Microbiome Profiling

CHAMP™ - Human Profiling

With our next-generation human microbiome profiler, CHAMP™, we offer high-resolution taxonomic and functional profiling that is unparalleled in accuracy, precision, and coverage to maximize insights from your study.

CHAMP™ employs over 400,000 metagenome-assembled genomes (MAGs), created from a collection of more than 30,000 microbiome samples from individuals across the world. These samples were sourced from 9 different human body sites including but not limited to: gut (stool and small intestine), vagina, skin and mouth. From this extensive human microbiome data collection, we have identified almost 7000 bacteria, archaea, and eukaryote species, including many newly discovered species. This is the foundation of our microbiome analysis services.

A more accurate future for microbiome research

CHAMP™ provides the most accurate and sensitive microbiome profiling data thanks to the following features:

• High sensitivity: Detects low abundant and rare species in samples.

• Superior specificity: Exclusively detects species actually present in samples.

• Abundance accuracy: Provides correct abundance estimations.

• Accurate taxonomic annotation: Microorganisms correctly annotated at species resolution.

CHAMP™ demonstrates best-in-class performance using both CAMI and NIBSC benchmarks against the best and most widely used profiling pipelines in the field.

Compared to MetaPhlAn4, CHAMP™ showed 16% greater sensitivity (recall) across different human body sites and showed an astounding 400 times lower false signal in the NIBSC mock community benchmark compared to state-of-the-art profilers (MetaPhlAn4, Centrifuge, Kraken, and Bracken). This means that when CHAMP™ detects something, you can trust it’s there.

CHAMP™ uses the latest GTDB annotation which includes more rare species and, compared to NCBI, often classifies sub-species as species in their own right.

Best-in-class microbiome profiling data

Combining the world’s most comprehensive reference catalogue for human-associated microbes with a proprietary algorithm for detection and abundance estimation of species, CHAMP™ provides you with the best microbiome data for further analysis.

For analyzing low biomass samples, searching for very rare species, profiling the microbiome of large-scale population health studies, or designing next generation probiotics or live biotherapeutics, CHAMP™ sets new standards in sensitivity and accuracy for microbiome research.

CHAMP™ also provides phage and virome profiling and advanced functional annotations. Furthermore, with the addition of our clonal-level analysis tool, it can separate very closely related strains, helping researchers understand the dynamics between probiotics/therapeutic microbes and endogenous populations of the same species.

CHAMP™ is available as part of our end-to-end human microbiome research services or as a standalone service for profiling or re-profiling of shotgun sequencing data.

Download our CHAMP™ poster presented at the EMBO | EMBL Human Microbiome Symposium 2023 in Heidelberg or contact us for more information.

Functional microbiome analysis

Understanding which microorganisms are present in a microbiome is essential, but it only tells part of the story. To truly comprehend how microbial communities influence health and disease, it's crucial to explore what these microorganisms are capable of doing—their functional potential.

Functional microbiome analysis goes beyond identifying microbial species. It predicts what microorganisms are equipped to do based on the genes they possess, providing deeper insights into their contributions to various physiological states.

Our Functional Profiling Services

We offer comprehensive functional profiling using:

• Kyoto Encyclopedia of Genes and Genomes (KEGG): Mapping genes to metabolic pathways and cellular processes.

• Curated Databases:

  • Gut Metabolic Modules

  • Gut-Brain Modules

  • Bile Acid Metabolism

  • Human Milk Oligosaccharide (HMO) Metabolism

  • Antimicrobial Resistance Genes (CARD)

  • Virulence Factors

  • And More

By leveraging these resources, we help you decode the functional capabilities of microbial communities, shedding light on how they contribute to health and disease.

Functional Species Groups (FSG)

Understanding Functional Redundancy

Different microbial species can occupy the same functional niche in various hosts—a phenomenon known as functional redundancy. Through our Functional Species Group (FSG) tool, we group species that share similar functional potentials regarding metabolic pathways, enzymatic activities, or disease associations. For example, species encoding the molecular pathway for butyrate production via transferase are grouped together.

Enrichment Testing for Functional Associations

Sometimes, a group of functionally related species may be associated with a treatment or condition, even if none of the individual species show significant abundance changes on their own. Our FSG enrichment tests can identify such associations by statistically evaluating whether multiple species within the same FSG collectively have a stronger link to a treatment group.

The power of this approach lies in its ability to identify functional enrichment even when there is no significant change at the species or strain level—pinpointing critical functions or pathways relevant to specific phenotypes or treatments.

