What is Microbiome?

A microbiome is a community of microorganisms that can usually be found living together in any given habitat, with the most standard reference being the human body. The microbiome, composed of trillions of microorganisms including bacteria, viruses, fungi, and their genetic material, primarily resides in the gut but also exists on the skin, in the mouth, and other parts of the body. Research into the microbiome has significantly expanded our understanding of its crucial role in human health.

From aiming to tailor treatment regimens for personalized medicines and improving dietary nutrition to regulating the immune system, and combating antibiotic resistance, microbiome data can help unlock the most business value across several use cases in the health sector.

Digestive Health

Digestion and Nutrient Absorption: Gut microbiota helps break down complex carbohydrates, synthesize vitamins (e.g., B vitamins, vitamin K), and facilitate the absorption of nutrients.

Gut Barrier Function: A healthy microbiome maintains the integrity of the gut lining, preventing leaky gut syndrome and reducing the risk of systemic inflammation.

Immune System Regulation

Immune Development: Microbiota are essential for the development and function of the immune system. They help distinguish between harmful and benign substances.

Inflammation Control: Balanced microbiota produces anti-inflammatory compounds and regulates immune responses to prevent chronic inflammation.

Metabolic Health

Energy Balance: Gut bacteria influence energy extraction from food and are involved in metabolic processes that affect body weight.

Metabolite Production: Microbiota produces short-chain fatty acids (SCFAs) like butyrate, acetate, and propionate, which have various health benefits including anti-inflammatory properties and regulating blood sugar levels.

Mental Health

Gut-Brain Axis: There is bidirectional communication between the gut and the brain. Gut bacteria produce neurotransmitters (e.g., serotonin, dopamine) and metabolites that can affect mood, stress response, and cognitive functions.

Mental Disorders: Dysbiosis (imbalance in the microbiome) has been linked to conditions like depression, anxiety, and autism spectrum disorders.

Chronic Diseases

Autoimmune Diseases: Changes in the microbiome composition are associated with autoimmune conditions like rheumatoid arthritis, multiple sclerosis, and type 1 diabetes.

Gut bacteria influence lipid metabolism and have been linked to conditions like atherosclerosis through the production of metabolites like trimethylamine N-oxide (TMAO).

Replicating Research on Intestinal Microbiome During Enteric Infections

Integrating microbiome data across health research has become widely recognized as useful for many high-business-value applications. Microbial data can open new avenues for diagnosing and managing enteric infections, which have substantial health impacts globally. Here are some links to additional literature and videos, that can help readers garner a better understanding of the real-world use cases of the microbiome.

We have added some links to additional literature and videos in the references section that can help readers garner a better understanding of the real-world use cases of the microbiome.

Working with Microbiome Data Involves Challenges Due to:

1. The scale of the data, which can introduce data management challenges
2. The high dimensionality of the data which may pose challenges when analyzing with traditional statistical methods

These challenges are well-suited to modern big data systems and modern machine learning (ML) and artificial intelligence (AI) algorithms. In this technical blog, we aim to demonstrate how to do a real-world microbiome analysis using ML algorithms to give a concrete sense of what it takes to bring such capabilities into your organization. Our aim is to show, from a technical perspective, how to unlock business value from your microbiome data with cutting edge methods.

We will do this by going through a step-by-step replication of a published research paper by Manning, et al, entitled “Intestinal microbial communities associated with acute enteric infections and disease recovery". Among other things, this paper analyzes a published dataset and demonstrates specific patterns of microbial populations correlated with clinical symptoms. The paper enhances our understanding of how intestinal microbial communities change during an enteric infection and the identification of factors that influence these changes. Of course, this understanding is crucial for the development of novel prevention and treatment strategies.

The research paper uses AI ML techniques to define data sets and streamline key highlights from the data points. We replicated the whole methodology and the results of the research paper, step by step using our own ML algorithms. This demonstrates that other health sector enterprises can seamlessly do the same with their data sets, ensuring substantial health impacts.

Approach

The research paper we are focusing on demonstrates that an individual's health status can be predicted and determined from their microbial abundance data using advanced AI ML techniques. This is significant because it opens new avenues for diagnosing and managing enteric infections, which have substantial health impacts globally.

This technical blog breaks down the key points from the research paper while demonstrating how enterprises in the healthcare sector can unlock value from such data.

We also reproduced the methodology step-by-step to demonstrate how microbiome data analysis is conducted. We aim to deliver a concrete understanding of how you can replicate such capabilities across your organization.

Additionally, this blog will explain the methodology in detail and show how to reproduce these results using publicly available data. Organizations with similar datasets can unlock immense value using these techniques, and we can help you do just that.

