Microbiome Landscape Research Articles

Are Infants and Children at Risk of Adverse Health Effects from Dietary Deoxynivalenol Exposure? An Integrative Review.

Deoxynivalenol (DON) is a foodborne mycotoxin produced by Fusarium molds that commonly infect cereal grains. It is a potent protein synthesis inhibitor that can significantly impact humans' gastrointestinal, immune, and nervous systems and can alter the microbiome landscape. Low-dose, chronic exposure to DON has been found to stimulate the immune system, inhibit protein synthesis, and cause appetite suppression, potentially leading to growth failure in children. At higher doses, DON has been shown to cause immune suppression, nausea, vomiting, abdominal pain, headache, diarrhea, gastroenteritis, the malabsorption of nutrients, intestinal hemorrhaging, dizziness, and fever. A provisional maximum tolerable daily intake (PMTDI) limit of 1 µg/kg/body weight has been established to protect humans, underscoring the potential health risks associated with DON intake. While the adverse effects of dietary DON exposure have been established, healthcare communities have not adequately investigated or addressed this threat to child health, possibly due to the assumption that current regulatory exposure limits protect the public appropriately. This integrative review investigated whether current dietary DON exposure rates in infants and children regularly exceed PMTDI limits, placing them at risk of negative health effects. On a global scale, the routine contamination of cereal grains, bakery products, pasta, and human milk with DON could lead to intake levels above PMTDI limits. Furthermore, evidence suggests that other food commodities, such as soy, coffee, tea, dried spices, nuts, certain seed oils, animal milk, and various water reservoirs, can be intermittently contaminated, further amplifying the scope of the issue. Better mitigation strategies and global measures are needed to safeguard vulnerable youth from this harmful toxicant.

Longitudinal microbiome investigation throughout prion disease course reveals pre- and symptomatic compositional perturbations linked to short-chain fatty acid metabolism and cognitive impairment in mice.

Commensal intestinal bacteria shape our microbiome and have decisive roles in preserving host metabolic and immune homeostasis. They conspicuously impact disease development and progression, including amyloid-beta (Aβ) and alpha (α)-synuclein pathology in neurodegenerative diseases, conveying the importance of the brain-gut-microbiome axis in such conditions. However, little is known about the longitudinal microbiome landscape and its potential clinical implications in other protein misfolding disorders, such as prion disease. We investigated the microbiome architecture throughout prion disease course in mice. Fecal specimens were assessed by 16S ribosomal RNA sequencing. We report a temporal microbiome signature in prion disease and uncovered alterations in Lachnospiraceae, Ruminococcaceae, Desulfovibrionaceae, and Muribaculaceae family members in this disease. Moreover, we determined the enrichment of Bilophila, a microorganism connected to cognitive impairment, long before the clinical manifestation of disease symptoms. Based on temporal microbial abundances, several associated metabolic pathways and resulting metabolites, including short-chain fatty acids, were linked to the disease. We propose that neuroinflammatory processes relate to perturbations of the intestinal microbiome and metabolic state by an interorgan brain-gut crosstalk. Furthermore, we describe biomarkers possibly suitable for early disease diagnostics and anti-prion therapy monitoring. While our study is confined to prion disease, our discoveries might be of equivalent relevance in other proteinopathies and central nervous system pathologies.

The microbiome landscape of early-onset colorectal cancer: A systematic review.

