Comparative analysis of the tumor microbiome, molecular profiles, and immune cell abundances by HPV status in mucosal head and neck cancers and their impact on survival.

Head and Neck Squamous Cell Carcinoma (HNSCC) comprises a diverse group of tumors with variable treatment response and prognosis. The tumor microenvironment (TME), which includes microbiome and immune cells, can impact outcomes. Here, we sought to relate the presence of specific microbes, gene expression, and tumor immune infiltration using tumor transcriptomics from The Cancer Genome Atlas (TCGA) and associate these with overall survival (OS). RNA sequencing (RNAseq) from HNSCC tumors in TCGA was processed through the exogenous sequences in tumors and immune cells (exotic) pipeline to identify and quantify low-abundance microbes. The detection of the Papillomaviridae family of viruses assessed HPV status. All statistical analyses were performed using R. A total of 499 RNAseq samples from TCGA were analyzed. HPV was detected in 111 samples (22%), most commonly Alphapapillomavirus 9 (90.1%). The presence of Alphapapillomavirus 9 was associated with improved OS [HR = 0.60 (95%CI: 0.40-0.89, p = .01)]. Among other microbes, Yersinia pseudotuberculosis was associated with the worst survival (HR = 3.88; p = .008), while Pseudomonas viridiflava had the best survival (HR = 0.05; p = .036). Microbial species found more abundant in HPV- tumors included several gram-negative anaerobes. HPV- tumors had a significantly higher abundance of M0 (p < .001) and M2 macrophages (p = .035), while HPV+ tumors had more T regulatory cells (p < .001) and CD8+ T-cells (p < .001). We identified microbes in HNSCC tumor samples significantly associated with survival. A greater abundance of certain anaerobic microbes was seen in HPV tumors and pro-tumorigenic macrophages. These findings suggest that TME can be used to predict patient outcomes and may help identify mechanisms of resistance to systemic therapies.

A comparison between full-length 16S rRNA Oxford nanopore sequencing and Illumina V3-V4 16S rRNA sequencing in head and neck cancer tissues.

Describing the microbial community within the tumour has been a key aspect in understanding the pathophysiology of the tumour microenvironment. In head and neck cancer (HNC), most studies on tissue samples have only performed 16S rRNA short-read sequencing (SRS) on V3-V5 region. SRS is mostly limited to genus level identification. In this study, we compared full-length 16S rRNA long-read sequencing (FL-ONT) from Oxford Nanopore Technology (ONT) to V3-V4 Illumina SRS (V3V4-Illumina) in 26 HNC tumour tissues. Further validation was also performed using culture-based methods in 16 bacterial isolates obtained from 4 patients using MALDI-TOF MS. We observed similar alpha diversity indexes between FL-ONT and V3V4-Illumina. However, beta-diversity was significantly different between techniques (PERMANOVA - R2 = 0.131, p < 0.0001). At higher taxonomic levels (Phylum to Family), all metrics were more similar among sequencing techniques, while lower taxonomy displayed more discrepancies. At higher taxonomic levels, correlation in relative abundance from FL-ONT and V3V4-Illumina were higher, while this correlation decreased at lower levels. Finally, FL-ONT was able to identify more isolates at the species level that were identified using MALDI-TOF MS (75% vs. 18.8%). FL-ONT was able to identify lower taxonomic levels at a better resolution as compared to V3V4-Illumina 16S rRNA sequencing.

Insights into the Adolescent Cystic Fibrosis Airway Microbiome Using Shotgun Metagenomics.

Cystic fibrosis (CF) is an inherited genetic disorder which manifests primarily in airway disease. Recent advances in molecular technologies have unearthed the diverse polymicrobial nature of the CF airway. Numerous studies have characterised the genus-level composition of this airway community using targeted 16S rDNA sequencing. Here, we employed whole-genome shotgun metagenomics to provide a more comprehensive understanding of the early CF airway microbiome. We collected 48 sputum samples from 11 adolescents and children with CF over a 12-month period and performed shotgun metagenomics on the Illumina NextSeq platform. We carried out functional and taxonomic analysis of the lung microbiome at the species and strain levels. Correlations between microbial diversity measures and independent demographic and clinical variables were performed. Shotgun metagenomics detected a greater diversity of bacteria than culture-based methods. A large proportion of the top 25 most-dominant species were anaerobes. Samples dominated by Staphylococcus aureus and Prevotella melaninogenica had significantly higher microbiome diversity, while no CF pathogen was associated with reduced microbial diversity. There was a diverse resistome present in all samples in this study, with 57.8% agreement between shotgun metagenomics and culture-based methods for detection of resistance. Pathogenic sequence types (STs) of S. aureus, Pseudomonas aeruginosa, Haemophilus influenzae and Stenotrophomonas maltophilia were observed to persist in young CF patients, while STs of S. aureus were both persistent and shared between patients. This study provides new insight into the temporal changes in strain level composition of the microbiome and the landscape of the resistome in young people with CF. Shotgun metagenomics could provide a very useful one-stop assay for detecting pathogens, emergence of resistance and conversion to persistent colonisation in early CF disease.

