Gendered agism in the media industry: Disavowal, discrimination and the pushback.

This study aimed to understand the experiences of older women media professionals whose age and gender had prompted discriminatory behavior (gendered ageism) towards them by their line managers and/or employers. It draws on the testimonies provided by 24 women media professionals who self-identified as 'older' and who were interviewed for the work. All the participants had worked (and some still work) as journalists, presenters, producers or actors, and alongside diverse and routinised micro-aggressions, their experiences included having their contracts summarily terminated or not renewed, being manoeuvred out of front-of-camera roles, seen their career opportunities evaporate when they reached their 40s or even earlier, and been replaced by younger, 'fresher' women. However, some participants are fighting back by creating their own media and developing opportunities for other women to thrive.

KSFinder-a knowledge graph model for link prediction of novel phosphorylated substrates of kinases.

Background: Aberrant protein kinase regulation leading to abnormal substrate phosphorylation is associated with several human diseases. Despite the promise of therapies targeting kinases, many human kinases remain understudied. Most existing computational tools predicting phosphorylation cover less than 50% of known human kinases. They utilize local feature selection based on protein sequences, motifs, domains, structures, and/or functions, and do not consider the heterogeneous relationships of the proteins. In this work, we present KSFinder, a tool that predicts kinase-substrate links by capturing the inherent association of proteins in a network comprising 85% of the known human kinases. We also postulate the potential role of two understudied kinases based on their substrate predictions from KSFinder. Methods: KSFinder learns the semantic relationships in a phosphoproteome knowledge graph using a knowledge graph embedding algorithm and represents the nodes in low-dimensional vectors. A multilayer perceptron (MLP) classifier is trained to discern kinase-substrate links using the embedded vectors. KSFinder uses a strategic negative generation approach that eliminates biases in entity representation and combines data from experimentally validated non-interacting protein pairs, proteins from different subcellular locations, and random sampling. We assess KSFinder's generalization capability on four different datasets and compare its performance with other state-of-the-art prediction models. We employ KSFinder to predict substrates of 68 "dark" kinases considered understudied by the Illuminating the Druggable Genome program and use our text-mining tool, RLIMS-P along with manual curation, to search for literature evidence for the predictions. In a case study, we performed functional enrichment analysis for two dark kinases - HIPK3 and CAMKK1 using their predicted substrates. Results: KSFinder shows improved performance over other kinase-substrate prediction models and generalized prediction ability on different datasets. We identified literature evidence for 17 novel predictions involving an understudied kinase. All of these 17 predictions had a probability score ≥0.7 (nine at >0.9, six at 0.8-0.9, and two at 0.7-0.8). The evaluation of 93,593 negative predictions (probability ≤0.3) identified four false negatives. The top enriched biological processes of HIPK3 substrates relate to the regulation of extracellular matrix and epigenetic gene expression, while CAMKK1 substrates include lipid storage regulation and glucose homeostasis. Conclusions: KSFinder outperforms the current kinase-substrate prediction tools with higher kinase coverage. The strategically developed negatives provide a superior generalization ability for KSFinder. We predicted substrates of 432 kinases, 68 of which are understudied, and hypothesized the potential functions of two dark kinases using their predicted substrates.

The insertion sequence excision enhancer: A PrimPol-based primer invasion system for immobilizing transposon-transmitted antibiotic resistance genes.