e15646 Background: The rise of early-onset colorectal cancer (EOCRC, age < 50 years old) necessitates the examination of potential risk factors to enhance prevention and control strategies. Microbial dysbiosis has been hypothesized as a potential contributor, yet studies are limited. To guide future research, we systematically reviewed studies of the EOCRC microbiome, emphasizing methodological heterogeneities. Methods: We systematically reviewed available literature for microbiome studies involving EOCRC and synthesized findings according to comparison groups (healthy/later-onset CRC [LOCRC]), sample type (fecal /tumor), and analyses (taxonomic/metabolomic). Results: Collection, storage, extraction, and analytical methods varied across the 10 studies included out of 2073 studies. There were 377 unique EOCRC patients, of which most originated from China (193 EOCRC cases), the United States (52 EOCRC cases), and Japan (42 EOCRC cases). Six studies analyzed 224 unique EOCRC fecal samples (4 performed 16S, 5 performed WGSS, 4 performed metabolomic); five compared EOCRC against healthy individuals and five to LOCRC. Four studies utilized 153 unique EOCRC tumor samples (3 performed 16S, 1 performed WGSS, 1 performed metabolomic); two compared with tumor-adjacent normal tissue, one to colonic tissue of healthy individuals, and four to LOCRC. Among studies comparing fecal samples of EOCRC to healthy individuals, EOCRC exhibited increased levels of Fusobacterium and Escherichia-Shigella, known for metabolizing 5-Fluorouracil, while butyrate-producing taxa like Faecalibacterium, protective against CRC, were diminished; EOCRC also showed decreased lung-associated pathogens and Clostridium difficile, but increased oral pathogens. Among studies comparing fecal samples of EOCRC to LOCRC, EOCRC fecal samples were more abundant in oral/gastrointestinal pathogens, with varying findings on Fusobacterium nucleatum. Faecalibacterium prausnitzii was more abundant in EOCRC than LOCRC, whereas Escherichia-Shigella was less abundant. Fecal metabolomic studies displayed increased LPS-related pathways and bile acids in EOCRC compared to healthy individuals. Compared to tumor-adjacent normal tissues or colonic tissues of healthy individuals, EOCRC tumors were characterized by higher levels of Fusobacterium, Actinomyces, and Escherichia coli ( E. coli), fewer butyrate producers, and more Bacteroides fragilis. Conclusions: While data is limited and methodologies varied, emerging evidence suggests that EOCRC may have unique microbial signatures compared to both healthy individuals and LOCRC. This may signal unique pathogenesis for EOCRC and present potential for prevention, early detection, and intervention. It is essential to harmonize study methods and analytical approaches to advance understanding of the role of microbiome in EOCRC.

The Landscape and Perspectives of the Human Gut Metaproteomics

The human gut microbiome is closely associated with human health and diseases. Metaproteomics has emerged as a valuable tool for studying the functionality of the gut microbiome by analyzing the entire proteins present in microbial communities. Recent advancements in liquid chromatography and tandem mass spectrometry (LC-MS/MS) techniques have expanded the detection range of metaproteomics. However, the overall coverage of the proteome in metaproteomics is still limited. While metagenomics studies have revealed substantial microbial diversity and functional potential of the human gut microbiome, few studies have summarized and studied the human gut microbiome landscape revealed with metaproteomics. In this article, we present the current landscape of human gut metaproteomics studies by re-analyzing the identification results from 15 published studies. We quantified the limited proteome coverage in metaproteomics and revealed a high proportion of annotation coverage of metaproteomics-identified proteins. We conducted a preliminary comparison between the metaproteomics view and the metagenomics view of the human gut microbiome, identifying key areas of consistency and divergence. Based on the current landscape of human gut metaproteomics, we discuss the feasibility of using metaproteomics to study functionally unknown proteins and propose a whole workflow peptide-centric analysis. Additionally, we suggest enhancing metaproteomics analysis by refining taxonomic classification and calculating confidence scores, as well as developing tools for analyzing the interaction between taxonomy and function.

Exploring the Microbiome Landscape of Dental Plaque: A Cross-Sectional Analysis in Periodontal Health and Disease.

