A Proof of Principle Proteomic Study Detects Dystrophin in Human Plasma: Implications in DMD Diagnosis and Clinical Monitoring

Duchenne muscular dystrophy (DMD) is a rare neuromuscular disease caused by pathogenic variations in the DMD gene. There is a need for robust DMD biomarkers for diagnostic screening and to aid therapy monitoring. Creatine kinase, to date, is the only routinely used blood biomarker for DMD, although it lacks specificity and does not correlate with disease severity. To fill this critical gap, we present here novel data about dystrophin protein fragments detected in human plasma by a suspension bead immunoassay using two validated anti-dystrophin-specific antibodies. Using both antibodies, a reduction of the dystrophin signal is detected in a small cohort of plasma samples from DMD patients when compared to healthy controls, female carriers, and other neuromuscular diseases. We also demonstrate the detection of dystrophin protein by an antibody-independent method using targeted liquid chromatography mass spectrometry. This last assay detects three different dystrophin peptides in all healthy individuals analysed and supports our finding that dystrophin protein is detectable in plasma. The results of our proof-of-concept study encourage further studies in larger sample cohorts to investigate the value of dystrophin protein as a low invasive blood biomarker for diagnostic screening and clinical monitoring of DMD

Thin Film Growth and Device Fabrication of Iron-Based Superconductors

Iron-based superconductors have received much attention as a new family of high-temperature superconductors owing to their unique properties and distinct differences from cuprates and conventional superconductors. This paper reviews progress in thin film research on iron-based superconductors since their discovery for each of five material systems with an emphasis on growth, physical properties, device fabrication, and relevant bulk material properties.Comment: To appear in J. Phys. Soc. Jp

The human secretome

The proteins secreted by human cells (collectively referred to as the secretome) are important not only for the basic understanding of human biology but also for the identification of potential targets for future diagnostics and therapies. Here, we present a comprehensive analysis of proteins predicted to be secreted in human cells, which provides information about their final localization in the human body, including the proteins actively secreted to peripheral blood. The analysis suggests that a large number of the proteins of the secretome are not secreted out of the cell, but instead are retained intracellularly, whereas another large group of proteins were identified that are predicted to be retained locally at the tissue of expression and not secreted into the blood. Proteins detected in the human blood by mass spectrometry-based proteomics and antibody-based immuno-assays are also presented with estimates of their concentrations in the blood. The results are presented in an updated version 19 of the Human Protein Atlas in which each gene encoding a secretome protein is annotated to provide an open-access knowledge resource of the human secretome, including body-wide expression data, spatial localization data down to the single-cell and subcellular levels, and data about the presence of proteins that are detectable in the blood

Proteome wide protein production

Over a decade after the completion of the human genome, researchers around the world are still wondering what information is hidden in the genome. Although the sequences of all human genes are known, it is still almost impossible to determine much more than the primary protein structure from the coding sequence of a gene. As a result of that, the need for recombinantly produced proteins to study protein structure and function is greater than ever. The main objective of this thesis has been to improve protein production, particularly using Escherichia coli. To improve protein production in Escherichia coli there are a number of different parameters to consider. Two very important parameters in the process of protein production are transcription and translation. To study the influence of differences in transcription rate, target proteins with different characteristics were produced under control of three promoters of different strength (lacUV5, trc and T7). Analyzing the total amount of target protein as well as the amount of soluble protein demonstrated the benefits of using a strong promoter such as T7. However, protein production is also highly dependent on translational efficiency, and a drawback associated with the use of Escherichia coli as host strain is that codons rarely used in this host can have a negative effect on the translation. The influence of using a strain supplied with genes for rare codon tRNAs, such as Rosetta(DE3), instead of the standard host strain BL21(DE3), was therefore evaluated. By using Rosetta(DE3) an improved protein yield for many of the poorly produced proteins was achieved, but more importantly the protein purity was significantly increased for a majority of the proteins. For further understanding of the underlying causes of the positive effects of Rosetta(DE3), the improved purity was thoroughly studied. The cause of this improvement was explained by the fact that Rosetta(DE3) has a significantly better read through of the full sequence during translation and thereby less truncated versions of the full-length protein is formed.  Moreover, the effect of supplementation of rare tRNAs was shown to be highly dependent on the target gene sequence. Surprisingly, it was not the total number of rare codons that determined the benefit of using Rosetta(DE3), instead it was shown that rare arginine codons and to some extent also rare codon clusters had a much bigger impact on the final outcome. As a result of the increased interest in large-scale studies in the field of proteomics, the need for high-throughput protein production pipelines is greater than ever. For that purpose, a protein production pipeline that allows handling of nearly 300 different proteins per week was set up within the Swedish Human Protein Atlas project. This was achieved by major and minor changes to the original protocol including protein production, purification and analysis. By using this standard setup almost 300 different proteins can be produced weekly, with an overall success rate of 81%. To further improve the success rate it has been shown that by adding an initial screening step, prior high-throughput protein production, unnecessary protein production can be avoided. A plate based micro-scale screening protocol for parallel production and verification of 96 proteins was developed. In that, protein production was performed using the EnBase® cultivation technology followed by purification based on immobilized metal ion affinity chromatography. The protein products were finally verified using matrix-assisted laser desorption ionization time-of-flight MS. By using this method, proteins that will be poorly produced can be sorted out prior high-throughput protein production.QC 20131120</p