We continually expand our FSG sets based on the latest scientific developments and our clients' interests. Functional analysis and FSG enrichment serve as natural extensions of taxonomic analysis, bringing you closer to understanding the mechanisms linking the microbiome to phenotypes, diseases, and treatment responses.

Applications

By including functional microbiome analysis in your study, you can:

Identify Potential Biomarkers

• Disease Diagnosis and Progression: Discover functional biomarkers associated with specific phenotypes or disease states.

• Machine Learning Integration: Use functional profiles as input for machine learning models to enhance predictive accuracy.

Understand Microbiome Adaptation

• Environmental Responses: Explore how microbial communities adapt to environmental changes like toxin exposure, antibiotic use, or dietary shifts.

• Mechanistic Insights: Gain a deeper understanding of microbial resilience and adaptation mechanisms.

Inform Treatment Development

• Mechanistic Understanding: Increase knowledge of how microbial functions impact health conditions, guiding the development of new treatments.

• Actionable Hypotheses: Generate testable hypotheses for follow-up in vivo or in vitro studies.

• Multi-Omics Integration: Combine functional analysis with other omics data (genomics, metabolomics) for a holistic view.

Illustration of the Functional Species Group (FSG) concept. Species 1, 2, and 3 all share functional potential associated with KEGG module X. Collectively, these three microbial species (MGSs) form Functional Species Group X. Individually, each species exhibits a weak association with the clinical parameter shown at the top (in this case, Body Mass Index [BMI]), and none shows a statistically significant signal on its own. However, because these species occupy the same functional niche across different hosts, evaluating them together as an FSG reveals a strong association between the functional module X and the clinical parameter. This approach demonstrates how aggregating species based on shared functional potential can uncover significant functional enrichments linked to phenotypes or treatments that might be missed when analyzing species individually.

Phage profiling

Insights into viral taxonomy and phage-host dynamics may inform development of microbiota-based (phage, probiotics, LBPs, etc) therapeutics. With the largest commercial virome database with >800,000 viral sequences from 64,000 virus species (vOTUs), CHAMP™ virus profiling offers an unmatched level of comprehensiveness in microbiome analysis. This approach can identify 64,000 virus species (vOTUs) and offers superior accuracy with nearly zero false positives compared to the current state-of-the-art publicly available tools.

Bacteriophage (phage) analysis

Recent studies underscore the significant influence of bacteriophages on human health and disease, despite their historical underrepresentation in microbiome studies. Around 5% of the sequencing reads in a shotgun metagenomic sample from feces corresponds to bacteriophages. Powered by the CHAMP™ profiler, microbiome analysis is now expanding to include the entire viral community, beyond just prokaryotic organisms. This broadened scope allows for a more comprehensive understanding of the microbiome’s impact on health.

Comprehensive Virus Database, including bacteriophages

Central to this is the extensive virus database built from curated genomes from previously isolated and sequenced viruses as well as *de novo-*identified phages from more than 30,000 human microbiome samples. Over 64,000 virus species (vOTUs) can be profiled, making it the largest commercial database in the human virome field.

Beyond identification, the CHAMP™ profiling platform enables phage lifestyle annotation to distinguish between lysogenic and lytic phases. This allows us to track over 14,000 virulent phage species that are candidates for targeting bacteria of interest such as pathobionts and pathogens. Simultaneously, we can track over 12,700 temperate phage species, which are capable of lysing bacteria but also integrating into their genomes, and therefore would require downstream genetic engineering for phage therapy applications. Additionally, phage-host mapping provides insights into phage relationships with their bacterial hosts for comprehensive viral analysis. The CHAMP™ profiler can determine the host affiliation down to genus level for >55% of all tailed bacteriophages in our database.

Increased Profiling Fidelity with CHAMP™

Paired with the industry-leading precision of the CHAMP™ Human Microbiome Profiler, benchmarking analyses show that the incidence of false positives is minimized while more viral species are detected, thereby exceeding metrics compared to competing tools [1]

Advanced Insights across Applications

In probiotic or LBP development, mapping phage-host relationships can explain different engraftment outcomes among patient populations.

For example, in figure A below, a lean phenotype is not only driven by the presence of certain gut bacteria in the mouse. A fecal viral transplant (FVT) from a lean mouse to an obese mouse shows the different phenotype outcomes after FVT based on the presence of possible phages (either red or orange) in the lean donor. If the orange phages are transferred, it has no effect on the obese phenotype. In this scenario, there is no matched host for the orange phages, or endogenous bacteria in the obese mouse are resistant to phage attack. A lean phenotype results from the transfer of red phages who find and lyse their bacterial host, thus shaping the bacterial structure toward the lean phenotype.