This study aimed to comprehensively analyze the microbiome of dental plaque in individuals with varying periodontal statuses, encompassing both periodontal health and disease. The primary objectives were to identify microbial markers associated with different clinical conditions, explore variations in microbial diversity, and investigate potential correlations between the oral microbiome and clinical parameters. A cross-sectional design was employed, involving 164 participants aged 18 to 65 years. Inclusion criteria comprised individuals with good oral and systemic health for the periodontal health group and those diagnosed with various stages of periodontal disease for the periodontal disease group. Dental plaque samples were meticulously collected from diverse tooth surfaces, and clinical examinations were conducted to assess periodontal health status. High-throughput sequencing of the 16S ribosomal RNA (rRNA) gene was utilized for microbiome analysis. Demographic characteristics revealed a balanced distribution between the periodontal health and disease groups. Clinical parameters, including probing depth, clinical attachment loss, and bleeding on probing, exhibited significant differences between the two groups (p < 0.001). Microbial diversity indices indicated a higher diversity in the periodontal health group compared to the disease group (p < 0.001). Analysis of relative abundance of bacterial phyla identified significant variations, with Firmicutes, Bacteroidetes, and Actinobacteria showing differential prevalence between health and disease (p < 0.05). Differentially abundant taxa analysis highlighted specific species associated with each clinical condition, including Prevotella intermedia and Porphyromonas gingivalis. Network analysis revealed complex microbial interactions within the oral microbiome. Functional predictions indicated variations in metabolic capabilities between health and disease, with potential implications for virulence and antibiotic resistance. This study provides a comprehensive analysis of the oral microbiome in periodontal health and disease, revealing significant associations between microbial composition and clinical parameters. The identification of microbial markers and functional insights enhances our understanding of the complex interplay within the oral ecosystem. These findings hold promise for advancing diagnostic and therapeutic approaches tailored to individual microbial profiles.

Abstract 994: Single analyte profiling of the mutational, immune and microbiome landscape in african american colon cancer patients

Abstract Colorectal cancer (CRC) disproportionately affects African American (AA) patients who experience a higher incidence and mortality than any other demographic groups in the United States. Yet molecular profiling in this population is scarce. Here, we performed whole-exome sequencing (WES) of 342 immediately snap-frozen tissues comprising AA CRC and matched, distant normal colon and identified distinct genomic drivers, DNA signatures, and, surprisingly, mosaic pathogenic mutations that are not present in the patient-matched CRC. We inferred T cell infiltration and tissue-resident microbiome (TR-M) directly from WES, enabling integrative analyses. Compared to normal colon, T cell infiltration is strongly decreased in tumours with microsatellite stability (MSS), while microsatellite unstable (MSI) and Polymerase Epsilon (POLE) mutant tumours had similar levels, suggesting immune evasion in MSS rather than increased T cell infiltration in MSI/POLE-mutants. Analyses of matched normal and tumour tissues revealed the evolving TR-M at unprecedented granularity and identified distinct niches of interactions with genomic and tumour-microenvironmental features. Together, this study represents the largest comprehensive genomic analysis of CRC in AA and provides a framework for performing a multi-layered analysis from a single analyte, thus, affording a cost-effective approach to expedite medical discoveries in underrepresented ancestries. Citation Format: Carino Gurjao, Edmond Chan, Michael J. Lee, Sho Hangai, Raul Rabadan, Tal Korem, Hanina Hibshoosh, Benjamin Izar. Single analyte profiling of the mutational, immune and microbiome landscape in african american colon cancer patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 994.

Uncovering the microbiome landscape in sashimi delicacies

It is widely believed that a significant portion of the gut microbiota, which play crucial roles in overall health and disease, originates from the food we consume. Sashimi is a type of popular raw seafood cuisine. Its microbiome, however, remained to be thoroughly explored. The objective of this study is to explore the microbiome composition in sashimi at the time when it is served and ready to be eaten. Specifically, our tasks include investigating the diversity and characteristics of microbial profiles in sashimi with respect to the fish types. We utilized the Sanger-sequencing based DNA barcoding technology for fish species authentication and next-generation sequencing for sashimi microbiome profiling. We investigated the microbiome profiles of amberjack, cobia, salmon, tuna and tilapia sashimi, which were all identified using the MT-CO1 DNA sequences regardless of their menu offering names. Chao1 and Shannon indexes, as well as Bray–Curtis dissimilarity index were used to evaluate the alpha and beta diversities of sashimi microbiome. We successfully validated our previous observation that tilapia sashimi has a significantly higher proportions of Pseudomonas compared to other fish sashimi, using independent samples (P = 0.0010). Salmon sashimi exhibited a notably higher Chao1 index in its microbiome in contrast to other fish species (P = 0.0031), indicating a richer and more diverse microbial ecosystem. Non-Metric Multidimensional Scaling (NMDS) based on Bray–Curtis dissimilarity index revealed distinct clusters of microbiome profiles with respect to fish types. Microbiome similarity was notably observed between amberjack and tuna, as well as cobia and salmon. The relationship of microbiome similarity can be depicted as a tree which resembles partly the phylogenetic tree of host species, emphasizing the close relationship between host evolution and microbial composition. Moreover, salmon exhibited a pronounced relative abundance of the Photobacterium genus, significantly surpassing tuna (P = 0.0079), observed consistently across various restaurant sources. In conclusion, microbiome composition of Pseudomonas is significantly higher in tilapia sashimi than in other fish sashimi. Salmon sashimi has the highest diversity of microbiome among all fish sashimi that we analyzed. The level of Photobacterium is significantly higher in salmon than in tuna across all the restaurants we surveyed. These findings provide critical insights into the intricate relationship between the host evolution and the microbial composition. These discoveries deepen our understanding of sashimi microbiota, facilitating our decision in selecting raw seafood.

