Tag Archives: Metagenomics

You are what you excrete

Guccione, C., McDonald, D., Fielding-Miller, R. et al. You are what you excrete.
Nat Microbiol (2023). https://doi.org/10.1038/s41564-023-01395-x

MGI Empowers the Completion of Nearly 60,000 Samples for The Million Microbiome of Humans Project

SHENZHEN, China, 10 May 2023 – MGI Tech Co. Ltd. (MGI), a company committed to building core tools and technology to lead life science, today shared that a total of nearly 60,000 samples have been sequenced among 21 institutes and over 10 participating nations throughout Europe, as part of the Million Microbiome of Humans Project (MMHP) that was officially launched in 2019.

Image Credit: MGI

The project was launched as a joint effort by the Karolinska Institute of Sweden, Shanghai National Clinical Research Center for Metabolic Diseases in China, the University of Copenhagen in Denmark, Technical University of Denmark, MetaGenoPolis at the National Research Institute for Agriculture, Food and Environment (INRAE) in France, and the Latvian Biomedical Research and Study Center. Relying on MGI’s core DNBSEQ™ technology, MMHP aims to sequence and analyze microbial DNA from a million human samples to construct a microbiome map of the human body and build the world’s largest human microbiome database.

“Countless studies have highlighted the importance of the microbiome in human health and disease. Yet, our knowledge of the composition of the microbiome in different parts of the body across countries, ages, sexes, and in relation to human health and disease remains limited,” said Duncan Yu, President of MGI. “Through MMHP, we are pushing forward microbial metagenomic research while empowering researchers within the microbiology community with access to MGI’s innovative sequencing technology. Despite a brief interruption by the COVID-19 pandemic, we are delighted to see such a monumental milestone merely four years into the project.”

The rise of microbial metagenomic sequencing​​​​​​​

Since the first description of human microbiome was published in 2010, the field of human microbiome has moved fast from sampling hundreds of individuals to thousands. Advances in genome sequencing has enabled researchers to better characterize the composition of the microbiome through identification of unculturable microbes. It has also allowed them the opportunity to study how the microbiome influences the development of some cancers and drug responses.

Metagenomics, coupled with high-throughput sequencing technologies, have revolutionized microbial ecology. Today, metagenomic sequencing has become both a powerful and popular tool for identifying and classifying complex microbial communities. It facilitates accelerated discovery of new markers that translate to virulence or antibiotic resistance, as well as de novo discovery and characterization of novel species and assembly of new genomes. Besides human microbiome, it is highly applicable in agricultural microbiome studies, environmental microbiome studies, pathogen surveillance and identification, and monitoring of antimicrobial resistance genes.

Indeed, the global metagenomic sequencing market was estimated to be worth USD 1.86 billion in revenue in 2022 and is poised to reach USD 4.33 billion by 2027, growing at a CAGR of 18.4% during the forecast period. In particular, Europe and Africa account for approximately 29.7% market share from the globe, ranking second after North America at 45.6%. Thanks to continuous technological innovations in high-throughput sequencing platforms, the metagenomic sequencing market within Europe and Africa is projected to grow from USD 551.7 million in 2022 to 1.29 billion by 2027, presenting huge market opportunities and providing local institutions with the impetus to invest and get involved.


Image Credit: MGI

An optimized workflow with MGI’s cutting-edge technology

Equipped with MGI’s innovative lab systems, the MMHP Consortium guarantees high-throughput processes, extreme precision, and high quality data output. The dedicated, one-stop workflow begins with sample transfer on MGISTP-7000* high-throughput automated sample transfer processing system. It then goes through nucleic acid extraction and library preparation on MGISP-960 high-throughput automated sample preparation system, a flexible and fully automated workstation capable of processing 96 samples per run. MGISP-960’s fully automatic operation design allows DNA extraction of 50,000 samples per year and library preparation of 25,000 samples per year. MGISP-Smart 8, the professional automated pipetting robot, equipped with an independent 8 pipetting channel can be used for the pooling, normalization and DNB making. Lastly, DNBSEQ-T7* ultra-high throughput sequencer and DNBSEQ-G400* versatile benchtop sequencer enables an efficient, productive, and streamlined sequencing experience.

“We are very focused on data quality, cost and time. After contrasting DNBSEQ™ technology by MGI with other sequencing technologies, we are convinced that MGI’s products have met high industry standards and provide a very good user experience,” commented Professor Lars Engstrand, Research Director of Center for Microbial Translational Research (CMTR) at Karolinska Institutet. “MGI’s platforms have enabled our team to upgrade our original microbiological research from 16SrRNA gene amplicon sequencing to shotgun metagenomic sequencing. I look forward to introducing more equipment and super-large projects as human microbiome emerges as a crucial diagnostic and treatment method in precision medicine.”

The next chapter in microbiomics

“Microbiomics will be part of precision medicine in the future, and data from the microbiome biobank that will result from MMHP will be leveraged for therapeutic R&D,” said Professor Stanislav Dusko Ehrlich of University College London, UK. “With 21 public and private institutions and 10+ countries currently involved in MMHP, we are actively looking for more research groups to take part in this landmark international microbiological research partnership and help generate the world’s biggest free-access human microbiome database.”

