Performance of a Shotgun Prediction Model for Colorectal Cancer When Using 16S rRNA Sequencing Data.

Colorectal cancer (CRC), the third most common cancer globally, has shown links to disturbed gut microbiota. While significant efforts have been made to establish a microbial signature indicative of CRC using shotgun metagenomic sequencing, the challenge lies in validating this signature with 16S ribosomal RNA (16S) gene sequencing. The primary obstacle is reconciling the differing outputs of these two methodologies, which often lead to divergent statistical models and conclusions. In this study, we introduce an algorithm designed to bridge this gap by mapping shotgun-derived taxa to their 16S counterparts. This mapping enables us to assess the predictive performance of a shotgun-based microbiome signature using 16S data. Our results demonstrate a reduction in performance when applying the 16S-mapped taxa in the shotgun prediction model, though it retains statistical significance. This suggests that while an exact match between shotgun and 16S data may not yet be feasible, our approach provides a viable method for comparative analysis and validation in the context of CRC-associated microbiome research.

DeepMPTB: a vaginal microbiome-based deep neural network as artificial intelligence strategy for efficient preterm birth prediction.

In recent decades, preterm birth (PTB) has become a significant research focus in the healthcare field, as it is a leading cause of neonatal mortality worldwide. Using five independent study cohorts including 1290 vaginal samples from 561 pregnant women who delivered at term (n = 1029) or prematurely (n = 261), we analysed vaginal metagenomics data for precise microbiome structure characterization. Then, a deep neural network (DNN) was trained to predict term birth (TB) and PTB with an accuracy of 84.10% and an area under the receiver operating characteristic curve (AUROC) of 0.875 ± 0.11. During a benchmarking process, we demonstrated that our DL model outperformed seven currently used machine learning algorithms. Finally, our results indicate that overall diversity of the vaginal microbiota should be taken in account to predict PTB and not specific species. This artificial-intelligence based strategy should be highly helpful for clinicians in predicting preterm birth risk, allowing personalized assistance to address various health issues. DeepMPTB is open source and free for academic use. It is licensed under a GNU Affero General Public License 3.0 and is available at https://deepmptb.streamlit.app/ . Source code is available at https://github.com/oschakoory/DeepMPTB and can be easily installed using Docker ( https://www.docker.com/ ).

Gut microbiota predicts the diagnosis of celiac disease in Saudi children.

BACKGROUND: Celiac disease (CeD) is a multisystem immune-mediated multifactorial condition strongly associated with the intestinal microbiota. AIM: To evaluate the predictive power of the gut microbiota in the diagnosis of CeD and to search for important taxa that may help to distinguish CeD patients from controls. METHODS: Microbial DNA from bacteria, viruses, and fungi, was isolated from mucosal and fecal samples of 40 children with CeD and 39 controls. All samples were sequenced using the HiSeq platform, the data were analyzed, and abundance and diversities were assessed. For this analysis, the predictive power of the microbiota was evaluated by calculating the area under the curve (AUC) using data for the entire microbiome. The Kruskal-Wallis test was used to evaluate the significance of the difference between AUCs. The Boruta logarithm, a wrapper built around the random forest classification algorithm, was used to identify important bacterial biomarkers for CeD. RESULTS: In fecal samples, AUCs for bacterial, viral, and fungal microbiota were 52%, 58%, and 67.7% respectively, suggesting weak performance in predicting CeD. However, the combination of fecal bacteria and viruses showed a higher AUC of 81.8 %, indicating stronger predictive power in the diagnosis of CeD. In mucosal samples, AUCs for bacterial, viral, and fungal microbiota were 81.2%, 58.6%, and 35%, respectively, indicating that mucosal bacteria alone had the highest predictive power. Two bacteria, Bacteroides intestinalis and Burkholderiales bacterium 1-1-47, in fecal samples and one virus, Human_endogenous _retrovirus_K, in mucosal samples are predicted to be "important" biomarkers, differentiating celiac from nonceliac disease groups. Bacteroides intestinalis is known to degrade complex arabinoxylans and xylan which have a protective role in the intestinal mucosa. Similarly, several Burkholderiales species have been reported to produce peptidases that hydrolyze gluten peptides, with the potential to reduce the gluten content of food. Finally, a role for Human_endogenous _retrovirus_K in immune-mediated disease such as CeD has been reported. CONCLUSION: The excellent predictive power of the combination of the fecal bacterial and viral microbiota with mucosal bacteria alone indicates a potential role in the diagnosis of difficult cases of CeD. Bacteroides intestinalis and Burkholderiales bacterium 1-1-47, which were found to be deficient in CeD, have a potential protective role in the development of prophylactic modalities. Further studies on the role of the microbiota in general and Human_endogenous _retrovirus_K in particular are needed.