Figure B shows a similar concept where the blue phages recognize an LBP intervention and lyse it, preventing engraftment in the gut. The orange phages recognize the LBP, but some of the bacterial strains have endogenous phage resistance resulting in some engraftment. In the red scenario, phages either do not find a suitable bacterial host or the bacteria hosts are present but are resistant to the phages. The red LBP shows strong engraftment.

Profiling and identification of such mechanisms can be used to engineer next-generation microbiome-based therapeutics with phage resistance and enhanced efficacy. This approach can also be used to enhance discovery pipelines by linking phage characteristics directly to specific phenotypes or health outcomes.

Created with BioRender.com

For the phage therapeutic developer, phage profiling offers a discovery tool for phages capable of remodelling bacteriome structures to reverse disease phenotypes and target pathobionts [2, 3]. Additionally, it can be used as a precision tool to identify a range of phage candidate(s) with specific host range that target only the intended bacteria. This focused strategy enhances the safety and efficacy of phage therapies and provides a funnel approach for enabling the development of highly specialized treatments that address bacterial infections without disrupting the beneficial microbiota.

Recently, phage analysis has been used to understand novel metabolic interactions in populations that are more resistant to aging due to enhanced mucosal integrity and resistance to pathobionts. [4] Bacteriophage are at the forefront of the fight against antimicrobial resistance (AMR) due to their ability to effectively disrupt AMR motifs and kill bacterial hosts [5]. These functions have unveiled a new toolbox for controlling infection [6].

Phage and viral analysis are an important component in microbiome science, enabling insights across applications including gut microbiome evolution, women’s health, infant microbiome studies, and the creation of live biotherapeutic products and probiotics. Recognizing phages as a significant modulator, the CHAMP™ profiler enables a deeper understanding of microbial ecosystems, highlighting their role in shaping health outcomes and therapeutic strategies. Get in touch to learn more about our phage services and whether phage analysis can add value to your study.

References:

[1] Pinto Y, Chakraborty M, Jain N, Bhatt AS. Phage-inclusive profiling of human gut microbiomes with Phanta. Nat Biotechnol. 2023 May 25. doi: 10.1038/s41587-023-01799-4. PMID: 37231259.

[2] Rasmussen TS, Mentzel CMJ, Kot W, Castro-Mejía JL, Zuffa S, Swann JR, Hansen LH, Vogensen FK, Hansen AK, Nielsen DS. Faecal virome transplantation decreases symptoms of type 2 diabetes and obesity in a murine model. Gut. 2020 Dec;69(12):2122-2130. doi: 10.1136/gutjnl-2019-320005. Epub 2020 Mar 12. PMID: 32165408.

[3] Ritz NL, Draper LA, Bastiaanssen TFS, Turkington CJR, Peterson VL, van de Wouw M, Vlckova K, Fülling C, Guzzetta KE, Burokas A, Harris H, Dalmasso M, Crispie F, Cotter PD, Shkoporov AN, Moloney GM, Dinan TG, Hill C, Cryan JF. The gut virome is associated with stress-induced changes in behaviour and immune responses in mice. Nat Microbiol. 2024 Feb;9(2):359-376. doi: 10.1038/s41564-023-01564-y. Epub 2024 Feb 5. PMID: 38316929; PMCID: PMC10847049.

[4] Johansen J, Atarashi K, Arai Y, Hirose N, Sørensen SJ, Vatanen T, Knip M, Honda K, Xavier RJ, Rasmussen S, Plichta DR. Centenarians have a diverse gut virome with the potential to modulate metabolism and promote healthy lifespan. Nat Microbiol. 2023 Jun;8(6):1064-1078. doi: 10.1038/s41564-023-01370-6. Epub 2023 May 15. PMID: 37188814.

[5] UK Parliment. The antimicrobial potential of bacteriophages. 3 January 2024. https://publications.parliament.uk/pa/cm5804/cmselect/cmsctech/328/report.html

[6] Strathdee SA, Hatfull GF, Mutalik VK, Schooley RT. Phage therapy: From biological mechanisms to future directions. Cell. 2023 Jan 5;186(1):17-31. doi: 10.1016/j.cell.2022.11.017. PMID: 36608652; PMCID: PMC9827498.

Kepler™ - Host-Agnostic Profiling

To complement our Human Profiler, CHAMP™, Kepler™ uses a host-agnostic curated database to process samples beyond the human host including but not limited to environmental, animal, soil, food samples and many others.