Since the inception of MMHP, MGI has played an important role in providing the program with state-of-the-art research platforms and technologies. Now entering its second phase towards sequencing and analyzing a final total of one million samples, the project welcomes further exchange and participation from relevant organizations to jointly promote research and applications of cutting-edge translational medicine in the field of microbiome. Those interested can fill the application form on www.mgi-tech.eu/mmhp.

About MGI

MGI Tech Co. Ltd. (MGI), headquartered in Shenzhen, is committed to building core tools and technology to lead life science through intelligent innovation. Based on its proprietary technology, MGI focuses on research & development, production and sales of sequencing instruments, reagents, and related products to support life science research, agriculture, precision medicine and healthcare. MGI is a leading producer of clinical high-throughput gene sequencers*, and its multi-omics platforms include genetic sequencing*, medical imaging, and laboratory automation. MGI’s mission is to develop and promote advanced life science tools for future healthcare. For more information, please visit the MGI website or connect with us on TwitterLinkedIn or YouTube.

*Unless otherwise informed, StandardMPS and CoolMPS sequencing reagents, and sequencers for use with such reagents are not available in Germany, Spain, UK, Sweden, Italy, Czech Republic, Switzerland and Hong Kong (CoolMPS is available in Hong Kong).

*For Research Use Only. Not for use in diagnostic procedures (except as specifically noted).

$(function() { Azom.wireUpVideoThumbnailLazyLoading(); });

Metagenomic sequencing offers rapid, accurate diagnosis of antimicrobial resistance in bloodstream infections

Metagenomic sequencing can provide rapid and actionable antimicrobial resistance predictions to treat bloodstream infections much faster than conventional laboratory tests, and has the potential to save lives and better manage the use of antibiotics, according to new research being presented at this year’s European Congress of Clinical Microbiology & Infectious Diseases (ECCMID) in Copenhagen, Denmark (15-18 April).

The study led by Dr Kumeren Govender from the John Radcliffe Hospital, University of Oxford, UK, indicates that rapid metagenomics can provide accurate results within just 6 hours of knowing bacteria are growing in a blood sample.

Antibiotic-resistant bloodstream infections are a leading killer in hospitals, and rapidly starting the right antibiotic saves lives. Our results suggests that metagenomics is a powerful tool for the rapid and accurate diagnosis of pathogenic organisms and antimicrobial resistance, allowing for effective treatment 18 to 42 hours earlier than would be possible using standard culture techniques.”

Dr Kumeren Govender, John Radcliffe Hospital, University of Oxford, UK

Bloodstream infections can rapidly lead to sepsis, multiple organ failure, and even death. Early and appropriate antibiotic therapy is vital for control of the infection.

Antimicrobial resistance (AMR) is a major challenge when treating bloodstream

Infections, causing around 370,000 deaths and associated with nearly 1.5 million deaths in 2019 [1].

The current method used in clinical settings to identify the pathogen causing the infection is long and laborious, requiring two time-consuming culture and sensitivity tests that take at least 1 to 3 days to complete-;first isolating and identifying the pathogen and then performing antimicrobial susceptibility testing (to expose the bacteria to various antibiotics to see exactly which it will respond to, plus the best route and dose).

if (g_displayableSlots.mobileMiddleMrec) {
pushDisplayAd(function() { googletag.display(‘div-gpt-mobile-middle-mrec’); });

In contrast, clinical metagenomics sequences all the genetic material including infectious pathogens in a sample all at once, so time spent running tests, waiting for results, and running more tests could be reduced.

To find out more, researchers randomly selected 210 positive and 61 negative blood culture specimens for metagenomic sequencing from the Oxford University Hospital’s microbiology laboratory between December 2020 and October 2022.

DNA was sequenced using the Oxford Nanopore GridION platform. Sequences were used to identify the species of pathogen causing infections and also to spot common species that can contaminate blood cultures.

Sequencing was able to identify 99% of infecting pathogens including polymicrobial infections and contaminants, as well as giving negative results in 100% of culture negative samples. In some instances, sequencing detected probable causes of infection missed by routine cultures, and in other instances identified uncultivable species where a result could not be determined.

Sequencing could also be used to detect antibiotic resistance in the ten most common causes of infections. A total of 741 resistant and 4047 sensitive combinations of antibiotics and pathogens were studied. Results of traditional culture-based testing and sequencing agreed 92% of the time. Similar performance could be obtained from raw reads after only two hours of sequencing, overall agreement was 90%.

The average time from sample extraction to sequencing was 4 hours with complete AMR prediction 2 hours later, producing actionable AMR results 18-42 hours before to the conventional laboratory.

David Eyre, Professor of Infectious Diseases at the University of Oxford, who co-led the study, commented, “This is a really exciting breakthrough that means we will be able to diagnose the cause of patients’ infections faster and more completely than has been possible before. We are working hard to continue to overcome some of the remaining barriers to metagenomic sequencing being used more widely, which include its current high cost, further improving accuracy, and creating improved laboratory expertise in these new technologies and simpler workflows for interpreting results.”