A New Perspective for Vineyard Terroir Identity: Looking for Microbial Indicator Species by Long Read Nanopore Sequencing.

Grapevine is one of the most important fruit crops worldwide, being Portugal one of the top wine producers. It is well established that wine sensory characteristics from a particular region are defined by the physiological responses of the grapevine to its environment and thus, the concept of terroir in viticulture was established. Among all the factors that contribute to terroir definition, soil microorganisms play a major role from nutrient recycling to a drastic influence on plant fitness (growth and protection) and of course wine production. Soil microbiome from four different terroirs in Quinta dos Murças vineyard was analysed through long-read Oxford Nanopore sequencing. We have developed an analytical pipeline that allows the identification of function, ecologies, and indicator species based on long read sequencing data. The Douro vineyard was used as a case study, and we were able to establish microbiome signatures of each terroir.

Specific Oral Microbial Differences in Proteobacteria and Bacteroidetes Are Associated with Distinct Sites When Moving from Healthy Mucosa to Oral Dysplasia-A Microbiome and Gene Profiling Study and Focused Review.

Oral potentially malignant disorders (OPMDs) are a group of conditions that carry a risk of oral squamous cell carcinoma (OSCC) development. Recent studies indicate that periodontal disease-associated pathogenic bacteria may play a role in the transition from healthy mucosa to dysplasia and to OSCC. Yet, the microbial signatures associated with the transition from healthy mucosa to dysplasia have not been established. To characterize oral microbial signatures at these different sites, we performed a 16S sequencing analysis of both oral swab and formalin-fixed, paraffin-embedded tissue (FFPE) samples. We collected oral swabs from healthy mucosa (from healthy patients), histologically normal mucosa adjacent to dysplasia, and low-grade oral dysplasia. Additionally, FFPE samples from histologically normal mucosa adjacent to OSCC, plus low grade and high-grade oral dysplasia samples were also collected. The collected data demonstrate significant differences in the alpha and beta microbial diversities of different sites in oral mucosa, dysplasia, and OSCC, as well as increased dissimilarities within these sites. We found that the Proteobacteria phyla abundance increased, concurrent with a progressive decrease in the Firmicutes phyla abundance, as well as altered levels of Enterococcus cecorum, Fusobacterium periodonticum, Prevotella melaninogenica, and Fusobacterium canifelinum when moving from healthy to diseased sites. Moreover, the swab sample analysis indicates that the oral microbiome may be altered in areas that are histologically normal, including in mucosa adjacent to dysplasia. Furthermore, trends in specific microbiome changes in oral swab samples preceded those in the tissues, signifying early detection opportunities for clinical diagnosis. In addition, we evaluated the gene expression profile of OSCC cells (HSC-3) infected with either P. gingivalis, T. denticola, F. nucelatum, or S. sanguinis and found that the three periodontopathogens enrich genetic processes related to cancer progression, including skin keratinization/cornification, while the commensal enriched processes related to RNA processing and adhesion. Finally, we reviewed the dysplasia microbiome literature and found a significant decrease in commensal bacteria, such as the Streptococci genus, and a simultaneous increase in pathogenic bacteria, mainly Bacteroidetes phyla and Fusobacterium genus. These findings suggest that features of the oral microbiome can serve as novel biomarkers for dysplasia and OSCC disease progression.

