Visual annotation display (VLAD): a tool for finding functional themes in lists of genes

Biochemical pathways represented by Gene Ontology-Causal Activity Models identify distinct phenotypes resulting from mutations in pathways

Gene inactivation can affect the process(es) in which that gene acts and causally downstream ones, yielding diverse mutant phenotypes. Identifying the genetic pathways resulting in a given phenotype helps us understand how individual genes interact in a functional network. Computable representations of biological pathways include detailed process descriptions in the Reactome Knowledgebase and causal activity flows between molecular functions in Gene Ontology-Causal Activity Models (GO-CAMs). A computational process has been developed to convert Reactome pathways to GO-CAMs. Laboratory mice are widely used models of normal and pathological human processes. We have converted human Reactome GO-CAMs to orthologous mouse GO-CAMs, as a resource to transfer pathway knowledge between humans and model organisms. These mouse GO-CAMs allowed us to define sets of genes that function in a causally connected way. To demonstrate that individual variant genes from connected pathways result in similar but distinguishable phenotypes, we used the genes in our pathway models to cross-query mouse phenotype annotations in the Mouse Genome Database (MGD). Using GO-CAM representations of 2 related but distinct pathways, gluconeogenesis and glycolysis, we show that individual causal paths in gene networks give rise to discrete phenotypic outcomes resulting from perturbations of glycolytic and gluconeogenic genes. The accurate and detailed descriptions of gene interactions recovered in this analysis of well-studied processes suggest that this strategy can be applied to less well-understood processes in less well-studied model systems to predict phenotypic outcomes of novel gene variants and to identify potential gene targets in altered processes.

Biochemical Pathways Represented by Gene Ontology Causal Activity Models Identify Distinct Phenotypes Resulting from Mutations in Pathways

Gene inactivation can affect the process(es) in which that gene acts and causally downstream ones, yielding diverse mutant phenotypes. Identifying the genetic pathways resulting in a given phenotype helps us understand how individual genes interact in a functional network. Computable representations of biological pathways include detailed process descriptions in the Reactome Knowledgebase, and causal activity flows between molecular functions in Gene Ontology-Causal Activity Models (GO-CAMs). A computational process has been developed to convert Reactome pathways to GO-CAMs. Laboratory mice are widely used models of normal and pathological human processes. We have converted human Reactome GO-CAMs to orthologous mouse GO-CAMs, as a resource to transfer pathway knowledge between humans and model organisms. These mouse GO-CAMs allowed us to define sets of genes that function in a connected and well-defined way. To test whether individual genes from well-defined pathways result in similar and distinguishable phenotypes, we used the genes in our pathway models to cross-query mouse phenotype annotations in the Mouse Genome Database (MGD). Using GO-CAM representations of two related but distinct pathways, gluconeogenesis and glycolysis, we can identify causal paths in gene networks that give rise to discrete phenotypic outcomes for perturbations of glycolysis and gluconeogenesis. The accurate and detailed descriptions of gene interactions recovered in this analysis of well-studied processes suggest that this strategy can be applied to less well-understood processes in less well-studied model systems to predict phenotypic outcomes of novel gene variants and to identify potential gene targets in altered processes.

Diet suppresses glioblastoma initiation in mice by maintaining quiescence of mutation-bearing neural stem cells

Glioblastoma is thought to originate from neural stem cells (NSCs) of the subventricular zone that acquire genetic alterations. In the adult brain, NSCs are largely quiescent, suggesting that deregulation of quiescence maintenance may be a prerequisite for tumor initiation. Although inactivation of the tumor suppressor p53 is a frequent event in gliomagenesis, whether or how it affects quiescent NSCs (qNSCs) remains unclear. Here, we show that p53 maintains quiescence by inducing fatty-acid oxidation (FAO) and that acute p53 deletion in qNSCs results in their premature activation to a proliferative state. Mechanistically, this occurs through direct transcriptional induction of PPARGC1a, which in turn activates PPARα to upregulate FAO genes. Dietary supplementation with fish oil containing omega-3 fatty acids, natural PPARα ligands, fully restores quiescence of p53-deficient NSCs and delays tumor initiation in a glioblastoma mouse model. Thus, diet can silence glioblastoma driver mutations, with important implications for cancer prevention.

GOing Forward With the Cardiac Conduction System Using Gene Ontology

The cardiac conduction system (CCS) comprises critical components responsible for the initiation, propagation, and coordination of the action potential. Aberrant CCS development can cause conduction abnormalities, including sick sinus syndrome, accessory pathways, and atrioventricular and bundle branch blocks. Gene Ontology (GO; http://geneontology.org/) is an invaluable global bioinformatics resource which provides structured, computable knowledge describing the functions of gene products. Many gene products are known to be involved in CCS development; however, this information is not comprehensively captured by GO. To address the needs of the heart development research community, this study aimed to describe the specific roles of proteins reported in the literature to be involved with CCS development and/or function. 14 proteins were prioritized for GO annotation which led to the curation of 15 peer-reviewed primary experimental articles using carefully selected GO terms. 152 descriptive GO annotations, including those describing sinoatrial node and atrioventricular node development were created and submitted to the GO Consortium database. A functional enrichment analysis of 35 key CCS development proteins confirmed that this work has improved the in-silico interpretation of this CCS dataset. This work may improve future investigations of the CCS with application of high-throughput methods such as genome-wide association studies analysis, proteomics, and transcriptomics.