Differences in gut microbiome diversity attributed to dietary patterns in children with obesity

In a recent study published in Microbiology Spectrum, researchers found that differences in the dietary patterns of children with normal weight and those who were overweight or obese contributed to variations in the gut microbiome diversity, virulence factors of gut bacteria, and metabolic function.

Study: Virulence factors of the gut microbiome are associated with BMI and metabolic blood parameters in children with obesity. Image Credit: Africa Studio / Shutterstock.com

Study: Virulence factors of the gut microbiome are associated with BMI and metabolic blood parameters in children with obesity. Image Credit: Africa Studio / Shutterstock.com


A growing body of evidence indicates that gut microbiota has a significant role in various aspects of host metabolism, including digestion, harvesting of energy, and induction of low-grade inflammation. In addition, the genetic factors of the host, as well as other characteristics such as age, diet, immunity, and gender, influence the gut microbiome composition.

Research shows that bacterial diversity in the gut and the individual’s functional capacity vary between those with normal weight and obese individuals. Gut microbiome profile variations have also been linked to metabolic disorders, lipid accumulation, and inflammation.

Lipogenesis in the liver and the regulation of appetite through hormones are also associated with gut microbiome genes.

Aside from its role in adipogenesis, superoxide reduction, and the metabolism of vitamins, gut microbiota also regulates innate immunity and the systemic, low-grade inflammatory state that can contribute to fat deposition and obesity. Therefore, Dysbiosis, which is the imbalance of gut microbiota, combined with diet, likely has a significant role in the development of obesity.

About the study

In the present study, researchers conducted a cross-sectional analysis of data from 45 children between the ages of six and 12 to determine the association between gut microbiota and obesity.

Questionnaires were used to obtain information on dietary frequencies, gender, age, and body mass index (BMI). Based on the World Health Organization (WHO) z-scores, in which BMI is adjusted for gender and age, the children were classified into two categories of overweight and obese (OWOB) and normal weight (NW).

Data from food frequency questionnaires were used to classify the dietary habits of children into two nutritional patterns. To this end, Pattern 1 was characterized by complex carbohydrates and proteins, whereas Pattern 2 comprised simple carbohydrates and saturated fats.

Shotgun metagenomics was used to assess the taxonomic diversity of the gut microbiota and metabolic capacity from genomic deoxyribonucleic acid (DNA) extracted from fecal samples. Clade-specific markers were used for the taxonomic and functional assessment of the gut bacteria. Additionally, reverse Simpson and Shannon diversity indices were calculated.

The virulence factor database was used to screen for virulence factor genes, whereas multivariate linear modeling was used to determine the association between the taxa, virulence factors, and function of gut microbes and covariates of diet, serology, and anthropometric measurements.

Study findings

Significant differences between the alpha and beta diversity of the gut microbiota were observed between the children in the NW and OWOB groups, thus suggesting that specific phyla of bacteria contribute to higher levels of energy harvest.

Furthermore, species such as Ruminococcus species, Victivallis vadensis, Mitsuokella multacida, Alistipes species, Clostridium species, and Acinetobacter johnsonii were linked to healthier metabolic parameters.

In contrast, an increase in the abundance of bacteria such as Veillonellaceae, Lactococcus, Fusicatenibacter saccharivorans, Fusicatenibacter prausnitzii, Eubacterium, Roseburia, Dialister, Coprococcus catus, Bifidobacterium, and Bilophila was identified in children with pro-inflammatory conditions and obesity.

Bacteria such as Citrobacter europaeus, Citrobacter youngae, Klebsiella variicola, Enterococcus mundtii, Gemella morbillorum, and Citrobacter portucalensis were associated with higher lipid and sugar intake, as well as higher blood biochemistry values and anthropometric measurements.

Diets high in fats and simple carbohydrates have been associated with the abundance of Citrobacter and Klebsiella species in the gut. Moreover, previous studies have indicated that these bacterial species are potential markers of inflammation, obesity, and an increase in fasting glucose.

The metabolism of menaquinones and gamma-glutamyl was negatively associated with BMI. Furthermore, the microbiomes of children in the NW group preserved a more consistent alpha diversity of virulence factors, while OWOB microbiomes exhibited a dominance of virulence factors.

Differences in the metabolic capacities pertaining to biosynthesis pathways of vitamins, carriers, amino acids, nucleotides, nucleosides, amines, and polyamines, as well as the degradation of nucleotides, nucleosides, and carbohydrate-sugars, were also found between the NW and OWOB groups.


Dietary profiles and the diversity of gut microbiota were found to be interconnected and associated with changes in metabolic parameters, the dominance of virulence factors, and obesity. Changes in gut microbiome diversity and relative abundance have been linked to obesity, inflammatory responses, and metabolic disorders.

Taken together, the study findings suggested that the prevalence of virulence factors, as well as the metabolic and genetic roles of gut microbiota in increasing inflammation, can help identify individuals at an increased risk of childhood obesity.

