Expression Atlas Research Articles

Integrative Modeling of Gene Expression and Metabolic Networks of Arabidopsis Embryos for Identification of Seed Oil Causal Genes.

Fatty acids in crop seeds are a major source for both vegetable oils and industrial applications. Genetic improvement of fatty acid composition and oil content is critical to meet the current and future demands of plant-based renewable seed oils. Addressing this challenge can be approached by network modeling to capture key contributors of seed metabolism and to identify underpinning genetic targets for engineering the traits associated with seed oil composition and content. Here, we present a dynamic model, using an Ordinary Differential Equations model and integrated time-course gene expression data, to describe metabolic networks during Arabidopsis thaliana seed development. Through in silico perturbation of genes, targets were predicted in seed oil traits. Validation and supporting evidence were obtained for several of these predictions using published reports in the scientific literature. Furthermore, we investigated two predicted targets using omics datasets for both gene expression and metabolites from the seed embryo, and demonstrated the applicability of this network-based model. This work highlights that integration of dynamic gene expression atlases generates informative models which can be explored to dissect metabolic pathways and lead to the identification of causal genes associated with seed oil traits.

USP42 protects ZNRF3/RNF43 from R-spondin-dependent clearance and inhibits Wnt signalling.

The tumour suppressors RNF43 and ZNRF3 play a central role in development and tissue homeostasis by promoting the turnover of the Wnt receptors LRP6 and Frizzled (FZD). The stem cell growth factor R‐spondin induces auto‐ubiquitination and membrane clearance of ZNRF3/RNF43 to promote Wnt signalling. However, the deubiquitinase stabilising ZNRF3/RNF43 at the plasma membrane remains unknown. Here, we show that the USP42 antagonises R‐spondin by protecting ZNRF3/RNF43 from ubiquitin‐dependent clearance. USP42 binds to the Dishevelled interacting region (DIR) of ZNRF3 and stalls the R‐spondin‐LGR4‐ZNRF3 ternary complex by deubiquitinating ZNRF3. Accordingly, USP42 increases the turnover of LRP6 and Frizzled (FZD) receptors and inhibits Wnt signalling. Furthermore, we show that USP42 functions as a roadblock for paracrine Wnt signalling in colon cancer cells and mouse small intestinal organoids. We provide new mechanistic insights into the regulation R‐spondin and conclude that USP42 is crucial for ZNRF3/RNF43 stabilisation at the cell surface.

LSTrAP-Kingdom: an automated pipeline to generate annotated gene expression atlases for kingdoms of life.

There are now more than two million RNA sequencing experiments for plants, animals, bacteria and fungi publicly available, allowing us to study gene expression within and across species and kingdoms. However, the tools allowing the download, quality control and annotation of this data for more than one species at a time are currently missing. To remedy this, we present the Large-Scale Transcriptomic Analysis Pipeline in Kingdom of Life (LSTrAP-Kingdom) pipeline, which we used to process 134521 RNA-seq samples, achieving ∼12000 processed samples per day. Our pipeline generated quality-controlled, annotated gene expression matrices that rival the manually curated gene expression data in identifying functionally related genes. LSTrAP-Kingdom is available from: https://github.com/wirriamm/plants-pipeline and is fully implemented in Python and Bash. Supplementary data are available at Bioinformatics online.

Single cell transcriptomics of the developing zebrafish lens and identification of putative controllers of lens development

The vertebrate lens is a valuable model system for investigating the gene expression changes that coordinate tissue differentiation due to its inclusion of two spatially separated cell types, the outer epithelial cells and the deeper denucleated fiber cells that they support. Zebrafish are a useful model system for studying lens development given the organ's rapid development in the first several days of life in an accessible, transparent embryo. While we have strong foundational knowledge of the diverse lens crystallin proteins and the basic gene regulatory networks controlling lens development, no study has detailed gene expression in a vertebrate lens at single cell resolution. Here we report an atlas of lens gene expression in zebrafish embryos and larvae at single cell resolution through five days of development, identifying a number of novel putative regulators of lens development. Our data address open questions about the temperospatial expression of α-crystallins during lens development that will support future studies of their function and provide the first detailed view of β- and γ-crystallin expression in and outside the lens. We describe divergent expression in transcription factor genes that occur as paralog pairs in the zebrafish. Finally, we examine the expression dynamics of cytoskeletal, membrane associated, RNA-binding, and transcription factor genes, identifying a number of novel patterns. Overall these data provide a foundation for identifying and characterizing lens developmental regulatory mechanisms and revealing targets for future functional studies with potential therapeutic impact.

