Association of Flavonifractor plautii, a Flavonoid-Degrading Bacterium, with the Gut Microbiome of Colorectal Cancer Patients in India

Virulence factor discovery identifies associations between the Fic gene family and Fap2+ fusobacteria in colorectal cancer microbiomes

Fusobacterium is a bacterium associated with colorectal cancer (CRC) tumorigenesis, progression, and metastasis. Fap2 is a fusobacteria-specific outer membrane galactose-binding lectin that mediates Fusobacterium adherence to and invasion of CRC tumors. Advances in omics analyses provide an opportunity to profile and identify microbial genomic features that correlate with the cancer-associated bacterial virulence factor Fap2. Here, we analyze genomes of Fusobacterium colon tumor isolates and find that a family of post-translational modification enzymes containing Fic domains is associated with Fap2 positivity in these strains. We demonstrate that Fic family genes expand with the presence of Fap2 in the fusobacterial pangenome. Through comparative genomic analysis, we find that Fap2+ Fusobacteriota are highly enriched with Fic gene families compared to other cancer-associated and human gut microbiome bacterial taxa. Using a global data set of CRC shotgun metagenomes, we show that fusobacterial Fic and Fap2 genes frequently co-occur in the fecal microbiomes of individuals with late-stage CRC. We further characterize specific Fic gene families harbored by Fap2+ Fusobacterium animalis genomes and detect recombination events and elements of horizontal gene transfer via synteny analysis of Fic gene loci. Exposure of a F. animalis strain to a colon adenocarcinoma cell line increases gene expression of fusobacterial Fic and virulence-associated adhesins. Finally, we demonstrate that Fic proteins are synthesized by F. animalis as Fic peptides are detectable in F. animalis monoculture supernatants. Taken together, our study uncovers Fic genes as potential virulence factors in Fap2+ fusobacterial genomes.IMPORTANCEAccumulating data support that bacterial members of the intra-tumoral microbiota critically influence colorectal cancer progression. Yet, relatively little is known about non-adhesin fusobacterial virulence factors that may influence carcinogenesis. Our genomic analysis and expression assays in fusobacteria identify Fic domain-containing genes, well-studied virulence factors in pathogenic bacteria, as potential fusobacterial virulence features. The Fic family proteins that we find are encoded by fusobacteria and expressed by Fusobacterium animalis merit future investigation to assess their roles in colorectal cancer development and progression.

Fecal microbial load is a major determinant of gut microbiome variation and a confounder for disease associations

The microbiota in individual habitats differ in both relative composition and absolute abundance. While sequencing approaches determine the relative abundances of taxa and genes, they do not provide information on their absolute abundances. Here, we developed a machine-learning approach to predict fecal microbial loads (microbial cells per gram) solely from relative abundance data. Applying our prediction model to a large-scale metagenomic dataset (n = 34,539), we demonstrated that microbial load is the major determinant of gut microbiome variation and is associated with numerous host factors, including age, diet, and medication. We further found that for several diseases, changes in microbial load, rather than the disease condition itself, more strongly explained alterations in patients' gut microbiome. Adjusting for this effect substantially reduced the statistical significance of the majority of disease-associated species. Our analysis reveals that the fecal microbial load is a major confounder in microbiome studies, highlighting its importance for understanding microbiome variation in health and disease.

Quantifying Metagenomic Strain Associations from Microbiomes with Anpan

Genetic and genomic variation among microbial strains can dramatically influence their phenotypes and environmental impact, including on human health. However, inferential methods for quantifying these differences have been lacking. Strain-level metagenomic profiling data has several features that make traditional statistical methods challenging to use, including high dimensionality, extreme variation among samples, and complex phylogenetic relatedness. We present Anpan, a set of quantitative methods addressing three key challenges in microbiome strain epidemiology. First, adaptive filtering designed to interrogate microbial strain gene carriage is combined with linear models to identify strain-specific genetic elements associated with host health outcomes and other phenotypes. Second, phylogenetic generalized linear mixed models are used to characterize the association of sub-species lineages with such phenotypes. Finally, random effects models are used to identify pathways more likely to be retained or lost by outcome-associated strains. We validated our methods by simulation, showing that we achieve more accurate effect size estimation and a lower false positive rate compared to alternative methodologies. We then applied our methods to a dataset of 1,262 colorectal cancer patients, identifying functionally adaptive genes and strong phylogenetic effects associated with CRC status, sometimes complementing and sometimes extending known species-level microbiome CRC biomarkers. Anpan's methods have been implemented as a publicly available R library to support microbial community strain and genetic epidemiology in a variety of contexts, environments, and phenotypes.

Study of Different Cultivated Plants Rhizosphere Soil Fungi-Mediated Pectinase: Insights into Production, Optimization, Purification, Biocompatibility, and Application

Microorganisms are preferred as an enzyme source due to their short lifespan, high production rate, affordability, and absence of harmful chemicals in enzymes generated from plant and animal sources. Fungi communities are biological factories for many bioactive compounds such as the important industrial enzyme pectinase. The current study dealt with production, optimization, purification, biocompatibility, and application of fungal pectinase obtained from five plant rhizospheres (banana, jarawa, lemon, tomato, and wheat) at Fayoum Governorate, Egypt. The highest pectinase degrading index (PDI) was scored for FB5, FJ2, and FW1 isolates. Pectinase production was also examined quantitively and the highest output of 1603.67, 1311.22, and 1264.83 U/ml was gained by FB5, FJ1, and FW1 fungal isolates, respectively. The most active pectinase-producing fungi were identified as Aspergillus niveus strain AUMC1624, A. niger strain AUMC16245, and A. brasiliensis strain AUMC16244, respectively. For pectinase production optimization, one factor at a time (OFAT) protocol was applied and revealed that A. niger, A. niveus, and A. brasiliensis reached maximum pectinase levels at 1% pectin after 5, 7, and 7 days, at 40, 45, and 45 °C, respectively. Obtained pectinases were partially purified using ammonium sulfate precipitation (ASP) and organic solvent precipitation (OSP) methods. The highest activity using the ASP method scored at 40-60% saturation with A. niger. The thermostability characterization of A. niger pectinase was reached with relative activities of 61.7, 69.0, 99.9, 91.3, and 90.6% at temperatures ranging between 30 and 70 °C. pH optimized at pH 5-7. The enzyme's molecular weight was approximately 30 kDa. The GC-mass analysis of pectinase end products included acetic acid ethyl ester, hexadecane carbonsaure methylase, and hexadecenoic acid. The biocompatibility was examined using a human skin cell line (HFb-4) for the first time, with a minimal half concentration (IC50) of 151.86 ± 0.76 U/ml. The biocompatible pectinase was applied as a clothes bioscouring agent with different concentrations of 1893.52 U/ml achieving the highest bioscouring with 20.0%.

GutMetaNet: an integrated database for exploring horizontal gene transfer and functional redundancy in the human gut microbiome

Metagenomic studies have revealed the critical roles of complex microbial interactions, including horizontal gene transfer (HGT) and functional redundancy (FR), in shaping the gut microbiome's functional capacity and resilience. However, the lack of comprehensive data integration and systematic analysis approaches has limited the in-depth exploration of HGT and FR dynamics across large-scale gut microbiome datasets. To address this gap, we present GutMetaNet (https://gutmetanet.deepomics.org/), a first-of-its-kind database integrating extensive human gut microbiome data with comprehensive HGT and FR analyses. GutMetaNet contains 21 567 human gut metagenome samples with whole-genome shotgun sequencing data related to various health conditions. Through systematic analysis, we have characterized the taxonomic profiles and FR profiles, and identified 14 636 HGT events using a shared reference genome database across the collected samples. These HGT events have been curated into 8049 clusters, which are annotated with categorized mobile genetic elements, including transposons, prophages, integrative mobilizable elements, genomic islands, integrative conjugative elements and group II introns. Additionally, GutMetaNet incorporates automated analyses and visualizations for the HGT events and FR, serving as an efficient platform for in-depth exploration of the interactions among gut microbiome taxa and their implications for human health.

PreLect: Prevalence leveraged consistent feature selection decodes microbial signatures across cohorts

The intricate nature of microbiota sequencing data-high dimensionality and sparsity-presents a challenge in identifying informative and reproducible microbial features for both research and clinical applications. Addressing this, we introduce PreLect, an innovative feature selection framework that harnesses microbes' prevalence to facilitate consistent selection in sparse microbiota data. Upon rigorous benchmarking against established feature selection methodologies across 42 microbiome datasets, PreLect demonstrated superior classification capabilities compared to statistical methods and outperformed machine learning-based methods by selecting features with greater prevalence and abundance. A significant strength of PreLect lies in its ability to reliably identify reproducible microbial features across varied cohorts. Applied to colorectal cancer, PreLect identifies key microbes and highlights crucial pathways, such as lipopolysaccharide and glycerophospholipid biosynthesis, in cancer progression. This case study exemplifies PreLect's utility in discerning clinically relevant microbial signatures. In summary, PreLect's accuracy and robustness make it a significant advancement in the analysis of complex microbiota data.

