginal microbiota and mycobiota in healthy women

BACKGROUND

Amplicon sequencing of kingdom-specific tags such as 16S rRNA gene for bacteria and internal transcribed spacer (ITS) region for fungi are widely used for investigating microbial communities. So far most human studies have focused on bacteria while studies on host-associated fungi in health and disease have only recently started to accumulate. To enable cost-effective parallel analysis of bacterial and fungal communities in human and environmental samples, we developed a method where 16S rRNA gene and ITS1 amplicons were pooled together for a single Illumina MiSeq or HiSeq run and analysed after primer-based segregation. Taxonomic assignments were performed with Blast in combination with an iterative text-extraction-based filtration approach, which uses extensive literature records from public databases to select the most probable hits that were further validated by shotgun metagenomic sequencing.

RESULTS

Using 50 vaginal samples, we show that the combined run provides comparable results on bacterial composition and diversity to conventional 16S rRNA gene amplicon sequencing. The text-extraction-based taxonomic assignment-guided tool provided ecosystem-specific bacterial annotations that were confirmed by shotgun metagenomic sequencing (VIRGO, MetaPhlAn, Kraken2). Fungi were identified in 39/50 samples with ITS sequencing while in the metagenome data fungi largely remained undetected due to their low abundance and database issues. Co-abundance analysis of bacteria and fungi did not show strong between-kingdom correlations within the vaginal ecosystem of healthy women.

CONCLUSION

Combined amplicon sequencing for bacteria and fungi provides a simple and cost-effective method for simultaneous analysis of microbiota and mycobiota within the same samples. Conventional metagenomic sequencing does not provide sufficient fungal genome coverage for their reliable detection in vaginal samples. Text extraction-based annotation tool facilitates ecosystem-specific characterization and interpretation of microbial communities by coupling sequence homology to microbe metadata readily available through public databases. Video Abstract.

Exploring alterations of gut/blood microbes in addressing iron overload-induced gut dysbiosis and cognitive impairment in thalassemia patients

Iron overload causes cognitive impairment in thalassemia patients. The gut-brain axis plays an important role in cognitive function. However, the association between gut/blood microbiome, cognition, and iron burden in thalassemia patients has not been thoroughly investigated. We aimed to determine those associations in thalassemia patients with different blood-transfusion regimens. Sixty participants: healthy controls, transfusion-dependent thalassemia (TDT) patients, and non-transfusion-dependent (NTDT) patients, were recruited to evaluate iron overload, cognition, and gut/blood microbiome. TDT patients exhibited greater iron overload than NTDT patients. Most thalassemia patients developed gut dysbiosis, and approximately 25% of the patients developed minor cognitive impairment. Increased Fusobacteriota and Verrucomicrobiota with decreased Fibrobacterota were observed in both TDT and NTDT groups. TDT patients showed more abundant beneficial bacteria: Verrucomicrobia. Iron overload was correlated with cognitive impairment. Increased Butyricimonas and decreased Paraclostridium were associated with higher cognitive function. No trace of blood microbiota was observed. Differences in blood bacterial profiles of thalassemia patients and controls were insignificant. These findings suggest iron overload plays a role in the imbalance of gut microbiota and impaired cognitive function in thalassemia patients. Harnessing probiotic potential from those microbes could prevent the gut-brain disturbance in thalassemia patients.

RAPiD: a rapid and accurate plant pathogen identification pipeline for on-site nanopore sequencing

Nanopore sequencing technology has enabled the rapid, on-site taxonomic identification of samples from anything and anywhere. However, sequencing errors, inadequate databases, as well as the need for bioinformatic expertise and powerful computing resources, have hampered the widespread use of the technology for pathogen identification in the agricultural sector. Here we present RAPiD, a lightweight and accurate real-time taxonomic profiling pipeline. Compared to other metagenomic profilers, RAPiD had a higher classification precision achieved through the use of a curated, non-redundant database of common agricultural pathogens and extensive quality filtering of alignments. On a fungal, bacterial and mixed mock community RAPiD was the only pipeline to detect all members of the communities. We also present a protocol for in-field sample processing enabling pathogen identification from plant sample to sequence within 3 h using low-cost equipment. With sequencing costs continuing to decrease and more high-quality reference genomes becoming available, nanopore sequencing provides a viable method for rapid and accurate pathogen identification in the field. A web implementation of the RAPiD pipeline for real-time analysis is available at https://agrifuture.senckenberg.de.

