Large-scale survey and database of high affinity ligands for peptide recognition modules

Many proteins involved in signal transduction contain peptide recognition modules (PRMs) that recognize short linear motifs (SLiMs) within their interaction partners. Here, we used large-scale peptide-phage display methods to derive optimal ligands for 163 unique PRMs representing 79 distinct structural families. We combined the new data with previous data that we collected for the large SH3, PDZ, and WW domain families to assemble a database containing 7,984 unique peptide ligands for 500 PRMs representing 82 structural families. For 74 PRMs, we acquired enough new data to map the specificity profiles in detail and derived position weight matrices and binding specificity logos based on multiple peptide ligands. These analyses showed that optimal peptide ligands resembled peptides observed in existing structures of PRM-ligand complexes, indicating that a large majority of the phage-derived peptides are likely to target natural peptide-binding sites and could thus act as inhibitors of natural protein-protein interactions. The complete dataset has been assembled in an online database (http://www.prm-db.org) that will enable many structural, functional, and biological studies of PRMs and SLiMs.

netDx: Software for building interpretable patient classifiers by multi-'omic data integration using patient similarity networks

Patient classification based on clinical and genomic data will further the goal of precision medicine. Interpretability is of particular relevance for models based on genomic data, where sample sizes are relatively small (in the hundreds), increasing overfitting risk netDx is a machine learning method to integrate multi-modal patient data and build a patient classifier. Patient data are converted into networks of patient similarity, which is intuitive to clinicians who also use patient similarity for medical diagnosis. Features passing selection are integrated, and new patients are assigned to the class with the greatest profile similarity. netDx has excellent performance, outperforming most machine-learning methods in binary cancer survival prediction. It handles missing data – a common problem in real-world data – without requiring imputation. netDx also has excellent interpretability, with native support to group genes into pathways for mechanistic insight into predictive features.

The netDx Bioconductor package provides multiple workflows for users to build custom patient classifiers. It provides turnkey functions for one-step predictor generation from multi-modal data, including feature selection over multiple train/test data splits. Workflows offer versatility with custom feature design, choice of similarity metric; speed is improved by parallel execution. Built-in functions and examples allow users to compute model performance metrics such as AUROC, AUPR, and accuracy. netDx uses RCy3 to visualize top-scoring pathways and the final integrated patient network in Cytoscape. Advanced users can build more complex predictor designs with functional building blocks used in the default design. Finally, the netDx Bioconductor package provides a novel workflow for pathway-based patient classification from sparse genetic data.

Tempora: Cell trajectory inference using time-series single-cell RNA sequencing data

Single-cell RNA sequencing (scRNA-seq) can map cell types, states and transitions during dynamic biological processes such as tissue development and regeneration. Many trajectory inference methods have been developed to order cells by their progression through a dynamic process. However, when time series data is available, most of these methods do not consider the available time information when ordering cells and are instead designed to work only on a single scRNA-seq data snapshot. We present Tempora, a novel cell trajectory inference method that orders cells using time information from time-series scRNA-seq data. In performance comparison tests, Tempora inferred known developmental lineages from three diverse tissue development time series data sets, beating state of the art methods in accuracy and speed. Tempora works at the level of cell clusters (types) and uses biological pathway information to help identify cell type relationships. This approach increases gene expression signal from single cells, processing speed, and interpretability of the inferred trajectory. Our results demonstrate the utility of a combination of time and pathway information to supervise trajectory inference for scRNA-seq based analysis.

Single-cell RNA sequencing (scRNA-seq) enables an unparalleled ability to map the heterogeneity of dynamic multicellular processes, such as tissue development, tumor growth, wound response and repair, and inflammation. Multiple methods have been developed to order cells along a pseudotime axis that represents a trajectory through such processes using the concept that cells that are closely related in a lineage will have similar transcriptomes. However, time series experiments provide another useful information source to order cells, from earlier to later time point. By introducing a novel use of biological pathway prior information, our Tempora algorithm improves the accuracy and speed of cell trajectory inference from time-series scRNA-seq data as measured by reconstructing known developmental trajectories from three diverse data sets. By analyzing scRNA-seq data at the cluster (cell type) level instead of at the single-cell level and by using known pathway information, Tempora amplifies gene expression signals from one cell using similar cells in a cluster and similar genes within a pathway. This approach also reduces computational time and resources needed to analyze large data sets because it works with a relatively small number of clusters instead of a potentially large number of cells. Finally, it eases interpretation, via operating on a relatively small number of clusters which usually represent known cell types, as well as by identifying time-dependent pathways. Tempora is useful for finding novel insights in dynamic processes.