A bacterial signature-based method for the identification of seven forensically relevant human body fluids.

Detection and identification of body fluids plays a crucial role in criminal investigation, as it provides information on the source of the DNA as well as corroborative evidence regarding the crime committed, scene, and/or association with persons of interest. Historically, forensic serological methods have been chemical, immunological, catalytic, spectroscopic, and/or microscopic in nature. However, most of these methods are presumptive, with few robust confirmatory exceptions. In recent years several new molecular methods (mRNA, miRNA, DNA methylation, etc.) have been proposed; although promising, these methods require high quality human DNA or RNA. Additional steps are required in RNA based methods. Additionally, RNA based methods cannot be used for old cases where only DNA extracts remain to sample from. In this study, a novel non-human DNA (microbiome) based method was developed for the identification of the majority of forensically relevant human biological samples. Eight hundred and twelve (n = 812) biological samples (semen, vaginal fluid, menstrual blood, saliva, feces, urine, and blood) were collected and preserved using methods commonly used in forensic laboratories for evidence collection. Variable region four (V4) of 16 S ribosomal DNA (16 S rDNA) was amplified using a dual-indexing strategy and then sequenced on the MiSeq FGx sequencing platform using the MiSeq Reagent Kit v2 (500 cycles) and following the manufacturer's protocol. Machine learning prediction models were used to assess the classification accuracy of the newly developed method. As there was no significant difference in bacterial communities between vaginal fluid, menstrual blood, and female urine, these were combined as female intimate samples. Except in urine, the bacterial structures associated with male and female body fluid samples were not significantly different from one another. The newly developed method accurately identified human body fluid samples with an overall accuracy of more than 88%. This newly developed bacterial signature-based method is fast (no additional steps are needed as the same DNA can be used for both body fluid identification and STR typing), efficient (consume less sample as a single test can identify all major body fluids), sensitive (needs only 5 pg of bacterial DNA), accurate, and can be easily added into a forensic high throughput sequencing (HTS) panel.

Exploring the patient-microbiome interaction patterns for pan-cancer.

Microbes play important roles in human health and disease. Immunocompromised cancer patients are more vulnerable to getting microbial infections. Regions of hypoxia and acidic tumor microenvironment shape the microbial community diversity and abundance. Each cancer has its own microbiome, making cancer-specific sets of microbiomes. High-throughput profiling technologies provide a culture-free approach for microbial profiling in tumor samples. Microbial compositional data was extracted and examined from the TCGA unmapped transcriptome data. Biclustering, correlation, and statistical analyses were performed to determine the seven patient-microbe interaction patterns. These two-dimensional patterns consist of a group of microbial species that show significant over-representation over the 7 pan-cancer subtypes (S1-S7), respectively. Approximately 60% of the untreated cancer patients have experienced tissue microbial composition and functional changes between subtypes and normal controls. Among these changes, subtype S5 had loss of microbial diversity as well as impaired immune functions. S1, S2, and S3 had been enriched with microbial signatures derived from the Gammaproteobacteria, Actinobacteria and Betaproteobacteria, respectively. Colorectal cancer (CRC) was largely composed of two subtypes, namely S4 and S6, driven by different microbial profiles. S4 patients had increased microbial load, and were enriched with CRC-related oncogenic pathways. S6 CRC together with other cancer patients, making up almost 40% of all cases were classified into the S6 subtype, which not only resembled the normal control's microbiota but also retained their original "normal-like" functions. Lastly, the S7 was a rare and understudied subtype. Our study investigated the pan-cancer heterogeneity at the microbial level. The identified seven pan-cancer subtypes with 424 subtype-specific microbial signatures will help us find new therapeutic targets and better treatment strategies for cancer patients.