Gene Ontology Curation of Neuroinflammation Biology Improves the Interpretation of Alzheimer's Disease Gene Expression Data

BACKGROUND

Gene Ontology (GO) is a major bioinformatic resource used for analysis of large biomedical datasets, for example from genome-wide association studies, applied universally across biological fields, including Alzheimer's disease (AD) research.

OBJECTIVE

We aim to demonstrate the applicability of GO for interpretation of AD datasets to improve the understanding of the underlying molecular disease mechanisms, including the involvement of inflammatory pathways and dysregulated microRNAs (miRs).

METHODS

We have undertaken a systematic full article GO annotation approach focused on microglial proteins implicated in AD and the miRs regulating their expression. PANTHER was used for enrichment analysis of previously published AD data. Cytoscape was used for visualizing and analyzing miR-target interactions captured from published experimental evidence.

RESULTS

We contributed 3,084 new annotations for 494 entities, i.e., on average six new annotations per entity. This included a total of 1,352 annotations for 40 prioritized microglial proteins implicated in AD and 66 miRs regulating their expression, yielding an average of twelve annotations per prioritized entity. The updated GO resource was then used to re-analyze previously published data. The re-analysis showed novel processes associated with AD-related genes, not identified in the original study, such as 'gliogenesis', 'regulation of neuron projection development', or 'response to cytokine', demonstrating enhanced applicability of GO for neuroscience research.

CONCLUSIONS

This study highlights ongoing development of the neurobiological aspects of GO and demonstrates the value of biocuration activities in the area, thus helping to delineate the molecular bases of AD to aid the development of diagnostic tools and treatments.

Cigarette Smoke Triggers Loss of Corneal Endothelial Cells and Disruption of Descemet's Membrane Proteins in Mice

Purpose

To investigate changes at a molecular level in the mouse corneal endothelium (CE) exposed to chronic cigarette smoke (CS).

Methods

Pregnant mice (gestation days 18–20) were placed in a whole-body exposure smoking chamber, and a few days later pups were born. After 3.5 months of CS exposure, a ConfoScan4 scanning microscope was used to examine the corneal endothelial cells (CECs) of CS-exposed and control (Ct) mice. The CE was peeled under a microscope and maintained as four biological replicates (two male and two female) for CS-exposed and Ct mice; each replicate consisted of 16 CEs. The proteome of the CE was investigated through mass spectrometry.

Results

The CE images of CS-exposed and Ct mice revealed a difference in the shape of CECs accompanied by a nearly 10% decrease in CEC density (P < 0.00003) following CS exposure. Proteome profiling identified a total of 524 proteins exhibiting statistically significant changes in CE from CS-exposed mice. Importantly, proteins associated with Descemet's membrane (DM), including COL4α1, COL4α2, COL4α3, COL4α4, COL4α5, COL4α6, COL8α1, COL8α2, and FN1, among others, exhibited diminished protein levels in the CE of CS-exposed mice.

Conclusions

Our data confirm that exposure to CS results in reduced CEC density accompanied by diminished levels of multiple collagen and extracellular matrix proteins associated with DM.

Selective sweep analysis reveals extensive parallel selection traits between large white and Duroc pigs

In the process of pig genetic improvement, different commercial breeds have been bred for the same purpose, improving meat production. Most of the economic traits, such as growth and fertility, have been selected similarly despite the discrepant selection pressure, which is known as parallel selection. Here, 28 whole-genome sequencing data of Danish large white pigs with an approximately 25-fold depth each were generated, resulting in about 12 million high-quality SNPs for each individual. Combined with the sequencing data of 27 Duroc and 23 European wild boars, we investigated the parallel selection of Danish large white and Duroc pigs using two complementary methods, Fst and iHS. In total, 67 candidate regions were identified as the signatures of parallel selection. The genes in candidate regions of parallel selection were mainly associated with sensory perception, growth rate, and body size. Further functional annotation suggested that the striking consistency of the terms may be caused by the polygenetic basis of quantitative traits, and revealing the complex genetic basis of parallel selection. Besides, some unique terms were enriched in population-specific selection regions, such as the limb development-related terms enriched in Duroc-specific selection regions, suggesting unique selections of breed-specific selected traits. These results will help us better understand the parallel selection process of different breeds. Moreover, we identified several potential causal SNPs that may contribute to the pig genetic breeding process.

Investigation of COVID-19 comorbidities reveals genes and pathways coincident with the SARS-CoV-2 viral disease

The emergence of the SARS-CoV-2 virus and subsequent COVID-19 pandemic initiated intense research into the mechanisms of action for this virus. It was quickly noted that COVID-19 presents more seriously in conjunction with other human disease conditions such as hypertension, diabetes, and lung diseases. We conducted a bioinformatics analysis of COVID-19 comorbidity-associated gene sets, identifying genes and pathways shared among the comorbidities, and evaluated current knowledge about these genes and pathways as related to current information about SARS-CoV-2 infection. We performed our analysis using GeneWeaver (GW), Reactome, and several biomedical ontologies to represent and compare common COVID-19 comorbidities. Phenotypic analysis of shared genes revealed significant enrichment for immune system phenotypes and for cardiovascular-related phenotypes, which might point to alleles and phenotypes in mouse models that could be evaluated for clues to COVID-19 severity. Through pathway analysis, we identified enriched pathways shared by comorbidity datasets and datasets associated with SARS-CoV-2 infection.

