Abstract Robust characterization of the protein corona—the layer of proteins that spontaneously forms on the surface of nanoparticles immersed in biological fluids—is vital for prediction of the safety, biodistribution, and diagnostic/therapeutic efficacy of nanomedicines. Protein corona identity and abundance characterization is entirely dependent on liquid chromatography coupled to mass spectroscopy (LC-MS/MS), though the variability of this technique for the purpose of protein corona characterization remains poorly understood. Here we investigate the variability of LC-MS/MS workflows in analysis of identical aliquots of protein coronas by sending them to different proteomics core-facilities and analyzing the retrieved datasets. While the shared data between the cores correlate well, there is considerable heterogeneity in the data retrieved from different cores. Specifically, out of 4022 identified unique proteins, only 73 (1.8%) are shared across the core facilities providing semiquantitative analysis. These findings suggest that protein corona datasets cannot be easily compared across independent studies and more broadly compromise the interpretation of protein corona research, with implications in biomarker discovery as well as the safety and efficacy of our nanoscale biotechnologies.
more »
« less
Inference of Protein-Protein Interaction Networks from Liquid-Chromatography Mass-Spectrometry Data by Approximate Bayesian Computation-Sequential Monte Carlo Sampling
We propose a new algorithm for inference of protein-protein interaction (PPI) networks from noisy time series of Liquid- Chromatography Mass-Spectrometry (LC-MS) proteomic expression data based on Approximate Bayesian Computation
- Sequential Monte Carlo sampling (ABC-SMC). The algorithm is an extension of our previous framework PALLAS. The proposed algorithm can be easily modified to handle other complex models of expression data, such as LC-MS data, for which the likelihood function is intractable. Results based on synthetic time series of cytokine LC-MS measurements cor- responding to a prototype immunomic network demonstrate that our algorithm is capable of inferring the network topology accurately.
more »
« less
- Award ID(s):
- 1718924
- PAR ID:
- 10199142
- Date Published:
- Journal Name:
- 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP)
- Page Range / eLocation ID:
- 1 to 6
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
more » « less
Abstract The wide applications of liquid chromatography - mass spectrometry (LC-MS) in untargeted metabolomics demand an easy-to-use, comprehensive computational workflow to support efficient and reproducible data analysis. However, current tools were primarily developed to perform specific tasks in LC-MS based metabolomics data analysis. Here we introduce MetaboAnalystR 4.0 as a streamlined pipeline covering raw spectra processing, compound identification, statistical analysis, and functional interpretation. The key features of MetaboAnalystR 4.0 includes an auto-optimized feature detection and quantification algorithm for LC-MS1 spectra processing, efficient MS2 spectra deconvolution and compound identification for data-dependent or data-independent acquisition, and more accurate functional interpretation through integrated spectral annotation. Comprehensive validation studies using LC-MS1 and MS2 spectra obtained from standards mixtures, dilution series and clinical metabolomics samples have shown its excellent performance across a wide range of common tasks such as peak picking, spectral deconvolution, and compound identification with good computing efficiency. Together with its existing statistical analysis utilities, MetaboAnalystR 4.0 represents a significant step toward a unified, end-to-end workflow for LC-MS based global metabolomics in the open-source R environment.
-
Abstract Protein–protein interaction (PPI) networks represent complex intra-cellular protein interactions, and the presence or absence of such interactions can lead to biological changes in an organism. Recent network-based approaches have shown that a phenotype’s PPI network’s resilience to environmental perturbations is related to its placement in the tree of life; though we still do not know how or why certain intra-cellular factors can bring about this resilience. Here, we explore the influence of gene expression and network properties on PPI networks’ resilience. We use publicly available data of PPIs for E. coli , S. cerevisiae , and H. sapiens , where we compute changes in network resilience as new nodes (proteins) are added to the networks under three node addition mechanisms—random, degree-based, and gene-expression-based attachments. By calculating the resilience of the resulting networks, we estimate the effectiveness of these node addition mechanisms. We demonstrate that adding nodes with gene-expression-based preferential attachment (as opposed to random or degree-based) preserves and can increase the original resilience of PPI network in all three species, regardless of gene expression distribution or network structure. These findings introduce a general notion of prospective resilience , which highlights the key role of network structures in understanding the evolvability of phenotypic traits.more » « less
-
more » « less
Ciona intestinalis, a common sea squirt, exhibits lower reproductive success at the upper extreme of water temperatures it experiences in coastal New England. In order to understand the changes in protein expression associated with elevated temperatures, and possible response to global temperature change, we reared C. intestinalis from embryos to adults at 18°C (a temperature at which they reproduce normally at our collection site in Rhode Island) and 22°C (the upper end of the local temperature range). We then dissected ovaries from animals at each temperature, extracted protein, and measured proteomic levels using shotgun mass spectrometry (LC-MS/MS). 1532 proteins were detected at a 1% false discovery rate present in both temperature groups by our LC-MS/MS method. 62 of those proteins are considered up or downregulated according to our statistical criteria. Principal component analysis shows a clear distinction in protein expression pattern between the control (18°C) group and high temperature (22°C) group. Similar to previous studies, cytoskeletal and chaperone proteins are upregulated in the high temperature group. Unexpectedly, we find evidence that proteolysis is downregulated at the higher temperature. We propose a working model for the high temperature response in C. intestinalis ovaries whereby increased temperature induces upregulation of signal transduction pathways involving PTPN11 and CrkL and activating coordinated changes in the proteome, especially in large lipid transport proteins, cellular stress responses, cytoskeleton, and downregulation of energy metabolism.
-
more » « less
Abstract The biomanufacturing industry is advancing toward continuous processes that will involve longer culture durations and older cell ages. These upstream trends may bring unforeseen challenges for downstream purification due to fluctuations in host cell protein (HCP) levels. To understand the extent of HCP expression instability exhibited by Chinese hamster ovary (CHO) cells over these time scales, an industry‐wide consortium collaborated to develop a study to characterize age‐dependent changes in HCP levels across 30, 60, and 90 cell doublings, representing a period of approximately 60 days. A monoclonal antibody (mAb)‐producing cell line with bulk productivity up to 3 g/L in a bioreactor was aged in parallel with its parental CHO‐K1 host. Subsequently, both cell types at each age were cultivated in an automated bioreactor system to generate harvested cell culture fluid (HCCF) for HCP analysis. More than 1500 HCPs were quantified using complementary proteomic techniques, two‐dimensional electrophoresis (2DE) and liquid chromatography coupled with tandem mass spectrometry (LC‐MS/MS). While up to 13% of proteins showed variable expression with age, more changes were observed when comparing between the two cell lines with up to 47% of HCPs differentially expressed. A small subset (50 HCPs) with age‐dependent expression were previously reported to be problematic as high‐risk and/or difficult‐to‐remove impurities; however, the vast majority of these were downregulated with age. Our findings suggest that HCP expression changes over this time scale may not be as dramatic and pose as great of a challenge to downstream processing as originally expected but that monitoring of variably expressed problematic HCPs remains critical.
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/MLSP49062.2020.9231824