Genome-Wide Histone Modifications and CTCF Enrichment Predict Gene Expression in Sheep Macrophages.

Alveolar macrophages function in innate and adaptive immunity, wound healing, and homeostasis in the lungs dependent on tissue-specific gene expression under epigenetic regulation. The functional diversity of tissue resident macrophages, despite their common myeloid lineage, highlights the need to study tissue-specific regulatory elements that control gene expression. Increasing evidence supports the hypothesis that subtle genetic changes alter sheep macrophage response to important production pathogens and zoonoses, for example, viruses like small ruminant lentiviruses and bacteria like Coxiella burnetii. Annotation of transcriptional regulatory elements will aid researchers in identifying genetic mutations of immunological consequence. Here we report the first genome-wide survey of regulatory elements in any sheep immune cell, utilizing alveolar macrophages. We assayed histone modifications and CTCF enrichment by chromatin immunoprecipitation with deep sequencing (ChIP-seq) in two sheep to determine cis-regulatory DNA elements and chromatin domain boundaries that control immunity-related gene expression. Histone modifications included H3K4me3 (denoting active promoters), H3K27ac (active enhancers), H3K4me1 (primed and distal enhancers), and H3K27me3 (broad silencers). In total, we identified 248,674 reproducible regulatory elements, which allowed assignment of putative biological function in macrophages to 12% of the sheep genome. Data exceeded the FAANG and ENCODE standards of 20 million and 45 million useable fragments for narrow and broad marks, respectively. Active elements showed consensus with RNA-seq data and were predictive of gene expression in alveolar macrophages from the publicly available Sheep Gene Expression Atlas. Silencer elements were not enriched for expressed genes, but rather for repressed developmental genes. CTCF enrichment enabled identification of 11,000 chromatin domains with mean size of 258 kb. To our knowledge, this is the first report to use immunoprecipitated CTCF to determine putative topological domains in sheep immune cells. Furthermore, these data will empower phenotype-associated mutation discovery since most causal variants are within regulatory elements.

Mediators of SARS-CoV-2 entry are preferentially enriched in cardiomyocytes

BackgroundThe coronavirus disease 2019 (COVID-19) has spread rapidly around the world. In addition to common respiratory symptoms such as cough and fever, some patients also have cardiac injury, however, the mechanism of cardiac injury is not clear. In this study, we analyzed the RNA expression atlases of angiotensin-converting enzyme 2(ACE2), cathepsin B (CTSB) and cathepsin L (CTSL) in the human embryonic heart at single-cell resolution.ResultsThe results showed that ACE2 was preferentially enriched in cardiomyocytes. Interestingly, serine protease transmembrane serine protease 2 (TMPRSS2) had less expression in cardiomyocytes, but CTSB and CTSL, which belonged to cell protease, could be found to be enriched in cardiomyocytes. The results of enrichment analysis showed that differentially expressed genes (DEGs) in ACE2-positive cardiomyocytes were mainly enriched in the processes of cardiac muscle contraction, regulation of cardiac conduction, mitochondrial respiratory chain, ion channel binding, adrenergic signaling in cardiomyocytes and viral transcription.ConclusionsOur study suggests that both atrial and ventricular cardiomyocytes are potentially susceptible to severe acute respiratory syndrome coronavirus-2(SARS-CoV-2), and SARS-CoV-2 may enter ventricular cardiomyocytes using CTSB/CTSL for S protein priming. This may be the partial cellular mechanism of cardiac injury in patients with COVID-19.

