Patient-derived xenograft (PDX) tumor models represent a valuable platform for identifying new biomarkers and novel targets, to evaluate therapy response and resistance mechanisms. This study aimed at establishment, characterization and therapy testing of colorectal carcinoma-derived PDX. We generated 49 PDX and validated identity between patient tumor and corresponding PDX. Sensitivity of PDX toward conventional and targeted drugs revealed that 92% of PDX responded toward irinotecan, 45% toward 5-FU, 65% toward bevacizumab, and 61% toward cetuximab. Expression of epidermal growth factor receptor (EGFR) ligands correlated to the sensitivity toward cetuximab. Proto-oncogene B-RAF, EGFR, Kirsten rat sarcoma virus oncogene homolog gene copy number correlated positively with cetuximab and erlotinib sensitivity. The mutational analyses revealed an individual mutational profile of PDX and mainly identical profiles of PDX from primary tumor vs corresponding metastasis. Mutation in PIK3CA was a determinant of accelerated tumor doubling time. PDX with wildtype Kirsten rat sarcoma virus oncogene homolog, proto-oncogene B-RAF, and phosphatidylinositol-4,5-bisphosphate 3-kinaseM catalytic subunit alfa showed higher sensitivity toward cetuximab and erlotinib. To study the molecular mechanism of cetuximab resistance, cetuximab resistant PDX models were generated, and changes in HER2, HER3, betacellulin, transforming growth factor alfa were observed. Global proteome and phosphoproteome profiling showed a reduction in canonical EGFR-mediated signaling via PTPN11 (SHP2) and AKT1S1 (PRAS40) and an increase in anti-apoptotic signaling as a consequence of acquired cetuximab resistance. This demonstrates that PDX models provide a multitude of possibilities to identify and validate biomarkers, signaling pathways and resistance mechanisms for clinically relevant improvement in cancer therapy.

Data-independent acquisition (DIA) is increasingly preferred over data-dependent acquisition due to its higher throughput and fewer missing values. Whereas data-dependent acquisition often uses stable isotope labeling to improve quantification, DIA mostly relies on label-free approaches. Efforts to integrate DIA with isotope labeling include chemical methods like mass differential tags for relative and absolute quantification and dimethyl labeling, which, while effective, complicate sample preparation. Stable isotope labeling by amino acids in cell culture (SILAC) achieves high labeling efficiency through the metabolic incorporation of heavy labels into proteins in vivo. However, the need for metabolic incorporation limits the direct use in clinical scenarios and certain high-throughput experiments. Spike-in SILAC (SiS) methods use an externally generated heavy sample as an internal reference, enabling SILAC-based quantification even for samples that cannot be directly labeled. Here, we combine DIA-SiS, leveraging the robust quantification of SILAC without the complexities associated with chemical labeling. We developed DIA-SiS and rigorously assessed its performance with mixed-species benchmark samples on bulk and single cell-like amount level. We demonstrate that DIA-SiS substantially improves proteome coverage and quantification compared to label-free approaches and reduces incorrectly quantified proteins. Additionally, DIA-SiS proves effective in analyzing proteins in low-input formalin-fixed paraffin-embedded tissue sections. DIA-SiS combines the precision of stable isotope-based quantification with the simplicity of label-free sample preparation, facilitating simple, accurate, and comprehensive proteome profiling.

The collapse of protein homeostasis manifests itself in a toxic protein aggregation cascade, which is associated with degenerative diseases and aging. To solubilize aggregates, dedicated protein disaggregases exist in unicellular organisms, but these have no nuclear/cytosolic orthologs in metazoa. Alternative metazoan disaggregation machines have been described, but how these are operated and regulated in vivo remained unknown. We show that protein disaggregases are functionally diversified in human cells to efficiently target different types of stress-induced aggregates in sequential and temporally distinct phases. In particular, we show the selective assembly of an Hsp70-DNAJA1-DNAJB1 trimeric disaggregase that forms during late phase of stress recovery., i.e. , after VCP-dependent solubilization of non-native proteins that accumulate in cellular condensates such as nucleoli or stress granules. When activated, the trimeric disaggregase provides resistance to stress toxicity and contributes to amyloid disposal. Strikingly, this disaggregase collapses early in cells undergoing replicative aging with important underlining pathophysiological consequences.

Protein glycosylation is an essential post-translational modification in all domains of life. Its impairment in humans can result in severe diseases named congenital disorders of glycosylation (CDGs). Most of the glycosyltransferases (GTs) responsible for proper glycosylation are polytopic membrane proteins that represent challenging targets in proteomics. We established a multiple reaction monitoring (MRM) assay to comprehensively quantify GTs involved in the processes of N-glycosylation and O- and C-mannosylation in the endoplasmic reticulum. High robustness was achieved by using an enriched membrane protein fraction of isotopically labeled HEK 293T cells as an internal protein standard. The analysis of primary skin fibroblasts from eight CDG type I patients with impaired ALG1, ALG2, and ALG11 genes, respectively, revealed a substantial reduction in the corresponding protein levels. The abundance of the other GTs, however, remained unchanged at the transcript and protein levels, indicating that there is no fail-safe mechanism for the early steps of glycosylation in the endoplasmic reticulum. The established MRM assay was shared with the scientific community via the commonly used open source Skyline software environment, including Skyline Batch for automated data analysis. We demonstrate that another research group could easily reproduce all analysis steps, even while using different LC-MS hardware.

Protein glycosylation is essential in all domains of life and its mutational impairment in humans can result in severe diseases named Congenital Disorders of Glycosylation (CDGs). Studies on molecular level are however challenging, because many glycosyltransferases in the endoplasmic reticulum (ER) are low abundance membrane proteins. We established a comprehensive multiple reaction monitoring (MRM) assay to quantify most human glycosyltransferases involved in the processes of N -glycosylation, O - and C -mannosylation in the ER. To increase reproducibility, a membrane protein fraction of isotopically labeled HEK 293T cells was used as an internal standard. With this internal standard the MRM assay is easily transferable between laboratories. 22 glycosyltransferases could be reliably quantified from whole cell lysates of HEK 293T cells, HeLa cells and skin fibroblast cell lines. We then analyzed fibroblasts derived from CDG type I patients with mutations in the ALG1 , ALG2 or ALG11 gene. Mutations in ALG1 or ALG2 gene strongly reduced the levels of the ALG1 and ALG2 protein, respectively. In contrast, the levels of all other glycosyltransferases remained unchanged, which was unexpected given evidence that the ALG1, ALG2 and ALG11 proteins form a stable complex. This study describes an efficient workflow for the development of MRM assays for low abundance proteins, establishes a ready-to-use tool for the comprehensive quantification of ER-localized glycosyltransferases and provides new insight into the organization of disease-relevant glycosylation processes.