Dietary selective effects manifest in the human gut microbiota from species composition to strain genetic makeup

Diet significantly influences the human gut microbiota, a key player in health. We analyzed shotgun metagenomic sequencing data from healthy individuals with long-term dietary patterns-vegan, flexitarian, or omnivore-and included detailed dietary surveys and blood biomarkers. Dietary patterns notably affected the bacterial community composition by altering the relative abundances of certain species but had a minimal impact on microbial functional repertoires. However, diet influenced microbial functionality at the strain level, with diet type linked to strain genetic variations. We also found molecular signatures of selective pressure in species enriched by specific diets. Notably, species enriched in omnivores exhibited stronger positive selection, such as multiple iron-regulating genes in the meat-favoring bacterium Odoribacter splanchnicus, an effect that was also validated in independent cohorts. Our findings offer insights into how diet shapes species and genetic diversity in the human gut microbiota.

Identifying fecal microbiota signatures of colorectal cancer in a Vietnamese cohort

Background

Colorectal cancer (CRC) is among the top three causes of global cancer mortality. In Vietnam, CRC is the third leading cause of death in women and the fourth cause of cancer mortality in men. A large number of metagenomic studies have reported the relationship between altered composition and function of the gut microbiota with CRC, but this relationship in low- and middle-income countries including Vietnam (with an estimated population of 100.3 million people in 2023, ranking 16th largest country by population in the world) is not well-explored.

Methods

We collected clinical data and fecal samples from 43 CRC patients and 44 healthy control subjects. The total community DNA of microorganisms was extracted from the fecal samples and analyzed for microbiota composition using Illumina MiSeq amplicon sequencing targeting the V3-V4 region of the 16S rRNA gene.

Results

We identified a significant difference in the overall fecal microbiota composition between CRC patients and healthy controls, and we detected several CRC-associated microbial signatures in fecal samples of Vietnamese patients with CRC, which overlapped with signatures from other countries and meta-analyses. Although patients with (n = 8) and without (n = 35) type 2 diabetes (T2D) exhibited distinct gut microbiota composition compared to healthy controls, increased relative abundances of putatively pathogenic species including Parvimonas micra, Peptostreptococcus stomatis, and Prevotella intermedia were consistent biomarkers for CRC. In contrast, several health-associated species were significantly depleted in CRC patients such as Lactobacillus johnsonii and Bifidobacterium longum in CRC/non-T2D patients, Ruminococcus species, Bacteroides uniformis, and Phascolarctobacterium faecium in CRC/T2D patients, and Butyricicoccus pullicaecorum in both CRC groups combined.

Conclusion

Our findings confirm alterations in gut microbiota composition in CRC in a pilot Vietnamese cohort and highlight several gut microbial taxa that may have inhibitory or driver roles in CRC. This and future studies will enable the development of cancer diagnostics and treatment strategies for CRC in Vietnam, with a focus on targeting the microbiota.

Impact of gestational diabetes mellitus in gut and human breast milk microbiome in Colombian women and their infants

Human breast milk (HBM) is a vital source of macronutrients and micronutrients that are crucial for an infant's development. Recent studies have shown that HBM contains diverse microorganisms, including bacteria, viruses, protozoa, and anaerobic fungi. Additionally, novel research has revealed that individuals with metabolic disorders, such as diabetes mellitus, are prone to dysbiosis in their gut microbiome. Our study aimed to investigate the impact of gestational diabetes mellitus (GDM) on HBM and the pair mother-infant gut microbiota. We conducted a comprehensive analysis of two groups from Pereira, Colombia: a GDM group and a non-GDM group. Each group consisted of five infants and their mothers. HBM and stool samples were collected from GDM and non-GDM mother-infant pairs. DNA was purified, and the 16S V3-V4 region was amplified and sequenced. Reads obtained were quality filtered and classified by homology according to the Ribosomal Small Subunit SILVA database. We found significant differences in the relative abundances of gut bacteria between GDM and non-GDM groups. Notably, Bifidobacterium, Serratia and Sutterella were negatively associated in women's gut with GDM. In HBM, Sutterella, Serratia and Lactococcus were found in low RA in the GDM group. Moreover, in the infants, Bifidobacterium, Lactobacillus, Sutterella, Serratia, Streptococcus, and Veillonella had a low presence in GDM. Our findings indicate that there are variations in gut bacteriome profiles between healthy women and those with GDM. These variations may impact the bacterial diversity in HBM, potentially leading to gut bacterial dysbiosis in their infants.

Combined analysis of cross-population healthy adult human microbiome reveals consistent differences in gut microbial characteristics between Western and non-Western countries

Despite extensive research on the gut microbiome of healthy individuals from a single country, there are still a limited number of population-level comparative studies. Moreover, the sequencing approach used in most related studies involves 16 S ribosomal RNA (rRNA) sequencing with a limited resolution, which cannot provide detailed functional profiles. In the present study, we applied a combined analysis approach to analyze whole metagenomic shotgun sequencing data from 2035 healthy adult samples from six countries across four continents. Analysis of core species revealed that 13 species were present in more than 90 % of all investigated individuals, the majority of which produced short-chain fatty acids (SCFA)-producing bacteria. Our analysis revealed consistently significant differences in gut microbial species and pathways between Western and non-Western countries, such as Escherichia coli and the relation of MetaCyc pathways to the TCA cycle. Specific changes in microbial species and pathways are potentially related to lifestyle and diet. Furthermore, we identified several noteworthy microbial species and pathways that exhibit distinct characteristics specific to China. Interestingly, we observed that China (CHN) was more similar to the United States (USA) and United Kingdom (GBR) in terms of the taxonomic and functional composition of the gut microbiome than India (IND) and Madagascar (MDG), which were more similar to the China (CHN) diet. The current study identified consistent microbial features associated with population and geography, which will inspire further clinical translations that consider paying attention to differences in microbiota backgrounds and confounding factors.

Mendelian randomization analyses support causal relationships between gut microbiome and longevity

BACKGROUND

Gut microbiome plays a significant role in longevity, and dysbiosis is indeed one of the hallmarks of aging. However, the causal relationship between gut microbiota and human longevity or aging remains elusive.

METHODS

Our study assessed the causal relationships between gut microbiome and longevity using Mendelian Randomization (MR). Summary statistics for the gut microbiome were obtained from four genome-wide association study (GWAS) meta-analysis of the MiBioGen consortium (N = 18,340), Dutch Microbiome Project (N = 7738), German individuals (N = 8956), and Finland individuals (N = 5959). Summary statistics for Longevity were obtained from Five GWAS meta-analysis, including Human healthspan (N = 300,447), Longevity (N = 36,745), Lifespans (N = 1,012,240), Parental longevity (N = 389,166), and Frailty (one of the primary aging-linked physiological hallmarks, N = 175,226).

RESULTS

Our findings reveal several noteworthy associations, including a negative correlation between Bacteroides massiliensis and longevity, whereas the genus Subdoligranulum and Alistipes, as well as species Alistipes senegalensis and Alistipes shahii, exhibited positive associations with specific longevity traits. Moreover, the microbial pathway of coenzyme A biosynthesis I, pyruvate fermentation to acetate and lactate II, and pentose phosphate pathway exhibited positive associations with two or more traits linked to longevity. Conversely, the TCA cycle VIII (helicobacter) pathway consistently demonstrated a negative correlation with lifespan and parental longevity.

CONCLUSIONS

Our findings of this MR study indicated many significant associations between gut microbiome and longevity. These microbial taxa and pathways may potentially play a protective role in promoting longevity or have a suppressive effect on lifespan.

Bidirectional Mendelian randomization links gut microbiota to primary biliary cholangitis

Primary biliary cholangitis (PBC) and gut microbiota (GM) are epidemiologically correlated but the causal inter-relationships remain poorly understood. We aim to explore the causal relationships between GM and PBC. Using the MiBioGen consortium, GWAS data for GM at the species level and the largest publicly available PBC GWAS data to date, we performed a bidirectional two-sample Mendelian randomization by the inverse variance weighted, MR-Egger, weighted median, weighted model and MR-PRESSO to elucidate the potential causal role of GM in PBC. To measure the heterogeneity of instrumental variables (IV), Cochran's Q statistic and MR-Egger intercept test were used. Genetically instrumented order Coriobacteriales (odds ratio [OR] = 2.18, 95% confidence interval [CI] 1.30-3.66, P = 0.004) significantly increased the risk for PBC, while genetically driven class Deltaproteobacteria (OR = 0.52, 95% CI 0.36-0.74, P = 0.002) causally decrease the NAFLD risk. Reverse MR analysis showed no significant association between PBC and the two specific GM. However, it indicated that PBC progression significantly increases the abundance of the class Bacteroidia, order Bacteroidales, and phylum Bacteroidetes (OR = 1.02, 95% CI 1.002-1.03, P = 0.026), while decreasing the abundance of the genus Lachnospiraceae UCG010 (OR = 0.98, 95% CI 0.96-0.995, P = 0.026). Our study demonstrated that genetically driven order Coriobacteriales and class Deltaproteobacteria were causally related to PBC risk. This causality provided a new perspective on ameliorating PBC by modulating GM. Our study demonstrated that genetically driven order Coriobacteriales and class Deltaproteobacteria were causally related to PBC risk. PBC was causally related to the abundance of four GM taxa(class Bacteroidia, order Bacteroidales, phylum Bacteroidetes and genus Lachnospiraceae UCG010). This causality provided a new perspective on ameliorating PBC by modulating GM.

