Integration Of Expression Data Research Articles

GEDI: An R Package for Integration of Transcriptomic Data from Multiple Platforms for Bioinformatics Applications.

Transcriptomic data is often expensive and difficult to generate in large cohorts relative to genomic data; therefore, it is often important to integrate multiple transcriptomic datasets from both microarray- and next generation sequencing (NGS)-based transcriptomic data across similar experiments or clinical trials to improve analytical power and discovery of novel transcripts and genes. However, transcriptomic data integration presents a few challenges including reannotation and batch effect removal. We developed the Gene Expression Data Integration (GEDI) R package to enable transcriptomic data integration by combining existing R packages. With just four functions, the GEDI R package makes constructing a transcriptomic data integration pipeline straightforward. Together, the functions overcome the complications in transcriptomic data integration by automatically reannotating the data and removing the batch effect. The removal of the batch effect is verified with principal component analysis and the data integration is verified using a logistic regression model with forward stepwise feature selection. To demonstrate the functionalities of the GEDI package, we integrated five bovine endometrial transcriptomic datasets from the NCBI Gene Expression Omnibus. These transcriptomic datasets were from multiple high-throughput platforms, namely, array-based Affymetrix and Agilent platforms, and NGS-based Illumina paired-end RNA-seq platform. Furthermore, we compared the GEDI package to existing tools and found that GEDI is the only tool that provides a full transcriptomic data integration pipeline including verification of both batch effect removal and data integration for downstream genomic and bioinformatics applications. © 2024 The Author(s). Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: ReadGE, a function to import gene expression datasets Basic Protocol 2: GEDI, a function to reannotate and merge gene expression datasets Basic Protocol 3: BatchCorrection, a function to remove batch effects from gene expression data Basic Protocol 4: VerifyGEDI, a function to confirm successful integration of gene expression data.

Unveiling circRNA-mediated ceRNA networks in ischemic stroke by integrative analysis of multi-source gene expression profiling

BackgroundCircular RNAs (circRNAs) are emerging as potential therapeutic targets for ischemic stroke (IS) due to their regulatory roles in inflammation and apoptosis. This study aimed to develop a comprehensive and robust IS-specific competing endogenous RNA (ceRNA) network to facilitate the identification of novel diagnostic and therapeutic targets. MethodsWe integrated expression data from 15 IS studies using the Rank-In algorithm to minimize batch effects. Differentially expressed circRNAs, miRNAs, and mRNAs were identified by comparing IS and control samples. Functional enrichment analysis of differentially expressed circRNA host genes revealed significantly enriched pathways and Gene Ontology (GO) terms relevant to IS pathogenesis. We further predicted miRNA-circRNA and mRNA-miRNA interactions, enabling the construction of a comprehensive ceRNA network to identify circRNA-related genes with diagnostic potential for IS. ResultsIntegrated analysis revealed 199 differentially expressed circRNAs, 103 miRNAs, and 1736 mRNAs in IS patients. Functional enrichment analysis implicated these molecules in relevant pathways like the neurotrophin signaling pathway and p53 signaling pathway. The constructed circRNA-miRNA-mRNA regulatory network provided insights into potential mechanisms underlying IS. Three circRNA-related genes (RGS2, CDK5R1, and NSF) displayed promising diagnostic potential for IS when combined. ConclusionsWe successfully constructed a robust and informative IS-specific ceRNA network by integrating data from diverse sources. This network identified differentially expressed RNAs and revealed enriched pathways potentially involved in IS pathogenesis. Notably, our analysis identified CDK5R1, RGS2, and NSF as potential diagnostic biomarkers for IS. This study sheds light on a circRNA-mediated regulatory network with potential diagnostic and therapeutic implications for ischemic stroke.

Mapping the Multiscale Proteomic Organization of Cellular and Disease Phenotypes.

While the primary sequences of human proteins have been cataloged for over a decade, determining how these are organized into a dynamic collection of multiprotein assemblies, with structures and functions spanning biological scales, is an ongoing venture. Systematic and data-driven analyses of these higher-order structures are emerging, facilitating the discovery and understanding of cellular phenotypes. At present, knowledge of protein localization and function has been primarily derived from manual annotation and curation in resources such as the Gene Ontology, which are biased toward richly annotated genes in the literature. Here, we envision a future powered by data-driven mapping of protein assemblies. These maps can capture and decode cellular functions through the integration of protein expression, localization, and interaction data across length scales and timescales. In this review, we focus on progress toward constructing integrated cell maps that accelerate the life sciences and translational research.

