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.

Oral microbiome dysbiosis among cigarette smokers and smokeless tobacco users compared to non-users.

Tobacco use significantly influences the oral microbiome. However, less is known about how different tobacco products specifically impact the oral microbiome over time. To address this knowledge gap, we characterized the oral microbiome of cigarette users, smokeless tobacco users, and non-users over 4 months (four time points). Buccal swab and saliva samples (n = 611) were collected from 85 participants. DNA was extracted from all samples and sequencing was carried out on an Illumina MiSeq, targeting the V3-V4 region of the 16S rRNA gene. Cigarette and smokeless tobacco users had more diverse oral bacterial communities, including a higher relative abundance of Firmicutes and a lower relative abundance of Proteobacteria, when compared to non-users. Non-users had a higher relative abundance of Actinomyces, Granulicatella, Haemophilus, Neisseria, Oribacterium, Prevotella, Pseudomonas, Rothia, and Veillonella in buccal swab samples, compared to tobacco users. While the most abundant bacterial genera were relatively constant over time, some species demonstrated significant shifts in relative abundance between the first and last time points. In addition, some opportunistic pathogens were detected among tobacco users including Neisseria subflava, Bulleidia moorei and Porphyromonas endodontalis. Overall, our results provide a more holistic understanding of the structure of oral bacterial communities in tobacco users compared to non-users.

The impact of Sangju Qingjie Decoction on the pulmonary microbiota in the prevention and treatment of chronic obstructive pulmonary disease.

Objective: Exploring the effect of SJQJD on the pulmonary microbiota of chronic obstructive pulmonary disease (COPD) rats through 16S ribosomal RNA (rRNA) sequencing. Methods: A COPD rat model was constructed through smoking and lipopolysaccharide (LPS) stimulation, and the efficacy of SJQJD was evaluated by hematoxylin and eosin (H&E) staining and Enzyme-Linked Immunosorbnent Assay (ELISA). The alveolar lavage fluid of rats was subjected to 16S rRNA sequencing. The diversity of lung microbiota composition and community structure was analyzed and differential microbiota were screened. Additionally, machine learning algorithms were used for screening biomarkers of each group of the microbiota. Results: SJQJD could improve lung structure and inflammatory response in COPD rats. 16s rRNA sequencing analysis showed that SJQJD could significantly improve the abundance and diversity of bacterial communities in COPD rats. Through differential analysis and machine learning methods, potential microbial biomarkers were identified as Mycoplasmataceae, Bacillaceae, and Lachnospiraceae. Conclusion: SJQJD could improve tissue morphology and local inflammatory response in COPD rats, and its effect may be related to improve pulmonary microbiota.

Multi-class boosting for the analysis of multiple incomplete views on microbiome data.

BACKGROUND: Microbiome dysbiosis has recently been associated with different diseases and disorders. In this context, machine learning (ML) approaches can be useful either to identify new patterns or learn predictive models. However, data to be fed to ML methods can be subject to different sampling, sequencing and preprocessing techniques. Each different choice in the pipeline can lead to a different view (i.e., feature set) of the same individuals, that classical (single-view) ML approaches may fail to simultaneously consider. Moreover, some views may be incomplete, i.e., some individuals may be missing in some views, possibly due to the absence of some measurements or to the fact that some features are not available/applicable for all the individuals. Multi-view learning methods can represent a possible solution to consider multiple feature sets for the same individuals, but most existing multi-view learning methods are limited to binary classification tasks or cannot work with incomplete views. RESULTS: We propose irBoost.SH, an extension of the multi-view boosting algorithm rBoost.SH, based on multi-armed bandits. irBoost.SH solves multi-class classification tasks and can analyze incomplete views. At each iteration, it identifies one winning view using adversarial multi-armed bandits and uses its predictions to update a shared instance weight distribution in a learning process based on boosting. In our experiments, performed on 5 multi-view microbiome datasets, the model learned by irBoost.SH always outperforms the best model learned from a single view, its closest competitor rBoost.SH, and the model learned by a multi-view approach based on feature concatenation, reaching an improvement of 11.8% of the F1-score in the prediction of the Autism Spectrum disorder and of 114% in the prediction of the Colorectal Cancer disease. CONCLUSIONS: The proposed method irBoost.SH exhibited outstanding performances in our experiments, also compared to competitor approaches. The obtained results confirm that irBoost.SH can fruitfully be adopted for the analysis of microbiome data, due to its capability to simultaneously exploit multiple feature sets obtained through different sequencing and preprocessing pipelines.