The miR-142 Suppresses U-87 Glioblastoma Cell Growth by Targeting EGFR Oncogenic Signaling Pathway.

Glioblastoma is the most lethal malignancy of the brain and is resistant to conventional cancer treatments. Gene-therapy approaches like using tumor suppressor miRNAs are promising in the treatment of glioblastoma. They control the expression of oncogenes and influence tumor features and behaviors. Therefore, in the present study, it was predicted that miR-142 regulates oncogenic epidermal growth factor receptor (EGFR) signaling pathway via TargetScan and miRWalk online tools. Its differential expression level was reduced in glioblastoma according to the previous microarray results, and its predicted target genes were upregulated, as shown by the Expression Atlas. The miR-142 was overexpressed in U-87 glioblastoma cells via lentiviral transduction, and the way it influences proliferation and migration of cells was investigated through MTT assay and wound healing assay. Apoptosis rate was also measured via the Annexin V assay, and cell-cycle analysis was done. Then, real-time polymerase chain reaction (real-time PCR) and western blotting were performed to assess fold changes in mRNA and protein levels of the miR-142 predicted targets. Direct target genes of miR-142 were confirmed through a dual-luciferase reporter assay. The miR-142 significantly suppressed cell proliferation and migration and induced apoptosis and cell-cycle arrest in U-87 glioblastoma cells. This was accompanied by a decrease in expression of SHC adaptor protein 4 (SHC4), phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA), v-akt murine thymoma viral oncogene homolog 1 (AKT1), Kirsten rat sarcoma viral oncogene homolog (KRAS), and mitogen-activated protein kinase 8 (MAPK8) oncogenes at mRNA and protein levels in glioblastoma cells. Also, AKT1 was demonstrated as a direct target of miR-142. Overall, miR-424 acts as tumor suppressor miRNA in glioblastoma cells.

Interacting Genetic Lesions of Melanoma in the Tumor Microenvironment: Defining a Viable Therapy.

Melanoma is the most aggressive form of skin cancer with an estimated 106,110 newly diagnosed cases in the United States of America in 2021 leading to an approximated 7180 melanoma-induced deaths. Cancer typically arises from an accumulation of somatic mutations and can be associated with mutagenic or carcinogenic exposure. A key characteristic of melanoma is the extensive somatic mutation rate of 16.8 mutations/Mb, which is largely attributed to UV exposure. Bearing the highest mutational load, many of them occur in key driver pathways, most commonly the BRAFV600E in the mitogen-activated protein kinase (MAPK) pathway. This driver mutation is targeted clinically with FDA-approved therapies using small molecule inhibitors of oncogenic BRAFV600E and MEK, which has greatly expanded therapeutic intervention following a melanoma diagnosis. Up until 2011, therapeutic options for metastatic melanoma were limited, and treatment typically fell under the spectrum of surgery, radiotherapy, and chemotherapy.Attributed to the extensive mutation rate, as well as having the highest number of neoepitopes, melanoma is deemed to be extremely immunogenic. However, despite this highly immunogenic nature, melanoma is notorious for inducing an immunosuppressive microenvironment which can be relieved by checkpoint inhibitor therapy. The two molecules currently approved clinically are ipilimumab and nivolumab, which target the molecules CTLA-4 and PD-1, respectively.A plethora of immunomodulatory molecules exist, many with redundant functions. Additionally, these molecules are expressed not only by immune cells but also by tumor cells within the tumor microenvironment. Tumor profiling of these cell surface checkpoint molecules is necessary to optimize a clinical response. The presence of immunomodulatory molecules in melanoma, using data from The Cancer Genome Atlas and validation of expression in two model systems, human melanoma tissues and patient-derived melanoma cells, revealed that the expression levels of B and T lymphocyte attenuator (BTLA), TIM1, and CD226, concurrently with the BRAFV600E mutation status, significantly dictated overall survival in melanoma patients. These molecules, along with herpesvirus entry mediator (HVEM) and CD160, two molecules that are a part of the HVEM/BTLA/CD160 axis, had a higher expression in human melanoma tissues when compared to normal skin melanocytes and have unique roles to play in T cell activation. New links are being uncovered between the expression of immunomodulatory molecules and the BRAFV600E genetic lesion in melanoma. Small molecule inhibitors of the MAPK pathway regulate the surface expression of this multifaceted molecule, making BTLA a promising target for immuno-oncology to be targeted in combination with small molecule inhibitors, potentially alleviating T regulatory cell activation and improving patient prognosis.