Abstract 3115: Mechanics And Genomic Profiles Reveal Aging And Developmental Lineage Heterogeneities In Human Induced Pluripotent Stem Cell-derived Smooth Muscle Cells.

Effective stem cell therapies and disease modeling depend on understanding epigenetic factors of aging. Accurately modeling age-dependent phenotypes such as aortic, and smooth muscle cell stiffening, requires cell models that reflect donor age. Improved knowledge of how donor aging influences stem cell-derived smooth muscle cells is essential for precise disease modeling and creating targeted therapeutics. We hypothesize that traits of vascular aging are retained in human induced pluripotent stem cells (iPSCs) and manifest in differentiated smooth muscle cells (SMCs). We aim to compare iPSC stiffness and gene expression between young (under 40, n=5 lines) and old (over 60, n=4 lines) donors, and examine age-dependent differences in their iPSC-SMCs. We measured cell stiffness using atomic force microscopy (AFM), and analyzed bulk and single-cell RNA gene expression in iPSCs and iPSC-SMCs of neuroectoderm (NE) or paraxial mesoderm (PM) lineages, respectively. AFM revealed no significant age-related differences in iPSC colony stiffness. Bulk RNA expression showed high similarity (>94% Pearson correlation) between young and old donor iPSCs, with principal component analysis indicating low variability and no clear age separation. Single-cell stiffness measurements found old NE-SMCs significantly stiffer than young ones (p=0.032), with no marked difference in PM-SMCs between ages. Old and young NE-SMCs were stiffer than their respective PM-SMC counterparts (old, p=0.006; young, p=0.007). UMAP analysis of single-cell RNA expression displayed distinct groupings for old and young NE-SMCs, but not for PM-SMCs. Cluster analysis revealed three clusters predominantly of old NE-SMCs and one of young NE-SMCs. Our study reveals age-related stiffening in NE lineage iPSC-SMCs, but not in PM lineage or iPSCs, indicating that iPSCs preserve certain donor age-related traits that manifest as lineage-specific phenotypes in SMCs post-differentiation. RNA sequencing showed similar expression patterns in young and old iPSCs, but age-specific differences in NE-SMCs. The integration of AFM and RNA expression data sheds light on the emergence of age-dependent phenotypes in differentiated, sensitive cell types, which are not present in immature cells.

Increased LACTB2 Expression Regulates Oxidative Phosphorylation and mTORC1 Signaling of Colorectal Cancer.

This study aimed to investigate the mechanisms of LACTB2 in colorectal cancer (CRC). Microarrays and sequencing data of CRC were acquired from UCSC Xena, GTEx, Gene Expression Omnibus, and TCGA. Pooled analysis of the mRNA expression of LACTB2 in CRC was performed using Stata software. The protein expression of LACTB2 in CRC tissues was evaluated by immunohistochemistry. The relationship between immune cell infiltration and LACTB2 expression was investigated using CIBERSORT. The potential signaling pathways and biological mechanisms of LACTB2 were explored using GSEA, KEGG, and GO. Subsequently, further screening of small molecular compounds with potential therapeutic effects on CRC was conducted through the HERB database, followed by molecular docking studies of these compounds with the LACTB2 protein. The integration and analysis of expression data obtained from 2294 CRC samples and 1286 noncancerous colorectal samples showed that LACTB2 was highly expressed in CRC. Immunohistochemistry performed on in-house tissue samples confirmed that LACTB2 protein expression was upregulated in CRC. CIBERSORT revealed lower B cell infiltration levels in the high LACTB2 expression group than in the low expression group. GO, KEGG, and GSEA analyses showed that LACTB2 expression and genes positively correlating with it were mainly related to DNA synthesis and repair, mitochondrial translational elongation and translational termination, phosphorylation, and mTORC1 signaling. Finally, molecular docking simulations confirmed the ability of quercitin to target and bind to LACTB2. This is the first study to demonstrate that LACTB2 is upregulated in CRC. LACTB2 promotes colorectal tumorigenesis and tumor progression.