Characterization of microbial communities from gut microbiota of hypercholesterolemic and control subjects.

Introduction: In recent years, several studies have evidenced the importance of the microbiome to host physiology as metabolism regulator, along with its potential role in triggering various diseases. In this study, we analyzed the gut microbiota in hypercholesterolemic (cases) and normocholesterolemic (controls) individuals to identify characteristic microbial signature for each condition. Methods: Stool samples were obtained from 57 adult volunteers (27 hypercholesterolemic and 30 controls). The taxonomic profiling of microbial communities was performed using high-throughput sequencing of 16S rRNA V3-V4 amplicons, followed by data analysis using Quantitative Insights Into Microbial Ecology 2 (QIIME2) and linear discriminant analysis (LDA) effect size (LEfSe). Results: Significant differences were observed in weight, height, body mass index (BMI) and serum levels of triglycerides, total cholesterol and low-density lipoprotein cholesterol (LDL-C) between the groups (p<0.05). LEfSe showed differentially abundant prokaryotic taxa (α=0.05, LDA score > 2.0) in the group of hypercholesterolemic individuals (Methanosphaera, Rothia, Chromatiales, Clostridiales, Bacillaceae and Coriobacteriaceae) and controls (Faecalibacterium, Victivallis and Selenomonas) at various taxonomic levels. In addition, through the application of Phylogenetic Investigation of Communities by Reconstruction of Unobserved States 2 (PICRUSt2), the predominance of pathways related to biosynthesis in hypercholesterolemic patients was established, compared to controls in which degradation pathways were predominant. Finally, in the analysis of co-occurrence networks, it was possible to identify associations between the microorganisms present in both studied groups. Conclusion: Our results point out to unique microbial signatures, which likely play a role on the cholesterol metabolism in the studied population.

The Machine-Learning-Mediated Interface of Microbiome and Genetic Risk Stratification in Neuroblastoma Reveals Molecular Pathways Related to Patient Survival.

Currently, most neuroblastoma patients are treated according to the Children's Oncology Group (COG) risk group assignment; however, neuroblastoma's heterogeneity renders only a few predictors for treatment response, resulting in excessive treatment. Here, we sought to couple COG risk classification with tumor intracellular microbiome, which is part of the molecular signature of a tumor. We determine that an intra-tumor microbial gene abundance score, namely M-score, separates the high COG-risk patients into two subpopulations (Mhigh and Mlow) with higher accuracy in risk stratification than the current COG risk assessment, thus sparing a subset of high COG-risk patients from being subjected to traditional high-risk therapies. Mechanistically, the classification power of M-scores implies the effect of CREB over-activation, which may influence the critical genes involved in cellular proliferation, anti-apoptosis, and angiogenesis, affecting tumor cell proliferation survival and metastasis. Thus, intracellular microbiota abundance in neuroblastoma regulates intracellular signals to affect patients' survival.

Stress response of the marine sponge Scopalina sp.. Can microbial community composition predict sponge disease?

Disease has become an increasingly recognised problem in the marine environment, but our understanding of the factors that drive disease or our ability to predict its occurrence is limited. Marine sponges are known for their close associations with microorganisms, which are generally accepted to underpin sponge health and function. The aim of this study is to explore whether the microbial community composition of sponges can act as a predictor of disease occurrence under stressful environmental conditions. The development of a naturally occurring disease in the temperate sponge species Scopalina sp. was reproducibly recreated in a flow-through aquarium environment using increasing temperature stress. Throughout the experiments, four morphological health states were observed and described. Fingerprinting based on terminal restriction fragment length polymorphism of the bacterial community uncovered a statistically significant signature in healthy sponges prior to stress or apparent symptoms that correlated with the time it took for the disease to occur. This shows that the bacterial community composition of individual sponges can act as predictors of necrotic disease development. To the best of our knowledge, this is the first time a microbial signature of this nature has been reported in marine sponges and this finding can contribute to unravelling cause-effect pathways for stress-related dysbiosis and disease.