Restoring the epigenetic landscape of lung microbiome: potential therapeutic approach for chronic respiratory diseases.

Chronic respiratory diseases, such as chronic obstructive pulmonary disease (COPD) and bronchiectasis, present significant threats to global health. Recent studies have revealed the crucial role of the lung microbiome in the development of these diseases. Pathogens have evolved complex strategies to evade the immune response, with the manipulation of host cellular epigenetic mechanisms playing a pivotal role. There is existing evidence regarding the effects of Pseudomonas on epigenetic modifications and their association with pulmonary diseases. Therefore, this study aims to directly assess the connection between Pseudomonas abundance and chronic respiratory diseases. We hope that our findings will shed light on the molecular mechanisms behind lung pathogen infections. We analyzed data from 366 participants, including individuals with COPD, acute exacerbations of COPD (AECOPD), bronchiectasis, and healthy individuals. Previous studies have given limited attention to the impact of Pseudomonas on these groups and their comparison with healthy individuals. Two independent datasets from different ethnic backgrounds were used for external validation. Each dataset separately analyzed bacteria at the genus level. The study reveals that Pseudomonas, a bacterium, was consistently found in high concentrations in all chronic lung disease datasets but it was present in very low abundance in the healthy datasets. This suggests that Pseudomonas may influence cellular mechanisms through epigenetics, contributing to the development and progression of chronic respiratory diseases. This study emphasizes the importance of understanding the relationship between the lung microbiome, epigenetics, and the onset of chronic pulmonary disease. Enhanced recognition of molecular mechanisms and the impact of the microbiome on cellular functions, along with a better understanding of these concepts, can lead to improved diagnosis and treatment.

Uncovering Microbial Composition of the Tissue Microenvironment in Bladder Cancer using RNA Sequencing Data.

Purpose: Bladder cancer (BC) is one of the top 10 common tumors in the world. It has been reported that microbiota can colonize tissues and play important roles in tumorigenesis and progression. However, the current understanding of microorganisms in the BC tissue microenvironment remains unclear. Methods: In this study, we integrated the RNA-seq data of 479 BC tissue samples from seven datasets combined with a range of bioinformatics tools to explore the landscape of microbiome in the BC tissue microenvironment. Results: The pan-microbiome was estimated to surpass 1,400 genera. A total of seven core microbiota (Bacillus, Corynebacterium, Cutibacterium, Escherichia, Halomonas, Pasteurella, and Streptomyces) were identified. Among them, Bacillus was widely distributed in all datasets with a high relative abundance (10.11% of all samples on average). Moreover, some biological factors, including tissue source and tumor grade, were found significant effects on the microbial composition of the bladder tissue. Pseudomonas, Porphyrobacter, and Acinetobacter were enriched in tumor tissues, while Mycolicibacterium and Streptomyces were enriched in patients who showed durable response to BCG therapy. In addition, we established microbial co-occurrence networks and found that the BCG therapy may attenuate the microbiological interactions. Conclusions: This study clearly provided a microbial landscape of the BC tissue microenvironment, which was important for exploring the interactions between microorganisms and BC tissues. The identified specific taxa might be potential biomarkers for BC.