Endometrial microbiota alteration in female patients with endometrial polyps based on 16S rRNA gene sequencing analysis.

Introduction: The potential role of the endometrial microbiota in the pathogenesis of endometrial polyps (EPs) warrants further investigation, given the current landscape of limited and inconclusive research findings. We aimed to explore the microecological characteristics of the uterine cavity in patients with EPs and investigate the potential of endometrial microbiota species as novel biomarkers for identifying EPs. Methods: Endometrial samples were collected from 225 patients who underwent hysteroscopies, of whom 167 had EPs, whereas 58 had non- hyperproliferative endometrium status. The endometrial microbiota was assessed using 16S rRNA gene sequencing. We characterized the endometrial microbiota and identified microbial biomarkers for predicting EPs. Results: The endometrial microbial diversity and composition were significantly different between the EP and control groups. Predictive functional analyses of the endometrial microbiota demonstrated significant alterations in pathways involved in sphingolipid metabolism, steroid hormone biosynthesis, and apoptosis between the two groups. Moreover, a classification model based on endometrial microbial ASV-based biomarkers along with the presence of abnormal uterine bleeding symptoms achieved powerful classification potential in identifying EPs in both the discovery and validation cohorts. Conclusion: Our study indicates a potential association between altered endometrial microbiota and EPs. Endometrial microbiota-based biomarkers may prove valuable for the diagnosis of EPs. Clinical trial registration: Chinese Clinical Trial Registry (ChiCTR2100052746).

Saliva­microbiome­derived signatures: expected to become a potential biomarker for pulmonary nodules (MCEPN-1).

BACKGROUND: Oral microbiota imbalance is associated with the progression of various lung diseases, including lung cancer. Pulmonary nodules (PNs) are often considered a critical stage for the early detection of lung cancer; however, the relationship between oral microbiota and PNs remains unknown. METHODS: We conducted a 'Microbiome with pulmonary nodule series study 1' (MCEPN-1) where we compared PN patients and healthy controls (HCs), aiming to identify differences in oral microbiota characteristics and discover potential microbiota biomarkers for non-invasive, radiation-free PNs diagnosis and warning in the future. We performed 16 S rRNA amplicon sequencing on saliva samples from 173 PN patients and 40 HCs to compare the characteristics and functional changes in oral microbiota between the two groups. The random forest algorithm was used to identify PN salivary microbial markers. Biological functions and potential mechanisms of differential genes in saliva samples were preliminarily explored using the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Cluster of Orthologous Groups (COG) analyses. RESULTS: The diversity of salivary microorganisms was higher in the PN group than in the HC group. Significant differences were noted in community composition and abundance of oral microorganisms between the two groups. Neisseria, Prevotella, Haemophilus and Actinomyces, Porphyromonas, Fusobacterium, 7M7x, Granulicatella and Selenomonas were the main differential genera between the PN and HC groups. Fusobacterium, Porphyromonas, Parvimonas, Peptostreptococcus and Haemophilus constituted the optimal marker sets (area under curve, AUC = 0.80), which can distinguish between patients with PNs and HCs. Further, the salivary microbiota composition was significantly correlated with age, sex, and smoking history (P < 0.001), but not with personal history of cancer (P > 0.05). Bioinformatics analysis of differential genes showed that patients with PN showed significant enrichment in protein/molecular functions related to immune deficiency and energy metabolisms, such as the cytoskeleton protein RodZ, nicotinamide adenine dinucleotide phosphate dehydrogenase (NADPH) dehydrogenase, major facilitator superfamily transporters and AraC family transcription regulators. CONCLUSIONS: Our study provides the first evidence that the salivary microbiota can serve as potential biomarkers for identifying PN. We observed a significant association between changes in the oral microbiota and PNs, indicating the potential of salivary microbiota as a new non-invasive biomarker for PNs. TRIAL REGISTRATION: Clinical trial registration number: ChiCTR2200062140; Date of registration: 07/25/2022.