Evolutionary studies often identify genes that have been exchanged between different organisms and the phrase Lateral or Horizontal Gene Transfer is often used in this context. However, they rarely provide any mechanistic information concerning how these gene transfers might have occurred. With the astonishing increase in the number of sequences in public databases over the past two or three decades, identical antibiotic resistance genes have been identified in many different sequence contexts. One explanation for this would be that genes are initially transmitted by transposons which have subsequently decayed and can no longer be detected. Here, we provide an overview of a protein, IEE (Insertion Sequence Excision Enhancer) observed to facilitate high-frequency excision of IS629 from clinically important Escherichia coli O157:H7 and subsequently shown to affect a large class of bacterial insertion sequences which all transpose using the copy-out-paste-in transposition mechanism. Excision depends on both IEE and transposase indicating association with the transposition process itself. We review genetic and biochemical data and propose that IEE immobilizes genes carried by compound transposons by removing the flanking insertion sequence (IS) copies. The biochemical activities of IEE as a primase with the capacity to recognize DNA microhomologies and the observation that its effect appears restricted to IS families which use copy-out-paste-in transposition, suggests IS deletion occurs by abortive transposition involving strand switching (primer invasion) during the copy-out step. This reinforces the proposal made for understanding the widespread phenomenon loss of ISApl1 flanking mcr-1 in the compound transposon Tn6330 which we illustrate with a detailed model. This model also provides a convincing way to explain the high levels of IEE-induced precise IS excision.

Distribution of [11C]-JNJ-42491293 in the marmoset brain: a positron emission tomography study.

JNJ-42491293 is a metabotropic glutamate 2 (mGlu2) positive allosteric modulator (PAM) that was radiolabelled with [11C]- to serve as a positron emission tomography (PET) ligand. Indeed, in vitro, the molecule displays high selectivity at mGlu2 receptors. However, PET experiments performed in rats, macaques and humans, have suggested that [11C]-JNJ-42491293 could interact with an unidentified, non-mGlu2 receptor binding site. The brain distribution of [11C]-JNJ-42491293 has not been determined in the brain of the common marmoset, a small non-human primate increasingly used in neuroscience research. Here, we investigated the distribution of [11C]-JNJ-42491293 in the marmoset brain. Three marmosets underwent brain magnetic resonance imaging (MRI) and 90-min dynamic PET scans with [11C]-JNJ-42491293 in combination with vehicle or the mGlu2 PAM AZD8529 (0.1, 1 and 10 mg/kg). In the scans in which [11C]-JNJ-42491293 was co-administered with vehicle, the brain areas with the highest standardised uptake values (SUVs) were the midbrain, cerebellum and thalamus, while the lowest SUVs were found in the pons. The addition of AZD8529 (0.1, 1 and 10 mg/kg) to [11C]-JNJ-42491293 did not modify the SUVs obtained with [11C]-JNJ-42491293 alone, and ex vivo blocking autoradiography with PAM AZD8529 (10, 100, 300 µM) on marmoset brain sections showed increased signals in the blocking conditions compared to vehicle, suggesting that no competition occurred between the 2 ligands. The results we obtained here do not suggest that [11C]-JNJ-42491293 interacts selectively, or even at all, with mGlu2 receptors in the marmoset, in agreement with findings previously reported in macaque and human.

Network models of protein phosphorylation, acetylation, and ubiquitination connect metabolic and cell signaling pathways in lung cancer.

We analyzed large-scale post-translational modification (PTM) data to outline cell signaling pathways affected by tyrosine kinase inhibitors (TKIs) in ten lung cancer cell lines. Tyrosine phosphorylated, lysine ubiquitinated, and lysine acetylated proteins were concomitantly identified using sequential enrichment of post translational modification (SEPTM) proteomics. Machine learning was used to identify PTM clusters that represent functional modules that respond to TKIs. To model lung cancer signaling at the protein level, PTM clusters were used to create a co-cluster correlation network (CCCN) and select protein-protein interactions (PPIs) from a large network of curated PPIs to create a cluster-filtered network (CFN). Next, we constructed a Pathway Crosstalk Network (PCN) by connecting pathways from NCATS BioPlanet whose member proteins have PTMs that co-cluster. Interrogating the CCCN, CFN, and PCN individually and in combination yields insights into the response of lung cancer cells to TKIs. We highlight examples where cell signaling pathways involving EGFR and ALK exhibit crosstalk with BioPlanet pathways: Transmembrane transport of small molecules; and Glycolysis and gluconeogenesis. These data identify known and previously unappreciated connections between receptor tyrosine kinase (RTK) signal transduction and oncogenic metabolic reprogramming in lung cancer. Comparison to a CFN generated from a previous multi-PTM analysis of lung cancer cell lines reveals a common core of PPIs involving heat shock/chaperone proteins, metabolic enzymes, cytoskeletal components, and RNA-binding proteins. Elucidation of points of crosstalk among signaling pathways employing different PTMs reveals new potential drug targets and candidates for synergistic attack through combination drug therapy.