Transcriptome Analysis Identifies SenZfp536, a Sense LncRNA that Suppresses Self-renewal of Cortical Neural Progenitors

Long non-coding RNAs (lncRNAs) regulate transcription to control development and homeostasis in a variety of tissues and organs. However, their roles in the development of the cerebral cortex have not been well elucidated. Here, a bioinformatics pipeline was applied to delineate the dynamic expression and potential cis-regulating effects of mouse lncRNAs using transcriptome data from 8 embryonic time points and sub-regions of the developing cerebral cortex. We further characterized a sense lncRNA, SenZfp536, which is transcribed downstream of and partially overlaps with the protein-coding gene Zfp536. Both SenZfp536 and Zfp536 were predominantly expressed in the proliferative zone of the developing cortex. Zfp536 was cis-regulated by SenZfp536, which facilitates looping between the promoter of Zfp536 and the genomic region that transcribes SenZfp536. Surprisingly, knocking down or activating the expression of SenZfp536 increased or compromised the proliferation of cortical neural progenitor cells (NPCs), respectively. Finally, overexpressing Zfp536 in cortical NPCs reversed the enhanced proliferation of cortical NPCs caused by SenZfp536 knockdown. The study deepens our understanding of how lncRNAs regulate the propagation of cortical NPCs through cis-regulatory mechanisms.

Prenatal Androgenization Alters the Development of GnRH Neuron and Preoptic Area RNA Transcripts in Female Mice

Polycystic ovary syndrome (PCOS) is the most common form of infertility in women. The causes of PCOS are not yet understood and both genetics and early-life exposure have been considered as candidates. With regard to the latter, circulating androgens are elevated in mid-late gestation in women with PCOS, potentially exposing offspring to elevated androgens in utero; daughters of women with PCOS are at increased risk for developing this disorder. Consistent with these clinical observations, prenatal androgenization (PNA) of several species recapitulates many phenotypes observed in PCOS. There is increasing evidence that symptoms associated with PCOS, including elevated luteinizing hormone (LH) (and presumably gonadotropin-releasing hormone [GnRH]) pulse frequency emerge during the pubertal transition. We utilized translating ribosome affinity purification coupled with ribonucleic acid (RNA) sequencing to examine GnRH neuron messenger RNAs from prepubertal (3 weeks) and adult female control and PNA mice. Prominent in GnRH neurons were transcripts associated with protein synthesis and cellular energetics, in particular oxidative phosphorylation. The GnRH neuron transcript profile was affected more by the transition from prepuberty to adulthood than by PNA treatment; however, PNA did change the developmental trajectory of GnRH neurons. This included families of transcripts related to both protein synthesis and oxidative phosphorylation, which were more prevalent in adults than in prepubertal mice but were blunted in PNA adults. These findings suggest that prenatal androgen exposure can program alterations in the translatome of GnRH neurons, providing a mechanism independent of changes in the genetic code for altered expression.

Investigation of COVID-19 comorbidities reveals genes and pathways coincident with the SARS-CoV-2 viral disease

The emergence of the SARS-CoV-2 virus and subsequent COVID-19 pandemic initiated intense research into the mechanisms of action for this virus. It was quickly noted that COVID-19 presents more seriously in conjunction with other hum an disease conditions such as hypertension, diabetes, and lung diseases. We conducted a bioinformatics analysis of COVID-19 comorbidity-associated gene sets, identifying genes and pathways shared among the comorbidities, and evaluated current know ledge about these genes and pathways as related to current information about SARS-CoV-2 infection. We performed our analysis using GeneWeaver (GW), Reactome, and several biomedical ontologies to represent and compare common COVID-19 comorbidities. Phenotypic analysis of shared genes revealed significant enrichment for immune system phenotypes and for cardiovascular-related phenotypes, which might point to alleles and phenotypes in mouse models that could be evaluated for clues to COVID-19 severity. Through pathway analysis, we identified enriched pathways shared by comorbidity datasets and datasets associated with SARS-CoV-2 infection.

Generation and proteome profiling of PBMC-originated, iPSC-derived lentoid bodies

Here, we report proteome profiling of peripheral blood mononuclear cell (PBMC)-originated, induced pluripotent stem cell (iPSC)-derived, lens-like organoids termed lentoid bodies at two differentiation time points. A small aliquot of the blood sample was ascertained to collect PBMCs that were reprogrammed to iPSCs. The PBMC-originated, iPSCs were differentiated to lentoid bodies employing the "fried egg" method. Quantitative real-time PCR (qRT-PCR) analysis revealed increased expression levels of lens-associated markers in lentoid bodies while transmission electron microscopy identified closely packed lens epithelial- and differentiating fiber-like cells in lentoid bodies. Total cellular protein was extracted from lentoid bodies at differentiation day 25 and mass spectrometry identified a total of 9,473 proteins. The low counts of crystallin proteins at differentiation day 25 prompted us to re-examine the proteome at differentiation day 35 as we reasoned that 10 additional days of differentiation will increase the crystallin count. However, we did not detect any substantial increase in crystallin protein counts at differentiation day 35. In conclusion, we report generation and proteome profiles of PBMC-originated, iPSC-derived lentoid bodies at multiple differentiation time points.