Multiple indicators of gut dysbiosis predict all-cause and cause-specific mortality in solid organ transplant recipients

OBJECTIVE

Gut microbiome composition is associated with multiple diseases, but relatively little is known about its relationship with long-term outcome measures. While gut dysbiosis has been linked to mortality risk in the general population, the relationship with overall survival in specific diseases has not been extensively studied. In the current study, we present results from an in-depth analysis of the relationship between gut dysbiosis and all-cause and cause-specific mortality in the setting of solid organ transplant recipients (SOTR).

DESIGN

We analysed 1337 metagenomes derived from faecal samples of 766 kidney, 334 liver, 170 lung and 67 heart transplant recipients part of the TransplantLines Biobank and Cohort-a prospective cohort study including extensive phenotype data with 6.5 years of follow-up. To analyze gut dysbiosis, we included an additional 8208 metagenomes from the general population of the same geographical area (northern Netherlands). Multivariable Cox regression and a machine learning algorithm were used to analyse the association between multiple indicators of gut dysbiosis, including individual species abundances, and all-cause and cause-specific mortality.

RESULTS

We identified two patterns representing overall microbiome community variation that were associated with both all-cause and cause-specific mortality. The gut microbiome distance between each transplantation recipient to the average of the general population was associated with all-cause mortality and death from infection, malignancy and cardiovascular disease. A multivariable Cox regression on individual species abundances identified 23 bacterial species that were associated with all-cause mortality, and by applying a machine learning algorithm, we identified a balance (a type of log-ratio) consisting of 19 out of the 23 species that were associated with all-cause mortality.

CONCLUSION

Gut dysbiosis is consistently associated with mortality in SOTR. Our results support the observations that gut dysbiosis is associated with long-term survival. Since our data do not allow us to infer causality, more preclinical research is needed to understand mechanisms before we can determine whether gut microbiome-directed therapies may be designed to improve long-term outcomes.

Clinical Characteristics, Prognosis Factors and Metagenomic Next-Generation Sequencing Diagnosis of Mucormycosis in patients With Hematologic Diseases

INTRODUCTION

New diagnostic methods and antifungal strategies may improve prognosis of mucormycosis. We describe the diagnostic value of metagenomic next⁃generation sequencing (mNGS) and identify the prognostic factors of mucormycosis.

METHODS

We conducted a retrospective study of hematologic patients suffered from mucormycosis and treated with monotherapy [amphotericin B (AmB) or posaconazole] or combination therapy (AmB and posaconazole). The primary outcome was 84-day all-cause mortality after diagnosis.

RESULTS

Ninety-five patients were included, with "proven" (n = 27), "probable" (n = 16) mucormycosis confirmed by traditional diagnostic methods, and "possible" (n = 52) mucormycosis with positive mNGS results. The mortality rate at 84 days was 44.2%. Possible + mNGS patients and probable patients had similar diagnosis processes, overall survival rates (44.2% vs 50.0%, p = 0.685) and overall response rates to effective drugs (44.0% vs 37.5%, p = 0.647). Furthermore, the median diagnostic time was shorter in possible + mNGS patients than proven and probable patients (14 vs 26 days, p < 0.001). Combination therapy was associated with better survival compared to monotherapy at six weeks after treatment (78.8% vs 53.1%, p = 0.0075). Multivariate analysis showed that combination therapy was the protective factor (HR = 0.338, 95% CI: 0.162-0.703, p = 0.004), though diabetes (HR = 3.864, 95% CI: 1.897-7.874, p < 0.001) and hypoxemia (HR = 3.536, 95% CI: 1.874-6.673, p < 0.001) were risk factors for mortality.

CONCLUSIONS

Mucormycosis is a life-threatening infection. Early management of diabetes and hypoxemia may improve the prognosis. Exploring effective diagnostic and treatment methods is important, and combination antifungal therapy seems to hold potential benefits.