The Glycan Structure Dictionary-a dictionary describing commonly used glycan structure terms.

Recent technological advances in glycobiology have resulted in a large influx of data and the publication of many papers describing discoveries in glycoscience. However, the terms used in describing glycan structural features are not standardized, making it difficult to harmonize data across biomolecular databases, hampering the harvesting of information across studies and hindering text mining and curation efforts. To address this shortcoming, the Glycan Structure Dictionary has been developed as a reference dictionary to provide a standardized list of widely used glycan terms that can help in the curation and mapping of glycan structures described in publications. Currently, the dictionary has 190 glycan structure terms with 297 synonyms linked to 3,332 publications. For a term to be included in the dictionary, it must be present in at least 2 peer-reviewed publications. Synonyms, annotations, and cross-references to GlyTouCan, GlycoMotif, and other relevant databases and resources are also provided when available. The purpose of this effort is to facilitate biocuration, assist in the development of text mining tools, improve the harmonization of search, and browse capabilities in glycoinformatics resources and help to map glycan structures to function and disease. It is also expected that authors will use these terms to describe glycan structures in their manuscripts over time. A mechanism is also provided for researchers to submit terms for potential incorporation. The dictionary is available at https://wiki.glygen.org/Glycan_structure_dictionary.

Overview of the COVID-19 text mining tool interactive demonstration track in BioCreative VII.

The coronavirus disease 2019 (COVID-19) pandemic has compelled biomedical researchers to communicate data in real time to establish more effective medical treatments and public health policies. Nontraditional sources such as preprint publications, i.e. articles not yet validated by peer review, have become crucial hubs for the dissemination of scientific results. Natural language processing (NLP) systems have been recently developed to extract and organize COVID-19 data in reasoning systems. Given this scenario, the BioCreative COVID-19 text mining tool interactive demonstration track was created to assess the landscape of the available tools and to gauge user interest, thereby providing a two-way communication channel between NLP system developers and potential end users. The goal was to inform system designers about the performance and usability of their products and to suggest new additional features. Considering the exploratory nature of this track, the call for participation solicited teams to apply for the track, based on their system's ability to perform COVID-19-related tasks and interest in receiving user feedback. We also recruited volunteer users to test systems. Seven teams registered systems for the track, and >30 individuals volunteered as test users; these volunteer users covered a broad range of specialties, including bench scientists, bioinformaticians and biocurators. The users, who had the option to participate anonymously, were provided with written and video documentation to familiarize themselves with the NLP tools and completed a survey to record their evaluation. Additional feedback was also provided by NLP system developers. The track was well received as shown by the overall positive feedback from the participating teams and the users. Database URL: https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vii/track-4/.

A knowledge graph representation learning approach to predict novel kinase-substrate interactions.

The human proteome contains a vast network of interacting kinases and substrates. Even though some kinases have proven to be immensely useful as therapeutic targets, a majority are still understudied. In this work, we present a novel knowledge graph representation learning approach to predict novel interaction partners for understudied kinases. Our approach uses a phosphoproteomic knowledge graph constructed by integrating data from iPTMnet, protein ontology, gene ontology and BioKG. The representations of kinases and substrates in this knowledge graph are learned by performing directed random walks on triples coupled with a modified SkipGram or CBOW model. These representations are then used as an input to a supervised classification model to predict novel interactions for understudied kinases. We also present a post-predictive analysis of the predicted interactions and an ablation study of the phosphoproteomic knowledge graph to gain an insight into the biology of the understudied kinases.

iPTMnet RESTful API for Post-translational Modification Network Analysis.

iPTMnet is a resource that combines rich information about protein post-translational modifications (PTM) from curated databases as well as text mining tools. Researchers can use the iPTMnet website to query, analyze and download the PTM data. In this chapter we describe the iPTMnet RESTful API which provides a way to streamline the integration of iPTMnet data into an automated data analysis workflow. In the first section, we give an overview of the architecture of the API. In the second section, we describe various function defined by the API and provide detailed examples of using these functions.