Identification of proteins and metabolic networks associated with sucrose accumulation in sugarcane (Saccharum spp. interspecific hybrids)

ABSTRACT The rate of improvement of sugar content in sugarcane remains low for decades worldwide. Our previous transcriptome studies provided an atlas of sucrose accumulation-related gene expression, but little is known about the proteins involved. Here, we conducted a proteomic analysis of experimentally altered sucrose accumulation in sugarcane. Analysis of stem proteomes of sugarcane ripener ethephon treated high- and low-sugar genotypes had identified 2983 proteins of which 139 were significantly differentially expressed (DEPs). These DEPs were found to be associated with sugar metabolism-related processes with 25 of them may have a regulatory role in sucrose accumulation. The key proteins identified include UDP-glucose 6-dehydrogenase associated with amino sugar and nucleotide sugar metabolism; those involved in carbon fixation; and fructokinase, β-D-glucosidase, and α-glucan phosphorylase involved in starch and sucrose metabolism. Distinct genotype- and ethephon-dependent DEP expression was evident providing new insights into one of the most intractable sugarcane traits to breeding.

Up-regulation of MARVEL domain-containing protein 1 (MARVELD1) accelerated the malignant phenotype of glioma cancer cells via mediating JAK/STAT signaling pathway.

This work aimed to research the function of MARVEL domain-containing protein 1 (MARVELD1) in glioma as well as its functioning mode. Bioinformatics analysis was utilized to assess the MARVELD1 expression in glioma tissues and its relationship with grade and prognosis, based on The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Chinese Glioma Genome Atlas (CGGA) databases. Cell Counting Kit-8 (CCK-8), colony formation, and Transwell assays were carried out to determine the impact of MARVELD1 on malignant biological behavior of glioma, such as proliferation, invasion, and migration. qRT-PCR was carried out to test the mRNA level of MARVELD1. Western blot assay was performed to measure the protein expression of MARVELD1 and JAK/STAT pathway-related proteins. MARVELD1 was expressed at high levels in glioma tissues and cell lines. Kaplan-Meier survival analysis revealed that the higher MARVELD1 expression, the shorter the survival time of patients with glioma. Also, the MARVELD1 expression in WHO IV was significantly enhanced compared to that in WHO II and WHO III. Furthermore, the functional analysis of MARVELD1 in vitro revealed that knockdown of MARVELD1 in U251 cells restrained cell proliferation, migration, and invasion, while up-regulation of MARVELD1 in U87 cells presented opposite outcomes. Finally, we found that JAK/STAT signaling pathway mediated the function of MARVELD1 in glioma. MARVELD1 contributed to promoting the malignant progression of glioma, which is the key driver of activation of JAK/STAT signaling pathway in gliomas.

Human CHR18: “Stakhanovite” Genes, Missing and uPE1 Proteins in Liver Tissue and HepG2 Cells