Dynamics of the cell-free DNA methylome of metastatic prostate cancer during androgen-targeting treatment

Aim: We examined methylation changes in cell-free DNA (cfDNA) in metastatic castration-resistant prostate cancer (mCRPC) during treatment. Patients & methods: Genome-wide methylation analysis of sequentially collected cfDNA samples derived from mCRPC patients undergoing androgen-targeting therapy was performed. Results: Alterations in methylation states of genes previously implicated in prostate cancer progression were observed and patients that maintained methylation changes throughout therapy tended to have a longer time to clinical progression. Importantly, we also report that markers associated with a highly aggressive form of the disease, neuroendocrine-CRPC, were associated with a faster time to clinical progression. Conclusion: Our findings highlight the potential of monitoring the cfDNA methylome during therapy in mCRPC, which may serve as predictive markers of response to androgen-targeting agents.

Towards reliable named entity recognition in the biomedical domain

MOTIVATION

Automatic biomedical named entity recognition (BioNER) is a key task in biomedical information extraction. For some time, state-of-the-art BioNER has been dominated by machine learning methods, particularly conditional random fields (CRFs), with a recent focus on deep learning. However, recent work has suggested that the high performance of CRFs for BioNER may not generalize to corpora other than the one it was trained on. In our analysis, we find that a popular deep learning-based approach to BioNER, known as bidirectional long short-term memory network-conditional random field (BiLSTM-CRF), is correspondingly poor at generalizing. To address this, we evaluate three modifications of BiLSTM-CRF for BioNER to improve generalization: improved regularization via variational dropout, transfer learning and multi-task learning.

RESULTS

We measure the effect that each strategy has when training/testing on the same corpus ('in-corpus' performance) and when training on one corpus and evaluating on another ('out-of-corpus' performance), our measure of the model's ability to generalize. We found that variational dropout improves out-of-corpus performance by an average of 4.62%, transfer learning by 6.48% and multi-task learning by 8.42%. The maximal increase we identified combines multi-task learning and variational dropout, which boosts out-of-corpus performance by 10.75%. Furthermore, we make available a new open-source tool, called Saber that implements our best BioNER models.

AVAILABILITY AND IMPLEMENTATION

Source code for our biomedical IE tool is available at https://github.com/BaderLab/saber. Corpora and other resources used in this study are available at https://github.com/BaderLab/Towards-reliable-BioNER.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Meta-analysis of gene expression profiles of lean and obese PCOS to identify differentially regulated pathways and risk of comorbidities

Polycystic ovary syndrome (PCOS) is a complex multigenic disorder and women with PCOS suffer from several comorbidities. Although, obesity is a known risk factor for PCOS, the incidence of lean women with PCOS is on the rise. A systematic and comparative study on lean and obese PCOS with respect to genes, pathways and comorbidity analysis has not been attempted so far. Analysis of differentially expressed genes (DEGs) across tissue types for lean and obese PCOS revealed that the majority of them were downregulated for lean and obese PCOS. Ovarian and endometrial tissues shared several commonly dysregulated genes, suggesting shared PCOS pathophysiology mechanisms exist across tissues. Several pathways for cellular homeostasis, such as inflammation and immune response, insulin signaling, steroidogenesis, hormonal and metabolic signaling, regulation of gonadotrophic hormone secretion, cell structure and signaling that are known to be affected in PCOS were found to be enriched in our gene expression analysis of lean and obese PCOS. The gene-disease network is denser for obese PCOS with a higher comorbidity score as compared to lean PCOS.