Radiosynthesis and In Vivo Evaluation of Four Positron Emission Tomography Tracer Candidates for Imaging of Melatonin Receptors.

Melatonin is a neurohormone that modulates several physiological functions in mammals through the activation of melatonin receptor type 1 and 2 (MT1 and MT2). The melatonergic system is an emerging therapeutic target for new pharmacological interventions in the treatment of sleep and mood disorders; thus, imaging tools to further investigate its role in the brain are highly sought-after. We aimed to develop selective radiotracers for in vivo imaging of both MT1 and MT2 by positron emission tomography (PET). We identified four previously reported MT ligands with picomolar affinities to the target based on different scaffolds which were also amenable for radiolabeling with either carbon-11 or fluorine-18. [11C]UCM765, [11C]UCM1014, [18F]3-fluoroagomelatine ([18F]3FAGM), and [18F]fluoroacetamidoagomelatine ([18F]FAAGM) have been synthesized in high radiochemical purity and evaluated in wild-type rats. All four tracers showed moderate to high brain permeability in rats with maximum standardized uptake values (SUVmax of 2.53, 1.75, 3.25, and 4.47, respectively) achieved 1-2 min after tracer administration, followed by a rapid washout from the brain. Several melatonin ligands failed to block the binding of any of the PET tracer candidates, while in some cases, homologous blocking surprisingly resulted in increased brain retention. Two 18F-labeled agomelatine derivatives were brought forward to PET scans in non-human primates and autoradiography on human brain tissues. No specific binding has been detected in blocking studies. To further investigate pharmacokinetic properties of the putative tracers, microsomal stability, plasma protein binding, log D, and membrane bidirectional permeability assays have been conducted. Based on the results, we conclude that the fast first pass metabolism by the enzymes in liver microsomes is the likely reason of the failure of our PET tracer candidates. Nevertheless, we showed that PET imaging can serve as a valuable tool to investigate the brain permeability of new therapeutic compounds targeting the melatonergic system.

High-yielding, automated radiosynthesis of [11 C]martinostat using [11 C]methyl triflate.

Histone deacetylases (HDACs) mediate epigenetic mechanisms implicated in a broad range of central nervous system dysfunction, including neurodegenerative diseases and neuropsychiatric disorders. [11 C]Martinostat allows in vivo quantification of class I/IIb HDACs and may be useful for the quantification of drug-occupancy relationship, facilitating drug development for disease modifying therapies. The present study reports a radiosynthesis of [11 C]martinostat using [11 C]methyl triflate in ethanol, as opposed to the originally described synthesis using [11 C]methyl iodide and DMSO. [11 C]Methyl triflate is trapped in a solution of 2 mg of precursor 1 dissolved in anhydrous ethanol (400 µl), reacted at ambient temperature for 5 min and purified by high-performance liquid chromatography; 1.5-1.8 GBq (41-48 mCi; n = 3) of formulated [11 C]martinostat was obtained from solid-phase extraction using a hydrophilic-lipophilic cartridge in a radiochemical yield of 11.4% ± 1.1% (nondecay corrected to trapped [11 C]MeI), with a molar activity of 369 ± 53 GBq/µmol (9.97 ± 1.3 Ci/µmol) at the end of synthesis (40 min) and validated for human use. This methodology was used at our production site to produce [11 C]martinostat in sufficient quantities of activity to scan humans, including losses incurred from decay during pre-release quality control testing.

Response from Varani et al. to "Comment on 'the IS6 family, a clinically important group of insertion sequences including IS26' by Ruth M. Hall".