Depletion of core microbiome forms the shared background against diverging dysbiosis patterns in Crohn's disease and intestinal tuberculosis: insights from an integrated multi-cohort analysis

BACKGROUND/AIMS

Crohn's disease (CD) and intestinal tuberculosis (ITB) are gastrointestinal (GI) inflammatory disorders with overlapping clinical presentations but diverging etiologies. The study aims to decipher CD and ITB-associated gut dysbiosis signatures and identify disease-associated co-occurring modules to evaluate whether this dysbiosis signature is a disease-specific trait or is a shared feature across diseases of diverging etiologies.

METHODS

Disease-associated gut microbial modules were identified using statistical machine learning and co-abundance network analysis in controls, CD and ITB patients recruited as part of this study. Module reproducibility was reinvestigated through meta-network analysis encompassing >5400 bacteriomes and ~900 mycobiomes. Subsequently, >1600 Indian gut microbiomes were analyzed to identify a central-core gut microbiome of 46 taxa, whose abundances aided in the formulation of an India-specific Core Gut Microbiome Score (CGMS) to measure the degree of core retention.

RESULTS

Both diseases witness similar patterns of alterations in [alpha]-diversity, characterized by a significant reduction in gut bacterial (i.e., bacterial/archaeal) diversity and a concomitant increase in the fungal [alpha]-diversity. Specific bacterial taxa, along with the diverging mycobiome enabled distinction between the diseases. Co-abundance network analysis of these taxa, validated by integrated meta-network analysis, revealed a 'disease-depleted' module, consistent across multiple cohorts, with >75% of this module constituting the central-core Indian gut microbiome. CGMS robustly assessed the core-microbiome loss across different stages of gut inflammatory disorders, in Indian and international cohorts.

CONCLUSIONS

While the disease-specific gain of detrimental bacteria forms an important component of gut dysbiosis, loss of the core microbiome is a shared phenomenon contributing to various GI disorders.

Insights into the gut microbiome of vitiligo patients from India

BACKGROUND

Vitiligo is an autoimmune disease characterized by loss of pigmentation in the skin. It affects 0.4 to 2% of the global population, but the factors that trigger autoimmunity remain elusive. Previous work on several immune-mediated dermatological disorders has illuminated the substantial roles of the gut microbiome in disease pathogenesis. Here, we examined the gut microbiome composition in a cohort of vitiligo patients and healthy controls from India, including patients with a family history of the disease.

RESULTS

Our results show significant alterations in the gut microbiome of vitiligo patients compared to healthy controls, affecting taxonomic and functional profiles as well as community structure. We observed a reduction in the abundance of several bacterial taxa commonly associated with a healthy gut microbiome and noted a decrease in the abundance of SCFA (Short Chain Fatty Acids) producing taxa in the vitiligo group. Observation of a higher abundance of genes linked to bacteria-mediated degradation of intestinal mucus suggested a potential compromise of the gut mucus barrier in vitiligo. Functional analysis also revealed a higher abundance of fatty acid and lipid metabolism-related genes in the vitiligo group. Combined analysis with data from a French cohort of vitiligo also led to the identification of common genera differentiating healthy and gut microbiome across populations.

CONCLUSION

Our observations, together with available data, strengthen the role of gut microbiome dysbiosis in symptom exacerbation and possibly pathogenesis in vitiligo. The reported microbiome changes also showed similarities with other autoimmune disorders, suggesting common gut microbiome-mediated mechanisms in autoimmune diseases. Further investigation can lead to the exploration of dietary interventions and probiotics for the management of these conditions.

A population-scale analysis of 36 gut microbiome studies reveals universal species signatures for common diseases

The gut microbiome has been implicated in various human diseases, though findings across studies have shown considerable variability. In this study, we reanalyzed 6314 publicly available fecal metagenomes from 36 case-control studies on different diseases to investigate microbial diversity and disease-shared signatures. Using a unified analysis pipeline, we observed reduced microbial diversity in many diseases, while some exhibited increased diversity. Significant alterations in microbial communities were detected across most diseases. A meta-analysis identified 277 disease-associated gut species, including numerous opportunistic pathogens enriched in patients and a depletion of beneficial microbes. A random forest classifier based on these signatures achieved high accuracy in distinguishing diseased individuals from controls (AUC = 0.776) and high-risk patients from controls (AUC = 0.825), and it also performed well in external cohorts. These results offer insights into the gut microbiome's role in common diseases in the Chinese population and will guide personalized disease management strategies.

Methanobrevibacter smithii cell variants in human physiology and pathology: A review

Methanobrevibacter smithii (M. smithii), initially isolated from human feces, has been recognised as a distinct taxon within the Archaea domain following comprehensive phenotypic, genetic, and genomic analyses confirming its uniqueness among methanogens. Its diversity, encompassing 15 genotypes, mirrors that of biotic and host-associated ecosystems in which M. smithii plays a crucial role in detoxifying hydrogen from bacterial fermentations, converting it into mechanically expelled gaseous methane. In microbiota in contact with host epithelial mucosae, M. smithii centres metabolism-driven microbial networks with Bacteroides, Prevotella, Ruminococcus, Veillonella, Enterococcus, Escherichia, Enterobacter, Klebsiella, whereas symbiotic association with the nanoarchaea Candidatus Nanopusillus phoceensis determines small and large cell variants of M. smithii. The former translocate with bacteria to induce detectable inflammatory and serological responses and are co-cultured from blood, urine, and tissular abscesses with bacteria, prototyping M. smithii as a model organism for pathogenicity by association. The sources, mechanisms and dynamics of in utero and lifespan M. smithii acquisition, its diversity, and its susceptibility to molecules of environmental, veterinary, and medical interest still have to be deeply investigated, as only four strains of M. smithii are available in microbial collections, despite the pivotal role this neglected microorganism plays in microbiota physiology and pathologies.

BCAA-producing Clostridium symbiosum promotes colorectal tumorigenesis through the modulation of host cholesterol metabolism

Identification of potential bacterial players in colorectal tumorigenesis has been a focus of intense research. Herein, we find that Clostridium symbiosum (C. symbiosum) is selectively enriched in tumor tissues of patients with colorectal cancer (CRC) and associated with higher colorectal adenoma recurrence after endoscopic polypectomy. The tumorigenic effect of C. symbiosum is observed in multiple murine models. Single-cell transcriptome profiling along with functional assays demonstrates that C. symbiosum promotes the proliferation of colonic stem cells and enhances cancer stemness. Mechanistically, C. symbiosum intensifies cellular cholesterol synthesis by producing branched-chain amino acids (BCAAs), which sequentially activates Sonic hedgehog signaling. Low dietary BCAA intake or blockade of cholesterol synthesis by statins could partially abrogate the C. symbiosum-induced cell proliferation in vivo and in vitro. Collectively, we reveal C. symbiosum as a bacterial driver of colorectal tumorigenesis, thus identifying a potential target in CRC prediction, prevention, and treatment.

Diversity in gut microbiota among colorectal cancer patients: findings from a case-control study conducted at a Tunisian University Hospital

PURPOSE

Globally, colorectal cancer (CRC) is among the most prevalent cancers. One distinctive feature of colorectal cancer is its close relationship to the gut microbiota, which is a crucial component of the tumor microenvironment. Over the last ten years, research has demonstrated that colorectal cancer is accompanied with dysbiosis of gut bacteria, fungi, viruses, and Archaea, and that these alterations may be causal.

OBJECTIVES

This study aimed to evaluate the disruption of the microorganism composition in the intestine, especially bacteria and to determine their relationship with colorectal cancer.

METHODS

An evaluation system for determining colorectal cancer (CRC) risk and prognosis can be established more easily with the help of accurate gut microbiota profiling. Stool samples from 14 CRC patients and 13 controls were collected and the flora relative abundance was measured using targeted quantitative PCR (qPCR) assays to evaluate diagnostic potential of selected biomarkers: Streptococcus gallolyticus and Enterococcus faecalis. Culture and MALDI-TOF mass spectrometry were coupled to identify the gut microbiota in both colorectal cancer and control groups.