Exploring mechanisms of increased cardiovascular disease risk with antipsychotic medications: Risperidone alters the cardiac proteomic signature in mice

Atypical antipsychotic (AA) medications including risperidone (RIS) and olanzapine (OLAN) are FDA approved for the treatment of psychiatric disorders including schizophrenia, bipolar disorder and depression. Clinical side effects of AA medications include obesity, insulin resistance, dyslipidemia, hypertension and increased cardiovascular disease risk. Despite the known pharmacology of these AA medications, the mechanisms contributing to adverse metabolic side-effects are not well understood. To evaluate drug-associated effects on the heart, we assessed changes in the cardiac proteomic signature in mice administered for 4 weeks with clinically relevant exposure of RIS or OLAN. Using proteomic and gene enrichment analysis, we identified differentially expressed (DE) proteins in both RIS- and OLAN-treated mouse hearts (p < 0.05), including proteins comprising mitochondrial respiratory complex I and pathways involved in mitochondrial function and oxidative phosphorylation. A subset of DE proteins identified were further validated by both western blotting and quantitative real-time PCR. Histological evaluation of hearts indicated that AA-associated aberrant cardiac gene expression occurs prior to the onset of gross pathomorphological changes. Additionally, RIS treatment altered cardiac mitochondrial oxygen consumption and whole body energy expenditure. Our study provides insight into the mechanisms underlying increased patient risk for adverse cardiac outcomes with chronic treatment of AA medications.

Effects of a male meiotic driver on male and female transcriptomes in the house mouse

Not all genetic loci follow Mendel's rules, and the evolutionary consequences of this are not yet fully known. Genomic conflict involving multiple loci is a likely outcome, as restoration of Mendelian inheritance patterns will be selected for, and sexual conflict may also arise when sexes are differentially affected. Here, we investigate effects of the t haplotype, an autosomal male meiotic driver in house mice, on genome-wide gene expression patterns in males and females. We analysed gonads, liver and brain in adult same-sex sibling pairs differing in genotype, allowing us to identify t-associated differences in gene regulation. In testes, only 40% of differentially expressed genes mapped to the approximately 708 annotated genes comprising the t haplotype. Thus, much of the activity of the t haplotype occurs in trans, and as upregulation. Sperm maturation functions were enriched among both cis and trans acting t haplotype genes. Within the t haplotype, we observed more downregulation and differential exon usage. In ovaries, liver and brain, the majority of expression differences mapped to the t haplotype, and were largely independent of the differences seen in the testis. Overall, we found widespread transcriptional effects of this male meiotic driver in the house mouse genome.

Cisplatin-resistant triple-negative breast cancer subtypes: multiple mechanisms of resistance

BACKGROUND

Understanding mechanisms underlying specific chemotherapeutic responses in subtypes of cancer may improve identification of treatment strategies most likely to benefit particular patients. For example, triple-negative breast cancer (TNBC) patients have variable response to the chemotherapeutic agent cisplatin. Understanding the basis of treatment response in cancer subtypes will lead to more informed decisions about selection of treatment strategies.

METHODS

In this study we used an integrative functional genomics approach to investigate the molecular mechanisms underlying known cisplatin-response differences among subtypes of TNBC. To identify changes in gene expression that could explain mechanisms of resistance, we examined 102 evolutionarily conserved cisplatin-associated genes, evaluating their differential expression in the cisplatin-sensitive, basal-like 1 (BL1) and basal-like 2 (BL2) subtypes, and the two cisplatin-resistant, luminal androgen receptor (LAR) and mesenchymal (M) subtypes of TNBC.

RESULTS

We found 20 genes that were differentially expressed in at least one subtype. Fifteen of the 20 genes are associated with cell death and are distributed among all TNBC subtypes. The less cisplatin-responsive LAR and M TNBC subtypes show different regulation of 13 genes compared to the more sensitive BL1 and BL2 subtypes. These 13 genes identify a variety of cisplatin-resistance mechanisms including increased transport and detoxification of cisplatin, and mis-regulation of the epithelial to mesenchymal transition.

CONCLUSIONS

We identified gene signatures in resistant TNBC subtypes indicative of mechanisms of cisplatin. Our results indicate that response to cisplatin in TNBC has a complex foundation based on impact of treatment on distinct cellular pathways. We find that examination of expression data in the context of heterogeneous data such as drug-gene interactions leads to a better understanding of mechanisms at work in cancer therapy response.

Improving Interpretation of Cardiac Phenotypes and Enhancing Discovery With Expanded Knowledge in the Gene Ontology

Supplemental Digital Content is available in the text.

Background:

A systems biology approach to cardiac physiology requires a comprehensive representation of how coordinated processes operate in the heart, as well as the ability to interpret relevant transcriptomic and proteomic experiments. The Gene Ontology (GO) Consortium provides structured, controlled vocabularies of biological terms that can be used to summarize and analyze functional knowledge for gene products.

