Periodic Reporting for period 1 - MICK (Translational Microbiome Platform)
Período documentado: 2022-09-01 hasta 2023-08-31
The human microbiome, encompassing all resident microbes and their genetic elements, is unique to each person and plays a crucial role in health. Dysbiosis, or microbial imbalance, is a significant risk factor for numerous chronic diseases, accounting for a substantial disease burden in Europe. As these conditions become more prevalent worldwide, early management through microbiome analysis is vital for public health.
Moreover, those with chronic conditions are more susceptible to additional health burdens, including infectious diseases. The microbiome can serve as a biomarker for infectious disease monitoring and diagnosis, revolutionising pathogen identification, outbreak characterization, and antimicrobial treatment selection.
In areas where microbiome analysis is pivotal, healthcare practitioners should leverage it to provide personalised patient care. However, translating microbiome data into clinical insights is complex, costly, and time-consuming. Existing solutions are designed for experts, limiting accessibility for healthcare professionals and hindering routine clinical use.
Our solution, MICK©, bridges this gap by making microbiome analysis accessible to healthcare professionals. It is the first platform to enable real-time, automated microbiome analysis with evidence-based, AI-powered recommendations. MICK© accurately profiles the microbiome, extracts critical insights, and translates results into patient-specific treatment suggestions. It simplifies and economises personalised treatments, considering patient phenotype and genetic makeup, without requiring coding skills or extensive IT infrastructure.
MICK© caters to various healthcare professionals, especially doctors and geneticists in both the private and public sector, enhancing clinical decision-making, particularly for complex conditions.
By harnessing microbiome advancements, MICK© aims to reduce health disparities and unlock the potential to improve global health and longevity.
Moreover, those with chronic conditions are more susceptible to additional health burdens, including infectious diseases. The microbiome can serve as a biomarker for infectious disease monitoring and diagnosis, revolutionising pathogen identification, outbreak characterization, and antimicrobial treatment selection.
In areas where microbiome analysis is pivotal, healthcare practitioners should leverage it to provide personalised patient care. However, translating microbiome data into clinical insights is complex, costly, and time-consuming. Existing solutions are designed for experts, limiting accessibility for healthcare professionals and hindering routine clinical use.
Our solution, MICK©, bridges this gap by making microbiome analysis accessible to healthcare professionals. It is the first platform to enable real-time, automated microbiome analysis with evidence-based, AI-powered recommendations. MICK© accurately profiles the microbiome, extracts critical insights, and translates results into patient-specific treatment suggestions. It simplifies and economises personalised treatments, considering patient phenotype and genetic makeup, without requiring coding skills or extensive IT infrastructure.
MICK© caters to various healthcare professionals, especially doctors and geneticists in both the private and public sector, enhancing clinical decision-making, particularly for complex conditions.
By harnessing microbiome advancements, MICK© aims to reduce health disparities and unlock the potential to improve global health and longevity.
The key scientific and technical achievements defined in the EIC project proposal and performed during the first year of the project are related to 4 main areas:
1.Enhancement of MICK© Functional Metagenomics Analysis Algorithm: The study of the microbiome encompasses both taxonomic and functional profiling of samples, such as those from patients. Prior to assessing dysbiosis (an alteration in the normal microbiome), the availability of a comprehensive toolkit of bioinformatics tools and pipelines is of utmost importance. Our team has developed a bioinformatics pipeline that not only identifies Antimicrobial Resistant (AMR) genes but also characterizes all proteins and metabolic pathways within microbiome samples (during D1.1).
Additionally, taxonomic profiling of samples is critical for dysbiosis assessment. Our team has upgraded and automated genomics databases to include a maximum number of complete genomes of microorganisms within a specific microbiome; further, given the high importance of bacterial strains in health, we've developed a Machine Learning (ML) algorithm for accurate detection of bacterial strains and phages.
2. Development of a Functional Dysbiotic Index Scoring System: Dysbiosis assessment involves comparing microorganism abundances with a reference healthy population. Through project development (D1.5) MICK© algorithm now conducts stratification analysis to assess taxa abundances against the closest population, thus enhancing analysis precision. Additionally, we've concentrated on establishing a scoring system, the Dysbiosis Index (DI), which considers all biomarkers. The DI quantifies the degree of dysbiosis, with a higher DI indicating a greater degree of dysbiosis. This achievement holds key clinical significance, as some diseases may exhibit elevated biomarkers with an accumulative impact on the disease.
3. Software Modularity: A central focus of MICK© development (under D1.4) has been creating a scalable and maintainable solution through the incorporation of software modularity and customization. MICK© platform adheres to a design principle of modularity, breaking the software into smaller, interchangeable components. This approach yields flexibility and scalability. The platform features two layers of modularity: one at the technology level, structuring IT architecture with independent microservices, and the other at the service level. The latter comprises the "MICK© Dashboard," a user-friendly web application for healthcare providers, the "MICK© Engine," the backend infrastructure, and the "MICK© Portal," facilitating effective doctor-patient communication.