Proteomic profiling reveals that ESR1 mutations enhance cyclin-dependent kinase signaling

Three quarters of all breast cancers express the estrogen receptor (ER, ESR1 gene), which promotes tumor growth and constitutes a direct target for endocrine therapies. ESR1 mutations have been implicated in therapy resistance in metastatic breast cancer, in particular to aromatase inhibitors. ESR1 mutations promote constitutive ER activity and affect other signaling pathways, allowing cancer cells to proliferate by employing mechanisms within and without direct regulation by the ER. Although subjected to extensive genetic and transcriptomic analyses, understanding of protein alterations remains poorly investigated. Towards this, we employed an integrated mass spectrometry based proteomic approach to profile the protein and phosphoprotein differences in breast cancer cell lines expressing the frequent Y537N and Y537S ER mutations. Global proteome analysis revealed enrichment of mitotic and immune signaling pathways in ER mutant cells, while phosphoprotein analysis evidenced enriched activity of proliferation associated kinases, in particular CDKs and mTOR. Integration of protein expression and phosphorylation data revealed pathway-dependent discrepancies (motility vs proliferation) that were observed at varying degrees across mutant and wt ER cells. Additionally, protein expression and phosphorylation patterns, while under different regulation, still recapitulated the estrogen-independent phenotype of ER mutant cells. Our study is the first proteome-centric characterization of ESR1 mutant models, out of which we confirm estrogen independence of ER mutants and reveal the enrichment of immune signaling pathways at the proteomic level.

Predicting prostate cancer progression with a Multi-lncRNA expression-based risk score and nomogram integrating ISUP grading

Prostate cancer is a highly heterogeneous disease; therefore, estimating patient prognosis accurately is challenging due to the lack of biomarkers with sufficient specificity and sensitivity. One of the current challenges lies in integrating genomic and transcriptomic data with clinico-pathological features and in incorporating their application in everyday clinical practice. Therefore, we aimed to model a risk score and nomogram containing long non-coding RNA (lncRNA) expression and clinico-pathological data to better predict the probability of prostate cancer progression. We performed bioinformatics analyses to identify lncRNAs differentially expressed across various prostate cancer stages and associated with progression-free survival. This information was further integrated into a prognostic risk score and nomogram containing transcriptomic and clinico-pathological features to estimate the risk of disease progression. We used RNA-seq data from 5 datasets from public repositories (total n = 178) comprising different stages of prostate cancer: pre-treatment primary prostate adenocarcinomas, post-treatment tumors and metastatic castration resistant prostate cancer. We found 30 lncRNAs with consistent differential expression in all comparisons made using two R-based packages. Multivariate progression-free survival analysis including the ISUP group as covariate, revealed that 7/30 lncRNAs were significantly associated with time-to-progression. Next, we combined the expression of these 7 lncRNAs into a multi-lncRNA score and dichotomized the patients into low- or high-score. Patients with a high-score showed a 4-fold risk of disease progression (HR = 4.30, 95 %CI = 2.66–6.97, p = 3.1e-9). Furthermore, we modelled a combined risk-score containing information on the multi-lncRNA score and ISUP group. We found that patients with a high-risk score had nearly 8-fold risk of progression (HR = 7.65, 95 %CI = 4.05–14.44, p = 3.4e-10). Finally, we created and validated a nomogram to help uro-oncologists to better predict patient's risk of progression at 3- and 5-years post-diagnosis. In conclusion, the integration of lncRNA expression data and clinico-pathological features of prostate tumors into predictive models might aid in tailored disease risk assessment and treatment for patients with prostate cancer.

Integration of multi-omics technologies for molecular diagnosis in ataxia patients.

