Secreted retrovirus-like GAG-domain-containing protein PEG10 is regulated by UBE3A and is involved in Angelman syndrome pathophysiology.

Angelman syndrome (AS) is a neurodevelopmental disorder caused by the loss of maternal UBE3A, a ubiquitin protein ligase E3A. Here, we study neurons derived from patients with AS and neurotypical individuals, and reciprocally modulate UBE3A using antisense oligonucleotides. Unbiased proteomics reveal proteins that are regulated by UBE3A in a disease-specific manner, including PEG10, a retrotransposon-derived GAG protein. PEG10 protein increase, but not RNA, is dependent on UBE3A and proteasome function. PEG10 binds to both RNA and ataxia-associated proteins (ATXN2 and ATXN10), localizes to stress granules, and is secreted in extracellular vesicles, modulating vesicle content. Rescue of AS patient-derived neurons by UBE3A reinstatement or PEG10 reduction reveals similarity in transcriptome changes. Overexpression of PEG10 during mouse brain development alters neuronal migration, suggesting that it can affect brain development. These findings imply that PEG10 is a secreted human UBE3A target involved in AS pathophysiology.

Inducers of the endothelial cell barrier identified through chemogenomic screening in genome-edited hPSC-endothelial cells.

The blood-retina barrier and blood-brain barrier (BRB/BBB) are selective and semipermeable and are critical for supporting and protecting central nervous system (CNS)-resident cells. Endothelial cells (ECs) within the BRB/BBB are tightly coupled, express high levels of Claudin-5 (CLDN5), a junctional protein that stabilizes ECs, and are important for proper neuronal function. To identify novel CLDN5 regulators (and ultimately EC stabilizers), we generated a CLDN5-P2A-GFP stable cell line from human pluripotent stem cells (hPSCs), directed their differentiation to ECs (CLDN5-GFP hPSC-ECs), and performed flow cytometry-based chemogenomic library screening to measure GFP expression as a surrogate reporter of barrier integrity. Using this approach, we identified 62 unique compounds that activated CLDN5-GFP. Among them were TGF-ß pathway inhibitors, including RepSox. When applied to hPSC-ECs, primary brain ECs, and retinal ECs, RepSox strongly elevated barrier resistance (transendothelial electrical resistance), reduced paracellular permeability (fluorescein isothiocyanate-dextran), and prevented vascular endothelial growth factor A (VEGFA)-induced barrier breakdown in vitro. RepSox also altered vascular patterning in the mouse retina during development when delivered exogenously. To determine the mechanism of action of RepSox, we performed kinome-, transcriptome-, and proteome-profiling and discovered that RepSox inhibited TGF-ß, VEGFA, and inflammatory gene networks. In addition, RepSox not only activated vascular-stabilizing and barrier-establishing Notch and Wnt pathways, but also induced expression of important tight junctions and transporters. Taken together, our data suggest that inhibiting multiple pathways by selected individual small molecules, such as RepSox, may be an effective strategy for the development of better BRB/BBB models and novel EC barrier-inducing therapeutics.

MSstatsTMT: Statistical Detection of Differentially Abundant Proteins in Experiments with Isobaric Labeling and Multiple Mixtures.

Tandem mass tag (TMT) is a multiplexing technology widely-used in proteomic research. It enables relative quantification of proteins from multiple biological samples in a single MS run with high efficiency and high throughput. However, experiments often require more biological replicates or conditions than can be accommodated by a single run, and involve multiple TMT mixtures and multiple runs. Such larger-scale experiments combine sources of biological and technical variation in patterns that are complex, unique to TMT-based workflows, and challenging for the downstream statistical analysis. These patterns cannot be adequately characterized by statistical methods designed for other technologies, such as label-free proteomics or transcriptomics. This manuscript proposes a general statistical approach for relative protein quantification in MS- based experiments with TMT labeling. It is applicable to experiments with multiple conditions, multiple biological replicate runs and multiple technical replicate runs, and unbalanced designs. It is based on a flexible family of linear mixed-effects models that handle complex patterns of technical artifacts and missing values. The approach is implemented in MSstatsTMT, a freely available open-source R/Bioconductor package compatible with data processing tools such as Proteome Discoverer, MaxQuant, OpenMS, and SpectroMine. Evaluation on a controlled mixture, simulated datasets, and three biological investigations with diverse designs demonstrated that MSstatsTMT balanced the sensitivity and the specificity of detecting differentially abundant proteins, in large-scale experiments with multiple biological mixtures.