Methods and Results:

In this study, we created a computational resource to facilitate genetic studies of cardiac physiology by integrating literature curation with attention to an improved and expanded ontological representation of heart processes in the Gene Ontology. As a result, the Gene Ontology now contains terms that comprehensively describe the roles of proteins in cardiac muscle cell action potential, electrical coupling, and the transmission of the electrical impulse from the sinoatrial node to the ventricles. Evaluating the effectiveness of this approach to inform data analysis demonstrated that Gene Ontology annotations, analyzed within an expanded ontological context of heart processes, can help to identify candidate genes associated with arrhythmic disease risk loci.

Conclusions:

We determined that a combination of curation and ontology development for heart-specific genes and processes supports the identification and downstream analysis of genes responsible for the spread of the cardiac action potential through the heart. Annotating these genes and processes in a structured format facilitates data analysis and supports effective retrieval of gene-centric information about cardiac defects.

The Transcriptomic Toolbox: Resources for Interpreting Large Gene Expression Data within a Precision Medicine Context for Metabolic Disease Atherosclerosis

As one of the most widespread metabolic diseases, atherosclerosis affects nearly everyone as they age; arteries gradually narrow from plaque accumulation over time reducing oxygenated blood flow to central and periphery causing heart disease, stroke, kidney problems, and even pulmonary disease. Personalized medicine promises to bring treatments based on individual genome sequencing that precisely target the molecular pathways underlying atherosclerosis and its symptoms, but to date only a few genotypes have been identified. A promising alternative to this genetic approach is the identification of pathways altered in atherosclerosis by transcriptome analysis of atherosclerotic tissues to target specific aspects of disease. Transcriptomics is a potentially useful tool for both diagnostics and discovery science, exposing novel cellular and molecular mechanisms in clinical and translational models, and depending on experimental design to identify and test novel therapeutics. The cost and time required for transcriptome analysis has been greatly reduced by the development of next generation sequencing. The goal of this resource article is to provide background and a guide to appropriate technologies and downstream analyses in transcriptomics experiments generating ever-increasing amounts of gene expression data.

Aligning the Aligners: Comparison of RNA Sequencing Data Alignment and Gene Expression Quantification Tools for Clinical Breast Cancer Research

The rapid expansion of transcriptomics and affordability of next-generation sequencing (NGS) technologies generate rocketing amounts of gene expression data across biology and medicine, including cancer research. Concomitantly, many bioinformatics tools were developed to streamline gene expression and quantification. We tested the concordance of NGS RNA sequencing (RNA-seq) analysis outcomes between two predominant programs for read alignment, HISAT2, and STAR, and two most popular programs for quantifying gene expression in NGS experiments, edgeR and DESeq2, using RNA-seq data from breast cancer progression series, which include histologically confirmed normal, early neoplasia, ductal carcinoma in situ and infiltrating ductal carcinoma samples microdissected from formalin fixed, paraffin embedded (FFPE) breast tissue blocks. We identified significant differences in aligners’ performance: HISAT2 was prone to misalign reads to retrogene genomic loci, STAR generated more precise alignments, especially for early neoplasia samples. edgeR and DESeq2 produced similar lists of differentially expressed genes, with edgeR producing more conservative, though shorter, lists of genes. Gene Ontology (GO) enrichment analysis revealed no skewness in significant GO terms identified among differentially expressed genes by edgeR versus DESeq2. As transcriptomics of FFPE samples becomes a vanguard of precision medicine, choice of bioinformatics tools becomes critical for clinical research. Our results indicate that STAR and edgeR are well-suited tools for differential gene expression analysis from FFPE samples.

Generation and Proteome Profiling of PBMC-Originated, iPSC-Derived Corneal Endothelial Cells

Purpose

Corneal endothelial cells (CECs) are critical in maintaining clarity of the cornea. This study was initiated to develop peripheral blood mononuclear cell (PBMC)-originated, induced pluripotent stem cell (iPSC)-derived CECs.

Methods

We isolated PBMCs and programmed the mononuclear cells to generate iPSCs, which were differentiated to CECs through the neural crest cells (NCCs). The morphology of differentiating iPSCs was examined at regular intervals by phase contrast microscopy. In parallel, the expression of pluripotent and corneal endothelium (CE)-associated markers was investigated by quantitative real-time PCR (qRT-PCR). The molecular architecture of the iPSC-derived CECs and human corneal endothelium (hCE) was examined by mass spectrometry–based proteome sequencing.

Results

The PBMC-originated, iPSC-derived CECs were tightly adherent, exhibiting a hexagonal-like shape, one of the cardinal characteristics of CECs. The CE-associated markers expressed at significantly higher levels in iPSC-derived CECs at days 13, 20, and 30 compared with their respective levels in iPSCs. It is of importance that only residual expression levels of pluripotency markers were detected in iPSC-derived CECs. Cryopreservation of iPSC-derived CECs did not affect the tight adherence of CECs and their hexagonal-like shape while expressing high levels of CE-associated markers. Mass spectrometry–based proteome sequencing identified 10,575 proteins in the iPSC-derived CEC proteome. In parallel, we completed proteome profiling of the hCE identifying 6345 proteins. Of these, 5763 proteins were identified in the iPSC-derived CECs, suggesting that 90.82% of the hCE proteome overlaps with the iPSC-derived CEC proteome.