Kepler maximizes the value derived from metagenomic data by blending the precision of K-mer exact-matching with the adaptability of probabilistic alignment. It accomplishes this by utilizing a curated, host-agnostic database of signature k-mers mapped across 30,000 species organized in a phylogenetic tree-like structure.

Kepler’s technology is at the heart of its innovation, holding patents in the US Patent office (US10108778B2, US20200294628A1) and the European Patent Office (ES2899879T3).

How Kepler Works

The Kepler multi-kingdom taxonomic profiler can be split into three parts:

Step 1. Harnessing a Curated Database of Microbial Genomes

Our Kepler database boasts a collection of meticulously curated microbial genomes, prioritizing high completeness, low contamination, and intra-species diversity. Through rigorous quality checks and selective curation, we ensure an optimal taxonomic signal-to-noise ratio, encompassing over 30,000 species spanning multiple microbial kingdoms.

Step 2. Identifying Relevant Signature Features

Within our curated genomes, a pre-computation phase dissects them into k-mers, categorizing them as shared or unique signature k-mers across taxa. Utilizing a phylogenetic tree-like structure, Kepler efficiently identifies shared and unique genomic signatures, essential for precise taxonomic classification.

Step 3. Searching the Taxonomy Database

During the computational phase, sequencing reads or contigs are meticulously compared against our database:

The first comparator employs k-mer sets to identify taxa present in the query, maintaining classification sensitivity and accuracy through composite statistics and coverage depth estimation.

The second comparator utilizes an edit distance-scoring probabilistic algorithm, ensuring precision and accuracy in taxonomic classification.

Benchmarking of Kepler with Standardized Mock Communities

Benchmarking Kepler against leading profilers such as Kraken2/Bracken and MetaPhlAn4 using real-world community standards demonstrated its superiority.

With exceptional F1-Scores, Kepler excels in detecting low-abundance taxa and differentiating closely related species, showcasing its precision even at the sub-species level.

Amplicon Sequencing Analysis Service

At cmbio, we leverage our expertise in database curation to build a quality-controlled OTU database for both 16S and ITS amplicon sequencing using a range of open-source databases. Combined with the cmbio Metagenomics Cloud, microbiome scientists can now can amplicon analysis in a matter of minutes, complete with charts and visualizations which are ready to export!

How does it work?

Methods: The Cmbio 16S data analysis pipeline starts with preprocessing of the raw reads from either paired-end or single-end fastq files through read trimming to remove adapters as well as reads and bases of low quality. If the reads are in paired-end format, the forward and reverse overlapping pairs are joined together; the unjoined R1 and R2 reads are then added to the end of the file. The file is then converted to fasta format and used as input for OTU picking. OTUs are identified against the Cmbio curated 16S database using a closed-reference OTU picker and 97% sequence similarity through the QIIME framework. The final results are then presented in tabular format with the taxonomic names, OTU ids, frequency, and relative abundance. Results can be downloaded, or compared to other 16S samples for visualizations through the cmbio Metagenomics Cloud.

What is the CosmosID-Hub?

Software developers at Cosmos-ID  have automated the Amplicon analysis pipeline and made it available through a user-friendly and interactive web-based application which means that you can start analysing your data regardless of how much computational infrastructure you have available to you!

The CosmosID-Hub features:

• Genus to species-level identification for Bacteria & Fungi
• Exportable abundance values, charts & visualizations including sunburts, bubble charts & stacked bar graphs
• Unlimited use of comparative analysis software, complete with Heatmaps, 3D PCA, Alpha Diversity plots & Beta Diversity PCoA

LAUNCH HUB

Why Choose Cmbio For Amplicon Analysis Services

Cmbio’s proficiency in amplicon analysis is unparalleled, making it a trusted choice for many researchers worldwide. Our robust pipeline allows us to handle raw sequencing data from amplicon sequencing services with ease and precision. One of our key starting points is the DNA extraction, a crucial step in sample preparation that allows us to accurately examine environmental samples.

Once we have the data, our experts begin processing amplicon data, focusing on the variable regions. These are the parts of the 16S rRNA gene that contain the greatest species-specific differences and are best for distinguishing among species. We then compare these regions to our comprehensive reference database, which is constantly updated to ensure accuracy.

The output from our bioinformatics analysis is a detailed report with insights into the relative abundance of different species within your sample. We provide a statistical analysis of the microbial communities revealed by 16S rRNA sequencing, assisting you in understanding the full scope of your data.