Journal reference:
  • Murga-Garrido, S. M., Ulloa-Pérez, E. J., Díaz-Benítez, C. E., et al. (2023). Virulence factors of the gut microbiome are associated with BMI and metabolic blood parameters in children with obesity. Microbiology Spectrum. doi:10.1128/spectrum.03382-22

MGI’s DNBSEQ-T7* recognized for its ultra-high throughput and excellent accuracy

Thanks to high-throughput sequencing technologies, shotgun metagenomic methods were made possible and had effectively transformed microbiology. Today, advances in both short- and long-read technologies are overcoming many of the previous challenges affecting metagenomic profiling, especially of highly complex samples and environment.

Researchers from France’s National Research Institute for Agriculture, Food, and Environment (INRAE) examined the performance of seven short- and long-read sequencing platforms in analyzing high-complexity metagenomic samples. The study, published in the Nature Portfolio journal Scientific Data, ran mock samples between 2018 and 2019 on various mainstream sequencers at the time, including MGI’s DNBSEQ-T7* and DNBSEQ-G400*.

Within this wide range of sequencing technologies tested, DNBSEQ-T7* was recognized for its ultra-high throughput and excellent accuracy. “We were surprised by the T7’s performance,” said senior author Mathieu Almeida, a research fellow at INRAE. “It provides ultra-deep sequencing in a single run with similar low error rate compared to the other platforms, making it at the time of our study one of the most affordable technologies for metagenomic sequencing.”

In the study, three uneven synthetic microbial communities were constructed, consisting of up to 87 genomic microbial strains DNAs each and spanning 29 bacterial and archaeal phyla. They represented some of the most complex and diverse communities used for sequencing technology comparisons. The mock1 (71 strains) was sequenced using all platforms, mock2 (64 strains) was additionally sequenced to estimate the impact of various microbial richness, while MGI’s platforms were not performed on mock3.

To assess the impact of sequencing depth, the team ran a subsampling analysis and compared observed and theoretical genome abundances across samples at multiple depth from 10,000 to 1 million reads. Overall, Spearman rank correlations for all platforms were high at above 0.9 when mapping at least 100,000 reads. Among them, the correlations of T7* and G400* were the best in mock1 and remained excellent in mock2.

In addition, differential analysis between observed and excepted species abundances was performed in mock1. Results showed that over or under abundance estimation for most genomes had little to do with the sequencing platform, read length, taxonomy, GC-content, genome size and genome completeness, even at a low depth of 500,000 reads. In fact, most genomes were accurately estimated on all sequencers, with the observed normalized abundances generated by T7* charting very close to the excepted values.

Based on performance analyses of the different sequencers, the study formed a microbial metagenomic sequencing benchmarking database, providing researchers and scientists a comprehensive and authentic reference for sequencing platform selection. In particular, the findings demonstrated the promising value of MGI’s DNBSEQ-T7* in metagenomic sequencing.

Boasting high stability and accuracy as shown in the data, combined with outstanding throughput, T7* makes a strong platform for the identification of species and functional genes in highly complex microbial communities. Its upgraded biochemical, fluidics, and optical systems are not only making sequencing more efficient and productive, but also continuing to support research into the structure and diversity of microbial communities.

Journal reference:

Meslier, V., et al. (2022) Benchmarking second and third-generation sequencing platforms for microbial metagenomics. Scientific Data. doi.org/10.1038/s41597-022-01762-z.

Understanding how the gut mycobiota respond to antibiotic treatment

Antibiotic treatment disrupts the balance of beneficial and harmful bacteria in a person’s gut. That disruption can lead to the overgrowth of fungal species in the gut mycobiota, including the common intestinal yeast Candida albicans. However, researchers only have a limited understanding of the underlying mechanisms.

This week in mBio, an open-access journal of the American Society for Microbiology, in a first of its kind study on human subjects, researchers in Europe report on how treatment with a common beta-lactam antibiotic led to significant changes in C. albicans in patients. Notably, they found that not all patients responded in the same way, and the degree to which C. albicans populations increased depended in large part on the microbiota of the individual. That variation suggests that the risk for C. albicans overgrowth, in response to antibiotic treatment, is not the same for everyone.

This study shows that the situation is more complex than previously thought, and with certain antibiotics such as beta-lactam, this increase in C. albicans varies from one person to another.”

Marie-Elisabeth Bougnoux, M.D., Ph.D., microbiologist and senior author, Institut Pasteur in Paris, France

Researchers have long studied the effects of antibiotics on the gut microbiota, but less attention has been paid to the mycobiota, or collection of gut fungal species. The authors of the new study point to 2 reasons.

“First, the mycobiota is difficult to study with metagenomics techniques,” said Margot Delavy, a Ph.D. student at the institute and first author on the paper, “and second, the concentration of fungi is much lower than that of bacteria,” making them harder to measure. “Repeatable metagenomic techniques to study the fungi of the gut have become available only recently,” she said.

For the new study, Bougnoux and her colleagues used fecal samples to track the changes in the gut mycobiota in 2 groups of 11 healthy patients before, during, and after they were treated with cefotaxime (in one group) or ceftriaxone (in the other). Both drugs are third-generation cephalosporin antibiotics.
The group first identified the fraction of the fecal DNA that was associated with fungal species. Then, they used high-throughput sequencing to identify which fungal species were present in the healthy gut of the volunteers, before antibiotic treatment. They found that both diversity and abundance of species varied not only from person to person, but also from one collection to another in the same individual. The team used specific qPCR to quantify levels of C. albicans and found the fungus present in 95% of the participants.