Generation of Functional Liver Sinusoidal Endothelial Cells from Human Pluripotent Stem-Cell-Derived Venous Angioblasts

Liver sinusoidal endothelial cells (LSECs) form a highly specialized microvasculature that plays a critical role in liver function and disease. To better understand this role, we developed a strategy to generate LSECs from human pluripotent stem cells (hPSCs) by first optimizing the specification of arterial and venous angioblasts and derivative endothelial populations. Induction of a LSEC-like fate by hypoxia, cyclic AMP (cAMP) agonism, and transforming growth factor β (TGF-β) inhibition revealed that venous endothelial cells responded more rapidly and robustly than the arterial cells to upregulate LSEC markers and functions in vitro. Upon intrahepatic transplantation in neonates, venous angioblasts engrafted the liver and generated mature, fenestrated LSECs with scavenger functions and molecular profiles of primary human LSECs. When transplanted into the liver of adult mice, angioblasts efficiently gave rise to mature LSECs with robust factor VIII (FVIII) production. Humanization of the murine liver with hPSC-derived LSECs provides a tractable system for studying the biology of this key liver cell type.

Disrupting Mitochondrial Copper Distribution Inhibits Leukemic Stem Cell Self-Renewal

Leukemic stem cells (LSCs) rely on oxidative metabolism and are differentially sensitive to targeting mitochondrial pathways, which spares normal hematopoietic cells. A subset of mitochondrial proteins is folded in the intermembrane space via the mitochondrial intermembrane assembly (MIA) pathway. We found increased mRNA expression of MIA pathway substrates in acute myeloid leukemia (AML) stem cells. Therefore, we evaluated the effects of inhibiting this pathway in AML. Genetic and chemical inhibition of ALR reduces AML growth and viability, disrupts LSC self-renewal, and induces their differentiation. ALR inhibition preferentially decreases its substrate COX17, a mitochondrial copper chaperone, and knockdown of COX17 phenocopies ALR loss. Inhibiting ALR and COX17 increases mitochondrial copper levels which in turn inhibit S-adenosylhomocysteine hydrolase (SAHH) and lower levels of S-adenosylmethionine (SAM), DNA methylation, and chromatin accessibility to lower LSC viability. These results provide insight into mechanisms through which mitochondrial copper controls epigenetic status and viability of LSCs.

Nanoparticle Uptake in a Spontaneous and Immunocompetent Woodchuck Liver Cancer Model

There is a tremendous focus on the application of nanomaterials for the treatment of cancer. Nonprimate models are conventionally used to assess the biomedical utility of nanomaterials. However, these animals often lack an intact immunological background, and the tumors in these animals do not develop spontaneously. We introduce a preclinical woodchuck hepatitis virus-induced liver cancer model as a platform for nanoparticle (NP)-based in vivo experiments. Liver cancer development in these out-bred animals occurs as a result of persistent viral infection, mimicking human hepatitis B virus-induced HCC development. We highlight how this model addresses key gaps associated with other commonly used tumor models. We employed this model to (1) track organ biodistribution of gold NPs after intravenous administration, (2) examine their subcellular localization in the liver, (3) determine clearance kinetics, and (4) characterize the identity of hepatic macrophages that take up NPs using RNA-sequencing (RNA-seq). We found that the liver and spleen were the primary sites of NP accumulation. Subcellular analyses revealed accumulation of NPs in the lysosomes of CD14⁺ cells. Through RNA-seq, we uncovered that immunosuppressive macrophages within the woodchuck liver are the major cell type that take up injected NPs. The woodchuck-HCC model has the potential to be an invaluable tool to examine NP-based immune modifiers that promote host anti-tumor immunity.

BraInMap Elucidates the Macromolecular Connectivity Landscape of Mammalian Brain

Connectivity webs mediate the unique biology of the mammalian brain. Yet, while cell circuit maps are increasingly available, knowledge of their underlying molecular networks remains limited. Here, we applied multi-dimensional biochemical fractionation with mass spectrometry and machine learning to survey endogenous macromolecules across the adult mouse brain. We defined a global "interactome" comprising over one thousand multi-protein complexes. These include hundreds of brain-selective assemblies that have distinct physical and functional attributes, show regional and cell-type specificity, and have links to core neurological processes and disorders. Using reciprocal pull-downs and a transgenic model, we validated a putative 28-member RNA-binding protein complex associated with amyotrophic lateral sclerosis, suggesting a coordinated function in alternative splicing in disease progression. This brain interaction map (BraInMap) resource facilitates mechanistic exploration of the unique molecular machinery driving core cellular processes of the central nervous system. It is publicly available and can be explored here https://www.bu.edu/dbin/cnsb/mousebrain/.