Missing (MP) and functionally uncharacterized proteins (uPE1) comprise less than 5% of the total number of proteins encoded by human Chr18 genes. Within half a year, since the January 2020 version of NextProt, the number of entries in the MP+uPE1 datasets changed, mainly due to the achievements of antibody-based proteomics. Assuming that the proteome is closely related to the transcriptome scaffold, quantitative PCR, Illumina HiSeq, and Oxford Nanopore Technology were applied to characterize the liver samples of three male donors in comparison with the HepG2 cell line. The data mining of the Expression Atlas (EMBL-EBI) and the profiling of biopsy samples by using orthogonal methods of transcriptome analysis have shown that in HepG2 cells and the liver, the genes encoding functionally uncharacterized proteins (uPE1) are expressed as low as for the missing proteins (less than 1 copy per cell), except the selected cases of HSBP1L1, TMEM241, C18orf21, and KLHL14. The initial expectation that uPE1 genes might be expressed at higher levels than MP genes, was compromised by severe discrepancies in our semi-quantitative gene expression data and in public databanks. Such discrepancy forced us to revisit the transcriptome of Chr18, the target of the Russian C-HPP Consortium. Tanglegram of highly expressed genes and further correlation analysis have shown the severe dependencies on the mRNA extraction method and the analytical platform. Targeted gene expression analysis by quantitative PCR (qPCR) and high-throughput transcriptome profiling (Illumina HiSeq and ONT MinION) for the same set of samples from normal liver tissue and HepG2 cells revealed the detectable expression of 250+ (92%) protein-coding genes of Chr18 (at least one method). The expression of slightly more than 50% protein-coding genes was detected simultaneously by all three methods. Correlation analysis of the gene expression profiles showed that the grouping of the datasets depended almost equally on both the type of biological material and the experimental method, particularly cDNA/mRNA isolation and library preparation.

Assessment of transcriptional importance of cell line-specific features based on GTRD and FANTOM5 data.

Creating a complete picture of the regulation of transcription seems to be an urgent task of modern biology. Regulation of transcription is a complex process carried out by transcription factors (TFs) and auxiliary proteins. Over the past decade, ChIP-Seq has become the most common experimental technology studying genome-wide interactions between TFs and DNA. We assessed the transcriptional significance of cell line-specific features using regression analysis of ChIP-Seq datasets from the GTRD database and transcriptional start site (TSS) activities from the FANTOM5 expression atlas. For this purpose, we initially generated a large number of features that were defined as the presence or absence of TFs in different promoter regions around TSSs. Using feature selection and regression analysis, we identified sets of the most important TFs that affect expression activity of TSSs in human cell lines such as HepG2, K562 and HEK293. We demonstrated that some TFs can be classified as repressors and activators depending on their location relative to TSS.

Polo like kinase 1 expression in cervical cancer tissues generated from multiple detection methods.

BackgroundExisting studies of PLK1 in cervical cancer had several flaws. The methods adopted by those studies of detecting PLK1 expression in cervical cancer were single and there lacks comprehensive evaluation of the clinico-pathological significance of PLK1 in cervical cancer.MethodsA total of 303 cervical tissue samples were collected for in-house tissue microarrays. Immunohistochemistry was performed for evaluating PLK1 expression between cervical cancer (including cervical squamous cell carcinoma (CESC) and cervical adenocarcinoma) and non-cancer samples. The Expression Atlas database was searched for querying PLK1 expression in different cervical cancer cell lines and different tissues in the context of pan-cancer. Standard mean difference (SMD) was calculated and the summarized receiver’s operating characteristics (SROC) curves were plotted for integrated tissue microarrays, exterior high-throughput microarrays and RNA sequencing data as further verification. The effect of PLK1 expression on the overall survival, disease-free survival and event-free survival of cervical cancer patients was analyzed through Kaplan Meier survival curves for cervical cancer patients from RNA-seq and GSE44001 datasets. The gene mutation and alteration status of PLK1 in cervical cancer was inspected in COSMIC and cBioPortal databases. Functional enrichment analysis was performed for genes correlated with PLK1 from aggregated RNA-seq and microarrays.ResultsA total of 963 cervical cancer samples and 178 non-cancer samples were collected from in-house tissue microarrays and exterior microarrays and RNA-seq datasets. The combined expression analysis supported overexpression of PLK1 in CESC, cervical adenocarcinoma and all types of cervical cancer (SMD = 1.59, 95%CI [0.56–2.63]; SMD = 2.99, 95%CI [0.75–5.24]; SMD = 1.57, 95% CI [0.85–2.29]) and the significant power of PLK1 expression in distinguishing CESC or all types of cervical cancer samples from non-cancer samples (AUC = 0.94, AUC = 0.92). Kaplan-Meier survival curves showed that the event-free survival rate of cervical cancer patients with higher expression of PLK1 was shorter than that of patients with lower PLK1 (HR = 2.020, P = 0.0197). Genetic alteration of PLK1 including missense mutation and mRNA low occurred in 6% of cervical cancer samples profiled in mRNA expression. Genes positively or negatively correlated with PLK1 were mainly assembled in pathways such as DNA replication, cell cycle, mismatch repair, Ras signaling pathway, melanoma, EGFR tyrosine kinase inhibitor resistance and homologous recombination (P < 0.05).ConclusionsHere, we provided sufficient evidence of PLK1 overexpression in cervical cancer. The overexpression of PLK1 in cervical cancer and the contributory effect of it on clinical progression indicated the hopeful prospect of PLK1 as a biomarker for cervical cancer.