The researchers carried out similar analyses during and after antibiotic treatment. They found that across the board, the fungal load -; the fraction of fecal DNA -; increased in all patients following treatment with antibiotics. But at the species level, those responses varied considerably. Some individuals experienced a significant increase in abundance of C. albicans and other species, while others didn’t. (At least one participant even showed a decrease.)

Further analyses of the samples revealed that the variations in fungal response to antibiotic treatment was connected to the activity of the enzyme beta-lactamase, which is produced by endogenous bacteria from the subject’s microbiota. People with lower levels of beta-lactamase experienced more growth of fungi, including C. albicans, than those with higher levels of the enzyme.

Bougnoux, whose previous work has focused on how intestinal C. albicans colonization leads to infection, said the group wanted to focus on antibiotic use because it’s a major risk factor for colonization. The new study, she noted, is a promising first step toward understanding how the mycobiota responds to treatment-;but it’s only the beginning.

“Our study was done on human volunteers who received only one antibiotic, but actual patients often receive several,” Bougnoux said. And those who receive the most are most likely to develop fungal infections, she added. “It remains to be seen if the relation we found between beta-lactams and reduced intestinal C. albicans colonization is also true in these patients.”

Journal reference:

Delavy, M., et al. (2022) A Clinical Study Provides the First Direct Evidence That Interindividual Variations in Fecal β-Lactamase Activity Affect the Gut Mycobiota Dynamics in Response to β-Lactam Antibiotics. mBio. doi.org/10.1128/mbio.02880-22.

One of the most significant consequences of climate change is the greenhouse gases generated from the microbial decomposition …

One of the most significant consequences of climate change is the greenhouse gases generated from the microbial decomposition of organic matter in thawing permafrost soil. Permafrost refers to ground soil frozen at 0℃ or lower, year after year. Permafrost regions of the Earth are mostly found in the north and south poles. During summer, some thawing of the permafrost landscape is considered normal, but with climate change, thawing has increased annually.

Studies of permafrost soil have previously identified ancient bacteria, viruses, fungi, and even protozoans that can potentially become infectious after several years of being frozen. Apart from identification, the global impact of the microbial composition of permafrost on human health remains largely undetermined. 

More recently, DNA was isolated from soil samples in the carbon-rich Yedoma permafrost of Siberia. The Yedoma permafrost is known to have preserved animal remains like mammoths and ancient microbial content. An international team of scientists from Russia and Germany conducted a ‘metagenomic’ analysis of various soil samples from the Yedoma permafrost, which involves the detailed characterization of all DNA extracted from multiple soil samples. Their studies from the Yedoma soils have identified bacterial genes from several bacterial species with no specific correlation to the age of the permafrost. Interestingly, a high frequency of the beta-lactamase gene was detected within the identified bacterial genomes. What does this mean? The DNA samples belong to diverse bacterial species, and all carry the gene for the enzyme beta-lactamase. Beta-lactamases are enzymes that cause the inactivation of penicillin-derived antibiotics, thereby conferring antibiotic resistance to the bacteria carrying them (in their genome or plasmids). 

Active microbial life has been discovered in the arctic before. But the discovery of bacterial DNA, a large proportion of which carries antibiotic resistance, is unexpected. This finding is even more perplexing to scientists because these soils have remained far removed from human civilization that have heavy antibiotic usage. The acquisition of antibiotic resistance is technically possible outside a clinical setting. Bacteria acquire genes from their environment all the time. However, the potential danger of thawing permafrost and the release of bacterial DNA offering antibiotic resistance is concerning.

Antimicrobial resistance (AMR) is a global health issue that severely challenges our ability to treat bacterial infections effectively. Tracking and early identification of AMR in clinical settings is key to reducing its spread. The discovery of antibiotic resistance in permafrost does not directly affect clinical care today but has implications for the future of AMR, especially with a rising concern about climate change. 

Anusha Naganathan

The vaginal microbiome through the lens of systems biology

The human organism is a complex ecosystem of coexisting microbiomes, including those in the gut, the skin, and the vagina in females. These play a crucial role in health and disease. However, a great deal remains to be learned about them.

A new paper recently published online in Trends in Microbiology journal reviews the systems biology approach to explore the vaginal microbiome (VMB), helping to understand its composition and function and the mechanisms by which it interacts with the host.

Review: New perspectives into the vaginal microbiome with systems biology. Image Credit: Design_Cells / ShutterstockReview: New perspectives into the vaginal microbiome with systems biology. Image Credit: Design_Cells / Shutterstock


The VMB is vital in female fertility, and disruptions can be associated with pregnancy disorders, gynecologic diseases such as pelvic inflammatory disease (PID), and an array of infections involving the female genitourinary and reproductive tract. In addition, the VMB may be instrumental in affecting drug efficacy in women.  