RESULTS

Compared with controls, the gut microbiota of CRC patients showed an increase in the abundance of Enterococcus, Fusobacterium and Streptococcus. At the species level, the CRC enriched bacterium including Escherichia coli, Enterococcus faecalis, Fusobacterium nucleatum, Streptococcus gallolyticus, Flavoni fractorplautii and Eggerthella lenta acted as promising biomarkers for early detection of CRC.

CONCLUSION

This study highlights the potential of gut microbiota biomarkers as a promising non-invasive tool for the accurate detection and distinction of individuals with CRC.

The genomic landscape of 2,023 colorectal cancers

Colorectal carcinoma (CRC) is a common cause of mortality1, but a comprehensive description of its genomic landscape is lacking2-9. Here we perform whole-genome sequencing of 2,023 CRC samples from participants in the UK 100,000 Genomes Project, thereby providing a highly detailed somatic mutational landscape of this cancer. Integrated analyses identify more than 250 putative CRC driver genes, many not previously implicated in CRC or other cancers, including several recurrent changes outside the coding genome. We extend the molecular pathways involved in CRC development, define four new common subgroups of microsatellite-stable CRC based on genomic features and show that these groups have independent prognostic associations. We also characterize several rare molecular CRC subgroups, some with potential clinical relevance, including cancers with both microsatellite and chromosomal instability. We demonstrate a spectrum of mutational profiles across the colorectum, which reflect aetiological differences. These include the role of Escherichia colipks+ colibactin in rectal cancers10 and the importance of the SBS93 signature11-13, which suggests that diet or smoking is a risk factor. Immune-escape driver mutations14 are near-ubiquitous in hypermutant tumours and occur in about half of microsatellite-stable CRCs, often in the form of HLA copy number changes. Many driver mutations are actionable, including those associated with rare subgroups (for example, BRCA1 and IDH1), highlighting the role of whole-genome sequencing in optimizing patient care.

Gut Microbiome and colorectal cancer: discovery of bacterial changes with metagenomics application in Turkısh population

BACKGROUND

Colorectal cancer (CRC) is the 3rd most common cancer in the world and colonic carcinogenesis is a multifactorial disease that involves environmental and genetic factors. Gut microbiota plays a critical role in the regulation of intestinal homeostasis. Increasing evidence shows that the gut microbiome plays a role in CRC development and may be a biomarker for early diagnosis.

OBJECTIVE

This study aimed to determine the clinical prognostic significance of gut microbiota in CRC patients in the Turkish population by metagenomic analysis and to determine the microbial composition in tumor tissue biopsy samples.

METHODS

Tissue biopsies were taken from the participants with sterile forceps during colonoscopy and stored at -80 °C. Then, DNA isolation was performed from the tissue samples and the V3-V4 region of the 16 S rRNA gene was sequenced on the Illumina MiSeq platform. Quality control of the obtained sequence data was performed. Operational taxonomic units (OTUs) were classified according to the Greengenes database. Alpha diversity (Shannon index) and beta diversity (Bray-Curtis distance) analyses were performed. The most common bacterial species in CRC patients and healthy controls were determined and whether there were statistically significant differences between the groups was tested.

RESULTS

A total of 40 individuals, 13 CRC patients and 20 healthy control individuals were included in our metagenomic study. The mean age of the patients was 64.83 and BMI was 25.85. In CRC patients, the level of Bacteroidetes at the phylum taxonomy was significantly increased (p = 0.04), the level of Clostridia at the class taxonomy was increased (p = 0.23), and the level of Enterococcus at the genus taxonomy was significantly increased (p = 0.01). When CRC patients were compared with the control group, significant increases were detected in the species of Gemmiger formicilis (p = 0.15), Prevotella copri (p = 0.02) and Ruminococcus bromii (p = 0.001) at the species taxonomy.

CONCLUSIONS

Metagenomic analysis of intestinal microbiota composition in CRC patients provides important data for determining the treatment options for these patients. The results of this study suggest that it may be beneficial in terms of early diagnosis, poor prognosis and survival rates in CRC patients. In addition, this metagenomic study is the first study on the colon microbiome associated with CRC mucosa in the Turkish population.

Predicting the potential deterioration of Barrett's esophagus based on gut microbiota: a Mendelian randomization analysis

Esophageal adenocarcinoma (EAC) is one of the most malignant tumors in the digestive system. To make thing worse, the scarcity of treatment options is disheartening. However, if detected early, there is a possibility of reversing the condition. Unfortunately, there is still a lack of relevant early screening methods. Considering that Barrett's esophagus (BE), a precursor lesion of EAC, has been confirmed as the only known precursor of EAC. Analyzing which BE cases will progress to EAC and understanding the processes and mechanisms involved is of great significance for early screening of such patients. Considering the significant alterations in the gut microbiota of patients with BE and its potential role in the progression to EAC, this study aims to analyze the relationship between BE, EAC, and GM to identify potential diagnostic biomarkers and therapeutic targets. This study utilized comprehensive statistical data on gut microbiota from a large-scale genome-wide association meta-analysis conducted by the MiBioGen consortium (n = 18,340). Subsequently, we selected a set of single nucleotide polymorphisms (SNPs) that fell below the genome-wide significance threshold (1 × 10-5) as instrumental variables. To investigate the causal relationship between gut microbiota and BE and EAC, we employed various MR analysis methods, including Inverse Variance Weighting (IVW), MR-Egger regression, weighted median (WM), and weighted mean. Additionally, we assessed the level of pleiotropy, heterogeneity, and stability of genetic variations through MR-Egger intercept test, MR-PRESSO, Cochran's Q test, and "leave-one-out" sensitivity analysis. Furthermore, we conducted reverse MR analysis to identify the causal relationships between gut microbiota and BE and EAC. The results from the Inverse Variance-Weighted (IVW) analysis indicate that Alistipes (P = 4.86 × 10-2), Lactobacillus (P = 2.11 × 10-2), Prevotella 7 (P = 4.28 × 10-2), and RuminococcaceaeUCG004 (P = 4.34 × 10-2) are risk factors for Barrett's esophagus (BE), while Flavonifractor (P = 8.81 × 10-3) and RuminococcaceaeUCG004 (P = 4.99 × 10-2) are risk factors for esophageal adenocarcinoma (EAC). On the other hand, certain gut microbiota genera appear to have a protective effect against both BE and EAC. These include Eubacterium (nodatum group) (P = 4.51 × 10-2), Holdemania (P = 1.22 × 10-2), and Lactococcus (P = 3.39 × 10-2) in the BE cohort, as well as Eubacterium (hallii group) (P = 4.07 × 10-2) and Actinomyces (P = 3.62 × 10-3) in the EAC cohort. According to the results of reverse MR analysis, no significant causal effects of BE and EAC on gut microbiota were observed. Furthermore, no significant heterogeneity or pleiotropy was detected in the instrumental variables. We have established a causal relationship between the gut microbiota and BE and EAC. This study holds profound significance for screening BE patients who may be at risk of deterioration, as it can provide them with timely medical interventions to reverse the condition.

Predictive modeling of colorectal cancer using exhaustive analysis of microbiome information layers available from public metagenomic data

This study aimed to compare the microbiome profiles of patients with colorectal cancer (CRC, n = 380) and colorectal adenomas (CRA, n = 110) against generally healthy participants (n = 2,461) from various studies. The overarching objective was to conduct a real-life experiment and develop a robust machine learning model applicable to the general population. A total of 2,951 stool samples underwent a comprehensive analysis using the in-house MetaBakery pipeline. This included various data matrices such as microbial taxonomy, functional genes, enzymatic reactions, metabolic pathways, and predicted metabolites. The study found no statistically significant difference in microbial diversity among individuals. However, distinct clusters were identified for healthy, CRC, and CRA groups through linear discriminant analysis (LDA). Machine learning analysis demonstrated consistent model performance, indicating the potential of microbiome layers (microbial taxa, functional genes, enzymatic reactions, and metabolic pathways) as prediagnostic indicators for CRC and CRA. Notable biomarkers on the taxonomy level and microbial functionality (gene families, enzymatic reactions, and metabolic pathways) associated with CRC were identified. The research presents promising avenues for practical clinical applications, with potential validation on external clinical datasets in future studies.

Intestinal microbiota composition is predictive of radiotherapy-induced acute gastrointestinal toxicity in prostate cancer patients

BACKGROUND

The search for factors beyond the radiotherapy dose that could identify patients more at risk of developing radio-induced toxicity is essential to establish personalised treatment protocols for improving the quality-of-life of survivors. To investigate the role of the intestinal microbiota in the development of radiotherapy-induced gastrointestinal toxicity, the MicroLearner observational cohort study characterised the intestinal microbiota of 136 (discovery) and 79 (validation) consecutive prostate cancer patients at baseline radiotherapy.