CoCas9 is a compact nuclease from the human microbiome for efficient and precise genome editing.

The expansion of the CRISPR-Cas toolbox is highly needed to accelerate the development of therapies for genetic diseases. Here, through the interrogation of a massively expanded repository of metagenome-assembled genomes, mostly from human microbiomes, we uncover a large variety (n = 17,173) of type II CRISPR-Cas loci. Among these we identify CoCas9, a strongly active and high-fidelity nuclease with reduced molecular size (1004 amino acids) isolated from an uncultivated Collinsella species. CoCas9 is efficiently co-delivered with its sgRNA through adeno associated viral (AAV) vectors, obtaining efficient in vivo editing in the mouse retina. With this study we uncover a collection of previously uncharacterized Cas9 nucleases, including CoCas9, which enriches the genome editing toolbox.

Anna Karenina as a promoter of microbial diversity in the cosmopolitan agricultural pest Zeugodacus cucurbitae (Diptera, Tephritidae).

Gut microbial communities are critical in determining the evolutive success of fruit fly phytophagous pests (Diptera, Tephritidae), facilitating their adaptation to suboptimal environmental conditions and to plant allelochemical defences. An important source of variation for the microbial diversity of fruit flies is represented by the crop on which larvae are feeding. However, a "crop effect" is not always the main driver of microbial patterns, and it is often observed in combination with other and less obvious processes. In this work, we aim at verifying if environmental stress and, by extension, changing environmental conditions, can promote microbial diversity in Zeugodacus cucurbitae (Coquillett), a cosmopolitan pest of cucurbit crops. With this objective, 16S rRNA metabarcoding was used to test differences in the microbial profiles of wild fly populations in a large experimental setup in Eastern Central Tanzania. The analysis of 2,973 unique ASV, which were assigned to 22 bacterial phyla, 221 families and 590 putative genera, show that microbial α diversity (as estimated by Abundance Coverage Estimator, Faith's Phylogenetic Diversity, Shannon-Weiner and the Inverse Simpson indexes) as well as ß microbial diversity (as estimated by Compositional Data analysis of ASVs and of aggregated genera) significantly change as the species gets closer to its altitudinal limits, in farms where pesticides and agrochemicals are used. Most importantly, the multivariate dispersion of microbial patterns is significantly higher in these stressful environmental conditions thus indicating that Anna Karenina effects contribute to the microbial diversity of Z. cucurbitae. The crop effect was comparably weaker and detected as non-consistent changes across the experimental sites. We speculate that the impressive adaptive potential of polyphagous fruit flies is, at least in part, related to the Anna Karenina principle, which promotes stochastic changes in the microbial diversity of fly populations exposed to suboptimal environmental conditions.

The comparative plastisphere microbial community profile at Kung Wiman beach unveils potential plastic-specific degrading microorganisms.

Background: Plastic waste is a global environmental issue that impacts the well-being of humans, animals, plants, and microorganisms. Microplastic contamination has been previously reported at Kung Wiman Beach, located in Chanthaburi province along with the Eastern Gulf of Thailand. Our research aimed to study the microbial population of the sand and plastisphere and isolate microorganisms with potential plastic degradation activity. Methods: Plastic and sand samples were collected from Kung Wiman Beach for microbial isolation on agar plates. The plastic samples were identified by Fourier-transform infrared spectroscopy. Plastic degradation properties were evaluated by observing the halo zone on mineral salts medium (MSM) supplemented with emulsified plastics, including polystyrene (PS), polylactic acid (PLA), polyvinyl chloride (PVC), and bis (2-hydroxyethyl) terephthalate (BHET). Bacteria and fungi were identified by analyzing nucleotide sequence analysis of the 16S rRNA and internal transcribed spacer (ITS) regions, respectively. 16S and ITS microbiomes analysis was conducted on the total DNA extracted from each sample to assess the microbial communities. Results: Of 16 plastic samples, five were identified as polypropylene (PP), four as polystyrene (PS), four as polyethylene terephthalate (PET), two as high-density polyethylene (HDPE), and one sample remained unidentified. Only 27 bacterial and 38 fungal isolates were found to have the ability to degrade PLA or BHET on MSM agar. However, none showed degradation capabilities for PS or PVC on MSM agar. Notably, Planococcus sp. PP5 showed the highest hydrolysis capacity of 1.64 ± 0.12. The 16S rRNA analysis revealed 13 bacterial genera, with seven showing plastic degradation abilities: Salipiger, Planococcus, Psychrobacter, Shewanella, Jonesia, Bacillus, and Kocuria. This study reports, for the first time of the BHET-degrading properties of the genera Planococcus and Jonesia. Additionally, The ITS analysis identified nine fungal genera, five of which demonstrated plastic degradation abilities: Aspergillus, Penicillium, Peacilomyces, Absidia, and Cochliobolus. Microbial community composition analysis and linear discriminant analysis effect size revealed certain dominant microbial groups in the plastic and sand samples that were absent under culture-dependent conditions. Furthermore, 16S and ITS amplicon microbiome analysis revealed microbial groups were significantly different in the plastic and sand samples collected. Conclusions: We reported on the microbial communities found on the plastisphere at Kung Wiman Beach and isolated and identified microbes with the capacity to degrade PLA and BHET.