However, the VMB is little understood beyond a vague idea that a preponderance of Lactobacillus is associated with a ‘good’ state with a homogeneous community structure. Conversely, an undesirable state of the VMB exists when more diverse species are identified in greater abundance.

This latter suboptimal state is often linked to bacterial vaginosis (BV), found in one in three women during their reproductive period, which can have severe consequences on their fertility. As such, research in this area is required to understand the directionality and magnitude of such associations.

The problem

While many studies have been performed in this area, it is difficult to understand what an optimal VMB looks like because of the complex interactions between microbes and other host factors. This means that the healthy VMB can differ considerably from woman to woman and at different points in the same individual’s life cycle.

Such changes occur within days, which contrasts with the much slower shift seen with the gut, skin, and oral microbiomes, which may change over months or even years. Unfortunately, this makes cross-sectional data quite non-representative when it comes to studying the association of VMB composition, function, and disease – and thus makes most of this data less useful than it could be.

Again, the human VMB differs significantly from that of animals, as well as from culture-based models. In the former, even non-human primates fail to show the characteristic conditions of the human vagina, including the acidic pH and Lactobacillus dominance.

In the latter, some microbes are incredibly resistant to culture in vitro, while various culture conditions are used in different laboratories, depending on the media. This could make the growth environment quite different from that of the human cervix and vagina, invalidating the results of such experiments.

As such, clinical samples from which vaginal microflora are cultured, identified, and quantified form the primary source of information about the human VMB. This information is colored by experimental and host variables, which require sophisticated statistical adaptations to achieve a valid conclusion.

While relevant to all microbiome sites, [this] is particularly applicable to the VMB because of its lack of experimental models that allow for interrogation of vaginal microbiota under controlled conditions.”

The solution

Such an impasse can be solved with a systems biology approach, where quantitative analyses are used to extract the important factors affecting the behavior and function of a microbial community. As such, “Leveraging systems biology techniques applied to other microbiomes, as well as developing novel techniques and applying these methods to the VMB, will have a significant impact on improving women’s health.”

The use of systems biology can overcome the challenges of such complex and multiple external and internal interactive networks. Furthermore, multiple approaches can be used, depending on the type of information available and the aim of the study.

Thus, statistical or data-driven methods are ideal when high-throughput data are abundant in a relatively new field of study. This can help suggest what microbial profiles are linked to disease or health. Since little is known so far about the VMB, data-driven models have predominated so far.

Conversely, based on hypotheses, mechanistic methods are better when much is already known about a system, or at least the fundamental data is available, and the need is to understand the mechanisms of cause-effect associations underlying biological function. In addition, they help to set the ranges within which microbial composition and interactions can occur in normal and abnormal situations.

Some mechanistic methods include mass-action kinetic or population dynamics models (based on differential equations), genome-scale metabolic models (GEMs), and agent-based models (ABMs).

What has been achieved?

The systems biology approach has already helped to identify and categorize community state types (CSTs) associated with health, disease, or transitions between the two. First defined by microbial abundance, they incorporated patient demographic and health data to form hierarchical clustering groups. In addition, other methods like nearest centroid classification have been developed to overcome the inherent variation in the dataset with the former approach.

CST groupings help simplify VMB composition and thus suggest associations with community composition and function. But this is at the cost of overlooking community-specific factors specific to different taxa.

Multi-omics approaches could be integrated with systems biology strategies to identify associations with different types of community and specific metabolomics, transcriptomics, and metagenomics profiles, for instance. In addition, random forest models and other advanced machine learning models are being pressed into service to help distinguish VMBs with a predominance of different microbes, such as L. crispatus vs. L. iners or Bifidobacteriaceae.

Interestingly, neural network models have shown the superiority of metabolomics in describing the cervicovaginal environment accurately compared to either VMB composition or immunoproteomics. The integrated use of these strategies could help pick out the important drivers of VMB states in health and disease.

Especially important could be the insights obtained regarding sexually transmitted infection (STI) risk with an increased abundance of ‘bad’ microbes. For instance, an increase in L. iners seems to be associated with a higher risk for STIs, while L. gasseri is associated with health. Conversely, Gardnerella vaginalis and Prevotella species are linked to Chlamydia infection.

Mechanistic models include the technique called MIMOSA (Model-based Integration of Metabolite Observations and Species Abundances) that uses metabolic network modeling to understand community function via its gene content. This helped identify Prevotella species and Atopobium vaginae as key modulators of the VMB, using a calculated community-based metabolite potential (CMP) score. The CMP shows the turnover of each metabolite by any given community.

Similarly, genome-scale network reconstructions (GENREs) could help understand the role of fastidious microbes in the VMB. Ordinary differential equation (ODE)-based models are being used to examine how drugs can affect the VMB and the ecology of this system, showing how the composition fluctuates following exposure to different factors.

What lies in the future?

A multitude of studies has focused on the gut microbiome, with almost $150 million being poured into developing and standardizing new tools for its exploration. VMB researchers may be able to use these to serve their aims. This includes BURRITO, a web tool that helps visualize a microbiome community by relative abundance. This could be extended to examine VMB metagenomics, showing how patient symptoms relate to the CSTs.

