Shotgun metagenomic insights into secondary metabolite biosynthetic gene clusters reveal taxonomic and functional profiles of microbiomes in natural farmland soil.

Antibiotic resistance is a worldwide problem that imposes a devastating effect on developing countries and requires immediate interventions. Initially, most of the antibiotic drugs were identified by culturing soil microbes. However, this method is prone to discovering the same antibiotics repeatedly. The present study employed a shotgun metagenomics approach to investigate the taxonomic diversity, functional potential, and biosynthetic capacity of microbiomes from two natural agricultural farmlands located in Bekeka and Welmera Choke Kebelle in Ethiopia for the first time. Analysis of the small subunit rRNA revealed bacterial domain accounting for 83.33% and 87.24% in the two selected natural farmlands. Additionally, the analysis showed the dominance of Proteobacteria representing 27.27% and 28.79% followed by Actinobacteria making up 12.73% and 13.64% of the phyla composition. Furthermore, the analysis revealed the presence of unassigned bacteria in the studied samples. The metagenome functional analysis showed 176,961 and 104, 636 number of protein-coding sequences (pCDS) from the two samples found a match with 172,655 and 102, 275 numbers of InterPro entries, respectively. The Genome ontology annotation suggests the presence of 5517 and 3293 pCDS assigned to the "biosynthesis process". Numerous Kyoto Encyclopedia of Genes and Genomes modules (KEGG modules) involved in the biosynthesis of terpenoids and polyketides were identified. Furthermore, both known and novel Biosynthetic gene clusters, responsible for the production of secondary metabolites, such as polyketide synthases, non-ribosomal peptide synthetase, ribosomally synthesized and post-translationally modified peptides (Ripp), and Terpene, were discovered. Generally, from the results it can be concluded that the microbiomes in the selected sampling sites have a hidden functional potential for the biosynthesis of secondary metabolites. Overall, this study can serve as a strong preliminary step in the long journey of bringing new antibiotics to the market.

Gut microbiome dynamics and predictive value in hospitalized COVID-19 patients: a comparative analysis of shallow and deep shotgun sequencing.

The COVID-19 pandemic caused by SARS-CoV-2 has led to a wide range of clinical presentations, with respiratory symptoms being common. However, emerging evidence suggests that the gastrointestinal (GI) tract is also affected, with angiotensin-converting enzyme 2, a key receptor for SARS-CoV-2, abundantly expressed in the ileum and colon. The virus has been detected in GI tissues and fecal samples, even in cases with negative results of the reverse transcription polymerase chain reaction in the respiratory tract. GI symptoms have been associated with an increased risk of ICU admission and mortality. The gut microbiome, a complex ecosystem of around 40 trillion bacteria, plays a crucial role in immunological and metabolic pathways. Dysbiosis of the gut microbiota, characterized by a loss of beneficial microbes and decreased microbial diversity, has been observed in COVID-19 patients, potentially contributing to disease severity. We conducted a comprehensive gut microbiome study in 204 hospitalized COVID-19 patients using both shallow and deep shotgun sequencing methods. We aimed to track microbiota composition changes induced by hospitalization, link these alterations to clinical procedures (antibiotics administration) and outcomes (ICU referral, survival), and assess the predictive potential of the gut microbiome for COVID-19 prognosis. Shallow shotgun sequencing was evaluated as a cost-effective diagnostic alternative for clinical settings. Our study demonstrated the diverse effects of various combinations of clinical parameters, microbiome profiles, and patient metadata on the precision of outcome prognostication in patients. It indicates that microbiological data possesses greater reliability in forecasting patient outcomes when contrasted with clinical data or metadata. Furthermore, we established that shallow shotgun sequencing presents a viable and cost-effective diagnostic alternative to deep sequencing within clinical environments.

Gut microbiome signatures associated with type 2 diabetes in obesity in Mongolia.