Leveraging genomic signatures of oral microbiome-associated antibiotic resistance genes for diagnosing pancreatic cancer.

Growing evidence has increasingly suggested a potential linkage between the oral microbiome and various diseases, including pancreatic ductal adenocarcinoma (PDAC). However, the utilization of gene-level information derived from the oral microbiome for diagnosing PDAC remains unexplored. In this study, we sought to investigate the novel potential of leveraging genomic signatures associated with antibiotic resistance genes (ARGs) within the oral microbiome for the diagnosis of PDAC. By conducting an analysis of oral microbiome samples obtained from PDAC patients, we successfully identified specific ARGs that displayed distinct sequence abundance profiles correlated with the presence of PDAC. In the healthy group, three ARGs were found to be enriched, whereas 21 ARGs were enriched in PDAC patients. Remarkably, these ARGs from oral microbiome exhibited promising diagnostic capabilities for PDAC (AUROC = 0.79), providing a non-invasive and early detection method. Our findings not only provide novel modal data for diagnosing PDAC but also shed light on the intricate interplay between the oral microbiome and PDAC.

HPV-associated cervicovaginal microbiome and host metabolome characteristics.

BACKGROUND: Cervicovaginal microbiome plays an important role in the persistence of HPV infection and subsequent disease development. However, cervicovaginal microbiota varied cross populations with different habits and regions. Identification of population-specific biomarkers from cervicovaginal microbiota and host metabolome axis may support early detection or surveillance of HPV-induced cervical disease at all sites. Therefore, in the present study, to identify HPV-specific biomarkers, cervicovaginal secretion and serum samples from HPV-infected patients (HPV group, n = 25) and normal controls (normal group, n = 17) in Xichang, China were collected for microbiome (16S rRNA gene sequencing) and metabolome (UHPLC-MS/MS) analysis, respectively. RESULTS: The results showed that key altered metabolites of 9,10-DiHOME, α-linolenic acid, ethylparaben, glycocholic acid, pipecolic acid, and 9,12,13-trihydroxy-10(E),15(Z)-octadecadienoic acid, correlating with Sneathia (Sneathia_amnii), Lactobacillus (Lactobacillus_iners), Atopobium, Mycoplasma, and Gardnerella, may be potential biomarkers of HPV infection. CONCLUSION: The results of current study would help to reveal the association of changes in cervicovaginal microbiota and serum metabolome with HPV infections.

Placental TLR recognition of salivary and subgingival microbiota is associated with pregnancy complications.

BACKGROUND: Pre-term birth, the leading cause of neonatal mortality, has been associated with maternal periodontal disease and the presence of oral pathogens in the placenta. However, the mechanisms that underpin this link are not known. This investigation aimed to identify the origins of placental microbiota and to interrogate the association between parturition complications and immune recognition of placental microbial motifs. Video Abstract METHODS: Saliva, plaque, serum, and placenta were collected during 130 full-term (FT), pre-term (PT), or pre-term complicated by pre-eclampsia (PTPE) deliveries and subjected to whole-genome shotgun sequencing. Real-time quantitative PCR was used to measure toll-like receptors (TLR) 1-10 expression in placental samples. Source tracking was employed to trace the origins of the placental microbiota. RESULTS: We discovered 10,007 functionally annotated genes representing 420 taxa in the placenta that could not be attributed to contamination. Placental microbial composition was the biggest discriminator of pregnancy complications, outweighing hypertension, BMI, smoking, and maternal age. A machine-learning algorithm trained on this microbial dataset predicted PTPE and PT with error rates of 4.05% and 8.6% (taxonomy) and 6.21% and 7.38% (function). Logistic regression revealed 32% higher odds of parturition complication (95% CI 2.8%, 81%) for every IQR increase in the Shannon diversity index after adjusting for maternal smoking status, maternal age, and gravida. We also discovered distinct expression patterns of TLRs that detect RNA- and DNA-containing antigens in the three groups, with significant upregulation of TLR9, and concomitant downregulation of TLR7 in PTPE and PT groups, and dense correlation networks between microbial genes and these TLRs. 70-82% of placental microbiota were traced to serum and thence to the salivary and subgingival microbiomes. The oral and serum microbiomes of PTPE and PT groups displayed significant enrichment of genes encoding iron transport, exosome, adhesion, quorum sensing, lipopolysaccharide, biofilm, and steroid degradation. CONCLUSIONS: Within the limits of cross-sectional analysis, we find evidence to suggest that oral bacteria might translocate to the placenta via serum and trigger immune signaling pathways capable of inducing placental vascular pathology. This might explain, in part, the higher incidence of obstetric syndromes in women with periodontal disease.

