Integrated Bottom-Up and Top-Down Liquid Chromatography-Mass Spectrometry for Characterization of Recombinant Human Growth Hormone Degradation Products.

With the advent of biosimilars to the U.S. market, it is important to have better analytical tools to ensure product quality from batch to batch. In addition, the recent popularity of using a continuous process for production of biopharmaceuticals, the traditional bottom-up method, alone for product characterization and quality analysis is no longer sufficient. Bottom-up method requires large amounts of material for analysis and is labor-intensive and time-consuming. Additionally, in this analysis, digestion of the protein with enzymes such as trypsin could induce artifacts and modifications which would increase the complexity of the analysis. On the other hand, a top-down method requires a minimum amount of sample and allows for analysis of the intact protein mass and sequence generated from fragmentation within the instrument. However, fragmentation usually occurs at the N-terminal and C-terminal ends of the protein with less internal fragmentation. Herein, we combine the use of the complementary techniques, a top-down and bottom-up method, for the characterization of human growth hormone degradation products. Notably, our approach required small amounts of sample, which is a requirement due to the sample constraints of small scale manufacturing. Using this approach, we were able to characterize various protein variants, including post-translational modifications such as oxidation and deamidation, residual leader sequence, and proteolytic cleavage. Thus, we were able to highlight the complementarity of top-down and bottom-up approaches, which achieved the characterization of a wide range of product variants in samples of human growth hormone secreted from Pichia pastoris.

Identification of novel glycans with disialylated structures in α3 integrin from mouse kidney cells with the phenotype of polycystic kidney disease.

Autosomal dominant polycystic kidney disease (ADPKD) is a common genetic disorder caused by mutations in the Pkd1 or Pkd2 genes, in which large cysts replace normal kidney tissue, leading to end-stage kidney disease. In this study we have utilized a powerful nano-HPLC-mass spectrometric approach to characterize patterns of normal and abnormal N-linked glycosylation of α3 integrin subunit in Pkd1(-/-) cells derived from mouse kidneys. Higher molecular weight glycan structures with a different monosaccharide composition were observed at two sites, namely, Asn-925 and Asn-928 sites in α3 integrin isolated from Pkd1(+/+) cells compared with Pkd1(-/-) cells. In addition, an unusual and unique disialic acid glycan structure was observed solely in Pkd1(-/-) cells. Thus, these studies suggest that abnormal protein glycosylation may have a role on the pathogenesis of cyst formation in ADPKD.

Genome wide proteomics of ERBB2 and EGFR and other oncogenic pathways in inflammatory breast cancer.

In this study we selected three breast cancer cell lines (SKBR3, SUM149 and SUM190) with different oncogene expression levels involved in ERBB2 and EGFR signaling pathways as a model system for the evaluation of selective integration of subsets of transcriptomic and proteomic data. We assessed the oncogene status with reads per kilobase per million mapped reads (RPKM) values for ERBB2 (14.4, 400, and 300 for SUM149, SUM190, and SKBR3, respectively) and for EGFR (60.1, not detected, and 1.4 for the same 3 cell lines). We then used RNA-Seq data to identify those oncogenes with significant transcript levels in these cell lines (total 31) and interrogated the corresponding proteomics data sets for proteins with significant interaction values with these oncogenes. The number of observed interactors for each oncogene showed a significant range, e.g., 4.2% (JAK1) to 27.3% (MYC). The percentage is measured as a fraction of the total protein interactions in a given data set vs total interactors for that oncogene in STRING (Search Tool for the Retrieval of Interacting Genes/Proteins, version 9.0) and I2D (Interologous Interaction Database, version 1.95). This approach allowed us to focus on 4 main oncogenes, ERBB2, EGFR, MYC, and GRB2, for pathway analysis. We used bioinformatics sites GeneGo, PathwayCommons and NCI receptor signaling networks to identify pathways that contained the four main oncogenes and had good coverage in the transcriptomic and proteomic data sets as well as a significant number of oncogene interactors. The four pathways identified were ERBB signaling, EGFR1 signaling, integrin outside-in signaling, and validated targets of C-MYC transcriptional activation. The greater dynamic range of the RNA-Seq values allowed the use of transcript ratios to correlate observed protein values with the relative levels of the ERBB2 and EGFR transcripts in each of the four pathways. This provided us with potential proteomic signatures for the SUM149 and 190 cell lines, growth factor receptor-bound protein 7 (GRB7), Crk-like protein (CRKL) and Catenin delta-1 (CTNND1) for ERBB signaling; caveolin 1 (CAV1), plectin (PLEC) for EGFR signaling; filamin A (FLNA) and actinin alpha1 (ACTN1) (associated with high levels of EGFR transcript) for integrin signalings; branched chain amino-acid transaminase 1 (BCAT1), carbamoyl-phosphate synthetase (CAD), nucleolin (NCL) (high levels of EGFR transcript); transferrin receptor (TFRC), metadherin (MTDH) (high levels of ERBB2 transcript) for MYC signaling; S100-A2 protein (S100A2), caveolin 1 (CAV1), Serpin B5 (SERPINB5), stratifin (SFN), PYD and CARD domain containing (PYCARD), and EPH receptor A2 (EPHA2) for PI3K signaling, p53 subpathway. Future studies of inflammatory breast cancer (IBC), from which the cell lines were derived, will be used to explore the significance of these observations.