Selection of Features with Consistent Profiles Improves Relative Protein Quantification in Mass Spectrometry Experiments.

In bottom-up mass spectrometry-based proteomics, relative protein quantification is often achieved with data-dependent acquisition (DDA), data-independent acquisition (DIA), or selected reaction monitoring (SRM). These workflows quantify proteins by summarizing the abundances of all the spectral features of the protein (e.g. precursor ions, transitions or fragments) in a single value per protein per run. When abundances of some features are inconsistent with the overall protein profile (for technological reasons such as interferences, or for biological reasons such as post-translational modifications), the protein-level summaries and the downstream conclusions are undermined. We propose a statistical approach that automatically detects spectral features with such inconsistent patterns. The detected features can be separately investigated, and if necessary, removed from the data set. We evaluated the proposed approach on a series of benchmark-controlled mixtures and biological investigations with DDA, DIA and SRM data acquisitions. The results demonstrated that it could facilitate and complement manual curation of the data. Moreover, it can improve the estimation accuracy, sensitivity and specificity of detecting differentially abundant proteins, and reproducibility of conclusions across different data processing tools. The approach is implemented as an option in the open-source R-based software MSstats.

An Introduction to Machine Learning.

In the last few years, machine learning (ML) and artificial intelligence have seen a new wave of publicity fueled by the huge and ever-increasing amount of data and computational power as well as the discovery of improved learning algorithms. However, the idea of a computer learning some abstract concept from data and applying them to yet unseen situations is not new and has been around at least since the 1950s. Many of these basic principles are very familiar to the pharmacometrics and clinical pharmacology community. In this paper, we want to introduce the foundational ideas of ML to this community such that readers obtain the essential tools they need to understand publications on the topic. Although we will not go into the very details and theoretical background, we aim to point readers to relevant literature and put applications of ML in molecular biology as well as the fields of pharmacometrics and clinical pharmacology into perspective.

HtrA1 activation is driven by an allosteric mechanism of inter-monomer communication.

The human protease family HtrA is responsible for preventing protein misfolding and mislocalization, and a key player in several cellular processes. Among these, HtrA1 is implicated in several cancers, cerebrovascular disease and age-related macular degeneration. Currently, HtrA1 activation is not fully characterized and relevant for drug-targeting this protease. Our work provides a mechanistic step-by-step description of HtrA1 activation and regulation. We report that the HtrA1 trimer is regulated by an allosteric mechanism by which monomers relay the activation signal to each other, in a PDZ-domain independent fashion. Notably, we show that inhibitor binding is precluded if HtrA1 monomers cannot communicate with each other. Our study establishes how HtrA1 trimerization plays a fundamental role in proteolytic activity. Moreover, it offers a structural explanation for HtrA1-defective pathologies as well as mechanistic insights into the degradation of complex extracellular fibrils such as tubulin, amyloid beta and tau that belong to the repertoire of HtrA1.

Binding to SMN2 pre-mRNA-protein complex elicits specificity for small molecule splicing modifiers.

Small molecule splicing modifiers have been previously described that target the general splicing machinery and thus have low specificity for individual genes. Several potent molecules correcting the splicing deficit of the SMN2 (survival of motor neuron 2) gene have been identified and these molecules are moving towards a potential therapy for spinal muscular atrophy (SMA). Here by using a combination of RNA splicing, transcription, and protein chemistry techniques, we show that these molecules directly bind to two distinct sites of the SMN2 pre-mRNA, thereby stabilizing a yet unidentified ribonucleoprotein (RNP) complex that is critical to the specificity of these small molecules for SMN2 over other genes. In addition to the therapeutic potential of these molecules for treatment of SMA, our work has wide-ranging implications in understanding how small molecules can interact with specific quaternary RNA structures.

Assessment of Preclinical Liver and Skeletal Muscle Biomarkers Following Clofibrate Administration in Wistar Rats.