Diversity and distribution of biosynthetic gene clusters in agricultural soil microbiomes.

Bacterial secondary metabolites serve as an important source of molecules for drug discovery. They also play an important function in mediating the interactions of microbial producers with their living environment and surrounding organisms. However, little is known about the genetic novelty, distribution, and community-level impacts of soil bacterial biosynthetic potential on a large geographic scale. Here, we constructed the first catalog of 11,149 biosynthetic gene clusters (BGCs) from agricultural soils across China and unearthed hidden biosynthetic potential for new natural product discovery from the not-yet-cultivated soil bacteria. Notably, we revealed soil pH as the strongest environmental driver of BGC biogeography and predicted that soil acidification and global climate change could damage the biosynthetic potential of the soil microbiome. The co-occurrence network of bacterial genomes revealed two BGC-rich species, i.e., Nocardia niigatensis from Actinobacteriota and PSRF01 from Acidobacteriota, as the module hub and connector, respectively, indicating their keystone positions in the soil microbial communities. We also uncovered a dominant role of BGC-inferred biotic interactions over environmental drivers in structuring the soil microbiome. Overall, this study achieved novel insights into the BGC landscape in agricultural soils of China, substantially expanding our understanding of the diversity and novelty of bacterial secondary metabolism and the potential role of secondary metabolites in microbiota assembly.IMPORTANCEBacterial secondary metabolites not only serve as the foundation for numerous therapeutics (e.g., antibiotics and anticancer drugs), but they also play critical ecological roles in mediating microbial interactions (e.g., competition and communication). However, our knowledge of bacterial secondary metabolism is limited to only a small fraction of cultured strains, thus restricting our comprehensive understanding of their diversity, novelty, and potential ecological roles in soil ecosystems. Here, we used culture-independent metagenomics to explore biosynthetic potentials in agricultural soils of China. Our analyses revealed a high degree of genetic diversity and novelty within biosynthetic gene clusters in agricultural soil environments, offering valuable insights for biochemists seeking to synthesize novel bioactive products. Furthermore, we uncovered the pivotal role of BGC-rich species in microbial communities and the significant relationship between BGC richness and microbial phylogenetic turnover. This information emphasizes the importance of biosynthetic potential in the assembly of microbial communities.

Bacterial communities associated with an island radiation of lichen-forming fungi.

Evolutionary radiations are one of the most striking processes biologists have studied in islands. A radiation is often sparked by the appearance of ecological opportunity, which can originate in processes like trophic niche segregation or the evolution of key innovations. Another recently proposed mechanism is facilitation mediated by the bacterial communities associated with the radiating species. Here we explore the role of the bacterial communities in a radiation of lichen-forming fungi endemic to Macaronesia. Bacterial diversity was quantified by high throughput sequencing of the V1-V2 hyper-variable region of 172 specimens. We characterized the taxonomic and phylogenetic diversity of the bacterial communities associated with the different species, tested for compositional differences between these communities, carried out a functional prediction, explored the relative importance of different factors in bacterial community structure, searched for phylosymbiosis and tried to identify the origin of this pattern. The species of the radiation differed in the composition of their bacterial communities, which were mostly comprised of Alphaproteobacteria and Acidobacteriia, but not in the functionality of those communities. A phylosimbiotic pattern was detected, but it was probably caused by environmental filtering. These findings are congruent with the combined effect of secondary chemistry and mycobiont identity being the main driver of bacterial community structure. Altogether, our results suggest that the associated bacterial communities are not the radiation's main driver. There is one possible exception, however, a species that has an abnormally diverse core microbiome and whose bacterial communities could be subject to a specific environmental filter at the functional level.