Supervised machine learning approaches to understand the VMB better include Data Integration Analysis for Biomarker Discovery using Latent cOmponents (DIABLO), where omics datasets are integrated by correlation, and Sparse regularized generalized canonical correlation analysis (SRGCCA), used in Crohn’s disease.

To overcome the limitations imposed by the lack of knowledge about the functional classification of the VMB, unsupervised learning strategies may be useful, such as multi-omic factor analysis (MOFA).

Many ODE models can also be used based on the Generalized Lotka–Volterra (gLV) models. These include web-gLV, Microbial dynamical systems inference engine for microbiome time-series analysis (MDSINE), and the learning interactions from microbial time series (LIMITS) method, as well as newer adaptations like the compositional Lotka–Volterra (cLV) and the ‘Biomass Estimation and Model Inference with an Expectation Maximization’ algorithm (BEEM), that are not dependent on the culturability of the community or on the availability of extensive longitudinal datasets.

Newer methods include algorithms like Constant yield expectation framework (conYE) and MMinte, that simulate conditions for community metabolism and growth based on dense interactions between the species. Such ingenious adaptations and approaches could help understand the factors that shape the dynamic VMB in health and disease in different populations.

Journal reference:

Archaea Sport Structures that Shuttle Genes Among Microbes

ABOVE: © iStock.com, Gerald Corsi

Earth’s first life forms eventually took one of three different paths, forming the domains of Eukarya, Bacteria, and Archaea. These domains have been evolving separately for billions of years.

Recent evidence suggests that the boundaries between the three domains are not so clean. Studies show that members of different domains can traffic genes back and forth, potentially fast-tracking evolution. How they do so remains unknown, but a study published today (November 16) in Science Advances provides a possible clue with the first report that archaea have integrons—gene exchange machinery previously thought only to exist in bacteria. This may allow microbes from the two domains to swap information and instantly acquire new functions.

“We’ve known for a while that there are a lot of genes that bacteria and archaea exchange,” says Olga Zhaxybayeva, an evolutionary biologist at Dartmouth College who was not involved in the study. If integrons turn out to be widespread in archaea, “it could be another mechanism for microbes to exchange the traits they need.”

See “Horizontal Gene Transfer Happens More Often Than Anyone Thought

Gene exchange can help bacteria survive in new, harsh environments, or strengthen their symbiotic relationships with plants. Study coauthor Timothy Ghaly, a microbiologist at Macquarie University in Sydney, says that he and his team had always been interested in how integrons allow bacteria to take on novel, sometimes incredibly useful traits such as antibiotic resistance.

It was unknown if archaea have integrons, partly because they’re hard to study, says Ghaly, as they live in a variety of difficult-to-access environments, from our guts to muddy, sulfuric hot springs. But recent advances in genomic sequencing, in particular a technique called metagenome-assembled genomes (MAG), have allowed researchers to piece together archaea genomes from environmental samples.

Ghaly and his team were curious if the prokaryotes might have similar gene exchange mechanisms to their distant bacterial relatives. If very different groups of organisms, like bacteria and archaea, are swapping genes, that could potentially help “the microbe that’s receiving a new function to occupy a new niche, and could have impacts on human and animal and plant health,” he adds. “Integrons are a big facilitator in the antibiotic resistance crisis . . . There are a lot of gene cassettes that are virulence genes or antibiotic resistance genes that could affect us negatively.” Some human methanogenic archaea are highly resistant to antibiotics, for example.

Bacteria swap genes in the form of a gene cassette that consists of a single gene and a gene recombination site called AttC. When they encounter stressful circumstances, bacteria exchange these cassettes like mixtapes, plugging them into and taking them out of their genomes.

To begin the DNA transfer process, bacteria use integron integrase (IntI), a protein in the tyrosine kinase family. Intl induces recombination between the gene cassette’s AttC site and a region on the bacterium’s genome called an integron attachment site, or AttI. Bacteria end up with a long string of gene cassettes, strung together by AttC sites, in their genomes.

On the bacterial genome, integrons consist of a gene for an IntI protein, Int, followed by a series of integrated gene cassettes. In the new study, the researchers screened all publicly available genomes of archaea, 95 percent of which were MAGs. They searched for AttC-like sequences and for sequences coding for IntI-like proteins. The researchers say they haven’t found a way to predict AttI sequences, and thus didn’t look for them.

In the nearly 6,700 archeal genomes they scanned, the researchers found 75, spanning nine phyla, that had evidence of integrons. All of the archaeal integrons had the same structure and components as bacterial integrons.

Based on the sequences they found, the researchers then synthesized archaeal AttC-containing cassettes and found that, when exposed, E. coli bacteria incorporated these cassettes into their genomes.

“It’s always interesting to find [horizontal gene transfer] in new organisms,” says Zhaxybayeva. She adds that, in the future, it would be useful to have a complete genome of a cultured archaea, as opposed to a constructed MAG as the team used in this study, and begin to piece together the mechanism behind the gene transfer. She’s particularly interested in whether archaea in the human gut have integrons, “and whether they participate in the exchange around antibiotic resistance.”