Clofibrate is a known rodent hepatotoxicant classically associated with hepatocellular hypertrophy and increased serum activities of cellular alanine aminotransferase/aspartate aminotransferase (ALT/AST) in the absence of microscopic hepatocellular degeneration. At toxic dose, clofibrate induces liver and skeletal muscle injury. The objective of this study was to assess novel liver and skeletal muscle biomarkers following clofibrate administration in Wistar rats at different dose levels for 7 days. In addition to classical biomarkers, liver injury was assessed by cytokeratin 18 (CK18) cleaved form, high-mobility group box 1, arginase 1 (ARG1), microRNA 122 (miR-122), and glutamate dehydrogenase. Skeletal muscle injury was evaluated with fatty acid binding protein 3 (Fabp3) and myosin light chain 3 (Myl3). Clofibrate-induced hepatocellular hypertrophy and skeletal muscle degeneration (type I rich muscles) were noted microscopically. CK, Fabp3, and Myl3 elevations correlated to myofiber degeneration. Fabp3 and Myl3 outperformed CK for detection of myofiber degeneration of minimal severity. miR-122 and ARG1 results were significantly correlated and indicated the absence of liver toxicity at low doses of clofibrate, despite increased ALT/AST activities. Moreover, combining classical and novel biomarkers (Fabp3, Myl3, ARG1, and miR-122) can be considered a valuable strategy for differentiating increased transaminases due to liver toxicity from skeletal muscle toxicity.

Implementing reusable software components for SNOMED CT diagram and expression concept representations.

SNOMED CT is a vital component in the future of semantic interoperability in healthcare as it provides the meaning to EHRs via its semantically rich, controlled terminology. Communicating the concepts of this terminology to both humans and machines is crucial therefore formal guidelines for diagram and expression representations have been developed by the curators of SNOMED CT. This paper presents a novel, model-based approach to implementing these guidelines that allows simultaneous editing of a concept via both diagram and expression editors. The implemented extensible software component can be embedded both both desktop and web applications.

Long noncoding RNAs are rarely translated in two human cell lines.

Data from the Encyclopedia of DNA Elements (ENCODE) project show over 9640 human genome loci classified as long noncoding RNAs (lncRNAs), yet only ~100 have been deeply characterized to determine their role in the cell. To measure the protein-coding output from these RNAs, we jointly analyzed two recent data sets produced in the ENCODE project: tandem mass spectrometry (MS/MS) data mapping expressed peptides to their encoding genomic loci, and RNA-seq data generated by ENCODE in long polyA+ and polyA- fractions in the cell lines K562 and GM12878. We used the machine-learning algorithm RuleFit3 to regress the peptide data against RNA expression data. The most important covariate for predicting translation was, surprisingly, the Cytosol polyA- fraction in both cell lines. LncRNAs are ~13-fold less likely to produce detectable peptides than similar mRNAs, indicating that ~92% of GENCODE v7 lncRNAs are not translated in these two ENCODE cell lines. Intersecting 9640 lncRNA loci with 79,333 peptides yielded 85 unique peptides matching 69 lncRNAs. Most cases were due to a coding transcript misannotated as lncRNA. Two exceptions were an unprocessed pseudogene and a bona fide lncRNA gene, both with open reading frames (ORFs) compromised by upstream stop codons. All potentially translatable lncRNA ORFs had only a single peptide match, indicating low protein abundance and/or false-positive peptide matches. We conclude that with very few exceptions, ribosomes are able to distinguish coding from noncoding transcripts and, hence, that ectopic translation and cryptic mRNAs are rare in the human lncRNAome.

Implementing an HL7 version 3 modeling tool from an Ecore model.

One of the main challenges of achieving interoperability using the HL7 V3 healthcare standard is the lack of clear definition and supporting tools for modeling, testing, and conformance checking. Currently, the knowledge defining the modeling is scattered around in MIF schemas, tools and specifications or simply with the domain experts. Modeling core HL7 concepts, constraints, and semantic relationships in Ecore/EMF encapsulates the domain-specific knowledge in a transparent way while unifying Java, XML, and UML in an abstract, high-level representation. Moreover, persisting and versioning the core HL7 concepts as a single Ecore context allows modelers and implementers to create, edit and validate message models against a single modeling context. The solution discussed in this paper is implemented in the new HL7 Static Model Designer as an extensible toolset integrated as a standalone Eclipse RCP application.

Content uniformity and assay requirements in current regulations.

The acceptance of a tablet batch is based both on the content uniformity test and on the assay. It is shown that these two characteristics are not independent, and the acceptance criteria for them are not even consistent. For content uniformity range three methods of calculation are compared: the present European Pharmacopoeia method, a tolerance range method with improved k tolerance factor and a one-way random effects analysis of variance model. To resolve the inconsistency several options are discussed: applying the holistic content uniformity range alone; using content uniformity standard deviation and assay mean simultaneously or applying a criterion based on Taguchi's quadratic loss function.