Microbiome structure and function in parallel full-scale aerobic granular sludge and activated sludge processes.

Aerobic granular sludge (AGS) and conventional activated sludge (CAS) are two different biological wastewater treatment processes. AGS consists of self-immobilised microorganisms that are transformed into spherical biofilms, whereas CAS has floccular sludge of lower density. In this study, we investigated the treatment performance and microbiome dynamics of two full-scale AGS reactors and a parallel CAS system at a municipal WWTP in Sweden. Both systems produced low effluent concentrations, with some fluctuations in phosphate and nitrate mainly due to variations in organic substrate availability. The microbial diversity was slightly higher in the AGS, with different dynamics in the microbiome over time. Seasonal periodicity was observed in both sludge types, with a larger shift in the CAS microbiome compared to the AGS. Groups important for reactor function, such as ammonia-oxidising bacteria (AOB), nitrite-oxidising bacteria (NOB), polyphosphate-accumulating organisms (PAOs) and glycogen-accumulating organisms (GAOs), followed similar trends in both systems, with higher relative abundances of PAOs and GAOs in the AGS. However, microbial composition and dynamics differed between the two systems at the genus level. For instance, among PAOs, Tetrasphaera was more prevalent in the AGS, while Dechloromonas was more common in the CAS. Among NOB, Ca. Nitrotoga had a higher relative abundance in the AGS, while Nitrospira was the main nitrifier in the CAS. Furthermore, network analysis revealed the clustering of the various genera within the guilds to modules with different temporal patterns, suggesting functional redundancy in both AGS and CAS. KEY POINTS: • Microbial community succession in parallel full-scale aerobic granular sludge (AGS) and conventional activated sludge (CAS) processes. • Higher periodicity in microbial community structure in CAS compared to in AGS. • Similar functional groups between AGS and CAS but different composition and dynamics at genus level.

Soil microbiota and herbivory drive the assembly of tomato plant-associated microbial communities through different mechanisms.

Plant-associated microbial communities are key to shaping many aspects of plant biology. In this study, we tested whether soil microbial communities and herbivory influence the bacterial community of tomato plants and whether their influence in different plant compartments is driven by microbial spillover between compartments or whether plants are involved in mediating this effect. We grew our plants in soils hosting three different microbial communities and covered (or not) the soil surface to prevent (or allow) passive microbial spillover between compartments, and we exposed them (or not) to herbivory by Manduca sexta. Here we show that the soil-driven effect on aboveground compartments is consistently detected regardless of soil coverage, whereas soil cover influences the herbivore-driven effect on belowground microbiota. Together, our results suggest that the soil microbiota influences aboveground plant and insect microbial communities via changes in plant metabolism and physiology or by sharing microorganisms via xylem sap. In contrast, herbivores influence the belowground plant microbiota via a combination of microbial spillover and changes in plant metabolism. These results demonstrate the important role of plants in linking aboveground and belowground microbiota, and can foster further research on soil microbiota manipulation for sustainable pest management.

Microbiome and cancer: from mechanistic implications in disease progression and treatment to development of novel antitumoral strategies.