Conclusions

We have successfully developed a personalized approach to generate CECs that closely mimic the molecular architecture of the hCE. To the best of our knowledge, this is the first report describing the development of PBMC-originated, iPSC-derived CECs.

Improving the Gene Ontology Resource to Facilitate More Informative Analysis and Interpretation of Alzheimer's Disease Data

The analysis and interpretation of high-throughput datasets relies on access to high-quality bioinformatics resources, as well as processing pipelines and analysis tools. Gene Ontology (GO, geneontology.org) is a major resource for gene enrichment analysis. The aim of this project, funded by the Alzheimer's Research United Kingdom (ARUK) foundation and led by the University College London (UCL) biocuration team, was to enhance the GO resource by developing new neurological GO terms, and use GO terms to annotate gene products associated with dementia. Specifically, proteins and protein complexes relevant to processes involving amyloid-beta and tau have been annotated and the resulting annotations are denoted in GO databases as 'ARUK-UCL'. Biological knowledge presented in the scientific literature was captured through the association of GO terms with dementia-relevant protein records; GO itself was revised, and new GO terms were added. This literature biocuration increased the number of Alzheimer's-relevant gene products that were being associated with neurological GO terms, such as 'amyloid-beta clearance' or 'learning or memory', as well as neuronal structures and their compartments. Of the total 2055 annotations that we contributed for the prioritised gene products, 526 have associated proteins and complexes with neurological GO terms. To ensure that these descriptive annotations could be provided for Alzheimer's-relevant gene products, over 70 new GO terms were created. Here, we describe how the improvements in ontology development and biocuration resulting from this initiative can benefit the scientific community and enhance the interpretation of dementia data.

Expanding the horizons of microRNA bioinformatics

MicroRNA regulation of key biological and developmental pathways is a rapidly expanding area of research, accompanied by vast amounts of experimental data. This data, however, is not widely available in bioinformatic resources, making it difficult for researchers to find and analyze microRNA-related experimental data and define further research projects. We are addressing this problem by providing two new bioinformatics data sets that contain experimentally verified functional information for mammalian microRNAs involved in cardiovascular-relevant, and other, processes. To date, our resource provides over 4400 Gene Ontology annotations associated with over 500 microRNAs from human, mouse, and rat and over 2400 experimentally validated microRNA:target interactions. We illustrate how this resource can be used to create microRNA-focused interaction networks with a biological context using the known biological role of microRNAs and the mRNAs they regulate, enabling discovery of associations between gene products, biological pathways and, ultimately, diseases. This data will be crucial in advancing the field of microRNA bioinformatics and will establish consistent data sets for reproducible functional analysis of microRNAs across all biological research areas.

Proteome Profiling of Developing Murine Lens Through Mass Spectrometry

Purpose

We previously completed a comprehensive profile of the mouse lens transcriptome. Here, we investigate the proteome of the mouse lens through mass spectrometry–based protein sequencing at the same embryonic and postnatal time points.

Methods

We extracted mouse lenses at embryonic day 15 (E15) and 18 (E18) and postnatal day 0 (P0), 3 (P3), 6 (P6), and 9 (P9). The lenses from each time point were preserved in three distinct pools to serve as biological replicates for each developmental stage. The total cellular protein was extracted from the lens, digested with trypsin, and labeled with isobaric tandem mass tags (TMT) for three independent TMT experiments.

Results

A total of 5404 proteins were identified in the mouse ocular lens in at least one TMT set, 4244 in two, and 3155 were present in all three TMT sets. The majority of the proteins exhibited steady expression at all six developmental time points; nevertheless, we identified 39 proteins that exhibited an 8-fold differential (higher or lower) expression during the developmental time course compared to their respective levels at E15. The lens proteome is composed of diverse proteins that have distinct biological properties and functional characteristics, including proteins associated with cataractogenesis and autophagy.

Conclusions

We have established a comprehensive profile of the developing murine lens proteome. This repository will be helpful in identifying critical components of lens development and processes essential for the maintenance of its transparency.

Identification of novel transcripts and peptides in developing murine lens

We previously investigated the transcriptome and proteome profiles of the murine ocular lens at six developmental time points including two embryonic (E15 and E18) and four postnatal time points (P0, P3, P6, and P9). Here, we extend our analyses to identify novel transcripts and peptides in developing  mouse lens. We identified a total of 9,707 novel transcripts and 325 novel fusion genes in developing mouse lens. Additionally, we identified 13,281 novel alternative splicing (AS) events in mouse lens including 6,990 exon skipping (ES), 2,447 alternative 3' splice site (A3SS), 1,900 alternative 5' splice site (A5SS), 1,771 mutually exclusive exons (MXE), and 173 intron retention (IR). Finally, we integrated our OMIC (Transcriptome and Proteome) datasets identifying 20 novel peptides in mouse lens. All 20 peptides were validated through matching MS/MS spectra of synthetic peptides. To the best of our knowledge, this is the first report integrating OMIC datasets to identify novel peptides in developing murine lens.