Scalp microbiome: a guide to better understanding scalp diseases and treatments

The scalp microbiome represents an array of microorganisms important in maintaining scalp homeostasis and mediating inflammation. Scalp microbial dysregulation has been implicated in dermatologic conditions including alopecia areata (AA), dandruff/seborrheic dermatitis (D/SD), scalp psoriasis (SP) and folliculitis decalvans (FD). Understanding the impact of scalp microbial dysbiosis gives insight on disease pathophysiology and guides therapeutic decision making. Herein we review the scalp microbiome and its functional role in scalp conditions by analysis of metagenomic medical literature in alopecia, D/SD, SP, and other dermatologic disease.Increased abundance of Malassezia, Staphylococcus, and Brevibacterium was associated with SD compared to healthy controls. A higher proportion of Corynebacterium, actinobacteria, and firmicutes are present in AA patients, and lower proportions of Staphylococcus caprae are associated with worse clinical outcomes. Decreased prevalence of actinobacteria and Propionibacterium and increased firmicutes, staphylococcus, and streptococcus are associated with scalp psoriasis. Studies of central centrifugal cicatricial alopecia (CCCA) suggest scalp microbial composition contributes to CCCA's pro-inflammatory status. The most common organisms associated with FD include methicillin-resistant S. aureus and S. lugdunensis. Antifungals have been a mainstay treatment for these diseases, while other alternatives including coconut oils and shampoos with heat-killed probiotics have shown considerable potential efficacy by replenishing the scalp microbiome.

Fine-scale characterization of the soybean rhizosphere microbiome via synthetic long reads and avidity sequencing

BACKGROUND

The rhizosphere microbiome displays structural and functional dynamism driven by plant, microbial, and environmental factors. While such plasticity is a well-evidenced determinant of host health, individual and community-level microbial activity within the rhizosphere remain poorly understood, due in part to the insufficient taxonomic resolution achieved through traditional marker gene amplicon sequencing. This limitation necessitates more advanced approaches (e.g., long-read sequencing) to derive ecological inferences with practical application. To this end, the present study coupled synthetic long-read technology with avidity sequencing to investigate eukaryotic and prokaryotic microbiome dynamics within the soybean (Glycine max) rhizosphere under field conditions.

RESULTS

Synthetic long-read sequencing permitted de novo reconstruction of the entire 18S-ITS1-ITS2 region of the eukaryotic rRNA operon as well as all nine hypervariable regions of the 16S rRNA gene. All full-length, mapped eukaryotic amplicon sequence variants displayed genus-level classification, and 44.77% achieved species-level classification. The resultant eukaryotic microbiome encompassed five kingdoms (19 genera) of protists in addition to fungi - a depth unattainable with conventional short-read methods. In the prokaryotic fraction, every full-length, mapped amplicon sequence variant was resolved at the species level, and 23.13% at the strain level. Thirteen species of Bradyrhizobium were thereby distinguished in the prokaryotic microbiome, with strain-level identification of the two Bradyrhizobium species most reported to nodulate soybean. Moreover, the applied methodology delineated structural and compositional dynamism in response to experimental parameters (i.e., growth stage, cultivar, and biostimulant application), unveiled a saprotroph-rich core microbiome, provided empirical evidence for host selection of mutualistic taxa, and identified key microbial co-occurrence network members likely associated with edaphic and agronomic properties.

CONCLUSIONS

This study is the first to combine synthetic long-read technology and avidity sequencing to profile both eukaryotic and prokaryotic fractions of a plant-associated microbiome. Findings herein provide an unparalleled taxonomic resolution of the soybean rhizosphere microbiota and represent significant biological and technological advancements in crop microbiome research.

Infection and the microbiome in bronchiectasis

Bronchiectasis is marked by bronchial dilatation, recurrent infections and significant morbidity, underpinned by a complex interplay between microbial dysbiosis and immune dysregulation. The identification of distinct endophenotypes have refined our understanding of its pathogenesis, including its heterogeneous disease mechanisms that influence treatment and prognosis responses. Next-generation sequencing (NGS) has revolutionised the way we view airway microbiology, allowing insights into the "unculturable". Understanding the bronchiectasis microbiome through targeted amplicon sequencing and/or shotgun metagenomics has provided key information on the interplay of the microbiome and host immunity, a central feature of disease progression. The rapid increase in translational and clinical studies in bronchiectasis now provides scope for the application of precision medicine and a better understanding of the efficacy of interventions aimed at restoring microbial balance and/or modulating immune responses. Holistic integration of these insights is driving an evolving paradigm shift in our understanding of bronchiectasis, which includes the critical role of the microbiome and its unique interplay with clinical, inflammatory, immunological and metabolic factors. Here, we review the current state of infection and the microbiome in bronchiectasis and provide views on the future directions in this field.