Background: Episodic ataxias are rare neurological disorders characterized by recurring episodes of imbalance and coordination difficulties. Obtaining definitive molecular diagnoses poses challenges, as clinical presentation is highly heterogeneous, and literature on the underlying genetics is limited. While the advent of high-throughput sequencing technologies has significantly contributed to Mendelian disorders genetics, interpretation of variants of uncertain significance and other limitations inherent to individual methods still leaves many patients undiagnosed. This study aimed to investigate the utility of multi-omics for the identification and validation of molecular candidates in a cohort of complex cases of ataxia with episodic presentation. Methods: Eight patients lacking molecular diagnosis despite extensive clinical examination were recruited following standard genetic testing. Whole genome and RNA sequencing were performed on samples isolated from peripheral blood mononuclear cells. Integration of expression and splicing data facilitated genomic variants prioritization. Subsequently, long-read sequencing played a crucial role in the validation of those candidate variants. Results: Whole genome sequencing uncovered pathogenic variants in four genes (SPG7, ATXN2, ELOVL4, PMPCB). A missense and a nonsense variant, both previously reported as likely pathogenic, configured in trans in individual #1 (SPG7: c.2228T>C/p.I743T, c.1861C>T/p.Q621*). An ATXN2 microsatellite expansion (CAG32) in another late-onset case. In two separate individuals, intronic variants near splice sites (ELOVL4: c.541 + 5G>A; PMPCB: c.1154 + 5G>C) were predicted to induce loss-of-function splicing, but had never been reported as disease-causing. Long-read sequencing confirmed the compound heterozygous variants configuration, repeat expansion length, as well as splicing landscape for those pathogenic variants. A potential genetic modifier of the ATXN2 expansion was discovered in ZFYVE26 (c.3022C>T/p.R1008*). Conclusion: Despite failure to identify pathogenic variants through clinical genetic testing, the multi-omics approach enabled the molecular diagnosis in 50% of patients, also giving valuable insights for variant prioritization in remaining cases. The findings demonstrate the value of long-read sequencing for the validation of candidate variants in various scenarios. Our study demonstrates the effectiveness of leveraging complementary omics technologies to unravel the underlying genetics in patients with unresolved rare diseases such as ataxia. Molecular diagnoses not only hold significant promise in improving patient care management, but also alleviates the burden of diagnostic odysseys, more broadly enhancing quality of life.

Integration of Web-Based Tools to Visualize, Integrate, and Interpret Glycogene Expression and Glycomics Data.

Glycosylation is the most abundant and diverse post-translational modification occurring on proteins. Glycans play important roles in modulating cell adhesion, growth, development, and differentiation. Changes in glycosylation affect protein structure and function and contribute to disease processes. Therefore, understanding glycosylation patterns is key for the identification of targets for the diagnosis of diseases, cellular states, and therapy. Glycosylation is a nontemplate-driven process governed by the action of numerous enzymes and substrate availability that varies among cell types and species. Therefore, qualitative and quantitative assessment of global glycosylation and individual glycans remains challenging because it requires integration of multiple complex data types. Glycan structure and quantity data are often integrated with assessments of gene expression to aid contextualization of observed glycosylation changes within biological processes. However, correlating glycogene expression to the glycan structure is challenging because transcriptional changes may not always concur with the final gene product; there is often a lack of information on nucleotide sugar pools, and the final glycan structure is the result of many different glycogenes acting in concert. To overcome these challenges, interactive online tools are emerging as key resources for facilitating the analysis and integration of glycomics and glycogene expression data. Importantly, these tools work in concurrence with glycan biosynthetic schemes and therefore provide a clear indication of the molecular pathways where the glycan and glycogene are involved. In this chapter, we describe the applications of four freely available online tools that can be used for integrated visualization, interpretation, and presentation of RNAseq and glycomics results.

Comprehensive Analysis of Ligand-receptor Interactions in Colon Adenocarcinoma to Identify of Tumor Microenvironment Oxidative Stress and Prognosis Model.

Single-cell technology enables a deep study on the mechanism of cancers. This work delineated the function of ligand-receptor interaction in colon adenocarcinoma (COAD), and developed a LR pairs-based prognostic model. For identifying important LR pairs, Single-cell RNA sequencing data of COAD was included. Unsupervised consensus clustering constructed molecular subtypes. LASSO established a prognostic model. Infiltration of 22 immune cells was evaluated by Cibersort. Enrichment score of oxidative stress related pathways was determined by SsGSEA in each patient. Forty-seven LR pairs were closely associated with the prognosis of COAD. Three molecular subtypes were differentiated according to 47 LR pairs, which displayed differential clinical features and molecular features. There were significant differences in immune T cell lytic activity among different subtypes. In clust1 with poor prognosis, significantly enriched oncogenic pathways were found, especially epithelial-mesenchymal transition (EMT). Additionally, it has been found that clust3 had significantly higher immune infiltration. A prognostic model containing eight LR pairs (PDGFB-PDGFRA, FLT4-VEGFC, CSF1R-CSF1, DLL1-NOTCH4, PDGFB-LRP1, DLL1- NOTCH3, FLT4-PDGFC, and NRP2-PGF) was established, which could effectively divide samples into low-risk and high-risk groups. Significantly higher oxidative stress was found among high-risk patients. This study integrated expression data and single-cell data for demonstrating the effectiveness of LR pairs in establishing the prognostic model and constructing molecular subtypes. Prognostic LR pairs may contribute to tumorigenesis and progression in COAD. The prognostic model was the potential for predicting prognosis and guiding immunotherapy for COAD patients.

