Industries · Infectious Diseases & Microbiome
Infectious Diseases & Microbiome
Multi-omics characterization of pathogens, host response and microbial ecosystems.
Industries · Infectious Diseases & Microbiome
Multi-omics characterization of pathogens, host response and microbial ecosystems.
Infection is not a single molecular event. The pathogen evolves, the host responds, and the surrounding microbial ecosystem can shift throughout disease progression or treatment.
GenXMap integrates pathogen genomics, host transcriptomics, microbiome and metagenomics, proteomics, and clinical data to characterize these changes and determine which molecular signals are associated with infection, disease severity, treatment response or progression.
A multi-layer approach to characterize infection from both sides of the interaction.
Understanding the pathogen requires more than identifying its presence.
Whole-genome and targeted sequencing, run on OMICS4, characterize pathogen populations and their evolution over time.
Analysis can include:
This provides a genomic view of how the pathogen changes alongside disease progression or therapeutic pressure.
In many infectious diseases, the surrounding microbial ecosystem is part of the biological response.
GenXMap combines targeted microbiome profiling and shotgun metagenomics depending on the required resolution.
For community-level characterization:
For deeper characterization:
Sequencing depth is adapted to the biological question, sample complexity and level of resolution required.
The host response provides the other side of the infection.
Bulk RNA sequencing can be used to characterize transcriptional changes associated with:
GenXMap identifies differentially expressed genes and biological pathways, then integrates these signals with pathogen and microbiome measurements to determine whether molecular changes converge on coherent biological processes.
The key question is not only what changed, but how the different molecular layers relate to one another.
GenXMap can integrate:
Pathogen genotype → microbial burden → host transcriptional response → immune response → clinical outcome
This allows molecular signals to be evaluated in their biological context rather than as isolated datasets.
For longitudinal studies, the same framework can be applied across time points to identify molecular changes associated with disease progression, treatment or pathogen evolution.
Transcriptomic changes do not always translate directly into protein-level changes.
Proteomic profiling adds a complementary layer of biological evidence, allowing GenXMap to examine whether transcriptional changes are reflected at the protein level and to identify biological processes supported by both datasets.
Proteomic data generated by mass spectrometry, including TIMS-TOF MS workflows, can be integrated with transcriptomic, genomic and microbiome data within the same analytical framework.
Molecular data become more informative when connected to the clinical context.
GenXMap integrates multi-omics measurements with relevant clinical and epidemiological metadata, including:
This makes it possible to distinguish molecular changes associated with infection from signals related to disease severity, treatment, patient variability or technical effects.
GenXMap identifies molecular signatures associated with clinically relevant phenotypes such as:
Where independent cohorts are available, candidate signatures can be tested in datasets that were not used for their initial discovery.
The objective is to determine whether a molecular signal is statistically supported, biologically coherent and reproducible across samples or cohorts.
A complete record of the study, including experimental design, methods, quality control, analytical parameters and statistical criteria.
Results across the relevant biological layers, including pathogen genomics, host transcriptomics, microbiome/metagenomics and proteomics.
Cross-layer analysis connecting molecular changes to biological processes, disease characteristics and clinical variables.
Every analytical step, filtering criterion and statistical decision is documented so that results can be reviewed, challenged and reproduced.
GenXMap can work from data generated within our own laboratory or from existing datasets produced by another laboratory or sequencing provider.
The analytical strategy is determined by the biological question, study design and available metadata—not by where the data were generated.
Different datasets, platforms and experiments can be integrated to identify converging biological signals while accounting for technical variation, batch effects and biological variability.
One infection. Multiple molecular layers.
Pathogen → Host → Microbiome → Immune response → Clinical outcome
Book the platforms on OMICS4Case studies for this industry are in preparation: get in touch to discuss a project directly.
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