The IS6 family of insertion sequences is a large but coherent group which was originally named to avoid confusion between a number of identical or nearly identical IS that were identified at about the same time and given different names (IS15D, IS26, IS46, IS140, IS160, IS176). The underlying common mechanistic feature of all IS6 family members which have been investigated is that they appear to transpose by replicative transposition and form pseudo compound transposons with the flanking IS in direct repeat and in which associated genes are simply transferred to the target replicon and lost from the donor.In the accompanying letter Hall raises a number of very serious and wide-ranging criticisms of our recent review article concerning the IS6 family of insertion sequences. She clearly feels that we have undervalued her work and that we question or ignore certain of her in vivo results. This impression is almost certainly the result of the standard of proof we generally apply to mechanistic aspects of transposition where we think it important to identify transposition intermediates including the types of synaptic, strand cleavage and strand transfer complexes involved.

Co-registration of Imaging Modalities (MRI, CT and PET) to Perform Frameless Stereotaxic Robotic Injections in the Common Marmoset.

The common marmoset has emerged as a popular model in neuroscience research, in part due to its reproductive efficiency, genetic and neuroanatomical similarities to humans and the successful generation of transgenic lines. Stereotaxic procedures in marmosets are guided by 2D stereotaxic atlases, which are constructed with a limited number of animals and fail to account for inter-individual variability in skull and brain size. Here, we developed a frameless imaging-guided stereotaxic system that improves upon traditional approaches by using subject-specific registration of computed tomography (CT), magnetic resonance imaging (MRI) and positron emission tomography (PET) data to identify a surgical target, namely the putamen, in two marmosets. The skull surface was laser-scanned to create a point cloud that was registered to the 3D reconstruction of the skull from CT. Reconstruction of the skull, as well as of the brain from MR images, was crucial for surgical planning. Localisation and injection into the putamen was done using a 6-axis robotic arm controlled by a surgical navigation software (Brainsight™). Integration of subject-specific registration and frameless stereotaxic navigation allowed target localisation specific to each animal. Injection of alpha-synuclein fibrils into the putamen triggered progressive neurodegeneration of the nigro-striatal system, a key feature of Parkinson's disease. Four months post-surgery, a PET scan found evidence of nigro-striatal denervation, supporting accurate targeting of the putamen during co-registration and subsequent surgery. Our results suggest that this approach, coupled with frameless stereotaxic neuronavigation, is accurate in localising surgical targets and can be used to assess endpoints for longitudinal studies.

COVID-19 Knowledge Graph from semantic integration of biomedical literature and databases.

SUMMARY: The global response to the COVID-19 pandemic has led to a rapid increase of scientific literature on this deadly disease. Extracting knowledge from biomedical literature and integrating it with relevant information from curated biological databases is essential to gain insight into COVID-19 etiology, diagnosis and treatment. We used Semantic Web technology RDF to integrate COVID-19 knowledge mined from literature by iTextMine, PubTator and SemRep with relevant biological databases and formalized the knowledge in a standardized and computable COVID-19 Knowledge Graph (KG). We published the COVID-19 KG via a SPARQL endpoint to support federated queries on the Semantic Web and developed a knowledge portal with browsing and searching interfaces. We also developed a RESTful API to support programmatic access and provided RDF dumps for download. AVAILABILITY AND IMPLEMENTATION: The COVID-19 Knowledge Graph is publicly available under CC-BY 4.0 license at https://research.bioinformatics.udel.edu/covid19kg/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

TnCentral: a Prokaryotic Transposable Element Database and Web Portal for Transposon Analysis.