Blocking MyD88 signaling with MyD88 inhibitor prevents colitis-associated colorectal cancer development by maintaining colonic microbiota homeostasis

Certain intestinal microbiota alterations appear to positively correlate with tumorigenesis of CAC due to the disruption of the balance between the host and microorganisms. It is proven that blocking MyD88 signaling can prevent colitis-associated colorectal cancer (CAC) development in mice. We are aim to reveal the role of MyD88 signaling of maintaining colonic microbiota homeostasis for preventing CAC development. We here analyzed the landscape of gut microbiome in the mice model of AOM/DSS-induced CAC with MyD88 inhibitor treatment. PCoA revealed significant reduction in Lactobacillus load and increase in Escherichia load in the mucosal microbial composition of mice with CAC, compared with normal controls (NCs). Inhibitor-treatment led to almost undetectable Proteobacteria (Escherichia) and the retention of the dominance of Firmicutes and Bacteroidota (Muribaculaceae) in the mucosa. RNA sequencing analysis identified genes were up-regulated (Hp, SAA3 and IL-1F9) and down-regulated (CYP3A44, SLC30A10, GPNMB and OTC) in Inhibitor-treated mice (vs. CAC). Meanwhile, Inhibitor-treated mice had higher percentage of MUC2-positive area in colon sections (vs. CAC, which was less than NCs) by IF staining and decreased Escherichia in the mucus layer (vs. CAC) by FISH. And intestinal microbiota from mice with MyD88 inhibitor treatment could lessen the outcome of CAC by fecal microbiota transplantation. The development of CAC was involved in the increasing and ectopic Escherichia in the decreasing colonic mucus layer. MyD88 signaling blockade may maintain the host-microbiota homeostasis by up-regulating MUC2 production, increasing probiotics and their protective effects, and inhibiting the reproduction of Escherichia.

Unveiling the Microbiome Landscape: A Metagenomic Study of Bacterial Diversity, Antibiotic Resistance, and Virulence Factors in the Sediments of the River Ganga, India.

The global rise in antibiotic resistance, fueled by indiscriminate antibiotic usage in medicine, aquaculture, agriculture, and the food industry, presents a significant public health challenge. Urban wastewater and sewage treatment plants have become key sources of antibiotic resistance proliferation. The present study focuses on the river Ganges in India, which is heavily impacted by human activities and serves as a potential hotspot for the spread of antibiotic resistance. We conducted a metagenomic analysis of sediment samples from six distinct locations along the river to assess the prevalence and diversity of antibiotic resistance genes (ARGs) within the microbial ecosystem. The metagenomic analysis revealed the predominance of Proteobacteria across regions of the river Ganges. The antimicrobial resistance (AMR) genes and virulence factors were determined by various databases. In addition to this, KEGG and COG analysis revealed important pathways related to AMR. The outcomes highlight noticeable regional differences in the prevalence of AMR genes. The findings suggest that enhancing health and sanitation infrastructure could play a crucial role in mitigating the global impact of AMR. This research contributes vital insights into the environmental aspects of antibiotic resistance, highlighting the importance of targeted public health interventions in the fight against AMR.

Pregnancy Protects against Abnormal Gut Permeability Promoted via the Consumption of a High-Fat Diet in Mice.

The consumption of large amounts of dietary fats and pregnancy are independent factors that can promote changes in gut permeability and the gut microbiome landscape. However, there is limited evidence regarding the impact of pregnancy on the regulation of such parameters in females fed a high-fat diet. Here, gut permeability and microbiome landscape were evaluated in a mouse model of diet-induced obesity in pregnancy. The results show that pregnancy protected against the harmful effects of the consumption of a high-fat diet as a disruptor of gut permeability; thus, there was a two-fold reduction in FITC-dextran passage to the bloodstream compared to non-pregnant mice fed a high-fat diet (p < 0.01). This was accompanied by an increased expression of gut barrier-related transcripts, particularly in the ileum. In addition, the beneficial effect of pregnancy on female mice fed the high-fat diet was accompanied by a reduced presence of bacteria belonging to the genus Clostridia, and by increased Lactobacillus murinus in the gut (p < 0.05). Thus, this study advances the understanding of how pregnancy can act during a short window of time, protecting against the harmful effects of the consumption of a high-fat diet by promoting an increased expression of transcripts encoding proteins involved in the regulation of gut permeability, particularly in the ileum, and promoting changes in the gut microbiome.

