Methods to identify protein targets of metal-based drugs

Metal-based anticancer agents occupy a distinct chemical space due to their particular coordination geometry and reactivity. Despite the initial DNA-targeting paradigm for this class of compounds, it is now clear that they can also be tuned to target proteins in cells, depending on the metal and ligand scaffold. Since metallodrug discovery is dominated by phenotypic screenings, tailored proteomics strategies were crucial to identify and validate protein targets of several investigative and clinically advanced metal-based drugs. Here, such experimental approaches are discussed, which showed that metallodrugs based on ruthenium, gold, rhenium and even platinum, can selectively and specifically target proteins with clear-cut down-stream effects. Target identification strategies are expected to support significantly the mechanism-driven clinical translation of metal-based drugs.     

Proteome Analysis of Human Neutrophil Granulocytes From Patients With Monogenic Disease Using Data-independent Acquisition

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Highlights

• •Data-independent acquisition proteomics analysis of naive neutrophils from patients with rare monogenic diseases.
• •Proteomics analysis helps guide targeted genetic diagnostics of patients for which routine clinical diagnostics proved inconclusive.

     

Drugging the undruggable proteins in cancer: A systems biology approach

In recent years, the research community has, with comprehensive systems biology approaches and related technologies, gained insight into the vast complexity of numerous cancers. These approaches allow an in-depth exploration that cannot be achieved solely using conventional low-throughput methods, which do not closely mimic the natural cellular environment. In this review, we discuss recent integrative multiple omics approaches for understanding and modulating previously identified ‘undruggable’ targets such as members of the RAS family, MYC, TP53, and various E3 ligases and deubiquitinases. We describe how these technologies have revolutionized drug discovery by overcoming an array of biological and technological challenges and how, in the future, they will be pivotal in assessing cancer states in individual patients, allowing for the prediction and application of personalized disease treatments.     

Combined metabolomics and proteomics reveals hypoxia as a cause of lower productivity on scale‐up to a 5000‐liter CHO bioprocess

Large‐scale bioprocessing is key to the successful manufacturing of a biopharmaceutical. However, cell viability and productivity are often lower in the scale‐up from laboratory to production. In this study, we analyzed CHO cells, which showed lower percent viabilities and productivity in a 5‐KL production scale bioreactor compared to a 20‐L bench‐top scale under seemingly identical process parameters. An increase in copper concentration in the media from 0.02 µM to 0.4 µM led to a doubling of percent viability in the production scale albeit still at a lower level than the bench‐top scale. Combined metabolomics and proteomics revealed the increased copper reduced the presence of reactive oxygen species (ROS) in the 5‐KL scale process. The reduction in oxidative stress was supported by the increased level of glutathione peroxidase in the lower copper level condition. The excess ROS was shown to be due to hypoxia (intermittent), as evidenced by the reduction in fibronectin with increased copper. The 20‐L scale showed much less hypoxia and thus less excess ROS generation, resulting in little to no impact to productivity with the increased copper in the media. The study illustrates the power of 'Omics in aiding in the understanding of biological processes in biopharmaceutical production.     

Integration,visualization and analysis of human interactome

Data integration and visualization are crucial to obtain meaningful hypotheses from the diversity of ‘omics’ fields and the large volume of heterogeneous and distributed data sets. In this review we focus on network analysis as a key technique to integrate, visualize and extrapolate relevant information from diverse data. We first describe challenges in integrating different types of data and then focus on systematically exploring network properties to gain insight into network function. We also describe the relationship between network structures and function of elements that form it. Next, we highlight the role of the interactome in connecting data derived from different experiments, and we stress the importance of network analysis to recognize interaction context-specific features. Finally, we present an example integration to demonstrate the value of the network approach in cancer research, and highlight the importance of dynamic data in the specific context of signaling pathways.     

Ezrin and HNRNP expression correlate with increased virus release rate and early onset of virus‐induced apoptosis of MDCK suspension cells

With the aim to adapt high‐yield adherent cell lines to suspension growth, Madin Darby canine kidney (MDCK) suspension cells were developed recently that achieved comparable influenza virus yields despite an early induction of apoptosis compared to the parental adherent cell line. For both cell lines, a comprehensive study under comparable infection conditions is performed comprising information on: time course of viral infection, antiviral state of cells, virus‐induced apoptosis, and virus‐induced cellular protein expression for early and late infection with influenza A/PuertoRico/8/34 H1N1. The proteomic analysis is performed with 2D differential gel electrophoreses followed by mass spectrometry. Based on flow cytometric data and on the differential expression of various stress and apoptosis‐related proteins, the earlier onset of virus‐induced apoptosis is confirmed for suspension cells. Surprisingly, the data indicated an increased virus release rate for suspension cells. These observations correlate with an increased expression of the apical marker protein ezrin, known to play a role in influenza‐induced cytoskeletal rearrangement, and the differential expression of heterogeneous nuclear ribonucleoproteins, known to bind actively influenza viral proteins and play a central role in regulating gene expression. Based on these findings, additional studies towards the design of MDCK suspension cells with further increase in influenza virus yields will be performed.     

KAGIANA: An Excel-Based Tool for Retrieving Summary Information on Arabidopsis Genes

Various public databases provide Arabidopsis gene informationvia the internet. It is useful to abstract information obtainedfrom such databases. We have developed the KAGIANA tool, whichallows a user to retrieve summary information obtained fromselective databases and to access pages for a gene of interestin those databases. The tool is based on Microsoft Excel andprovides several macro programs for gene expression analyses.It can assist plant biologists in accessing omics informationfor plant biology. The KAGIANA tool is freely available at http://pmnedo.kazusa.or.jp/kagiana/.