METHODS

Gastrointestinal toxicity was assessed weekly during RT using CTCAE. An average grade >1.3 over time points was used to identify patients suffering from persistent acute toxicity (endpoint). The microbiota of patients was quantified from the baseline faecal samples using 16S rRNA gene sequencing technology and the Ion Reporter metagenomic pipeline. Statistical techniques and computational and machine learning tools were used to extract, functionally characterise, and predict core features of the bacterial communities of patients who developed acute gastrointestinal toxicity.

FINDINGS

Analysis of the core bacterial composition in the discovery cohort revealed a cluster of patients significantly enriched for toxicity, displaying a toxicity rate of 60%. Based on selected high-risk microbiota compositional features, we developed a clinical decision tree that could effectively predict the risk of toxicity based on the relative abundance of genera Faecalibacterium, Bacteroides, Parabacteroides, Alistipes, Prevotella and Phascolarctobacterium both in internal and external validation cohorts.

INTERPRETATION

We provide evidence showing that intestinal bacteria profiling from baseline faecal samples can be effectively used in the clinic to improve the pre-radiotherapy assessment of gastrointestinal toxicity risk in prostate cancer patients.

FUNDING

Italian Ministry of Health (Promotion of Institutional Research INT-year 2016, 5 × 1000, Ricerca Corrente funds). Fondazione Regionale per la Ricerca Biomedica (ID 2721017). AIRC (IG 21479).

Species-level identification of enterotype-specific microbial markers for colorectal cancer and adenoma

Enterotypes have been shown to be an important factor for population stratification based on gut microbiota composition, leading to a better understanding of human health and disease states. Classifications based on compositional patterns will have implications for personalized microbiota-based solutions. There have been limited enterotype based studies on colorectal adenoma and cancer. Here, an enterotype-based meta-analysis of fecal shotgun metagenomic studies was performed, including 1579 samples of healthy controls (CTR), colorectal adenoma (ADN) and colorectal cancer (CRC) in total. Gut microbiota of healthy people were clustered into three enterotypes (Ruminococcus-, Bacteroides- and Prevotella-dominated enterotypes). Reference-based enterotype assignments were performed for CRC and ADN samples, using the supervised machine learning algorithm, K-nearest neighbors. Differential abundance analyses and random forest classification were conducted on each enterotype between healthy controls and CRC-ADN groups, revealing novel enterotype-specific microbial markers for non-invasive CRC screening strategies. Furthermore, we identified microbial species unique to each enterotype that play a role in the production of secondary bile acids and short-chain fatty acids, unveiling the correlation between cancer-associated gut microbes and dietary patterns. The enterotype-based approach in this study is promising in elucidating the mechanisms of differential gut microbiome profiles, thereby improving the efficacy of personalized microbiota-based solutions.

Flavonifractor plautii Bacteremia With Generalized Peritonitis: A Case Report and Literature Review

Flavonifractor plautii is an obligate anaerobic rod bacterium that is part of the human gut microbiota. We describe a case of bacteremia caused by F. plautii in a mildly immunocompromised patient with acute generalized peritonitis. The patient is an 83-year-old male, with a history of stage III hepatocellular carcinoma 11 months prior, stage I gastric cancer, and cerebral infarction three months prior. He visited the emergency room of our hospital with a chief complaint of right-sided abdominal pain. A partial resection of the colon was performed due to stenosis of the transverse colon. Due to increasing abdominal pain, the patient underwent surgery for acute generalized peritonitis on the 11th postoperative day. F. plautii was detected in blood cultures collected prior to surgery, and the patient was treated with piperacillin/tazobactam 2.25 g four times a day for 11 days. The patient resumed eating and was discharged with no recurrence. This species may also stain gram-negative, and caution should be exercised in reporting results due to the potential impact on initial antimicrobial therapy. Gram staining showed variation in the length of the bacterium, which is considered a characteristic of this species. Appropriate antimicrobial therapy for F. plautii has yet to be established, and further accumulation of cases is needed to understand the resistance mechanism and confirm the effectiveness of different antimicrobials.

Genome Sequencing Analysis of a Rare Case of Blood Infection Caused by Flavonifractor plautii

BACKGROUND Flavonifractor plautii belongs to the clostridium family, which can lead to local infections as well as the bloodstream infections. Flavonifractor plautii caused infection is rarely few in the clinic. To understand better Flavonifractor plautii, we investigated the drug sensitivity and perform genome sequencing of Flavonifractor plautii isolated from blood samples in China and explored the drug resistance and pathogenic mechanism of the bacteria. CASE REPORT The Epsilometer test method was used to detect the sensitivity of flavonoid bacteria to antimicrobial agents. PacBio sequencing technology was employed to sequence the whole genome of Flavonifractor plautii, and gene prediction and functional annotation were also analyzed. Flavonifractor plautii displayed sensitivity to most drugs but resistance to fluoroquinolones and tetracycline, potentially mediated by tet (W/N/W). The total genome size of Flavonifractor plautii was 4,573,303 bp, and the GC content was 59.78%. Genome prediction identified 4,506 open reading frames, including 9 ribosomal RNAs and 66 transfer RNAs. It was detected that the main virulence factor-coding genes of the bacteria were the capsule, polar flagella and FbpABC, which may be associated with bacterial movement, adhesion, and biofilm formation. CONCLUSIONS The results of whole-genome sequencing could provide relevant information about the drug resistance mechanism and pathogenic mechanism of bacteria and offer a basis for clinical diagnosis and treatment.

Evaluation of normalization methods for predicting quantitative phenotypes in metagenomic data analysis

Genotype-to-phenotype mapping is an essential problem in the current genomic era. While qualitative case-control predictions have received significant attention, less emphasis has been placed on predicting quantitative phenotypes. This emerging field holds great promise in revealing intricate connections between microbial communities and host health. However, the presence of heterogeneity in microbiome datasets poses a substantial challenge to the accuracy of predictions and undermines the reproducibility of models. To tackle this challenge, we investigated 22 normalization methods that aimed at removing heterogeneity across multiple datasets, conducted a comprehensive review of them, and evaluated their effectiveness in predicting quantitative phenotypes in three simulation scenarios and 31 real datasets. The results indicate that none of these methods demonstrate significant superiority in predicting quantitative phenotypes or attain a noteworthy reduction in Root Mean Squared Error (RMSE) of the predictions. Given the frequent occurrence of batch effects and the satisfactory performance of batch correction methods in predicting datasets affected by these effects, we strongly recommend utilizing batch correction methods as the initial step in predicting quantitative phenotypes. In summary, the performance of normalization methods in predicting metagenomic data remains a dynamic and ongoing research area. Our study contributes to this field by undertaking a comprehensive evaluation of diverse methods and offering valuable insights into their effectiveness in predicting quantitative phenotypes.

Establishment and perturbation of human gut microbiome: common trends and variations between Indian and global populations

Human gut microbial species are crucial for dietary metabolism and biosynthesis of micronutrients. Digested products are utilised by the host as well as several gut bacterial species. These species are influenced by various factors such as diet, age, geographical location, and ethnicity. India is home to the largest human population in the world. It is spread across diverse ecological and geographical locations. With variable dietary habits and lifestyles, Indians have unique gut microbial composition. This review captures contrasting and common trends of gut bacterial community establishment in infants (born through different modes of delivery), and how that bacterial community manifests itself along infancy, through old age between Indian and global populations. Because dysbiosis of the gut community structure is associated with various diseases, this review also highlights the common and unique bacterial species associated with various communicable as well as noncommunicable diseases such as diarrhoea, amoebiasis, malnutrition, type 2 diabetes, obesity, colorectal cancer, inflammatory bowel disease, and gut inflammation and damage to the brain in the global and Indian population.

BugSigDB captures patterns of differential abundance across a broad range of host-associated microbial signatures

The literature of human and other host-associated microbiome studies is expanding rapidly, but systematic comparisons among published results of host-associated microbiome signatures of differential abundance remain difficult. We present BugSigDB, a community-editable database of manually curated microbial signatures from published differential abundance studies accompanied by information on study geography, health outcomes, host body site and experimental, epidemiological and statistical methods using controlled vocabulary. The initial release of the database contains >2,500 manually curated signatures from >600 published studies on three host species, enabling high-throughput analysis of signature similarity, taxon enrichment, co-occurrence and coexclusion and consensus signatures. These data allow assessment of microbiome differential abundance within and across experimental conditions, environments or body sites. Database-wide analysis reveals experimental conditions with the highest level of consistency in signatures reported by independent studies and identifies commonalities among disease-associated signatures, including frequent introgression of oral pathobionts into the gut.