Challenges and possible solutions for decoding extranasal olfactory receptors.

Olfactory receptors are primarily known to be expressed in the olfactory epithelium of the nasal cavity and therefore assist in odor perception. With the advent of high-throughput omics technologies such as tissue microarray or RNA sequencing, a large number of olfactory receptors have been reported to be expressed in the nonolfactory tissues. Although these technologies uncovered the expression of these olfactory receptors in the nonchemosensory tissues, unfortunately, they failed to reveal the information about their cell type of origin. Accurate characterization of the cell types should be the first step towards devising cell type-specific assays for their functional evaluation. Single-cell RNA-sequencing technology resolved some of these apparent limitations and opened new means to interrogate the expression of these extranasal olfactory receptors at the single-cell resolution. Moreover, the availability of large-scale, multi-organ/species single-cell expression atlases offer ample resources for the systematic reannotation of these receptors in a cell type-specific manner. In this Viewpoint article, we discuss some of the technical limitations that impede the in-depth understanding of these extranasal olfactory receptors, with a special focus on odorant receptors. Moreover, we also propose a list of single cell-based omics technologies that could further promulgate the opportunity to decipher the regulatory network that drives the odorant receptors expression at atypical locations.

The mouse Gene Expression Database (GXD): 2021 update

The Gene Expression Database (GXD; www.informatics.jax.org/expression.shtml) is an extensive and well-curated community resource of mouse developmental gene expression information. For many years, GXD has collected and integrated data from RNA in situ hybridization, immunohistochemistry, RT-PCR, northern blot, and western blot experiments through curation of the scientific literature and by collaborations with large-scale expression projects. Since our last report in 2019, we have continued to acquire these classical types of expression data; developed a searchable index of RNA-Seq and microarray experiments that allows users to quickly and reliably find specific mouse expression studies in ArrayExpress (https://www.ebi.ac.uk/arrayexpress/) and GEO (https://www.ncbi.nlm.nih.gov/geo/); and expanded GXD to include RNA-Seq data. Uniformly processed RNA-Seq data are imported from the EBI Expression Atlas and then integrated with the other types of expression data in GXD, and with the genetic, functional, phenotypic and disease-related information in Mouse Genome Informatics (MGI). This integration has made the RNA-Seq data accessible via GXD’s enhanced searching and filtering capabilities. Further, we have embedded the Morpheus heat map utility into the GXD user interface to provide additional tools for display and analysis of RNA-Seq data, including heat map visualization, sorting, filtering, hierarchical clustering, nearest neighbors analysis and visual enrichment.