A reference map of the human binary protein interactome

Global insights into cellular organization and genome function require comprehensive understanding of the interactome networks that mediate genotype-phenotype relationships^1,2. Here, we present a human “all-by-all” reference interactome map of human binary protein interactions, or “HuRI”. With ~53,000 high-quality protein-protein interactions (PPIs), HuRI has approximately four times more such interactions than high-quality curated interactions from small-scale studies. Integrating HuRI with genome³, transcriptome⁴, and proteome⁵ data enables the study of cellular function within most physiological or pathological cellular contexts. We demonstrate the utility of HuRI in identifying specific subcellular roles of PPIs. Inferred tissue-specific networks reveal general principles for the formation of cellular context-specific functions and elucidate potential molecular mechanisms underlying tissue-specific phenotypes of Mendelian diseases. HuRI represents a systematic proteome-wide reference linking genomic variation to phenotypic outcomes.

Potential therapeutic targets for lung repair during human ex vivo lung perfusion

Introduction

The ex vivo lung perfusion (EVLP) technique has been developed to assess the function of marginal donor lungs and has significantly increased donor lung utilisation. EVLP has also been explored as a platform for donor lung repair through injury-specific treatments such as antibiotics or fibrinolytics. We hypothesised that actively expressed pathways shared between transplantation and EVLP may reveal common mechanisms of injury and potential therapeutic targets for lung repair prior to transplantation.

Materials and methods

Retrospective transcriptomics analyses were performed with peripheral tissue biopsies from “donation after brain death” lungs, with 46 pre-/post-transplant pairs and 49 pre-/post-EVLP pairs. Pathway analysis was used to identify and compare the responses of donor lungs to transplantation and to EVLP.

Results

22 pathways were enriched predominantly in transplantation, including upregulation of lymphocyte activation and cell death and downregulation of metabolism. Eight pathways were enriched predominantly in EVLP, including downregulation of leukocyte functions and upregulation of vascular processes. 27 pathways were commonly enriched, including activation of innate inflammation, cell death, heat stress and downregulation of metabolism and protein synthesis. Of the inflammatory clusters, Toll-like receptor/innate immune signal transduction adaptor signalling had the greatest number of nodes and was central to inflammation. These mechanisms have been previously speculated as major mechanisms of acute lung injury in animal models.

Conclusion

EVLP and transplantation share common molecular features of injury including innate inflammation and cell death. Blocking these pathways during EVLP may allow for lung repair prior to transplantation.

Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale1-3. Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter4; identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation5,6; analyses timings and patterns of tumour evolution7; describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity8,9; and evaluates a range of more-specialized features of cancer genomes8,10-18.

A network analysis to identify mediators of germline-driven differences in breast cancer prognosis

Identifying the underlying genetic drivers of the heritability of breast cancer prognosis remains elusive. We adapt a network-based approach to handle underpowered complex datasets to provide new insights into the potential function of germline variants in breast cancer prognosis. This network-based analysis studies ~7.3 million variants in 84,457 breast cancer patients in relation to breast cancer survival and confirms the results on 12,381 independent patients. Aggregating the prognostic effects of genetic variants across multiple genes, we identify four gene modules associated with survival in estrogen receptor (ER)-negative and one in ER-positive disease. The modules show biological enrichment for cancer-related processes such as G-alpha signaling, circadian clock, angiogenesis, and Rho-GTPases in apoptosis.