Integrating gene expression data into a genome-scale metabolic model to identify reprogramming during adaptive evolution.

The development of a method for identifying latent reprogramming in gene expression data resulting from adaptive laboratory evolution (ALE) in response to genetic or environmental perturbations has been a challenge. In this study, a method called Metabolic Reprogramming Identifier (MRI), based on the integration of expression data to a genome-scale metabolic model has been developed. To identify key genes playing the main role in reprogramming, a MILP problem is presented and maximization of an adaptation score as a criterion indicating a pattern of using metabolism with maximum utilization of gene expression resources is defined as an objective function. Then, genes with complete expression usage and significant expression differences between wild-type and evolved strains were selected as key genes for reprogramming. This score is also applied to evaluate the compatibility of expression patterns with maximal use of key genes. The method was implemented to investigate the reprogramming of Escherichia coli during adaptive evolution caused by changing carbon sources. cyoC and cydB responsible for establishing proton gradient across the inner membrane were identified to be vital in the E. coli reprogramming when switching from glucose to lactate. These results indicate the importance of the inner membrane in reprogramming of E. coli to adapt to the new environment. The method predicts no reprogramming occurs during the evolution for growth on glycerol.

A Bayesian method for estimating gene-level polygenicity under the framework of transcriptome-wide association study.

Polygenicity refers to the phenomenon that multiple genetic variants have a nonzero effect on a complex trait. It is defined as the proportion of genetic variants with a nonzero effect on the trait. Evaluation of polygenicity can provide valuable insights into the genetic architecture of the trait. Several recent works have attempted to estimate polygenicity at the single nucleotide polymorphism level. However, evaluating polygenicity at the gene level can be biologically more meaningful. We propose the notion of gene-level polygenicity, defined as the proportion of genes having a nonzero effect on the trait under the framework of a transcriptome-wide association study. We introduce a Bayesian approach genepoly to estimate this quantity for a trait. The method is based on spike and slab prior and simultaneously estimates the subset of non-null genes. Our simulation study shows that genepoly efficiently estimates gene-level polygenicity. The method produces a downward bias for small choices of trait heritability due to a non-null gene, which diminishes rapidly with an increase in the genome-wide association study (GWAS) sample size. While identifying the subset of non-null genes, genepoly offers a high level of specificity and an overall good level of sensitivity-the sensitivity increases as the sample size of the reference panel expression and GWAS data increase. We applied the method to seven phenotypes in the UK Biobank, integrating expression data. We find height to be the most polygenic and asthma to be the least polygenic.

Integration of single-cell sequencing and bulk expression data reveals chemokine signaling pathway in proliferating cells is associated with the survival outcome of osteosarcoma

BackgroundOsteosarcoma, as the most common primary bone malignancy, is urgent to be well-studied on the biomarkers and therapeutic targets to improve the five-year survival rate. Transcriptomic analysis using single-cell RNA or bulk RNA sequencing has been developed to detect biomarkers in various cancer types.Methods and resultsWe applied Scissor to combine single-cell RNA-seq data and bulk transcriptome data of osteosarcoma, providing cell-level information and sample phenotypes to identify the survival-associated cell subpopulations. By investigating the differences between the survival-associated cell subpopulations, we identified CCL21, CCL22, CCL24, CXCL11, CXCL12, CXCL13, GNAI2, and RAC2 in the proliferating cells that are significantly associated with osteosarcoma patient outcome. Then we assigned the risk score for each sample based on the cell proportion-normalized gene expression and validated it in the public dataset.ConclusionsThis study provides the clinical insight that chemokine signaling pathway genes (CCL21, CCL22, CCL24, CXCL11, CXCL12, CXCL13, GNAI2, and RAC2) in proliferating cells might be the potential biomarkers for treatment of osteosarcoma.