Sequencing, Fast and Slow: Profiling Microbiomes in Human Samples with Nanopore Sequencing

Rapid and accurate pathogen identification is crucial in effectively combating infectious diseases. However, the current diagnostic tools for bacterial infections predominantly rely on century-old culture-based methods. Furthermore, recent research highlights the significance of host–microbe interactions within the host microbiota in influencing the outcome of infection episodes. As our understanding of science and medicine advances, there is a pressing need for innovative diagnostic methods that can identify pathogens and also rapidly and accurately profile the microbiome landscape in human samples. In clinical settings, such diagnostic tools will become a powerful predictive instrument in directing the diagnosis and prognosis of infectious diseases by providing comprehensive insights into the patient’s microbiota. Here, we explore the potential of long-read sequencing in profiling the microbiome landscape from various human samples in terms of speed and accuracy. Using nanopore sequencers, we generate native DNA sequences from saliva and stool samples rapidly, from which each long-read is basecalled in real-time to provide downstream analyses such as taxonomic classification and antimicrobial resistance through the built-in software (&lt;12 h). Subsequently, we utilize the nanopore sequence data for in-depth analysis of each microbial species in terms of host–microbe interaction types and deep learning-based classification of unidentified reads. We find that the nanopore sequence data encompass complex information regarding the microbiome composition of the host and its microbial communities, and also shed light on the unexplored human mobilome including bacteriophages. In this study, we use two different systems of long-read sequencing to give insights into human microbiome samples in the ‘slow’ and ‘fast’ modes, which raises additional inquiries regarding the precision of this novel technology and the feasibility of extracting native DNA sequences from other human microbiomes.

Colon Cancer Microbiome Landscaping: Differences in Right- and Left-Sided Colon Cancer and a Tumor Microbiome-Ileal Microbiome Association.

In the current era of precision oncology, it is widely acknowledged that CRC is a heterogeneous disease entity. Tumor location (right- or left-sided colon cancer or rectal cancer) is a crucial factor in determining disease progression as well as prognosis and influences disease management. In the last decade, numerous works have reported that the microbiome is an important element of CRC carcinogenesis, progression and therapy response. Owing to the heterogeneous nature of microbiomes, the findings of these studies were inconsistent. The majority of the studies combined colon cancer (CC) and rectal cancer (RC) samples as CRC for analysis. Furthermore, the small intestine, as the major site for immune surveillance in the gut, is understudied compared to the colon. Thus, the CRC heterogeneity puzzle is far from being solved, and more research is necessary for prospective trials that separately investigate CC and RC. Our prospective study aimed to map the colon cancer landscape using 16S rRNA amplicon sequencing in biopsy samples from the terminal ileum, healthy colon tissue, healthy rectal tissue and tumor tissue as well as in preoperative and postoperative stool samples of 41 patients. While fecal samples provide a good approximation of the average gut microbiome composition, mucosal biopsies allow for detecting subtle variations in local microbial communities. In particular, the small bowel microbiome has remained poorly characterized, mainly because of sampling difficulties. Our analysis revealed the following: (i) right- and left-sided colon cancers harbor distinct and diverse microbiomes, (ii) the tumor microbiome leads to a more consistent cancer-defined microbiome between locations and reveals a tumor microbiome-ileal microbiome association, (iii) the stool only partly reflects the microbiome landscape in patients with CC, and (iv) mechanical bowel preparation and perioperative antibiotics together with surgery result in major changes in the stool microbiome, characterized by a significant increase in the abundance of potentially pathogenic bacteria, such as Enterococcus. Collectively, our results provide new and valuable insights into the complex microbiome landscape in patients with colon cancer.