Mongolian people possess a unique dietary habit characterized by high consumption of meat and dairy products and fewer vegetables, resulting in the highest obesity rate in East Asia. Although obesity is a known cause of type 2 diabetes (T2D), the T2D rate is moderate in this population; this is known as the "Mongolian paradox." Since the gut microbiota plays a key role in energy and metabolic homeostasis as an interface between food and body, we investigated gut microbial factors involved in the prevention of the co-occurrence of T2D with obesity in Mongolians. We compared the gut microbiome and metabolome of Mongolian adults with obesity with T2D (DO: n = 31) or without T2D (NDO: n = 35). Dysbiotic signatures were found in the gut microbiome of the DO group; lower levels of Faecalibacterium and Anaerostipes which are known as short-chain fatty acid (SCFA) producers and higher levels of Methanobrevibacter, Desulfovibrio, and Solobacterium which are known to be associated with certain diseases. On the other hand, the NDO group exhibited a higher level of fecal SCFA concentration, particularly acetate. This is consistent with the results of the whole shotgun metagenomic analysis, which revealed a higher relative abundance of SCFA biosynthesis-related genes encoded largely by Anaerostipes hadrus in the NDO group. Multiple logistic regression analysis including host demographic parameters indicated that acetate had the highest negative contribution to the onset of T2D. These findings suggest that SCFAs produced by the gut microbial community participate in preventing the development of T2D in obesity in Mongolians.

Plasma Microbial Cell-free DNA Next-generation Sequencing Can Be a Useful Diagnostic Tool in Patients With Osteoarticular Infections.

Background: Recent advances in shotgun metagenomic sequencing (sMGS) for detecting microbial cell-free DNA (mcfDNA) in peripheral blood have shown promise across various patient populations. This study evaluates the application of sMGS for diagnosing osteoarticular infections (OAIs), a condition with significant diagnostic challenges. Methods: We conducted a retrospective analysis on 73 patients suspected of OAIs at the Mayo Clinic from 2019 to 2023, incorporating mcfDNA sMGS (Karius test [KT]) into their diagnostic evaluation. We categorized the clinical impact of KT on OAI diagnoses and management into 4 distinct outcomes. (1) KT was able to confirm an established diagnosis, (2) KT supported noninfectious diseases diagnosis, (3) KT established an unsuspected diagnosis, (4) KT did not add relevant information. Results: In our cohort, KT was performed in 73 patients. Among the infected individuals, KT yielded positive results in 22 of 43 (51.2%) cases. Of these 22 cases, 11 (50%) showed agreement with conventional diagnostic workup, whereas in 5 (22.7%) cases, the KT established an unsuspected diagnosis. Native vertebral osteomyelitis diagnosis (P < .001) or OAIs with concomitant presence of endocarditis or endovascular infection (P = .005) were statistically associated with a definite, probable, or possible diagnostic certainty of KT result. Conclusions: In complex OAIs, KT enhanced diagnostic accuracy by 11.6%, proving especially beneficial in diagnosing native vertebral osteomyelitis and infections with concurrent endocarditis or endovascular complications. Our findings underscore the utility of KT in the diagnostic workflow for challenging OAI cases, potentially altering clinical management for a significant subset of patients.

Comprehensive assessment of machine learning methods for diagnosing gastrointestinal diseases through whole metagenome sequencing data.

The gut microbiome, linked significantly to host diseases, offers potential for disease diagnosis through machine learning (ML) pipelines. These pipelines, crucial in modeling diseases using high-dimensional microbiome data, involve selecting profile modalities, data preprocessing techniques, and classification algorithms, each impacting the model accuracy and generalizability. Despite whole metagenome shotgun sequencing (WMS) gaining popularity for human gut microbiome profiling, a consensus on the optimal methods for ML pipelines in disease diagnosis using WMS data remains elusive. Addressing this gap, we comprehensively evaluated ML methods for diagnosing Crohn's disease and colorectal cancer, using 2,553 fecal WMS samples from 21 case-control studies. Our study uncovered crucial insights: gut-specific, species-level taxonomic features proved to be the most effective for profiling; batch correction was not consistently beneficial for model performance; compositional data transformations markedly improved the models; and while nonlinear ensemble classification algorithms typically offered superior performance, linear models with proper regularization were found to be more effective for diseases that are linearly separable based on microbiome data. An optimal ML pipeline, integrating the most effective methods, was validated for generalizability using holdout data. This research offers practical guidelines for constructing reliable disease diagnostic ML models with fecal WMS data.