Pathway Commons 2019 Update: integration, analysis and exploration of pathway data

Pathway Commons (https://www.pathwaycommons.org) is an integrated resource of publicly available information about biological pathways including biochemical reactions, assembly of biomolecular complexes, transport and catalysis events and physical interactions involving proteins, DNA, RNA, and small molecules (e.g. metabolites and drug compounds). Data is collected from multiple providers in standard formats, including the Biological Pathway Exchange (BioPAX) language and the Proteomics Standards Initiative Molecular Interactions format, and then integrated. Pathway Commons provides biologists with (i) tools to search this comprehensive resource, (ii) a download site offering integrated bulk sets of pathway data (e.g. tables of interactions and gene sets), (iii) reusable software libraries for working with pathway information in several programming languages (Java, R, Python and Javascript) and (iv) a web service for programmatically querying the entire dataset. Visualization of pathways is supported using the Systems Biological Graphical Notation (SBGN). Pathway Commons currently contains data from 22 databases with 4794 detailed human biochemical processes (i.e. pathways) and ∼2.3 million interactions. To enhance the usability of this large resource for end-users, we develop and maintain interactive web applications and training materials that enable pathway exploration and advanced analysis.

ARGLU1 is a transcriptional coactivator and splicing regulator important for stress hormone signaling and development

Stress hormones bind and activate the glucocorticoid receptor (GR) in many tissues including the brain. We identified arginine and glutamate rich 1 (ARGLU1) in a screen for new modulators of glucocorticoid signaling in the CNS. Biochemical studies show that the glutamate rich C-terminus of ARGLU1 coactivates multiple nuclear receptors including the glucocorticoid receptor (GR) and the arginine rich N-terminus interacts with splicing factors and binds to RNA. RNA-seq of neural cells depleted of ARGLU1 revealed significant changes in the expression and alternative splicing of distinct genes involved in neurogenesis. Loss of ARGLU1 is embryonic lethal in mice, and knockdown in zebrafish causes neurodevelopmental and heart defects. Treatment with dexamethasone, a GR activator, also induces changes in the pattern of alternatively spliced genes, many of which were lost when ARGLU1 was absent. Importantly, the genes found to be alternatively spliced in response to glucocorticoid treatment were distinct from those under transcriptional control by GR, suggesting an additional mechanism of glucocorticoid action is present in neural cells. Our results thus show that ARGLU1 is a novel factor for embryonic development that modulates basal transcription and alternative splicing in neural cells with consequences for glucocorticoid signaling.

Sphingolipid Modulation Activates Proteostasis Programs to Govern Human Hematopoietic Stem Cell Self-Renewal

Cellular stress responses serve as crucial decision points balancing persistence or culling of hematopoietic stem cells (HSCs) for lifelong blood production. Although strong stressors cull HSCs, the linkage between stress programs and self-renewal properties that underlie human HSC maintenance remains unknown, particularly at quiescence exit when HSCs must also dynamically shift metabolic state. Here, we demonstrate distinct wiring of the sphingolipidome across the human hematopoietic hierarchy and find that genetic or pharmacologic modulation of the sphingolipid enzyme DEGS1 regulates lineage differentiation. Inhibition of DEGS1 in hematopoietic stem and progenitor cells during the transition from quiescence to cellular activation with N-(4-hydroxyphenyl) retinamide activates coordinated stress pathways that coalesce on endoplasmic reticulum stress and autophagy programs to maintain immunophenotypic and functional HSCs. Thus, our work identifies a linkage between sphingolipid metabolism, proteostatic quality control systems, and HSC self-renewal and provides therapeutic targets for improving HSC-based cellular therapeutics.

Transfer learning for biomedical named entity recognition with neural networks

Motivation

The explosive increase of biomedical literature has made information extraction an increasingly important tool for biomedical research. A fundamental task is the recognition of biomedical named entities in text (BNER) such as genes/proteins, diseases and species. Recently, a domain-independent method based on deep learning and statistical word embeddings, called long short-term memory network-conditional random field (LSTM-CRF), has been shown to outperform state-of-the-art entity-specific BNER tools. However, this method is dependent on gold-standard corpora (GSCs) consisting of hand-labeled entities, which tend to be small but highly reliable. An alternative to GSCs are silver-standard corpora (SSCs), which are generated by harmonizing the annotations made by several automatic annotation systems. SSCs typically contain more noise than GSCs but have the advantage of containing many more training examples. Ideally, these corpora could be combined to achieve the benefits of both, which is an opportunity for transfer learning. In this work, we analyze to what extent transfer learning improves upon state-of-the-art results for BNER.