The genital microbiome and its potential for detecting sexual assault.

Since its inception, the Human Microbiome Project (HMP) has provided key discoveries that can be applied to forensics, in addition to those of obvious medical value. Whether for postmortem interval estimation, geolocation, or human identification, there are many applications of the microbiome as an investigative lead for forensic casework. The human skin microbiome has shown great potential for use in studies of transfer and human identification, however there has been little focus on the genital microbiome, in particular penile skin which differs from other body sites. Our preliminary data on both the penile and vaginal microbiome demonstrates potential value in cases of sexual assault. In this study we describe genital microbial signatures based on the analysis of five male and five female genital samples and compare these results to those from longitudinal studies. Selected taxa, e.g., Gardnerella, Lactobacilli, Finegoldia, Peptoniphilus, and Anaerococci, are shown to be candidate constituents of the genital microbiome that merit investigation for use in sexual assault casework.

Vultures from different trophic guilds show distinct oral pathogenic yeast signatures and co-occurrence networks.

Vultures have evolved adaptive mechanisms to prevent infections associated with their scavenging lifestyle. However, food-borne exposure to antimicrobial pharmaceuticals can promote opportunistic infections with adverse outcomes. Here, we used multivariate and network analyses to increase understanding of the behavior of the yeast communities causing oral mycosis outbreaks recently reported in wild nestling cinereous (Aegypius monachus), griffon (Gyps fulvus) and Egyptian (Neophron percnopterus) vultures (CV, GV and EV, respectively) exposed to antibiotics from livestock farming. Common and unique yeast signatures (of Candida, Debaromyces, Diutina, Meyerozyma, Naganishia, Pichia, Rhodotorula, Trichosporon and Yarrowia species) associated with oral mycoses were identified in the three vulture species. Hierarchical clustering analysis (HCA) and principal component analysis (PCA) highlighted that oral lesions from CV and GV shared similar yeast signatures (of major causative pathogens of opportunistic mycoses, such as Candida albicans, Candida parapsilosis and Candida tropicalis), while EV had a distinct yeast signature (of uncommon pathogenic species, such as Candida dubliniensis, Candida zeylanoides, Pichia fermentans and Rhodotorula spp.). Synergistic interactions between yeast species from distinct fungal phyla were found in lesions from CV and GV, but not in EV. These formed co-occurrence subnetworks with partially or fully connected topology. This study reveals that the composition, assembly and co-occurrence patterns of the yeast communities causing oral mycoses differ between vulture species with distinct feeding habits and scavenging lifestyles. Yeast species widely pathogenic to humans and animals, and yeast co-occurrence relationships, are distinctive hallmarks of oral mycoses in CV and GV. These vulture species are more exposed to antibiotics from intensively medicated livestock carcasses provided in supplementary feeding stations and show higher incidence of thrush-like oral lesions than EV. These findings may be useful for development of new initiatives or changes in the conservation of these avian scavengers affected by anthropogenic activities.

Gut microbial signatures associated with moxidectin treatment efficacy of Haemonchus contortus in infected goats.

Parasitic infections are associated with profound changes in the structure and function of the gut microbiome in various host-parasite systems. Here we examined the microbial composition and function in the abomasum, proximal colon and feces of Haemonchus contortus-infected goats after a partial anthelmintic drug clearance. A single-dose treatment of H. contortus-infected goats with Cydectin (moxidectin) resulted in an 83.9 % and 61.8 % reduction in fecal egg counts (EPG) and worm burden, respectively (P < 0.01), and restored abomasal pH to a normal baseline level. The treatment significantly increased the abundance of Proteobacteria, particularly that of Campylobacter, in the proximal colon. It also significantly affected several basic pathways, including bacterial secretion, butyrate metabolism, and LPS biosynthesis, and seemingly reduced the cellulolytic capacity in the colon. Several network modules displayed a strong correlation with EPG and worm burden. The Mantel test indicated a strong correlation between treatment related network topologies of the operational taxonomic units (OTU) belonging to Actinobacteria and Rikenellaceae and EPG and worm burden levels, respectively. Furthermore, microbial signatures that may better predict anthelmintic efficacy were identified. A signature or balance represented by the log ratio of the abundance of Verrucomicrobiaceae and Camplyobacteraceae had a strong correlation with EPG (r = 0.80). These novel insights into the interactions between H. contortus and gut microbiome in the caprine host and the consequence of a partial anthelmintic clearance on animal health and well-being may facilitate the design of more effective next-generation anthelmintics.