No evidence on infectious DNA-based agents in pediatric acute lymphoblastic leukemia using whole metagenome shotgun sequencing.

The etiology of pediatric acute lymphatic leukemia (ALL) is still unclear. Whole-metagenome shotgun sequencing of bone marrow samples in patients with treatment-naïve ALL (n=6) was performed for untargeted investigation of bacterial and viral DNA. The control group consisted of healthy children (n=4) and children with non-oncologic diseases (n=2) undergoing bone marrow sampling. Peripheral blood of all participants was investigated at the same time. After bioinformatical elimination of potential contaminants by comparison with the employed controls, no significant amounts of microbial or viral DNA were identified.

Multikingdom characterization of gut microbiota in patients with rheumatoid arthritis and rheumatoid arthritis-associated interstitial lung disease.

Rheumatoid arthritis-associated interstitial lung disease (RA-ILD) is a serious and common extra-articular disease manifestation. Patients with RA-ILD experience reduced bacterial diversity and gut bacteriome alterations. However, the gut mycobiome and virome in these patients have been largely neglected. In this study, we performed whole-metagenome shotgun sequencing on fecal samples from 30 patients with RA-ILD, and 30 with RA-non-ILD, and 40 matched healthy controls. The gut bacteriome and mycobiome were explored using a reference-based approach, while the gut virome was profiled based on a nonredundant viral operational taxonomic unit (vOTU) catalog. The results revealed significant alterations in the gut microbiomes of both RA-ILD and RA-non-ILD groups compared with healthy controls. These alterations encompassed changes in the relative abundances of 351 bacterial species, 65 fungal species, and 4,367 vOTUs. Bacteria such as Bifidobacterium longum, Dorea formicigenerans, and Collinsella aerofaciens were enriched in both patient groups. Ruminococcus gnavus (RA-ILD), Gemmiger formicilis, and Ruminococcus bromii (RA-non-ILD) were uniquely enriched. Conversely, Faecalibacterium prausnitzii, Bacteroides spp., and Roseburia inulinivorans showed depletion in both patient groups. Mycobiome analysis revealed depletion of certain fungi, including Saccharomyces cerevisiae and Candida albicans, in patients with RA compared with healthy subjects. Notably, gut virome alterations were characterized by an increase in Siphoviridae and a decrease in Myoviridae, Microviridae, and Autographiviridae in both patient groups. Hence, multikingdom gut microbial signatures showed promise as diagnostic indicators for both RA-ILD and RA-non-ILD. Overall, this study provides comprehensive insights into the fecal virome, bacteriome, and mycobiome landscapes of RA-ILD and RA-non-ILD gut microbiota, thereby offering potential biomarkers for further mechanistic and clinical research.

Spectra without stories: reporting 94% dark and unidentified ancient proteomes.

Background: Data-dependent, bottom-up proteomics is widely used for identifying proteins and peptides. However, one key challenge is that 70% of fragment ion spectra consistently fail to be assigned by conventional database searching. This 'dark matter' of bottom-up proteomics seems to affect fields where non-model organisms, low-abundance proteins, non-tryptic peptides, and complex modifications may be present. While palaeoproteomics may appear as a niche field, understanding and reporting unidentified ancient spectra require collaborative innovation in bioinformatics strategies. This may advance the analysis of complex datasets. Methods: 14.97 million high-impact ancient spectra published in Nature and Science portfolios were mined from public repositories. Identification rates, defined as the proportion of assigned fragment ion spectra, were collected as part of deposited database search outputs or parsed using open-source python packages. Results and Conclusions: We report that typically 94% of the published ancient spectra remain unidentified. This phenomenon may be caused by multiple factors, notably the limitations of database searching and the selection of user-defined reference data with advanced modification patterns. These 'spectra without stories' highlight the need for widespread data sharing to facilitate methodological development and minimise the loss of often irreplaceable ancient materials. Testing and validating alternative search strategies, such as open searching and de novo sequencing, may also improve overall identification rates. Hence, lessons learnt in palaeoproteomics may benefit other fields grappling with challenging data.

Identification of individuals from low template blood samples using whole transcriptome shotgun sequencing.