Differential network connectivity analysis for microbiome data adjusted for clinical covariates using jackknife pseudo-values.

BACKGROUND: A recent breakthrough in differential network (DN) analysis of microbiome data has been realized with the advent of next-generation sequencing technologies. The DN analysis disentangles the microbial co-abundance among taxa by comparing the network properties between two or more graphs under different biological conditions. However, the existing methods to the DN analysis for microbiome data do not adjust for other clinical differences between subjects. RESULTS: We propose a Statistical Approach via Pseudo-value Information and Estimation for Differential Network Analysis (SOHPIE-DNA) that incorporates additional covariates such as continuous age and categorical BMI. SOHPIE-DNA is a regression technique adopting jackknife pseudo-values that can be implemented readily for the analysis. We demonstrate through simulations that SOHPIE-DNA consistently reaches higher recall and F1-score, while maintaining similar precision and accuracy to existing methods (NetCoMi and MDiNE). Lastly, we apply SOHPIE-DNA on two real datasets from the American Gut Project and the Diet Exchange Study to showcase the utility. The analysis of the Diet Exchange Study is to showcase that SOHPIE-DNA can also be used to incorporate the temporal change of connectivity of taxa with the inclusion of additional covariates. As a result, our method has found taxa that are related to the prevention of intestinal inflammation and severity of fatigue in advanced metastatic cancer patients. CONCLUSION: SOHPIE-DNA is the first attempt of introducing the regression framework for the DN analysis in microbiome data. This enables the prediction of characteristics of a connectivity of a network with the presence of additional covariate information in the regression. The R package with a vignette of our methodology is available through the CRAN repository ( https://CRAN.R-project.org/package=SOHPIE ), named SOHPIE (pronounced as Sofie). The source code and user manual can be found at https://github.com/sjahnn/SOHPIE-DNA .

Multi-omic integration of microbiome data for identifying disease-associated modules.

Multi-omic studies of the human gut microbiome are crucial for understanding its role in disease across multiple functional layers. Nevertheless, integrating and analyzing such complex datasets poses significant challenges. Most notably, current analysis methods often yield extensive lists of disease-associated features (e.g., species, pathways, or metabolites), without capturing the multi-layered structure of the data. Here, we address this challenge by introducing "MintTea", an intermediate integration-based approach combining canonical correlation analysis extensions, consensus analysis, and an evaluation protocol. MintTea identifies "disease-associated multi-omic modules", comprising features from multiple omics that shift in concord and that collectively associate with the disease. Applied to diverse cohorts, MintTea captures modules with high predictive power, significant cross-omic correlations, and alignment with known microbiome-disease associations. For example, analyzing samples from a metabolic syndrome study, MintTea identifies a module with serum glutamate- and TCA cycle-related metabolites, along with bacterial species linked to insulin resistance. In another dataset, MintTea identifies a module associated with late-stage colorectal cancer, including Peptostreptococcus and Gemella species and fecal amino acids, in line with these species' metabolic activity and their coordinated gradual increase with cancer development. This work demonstrates the potential of advanced integration methods in generating systems-level, multifaceted hypotheses underlying microbiome-disease interactions.

Global abundance patterns, diversity, and ecology of Patescibacteria in wastewater treatment plants.

BACKGROUND: Microorganisms are responsible for nutrient removal and resource recovery in wastewater treatment plants (WWTPs), and their diversity is often studied by 16S rRNA gene amplicon sequencing. However, this approach underestimates the abundance and diversity of Patescibacteria due to the low coverage of commonly used PCR primers for this highly divergent bacterial phylum. Therefore, our current understanding of the global diversity, distribution, and ecological role of Patescibacteria in WWTPs is very incomplete. This is particularly relevant as Patescibacteria are considered to be associated with microbial host cells and can therefore influence the abundance and temporal variability of other microbial groups that are important for WWTP functioning. RESULTS: Here, we evaluated the in silico coverage of widely used 16S rRNA gene-targeted primer pairs and redesigned a primer pair targeting the V4 region of bacterial and archaeal 16S rRNA genes to expand its coverage for Patescibacteria. We then experimentally evaluated and compared the performance of the original and modified V4-targeted primers on 565 WWTP samples from the MiDAS global sample collection. Using the modified primer pair, the percentage of ASVs classified as Patescibacteria increased from 5.9 to 23.8%, and the number of detected patescibacterial genera increased from 560 to 1576, while the detected diversity of the remaining microbial community remained similar. Due to this significantly improved coverage of Patescibacteria, we identified 23 core genera of Patescibacteria in WWTPs and described the global distribution pattern of these unusual microbes in these systems. Finally, correlation network analysis revealed potential host organisms that might be associated with Patescibacteria in WWTPs. Interestingly, strong indications were found for an association between Patescibacteria of the Saccharimonadia and globally abundant polyphosphate-accumulating organisms of the genus Ca. Phosphoribacter. CONCLUSIONS: Our study (i) provides an improved 16S rRNA gene V4 region-targeted amplicon primer pair inclusive of Patescibacteria with little impact on the detection of other taxa, (ii) reveals the diversity and distribution patterns of Patescibacteria in WWTPs on a global scale, and (iii) provides new insights into the ecological role and potential hosts of Patescibacteria in WWTPs. Video Abstract.