Consistent signatures in the human gut microbiome of old- and young-onset colorectal cancer

The incidence of young-onset colorectal cancer (yCRC) has been increasing in recent decades, but little is known about the gut microbiome of these patients. Most studies have focused on old-onset CRC (oCRC), and it remains unclear whether CRC signatures derived from old patients are valid in young patients. To address this, we assembled the largest yCRC gut metagenomes to date from two independent cohorts and found that the CRC microbiome had limited association with age across adulthood. Differential analysis revealed that well-known CRC-associated taxa, such as Clostridium symbiosum, Peptostreptococcus stomatis, Parvimonas micra and Hungatella hathewayi were significantly enriched (false discovery rate <0.05) in both old- and young-onset patients. Similar strain-level patterns of Fusobacterium nucleatum, Bacteroides fragilis and Escherichia coli were observed for oCRC and yCRC. Almost all oCRC-associated metagenomic pathways had directionally concordant changes in young patients. Importantly, CRC-associated virulence factors (fadA, bft) were enriched in both oCRC and yCRC compared to their respective controls. Moreover, the microbiome-based classification model had similar predication accuracy for CRC status in old- and young-onset patients, underscoring the consistency of microbial signatures across different age groups.

Comparison of the effectiveness of different normalization methods for metagenomic cross-study phenotype prediction under heterogeneity

The human microbiome, comprising microorganisms residing within and on the human body, plays a crucial role in various physiological processes and has been linked to numerous diseases. To analyze microbiome data, it is essential to account for inherent heterogeneity and variability across samples. Normalization methods have been proposed to mitigate these variations and enhance comparability. However, the performance of these methods in predicting binary phenotypes remains understudied. This study systematically evaluates different normalization methods in microbiome data analysis and their impact on disease prediction. Our findings highlight the strengths and limitations of scaling, compositional data analysis, transformation, and batch correction methods. Scaling methods like TMM show consistent performance, while compositional data analysis methods exhibit mixed results. Transformation methods, such as Blom and NPN, demonstrate promise in capturing complex associations. Batch correction methods, including BMC and Limma, consistently outperform other approaches. However, the influence of normalization methods is constrained by population effects, disease effects, and batch effects. These results provide insights for selecting appropriate normalization approaches in microbiome research, improving predictive models, and advancing personalized medicine. Future research should explore larger and more diverse datasets and develop tailored normalization strategies for microbiome data analysis.

Metagenomic analysis of fecal samples in colorectal cancer Egyptians patients post colectomy: A pilot study

One of the most prevalent malignancies that significantly affects world health is colorectal cancer (CRC). While genetics are involved in a portion of CRC patients, most cases are sporadic. The microbiome composition could be a new source of tumor initiation and progression. This research was conducted to investigate the microbiota composition of CRC patients post colectomy at taxonomic and functional levels. Using a next-generation sequencing approach, using an Illumina Novaseq 6000, the fecal samples of 13 patients were analyzed and the obtained data was subjected to a bioinformatics analysis. The bacterial abundance and uniqueness varied in CRC patients alongside differences in bacterial counts between patients. Bacteroides fragilis, Bacteroides vulgatus, Escherichia coli, and Fusobacterium nucleatum were among the pro-cancerous microorganisms found. Concurrently, bacteria linked to CRC progression were detected that have been previously linked to metastasis and recurrence. At the same time, probiotic bacteria such as Bifidobacterium dentium, Bifidobacterium bifidum, and Akkermansia muciniphila increased in abundance after colectomies. Additionally, numerous pathways were deferentially enriched in CRC, which emerged from functional pathways based on bacterial shotgun data. CRC-specific microbiome signatures include an altered bacterial composition. Our research showed that microbial biomarkers could be more usefully employed to explore the link between gut microbiota and CRC using metagenomic techniques in the diagnosis, prognosis, and remission of CRC, thereby opening new avenues for CRC treatment.

Processing-bias correction with DEBIAS-M improves cross-study generalization of microbiome-based prediction models

Every step in common microbiome profiling protocols has variable efficiency for each microbe. For example, different DNA extraction kits may have different efficiency for Gram-positive and -negative bacteria. These variable efficiencies, combined with technical variation, create strong processing biases, which impede the identification of signals that are reproducible across studies and the development of generalizable and biologically interpretable prediction models. "Batch-correction" methods have been used to alleviate these issues computationally with some success. However, many make strong parametric assumptions which do not necessarily apply to microbiome data or processing biases, or require the use of an outcome variable, which risks overfitting. Lastly and importantly, existing transformations used to correct microbiome data are largely non-interpretable, and could, for example, introduce values to features that were initially mostly zeros. Altogether, processing bias currently compromises our ability to glean robust and generalizable biological insights from microbiome data. Here, we present DEBIAS-M (Domain adaptation with phenotype Estimation and Batch Integration Across Studies of the Microbiome), an interpretable framework for inference and correction of processing bias, which facilitates domain adaptation in microbiome studies. DEBIAS-M learns bias-correction factors for each microbe in each batch that simultaneously minimize batch effects and maximize cross-study associations with phenotypes. Using benchmarks of HIV and colorectal cancer classification from gut microbiome data, and cervical neoplasia prediction from cervical microbiome data, we demonstrate that DEBIAS-M outperforms batch-correction methods commonly used in the field. Notably, we show that the inferred bias-correction factors are stable, interpretable, and strongly associated with specific experimental protocols. Overall, we show that DEBIAS-M allows for better modeling of microbiome data and identification of interpretable signals that are reproducible across studies.

metaProbiotics: a tool for mining probiotic from metagenomic binning data based on a language model

Beneficial bacteria remain largely unexplored. Lacking systematic methods, understanding probiotic community traits becomes challenging, leading to various conclusions about their probiotic effects among different publications. We developed language model-based metaProbiotics to rapidly detect probiotic bins from metagenomes, demonstrating superior performance in simulated benchmark datasets. Testing on gut metagenomes from probiotic-treated individuals, it revealed the probioticity of intervention strains-derived bins and other probiotic-associated bins beyond the training data, such as a plasmid-like bin. Analyses of these bins revealed various probiotic mechanisms and bai operon as probiotic Ruminococcaceae's potential marker. In different health-disease cohorts, these bins were more common in healthy individuals, signifying their probiotic role, but relevant health predictions based on the abundance profiles of these bins faced cross-disease challenges. To better understand the heterogeneous nature of probiotics, we used metaProbiotics to construct a comprehensive probiotic genome set from global gut metagenomic data. Module analysis of this set shows that diseased individuals often lack certain probiotic gene modules, with significant variation of the missing modules across different diseases. Additionally, different gene modules on the same probiotic have heterogeneous effects on various diseases. We thus believe that gene function integrity of the probiotic community is more crucial in maintaining gut homeostasis than merely increasing specific gene abundance, and adding probiotics indiscriminately might not boost health. We expect that the innovative language model-based metaProbiotics tool will promote novel probiotic discovery using large-scale metagenomic data and facilitate systematic research on bacterial probiotic effects. The metaProbiotics program can be freely downloaded at https://github.com/zhenchengfang/metaProbiotics.

The microbiome and rise of early-onset cancers: knowledge gaps and research opportunities

Accumulating evidence indicates an alarming increase in the incidence of early-onset cancers, which are diagnosed among adults under 50 years of age, in the colorectum, esophagus, extrahepatic bile duct, gallbladder, liver, stomach, pancreas, as well as the bone marrow (multiple myeloma), breast, head and neck, kidney, prostate, thyroid, and uterine corpus (endometrium). While the early-onset cancer studies have encompassed research on the wide variety of organs, this article focuses on research on digestive system cancers. While a minority of early-onset cancers in the digestive system are associated with cancer-predisposing high penetrance germline genetic variants, the majority of those cancers are sporadic and multifactorial. Although potential etiological roles of diets, lifestyle, environment, and the microbiome from early life to adulthood (i.e. in one's life course) have been hypothesized, exact contribution of each of these factors remains uncertain. Diets, lifestyle patterns, and environmental exposures have been shown to alter the oral and intestinal microbiome. To address the rising trend of early-onset cancers, transdisciplinary research approaches including lifecourse epidemiology and molecular pathological epidemiology frameworks, nutritional and environmental sciences, multi-omics technologies, etc. are needed. We review current evidence and discuss emerging research opportunities, which can improve our understanding of their etiologies and help us design better strategies for prevention and treatment to reduce the cancer burden in populations.

GDmicro: classifying host disease status with GCN and deep adaptation network based on the human gut microbiome data

MOTIVATION

With advances in metagenomic sequencing technologies, there are accumulating studies revealing the associations between the human gut microbiome and some human diseases. These associations shed light on using gut microbiome data to distinguish case and control samples of a specific disease, which is also called host disease status classification. Importantly, using learning-based models to distinguish the disease and control samples is expected to identify important biomarkers more accurately than abundance-based statistical analysis. However, available tools have not fully addressed two challenges associated with this task: limited labeled microbiome data and decreased accuracy in cross-studies. The confounding factors, such as the diet, technical biases in sample collection/sequencing across different studies/cohorts often jeopardize the generalization of the learning model.