CrossDome: an interactive R package to predict cross-reactivity risk using immunopeptidomics databases.

T-cell-based immunotherapies hold tremendous potential in the fight against cancer, thanks to their capacity to specifically targeting diseased cells. Nevertheless, this potential has been tempered with safety concerns regarding the possible recognition of unknown off-targets displayed by healthy cells. In a notorious example, engineered T-cells specific to MAGEA3 (EVDPIGHLY) also recognized a TITIN-derived peptide (ESDPIVAQY) expressed by cardiac cells, inducing lethal damage in melanoma patients. Such off-target toxicity has been related to T-cell cross-reactivity induced by molecular mimicry. In this context, there is growing interest in developing the means to avoid off-target toxicity, and to provide safer immunotherapy products. To this end, we present CrossDome, a multi-omics suite to predict the off-target toxicity risk of T-cell-based immunotherapies. Our suite provides two alternative protocols, i) a peptide-centered prediction, or ii) a TCR-centered prediction. As proof-of-principle, we evaluate our approach using 16 well-known cross-reactivity cases involving cancer-associated antigens. With CrossDome, the TITIN-derived peptide was predicted at the 99+ percentile rank among 36,000 scored candidates (p-value < 0.001). In addition, off-targets for all the 16 known cases were predicted within the top ranges of relatedness score on a Monte Carlo simulation with over 5 million putative peptide pairs, allowing us to determine a cut-off p-value for off-target toxicity risk. We also implemented a penalty system based on TCR hotspots, named contact map (CM). This TCR-centered approach improved upon the peptide-centered prediction on the MAGEA3-TITIN screening (e.g., from 27th to 6th, out of 36,000 ranked peptides). Next, we used an extended dataset of experimentally-determined cross-reactive peptides to evaluate alternative CrossDome protocols. The level of enrichment of validated cases among top 50 best-scored peptides was 63% for the peptide-centered protocol, and up to 82% for the TCR-centered protocol. Finally, we performed functional characterization of top ranking candidates, by integrating expression data, HLA binding, and immunogenicity predictions. CrossDome was designed as an R package for easy integration with antigen discovery pipelines, and an interactive web interface for users without coding experience. CrossDome is under active development, and it is available at https://github.com/AntunesLab/crossdome.

ScRNA-seq revealed targeting regulator of G protein signaling 1 to mediate regulatory T cells in Hepatocellular carcinoma.

Regulatory T cells (Tregs) are central to determine immune response, thus targeting Tregs for immunotherapy is a promising strategy against tumor development and metastasis. The objective of this study was to identify genes for targeting Tregs to improve the outcome of HCC. We integrated expression data from different samples to remove batch effects and further applied embedding function in Scanpy to conduct sub-clustering of CD4+ T cells in HCC for each of two independent scRNA-seq data. The activity of transcription factors (TFs) was inferred by DoRothEA. Gene expression network analysis was performed in WGCNA R package. We finally used R packages (survminer and survival) to conduct survival analysis. Multiplex immunofluorescence analysis was performed to validate the result from bioinformatic analyses. We found that regulator of G protein signaling 1 (RGS1) expression was significantly elevated in Tregs compared to other CD4+ T cells in two independent public scRNA-seq datasets, and increased RGS1 predicted inferior clinical outcome of HCC patients. Multiplex immunofluorescence analysis supported that the higher expression of RGS1 in HCC Tregs in tumor tissue compared to it in adjacent tissue. Moreover, RGS1 expression in Tregs was positively correlated with the expression of marker genes of Tregs, C-X-C chemokine receptor 4 (CXCR4), and three CXCR4-dependent genes in both scRNA-seq and bulk RNA-seq data. We further identified that these three genes were selectively expressed in Tregs as compared to other CD4+ T cells. The activities of two transcription factors, recombination signal binding protein for immunoglobulin kappa J region (RBPJ) and yin yang 1 (YY1), were significantly different in HCC Tregs with RGS1 high and RGS1 low. Our findings suggested that RGS1 may regulate Treg function possibly through CXCR4 signaling and RGS1 could be a potential target to improve responses for immunotherapy in HCC.