Cancer is a very aggressive disease and one of mankind's most important health problems, causing numerous deaths each year. Its etiology is complex, including genetic, gender-related, infectious diseases, dysbiosis, immunological imbalances, lifestyle, including dietary factors, pollution etc. Cancer patients also become immunosuppressed, frequently as side effects of chemotherapy and radiotherapy, and prone to infections, which further promote the proliferation of tumor cells. In recent decades, the role and importance of the microbiota in cancer has become a hot spot in human biology research, bringing together oncology and human microbiology. In addition to their roles in the etiology of different cancers, microorganisms interact with tumor cells and may be involved in modulating their response to treatment and in the toxicity of anti-tumor therapies. In this review, we present an update on the roles of microbiota in cancer with a focus on interference with anticancer treatments and anticancer potential.

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.

Growing a Cystic Fibrosis-Relevant Polymicrobial Biofilm to Probe Community Phenotypes.

Most in vitro models lack the capacity to fully probe bacterial phenotypes emerging from the complex interactions observed in real-life environments. This is particularly true in the context of hard-to-treat, chronic, and polymicrobial biofilm-based infections detected in the airways of individuals living with cystic fibrosis (CF), a multiorgan genetic disease. While multiple microbiome studies have defined the microbial compositions detected in the airway of people with CF (pwCF), no in vitro models thus far have fully integrated critical CF-relevant lung features. Therefore, a significant knowledge gap exists in the capacity to investigate the mechanisms driving the pathogenesis of mixed species CF lung infections. Here, we describe a recently developed four-species microbial community model, including Pseudomonas aeruginosa, Staphylococcus aureus, Streptococcus sanguinis, and Prevotella melaninogenica grown in CF-like conditions. Through the utilization of this system, clinically relevant phenotypes such as antimicrobial recalcitrance of several pathogens were observed and explored at the molecular level. The usefulness of this in vitro model resides in its standardized workflow that can facilitate the study of interspecies interactions in the context of chronic CF lung infections.

Differentially expressed proteins and microbial communities of the skin regulate disease resistance to Chinese tongue sole (Cynoglossus semilaevis).

Vibriosis, caused by Vibrio, seriously affects the health of fish, shellfish, and shrimps, causing large economic losses. Teleosts are represent the first bony vertebrates with both innate and adaptive immune responses against pathogens. Aquatic animals encounter hydraulic pressure and more pathogens, compared to terrestrial animals. The skin is the first line of defense in fish, constituting the skin-associated lymphoid tissue (SALT), which belongs to the main mucosa-associated lymphoid tissues (MALT). However, little is known about the function of immunity related proteins in fish. Therefore, this study used iTRAQ (isobaric tags for relative and absolute quantitation) to compare the skin proteome between the resistant and susceptible families of Cynoglossus semilaevis. The protein integrin beta-2, the alpha-enolase isoform X1, subunit B of V-type proton ATPase, eukaryotic translation initiation factor 6, and ubiquitin-like protein ISG15, were highly expressed in the resistant family. The 16S sequencing of the skin tissues of the resistant and susceptible families showed significant differences in the microbial communities of the two families. The protein-microbial interaction identified ten proteins associated with skin microbes, including immunoglobulin heavy chain gene (IGH), B-cell lymphoma/leukemia 10 (BCL10) and pre-B-cell leukemia transcription factor 1 isoform X2 (PBX2). This study highlights the interaction between skin proteins and the microbial compositions of C. semilaevis and provides new insights into understanding aquaculture breeding research.

Combined application of arbuscular mycorrhizal fungi and selenium fertilizer increased wheat biomass under cadmium stress and shapes rhizosphere soil microbial communities.