Biological trace samples consisting of very few cells pose a challenge to conventional forensic genetic DNA analysis. RNA may be an alternative to DNA when handling low template samples. Whereas each cell only contains two copies of an autosomal DNA segment, the transcriptome retains much of the genomic variation replicated in abundant RNA fragments. In this study, we describe the development of a prototype RNA-based SNP selection set for forensic human identification from low template samples (50 pg gDNA). Whole blood from a subset of the Danish population (41 individuals) and blood stains subjected to degradation at room temperature for up to two weeks were analysed by whole transcriptome shotgun sequencing. Concordance was determined by DNA genotyping with the Infinium Omni5-4 SNP chip. In the 100 protein-coding genes with the most reads, 5214 bi-allelic SNPs with gnomAD minor allele frequencies > 0.1 in the African/African American, East Asian, and (non-Finnish) European populations were identified. Of these, 24 SNPs in 21 genes passed screening in whole blood and degraded blood stains, with a resulting mean match probability of 4.5 ∙ 10-9. Additionally, ancestry informative SNPs and SNPs in genes useful for body fluid identification were identified in the transcriptome. Consequently, shotgun sequencing of RNA from low template samples may be used for a vast host of forensic genetics purposes, including simultaneous human and body fluid identification, leading to direct donor identification in the identified body fluid.

Nanoliter-Scale Sample Preparation for Single-Cell Proteomic Analysis Using Glass-Oil-Air-Droplet Chip.

Single-cell proteomic analyses are of fundamental importance in order to capture biological heterogeneity within complex cell systems' heterogeneous populations. Mass spectrometry (MS)-based proteomics is a promising alternative for quantitative single-cell proteomics. Various techniques are continually evolving to address the challenges of limited sample material, detection sensitivity, and throughput constraints. In this chapter, we describe a nanoliter-scale glass-oil-air-droplet (gOAD) chip engineered for heat tolerance, which combines droplet-based microfluidics and shotgun proteomic analysis techniques to enable multistep sample pretreatment.

Shotgun metagenomic investigation of foodborne pathogens and antimicrobial resistance genes in artisanal fermented meat products from the Mediterranean area.

In this pilot study, we compared the metagenomic profiles of different types of artisanal fermented meat products collected in Italy, Greece, Portugal, and Morocco to investigate their taxonomic profile, also in relation to the presence of foodborne pathogens and antimicrobial resistance genes. In addition, technical replicates of the same biological sample were tested to estimate the reproducibility of shotgun metagenomics. The taxonomic analysis showed a high level of variability between different fermented meat products at both the phylum and genus levels. Staphylococcus aureus was identified with the highest abundance in Italian fermented meat; Escherichia coli in fermented meat from Morocco; Salmonella enterica in fermented meat from Greece; Klebsiella pneumoniae and Yersinia enterocolitica in fermented meat from Portugal. The fungi Aspergillus, Neosartoria, Emericella, Penicillum and Debaryomyces showed a negative correlation with Lactococcus, Enterococcus, Streptococcus, Leuconostoc and Lactobacillus. The resistome analysis indicated that genes conferring resistance to aminoglycoside, macrolide, and tetracycline were widely spread in all samples. Our results showed that the reproducibility between technical replicates tested by shotgun metagenomic was very high under the same conditions of analysis (either DNA extraction, library preparation, sequencing analysis, and bioinformatic analysis), considering both the degree of overlapping and the pairwise correlation.

Exploring the Ocular Surface Microbiome and Tear Proteome in Glaucoma.

Although glaucoma is a leading cause of irreversible blindness worldwide, its pathogenesis is incompletely understood, and intraocular pressure (IOP) is the only modifiable risk factor to target the disease. Several associations between the gut microbiome and glaucoma, including the IOP, have been suggested. There is growing evidence that interactions between microbes on the ocular surface, termed the ocular surface microbiome (OSM), and tear proteins, collectively called the tear proteome, may also play a role in ocular diseases such as glaucoma. This study aimed to find characteristic features of the OSM and tear proteins in patients with glaucoma. The whole-metagenome shotgun sequencing of 32 conjunctival swabs identified Actinobacteria, Firmicutes, and Proteobacteria as the dominant phyla in the cohort. The species Corynebacterium mastitidis was only found in healthy controls, and their conjunctival microbiomes may be enriched in genes of the phospholipase pathway compared to glaucoma patients. Despite these minor differences in the OSM, patients showed an enrichment of many tear proteins associated with the immune system compared to controls. In contrast to the OSM, this emphasizes the role of the proteome, with a potential involvement of immunological processes in glaucoma. These findings may contribute to the design of new therapeutic approaches targeting glaucoma and other associated diseases.