Effects of whole-body vibration on body composition, microbiota, cardiometabolic markers, physical fitness, and quality of life after bariatric surgery: protocol for a randomized controlled trial

BACKGROUND

Morbid obesity is a complex chronic condition characterized by a body mass index of 40 kg/m2 or higher. The incidence of the condition is on the rise in developed countries, and bariatric surgery has been proposed as a potential solution to address this trend. Nonetheless, bariatric surgery may also result in adverse effects, including a reduction in bone mineral density (BMD) and muscle mass, as well as an increased risk of fractures. The present study aims to elucidate the effects of bariatric surgery and whole-body vibration (WBV) training on body composition, microbiota, physical fitness, quality of life, and cardiometabolic markers.

METHODS

Twenty-eight participants (14 females), aged 18 to 50 years, will undergo sleeve gastrectomy surgery. They will be randomly allocated into a control group or a WBV training group. The WBV group will train three times per week with increasing intensities and duration ranging from 30 to 45 min over the 4-month training period. Measurements of body composition (dual-energy X-ray absorptiometry and peripheral quantitative computed tomography), physical fitness (muscular strength, agility, cardiorespiratory fitness, and balance), gait biomechanics, cardiometabolic markers, gut microbiota, quality of life, and physical activity levels will be collected at four different time points: (1) prior to the surgery, (2) 45 days post-surgery, (3) 6 months post-surgery, and (4) 18 months post-surgery.

DISCUSSION

Both groups are expected to experience improvements in most of the aforementioned variables. Nonetheless, we expect the WBV group to show larger improvements proving that the training is effective and safe.

TRIAL REGISTRATION

Clinicaltrials.gov NCT05695599. Registered on January 25, 2023.

Comparative microbiome analysis in cystic fibrosis and non-cystic fibrosis bronchiectasis

BACKGROUND

Bronchiectasis is a condition characterized by abnormal and irreversible bronchial dilation resulting from lung tissue damage and can be categorized into two main groups: cystic fibrosis (CF) and non-CF bronchiectasis (NCFB). Both diseases are marked by recurrent infections, inflammatory exacerbations, and lung damage. Given that infections are the primary drivers of disease progression, characterization of the respiratory microbiome can shed light on compositional alterations and susceptibility to antimicrobial drugs in these cases compared to healthy individuals.

METHODS

To assess the microbiota in the two studied diseases, 35 subjects were recruited, comprising 10 NCFB and 13 CF patients and 12 healthy individuals. Nasopharyngeal swabs and induced sputum were collected, and total DNA was extracted. The DNA was then sequenced by the shotgun method and evaluated using the SqueezeMeta pipeline and R.

RESULTS

We observed reduced species diversity in both disease cohorts, along with distinct microbial compositions and profiles of antimicrobial resistance genes, compared to healthy individuals. The nasopharynx exhibited a consistent microbiota composition across all cohorts. Enrichment of members of the Burkholderiaceae family and an increased Firmicutes/Bacteroidetes ratio in the CF cohort emerged as key distinguishing factors compared to NCFB group. Staphylococcus aureus and Prevotella shahii also presented differential abundance in the CF and NCFB cohorts, respectively, in the lower respiratory tract. Considering antimicrobial resistance, a high number of genes related to antibiotic efflux were detected in both disease groups, which correlated with the patient's clinical data.

CONCLUSIONS

Bronchiectasis is associated with reduced microbial diversity and a shift in microbial and resistome composition compared to healthy subjects. Despite some similarities, CF and NCFB present significant differences in microbiome composition and antimicrobial resistance profiles, suggesting the need for customized management strategies for each disease.

Gut microbiota, blood metabolites, and left ventricular diastolic dysfunction in US Hispanics/Latinos

BACKGROUND

Left ventricular diastolic dysfunction (LVDD) is an important precursor of heart failure (HF), but little is known about its relationship with gut dysbiosis and microbial-related metabolites. By leveraging the multi-omics data from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL), a study with population at high burden of LVDD, we aimed to characterize gut microbiota associated with LVDD and identify metabolite signatures of gut dysbiosis and incident LVDD.