BACKGROUND: Selenium (Se) fertilizer and arbuscular mycorrhizal fungi (AMF) are known to modulate cadmium (Cd) toxicity in plants. However, the effects of their co-application on wheat growth and soil microbial communities in Cd-contaminated soil are unclear. RESULTS: A pot experiment inoculation with two types of AMF and the application of Se fertilizer under Cd stress in wheat showed that inoculation AMF alone or combined with Se fertilizer significantly increased wheat biomass. Se and AMF alone or in combination significantly reduced available Cd concentration in wheat and soil, especially in the Se combined with Ri treatment. High throughput sequencing of soil samples indicated that Se and AMF application had stronger influence on bacterial community compared to fungal community and the bacterial network seemed to have more complex interconnections than the fungal network, and finally shaped the formation of specific microflora to affect Cd availability. CONCLUSION: These results indicate that the application of Se and AMF, particularly in combination, could successfully decrease soil Cd availability and relieve the harm of Cd in wheat by modifying rhizosphere soil microbial communities.

Helicobacter pylori infection in infant rhesus macaque monkeys is associated with an altered lung and oral microbiome.

It is estimated that more than half of the world population has been infected with Helicobacter pylori. Most newly acquired H. pylori infections occur in children before 10 years of age. We hypothesized that early life H. pylori infection could influence the composition of the microbiome at mucosal sites distant to the stomach. To test this hypothesis, we utilized the infant rhesus macaque monkey as an animal model of natural H. pylori colonization to determine the impact of infection on the lung and oral microbiome during a window of postnatal development. From a cohort of 4-7 month-old monkeys, gastric biopsy cultures identified 44% of animals infected by H. pylori. 16S ribosomal RNA gene sequencing of lung washes and buccal swabs from animals showed distinct profiles for the lung and oral microbiome, independent of H. pylori infection. In order of relative abundance, the lung microbiome was dominated by the phyla Proteobacteria, Firmicutes, Bacteroidota, Fusobacteriota, Campilobacterota and Actinobacteriota while the oral microbiome was dominated by Proteobacteria, Firmicutes, Bacteroidota, and Fusobacteriota. In comparison to the oral cavity, the lung was composed of more genera and species that significantly differed by H. pylori status, with a total of 6 genera and species that were increased in H. pylori negative infant monkey lungs. Lung, but not plasma IL-8 concentration was also associated with gastric H. pylori load and lung microbial composition. We found the infant rhesus macaque monkey lung harbors a microbiome signature that is distinct from that of the oral cavity during postnatal development. Gastric H. pylori colonization and IL-8 protein were linked to the composition of microbial communities in the lung and oral cavity. Collectively, these findings provide insight into how H. pylori infection might contribute to the gut-lung axis during early childhood and modulate future respiratory health.

Biosynthetic potential of uncultured anammox community bacteria revealed through multi-omics analysis.

Microbial secondary metabolites (SMs) and their derivatives have been widely used in medicine, agriculture, and energy. Growing needs for renewable energy and the challenges posed by antibiotic resistance, cancer, and pesticides emphasize the crucial hunt for new SMs. Anaerobic ammonium-oxidation (anammox) systems harbor many uncultured or underexplored bacteria, representing potential resources for discovering novel SMs. Leveraging HiFi long-read metagenomic sequencing, 1,040 biosynthetic gene clusters (BGCs) were unearthed from the anammox microbiome with 58% being complete and showcasing rich diversity. Most of them showed distant relations to known BGCs, implying novelty. Members of the underexplored lineages (Chloroflexota and Planctomycetota) and Proteobacteria contained lots of BGCs, showcasing substantial biosynthetic potential. Metaproteomic results indicated that Planctomycetota members harbored the most active BGCs, particularly those involved in producing potential biofuel-ladderane. Overall, these findings underscore that anammox microbiomes could serve as valuable resources for mining novel BGCs and discovering new SMs for practical application.

Association between ascites Gustave Roussy immune score and the intratumoural microbiome in malignant ascites secondary to hepatocellular carcinoma.