Association between Clinical Characteristics and Microbiota in Bronchiectasis Patients Based on Metagenomic Next-Generation Sequencing Technology.

This study aimed to investigate the disparities between metagenomic next-generation sequencing (mNGS) and conventional culture results in patients with bronchiectasis. Additionally, we sought to investigate the correlation between the clinical characteristics of patients and their microbiome profiles. The overarching goal was to enhance the effective management and treatment of bronchiectasis patients, providing a theoretical foundation for healthcare professionals. A retrospective survey was conducted on 67 bronchiectasis patients admitted to The First Hospital of Jiaxing from October 2019 to March 2023. Clinical baseline information, inflammatory indicators, and pathogen detection reports, including mNGS, conventional blood culture, bronchoalveolar lavage fluid (BALF) culture, and sputum culture results, were collected. By comparing the results of mNGS and conventional culture, the differences in pathogen detection rate and pathogen types were explored, and the diagnostic performance of mNGS compared to conventional culture was evaluated. Based on the various pathogens detected by mNGS, the association between clinical characteristics of bronchiectasis patients and mNGS microbiota results was analyzed. The number and types of pathogens detected by mNGS were significantly larger than those detected by conventional culture. The diagnostic efficacy of mNGS was significantly superior to conventional culture for all types of pathogens, particularly in viral detection (p < 0.01). Regarding pathogen detection rate, the bacteria with the highest detection rate were Pseudomonas aeruginosa (17/58) and Haemophilus influenzae (11/58); the fungus with the highest detection rate was Aspergillus fumigatus (10/21), and the virus with the highest detection rate was human herpes virus 4 (4/11). Differences were observed between the positive and negative groups for P. aeruginosa in terms of common scoring systems for bronchiectasis and whether the main symptom of bronchiectasis manifested as thick sputum (p < 0.05). Significant distinctions were also noted between the positive and negative groups for A. fumigatus regarding Reiff score, neutrophil percentage, bronchiectasis etiology, and alterations in treatment plans following mNGS results reporting (p < 0.05). Notably, 70% of patients with positive A. fumigatus infection opted to change their treatment plans. The correlation study between clinical characteristics of bronchiectasis patients and mNGS microbiological results revealed that bacteria, such as P. aeruginosa, and fungi, such as A. fumigatus, were associated with specific clinical features of patients. This underscored the significance of mNGS in guiding personalized treatment approaches. mNGS could identify multiple pathogens in different types of bronchiectasis samples and was a rapid and effective diagnostic tool for pathogen identification. Its use was recommended for diagnosing the causes of infections in bronchiectasis patients.

Improved DNA Extraction and Amplification Strategy for 16S rRNA Gene Amplicon-Based Microbiome Studies.