RESULTS

We included up to 1996 Hispanic/Latino adults (mean age: 59.4 years; 67.1% female) with comprehensive echocardiography assessments, gut microbiome, and blood metabolome data. LVDD was defined through a composite criterion involving tissue Doppler assessment and left atrial volume index measurements. Among 1996 participants, 916 (45.9%) had prevalent LVDD, and 212 out of 594 participants without LVDD at baseline developed incident LVDD over a median 4.3 years of follow-up. Using multivariable-adjusted analysis of compositions of microbiomes (ANCOM-II) method, we identified 7 out of 512 dominant gut bacterial species (prevalence > 20%) associated with prevalent LVDD (FDR-q < 0.1), with inverse associations being found for Intestinimonas_massiliensis, Clostridium_phoceensis, and Bacteroide_coprocola and positive associations for Gardnerella_vaginali, Acidaminococcus_fermentans, Pseudomonas_aeruginosa, and Necropsobacter_massiliensis. Using multivariable adjusted linear regression, 220 out of 669 circulating metabolites with detection rate > 75% were associated with the identified LVDD-related bacterial species (FDR-q < 0.1), with the majority being linked to Intestinimonas_massiliensis, Clostridium_phoceensis, and Acidaminococcus_fermentans. Furthermore, 46 of these bacteria-associated metabolites, mostly glycerophospholipids, secondary bile acids, and amino acids, were associated with prevalent LVDD (FDR-q < 0.1), 21 of which were associated with incident LVDD (relative risk ranging from 0.81 [p = 0.001, for guanidinoacetate] to 1.25 [p = 9 × 10-5, for 1-stearoyl-2-arachidonoyl-GPE (18:0/20:4)]). The inclusion of these 21 bacterial-related metabolites significantly improved the prediction of incident LVDD compared with a traditional risk factor model (the area under the receiver operating characteristic curve [AUC] = 0.73 vs 0.70, p = 0.001). Metabolite-based proxy association analyses revealed the inverse associations of Intestinimonas_massilliensis and Clostridium_phoceensis and the positive association of Acidaminococcus_fermentans with incident LVDD.

CONCLUSION

In this study of US Hispanics/Latinos, we identified multiple gut bacteria and related metabolites linked to LVDD, suggesting their potential roles in this preclinical HF entity. Video Abstract.

The Role of Prevotella Species in Female Genital Tract Infections

Female genital tract infections (FGTIs) include vaginal infections (e.g., bacterial vaginosis [BV]), endometritis, pelvic inflammatory disease [PID], and chorioamnionitis [amniotic fluid infection]. They commonly occur in women of reproductive age and are strongly associated with multiple adverse health outcomes including increased risk of HIV/sexually transmitted infection acquisition and transmission, infertility, and adverse birth outcomes such as preterm birth. These FGTIs are characterized by a disruption of the cervicovaginal microbiota which largely affects host immunity through the loss of protective, lactic acid-producing Lactobacillus spp. and the overgrowth of facultative and strict anaerobic bacteria. Prevotella species (spp.), anaerobic Gram-negative rods, are implicated in the pathogenesis of multiple bacterial FGTIs. Specifically, P. bivia, P. amnii, and P. timonensis have unique virulence factors in this setting, including resistance to antibiotics commonly used in treatment. Additionally, evidence suggests that the presence of Prevotella spp. in untreated BV cases can lead to infections of the upper female genital tract by ascension into the uterus. This narrative review aims to explore the most common Prevotella spp. in FGTIs, highlight their important role in the pathogenesis of FGTIs, and propose future research in this area.

Gut microbiome is associated with recurrence-free survival in patients with resected Stage IIIB-D or Stage IV melanoma treated with immune checkpoint inhibitors

The gut microbiome (GMB) has been associated with outcomes of immune checkpoint blockade therapy in melanoma, but there is limited consensus on the specific taxa involved, particularly across different geographic regions. We analyzed pre-treatment stool samples from 674 melanoma patients participating in a phase-III trial of adjuvant nivolumab plus ipilimumab versus nivolumab, across three continents and five regions. Longitudinal analysis revealed that GMB was largely unchanged following treatment, offering promise for lasting GMB-based interventions. In region-specific and cross-region meta-analyses, we identified pre-treatment taxonomic markers associated with recurrence, including Eubacterium, Ruminococcus, Firmicutes, and Clostridium. Recurrence prediction by these markers was best achieved across regions by matching participants on GMB compositional similarity between the intra-regional discovery and external validation sets. AUCs for prediction ranged from 0.83-0.94 (depending on the initial discovery region) for patients closely matched on GMB composition (e.g., JSD ≤0.11). This evidence indicates that taxonomic markers for prediction of recurrence are generalizable across regions, for individuals of similar GMB composition.