Results

We demonstrate that transferring a deep neural network (DNN) trained on a large, noisy SSC to a smaller, but more reliable GSC significantly improves upon state-of-the-art results for BNER. Compared to a state-of-the-art baseline evaluated on 23 GSCs covering four different entity classes, transfer learning results in an average reduction in error of approximately 11%. We found transfer learning to be especially beneficial for target datasets with a small number of labels (approximately 6000 or less).

Availability and implementation

Source code for the LSTM-CRF is available at https://github.com/Franck-Dernoncourt/NeuroNER/ and links to the corpora are available at https://github.com/BaderLab/Transfer-Learning-BNER-Bioinformatics-2018/.

Supplementary information

Supplementary data are available at Bioinformatics online.

Rewiring of the Human Mitochondrial Interactome during Neuronal Reprogramming Reveals Regulators of the Respirasome and Neurogenesis

Mitochondrial protein (MP) assemblies undergo alterations during neurogenesis, a complex process vital in brain homeostasis and disease. Yet which MP assemblies remodel during differentiation remains unclear. Here, using mass spectrometry-based co-fractionation profiles and phosphoproteomics, we generated mitochondrial interaction maps of human pluripotent embryonal carcinoma stem cells and differentiated neuronal-like cells, which presented as two discrete cell populations by single-cell RNA sequencing. The resulting networks, encompassing 6,442 high-quality associations among 600 MPs, revealed widespread changes in mitochondrial interactions and site-specific phosphorylation during neuronal differentiation. By leveraging the networks, we show the orphan C20orf24 as a respirasome assembly factor whose disruption markedly reduces respiratory chain activity in patients deficient in complex IV. We also find that a heme-containing neurotrophic factor, neuron-derived neurotrophic factor [NENF], couples with Parkinson disease-related proteins to promote neurotrophic activity. Our results provide insights into the dynamic reorganization of mitochondrial networks during neuronal differentiation and highlights mechanisms for MPs in respirasome, neuronal function, and mitochondrial diseases.

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Rewiring of mitochondrial (mt) protein interaction network in distinct cell states

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Dramatic changes in site-specific phosphorylation during neuronal differentiation

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C20orf24 is a respirasome assembly factor depleted in patients deficient in CIV

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NENF binding with DJ-1/PINK1 promotes neurotrophic activity and neuronal survival

Identification of CDC25 as a Common Therapeutic Target for Triple-Negative Breast Cancer

CDK4/6 inhibitors are effective against cancer cells expressing the tumor suppressor RB1, but not RB1-deficient cells, posing the challenge of how to target RB1 loss. In triple-negative breast cancer (TNBC), RB1 and PTEN are frequently inactivated together with TP53. We performed kinome/phosphatase inhibitor screens on primary mouse Rb/p53-, Pten/p53-, and human RB1/PTEN/TP53-deficient TNBC cell lines and identified CDC25 phosphatase as a common target. Pharmacological or genetic inhibition of CDC25 suppressed growth of RB1-deficient TNBC cells that are resistant to combined CDK4/6 plus CDK2 inhibition. Minimal cooperation was observed in vitro between CDC25 antagonists and CDK1, CDK2, or CDK4/6 inhibitors, but strong synergy with WEE1 inhibition was apparent. In accordance with increased PI3K signaling following long-term CDC25 inhibition, CDC25 and PI3K inhibitors effectively synergized to suppress TNBC growth both in vitro and in xenotransplantation models. These results provide a rationale for the development of CDC25-based therapies for diverse RB1/PTEN/TP53-deficient and -proficient TNBCs.

Liu et al. report that inhibition of the protein phosphatase CDC25 kills diverse triple-negative breast cancer (TNBC) cells. Moreover, CDC25 antagonists cooperate with other drugs, such as PI3K inhibitors, to efficiently suppress growth of human TNBC engrafted into mice.