Next-generation sequencing technology has driven the rapid advancement of human microbiome studies by enabling community-level sequence profiling of microbiomes. Although all microbiome sequencing methods depend on recovering the DNA from a sample as a first critical step, lysis methods can be a major determinant of microbiome profile bias. Gentle enzyme-based DNA preparation methods preserve DNA quality but can bias the results by failing to open difficult-to-lyse bacteria. Mechanical methods like bead beating can also bias DNA recovery because the mechanical energy required to break tougher cell walls may shear the DNA of the more easily lysed microbes, and shearing can vary depending on the time and intensity of beating, influencing reproducibility. We introduce a non-mechanical, non-enzymatic, novel rapid microbial DNA extraction procedure suitable for 16S rRNA gene-based microbiome profiling applications that eliminates bead beating. The simultaneous application of alkaline, heat, and detergent ('Rapid' protocol) to milligram quantity samples provided consistent representation across the population of difficult and easily lysed bacteria equal to or better than existing protocols, producing sufficient high-quality DNA for full-length 16S rRNA gene PCR. The novel 'Rapid' method was evaluated using mock bacterial communities containing both difficult and easily lysed bacteria. Human fecal sample testing compared the novel Rapid method with a standard Human Microbiome Project (HMP) protocol for samples from lung cancer patients and controls. DNA recovered from both methods was analyzed using 16S rRNA gene sequencing of the V1V3 and V4 regions on the Illumina platform and the V1V9 region on the PacBio platform. Our findings indicate that the 'Rapid' protocol consistently yielded higher levels of Firmicutes species, which reflected the profile of the bacterial community structure more accurately, which was confirmed by mock community evaluation. The novel 'Rapid' DNA lysis protocol reduces population bias common to bead beating and enzymatic lysis methods, presenting opportunities for improved microbial community profiling, combined with the reduction in sample input to 10 milligrams or less, and it enables rapid transfer and simultaneous lysis of 96 samples in a standard plate format. This results in a 20-fold reduction in sample handling time and an overall 2-fold time advantage when compared to widely used commercial methods. We conclude that the novel 'Rapid' DNA extraction protocol offers a reliable alternative for preparing fecal specimens for 16S rRNA gene amplicon sequencing.

Impact of vaginal microecological differences on pregnancy outcomes and endometrial microbiota in frozen embryo transfer cycles.

PURPOSE: This prospective study investigates the correlation between vaginal microecology and pregnancy outcomes and explores their impact on endometrial microbiota composition during frozen embryo transfer (FET) cycles. Additionally, the impact of transvaginal Lactobacillus supplementation on reproductive outcomes in patients with previous failed cycles was assessed. METHODS: A total of 379 patients undergoing FET at a reproductive medicine center were categorized into clinical pregnancy (CP), miscarriage (MISC), and non-pregnant (NP) groups. Vaginal specimens were collected for microecological evaluation prior to embryo transfer. Endometrial microbiota samples were obtained during embryo transfer for 16S rRNA gene sequencing analysis to assess endometrial microbiota composition. Vaginal microecological indicators, including pH, Lactobacillus dominance, and leukocyte esterase activity, were measured. Transvaginal Lactobacillus supplementation was investigated in 60 patients with previous failed cycles. RESULTS: Vaginal microecology significantly correlated with pregnancy outcomes, with normal microecology associated with a higher clinical pregnancy rate. Vaginal pH and leukocyte esterase activity were significantly associated with clinical pregnancy. Furthermore, vaginal microecological differences significantly impacted endometrial microbiota composition. However, no significant differences were observed in endometrial microbiota composition among the CP, MISC, and NP groups. Notably, transvaginal Lactobacillus supplementation increased the clinical pregnancy rate without affecting the miscarriage rate. CONCLUSION: This study highlights that normal vaginal microecology, characterized by lower pH and leukocyte esterase negativity, is associated with a higher likelihood of clinical pregnancy following FET. Importantly, vaginal microecological differences influence endometrial microbiota composition. Moreover, transvaginal Lactobacillus supplementation appears promising in improving clinical pregnancy rates in patients with previous failed cycles. These findings contribute to a better understanding of the interplay between vaginal and endometrial microbiota and offer potential interventions to enhance reproductive success in assisted reproductive technologies.

Robustness of cancer microbiome signals over a broad range of methodological variation.

In 2020, we identified cancer-specific microbial signals in The Cancer Genome Atlas (TCGA) [1]. Multiple peer-reviewed papers independently verified or extended our findings [2-12]. Given this impact, we carefully considered concerns by Gihawi et al. [13] that batch correction and database contamination with host sequences artificially created the appearance of cancer type-specific microbiomes. (1) We tested batch correction by comparing raw and Voom-SNM-corrected data per-batch, finding predictive equivalence and significantly similar features. We found consistent results with a modern microbiome-specific method (ConQuR [14]), and when restricting to taxa found in an independent, highly-decontaminated cohort. (2) Using Conterminator [15], we found low levels of human contamination in our original databases (~1% of genomes). We demonstrated that the increased detection of human reads in Gihawi et al. [13] was due to using a newer human genome reference. (3) We developed Exhaustive, a method twice as sensitive as Conterminator, to clean RefSeq. We comprehensively host-deplete TCGA with many human (pan)genome references. We repeated all analyses with this and the Gihawi et al. [13] pipeline, and found cancer type-specific microbiomes. These extensive re-analyses and updated methods validate our original conclusion that cancer type-specific microbial signatures exist in TCGA, and show they are robust to methodology.