Forensic investigation of complex and complicated suicides in Attica, Greece: a 12-year retrospective study.

The purpose of this study was to examine the prevalence and characteristics (victims' profiles, circumstances surrounding the incidents, and methods employed) of complex and complicated suicides over a 12-year period in the broader area of Athens, Greece. A retrospective analysis of 5,568 autopsy cases performed at the Department of Forensic Medicine and Toxicology of the National and Kapodistrian University of Athens from January 1, 2011, to December 31, 2022, was carried out. Out of a total sample of 5,568 autopsies, 360 suicide cases were identified, among which 14 (3.9%) were classified as complex suicides, and one case (0.3%) was identified as complicated suicide. Among the victims, 78.6% were males. The age range of the victims varied between 25 and 82 years old. The most prevalent method of complex suicide was the use of sharp objects followed by jumping from a height (42.8%). The next most common combination of methods was poisoning (21.4%) along with hanging. Prior suicidal attempts and suicide note were mentioned in 16.7% and 8.3% of the cases respectively. Overall, a total of 9 different methods were used in the above 14 cases. Only half (50.0%) of the victims had an established psychiatric diagnosis. In determining the cause of death in cases of a complex or complicated suicides, it is of utmost importance for the forensic pathologist to gather and analyze all available information provided by the police, the victim's relatives along with a thorough investigation of the scene, a detailed autopsy and a toxicological analysis.

Species identification of biological ingredients in herbal product, Gurigumu-7, based on DNA barcoding and shotgun metagenomics.

Accurate identification the species composition in mixtures poses a significant challenge, especially in processed mixtures comprising multiple species, such as those found in food and pharmaceuticals. Therefore, we have attempted to utilize shotgun metabarcoding technology to tackle this issue. In this study, the method was initially established using two mock samples of the Mongolian compound preparation Gurigumu-7 (G-7), which was then applied to three pharmaceutical products and 12 hospital-made preparations. A total of 119.72 Gb of raw data sets were obtained through shotgun metagenomic sequencing. By combining ITS2, matK, and rbcL, all the labeled bio-ingredients specified in the G-7 prescription can be detected, although some species may not be detectable in all samples. The prevalent substitution of Akebia quinata can be found in all the pharmaceutical and hospital samples, except for YN02 and YN12. The toxic alternative to Akebia quinata, Aristolochia manshuriensis, was exclusively identified in the YN02 sample. To further confirm this result, we validated it in YN02 using HPLC and real-time PCR with TaqMan probes. The results showed that aristolochic acid A (AAA) was detected in YN02 using HPLC, and the ITS2 sequence of Aristolochia manshuriensis has been validated in YN02 through qPCR and the use of a TaqMan probe. This study confirms that shotgun metabarcoding can effectively identify the biological components in Mongolian medicine compound preparation G-7. It also demonstrates the method's potential to be utilized as a general identification technique for mixtures containing a variety of plants.

Metagenomic analysis of the bacterial microbiome, resistome and virulome distinguishes Portuguese Serra da Estrela PDO cheeses from similar non-PDO cheeses: An exploratory approach.

