The transcriptomic analytical level determines the human monocyte-derived macrophage response toward either the infectious agent or the host.

Macrophages exhibit multifunctional activity and play a central role in the response to infectious agents. It is commonly accepted that the plasticity of the response of macrophages depends on the type of stimuli. Here we re-evaluate whether the macrophage response is only dependent on the stimulus. We analyzed the transcriptomic profile of monocyte-derived macrophages (MDMs) that were activated with several pathogens and multiple in vitro-stimulations. The transcriptomic data were normalized using matched-pair analysis. Further analysis showed a clustering association with (i) specific signatures of the infectious agent and its strategy as well as (ii) a preponderance of MDM overall responses related to individuals. Currently, the null hypothesis H0 is that the innate MDM response is globally associated with the pathogen. Our results reveal that the global innate MDM response is intrinsically and predominantly associated with the individual. Thus, the hypothesis is supported or negated depending on the transcriptomic analytical level. AUTHOR SUMMARY: In modern medicine, diagnosis is based on objective criteria. Scientists are focused on the common denominators indicative of an infection. Analytical studies are based on this oriented approach, which defines the null hypothesis H0: the host immune response depends on the stimulus. We observe that the macrophage response to a given pathogen represents <0.4% of the expressed transcripts. The events to which the remaining 99.6% of transcripts are associated remain unclear. We find that 10.3% of the genes modulated during the response to the stimulus are related to the individual. They represent the overall response of the host, which integrates two responses: one associated with the stimulus and the other associated with the individual.

Intertwining threshold settings, biological data and database knowledge to optimize the selection of differentially expressed genes from microarray.

BACKGROUND: Many tools used to analyze microarrays in different conditions have been described. However, the integration of deregulated genes within coherent metabolic pathways is lacking. Currently no objective selection criterion based on biological functions exists to determine a threshold demonstrating that a gene is indeed differentially expressed. METHODOLOGY/PRINCIPAL FINDINGS: To improve transcriptomic analysis of microarrays, we propose a new statistical approach that takes into account biological parameters. We present an iterative method to optimise the selection of differentially expressed genes in two experimental conditions. The stringency level of gene selection was associated simultaneously with the p-value of expression variation and the occurrence rate parameter associated with the percentage of donors whose transcriptomic profile is similar. Our method intertwines stringency level settings, biological data and a knowledge database to highlight molecular interactions using networks and pathways. Analysis performed during iterations helped us to select the optimal threshold required for the most pertinent selection of differentially expressed genes. CONCLUSIONS/SIGNIFICANCE: We have applied this approach to the well documented mechanism of human macrophage response to lipopolysaccharide stimulation. We thus verified that our method was able to determine with the highest degree of accuracy the best threshold for selecting genes that are truly differentially expressed.

"Pathogeno-proteomics".

Many scientists working on pathogens (viruses, bacteria, fungi, parasites) are betting heavily on data generated by longitudinal genomic-transcriptomic-proteomic studies to explain biochemical host-vector-pathogen interactions and thus to contribute to disease control. Availability of genome sequences of various organisms, from viruses to complex metazoans, led to the discovery of the functions of the genes themselves. The postgenomic era stimulated the development of proteomic and bioinformatics tools to identify the locations, functions, and interactions of the gene products in tissues and/or cells of living organisms. Because of the diversity of available methods and the level of integration they promote, proteomics tools are potentially able to resolve interesting issues specific not only to host-vector-pathogen interactions in cell immunobiology, but also to ecology and evolution, population biology, and adaptive processes. These new analytical tools, as all new tools, contain pitfalls directly related to experimental design, statistical treatment, and protein identification. Nevertheless, they offer the potency of building large protein-protein interaction networks for in silico analysis of novel biological entities named "interactomes," a way of modeling host-vector-pathogen interactions to define new interference strategies.