✎ pip:fct16713_2024 [Molecular Microbiology and Infection Unit]

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====== RESONANCE - Promoting large-scale use of genomics in understanding bacterial pathogen dynamics and evolution ======

Acronym: RESONANCE

FCT reference: 2023.16713.ICDT/LISBOA2030-FEDER-00866000

Principal investigator: M. Ramirez

Total Funding: €249,436.80

Dates: 01-03-2026 to 27-02-2029

===== Summary =====
Identiﬁcation and tracking of bacterial pathogens are essential for outbreak investigations, epidemiological surveillance, and understanding pathogen evolution. The current proposal will create software facilitating the genomic surveillance of novel pathogens, expanding the discrimination of existing systems and facilitating leveraging the population information to design novel prophylactic and diagnostic approaches.

===== Summary =====
The identification and tracking of microbial strains or lineages have become indispensable for a multitude of applications, including outbreak investigations, epidemiological surveillance, and understanding microbial evolution. This information has been translated into more informed public health decisions, including measures for outbreak prevention and control or the development and application of new diagnostic and therapeutic approaches. Genomics quickly became the paradigm for these activities and assumed a pivotal role in public health preparedness, prevention, and response. High throughput sequencing (HTS), generating high-quality draft or complete microbial genomes, decentralized and revolutionized these activities. The SARS-CoV-2 pandemic universalized access to HTS and raised the awareness of the benefits that could be reaped from genomic pathogen data. The continuous recognition of novel bacterial pathogens and the emergence of novel bacterial strains of public health concern alert to the fact that although “pathogen X” is usually taken to refer to viral threats, we should also prepare to address potential bacterial pathogens X, as recognized recently by the [[https://cdn.who.int/media/docs/default-source/blue-print/agenda_pathogen-x-august-2022_v8.pdf|WHO]].

Genomic information is considered to offer the ultimate discriminatory power for lineage identification and is now the recognized gold standard, but the choice of suitable methods to extract meaningful information from genomic data remains challenging. Current approaches fall into two groups: they focus on the identification of single-nucleotide polymorphisms (SNPs), commonly by mapping short reads to a single reference genome; or take a high-resolution view of microbial genomes by targeting a predefined set of genes (schema) through a gene-by-gene allele calling approach. Although in principle SNPs could allow for a finer resolution, in practice they meet several challenges. On the other hand, [[https://pubmed.ncbi.nlm.nih.gov/35531659/|wg/cgMLST approaches were shown to be equally discriminant and useful in outbreak investigations]]. The allelic profiles generated by wg/cgMLST can be easily shared and compared between different laboratories, allowing distributed surveillance efforts to participate in a unified global picture of bacterial evolution and transmission if the same schema and allele calling pipeline are used, enabling global “precision public health” in real-time. This led to the adoption of wg/cgMLST by leading international agencies, such as [[10.2903/sp.efsa.2022.EN-7413|EFSA]], ECDC and the US CDC.

A unified wg/cgMLST-based surveillance was already shown to be able to [[doi.org/10.2903/sp.efsa.2023.EN-8538|detect unsuspected and transnational outbreaks]]. However, genomic surveillance systems need to be user friendly, able to handle large amounts of data and use modest computational resources if they are to allow a truly distributed surveillance effort. Commercial, closed source solutions, and server based solutions have problems of transparency, sustainability and confidentiality. We created [[https://pubmed.ncbi.nlm.nih.gov/41877275/|chewBBACA]] as an open source software (OSS) solution to this problem. However, the growth in available data, changes in software architecture and the possibility of extending the discrimination and usefulness of the software for understanding pathogen evolution has created new needs that we plan to address in the current project.
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