This study aimed to evaluate the microbiome, resistome and virulome of two types of Portuguese cheese using high throughput sequencing (HTS). Culture-dependent chromogenic methods were also used for certain groups/microorganisms. Eight samples of raw ewe's milk cheese were obtained from four producers: two producers with cheeses with a PDO (Protected Designation of Origin) label and the other two producers with cheeses without a PDO label. Agar-based culture methods were used to quantify total mesophiles, Enterobacteriaceae, Escherichia coli, Staphylococcus, Enterococcus and lactic acid bacteria. The presence of Listeria monocytogenes and Salmonella was also investigated. The selected isolates were identified by 16S rRNA gene sequencing and evaluated to determine antibiotic resistance and the presence of virulence genes. The eight cheese samples analyzed broadly complied with EC regulations in terms of the microbiological safety criteria. The HTS results demonstrated that Leuconostoc mesenteroides, Lactococcus lactis, Lactobacillus plantarum, Lacticaseibacillus rhamnosus, Enterococcus durans and Lactobacillus coryniformis were the most prevalent bacterial species in cheeses. The composition of the bacterial community varied, not only between PDO and non-PDO cheeses, but also between producers, particularly between the two non-PDO cheeses. Alpha-diversity analyses showed that PDO cheeses had greater bacterial diversity than non-PDO cheeses, demonstrating that the diversity of spontaneously fermented foods is significantly higher in cheeses produced without the addition of food preservatives and dairy ferments. Despite complying with microbiological regulations, both PDO and non-PDO cheeses harbored potential virulence genes as well as antibiotic resistance genes. However, PDO cheeses exhibited fewer of these virulence and antibiotic resistance genes compared to non-PDO cheeses. Therefore, the combination of conventional microbiological methods and the metagenomic approach could contribute to improving the attribution of the PDO label to this type of cheese.

Associations between specific dietary patterns, gut microbiome composition, and incident subthreshold depression in Chinese young adults.

INTRODUCTION: The interplay between influential factors and the incidence of subthreshold depression (SD) in young adults remains poorly understood. OBJECTIVES: This study sought to understand the dietary habits, gut microbiota composition, etc. among individuals with SD in young adults and to investigate their association with SD occurrence. METHODS: Employing a cross-sectional approach, 178 individuals with SD, aged 18-32 years, were matched with 114 healthy counterparts. SD status was evaluated using the Zung Self-rating Depression Scale (SDS), Zung Self-rating Anxiety Scale (SAS), Beck Depression Inventory 2nd version (BDI-II), the 17-item Hamilton Rating Scales of Depression (HAMD-17), and Pittsburgh Sleep Quality Index (PSQI). Metagenomic sequencing was utilized to identify fecal microbial profiles. Dietary patterns were discerned via factor analysis of a 25-item food frequency questionnaire (FFQ). Logistic regression analysis and mediation analysis were performed to explore the potential links between gut microbiota, dietary patterns, and incident SD. RESULTS: Data on dietary habits were available for 292 participants (mean [SD] age, 22.1 [2.9] years; 216 [73.9 %] female). Logistic regression analysis revealed that dietary patterns Ⅰ (odds ratio [OR], 0.34; 95 % CI, 0.15-0.75) and IV (OR, 0.39; 95 % CI, 0.17-0.86 and OR, 0.39; 95 % CI, 0.18-0.84) were associated with reduced risk of SD. Distinct microbial profiles were observed in young adults with SD, marked by increased microbial diversity and taxonomic alterations. Moreover, mediation analysis suggested Veillonella atypica as a potential mediator linking SDS or BDI-II scores with a healthy dietary pattern rich in bean products, coarse grains, nuts, fruits, mushrooms, and potatoes (ß = 0.25, 95 % CI: 0.02-0.78 and ß = 0.18, 95 % CI: 0.01-0.54). CONCLUSIONS: Our findings highlight the complex interplay between dietary patterns, gut microbiota, and the risk of developing SD in young adults, underscoring the potential for dietary interventions and microbiome modulation in mental health promotion.

A Shotgun Metagenomics Investigation into Labeling Inaccuracies in Widely Sold Probiotic Supplements in the USA.

While probiotics are generally considered safe, concerns persist regarding the accuracy of labels on these supplements and their potential contribution to the spread of antibiotic resistance genes. Given that probiotics are predominantly ingested with a view towards obtaining particular health benefits. The objective of this study is to assess the composition of 50 widely available probiotic supplements in the USA using shotgun metagenome sequencing. The study also determines the potential resistome profile, and the functional characteristics of these products. This study finds that 67% of products does not contain any labeling inaccuracies. Antimicrobial Resistance Genes (ARGs) are identified in several products, particularly Bacillus-based products carrying between 10 and 56 genes. The risk posed by the presence of these ARGs requires further study. Functional analysis reveals differences in metabolic profiles among probiotic supplements, indicating the importance of strain-level selection for personalized probiotics. This study provides updated and comprehensive analysis to evaluate a snapshot of the USA market. The study demonstrates that label inaccuracies occur on approximately one third of popular dietary supplement products sold in the USA, supporting the need for improved approaches to marketing and quality control. Further, the risk of antibiotic resistance, especially in Bacillus-based formulations, should be assessed.