Gut-Lung Axis-A Compass to Navigate Microbiome Landscape in COVID-19

The waves of the Coronavirus Disease 2019 (COVID-19) pandemic have disrupted healthcare systems globally, with a staggering number of confirmed cases reaching 593 million and a death toll of six million. While the primary target organ of COVID-19 is the lungs, the novel coronavirus, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) also shows a preference for the intestinal epithelium, which expresses Angiotensin Converting Enzyme 2 (ACE2), a receptor necessary for viral entry into host cells. The composition and abundance of beneficial gut microbiota play a critical role in protecting against severe disease and complications, whereas dysbiosis contribute to systemic inflammation and immune imbalances during SARS-CoV-2 infection. Recent insights into the microbiome have emphasised the importance of the gut-lung axis and the microenvironments of the gut and lungs in the context of COVID-19. These findings offer opportunities for the development of new diagnostic and therapeutic strategies. In this review, the significance of the gut-lung axis, as well as the intestinal and pulmonary microbiota, in COVID-19, were explored with a particular focus on their potential applications

Machine learning-based feature selection to search stable microbial biomarkers: application to inflammatory bowel disease.

Biomarker discovery exploiting feature importance of machine learning has risen recently in the microbiome landscape with its high predictive performance in several disease states. To have a concrete selection among a high number of features, recursive feature elimination (RFE) has been widely used in the bioinformatics field. However, machine learning-based RFE has factors that decrease the stability of feature selection. In this article, we suggested methods to improve stability while sustaining performance. We exploited the abundance matrices of the gut microbiome (283 taxa at species level and 220 at genus level) to classify between patients with inflammatory bowel disease (IBD) and healthy control (1,569 samples). We found that applying an already published data transformation before RFE improves feature stability significantly. Moreover, we performed an in-depth evaluation of different variants of the data transformation and identify those that demonstrate better improvement in stability while not sacrificing classification performance. To ensure a robust comparison, we evaluated stability using various similarity metrics, distances, the common number of features, and the ability to filter out noise features. We were able to confirm that the mapping by the Bray-Curtis similarity matrix before RFE consistently improves the stability while maintaining good performance. Multilayer perceptron algorithm exhibited the highest performance among 8 different machine learning algorithms when a large number of features (a few hundred) were considered based on the best performance across 100 bootstrapped internal test sets. Conversely, when utilizing only a limited number of biomarkers as a trade-off between optimal performance and method generalizability, the random forest algorithm demonstrated the best performance. Using the optimal pipeline we developed, we identified 14 biomarkers for IBD at the species level and analyzed their roles using Shapley additive explanations. Taken together, our work not only showed how to improve biomarker discovery in the metataxonomic field without sacrificing classification performance but also provided useful insights for future comparative studies.

Microbiome landscape of lesions and adjacent normal mucosal areas in oral lichen planus patient.

The pathogenesis of oral lichen planus (OLP) remains unclear, and microbial dysbiosis has been proposed to play a role in the pathogenesis of OLP. Oral mucosal swabs from 77 OLP patients and 76 healthy subjects were collected. The bacterial community among the OLP lesion, the adjacent normal mucosal, and the oral mucosal surface in healthy people were analyzed by 16S sequencing. The factor of gender and age that may affect the flora distribution of OLP patients were explored. Results indicate no significant difference in microbiota between OLP and the adjacent group. Compared with the healthy group, Neisseria, Haemophilus, Fusobacterium, Porphyromonas, Rothia, Actinomyces, and Capnocytophaga significantly increased in the OLP group. Actinomyces increased in male OLP patients, and the other six bacteria increased in female OLP patients. In female OLP patients, Lautropia and Dialister were positively correlated with age. While in male OLP patients, Moraxella, Porphyromonas, and Fusobacterium were positively correlated with age. Functional enrichment analysis suggested that abnormal energy metabolism related to ATP synthases, abnormal transport and metabolism of glycans, amino acids, and vitamins, and disorders of the local immune microenvironment might exist in OLP lesion.