RESULTS

To address these challenges, we develop a new tool GDmicro, which combines semi-supervised learning and domain adaptation to achieve a more generalized model using limited labeled samples. We evaluated GDmicro on human gut microbiome data from 11 cohorts covering 5 different diseases. The results show that GDmicro has better performance and robustness than state-of-the-art tools. In particular, it improves the AUC from 0.783 to 0.949 in identifying inflammatory bowel disease. Furthermore, GDmicro can identify potential biomarkers with greater accuracy than abundance-based statistical analysis methods. It also reveals the contribution of these biomarkers to the host's disease status.

AVAILABILITY AND IMPLEMENTATION

https://github.com/liaoherui/GDmicro.

Gut virome profiling identifies an association between temperate phages and colorectal cancer promoted by Helicobacter pylori infection

Colorectal cancer (CRC) is one of the most commonly diagnosed cancers worldwide. While a close correlation between chronic Helicobacter pylori infection and CRC has been reported, the role of the virome has been overlooked. Here, we infected Apc-mutant mouse models and C57BL/6 mice with H. pylori and conducted a comprehensive metagenomics analysis of H. pylori-induced changes in lower gastrointestinal tract bacterial and viral communities. We observed an expansion of temperate phages in H. pylori infected Apc+/1638N mice at the early stage of carcinogenesis. Some of the temperate phages were predicted to infect bacteria associated with CRC, including Enterococcus faecalis. We also observed a high prevalence of virulent genes, such as flgJ, cwlJ, and sleB, encoded by temperate phages. In addition, we identified phages associated with pre-onset and onset of H. pylori-promoted carcinogenesis. Through co-occurrence network analysis, we found strong associations between the viral and bacterial communities in infected mice before the onset of carcinogenesis. These findings suggest that the expansion of temperate phages, possibly caused by prophage induction triggered by H. pylori infection, may have contributed to the development of CRC in mice by interacting with the bacterial community.

Role of the intestinal microbiota and diet in the onset and progression of colorectal and breast cancers and the interconnection between both types of tumours

Colorectal cancer (CRC) is among the leading causes of mortality in adults of both sexes worldwide, while breast cancer (BC) is among the leading causes of death in women. In addition to age, gender, and genetic predisposition, environmental and lifestyle factors exert a strong influence. Global diet, including alcohol consumption, is one of the most important modifiable factors affecting the risk of CRC and BC. Western dietary patterns promoting high intakes of xenobiotics from food processing and ethanol have been associated with increased cancer risk, whereas the Mediterranean diet, generally leading to a higher intake of polyphenols and fibre, has been associated with a protective effect. Gut dysbiosis is a common feature in CRC, where the usual microbiota is progressively replaced by opportunistic pathogens and the gut metabolome is altered. The relationship between microbiota and BC has been less studied. The estrobolome is the collection of genes from intestinal bacteria that can metabolize oestrogens. In a dysbiosis condition, microbial deconjugating enzymes can reactivate conjugated-deactivated oestrogens, increasing the risk of BC. In contrast, intestinal microorganisms can increase the biological activity and bioavailability of dietary phytochemicals through diverse microbial metabolic transformations, potentiating their anticancer activity. Members of the intestinal microbiota can increase the toxicity of xenobiotics through metabolic transformations. However, most of the microorganisms involved in diet-microbiota interactions remain poorly characterized. Here, we provide an overview of the associations between microbiota and diet in BC and CRC, considering the diverse types and heterogeneity of these cancers and their relationship between them and with gut microbiota.

Multi-site microbiota alteration is a hallmark of kidney stone formation

BACKGROUND

Inquiry of microbiota involvement in kidney stone disease (KSD) has largely focussed on potential oxalate handling abilities by gut bacteria and the increased association with antibiotic exposure. By systematically comparing the gut, urinary, and oral microbiota of 83 stone formers (SF) and 30 healthy controls (HC), we provide a unified assessment of the bacterial contribution to KSD.

RESULTS

Amplicon and shotgun metagenomic sequencing approaches were consistent in identifying multi-site microbiota disturbances in SF relative to HC. Biomarker taxa, reduced taxonomic and functional diversity, functional replacement of core bioenergetic pathways with virulence-associated gene markers, and community network collapse defined SF, but differences between cohorts did not extend to oxalate metabolism.

CONCLUSIONS

We conclude that multi-site microbiota alteration is a hallmark of SF, and KSD treatment should consider microbial functional restoration and the avoidance of aberrant modulators such as poor diet and antibiotics where applicable to prevent stone recurrence. Video Abstract.

NCRD: A non-redundant comprehensive database for detecting antibiotic resistance genes

Antibiotic resistance genes (ARGs) are emerging pollutants present in various environments. Identifying ARGs has become a growing concern in recent years. Several databases, including the Antibiotic Resistance Genes Database (ARDB), Comprehensive Antibiotic Resistance Database (CARD), and Structured Antibiotic Resistance Genes (SARG), have been applied to detect ARGs. However, these databases have limitations, which hinder the comprehensive profiling of ARGs in environmental samples. To address these issues, we constructed a non-redundant antibiotic resistance genes database (NRD) by consolidating sequences from ARDB, CARD, and SARG. We identified the homologous proteins of NRD from Non-redundant Protein Database (NR) and the Protein DataBank Database (PDB) and clustered them to establish a non-redundant comprehensive antibiotic resistance genes database (NCRD) with similarities of 100% (NCRD100) and 95% (NCRD95). To demonstrate the advantages of NCRD, we compared it with other databases by using metagenome datasets. Results revealed its strong ability in detecting potential ARGs.

The Archaeome's Role in Colorectal Cancer: Unveiling the DPANN Group and Investigating Archaeal Functional Signatures

BACKGROUND AND AIMS

Gut microbial imbalances are linked to colorectal cancer (CRC), but archaea's role remains underexplored. Here, using previously published metagenomic data from different populations including Austria, Germany, Italy, Japan, China, and India, we performed bioinformatic and statistical analysis to identify archaeal taxonomic and functional signatures related to CRC.

METHODS

We analyzed published fecal metagenomic data from 390 subjects, comparing the archaeomes of CRC and healthy individuals. We conducted a biostatistical analysis to investigate the relationship between Candidatus Mancarchaeum acidiphilum (DPANN superphylum) and other archaeal species associated with CRC. Using the Prokka tool, we annotated the data focusing on archaeal genes, subsequently linking them to CRC and mapping them against UniprotKB and GO databases for specific archaeal gene functions.

RESULTS

Our analysis identified enrichment of methanogenic archaea in healthy subjects, with an exception for Methanobrevibacter smithii, which correlated with CRC. Notably, CRC showed a strong association with archaeal species, particularly Natrinema sp. J7-2, Ferroglobus placidus, and Candidatus Mancarchaeum acidiphilum. Furthermore, the DPANN archaeon exhibited a significant correlation with other CRC-associated archaea (p < 0.001). Functionally, we found a marked association between MvhB-type polyferredoxin and colorectal cancer. We also highlighted the association of archaeal proteins involved in the biosynthesis of leucine and the galactose metabolism process with the healthy phenotype.

CONCLUSIONS

The archaeomes of CRC patients show identifiable alterations, including a decline in methanogens and an increase in Halobacteria species. MvhB-type polyferredoxin, linked with CRC and species like Candidatus Mancarchaeum acidiphilum, Natrinema sp. J7-2, and Ferroglobus placidus emerge as potential archaeal biomarkers. Archaeal proteins may also offer gut protection, underscoring archaea's role in CRC dynamics.

Machine learning: a powerful tool for identifying key microbial agents associated with specific cancer types

Machine learning (ML) includes a broad class of computer programs that improve with experience and shows unique strengths in performing tasks such as clustering, classification and regression. Over the past decade, microbial communities have been implicated in influencing the onset, progression, metastasis, and therapeutic response of multiple cancers. Host-microbe interaction may be a physiological pathway contributing to cancer development. With the accumulation of a large number of high-throughput data, ML has been successfully applied to the study of human cancer microbiomics in an attempt to reveal the complex mechanism behind cancer. In this review, we begin with a brief overview of the data sources included in cancer microbiomics studies. Then, the characteristics of the ML algorithm are briefly introduced. Secondly, the application progress of ML in cancer microbiomics is also reviewed. Finally, we highlight the challenges and future prospects facing ML in cancer microbiomics. On this basis, we conclude that the development of cancer microbiomics can not be achieved without ML, and that ML can be used to develop tumor-targeting microbial therapies, ultimately contributing to personalized and precision medicine.

tRNA queuosine modification is involved in biofilm formation and virulence in bacteria

tRNA modifications are crucial for fine-tuning of protein translation. Queuosine (Q) modification of tRNAs is thought to modulate the translation rate of NAU codons, but its physiological role remains elusive. Therefore, we hypothesize that Q-tRNAs control those physiological processes involving NAU codon-enriched genes (Q-genes). Here, we report a novel bioinformatic strategy to predict Q-genes, revealing a widespread enrichment in functions, especially those related to biofilm formation and virulence in bacteria, and particularly in human pathogens. Indeed, we experimentally verified that these processes were significantly affected by altering the degree of tRNA Q-modification in different model bacteria, representing the first report of a general mechanism controlling biofilm formation and virulence in Gram-positive and Gram-negative bacteria possibly through the coordination of the expression of functionally related genes. Furthermore, we propose that changes in Q availability in a microbiome would affect its functionality. Our findings open the door to the control of bacterial infections and biofilm formation by inhibition of tRNA Q-modification.