We describe here the structure and organization of TnCentral (https://tncentral.proteininformationresource.org/ [or the mirror link at https://tncentral.ncc.unesp.br/]), a web resource for prokaryotic transposable elements (TE). TnCentral currently contains ∼400 carefully annotated TE, including transposons from the Tn3, Tn7, Tn402, and Tn554 families; compound transposons; integrons; and associated insertion sequences (IS). These TE carry passenger genes, including genes conferring resistance to over 25 classes of antibiotics and nine types of heavy metal, as well as genes responsible for pathogenesis in plants, toxin/antitoxin gene pairs, transcription factors, and genes involved in metabolism. Each TE has its own entry page, providing details about its transposition genes, passenger genes, and other sequence features required for transposition, as well as a graphical map of all features. TnCentral content can be browsed and queried through text- and sequence-based searches with a graphic output. We describe three use cases, which illustrate how the search interface, results tables, and entry pages can be used to explore and compare TE. TnCentral also includes downloadable software to facilitate user-driven identification, with manual annotation, of certain types of TE in genomic sequences. Through the TnCentral homepage, users can also access TnPedia, which provides comprehensive reviews of the major TE families, including an extensive general section and specialized sections with descriptions of insertion sequence and transposon families. TnCentral and TnPedia are intuitive resources that can be used by clinicians and scientists to assess TE diversity in clinical, veterinary, and environmental samples. IMPORTANCE The ability of bacteria to undergo rapid evolution and adapt to changing environmental circumstances drives the public health crisis of multiple antibiotic resistance, as well as outbreaks of disease in economically important agricultural crops and animal husbandry. Prokaryotic transposable elements (TE) play a critical role in this. Many carry "passenger genes" (not required for the transposition process) conferring resistance to antibiotics or heavy metals or causing disease in plants and animals. Passenger genes are spread by normal TE transposition activities and by insertion into plasmids, which then spread via conjugation within and across bacterial populations. Thus, an understanding of TE composition and transposition mechanisms is key to developing strategies to combat bacterial pathogenesis. Toward this end, we have developed TnCentral, a bioinformatics resource dedicated to describing and exploring the structural and functional features of prokaryotic TE whose use is intuitive and accessible to users with or without bioinformatics expertise.

The IS6 family, a clinically important group of insertion sequences including IS26.

The IS6 family of bacterial and archaeal insertion sequences, first identified in the early 1980s, has proved to be instrumental in the rearrangement and spread of multiple antibiotic resistance. Two IS, IS26 (found in many enterobacterial clinical isolates as components of both chromosome and plasmids) and IS257 (identified in the plasmids and chromosomes of gram-positive bacteria), have received particular attention for their clinical impact. Although few biochemical data are available concerning the transposition mechanism of these elements, genetic studies have provided some interesting observations suggesting that members of the family might transpose using an unexpected mechanism. In this review, we present an overview of the family, the distribution and phylogenetic relationships of its members, their impact on their host genomes and analyse available data concerning the particular transposition pathways they may use. We also provide a mechanistic model that explains the recent observations on one of the IS6 family transposition pathways: targeted cointegrate formation between replicons.

Alterations in the Global Proteome and Phosphoproteome in Third Generation EGFR TKI Resistance Reveal Drug Targets to Circumvent Resistance.

Lung cancer is the leading cause of cancer mortality worldwide. The treatment of patients with lung cancer harboring mutant EGFR with orally administered EGFR tyrosine kinase inhibitors (TKI) has been a paradigm shift. Osimertinib and rociletinib are third-generation irreversible EGFR TKIs targeting the EGFR T790M mutation. Osimertinib is the current standard of care for patients with EGFR mutations due to increased efficacy, lower side effects, and enhanced brain penetrance. Unfortunately, all patients develop resistance. Genomic approaches have primarily been used to interrogate resistance mechanisms. Here we characterized the proteome and phosphoproteome of a series of isogenic EGFR-mutant lung adenocarcinoma cell lines that are either sensitive or resistant to these drugs, comprising the most comprehensive proteomic dataset resource to date to investigate third generation EGFR TKI resistance in lung adenocarcinoma. Unbiased global quantitative mass spectrometry uncovered alterations in signaling pathways, revealed a proteomic signature of epithelial-mesenchymal transition, and identified kinases and phosphatases with altered expression and phosphorylation in TKI-resistant cells. Decreased tyrosine phosphorylation of key sites in the phosphatase SHP2 suggests its inhibition, resulting in subsequent inhibition of RAS/MAPK and activation of PI3K/AKT pathways. Anticorrelation analyses of this phosphoproteomic dataset with published drug-induced P100 phosphoproteomic datasets from the Library of Integrated Network-Based Cellular Signatures program predicted drugs with the potential to overcome EGFR TKI resistance. The PI3K/MTOR inhibitor dactolisib in combination with osimertinib overcame resistance both in vitro and in vivo. Taken together, this study reveals global proteomic alterations upon third generation EGFR TKI resistance and highlights potential novel approaches to overcome resistance. SIGNIFICANCE: Global quantitative proteomics reveals changes in the proteome and phosphoproteome in lung cancer cells resistant to third generation EGFR TKIs, identifying the PI3K/mTOR inhibitor dactolisib as a potential approach to overcome resistance.