Scalability is a key element in MICK© development, as it allows the system to handle an increasing amount of data, targets and users without impacting its performance, thus allowing MICK© to grow and evolve.
4. Enhancement of MICK© Functional Automated Reports: As part of first-year development actions (D1.6) our team improved platform performance to optimize user utilization of clinical results. This enhancement involves technical improvements and new features within the MICK Dashboard, report generation enhancements in terms of capabilities and execution time, and optimization of report delivery, including email notifications and ( shortly ) patient access through the user portal.
1.Enhancement of MICK© Functional Metagenomics Analysis Algorithm: The study of the microbiome encompasses both taxonomic and functional profiling of samples, such as those from patients. Prior to assessing dysbiosis (an alteration in the normal microbiome), the availability of a comprehensive toolkit of bioinformatics tools and pipelines is of utmost importance. Our team has developed a bioinformatics pipeline that not only identifies Antimicrobial Resistant (AMR) genes but also characterizes all proteins and metabolic pathways within microbiome samples (during D1.1).
Additionally, taxonomic profiling of samples is critical for dysbiosis assessment. Our team has upgraded and automated genomics databases to include a maximum number of complete genomes of microorganisms within a specific microbiome; further, given the high importance of bacterial strains in health, we've developed a Machine Learning (ML) algorithm for accurate detection of bacterial strains and phages.
2. Development of a Functional Dysbiotic Index Scoring System: Dysbiosis assessment involves comparing microorganism abundances with a reference healthy population. Through project development (D1.5) MICK© algorithm now conducts stratification analysis to assess taxa abundances against the closest population, thus enhancing analysis precision. Additionally, we've concentrated on establishing a scoring system, the Dysbiosis Index (DI), which considers all biomarkers. The DI quantifies the degree of dysbiosis, with a higher DI indicating a greater degree of dysbiosis. This achievement holds key clinical significance, as some diseases may exhibit elevated biomarkers with an accumulative impact on the disease.
3. Software Modularity: A central focus of MICK© development (under D1.4) has been creating a scalable and maintainable solution through the incorporation of software modularity and customization. MICK© platform adheres to a design principle of modularity, breaking the software into smaller, interchangeable components. This approach yields flexibility and scalability. The platform features two layers of modularity: one at the technology level, structuring IT architecture with independent microservices, and the other at the service level. The latter comprises the "MICK© Dashboard," a user-friendly web application for healthcare providers, the "MICK© Engine," the backend infrastructure, and the "MICK© Portal," facilitating effective doctor-patient communication.
Scalability is a key element in MICK© development, as it allows the system to handle an increasing amount of data, targets and users without impacting its performance, thus allowing MICK© to grow and evolve.
4. Enhancement of MICK© Functional Automated Reports: As part of first-year development actions (D1.6) our team improved platform performance to optimize user utilization of clinical results. This enhancement involves technical improvements and new features within the MICK Dashboard, report generation enhancements in terms of capabilities and execution time, and optimization of report delivery, including email notifications and ( shortly ) patient access through the user portal.
MICK results and related impact spans critical clinical areas:
1. Accurate Microbiome Profiling: MICK© delivers precise patient microbiome profiles and detects dysbiosis.
2. Evidence-Based Recommendations: MICK© offers AI-backed guidance to restore microbiome balance, including dietary interventions, probiotics, and FMT.
3. Pathogen Detection and Insights: MICK© provides high-spectrum pathogens detection, aiding in understanding pathogenesis and antimicrobial resistance.
4. Disease-Associated Signatures: MICK© supports the identification of disease-related microbiota signatures in early detection, disease monitoring and treatment evaluation.
5. MICK supports therapeutics companies in developing innovative microbiome-based treatments.
A key need to ensure further uptake and success of MICK is the access to equity capital for commercialization and internationalisation.
1. Accurate Microbiome Profiling: MICK© delivers precise patient microbiome profiles and detects dysbiosis.
2. Evidence-Based Recommendations: MICK© offers AI-backed guidance to restore microbiome balance, including dietary interventions, probiotics, and FMT.
3. Pathogen Detection and Insights: MICK© provides high-spectrum pathogens detection, aiding in understanding pathogenesis and antimicrobial resistance.
4. Disease-Associated Signatures: MICK© supports the identification of disease-related microbiota signatures in early detection, disease monitoring and treatment evaluation.
5. MICK supports therapeutics companies in developing innovative microbiome-based treatments.
A key need to ensure further uptake and success of MICK is the access to equity capital for commercialization and internationalisation.