Metagenomic Next-Generation Sequencing Contributes to the Early Diagnosis of Mixed Infections in Central Nervous System

Central nervous system (CNS) infections represent a challenge due to the complexities associated with their diagnosis and treatment, resulting in a high incidence rate and mortality. Here, we presented a case of CNS mixed infection involving Candida and human cytomegalovirus (HCMV), successfully diagnosed through macrogenomic next-generation sequencing (mNGS) in China. A comprehensive review and discussion of previously reported cases were also provided. Our study emphasizes the critical role of early pathogen identification facilitated by mNGS, underscoring its significance. Notably, the integration of mNGS with traditional methods significantly enhances the diagnostic accuracy of CNS infections. This integrated approach has the potential to provide valuable insights for clinical practice, facilitating early diagnosis, allowing for treatment adjustments, and ultimately, improving the prognosis for patients with CNS infections.

Combining compositional data sets introduces error in covariance network reconstruction

Microbial communities are diverse biological systems that include taxa from across multiple kingdoms of life. Notably, interactions between bacteria and fungi play a significant role in determining community structure. However, these statistical associations across kingdoms are more difficult to infer than intra-kingdom associations due to the nature of the data involved using standard network inference techniques. We quantify the challenges of cross-kingdom network inference from both theoretical and practical points of view using synthetic and real-world microbiome data. We detail the theoretical issue presented by combining compositional data sets drawn from the same environment, e.g. 16S and ITS sequencing of a single set of samples, and we survey common network inference techniques for their ability to handle this error. We then test these techniques for the accuracy and usefulness of their intra- and inter-kingdom associations by inferring networks from a set of simulated samples for which a ground-truth set of associations is known. We show that while the two methods mitigate the error of cross-kingdom inference, there is little difference between techniques for key practical applications including identification of strong correlations and identification of possible keystone taxa (i.e. hub nodes in the network). Furthermore, we identify a signature of the error caused by transkingdom network inference and demonstrate that it appears in networks constructed using real-world environmental microbiome data.

Current Uses and Future Perspectives of Genomic Technologies in Clinical Microbiology

Recent advancements in sequencing technology and data analytics have led to a transformative era in pathogen detection and typing. These developments not only expedite the process, but also render it more cost-effective. Genomic analyses of infectious diseases are swiftly becoming the standard for pathogen analysis and control. Additionally, national surveillance systems can derive substantial benefits from genomic data, as they offer profound insights into pathogen epidemiology and the emergence of antimicrobial-resistant strains. Antimicrobial resistance (AMR) is a pressing global public health issue. While clinical laboratories have traditionally relied on culture-based antimicrobial susceptibility testing, the integration of genomic data into AMR analysis holds immense promise. Genomic-based AMR data can furnish swift, consistent, and highly accurate predictions of resistance phenotypes for specific strains or populations, all while contributing invaluable insights for surveillance. Moreover, genome sequencing assumes a pivotal role in the investigation of hospital outbreaks. It aids in the identification of infection sources, unveils genetic connections among isolates, and informs strategies for infection control. The One Health initiative, with its focus on the intricate interconnectedness of humans, animals, and the environment, seeks to develop comprehensive approaches for disease surveillance, control, and prevention. When integrated with epidemiological data from surveillance systems, genomic data can forecast the expansion of bacterial populations and species transmissions. Consequently, this provides profound insights into the evolution and genetic relationships of AMR in pathogens, hosts, and the environment.

Multi-level analysis of the gut-brain axis shows autism spectrum disorder-associated molecular and microbial profiles

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by heterogeneous cognitive, behavioral and communication impairments. Disruption of the gut-brain axis (GBA) has been implicated in ASD although with limited reproducibility across studies. In this study, we developed a Bayesian differential ranking algorithm to identify ASD-associated molecular and taxa profiles across 10 cross-sectional microbiome datasets and 15 other datasets, including dietary patterns, metabolomics, cytokine profiles and human brain gene expression profiles. We found a functional architecture along the GBA that correlates with heterogeneity of ASD phenotypes, and it is characterized by ASD-associated amino acid, carbohydrate and lipid profiles predominantly encoded by microbial species in the genera Prevotella, Bifidobacterium, Desulfovibrio and Bacteroides and correlates with brain gene expression changes, restrictive dietary patterns and pro-inflammatory cytokine profiles. The functional architecture revealed in age-matched and sex-matched cohorts is not present in sibling-matched cohorts. We also show a strong association between temporal changes in microbiome composition and ASD phenotypes. In summary, we propose a framework to leverage multi-omic datasets from well-defined cohorts and investigate how the GBA influences ASD.