BACKGROUNDS: The Gustave Roussy Immune (GRIm) score predicts survival outcomes in several cancers. However, the prognostic significance of the GRIm score in patients with malignant ascites has not yet been investigated. METHODS: Clinical samples were collected from a cohort of patients with malignant ascites secondary to hepatocellular carcinoma (HCC). We calculated serum GRIm (sGRIm) and ascites GRIm (aGRIm) scores and divided the samples into low and high GRIm score groups. Survival analysis was used to compare the prognostic significance of the sGRIm and aGRIm scores. 16S rRNA sequencing was performed to determine the profiles of the intratumoral microbiota in the groups. A fluorescent multiplex immunohistochemistry (mIHC) assay was used to detect the expression of different immune cells by labeling seven markers of malignant ascites. RESULTS: 155 patients with HCC and malignant ascites were enrolled in this study. Survival analysis revealed that the aGRIm score showed a superior prognostic significance compared to the sGRIm score. Microbial analysis demonstrated that the bacterial richness and diversity were higher in the low aGRIm score group than in the high aGRIm score group. LefSe analysis revealed that certain bacteria were correlated with high aGRIm scores. Fluorescent mIHC displayed the tumor microenvironment of malignant ascites and found that the density of CD8 + T cells was significantly higher in the low aGRIm score group than in the high aGRIm score group. CONCLUSIONS: Our present study identified a novel scoring system (aGRIm score) that can predict the survival outcome of patients with malignant ascites secondary to HCC.

Plastiome: Plastisphere-enriched mobile resistome in aquatic environments.

Aquatic microplastics (MPs) act as reservoirs for microbial communities, fostering the formation of a mobile resistome encompassing diverse antibiotic (ARGs) and biocide/metal resistance genes (BMRGs), and mobile genetic elements (MGEs). This collective genetic repertoire, referred to as the "plastiome," can potentially perpetuate environmental antimicrobial resistance (AMR). Our study examining two Japanese rivers near Tokyo revealed that waterborne MPs are primarily composed of polyethylene and polypropylene fibers and sheets of diverse origin. Clinically important genera like Exiguobacterium and Eubacterium were notably enriched on MPs. Metagenomic analysis uncovered a 3.46-fold higher enrichment of ARGs on MPs than those in water, with multidrug resistance genes (MDRGs) and BMRGs prevailing, particularly within MPs. Specific ARG and BMRG subtypes linked to resistance to vancomycin, beta-lactams, biocides, arsenic, and mercury showed selective enrichment on MPs. Network analysis revealed intense associations between host genera with ARGs, BMRGs, and MGEs on MPs, emphasizing their role in coselection. In contrast, river water exhibited weaker associations. This study underscores the complex interactions shaping the mobile plastiome in aquatic environments and emphasizes the global imperative for research to comprehend and effectively control AMR within the One Health framework.

BN-BacArena: Bayesian network extension of BacArena for the dynamic simulation of microbial communities.

MOTIVATION: Simulating gut microbial dynamics is extremely challenging. Several computational tools, notably the widely used BacArena, enable modeling of dynamic changes in the microbial environment. These methods, however, do not comprehensively account for microbe-microbe stimulant or inhibitory effects or for nutrient-microbe inhibitory effects, typically observed in different compounds present in the daily diet. RESULTS: Here, we present BN-BacArena, an extension of BacArena consisting on the incorporation within the native computational framework of a Bayesian network model that accounts for microbe-microbe and nutrient-microbe interactions. Using in vitro experiments, 16S rRNA gene sequencing data and nutritional composition of 55 foods, the output Bayesian network showed 23 significant nutrient-bacteria interactions, suggesting the importance of compounds such as polyols, ascorbic acid, polyphenols and other phytochemicals, and 40 bacteria-bacteria significant relationships. With test data, BN-BacArena demonstrates a statistically significant improvement over BacArena to predict the time-dependent relative abundance of bacterial species involved in the gut microbiota upon different nutritional interventions. As a result, BN-BacArena opens new avenues for the dynamic modeling and simulation of the human gut microbiota metabolism. AVAILABILITY AND IMPLEMENTATION: MATLAB and R code are available in https://github.com/PlanesLab/BN-BacArena.