At Cmbio, we don’t just deliver data; we provide meaningful insights that drive impactful discoveries. Choose Cmbio for your amplicon analysis needs, and let’s explore the microbial universe together.

How do I get started?

No licensing or subscription required! Simply send us your samples and the analysis will be completed for you as part of the service or if you have data ready, purchase your credits, upload your data and unlock the microbiome!

TRY IT HERE

Antimicrobial resistance profiling

Microbial communities are heavily influenced by antibiotics and other antimicrobials. Microbiome compositions, both relative and absolute abundances, not only change, but species also respond to antibiotic treatments by acquiring and disseminating antibiotic resistance genes (ARGs). With the CARD database and our proprietary annotation and organization of species we provide clients with a detailed resistome analysis that provides insights on antibiotic resistance genes (ARG), classes and whether the ARGs can be transferred horizontally or is comprised of species that are considered commensal, pathogenic or an opportunistic pathogen.

Antimicrobial resistance (resistome) analysis

The development and spread of microbial antibiotic resistance causes previously reliable antibiotics to fail, posing serious global health concerns. Microbial communities respond to antibiotics not only by changing their composition, but also by acquiring and disseminating antibiotic resistance genes (ARGs). The resistome is the set of ARGs in a microbial community.

Antimicrobial resistance is a serious global health problem that threatens our ability to effectively treat infections and is leading to increased morbidity, mortality, and healthcare costs.

Microbial communities are heavily influenced by antibiotics. Microbiome compositions, both relative and absolute abundances, not only change, but species also respond to antibiotic treatments by acquiring and disseminating antibiotic resistance genes (ARGs). The emergence of antibiotic-resistant bacteria are rendering antimicrobial drugs ineffective in treating infections. This poses a significant threat to public health.

Developing new antibiotics to combat these challenges is a complex and arduous task.

We provide clients with detailed resistome analysis to enable insights on the antimicrobial resistance associated with new or already approved antimicrobial treatments or specific diseases.

Our Approach

We annotate our Cmbio Human Microbiome Reference (HMR) for ARGs using the Comprehensive Antibiotic Resistance Database (CARD)¹, which is a manually curated and constantly updated collection of resistance genes, products, and associated phenotypes.

We assign catalog genes to a CARD model by using the CARD Resistance Gene Identifier software (using DIAMOND as search method). First, our HMR catalog genes are translated into proteins and subsequently aligned to CARD ARG models. To avoid false ARG detections, we only include perfect matches to protein homolog models.

This technique allows detection of ARGs conferring resistance to the 21 most commonly used classes of antibiotics: Aminocyclitol, Aminoglycoside, Beta-lactam, Diaminopyrimidine, Fluoroquinolone, Fosfomycin, fusidic acids, Glycopeptide, Iminophenazine, Isoniazide, Lincosamide, Macrolide, Oxazolidinone, Phenicol, Pleuromutilin, Polymyxin, Rifamycin, Streptogramin, Sulfonamide, Tetracycline, and Multidrug efflux pumps (MEPs).

ARG relative abundance is extracted from gene relative abundance, and we provide resistome analysis at the level of ARG richness, total ARG abundance, or ARG level. We aggregate the relative abundance of ARGs conferring resistance to each type of antibiotic to provide a resistome analysis at the level of antibiotic class. Furthermore, we summarize ARG relative abundance per resistance threat (e.g extended spectrum beta-lactam, carbapenem resistance, or vancomycin resistance). Finally, we mark which ARGs associated with disease or treatment have been seen in plasmids and/or pathogens to pinpoint the most threatening ARGs.

Contact us to learn more about the applicability of resistome analysis to your study and further details about our methodology.

References

¹ Alcock BP, Raphenya AR, Lau TTY, et al. CARD 2020: antibiotic resistome surveillance with the comprehensive antibiotic resistance database. Nucleic Acids Res**.** 2020 Jan 8;48(D1):D517-D525. doi: 10.1093/nar/gkz935.

Have a look at some of our published work involving resistome analyses:

Pallejá, A., Mikkelsen, K.H., Forslund, S.K. et al. Recovery of gut microbiota of healthy adults following antibiotic exposure. Nature Microbiology 3, 1255–1265 (2018). https://doi.org/10.1038/s41564-018-0257-9

Nielsen KL, Olsen MH, Pallejá A, et al. Microbiome Compositions and Resistome Levels after Antibiotic Treatment of Critically Ill Patients: An Observational Cohort Study. Microorganisms 12, 2542 (2021). doi: 10.3390/microorganisms9122542