Mechanistic insights into the attenuation of intestinal inflammation and modulation of the gut microbiome by krill oil using in vitro and in vivo models.

BACKGROUND: The anti-inflammatory property of ω-3 polyunsaturated fatty acids (PUFA) has been exploited in the management of inflammatory bowel disease (IBD) with promising results. However, it remains unclear if PUFA play a significant role in the resolution of inflammation and promotion of mucosal healing. Krill oil (KO) is a natural product rich in PUFA and the potent antioxidant, astaxanthin. In this study, we attempted to understand the mechanisms through which KO modulates the gut microbiome and metabolome using in vitro and in vivo colitis models and a multi-omics based approach. RESULTS: KO significantly decreased LPS-induced IL1ß and TNFα expression in human macrophages in vitro in a dose-dependent manner by regulating a broad spectrum of signaling pathways, including NF-κB and NOD-like receptor signaling, and displayed a synergistic effect with COX2 and IKK2 inhibitors in attenuating inflammatory pathways. Moreover, KO was involved in the resolution of inflammation by promoting M2 polarization and enhancing macrophage-mediated intracellular bacterial killing. Parasite-dependent intestinal mucosal damage and microbial dysbiosis induced by Trichuris suis infection in pigs were partially restored by feeding KO. KO supplementation reduced the abundance of Rickettsiales and several species of Lactobacillus, which were among the important features identified by random forests analysis contributing to classification accuracy for KO supplementation. Several microbial signatures with strong predictive power for the status of both infection and supplementation were identified. The inhibitory effect of KO on histidine metabolism was identified using untargeted metabolomics. KO supplementation reduced several key metabolites related to histamine metabolism by suppressing the expression of a gene encoding L-histidine decarboxylase in the colon mucosa and reducing histamine biosynthesis of microbial origin. Moreover, the pro-resolving properties of KO were validated using a Citrobacter rodentium-induced Th1-dependent colitis murine model. Further, microbial signatures with high prediction accuracy for colitis-related pathophysiological traits were identified in mice. CONCLUSION: The findings from this study provided a mechanistic basis for optimizing microbiome-inspired alternative therapeutics in the management of IBD. The microbial signatures identified, particularly those with strong predictive accuracy for colitis phenotypes, will facilitate the development of biomarkers associated with appropriate dietary intervention to manage intestinal inflammation. Video abstract.

The Environmental Exposures and Inner- and Intercity Traffic Flows of the Metro System May Contribute to the Skin Microbiome and Resistome.

The skin functions as the primary interface between the human body and the external environment. To understand how the microbiome varies within urban mass transit and influences the skin microbiota, we profiled the human palm microbiome after contact with handrails within the Hong Kong Mass Transit Railway (MTR) system. Intraday sampling time was identified as the primary determinant of the variation and recurrence of the community composition, whereas human-associated species and clinically important antibiotic resistance genes (ARGs) were captured as p.m. signatures. Line-specific signatures were notably correlated with line-specific environmental exposures and city characteristics. The sole cross-border line appeared as an outlier in most analyses and showed high relative abundance and a significant intraday increment of clinically important ARGs (24.1%), suggesting potential cross-border ARG transmission, especially for tetracycline and vancomycin resistance. Our study provides an important reference for future public health strategies to mitigate intracity and cross-border pathogen and ARG transmission.