MaizeMine: A Data Mining Warehouse for the Maize Genetics and Genomics Database

MaizeMine is the data mining resource of the Maize Genetics and Genome Database (MaizeGDB; http://maizemine.maizegdb.org). It enables researchers to create and export customized annotation datasets that can be merged with their own research data for use in downstream analyses. MaizeMine uses the InterMine data warehousing system to integrate genomic sequences and gene annotations from the Zea mays B73 RefGen_v3 and B73 RefGen_v4 genome assemblies, Gene Ontology annotations, single nucleotide polymorphisms, protein annotations, homologs, pathways, and precomputed gene expression levels based on RNA-seq data from the Z. mays B73 Gene Expression Atlas. MaizeMine also provides database cross references between genes of alternative gene sets from Gramene and NCBI RefSeq. MaizeMine includes several search tools, including a keyword search, built-in template queries with intuitive search menus, and a QueryBuilder tool for creating custom queries. The Genomic Regions search tool executes queries based on lists of genome coordinates, and supports both the B73 RefGen_v3 and B73 RefGen_v4 assemblies. The List tool allows you to upload identifiers to create custom lists, perform set operations such as unions and intersections, and execute template queries with lists. When used with gene identifiers, the List tool automatically provides gene set enrichment for Gene Ontology (GO) and pathways, with a choice of statistical parameters and background gene sets. With the ability to save query outputs as lists that can be input to new queries, MaizeMine provides limitless possibilities for data integration and meta-analysis.

A multigenotype maize silk expression atlas reveals how exposure-related stresses are mitigated following emergence from husk leaves.

The extraordinarily long stigmatic silks of corn (Zea mays L.) are critical for grain production but the biology of their growth and emergence from husk leaves has remained underexplored. Accordingly, gene expression was assayed for inbreds 'B73' and 'Mo17' across five contiguous silk sections. Half of the maize genes (∼20,000) are expressed in silks, mostly in spatiotemporally dynamic patterns. In particular, emergence triggers strong differential expression of ∼1,500 genes collectively enriched for gene ontology terms associated with abiotic and biotic stress responses, hormone signaling, cell-cell communication, and defense metabolism. Further, a meta-analysis of published maize transcriptomic studies on seedling stress showed that silk emergence elicits an upregulated transcriptomic response that overlaps strongly with both abiotic and biotic stress responses. Although the two inbreds revealed similar silk transcriptomic programs overall, genotypic expression differences were observed for 5,643 B73-Mo17 syntenic gene pairs and collectively account for>50% of genome-wide expression variance. Coexpression clusters, including many based on genotypic divergence, were identified and interrogated via ontology-term enrichment analyses to generate biological hypotheses for future research. Ultimately, dissecting how gene expression changes along the length of silks and between husk-encased and emerged states offers testable models for silk development and plant response to environmental stresses.

A simple, scalable approach to building a cross-platform transcriptome atlas.

Gene expression atlases have transformed our understanding of the development, composition and function of human tissues. New technologies promise improved cellular or molecular resolution, and have led to the identification of new cell types, or better defined cell states. But as new technologies emerge, information derived on old platforms becomes obsolete. We demonstrate that it is possible to combine a large number of different profiling experiments summarised from dozens of laboratories and representing hundreds of donors, to create an integrated molecular map of human tissue. As an example, we combine 850 samples from 38 platforms to build an integrated atlas of human blood cells. We achieve robust and unbiased cell type clustering using a variance partitioning method, selecting genes with low platform bias relative to biological variation. Other than an initial rescaling, no other transformation to the primary data is applied through batch correction or renormalisation. Additional data, including single-cell datasets, can be projected for comparison, classification and annotation. The resulting atlas provides a multi-scaled approach to visualise and analyse the relationships between sets of genes and blood cell lineages, including the maturation and activation of leukocytes in vivo and in vitro. In allowing for data integration across hundreds of studies, we address a key reproduciblity challenge which is faced by any new technology. This allows us to draw on the deep phenotypes and functional annotations that accompany traditional profiling methods, and provide important context to the high cellular resolution of single cell profiling. Here, we have implemented the blood atlas in the open access Stemformatics.org platform, drawing on its extensive collection of curated transcriptome data. The method is simple, scalable and amenable for rapid deployment in other biological systems or computational workflows.

Digging for Stress-Responsive Cell Wall Proteins for Developing Stress-Resistant Maize.