Metaproteomic Analysis of Fecal Samples from Human Subjects and Rodent Models.

Fecal metaproteomics is a useful approach to measure changes in microbial and host protein abundance and to infer which members of the gut microbiota are involved in specific functions and pathways. This chapter describes a protocol enabling analysis and characterization of fecal metaproteomes, successfully applied to human, mouse, and rat stool samples. The protocol combines mechanical and thermal treatments for protein extraction, a centrifugal filter-based procedure for cleanup and digestion, long-gradient liquid chromatography for peptide separation, and high-resolution mass spectrometry for peptide detection.

The urinary microbiota composition and functionality of calcium oxalate stone formers.

Background: Accumulated evidences indicate that dysbiosis of the urinary microbiota is associated with kidney stone formation. In the present study, we aimed to investigate the urinary microbiota composition and functionality of patients with calcium oxalate stones and compare it with those of healthy individuals. Method: We collected bladder urine samples from 68 adult patients with calcium oxalate stones and 54 age-matched healthy controls by transurethral catheterization. 16S rRNA gene and shotgun sequencing were utilized to characterize the urinary microbiota and functionality associated with calcium oxalate stones. Results: After further exclusion, a total of 100 subjects was finally included and analyzed. The diversity of the urinary microbiota in calcium oxalate stone patients was not significantly different from that of healthy controls. However, the urinary microbiota structure of calcium oxalate stone formers significantly differed from that of healthy controls (PERMANOVA, r = 0.026, P = 0.019). Differential representation of bacteria (e.g., Bifidobacterium) and several enriched functional pathways (e.g., threonine biosynthesis) were identified in the urine of calcium oxalate stone patients. Conclusion: Our results showed significantly different urinary microbiota structure and several enriched functional pathways in calcium oxalate stone patients, which provide new insight into the pathogenesis of calcium oxalate stones.

Exploring the Complexity of the Human Respiratory Virome through an In Silico Analysis of Shotgun Metagenomic Data Retrieved from Public Repositories.

BACKGROUND: Respiratory viruses significantly impact global morbidity and mortality, causing more disease in humans than any other infectious agent. Beyond pathogens, various viruses and bacteria colonize the respiratory tract without causing disease, potentially influencing respiratory diseases' pathogenesis. Nevertheless, our understanding of respiratory microbiota is limited by technical constraints, predominantly focusing on bacteria and neglecting crucial populations like viruses. Despite recent efforts to improve our understanding of viral diversity in the human body, our knowledge of viral diversity associated with the human respiratory tract remains limited. METHODS: Following a comprehensive search in bibliographic and sequencing data repositories using keyword terms, we retrieved shotgun metagenomic data from public repositories (n = 85). After manual curation, sequencing data files from 43 studies were analyzed using EVEREST (pipEline for Viral assEmbly and chaRactEriSaTion). Complete and high-quality contigs were further assessed for genomic and taxonomic characterization. RESULTS: Viral contigs were obtained from 194 out of the 868 FASTQ files processed through EVEREST. Of the 1842 contigs that were quality assessed, 8% (n = 146) were classified as complete/high-quality genomes. Most of the identified viral contigs were taxonomically classified as bacteriophages, with taxonomic resolution ranging from the superkingdom level down to the species level. Captured contigs were spread across 25 putative families and varied between RNA and DNA viruses, including previously uncharacterized viral genomes. Of note, airway samples also contained virus(es) characteristic of the human gastrointestinal tract, which have not been previously described as part of the lung virome. Additionally, by performing a meta-analysis of the integrated datasets, ecological trends within viral populations linked to human disease states and their biogeographical distribution along the respiratory tract were observed. CONCLUSION: By leveraging publicly available repositories of shotgun metagenomic data, the present study provides new insights into viral genomes associated with specimens from the human respiratory tract across different disease spectra. Further studies are required to validate our findings and evaluate the potential impact of these viral communities on respiratory tract physiology.