Modeling intra- and intercellular communication in the context of human cancer from high-throughput data

Understanding cell signaling is probably one of the biggest challenges in modern biology. Thanks to the advance in new technologies, like next generation sequencing and other high throughput techniques, commonly referred as omics technologies, researches can generate great amounts of comprehensive biological data. More recently, these technologies have advanced to the point where one can analyze genes or proteins in single cells, even with spatial resolution. The ability of these approaches to generate great amounts of data requires of complementary techniques to analyze it. Analyzing and contextualizing this amounts of data has provided great insight and development in our current understanding and treatment of many diseases. Current research on disease mechanisms focuses mainly on molecular processes in order to understand the underlying systems driving them. Many approaches have so far focused on intracellular signaling and it has not been until recent years that the role of cell-to-cell communication in health disorders has gained importance. With this goal in mind, I will be presenting approaches to model and analyze biological data accounting for intra- and intercellular communication. The models and analyses presented combine omics data with prior biological knowledge. For this, I rely and our in-house resource OmniPath to extract the relevant intra- and intercellular interactions.The analytical approaches presented in this thesis range from the more classical differential expression and gene set enrichment analysis to more advanced and recent machine learning methods. Thanks to the different strategies applied across different settings, I was able to extract relevant biological insights with applications in clinical and biological research. Despite the approaches presented in this thesis being mainly focused on cancer, these surely can be further extended and applicable in many other contexts

Integrated intra‐ and intercellular signaling knowledge for multicellular omics analysis

Abstract Molecular knowledge of biological processes is a cornerstone in omics data analysis. Applied to single‐cell data, such analyses provide mechanistic insights into individual cells and their interactions. However, knowledge of intercellular communication is scarce, scattered across resources, and not linked to intracellular processes. To address this gap, we combined over 100 resources covering interactions and roles of proteins in inter‐ and intracellular signaling, as well as transcriptional and post‐transcriptional regulation. We added protein complex information and annotations on function, localization, and role in diseases for each protein. The resource is available for human, and via homology translation for mouse and rat. The data are accessible via OmniPath’s web service (https://omnipathdb.org/), a Cytoscape plug‐in, and packages in R/Bioconductor and Python, providing access options for computational and experimental scientists. We created workflows with tutorials to facilitate the analysis of cell–cell interactions and affected downstream intracellular signaling processes. OmniPath provides a single access point to knowledge spanning intra‐ and intercellular processes for data analysis, as we demonstrate in applications studying SARS‐CoV‐2 infection and ulcerative colitis

Phosphoproteomics of primary AML patient samples reveals rationale for AKT combination therapy and p53 context to overcome selinexor resistance

Acute myeloid leukemia (AML) is a heterogeneous disease with variable patient responses to therapy. Selinexor, an inhibitor of nuclear export, has shown promising clinical activity for AML. To identify the molecular context for monotherapy sensitivity as well as rational drug combinations, we profile selinexor signaling responses using phosphoproteomics in primary AML patient samples and cell lines. Functional phosphosite scoring reveals that p53 function is required for selinexor sensitivity consistent with enhanced efficacy of selinexor in combination with the MDM2 inhibitor nutlin-3a. Moreover, combining selinexor with the AKT inhibitor MK-2206 overcomes dysregulated AKT-FOXO3 signaling in resistant cells, resulting in synergistic anti-proliferative effects. Using high-throughput spatial proteomics to profile subcellular compartments, we measure global proteome and phospho-proteome dynamics, providing direct evidence of nuclear translocation of FOXO3 upon combination treatment. Our data demonstrate the potential of phosphoproteomics and functional phosphorylation site scoring to successfully pinpoint key targetable signaling hubs for rational drug combinations

The proteome microenvironment determines the protective effect of preconditioning in cisplatin-induced acute kidney injury

Acute kidney injury (AKI) leads to significant morbidity and mortality; unfortunately, strategies to prevent or treat AKI are lacking. In recent years, several preconditioning protocols have been shown to be effective in inducing organ protection in rodent models. Here, we characterized two of these interventions—caloric restriction and hypoxic preconditioning—in a mouse model of cisplatin-induced AKI and investigated the underlying mechanisms by acquisition of multi-layered omic data (transcriptome, proteome, N-degradome) and functional parameters in the same animals. Both preconditioning protocols markedly ameliorated cisplatin-induced loss of kidney function, and caloric restriction also induced lipid synthesis. Bioinformatic analysis revealed mRNA-independent proteome alterations affecting the extracellular space, mitochondria, and transporters. Interestingly, our analyses revealed a strong dissociation of protein and RNA expression after cisplatin treatment that showed a strong correlation with the degree of damage. N-degradomic analysis revealed that most posttranscriptional changes were determined by arginine-specific proteolytic processing. This included a characteristic cisplatin-activated complement signature that was prevented by preconditioning. Amyloid and acute-phase proteins within the cortical parenchyma showed a similar response. Extensive analysis of disease-associated molecular patterns suggested that transcription-independent deposition of amyloid P-component serum protein may be a key component in the microenvironmental contribution to kidney damage. This proof-of-principle study provides new insights into the pathogenesis of cisplatin-induced AKI and the molecular mechanisms underlying organ protection by correlating phenotypic and multi-layered omics data