Ascitic microbiota alteration is associated with portal vein tumor thrombosis occurrence and prognosis in hepatocellular carcinoma.

Portal vein tumor thrombosis (PVTT) frequently leads to malignant ascites (MA) in individuals with hepatocellular carcinoma (HCC), remaining a bottleneck in the treatment. This study aimed to explore the differences in microbes in paired groups and provide novel insights into PVTT and MA-related treatments. Formalin-fixed paraffin embedding ascite samples were collected from MA secondary to HCC and benign ascites (BA) secondary to liver cirrhosis (LC). Ascitic microbiota profiles were determined in the HCC and LC groups by 16S rRNA sequencing. Prognostic risk factors were screened using survival analysis. The correlation between the significantly different microbial signatures in the groups with PVTT (WVT) and non-PVTT (NVT) and clinical characteristics was explored. The expression of different immune cells was determined by labeling four markers in the MA tissue chips using multiplex immunohistochemistry. A total of 240 patients (196 with HCC with MA and 44 with LC with BA) were included in this study. Microbial profiles differed between the HCC and LC groups. PVTT and Barcelona Clinic Liver Cancer stage were shown to be prognostic risk factors. Significant differences in the alpha and beta diversities were observed between the WVT and NVT groups. Gammaproteobacteria and Acinetobacter were the most abundant in the HCC MA. Differences in microbial signatures between the WVT and NVT groups were correlated with the level of C-reactive protein and apolipoprotein A1. This study revealed the microbial differences in the tumor microenvironment of MA secondary to HCC and BA secondary to LC.IMPORTANCEFirst, we explored the alteration of the ascites ecosystem through the microbiota in patients with hepatocellular carcinoma (HCC) and liver cirrhosis. Second, this is the first clinical study to investigate the differences between patients with HCC with and without portal vein tumor thrombosis via 16S rRNA sequencing. These results revealed a decreased microbial diversity and metabolic dysregulation in individuals with HCC and portal vein tumor thrombosis. Gammaproteobacteria and Acinetobacter were the most abundant in the HCC malignant ascitic fluid. Our study provides a new perspective on treating malignant ascites secondary to HCC.

The microbiome of the appendix differs in patients with and without appendicitis: A prospective cohort study.

BACKGROUND: Appendicitis seems to be a disease of infectious origin, but the detailed pathogenesis is unknown. We aimed to investigate the microbiome of the appendix lumen in patients with and without appendicitis, including a comparison of the subgroups of complicated versus uncomplicated appendicitis. METHODS: This prospective observational cohort study included adult patients undergoing laparoscopic appendectomy for suspected appendicitis. According to histopathologic findings, the investigated groups consisted of patients with and without appendicitis, including subgroups of complicated versus uncomplicated appendicitis based on the surgical report. A swab of the appendix lumen was analyzed for genetic material from bacteria with shotgun metagenomics, and outcomes included analyses of microbiome diversity and differential abundance of bacteria. RESULTS: A total of 53 swabs from patients with suspected appendicitis were analyzed: 42 with appendicitis (16 complicated) and 11 without appendicitis. When comparing patients with and without appendicitis, they were equally rich in bacteria (alpha diversity), but the microbiome composition was dissimilar between these groups (beta diversity) (P < .01). No consistent bacterial species were detected in all patients with appendicitis, but a least 3 genera (Blautia, Faecalibacterium, and Fusicatenibacter) and 2 species, Blautia faecis and Blautia wexlerae, were more abundant in patients without appendicitis. For the subgroups complicated versus uncomplicated appendicitis, both measures for microbiome diversity were similar. CONCLUSION: The appendix microbiome composition of genetic material from bacteria in adult patients with and without appendicitis differed, but the microbiome was similar for patients with complicated versus uncomplicated appendicitis. Trial registration NCT03349814.