An Affibody Molecule Is Actively Transported into the Cerebrospinal Fluid via Binding to the Transferrin Receptor

The use of biotherapeutics for the treatment of diseases of the central nervous system (CNS) is typically impeded by insufficient transport across the blood&ndash;brain barrier. Here, we investigate a strategy to potentially increase the uptake into the CNS of an affibody molecule (ZSYM73) via binding to the transferrin receptor (TfR). ZSYM73 binds monomeric amyloid beta, a peptide involved in Alzheimer&rsquo;s disease pathogenesis, with subnanomolar affinity. We generated a tri-specific fusion protein by genetically linking a single-chain variable fragment of the TfR-binding antibody 8D3 and an albumin-binding domain to the affibody molecule ZSYM73. Simultaneous tri-specific target engagement was confirmed in a biosensor experiment and the affinity for murine TfR was determined to 5 nM. Blockable binding to TfR on endothelial cells was demonstrated using flow cytometry and in a preclinical study we observed increased uptake of the tri-specific fusion protein into the cerebrospinal fluid 24 h after injection

Machine learning in computational biology to accelerate high-throughput protein expression.

MotivationThe Human Protein Atlas (HPA) enables the simultaneous characterization of thousands of proteins across various tissues to pinpoint their spatial location in the human body. This has been achieved through transcriptomics and high-throughput immunohistochemistry-based approaches, where over 40 000 unique human protein fragments have been expressed in E. coli. These datasets enable quantitative tracking of entire cellular proteomes and present new avenues for understanding molecular-level properties influencing expression and solubility.ResultsCombining computational biology and machine learning identifies protein properties that hinder the HPA high-throughput antibody production pipeline. We predict protein expression and solubility with accuracies of 70% and 80%, respectively, based on a subset of key properties (aromaticity, hydropathy and isoelectric point). We guide the selection of protein fragments based on these characteristics to optimize high-throughput experimentation.Availability and implementationWe present the machine learning workflow as a series of IPython notebooks hosted on GitHub (https://github.com/SBRG/Protein_ML). The workflow can be used as a template for analysis of further expression and solubility [email protected] or [email protected] informationSupplementary data are available at Bioinformatics online

Machine learning in computational biology to accelerate high-throughput protein expression

MotivationThe Human Protein Atlas (HPA) enables the simultaneous characterization of thousands of proteins across various tissues to pinpoint their spatial location in the human body. This has been achieved through transcriptomics and high-throughput immunohistochemistry-based approaches, where over 40 000 unique human protein fragments have been expressed in E. coli. These datasets enable quantitative tracking of entire cellular proteomes and present new avenues for understanding molecular-level properties influencing expression and solubility.ResultsCombining computational biology and machine learning identifies protein properties that hinder the HPA high-throughput antibody production pipeline. We predict protein expression and solubility with accuracies of 70% and 80%, respectively, based on a subset of key properties (aromaticity, hydropathy and isoelectric point). We guide the selection of protein fragments based on these characteristics to optimize high-throughput experimentation.Availability and implementationWe present the machine learning workflow as a series of IPython notebooks hosted on GitHub (https://github.com/SBRG/Protein_ML). The workflow can be used as a template for analysis of further expression and solubility [email protected] or [email protected] informationSupplementary data are available at Bioinformatics online

Solid-phase cloning for high-throughput assembly of single and multiple DNA parts

We describe solid-phase cloning (SPC) for high-throughput assembly of expression plasmids. Our method allows PCR products to be put directly into a liquid handler for capture and purification using paramagnetic streptavidin beads and conversion into constructs by subsequent cloning reactions. We present a robust automated protocol for restriction enzyme based SPC and its performance for the cloning of &gt;60 000 unique human gene fragments into expression vectors. In addition, we report on SPC-based single-strand assembly for applications where exact control of the sequence between fragments is needed or where multiple inserts are to be assembled. In this approach, the solid support allows for head-to-tail assembly of DNA fragments based on hybridization and polymerase fill-in. The usefulness of head-to-tail SPC was demonstrated by assembly of &gt;150 constructs with up to four DNA parts at an average success rate above 80%. We report on several applications for SPC and we suggest it to be particularly suitable for high-throughput efforts using laboratory workstations.QC 20150203</p

An easy-to-use high-throughput selection system for the discovery of recombinant protein binders from alternative scaffold libraries

QC 20231026</p