Benchmarking to the Gold Standard: Hyaluronan-Oxime Hydrogels Recapitulate Xenograft Models with In Vitro Breast Cancer Spheroid Culture

Many 3D in vitro models induce breast cancer spheroid formation; however, this alone does not recapitulate the complex in vivo phenotype. To effectively screen therapeutics, it is urgently needed to validate in vitro cancer spheroid models against the gold standard of xenografts. A new oxime-crosslinked hyaluronan (HA) hydrogel is designed, manipulating gelation rate and mechanical properties to grow breast cancer spheroids in 3D. This HA-oxime breast cancer model maintains the gene expression profile most similar to that of tumor xenografts based on a pan-cancer gene expression profile (comprising 730 genes) of three different human breast cancer subtypes compared to Matrigel or conventional 2D culture. Differences in gene expression between breast cancer cultures in HA-oxime versus Matrigel or 2D are confirmed for 12 canonical pathways by gene set variation analysis. Importantly, drug response is dependent on the culture method. Breast cancer cells respond better to the Rac inhibitor (EHT-1864) and the PI3K inhibitor (AZD6482) when cultured in HA-oxime versus Matrigel. This study demonstrates the superiority of an HA-based hydrogel as a platform for in vitro breast cancer culture of both primary, patient-derived cells and cell lines, and provides a hydrogel culture model that closely matches that in vivo.

GeneMANIA update 2018

GeneMANIA (http://genemania.org) is a flexible user-friendly web site for generating hypotheses about gene function, analyzing gene lists and prioritizing genes for functional assays. Given a query gene list, GeneMANIA finds functionally similar genes using a wealth of genomics and proteomics data. In this mode, it weights each functional genomic dataset according to its predictive value for the query. Another use of GeneMANIA is gene function prediction. Given a single query gene, GeneMANIA finds genes likely to share function with it based on their interactions with it. Enriched Gene Ontology categories among this set can point to the function of the gene. Nine organisms are currently supported (Arabidopsis thaliana, Caenorhabditis elegans, Danio rerio, Drosophila melanogaster, Escherichia coli, Homo sapiens, Mus musculus, Rattus norvegicus and Saccharomyces cerevisiae). Hundreds of data sets and hundreds of millions of interactions have been collected from GEO, BioGRID, IRefIndex and I2D, as well as organism-specific functional genomics data sets. Users can customize their search by selecting specific data sets to query and by uploading their own data sets to analyze. We have recently updated the user interface to GeneMANIA to make it more intuitive and make more efficient use of visual space. GeneMANIA can now be used effectively on a variety of devices.

EPIC: software toolkit for elution profile-based inference of protein complexes

Protein complexes are key macromolecular machines of the cell, but their description remains incomplete. We and others previously reported an experimental strategy for global characterization of native protein assemblies based on chromatographic fractionation of biological extracts coupled to precision mass spectrometry analysis (chromatographic fractionation-mass spectrometry, CF-MS), but the resulting data are challenging to process and interpret. Here, we describe EPIC (elution profile-based inference of complexes), a software toolkit for automated scoring of large-scale CF-MS data to define high-confidence multi-component macromolecules from diverse biological specimens. As a case study, we used EPIC to map the global interactome of Caenorhabditis elegans, defining 612 putative worm protein complexes linked to diverse biological processes. These included novel subunits and assemblies unique to nematodes that we validated using orthogonal methods. The open source EPIC software is freely available as a Jupyter notebook packaged in a Docker container (https://hub.docker.com/r/baderlab/bio-epic/).

Specifications of Standards in Systems and Synthetic Biology: Status and Developments in 2019

This special issue of the Journal of Integrative Bioinformatics presents an overview of COMBINE standards and their latest specifications. The standards cover representation formats for computational modeling in synthetic and systems biology and include BioPAX, CellML, NeuroML, SBML, SBGN, SBOL and SED-ML. The articles in this issue contain updated specifications of SBGN Process Description Level 1 Version 2, SBML Level 3 Core Version 2 Release 2, SBOL Version 2.3.0, and SBOL Visual Version 2.1.