Background

The intestinal microbiome is a complex network of microbes crucial for human health and pathogen prevention. Research comparing intestinal microbes in individuals with and without enteric infections (occurring in the intestines) helps identify microbes that influence intestinal health. By leveraging machine learning techniques, we can deepen our understanding of these microbial communities and their health impacts.

Key Insights

The step-by-step reproduction of the research paper explores the role of the intestinal microbiome in enteric infections, uncovering key findings such as:

Lower diversity in patients: Patients exhibited lower microbial species diversity compared to healthy individuals.

Variation in key phyla: Differences in community composition were primarily due to variations in the abundance of Proteobacteria, Bacteroidetes (a phylum of Gram-negative bacteria found in all ecosystems), and Firmicutes (type of bacteria that live in the human gut).

Post-infection recovery: Intestinal communities showed an increased abundance of Bacteroidetes and Firmicutes post-infection, resembling healthy communities.

Data Validation

For the sample size, stool was collected from 200 patients with enteric infections and 75 healthy family members. Additionally, stool samples from 13 patients were collected post-infection to observe microbial population changes. The dataset included microbial abundance data at various taxonomic levels (phylum, class, order, family, genus) along with the health status of the patient (healthy or infected) and the time of sample collection (pre- or post-infection).

The datasets to be used by the Machine Learning algorithms were obtained from the public repository associated with the research paper. The microbial community data was transformed into a format suitable for machine learning, with each row representing an individual and columns indicating microbial abundances and health status.

For "Target Variable Encoding", a binary target variable was created labeling individuals as "Healthy" or "Enteric Diseased (EDD)".

The dataset was split into training and testing sets, with the training set consisting of 152 “Enteric Diseased” patient samples and 61 healthy patient samples and the testing set consisting of 40 “Enteric Diseased” patient samples and 14 healthy patient samples.

A powerful and efficient machine learning model, called the XGBoost algorithm was used to classify individuals based on their microbial abundance composition. The XGBoost model was trained using the training dataset to learn the patterns and relationships between microbial abundances and health status.

Results

Metrics from Cambridge Technology's approach:

Confusion matrix: (rows represent true labels, and columns represent predicted labels)

	EDD	Healthy
EDD	35	1
Healthy	4	17

• Precision: 0.90
• Recall: 0.97
• F1-Score: 0.93
• Area under roc curve: 0.9616

Metrics from Pallavi Singh's research paper and github code: (rows represent true labels, and columns represent predicted labels)

	EDD	Healthy
EDD	39	1
Healthy	5	11

• Precision: 0.8863
• Recall: 0.9750
• F1-Score: 0.9285
• Area under roc curve: 0.9633

Our approach successfully reproduced the research paper's findings by leveraging our own machine learning expertise. In our findings, we were able to efficiently and accurately differentiate between healthy and “Enteric Diseased” patient samples based on their microbiome composition.

By automating the analysis of microbial abundance data, we uncovered subtle patterns and relationships that traditional methods might miss. Using the raw sequence data provided by Pallavi Singh from the original paper, we successfully reproduced the study's results with our own model using gradient-boosted decision trees. Our model achieved an F1-score of 93%, closely matching the results obtained by Singh. This validates the effectiveness of machine learning in analyzing high-dimensional microbiome data and constructing accurate health outcome models, both in the original research paper and our replication efforts.

Metrics from Cambridge Technology's Approach

Metrics from Pallavi Singh’s Research Paper

Such methodologies can significantly enhance predictive capabilities in clinical settings, enabling the automatic prediction of disease and health outcomes. This also opens the path for understanding longitudinal health aspects using microbiome data, thereby providing valuable insights for future health interventions.

Unlocking Value for Your Organization

By leveraging these techniques, your organization can unlock significant value from similar datasets, paving the way for improved health outcomes and targeted interventions. Contact us to learn how we can assist you in harnessing the power of AI for your microbiome research and beyond.

References

Singh P, Teal TK, Marsh TL, Tiedje JM, Mosci R, Jernigan K, Zell A, Newton DW, Salimnia H, Lephart P, Sundin D, Khalife W, Britton RA, Rudrik JT, Manning SD. Intestinal microbial communities associated with acute enteric infections and disease recovery. Microbiome. 2015 Sep 22;3:45. doi: 10.1186/s40168-015-0109-2. PMID: 26395244; PMCID: PMC4579588.

https://link.springer.com/chapter/10.1007/978-981-16-3156-6_1 https://pmc.ncbi.nlm.nih.gov/articles/PMC7043356/ https://www.youtube.com/watch?v=XCaTQzjX2rQ&t=1s https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4579588/ http://dx.doi.org/10.6084/m9.figshare.1447256 https://github.com/glbio-mlmb/MLMB_materials/tree/main/Session_I https://github.com/glbio-mlmb/MLMB_materials/blob/main/Session_I/MLMB_session_1_tutorial.R