Integration of single-cell sequencing and bulk expression data reveals chemokine signaling pathway in proliferating cells is associated with the survival outcome of osteosarcoma

Integration of single-cell sequencing and bulk expression data reveals chemokine signaling pathway in proliferating cells is associated with the survival outcome of osteosarcoma

Integration of single-cell sequencing and bulk expression data reveals chemokine signaling pathway in proliferating cells is associated with the survival outcome of osteosarcoma

Integration of single-cell sequencing and bulk expression data reveals chemokine signaling pathway in proliferating cells is associated with the survival outcome of osteosarcoma

Integration of mRNA and protein expression data for the identification of potential biomarkers associated with pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is one of the most death-dealing tumors, with a tremendously poor prognosis. Here, we, through interrogation of mRNA and protein data combined with a system biology approach, identified several key genes, functional processes, and pathways that can have critical roles in PDAC. We detected an interesting module related to the clinical traits that enriched in the ribosome, hematopoietic cell lineage, and cell adhesion molecules-related pathways. We also identified several hub genes in important modules that are associated with immune system processes. The results also indicated some lncRNAs, such as FAM30A, and MIR223HG with essential functions that are involved in PDAC. Additionally, five genes, including CD53, ITGAL, WDFY4, TLX1, and LMAN1L were screened by survival analysis and can be considered as candidate biomarkers or therapeutic targets. According to our strategy, the findings of this study may provide a better understanding of the molecular mechanisms and suggest potential prognostic and therapeutic targets for PDAC.

Regulatory dynamics distinguishing desiccation tolerance strategies within resurrection grasses.

Desiccation tolerance has evolved recurrently in grasses using two unique strategies of either protecting or dismantling the photosynthetic apparatus to minimize photooxidative damage under life without water (anhydrobiosis). Here, we surveyed chromatin architecture and gene expression during desiccation in two closely related grasses with distinguishing desiccation tolerance strategies to identify regulatory dynamics underlying these unique adaptations. In both grasses, we observed a strong association between nearby chromatin accessibility and gene expression in desiccated tissues compared to well-watered, reflecting an unusual chromatin stability under anhydrobiosis. Integration of chromatin accessibility (ATACseq) and expression data (RNAseq) revealed a core desiccation response across these two grasses. This includes many genes with binding sites for the core seed development transcription factor ABI5, supporting the long-standing hypothesis that vegetative desiccation tolerance evolved from rewiring seed pathways. Oropetium thomaeum has a unique set of desiccation induced genes and regulatory elements associated with photoprotection, pigment biosynthesis, and response to high light, reflecting its adaptation of protecting the photosynthetic apparatus under desiccation (homoiochlorophyly). By contrast, Eragrostis nindensis has unique accessible and expressed genes related to chlorophyll catabolism, scavenging of amino acids, and hypoxia, highlighting its poikilochlorophyllous adaptations of dismantling the photosynthetic apparatus and degrading chlorophyll under desiccation. Together, our results highlight the complex regulatory and expression dynamics underlying desiccation tolerance in grasses.

Single-nuclei transcriptome analysis of the shoot apex vascular system differentiation in Populus.

Differentiation of stem cells in the plant apex gives rise to aerial tissues and organs. Presently, we lack a lineage map of the shoot apex cells in woody perennials - a crucial gap considering their role in determining primary and secondary growth. Here, we used single-nuclei RNA-sequencing to determine cell type-specific transcriptomes of the Populus vegetative shoot apex. We identified highly heterogeneous cell populations clustered into seven broad groups represented by 18 transcriptionally distinct cell clusters. Next, we established the developmental trajectories of the epidermis, leaf mesophyll and vascular tissue. Motivated by the high similarities between Populus and Arabidopsis cell population in the vegetative apex, we applied a pipeline for interspecific single-cell gene expression data integration. We contrasted the developmental trajectories of primary phloem and xylem formation in both species, establishing the first comparison of vascular development between a model annual herbaceous and a woody perennial plant species. Our results offer a valuable resource for investigating the principles underlying cell division and differentiation conserved between herbaceous and perennial species while also allowing us to examine species-specific differences at single-cell resolution.