Protein ontology on the semantic web for knowledge discovery.

The Protein Ontology (PRO) provides an ontological representation of protein-related entities, ranging from protein families to proteoforms to complexes. Protein Ontology Linked Open Data (LOD) exposes, shares, and connects knowledge about protein-related entities on the Semantic Web using Resource Description Framework (RDF), thus enabling integration with other Linked Open Data for biological knowledge discovery. For example, proteins (or variants thereof) can be retrieved on the basis of specific disease associations. As a community resource, we strive to follow the Findability, Accessibility, Interoperability, and Reusability (FAIR) principles, disseminate regular updates of our data, support multiple methods for accessing, querying and downloading data in various formats, and provide documentation both for scientists and programmers. PRO Linked Open Data can be browsed via faceted browser interface and queried using SPARQL via YASGUI. RDF data dumps are also available for download. Additionally, we developed RESTful APIs to support programmatic data access. We also provide W3C HCLS specification compliant metadata description for our data. The PRO Linked Open Data is available at https://lod.proconsortium.org/ .

Understanding ER+ Breast Cancer Dormancy Using Bioinspired Synthetic Matrices for Long-Term 3D Culture and Insights into Late Recurrence.

Late recurrences of breast cancer are hypothesized to originate from disseminated tumor cells that re-activate after a long period of dormancy, ≥5 years for estrogen-receptor positive (ER+) tumors. An outstanding question remains as to what the key microenvironment interactions are that regulate this complex process, and well-defined human model systems are needed for probing this. Here, a robust, bioinspired 3D ER+ dormancy culture model is established and utilized to probe the effects of matrix properties for common sites of late recurrence on breast cancer cell dormancy. Formation of dormant micrometastases over several weeks is examined for ER+ cells (T47D, BT474), where the timing of entry into dormancy versus persistent growth depends on matrix composition and cell type. In contrast, triple negative cells (MDA-MB-231), associated with early recurrence, are not observed to undergo long-term dormancy. Bioinformatic analyses quantitatively support an increased "dormancy score" gene signature for ER+ cells (T47D) and reveal differential expression of genes associated with different biological processes based on matrix composition. Further, these analyses support a link between dormancy and autophagy, a potential survival mechanism. This robust model system will allow systematic investigations of other cell-microenvironment interactions in dormancy and evaluation of therapeutics for preventing late recurrence.

RESTful API for iPTMnet: a resource for protein post-translational modification network discovery.

iPTMnet is a bioinformatics resource that integrates protein post-translational modification (PTM) data from text mining and curated databases and ontologies to aid in knowledge discovery and scientific study. The current iPTMnet website can be used for querying and browsing rich PTM information but does not support automated iPTMnet data integration with other tools. Hence, we have developed a RESTful API utilizing the latest developments in cloud technologies to facilitate the integration of iPTMnet into existing tools and pipelines. We have packaged iPTMnet API software in Docker containers and published it on DockerHub for easy redistribution. We have also developed Python and R packages that allow users to integrate iPTMnet for scientific discovery, as demonstrated in a use case that connects PTM sites to kinase signaling pathways.