Metagenome-wide analysis uncovers gut microbial signatures and implicates taxon-specific functions in end-stage renal disease

BACKGROUND

The gut microbiota plays a crucial role in regulating host metabolism and producing uremic toxins in patients with end-stage renal disease (ESRD). Our objective is to advance toward a holistic understanding of the gut ecosystem and its functional capacity in such patients, which is still lacking.

RESULTS

Herein, we explore the gut microbiome of 378 hemodialytic ESRD patients and 290 healthy volunteers from two independent cohorts via deep metagenomic sequencing and metagenome-assembled-genome-based characterization of their feces. Our findings reveal fundamental alterations in the ESRD microbiome, characterized by a panel of 348 differentially abundant species, including ESRD-elevated representatives of Blautia spp., Dorea spp., and Eggerthellaceae, and ESRD-depleted Prevotella and Roseburia species. Through functional annotation of the ESRD-associated species, we uncover various taxon-specific functions linked to the disease, such as antimicrobial resistance, aromatic compound degradation, and biosynthesis of small bioactive molecules. Additionally, we show that the gut microbial composition can be utilized to predict serum uremic toxin concentrations, and based on this, we identify the key toxin-contributing species. Furthermore, our investigation extended to 47 additional non-dialyzed chronic kidney disease (CKD) patients, revealing a significant correlation between the abundance of ESRD-associated microbial signatures and CKD progression.

CONCLUSION

This study delineates the taxonomic and functional landscapes and biomarkers of the ESRD microbiome. Understanding the role of gut microbiota in ESRD could open new avenues for therapeutic interventions and personalized treatment approaches in patients with this condition.

Overview of data preprocessing for machine learning applications in human microbiome research

Although metagenomic sequencing is now the preferred technique to study microbiome-host interactions, analyzing and interpreting microbiome sequencing data presents challenges primarily attributed to the statistical specificities of the data (e.g., sparse, over-dispersed, compositional, inter-variable dependency). This mini review explores preprocessing and transformation methods applied in recent human microbiome studies to address microbiome data analysis challenges. Our results indicate a limited adoption of transformation methods targeting the statistical characteristics of microbiome sequencing data. Instead, there is a prevalent usage of relative and normalization-based transformations that do not specifically account for the specific attributes of microbiome data. The information on preprocessing and transformations applied to the data before analysis was incomplete or missing in many publications, leading to reproducibility concerns, comparability issues, and questionable results. We hope this mini review will provide researchers and newcomers to the field of human microbiome research with an up-to-date point of reference for various data transformation tools and assist them in choosing the most suitable transformation method based on their research questions, objectives, and data characteristics.

Male-Biased Gut Microbiome and Metabolites Aggravate Colorectal Cancer Development

Men demonstrate higher incidence and mortality rates of colorectal cancer (CRC) than women. This study aims to explain the potential causes of such sexual dimorphism in CRC from the perspective of sex-biased gut microbiota and metabolites. The results show that sexual dimorphism in colorectal tumorigenesis is observed in both ApcMin/ + mice and azoxymethane (AOM)/dextran sulfate sodium (DSS)-treated mice with male mice have significantly larger and more tumors, accompanied by more impaired gut barrier function. Moreover, pseudo-germ mice receiving fecal samples from male mice or patients show more severe intestinal barrier damage and higher level of inflammation. A significant change in gut microbiota composition is found with increased pathogenic bacteria Akkermansia muciniphila and deplets probiotic Parabacteroides goldsteinii in both male mice and pseudo-germ mice receiving fecal sample from male mice. Sex-biased gut metabolites in pseudo-germ mice receiving fecal sample from CRC patients or CRC mice contribute to sex dimorphism in CRC tumorigenesis through glycerophospholipids metabolism pathway. Sexual dimorphism in tumorigenesis of CRC mouse models. In conclusion, the sex-biased gut microbiome and metabolites contribute to sexual dimorphism in CRC. Modulating sex-biased gut microbiota and metabolites could be a potential sex-targeting therapeutic strategy of CRC.

Primary nephrotic syndrome relapse within 1 year after glucocorticoid therapy in children is associated with gut microbiota composition at syndrome onset

BACKGROUND

Children with primary nephrotic syndrome (PNS) who relapse after glucocorticoid therapy are shown to have a decreased total proportion of butyrate-producing bacteria in the gut at onset. Glucocorticoid treatment changes the gut microbiota composition. It is unclear whether gut microbiota at remission right after therapy and gut bacteria other than butyrate-producing bacteria are associated with PNS relapse.

METHODS

PNS relapse of paediatric patients within 1 year after glucocorticoid therapy was recorded. The gut microbiota composition, profiled with 16S rRNA gene V3-V4 region sequencing, was compared between relapsing and non-relapsing PNS children at onset before glucocorticoid treatment (preT group) and in PNS children at remission right after treatment (postT group), respectively.

RESULTS

The gut microbiota composition of postT children significantly differed from that of preT children by having lower levels of Bacteroides, Lachnoclostridium, Flavonifractor, Ruminococcaceae UBA1819, Oscillibacter, Hungatella and Coprobacillus and higher levels of Ruminococcaceae UCG-013 and Clostridium sensu stricto 1 group. In the preT group, compared with non-relapsing patients, relapsing patients showed decreased Blautia, Dialister and total proportion of butyrate-producing bacteria and increased Oscillibacter, Anaerotruncus and Ruminococcaceae UBA1819. However, relapsing and non-relapsing postT children showed no difference in gut microbiota composition.

CONCLUSIONS

PNS relapse-associated gut microbiota dysbiosis at onset, which includes alterations of both butyrate-producing and non-butyrate-producing bacteria, disappeared right after glucocorticoid therapy. It is necessary to study the association of the longitudinal changes in the complete profiles of gut microbiota after glucocorticoid treatment with later PNS relapse.

Gut microbiome and nutrition-related predictors of response to immunotherapy in cancer: making sense of the puzzle

The gut microbiome is emerging as an important predictor of response to immune checkpoint inhibitor (ICI) therapy for patients with cancer. However, several nutrition-related patient characteristics, which are themselves associated with changes in gut microbiome, are also prognostic markers for ICI treatment response and survival. Thus, increased abundance of Akkermansia muciniphila, Phascolarctobacterium, Bifidobacterium and Rothia in stool are consistently associated with better response to ICI treatment. A. muciniphila is also more abundant in stool in patients with higher muscle mass, and muscle mass is a strong positive prognostic marker in cancer, including after ICI treatment. This review explores the complex inter-relations between the gut microbiome, diet and patient nutritional status and the correlations with response to ICI treatment. Different multivariate approaches, including archetypal analysis, are discussed to help identify the combinations of features which may select patients most likely to respond to ICI treatment.

Gut microbiota of healthy Asians and their discriminative features revealed by metagenomics approach

This study is conducted to identify the microbial architecture and its functional capacity in the Asian population via the whole metagenomics approach. A brief comparison of the Asian countries namely Malaysia, India, China, and Thailand, was conducted, giving a total of 916 taxa under observation. Results show a close representation of the taxonomic diversity in the gut microbiota of Malaysia, India, and China, where Bacteroidetes, Firmicutes, and Actinobacteria were more predominant compared to other phyla. Mainly due to the multi-racial population in Malaysia, which also consists of Malays, Indian, and Chinese, the population tend to share similar dietary preferences, culture, and lifestyle, which are major influences that shapes the structure of the gut microbiota. Moreover, Thailand showed a more distinct diversity in the gut microbiota which was highly dominated by Firmicutes. Meanwhile, functional profiles show 1034 gene families that are common between the four countries. The Malaysia samples are having the most unique gene families with a total of 67,517 gene families, and 51 unique KEGG Orthologs, mainly dominated by the metabolic pathways, followed by microbial metabolism in diverse environments. In conclusion, this study provides some general overview on the structure of the Asian gut microbiota, with some additional highlights on the Malaysian population.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-023-03671-3.