1502P Heavy pre-treatment is associated with microbiome dysbiosis, reduced immune infiltration, and potential resistance to immune checkpoint inhibitors in metastatic sarcoma

A unique feature of sarcoma metastasis is the escape from aseptic positions(mesenchymal) to aseptic organ such as lung. Here, we explore the lung microbiome of sarcoma in association with immune-microenvironment and immunotherapy. We investigated the microbiome landscape of 100 recurrent sarcoma samples by shot-gun metagenomic sequencing of osteosarcoma(n=90) and other sarcoma(n=10), including 24 tumor-adjacent lung tissue, 73 pleural effusion, and 3 abdomen/subdermal effusion. The microbiome characteristics were investigated in association with immune infiltration and T-cell receptor(TCR) repertoire in 58 samples, and with the clinical efficacy of PD-1 blockade in 22 patients. We identified a mean of 99 genera in lung tissue, 41 in pleural effusion and 18 in other effusion. There was a significant association of prior-lines of chemotherapy with microbiome diversity in terms of quantity (Chao) and diversity (Shannon). Hierarchical clustering suggested 3 clusters of microbiome composition-C1:abundance subtype (high-quantity and diversity, n=43); C2:disrupted subtype (high-quantity, low-diversity, n=35) and C3:depleted subtype (low-quantity, low diversity, n=22). Furthermore, patients with heavier therapy were enriched in C2 (disrupted) and C3 (depleted) subtypes. Interestingly, we observed a significantly higher TCR diversity, IFN-gamma, and CD8-T cells in the C1 (abundance) microenvironment. Comparing 15 samples from responders (cutoff=4 months) versus 18 non-responders, we found 61 genera associated with PD-1 inhibition efficacy, including known immune-stimulatory microbes, such as Streptococcus (reminiscent of Coley-toxins), Rhodanobacter, Bifidobacterium, Anaerococcus, etc. Surprisingly, these microbiome was highly prosperous in C1 (abundance) and low in the C2 (disrupted), but were either missing or in complete dysbiosis in the C3 (depleted) subtype. Our report depicts the first landscape of lung microbiome associated with metastatic sarcoma, which suggests the potential of avoiding microbiome dysbiosis during chemotherapy and boosting microbiome for immunotherapy in metastatic sarcoma.

Computational approach to modeling microbiome landscapes associated with chronic human disease progression.

A microbial community is a dynamic system undergoing constant change in response to internal and external stimuli. These changes can have significant implications for human health. However, due to the difficulty in obtaining longitudinal samples, the study of the dynamic relationship between the microbiome and human health remains a challenge. Here, we introduce a novel computational strategy that uses massive cross-sectional sample data to model microbiome landscapes associated with chronic disease development. The strategy is based on the rationale that each static sample provides a snapshot of the disease process, and if the number of samples is sufficiently large, the footprints of individual samples populate progression trajectories, which enables us to recover disease progression paths along a microbiome landscape by using computational approaches. To demonstrate the validity of the proposed strategy, we developed a bioinformatics pipeline and applied it to a gut microbiome dataset available from a Crohn’s disease study. Our analysis resulted in one of the first working models of microbial progression for Crohn’s disease. We performed a series of interrogations to validate the constructed model. Our analysis suggested that the model recapitulated the longitudinal progression of microbial dysbiosis during the known clinical trajectory of Crohn’s disease. By overcoming restrictions associated with complex longitudinal sampling, the proposed strategy can provide valuable insights into the role of the microbiome in the pathogenesis of chronic disease and facilitate the shift of the field from descriptive research to mechanistic studies.

Look-alike humans identified by facial recognition algorithms show genetic similarities.

The human face is one of the most visible features of our unique identity as individuals. Interestingly, monozygotic twins share almost identical facial traits and the same DNA sequence but could exhibit differences in other biometrical parameters. The expansion of the world wide web and the possibility to exchange pictures of humans across the planet has increased the number of people identified online as virtual twins or doubles that are not family related. Herein, we have characterized in detail a set of "look-alike" humans, defined by facial recognition algorithms, for their multiomics landscape. We report that these individuals share similar genotypes and differ in their DNA methylation and microbiome landscape. These results not only provide insights about the genetics that determine our face but also might have implications for the establishment of other human anthropometric properties and even personality characteristics.