Abstract
Leishmaniasis is a major public health problem, causing diseases ranging from self-healing skin lesions to life-threatening chronic infections. Understanding how Leishmania parasites evade the host defense system is crucial for understanding the different manifestations of the disease and for improving diagnostic tools and drug development. We performed high-resolution proteome profiling of Leishmania spp. across three species during macrophage infection and identified distinct temporal expression patterns. Clustering analysis revealed unique protein expression profiles for each Leishmania species, whereas pairwise enrichment analysis revealed specific up- and downregulation patterns at different infection stages. Our results confirmed known virulence factors and highlighted new ones, demonstrating how our dataset could be used. We validated the dataset by showing that deletion of putative L. mexicana virulence factors resulted in reduced stage differentiation capacity and infectivity.
For more information please visit our publication at PLOS Pathogens
Nicolas Hagedorn 1,+ , Albert Fradera-Sola 2,+ , Melina Mitnacht 1 , Tobias Gold 3 , Ulrike Schleicher 3 , Falk Butter 2,# Christian J. Janzen 1,#
+ Indicates equal contribution, # Indicates correspondance1 Department of Cell & Developmental Biology, Biocenter, University of Würzburg, Würzburg, 97074, Germany
2 Institute of Molecular Virology and Cell Biology, Freidrich-Loeffler-Institute, Greifswald - Insel Riems, 17493, Germany
3 Microbiology Institute-Clinical Microbiology, Immunology and Hygiene, University Hospital Erlangen and Friedrich-Alexander-University, Erlangen, 91054, Germany
Comments and bug reports to the following e-mail: albert.fraderasola@dkfz-heidelberg.de
Data selection
Here you can select which data to work with. First (1) you can filter out your data per species and timepoint. Then (2), a table showing all quantified proteins per species and timepoint is generated. You can search for proteins in the table using both the protein ID and the gene name. Once you find an interesting ID, you can click on the table to select it and highlight it at the proteome analysis (lower tab). Finally (3), you have an overview of which ID are you currently working with.
(1): Settings
(2): Quantified proteins
(3): Selected proteins:
PCA plot
HeatMap plot: Sample's Spearman Correlation
Boxplot: Proteome distribution along the time course
Dotplot: Selected ID statiscal significance
Volcano plot: Individual IDs fold change and p-value
Data selection
Here you can select which data to work with. First (1) you can filter out your data per species and timepoint. Then (2), a table showing all quantified proteins per species and timepoint is generated. You can search for proteins in the table using both the protein ID and the gene name. Once you find an interesting ID, you can click on the table to select it and highlight it at the proteome analysis (lower tab). Finally (3), you have an overview of which ID are you currently working with.
(1): Settings
(2): Quantified proteins
(3): Selected proteins:
PCA plot
HeatMap plot: Sample's Spearman Correlation
Boxplot: Proteome distribution along the time course
Dotplot: Selected ID statiscal significance
Volcano plot: Individual IDs fold change and p-value
Data selection
Here you can select which data to work with. First (1) you can filter out your data per species and timepoint. Then (2), a table showing all quantified proteins per species and timepoint is generated. You can search for proteins in the table using both the protein ID and the gene name. Once you find an interesting ID, you can click on the table to select it and highlight it at the proteome analysis (lower tab). Finally (3), you have an overview of which ID are you currently working with.
(1): Settings
(2): Quantified proteins
(3): Selected proteins:
PCA plot
HeatMap plot: Sample's Spearman Correlation
Boxplot: Proteome distribution along the time course
Dotplot: Selected ID statiscal significance
Volcano plot: Individual IDs fold change and p-value
Data selection
Here you can select which data to work with. First (1) you can filter out your data per species and timepoint. Then (2), a table showing all quantified proteins per species and timepoint is generated. You can search for proteins in the table using both the protein ID and the gene name. Once you find an interesting ID, you can click on the table to select it and highlight it at the proteome analysis (lower tab). Finally (3), you have an overview of which ID are you currently working with.
(1): Settings
(2): Proteins included in SOM analysis
(3): Selected proteins:
Data selection
Here you can select which data to work with. The table (1) shows all Leishmania spp. quantified proteins. You can search for proteins in the table using the protein ID and find, this way, its assigned OrthoMCL ID. Once you find an interesting ID, you can click on the table to select it and check its expression profile and the one for all its orthologs. Additionally (2) you have an overview of which ID are you currently working with.