As a vital component of plant cell walls, proteins play important roles in stress response by modifying the structure of cell walls and involving in the wall integrity signaling pathway. Recently, we have critically reviewed the predictors, databases, and cross-referencing of the subcellular locations of possible cell wall proteins (CWPs) in plants (Briefings in Bioinformatics 2018;19:1130–1140). Here, we briefly introduce strategies for isolating CWPs during proteomic analysis. Taking maize (Zea mays) as an example, we retrieved 1873 probable maize CWPs recorded in the UniProt KnowledgeBase (UniProtKB). After curation, 863 maize CWPs were identified and classified into 59 kinds of protein families. By referring to gene ontology (GO) annotations and gene differential expression in the Expression Atlas, we have highlighted the potential of CWPs acting in the front line of defense against biotic and abiotic stresses. Moreover, the analysis results of cis-acting elements revealed the responsiveness of the genes encoding CWPs toward phytohormones and various stresses. We suggest that the stress-responsive CWPs could be promising candidates for applications in developing varieties of stress-resistant maize.

Abstract 2535: Screening PTEN-loss associated lncRNAs in prostate cancer

Abstract Phosphatase and tensin homologue (PTEN) is a tumor suppressor gene that is frequently inactivated by deletion in prostate cancer (PCa). Occurring in around 20% of primary PCa tumors, and up to 50% in castration resistant tumors, it is the most frequent genomic aberration in PCa. Loss of PTEN activates the phosphoinositide 3-kinase-RAC-alpha serine/threonine-protein kinase (PI3K-AKT) pathway, which is associated with poor clinical outcomes. Despite the consequences of PTEN loss being well studied, most of what is known is restricted to protein-coding genes, with relatively little information about the role of non-coding genes. Using our recently created resource - the FC-R2 expression atlas, which encompasses expression levels for thousands of lncRNAs recently unveiled by the FANTOM consortium - we analyzed differential gene expression of PTEN-null vs PTEN-intact tumors with the goal of characterizing the molecular landscape of PTEN loss. First, we generated a consensus signature using two large PCa cohorts with experimentally validated PTEN status by Immunohistochemistry (IHC), applying a meta-analysis approach. This signature encompassed mainly protein coding genes due it being microarray based. In order to expand this signature beyond the coding genes, we relied on FC-R2-based TCGA-PRAD data. Since PTEN status was not available by IHC, we opted to call the status based on CNV. Then, we proceed to generate a PTEN-null signature using a generalized linear model approach. Both signatures were compared for concordance using correspondence-at-the-top plots and hypergeometric confidence intervals. Gene set enrichment analysis was performed in both signatures using a collection of obtained from the MSigDB database in order to characterize pathways involved in this event. Our results showed that the signature based on IHC validated samples agreed significantly with the CNV-based signature from TCGA for the genes in common. In the differential gene expression analysis on the TCGA cohort we observed 203 significant coding genes and 171 significant non-coding genes (FDR ≤ 0.01, LogFC ≥ 1). Notably, we identified several lncRNAs that have not been associated with PCa or PTEN loss, these include many classes of non-coding RNAs characterized by the FANTOM consortium such as: enhancers and promoters genes. Gene set enrichment analysis revealed that PTEN-null tumors are associated with epithelial-mesenchymal transition suggesting a possible role for these lncRNAs. In conclusion, by leveraging our resources, we were able to obtain comprehensive landscape of the PTEN loss in PCa for both the coding and non-coding counterpart. Furthermore, the association of many lncRNAs with PTEN loss was observed, many recently annotated by the FANTOM consortium, which can help us understand how genes are regulated in this event. In this work we show that despite being widely studied, there are still many components of PTEN loss in the form of lncRNAs highlighting potential markers for PTEN loss and clinical outcomes. Citation Format: Eddie Luidy Imada, Diego Fernando Sanchez, Wikum Dinalankara, Tamara Lotan, Luigi Marchionni. Screening PTEN-loss associated lncRNAs in prostate cancer [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2535.