A retinoic acid-dependent stroma-leukemia crosstalk promotes chronic lymphocytic leukemia progression

In chronic lymphocytic leukemia (CLL), the non-hematopoietic stromal microenvironment plays a critical role in promoting tumor cell recruitment, activation, survival, and expansion. However, the nature of the stromal cells and molecular pathways involved remain largely unknown. Here, we demonstrate that leukemic B lymphocytes induce the activation of retinoid acid synthesis and signaling in the microenvironment. Inhibition of RA-signaling in stromal cells causes deregulation of genes associated with adhesion, tissue organization and chemokine secretion including the B-cell chemokine CXCL13. Notably, reducing retinoic acid precursors from the diet or inhibiting RA-signaling through retinoid-antagonist therapy prolong survival by preventing dissemination of leukemia cells into lymphoid tissues. Furthermore, mouse and human leukemia cells could be distinguished from normal B-cells by their increased expression of Rarγ2 and RXRα, respectively. These findings establish a role for retinoids in murine CLL pathogenesis, and provide new therapeutic strategies to target the microenvironment and to control disease progression.

The stromal microenvironment plays a key role in the expansion of chronic lymphocytic leukemia. Here, the authors use the Eµ-TCL1 mouse model to show that leukemic B-cells induce the activation of retinoic acid synthesis in stromal cells of the lymphoid microenvironment, and that impacting on retinoic acid signalling via diet or chemical inhibition prolonged survival by preventing leukemia dissemination and accumulation in lymphoid tissues.

Targeting of NAT10 enhances healthspan in a mouse model of human accelerated aging syndrome

Hutchinson-Gilford Progeria Syndrome (HGPS) is a rare, but devastating genetic disease characterized by segmental premature aging, with cardiovascular disease being the main cause of death. Cells from HGPS patients accumulate progerin, a permanently farnesylated, toxic form of Lamin A, disrupting the nuclear shape and chromatin organization, leading to DNA-damage accumulation and senescence. Therapeutic approaches targeting farnesylation or aiming to reduce progerin levels have provided only partial health improvements. Recently, we identified Remodelin, a small-molecule agent that leads to amelioration of HGPS cellular defects through inhibition of the enzyme N-acetyltransferase 10 (NAT10). Here, we show the preclinical data demonstrating that targeting NAT10 in vivo, either via chemical inhibition or genetic depletion, significantly enhances the healthspan in a Lmna G609G HGPS mouse model. Collectively, the data provided here highlights NAT10 as a potential therapeutic target for HGPS.

Sensing the cilium, digital capture of ciliary data for comparative genomics investigations

Background

Cilia are specialized, hair-like structures that project from the cell bodies of eukaryotic cells. With increased understanding of the distribution and functions of various types of cilia, interest in these organelles is accelerating. To effectively use this great expansion in knowledge, this information must be made digitally accessible and available for large-scale analytical and computational investigation. Capture and integration of knowledge about cilia into existing knowledge bases, thus providing the ability to improve comparative genomic data analysis, is the objective of this work.

Methods

We focused on the capture of information about cilia as studied in the laboratory mouse, a primary model of human biology. The workflow developed establishes a standard for capture of comparative functional data relevant to human biology. We established the 310 closest mouse orthologs of the 302 human genes defined in the SYSCILIA Gold Standard set of ciliary genes. For the mouse genes, we identified biomedical literature for curation and used Gene Ontology (GO) curation paradigms to provide functional annotations from these publications.

Results

Employing a methodology for comprehensive capture of experimental data about cilia genes in structured, digital form, we established a workflow for curation of experimental literature detailing molecular function and roles of cilia proteins starting with the mouse orthologs of the human SYSCILIA gene set. We worked closely with the GO Consortium ontology development editors and the SYSCILIA Consortium to improve the representation of ciliary biology within the GO. During the time frame of the ontology improvement project, we have fully curated 134 of these 310 mouse genes, resulting in an increase in the number of ciliary and other experimental annotations.

Conclusions

We have improved the GO annotations available for mouse genes orthologous to the human genes in the SYSCILIA Consortium’s Gold Standard set. In addition, ciliary terminology in the GO itself was improved in collaboration with GO ontology developers and the SYSCILIA Consortium. These improvements to the GO terms for the functions and roles of ciliary proteins, along with the increase in annotations of the corresponding genes, enhance the representation of ciliary processes and localizations and improve access to these data during large-scale bioinformatic analyses.

Electronic supplementary material

The online version of this article (10.1186/s13630-018-0057-0) contains supplementary material, which is available to authorized users.

Split-BioID a conditional proteomics approach to monitor the composition of spatiotemporally defined protein complexes

Understanding the function of the thousands of cellular proteins is a central question in molecular cell biology. As proteins are typically part of multiple dynamic and often overlapping macromolecular complexes exerting distinct functions, the identification of protein–protein interactions (PPI) and their assignment to specific complexes is a crucial but challenging task. We present a protein fragments complementation assay integrated with the proximity-dependent biotinylation technique BioID. Activated on the interaction of two proteins, split-BioID is a conditional proteomics approach that allows in a single and simple assay to both experimentally validate binary PPI and to unbiasedly identify additional interacting factors. Applying our method to the miRNA-mediated silencing pathway, we can probe the proteomes of two distinct functional complexes containing the Ago2 protein and uncover the protein GIGYF2 as a regulator of miRNA-mediated translation repression. Hence, we provide a novel tool to study dynamic spatiotemporally defined protein complexes in their native cellular environment.

The BioID approaches takes advantage of the promiscuous biotinylation enzyme (BirA*) to identify proteins that closely interact. Here the authors improve the resolution of BioID using a protein fragment complementation approach that allows the assignment of protein-protein interactions to specific complexes within a common interactome.

NaviGO: interactive tool for visualization and functional similarity and coherence analysis with gene ontology

Background

The number of genomics and proteomics experiments is growing rapidly, producing an ever-increasing amount of data that are awaiting functional interpretation. A number of function prediction algorithms were developed and improved to enable fast and automatic function annotation. With the well-defined structure and manual curation, Gene Ontology (GO) is the most frequently used vocabulary for representing gene functions. To understand relationship and similarity between GO annotations of genes, it is important to have a convenient pipeline that quantifies and visualizes the GO function analyses in a systematic fashion.

Results

NaviGO is a web-based tool for interactive visualization, retrieval, and computation of functional similarity and associations of GO terms and genes. Similarity of GO terms and gene functions is quantified with six different scores including protein-protein interaction and context based association scores we have developed in our previous works. Interactive navigation of the GO function space provides intuitive and effective real-time visualization of functional groupings of GO terms and genes as well as statistical analysis of enriched functions.

Conclusions

We developed NaviGO, which visualizes and analyses functional similarity and associations of GO terms and genes. The NaviGO webserver is freely available at: http://kiharalab.org/web/navigo.

Mouse Genome Informatics (MGI): Resources for Mining Mouse Genetic, Genomic, and Biological Data in Support of Primary and Translational Research

The Mouse Genome Informatics (MGI), resource ( www.informatics.jax.org ) has existed for over 25 years, and over this time its data content, informatics infrastructure, and user interfaces and tools have undergone dramatic changes (Eppig et al., Mamm Genome 26:272-284, 2015). Change has been driven by scientific methodological advances, rapid improvements in computational software, growth in computer hardware capacity, and the ongoing collaborative nature of the mouse genomics community in building resources and sharing data. Here we present an overview of the current data content of MGI, describe its general organization, and provide examples using simple and complex searches, and tools for mining and retrieving sets of data.

Temporal dynamics of the developing lung transcriptome in three common inbred strains of laboratory mice reveals multiple stages of postnatal alveolar development

To characterize temporal patterns of transcriptional activity during normal lung development, we generated genome wide gene expression data for 26 pre- and post-natal time points in three common inbred strains of laboratory mice (C57BL/6J, A/J, and C3H/HeJ). Using Principal Component Analysis and least squares regression modeling, we identified both strain-independent and strain-dependent patterns of gene expression. The 4,683 genes contributing to the strain-independent expression patterns were used to define a murine Developing Lung Characteristic Subtranscriptome (mDLCS). Regression modeling of the Principal Components supported the four canonical stages of mammalian embryonic lung development (embryonic, pseudoglandular, canalicular, saccular) defined previously by morphology and histology. For postnatal alveolar development, the regression model was consistent with four stages of alveolarization characterized by episodic transcriptional activity of genes related to pulmonary vascularization. Genes expressed in a strain-dependent manner were enriched for annotations related to neurogenesis, extracellular matrix organization, and Wnt signaling. Finally, a comparison of mouse and human transcriptomics from pre-natal stages of lung development revealed conservation of pathways associated with cell cycle, axon guidance, immune function, and metabolism as well as organism-specific expression of genes associated with extracellular matrix organization and protein modification. The mouse lung development transcriptome data generated for this study serves as a unique reference set to identify genes and pathways essential for normal mammalian lung development and for investigations into the developmental origins of respiratory disease and cancer. The gene expression data are available from the Gene Expression Omnibus (GEO) archive (GSE74243). Temporal expression patterns of mouse genes can be investigated using a study specific web resource (http://lungdevelopment.jax.org).

Altered gene expression in the lower respiratory tract of Car6 (-/-) mice

From birth, the respiratory tract mucosa is exposed to various chemical, physical, and microbiological stress factors. Efficient defense mechanisms and strictly regulated renewal systems in the mucosa are thus required. Carbonic anhydrase VI (CA VI) is the only secreted isoenzyme of the α-CA gene family. It is transported in high concentrations in saliva and milk into the alimentary tract where it contributes to optimal pH homeostasis. Earlier study of transcriptomic responses of Car6 (-/-) mice has shown changes in the response to oxidative stress and brown fat cell differentiation in the submandibular gland. It has been suggested that CA VI delivered to the mucosal surface of the bronchiolar epithelium is an essential factor in defense and renewal of the lining epithelium. In this study, the transcriptional effects of CA VI deficiency were investigated in both trachea and lung of Car6 (-/-) mice using a cDNA microarray analysis. Functional clustering of the results indicated significant changes of gene transcription in the lower airways. The altered biological processes included antigen transport by M-cells, potassium transport, muscle contraction, and thyroid hormone synthesis. Immunohistochemical staining confirmed the absence of CA VI in the submandibular gland of Car6 (-/-) mice. Immunostaining of the trachea and lung samples revealed no differences between the knockout and wild type groups nor were any morphological changes observed. The present findings can help us to recognize novel functions for CA VI-one of the major protein constituents of saliva and milk.