Combining host gene expression profiling and metagenomic pathogen detection from plasma nucleic acid enables accurate sepsis diagnosis

In a recent study published in Nature Microbiology, researchers developed integrated host-microbe plasma metagenomics to facilitate sepsis diagnosis.

Study: Integrated host-microbe plasma metagenomics for sepsis diagnosis in a prospective cohort of critically ill adults. Image Credit: Kateryna Kon/Shutterstock
Study: Integrated host-microbe plasma metagenomics for sepsis diagnosis in a prospective cohort of critically ill adults. Image Credit: Kateryna Kon/Shutterstock


Sepsis accounts for 20% of all fatalities worldwide and 20% to 50% of hospital deaths in the United States. For timely and effective antibiotic therapy crucial for sepsis survival, initial detection and identification of microbial infections are required. However, no etiologic pathogens are identified in more than 30% of cases. Distinguishing sepsis from non-infectious systemic disorders is essential since they frequently appear clinically similar during hospitalization.

About the study

In the present study, researchers created a sepsis diagnostic tool that combined host transcriptional profiling along with broad-range pathogen identification.

At two tertiary care hospitals, the team conducted a prospective observational examination of critically ill adult patients admitted to the intensive care unit (ICU) from the emergency department (ED). Patients were divided into five subgroups based on the presence or absence of sepsis. These patients included those who had: (1) clinically adjudicated sepsis as well as confirmed bacterial bloodstream infection (SepsisBSI); (2) clinically adjudicated sepsis as well as a confirmed non-bloodstream infection (Sepsisnon-BSI); (3) suspected sepsis characterized with negative clinical microbiological testing (Sepsissuspected); (4) patients having no evidence of sepsis and an explanation for their critical disease (No-sepsis); or (5) patients with an indeterminate status (Indeterm).

By conducting ribonucleic acid (RNA) sequencing on whole blood samples, the team first examined transcriptional variations between patients having clinically and microbiologically proven sepsis and those without symptoms of infection. A technique called gene set enrichment analysis (GSEA) detects clusters of genes within a dataset with related biological functions.

A differential gene expression (DE) study across the SepsisBSI and Sepsisnon-BSI groups was conducted to identify further variations between sepsis patients with infections in the bloodstream versus peripheral sites. The team developed a universal sepsis diagnostic classifier based on whole-blood gene expression patterns in response to the practical requirement to diagnose sepsis in SepsisBSI as well as Sepsisnon-BSI patients. The team utilized a bagged support vector machine (bSVM) learning strategy to choose the genes that most successfully differentiated patients with sepsis (SepsisBSI and Sepsisnon-BSI) and those without sepsis (No-sepsis).

A median of 2.3 × 107 reads was acquired after sequencing the RNA from obtained patients whose plasma specimens were available. Furthermore, DE analysis was performed to determine if a biologically plausible signal could be used to differentiate patients who did and did not have sepsis.


Heart failure exacerbation, overdose/poisoning, cardiac arrest, and pulmonary embolism were the most frequently diagnosed conditions in the No-sepsis group. Irrespective of the subgroup, most patients required vasopressor support and mechanical ventilation. Patients in the SepsisBSI and Sepsisnon-BSI who had proven sepsis did not show any difference from No-sepsis patients with respect to age, sex, race, ethnicity, APACHE III score, immunocompromise, intubation status,  maximal white blood cell count, or 28-day mortality. In the group of patients without sepsis, all but one patient demonstrated two or more systemic inflammatory response syndrome (SIRS) criteria.

The study also revealed the downregulation of pathways linked to ribosomal RNA processing and translation along with the upregulation of genes involved in innate immune signaling and neutrophil degranulation in sepsis patients. Using DE analysis, the team found 5,227 genes. The Sepsisnon-BSI cohort displayed enrichment in genes associated with defensins, antimicrobial peptides, and G alpha signaling as well as other pathways. On the other hand, the SepsisBSI cohort showed enrichment in genes associated with immunoregulatory interactions between non-lymphoid and lymphoid cells and CD28 signaling, among other functions.

The bSVM model displayed a mean cross-validation area under the receiver operating characteristic (ROC) curve (AUC) of 0.81. Samples with transcript counts lower than the quality control (QC) threshold had a lower mean input mass than samples with sufficient counts.

Interestingly, a number of differentially expressed genes have been identified as sepsis biomarkers, including increased CD177, repressed human leukocyte antigen – DR isotype (HLA-DRA), indicating a biologically significant transcriptome signature from plasma RNA. In the Sepsisnon-BSI group, plasma deoxyribonucleic acid (DNA) metagenomic next-generation sequencing (mNGS) revealed three out of eight bacterial urinary tract infection (UTI) pathogens and two out of 25 bacterial lower respiratory tract infection (LRTI) pathogens. None of the three patients with severe colitis caused by C. difficile had this pathogen. In eight out of 73 patients with proven sepsis, additional potential bacterial pathogens not identified by culture were found.


Overall, the study findings showed that reliable sepsis diagnosis is facilitated by the combination of host gene expression profiling with metagenomic pathogen identification from plasma nucleic acid. Future research is required to verify and gauge the therapeutic utility of this culture-independent diagnostic strategy.

Journal reference: