Clusters Of Co-expressed Genes Research Articles

SSBP1 is a novel prognostic marker and promotes disease progression via p38MAPK signaling pathway in multiple myeloma.

Multiple myeloma (MM) remains an incurable disease. Identification of meaningful co-expressed gene clusters or representative biomarkers of MM may help to identify new pathological mechanisms and promote the development of new therapies. Here, we performed weighted sgene co-expression network analysis and a series of bioinformatics analysis to identify single stranded DNA binding protein 1 (SSBP1) as novel hub gene associated with MM development and prognosis. In vitro, CRISPR/cas9 mediated knockdown of SSBP1 can significantly inhibit the proliferation of MM cells through inducing apoptosis and cell cycle arrest in G0/G1 phase. We also found that decreased SSBP1 expression significantly increased mitochondrial reactive oxygen species (mtROS) generation and the level of phosphorylated p38MAPK. Furthermore, it was further verified that disruption of SSBP1 expression could inhibit the tumor growth via p38MAPK pathway in a human myeloma xenograft model. In summary, our study is the first to demonstrate that SSBP1 promotes MM development by regulating the p38MAPK pathway.

Transcriptome analysis reveals temporally regulated genetic networks during Drosophila border cell collective migration

BackgroundCollective cell migration underlies many essential processes, including sculpting organs during embryogenesis, wound healing in the adult, and metastasis of cancer cells. At mid-oogenesis, Drosophila border cells undergo collective migration. Border cells round up into a small group at the pre-migration stage, detach from the epithelium and undergo a dynamic and highly regulated migration at the mid-migration stage, and stop at the oocyte, their final destination, at the post-migration stage. While specific genes that promote cell signaling, polarization of the cluster, formation of protrusions, and cell-cell adhesion are known to regulate border cell migration, there may be additional genes that promote these distinct active phases of border cell migration. Therefore, we sought to identify genes whose expression patterns changed during border cell migration.ResultsWe performed RNA-sequencing on border cells isolated at pre-, mid-, and post-migration stages. We report that 1,729 transcripts, in nine co-expression gene clusters, are temporally and differentially expressed across the three migration stages. Gene ontology analyses and constructed protein-protein interaction networks identified genes expected to function in collective migration, such as regulators of the cytoskeleton, adhesion, and tissue morphogenesis, but also uncovered a notable enrichment of genes involved in immune signaling, ribosome biogenesis, and stress responses. Finally, we validated the in vivo expression and function of a subset of identified genes in border cells.ConclusionsOverall, our results identified differentially and temporally expressed genetic networks that may facilitate the efficient development and migration of border cells. The genes identified here represent a wealth of new candidates to investigate the molecular nature of dynamic collective cell migrations in developing tissues.

Development of a versatile LCM-Seq method for spatial transcriptomics of fluorescently tagged cholinergic neuron populations

Single-cell transcriptomics are powerful tools to define neuronal cell types based on co-expressed gene clusters. Limited RNA input in these technologies necessarily compromises transcriptome coverage and accuracy of differential expression analysis. We propose that bulk RNA-Seq of neuronal pools defined by spatial position offers an alternative strategy to overcome these technical limitations. We report a laser-capture microdissection (LCM)-Seq method which allows deep transcriptome profiling of fluorescently tagged neuron populations isolated with LCM from histological sections of transgenic mice. Mild formaldehyde fixation of ZsGreen marker protein, LCM sampling of ∼300 pooled neurons, followed by RNA isolation, library preparation and RNA-Seq with methods optimized for nanogram amounts of moderately degraded RNA enabled us to detect ∼15,000 different transcripts in fluorescently labeled cholinergic neuron populations. The LCM-Seq approach showed excellent accuracy in quantitative studies, allowing us to detect 2891 transcripts expressed differentially between the spatially defined and clinically relevant cholinergic neuron populations of the dorsal caudate-putamen and medial septum. In summary, the LCM-Seq method we report in this study is a versatile, sensitive, and accurate bulk sequencing approach to study the transcriptome profile and differential gene expression of fluorescently tagged neuronal populations isolated from transgenic mice with high spatial precision.

JGI Plant Gene Atlas: an updateable transcriptome resource to improve functional gene descriptions across the plant kingdom.

Gene functional descriptions offer a crucial line of evidence for candidate genes underlying trait variation. Conversely, plant responses to environmental cues represent important resources to decipher gene function and subsequently provide molecular targets for plant improvement through gene editing. However, biological roles of large proportions of genes across the plant phylogeny are poorly annotated. Here we describe the Joint Genome Institute (JGI) Plant Gene Atlas, an updateable data resource consisting of transcript abundance assays spanning 18 diverse species. To integrate across these diverse genotypes, we analyzed expression profiles, built gene clusters that exhibited tissue/condition specific expression, and tested for transcriptional response to environmental queues. We discovered extensive phylogenetically constrained and condition-specific expression profiles for genes without any previously documented functional annotation. Such conserved expression patterns and tightly co-expressed gene clusters let us assign expression derived additional biological information to 64495 genes with otherwise unknown functions. The ever-expanding Gene Atlas resource is available at JGI Plant Gene Atlas (https://plantgeneatlas.jgi.doe.gov) and Phytozome (https://phytozome.jgi.doe.gov/), providing bulk access to data and user-specified queries of gene sets. Combined, these web interfaces let users access differentially expressed genes, track orthologs across the Gene Atlas plants, graphically represent co-expressed genes, and visualize gene ontology and pathway enrichments.

Comparison of red raspberry and wild strawberry fruits reveals mechanisms of fruit type specification.

Belonging to Rosaceae, red raspberry (Rubus idaeus) and wild strawberry (Fragaria vesca) are closely related species with distinct fruit types. While the numerous ovaries become the juicy drupelet fruits in raspberry, their strawberry counterparts become dry and tasteless achenes. In contrast, while the strawberry receptacle, the stem tip, enlarges to become a red fruit, the raspberry receptacle shrinks and dries. The distinct fruit-forming ability of homologous organs in these two species allows us to investigate fruit type determination. We assembled and annotated the genome of red raspberry (Rubus idaeus) and characterized its fruit development morphologically and physiologically. Subsequently, transcriptomes of dissected and staged raspberry fruit tissues were compared to those of strawberry from a prior study. Class B MADS box gene expression was negatively associated with fruit-forming ability, which suggested a conserved inhibitory role of class B heterodimers, PISTILLATA/TM6 or PISTILLATA/APETALA3, for fruit formation. Additionally, the inability of strawberry ovaries to develop into fruit flesh was associated with highly expressed lignification genes and extensive lignification of the ovary pericarp. Finally, co-expressed gene clusters preferentially expressed in the dry strawberry achenes were enriched in "cell wall biosynthesis" and "ABA signaling", while co-expressed clusters preferentially expressed in the fleshy raspberry drupelets were enriched in "protein translation". Our work provides extensive genomic resources as well as several potential mechanisms underlying fruit type specification. These findings provide the framework for understanding the evolution of different fruit types, a defining feature of angiosperms.

Y-Chromosome-Linked Genes Are Associated With Sex-Related Head-Neck Squamous Cell Carcinoma Survival.

To define novel gene biomarkers for prognosis of head and neck squamous cell carcinoma (HNSCC) patients' survival. Retrospective study. The Cancer Genome Atlas (TCGA) HNSCC RNA-Seq dataset. Coexpressed gene clusters were extracted from TCGA RNA-seq data using our previously published method (EPIG). Kaplan-Meier estimator was then used for overall survival-relevant analysis, with patients partitioned into 3 groups based on gene expression levels: female, male_low, and male_high. Male had better overall survival than female and male with higher expression level of Y-chromosome-linked (Y-linked) genes had significantly better survival than those with lower expression levels. In addition, male with a higher expression level of Y-linked genes showed even better survival when they have a higher level of coexpressed cluster of genes related to B or T cell immune response. Other clinical conditions related to immune responses also consistently showed favorable effects on the Y-linked genes for survival estimation. Male patients with higher expression level of Y-linked genes also have significantly higher tumor/normal tissue (T/N) ratio of those genes and higher level of several immune responses related clinical measurements (eg, lymphocyte and TCR related). Male patients with lower expression level of Y-linked genes benefited from radiation-only treatment. The favorable role of a cluster of coexpressed Y-linked genes in HNSCC patients' survival is potentially associated with elevated level of immune responses. These Y-linked genes could serve as useful prognostic biomarkers for HNSCC patients' survival estimation and treatment.

Adaptations in Nucleus Accumbens Neuron Subtypes Mediate Negative Affective Behaviors in Fentanyl Abstinence

BackgroundOpioid discontinuation generates a withdrawal syndrome marked by increased negative affect. Increased symptoms of anxiety and dysphoria during opioid discontinuation are significant barriers to achieving long-term abstinence in opioid-dependent individuals. While adaptations in the nucleus accumbens are implicated in opioid abstinence syndrome, the precise neural mechanisms are poorly understood. Additionally, our current knowledge is limited to changes following natural and semisynthetic opioids, despite recent increases in synthetic opioid use and overdose. MethodsWe used a combination of cell subtype–specific viral labeling and electrophysiology in male and female mice to investigate structural and functional plasticity in nucleus accumbens medium spiny neuron (MSN) subtypes after fentanyl abstinence. We characterized molecular adaptations after fentanyl abstinence with subtype-specific RNA sequencing and weighted gene co-expression network analysis. We used viral-mediated gene transfer to manipulate the molecular signature of fentanyl abstinence in D1-MSNs. ResultsHere, we show that fentanyl abstinence increases anxiety-like behavior, decreases social interaction, and engenders MSN subtype–specific plasticity in both sexes. D1-MSNs, but not D2-MSNs, exhibit dendritic atrophy and an increase in excitatory drive. We identified a cluster of coexpressed dendritic morphology genes downregulated selectively in D1-MSNs that are transcriptionally coregulated by E2F1. E2f1 expression in D1-MSNs protects against loss of dendritic complexity, altered physiology, and negative affect–like behaviors caused by fentanyl abstinence. ConclusionsOur findings indicate that fentanyl abstinence causes unique structural, functional, and molecular changes in nucleus accumbens D1-MSNs that can be targeted to alleviate negative affective symptoms during abstinence.

Analyzing Multifactorial RNA-Seq Experiments with DicoExpress.

The proper use of statistical modeling in NGS data analysis requires an advanced level of expertise. There has recently been a growing consensus on using generalized linear models for differential analysis of RNA-Seq data and the advantage of mixture models to perform co-expression analysis. To offer a managed setting to use these modeling approaches, we developed DiCoExpress that provides a standardized R pipeline to perform an RNA-Seq analysis. Without any particular knowledge in statistics or R programming, beginners can perform a complete RNA-Seq analysis from quality controls to co-expression through differential analysis based on contrasts inside a generalized linear model. An enrichment analysis is proposed both on the lists of differentially expressed genes, and the co-expressed gene clusters. This video tutorial is conceived as a step-by-step protocol to help users take full advantage of DiCoExpress and its potential in empowering the biological interpretation of an RNA-Seq experiment.

Characterization of gene cluster heterogeneity in single-cell transcriptomic data within and across cancer types.

ABSTRACTDespite the remarkable progress in probing tumor transcriptomic heterogeneity by single-cell RNA sequencing (sc-RNAseq) data, several gaps exist in prior studies. Tumor heterogeneity is frequently mentioned but not quantified. Clustering analyses typically target cells rather than genes, and differential levels of transcriptomic heterogeneity of gene clusters are not characterized. Relations between gene clusters inferred from multiple datasets remain less explored. We provided a series of quantitative methods to analyze cancer sc-RNAseq data. First, we proposed two quantitative measures to assess intra-tumoral heterogeneity/homogeneity. Second, we established a hierarchy of gene clusters from sc-RNAseq data, devised an algorithm to reduce the gene cluster hierarchy to a compact structure, and characterized the gene clusters with functional enrichment and heterogeneity. Third, we developed an algorithm to align the gene cluster hierarchies from multiple datasets to a small number of meta gene clusters. By applying these methods to nine cancer sc-RNAseq datasets, we discovered that cancer cell transcriptomes were more homogeneous within tumors than the accompanying normal cells. Furthermore, many gene clusters from the nine datasets were aligned to two large meta gene clusters, which had high and low heterogeneity and were enriched with distinct functions. Finally, we found the homogeneous meta gene cluster retained stronger expression coherence and associations with survival times in bulk level RNAseq data than the heterogeneous meta gene cluster, yet the combinatorial expression patterns of breast cancer subtypes in bulk level data were not preserved in single-cell data. The inference outcomes derived from nine cancer sc-RNAseq datasets provide insights about the contributing factors for transcriptomic heterogeneity of cancer cells and complex relations between bulk level and single-cell RNAseq data. They demonstrate the utility of our methods to enable a comprehensive characterization of co-expressed gene clusters in a wide range of sc-RNAseq data in cancers and beyond.

HIVEP3 cooperates with ferroptosis gene signatures to confer adverse prognosis in acute myeloid leukemia.

The human immunodeficiency virus type I enhancer binding protein (HIVEP) family, which contains zinc finger and acid-rich (ZAS) domains, has been demonstrated to be implicated in vital biological processes, such as cell survival, tumor necrosis factor (TNF) signaling, and tumor formation. However, its expression patterns, prognostic relevance, and functional implications in acute myeloid leukemia (AML) remain elusive. We inspected HIVEP mRNA expression levels in datasets from The Cancer Genome Atlas (TCGA) and GSE24006. Survival analyses were orchestrated using the web-based bioinformatics platforms and R studio in two AML cohorts. Prognostic value and capacity were assessed by Cox regression analyses. Association of HIVEP3 expression levels with clinical characteristics were analyzed with R and UALCAN. Subsequentially, functional enrichment analyses were operated to interpret HIVEP3 co-expressed gene clusters. A prognostic gene signature was created by the least absolute shrinkage and selection operator (LASSO) regression algorithm. Moreover, bone marrow aspirate smears of AML patients were stained for HIVEP3 by immunohistochemistry (IHC). HIVEP3 expression was examined by qRT-PCR in leukemia cell lines treated with ferroptosis compounds in vitro. Augmented transcriptional levels of HIVEP2 and 3 were noted in AML patients (p<0.001). HIVEP3 not only could confer adverse prognosis independently in AML patients, but also was associated with AML subtypes, age, cytogenetic risk, and disease-related molecules. Co-expressed gene clusters of HIVEP3 were enriched in functional pathways related to AML leukemogenesis, such as ribosome, metabolism, and calcium signaling. Combined with multiple tumorigenesis signaling pathways, we proposed an integrated LASSO model with HIVEP3 and ferroptosis regulators AIFM2 and LPCAT3, to predict the outcome for AML patients. Furthernore, altered HIVEP3 expression at the mRNA or protein level was confirmed in sorted leukemia cells and blast cells in bone marrow tissues. In vitro experiments authenticated the involvement of HIVEP3 in ferroptosis signaling pathways. Our findings suggest that HIVEP3 is a de novo independent prognostic indicator, and the crosstalk between HIVEP3 and ferroptosis signaling pathways may inspire a specific perspective on the oncological network of AML.

Integrated Bioinformatics and Machine Learning Algorithms Analyses Highlight Related Pathways and Genes Associated with Alzheimer's Disease

Background: The pathophysiology of Alzheimer's disease (AD) is still not fully studied. Objective: This study aimed to explore the differently expressed key genes in AD and build a predictive model of diagnosis and treatment. Methods: Gene expression data of the entorhinal cortex of AD, asymptomatic AD, and control samples from the GEO database were analyzed to explore the relevant pathways and key genes in the progression of AD. Differentially expressed genes between AD and the other two groups in the module were selected to identify biological mechanisms in AD through KEGG and PPI network analysis in Metascape. Furthermore, genes with a high connectivity degree by PPI network analysis were selected to build a predictive model using different machine learning algorithms. Besides, model performance was tested with five-fold cross-validation to select the best fitting model. Results: A total of 20 co-expression gene clusters were identified after the network was constructed. Module 1 (in black) and module 2 (in royal blue) were most positively and negatively correlated with AD, respectively. Total 565 genes in module 1 and 215 genes in module 2, respectively, overlapped in two differentially expressed genes lists. They were enriched in the G protein-coupled receptor signaling pathway, immune-related processes, and so on. 11 genes were screened by using lasso logistic regression, and they were considered to play an important role in predicting AD samples. The model built by the support vector machine algorithm with 11 genes showed the best performance. Conclusion: This result shed light on the diagnosis and treatment of AD.

Gene Co-expression Network Analysis.

Gene co-expression analysis is a data analysis technique that helps identify groups of genes with similar expression patterns across several different conditions. By means of these techniques, different groups have been able to assign putative metabolic pathways and functions to understudied genes and to identify novel metabolic regulation networks for different metabolites. Some groups have even used network comparative studies to understand the evolution of these networks from green algae to land plants. In this chapter, we will go over the basic definitions required to understand network topology and gene module identification. Additionally, we offer the reader a walk-through a standard analysis pipeline as implemented in the package WGCNA that takes as input raw fastq files and obtains co-expressed gene clusters and representative gene expression patterns from each module for downstream applications.

Detecting spatially co-expressed gene clusters with functional coherence by graph-regularized convolutional neural network.

Clustering spatial-resolved gene expression is an essential analysis to reveal gene activities in the underlying morphological context by their functional roles. However, conventional clustering analysis does not consider gene expression co-localizations in tissue for detecting spatial expression patterns or functional relationships among the genes for biological interpretation in the spatial context. In this article, we present a convolutional neural network (CNN) regularized by the graph of protein-protein interaction (PPI) network to cluster spatially resolved gene expression. This method improves the coherence of spatial patterns and provides biological interpretation of the gene clusters in the spatial context by exploiting the spatial localization by convolution and gene functional relationships by graph-Laplacian regularization. In this study, we tested clustering the spatially variable genes or all expressed genes in the transcriptome in 22 Visium spatial transcriptomics datasets of different tissue sections publicly available from 10× Genomics and spatialLIBD. The results demonstrate that the PPI-regularized CNN constantly detects gene clusters with coherent spatial patterns and significantly enriched by gene functions with the state-of-the-art performance. Additional case studies on mouse kidney tissue and human breast cancer tissue suggest that the PPI-regularized CNN also detects spatially co-expressed genes to define the corresponding morphological context in the tissue with valuable insights. Source code is available at https://github.com/kuanglab/CNN-PReg. Supplementary data are available at Bioinformatics online.

A metric for evaluating biological information in gene sets and its application to identify co-expressed gene clusters in PBMC.

Recent technological advances have made the gathering of comprehensive gene expression datasets a commodity. This has shifted the limiting step of transcriptomic studies from the accumulation of data to their analyses and interpretation. The main problem in analyzing transcriptomics data is that the number of independent samples is typically much lower (<100) than the number of genes whose expression is quantified (typically >14,000). To address this, it would be desirable to reduce the gathered data's dimensionality without losing information. Clustering genes into discrete modules is one of the most commonly used tools to accomplish this task. While there are multiple clustering approaches, there is a lack of informative metrics available to evaluate the resultant clusters' biological quality. Here we present a metric that incorporates known ground truth gene sets to quantify gene clusters' biological quality derived from standard clustering techniques. The GECO (Ground truth Evaluation of Clustering Outcomes) metric demonstrates that quantitative and repeatable scoring of gene clusters is not only possible but computationally lightweight and robust. Unlike current methods, it allows direct comparison between gene clusters generated by different clustering techniques. It also reveals that current cluster analysis techniques often underestimate the number of clusters that should be formed from a dataset, which leads to fewer clusters of lower quality. As a test case, we applied GECO combined with k-means clustering to derive an optimal set of co-expressed gene modules derived from PBMC, which we show to be superior to previously generated modules generated on whole-blood. Overall, GECO provides a rational metric to test and compare different clustering approaches to analyze high-dimensional transcriptomic data.

Early transcriptional response to gravistimulation in poplar without phototropic confounding factors.

In response to gravistimulation under anisotropic light, tree stems showing an active cambium produce reaction wood that redirects the axis of the trees. Several studies have described transcriptomic or proteomic models of reaction wood relative to the opposite wood. However, the mechanisms leading to the formation of reaction wood are difficult to decipher because so many environmental factors can induce various signalling pathways leading to this developmental reprogramming. Using an innovative isotropic device where the phototropic response does not interfere with gravistimulation we characterized the early molecular responses occurring in the stem of poplar after gravistimulation in an isotropic environment, and without deformation of the stem. After 30 min tilting at 35° under anisotropic light, we collected the upper and lower xylems from the inclined stems. Controls were collected from vertical stems. We used a microarray approach to identify differentially expressed transcripts. High-throughput real-time PCR allowed a kinetic experiment at 0, 30, 120 and 180 min after tilting at 35°, with candidate genes. We identified 668 differentially expressed transcripts, from which we selected 153 candidates for additional Fluidigm qPCR assessment. Five candidate co-expression gene clusters have been identified after the kinetic monitoring of the expression of candidate genes. Gene ontology analyses indicate that molecular reprogramming of processes such as ‘wood cell expansion’, ‘cell wall reorganization’ and ‘programmed cell death’ occur as early as 30 min after gravistimulation. Of note is that the change in the expression of different genes involves a fine regulation of gibberellin and brassinosteroid pathways as well as flavonoid and phosphoinositide pathways. Our experimental set-up allowed the identification of genes regulated in early gravitropic response without the bias introduced by phototropic and stem bending responses.

Ocean Acidification Induces Subtle Shifts in Gene Expression and DNA Methylation in Mantle Tissue of the Eastern Oyster (Crassostrea virginica)

Early evidence suggests that DNA methylation can mediate phenotypic responses of marine calcifying species to ocean acidification (OA). Few studies, however, have explicitly studied DNA methylation in calcifying tissues through time. Here, we examined the phenotypic and molecular responses in the extrapallial fluid and mantle (fluid and tissue at the calcification site) in the Eastern oyster (Crassostrea virginica) exposed to experimental OA over 80 days. Oysters were reared under three experimental pCO2 treatments (‘control’, 580 μatm; ‘moderate OA’, 1000 uatm; ‘high OA’, 2800 μatm) and sampled at 6 time points (24 hours - 80 days). We found that high OA initially induced changes in the pH of the extrapallial fluid (pHEPF) relative to the external seawater, but the magnitude of this difference was highest at 9 days and diminished over time. Calcification rates were significantly lower in the high OA treatment compared to the other treatments. To explore how oysters regulate their extrapallial fluid, gene expression and DNA methylation were examined in the mantle-edge tissue of oysters from day 9 and 80 in the control and high OA treatments. Mantle tissue mounted a significant global molecular response (both in the transcriptome and methylome) to OA that shifted through time. Although we did not find individual genes that were significantly differentially expressed to OA, the pHEPF was correlated with the eigengene expression of several co-expressed gene clusters. A small number of OA-induced differentially methylated loci were discovered, which corresponded with a weak association between OA-induced changes in genome-wide gene body DNA methylation and gene expression. Gene body methylation, however, was not significantly correlated with the eigengene expression of pHEPF correlated gene clusters. These results suggest that in C. virginica, OA induces a subtle response in a large number of genes, but also indicates that plasticity at the molecular level may be limited. Our study highlights the need to re-assess the plasticity of tissue-specific molecular responses in marine calcifiers, as well as the role of DNA methylation and gene expression in mediating physiological and biomineralization responses to OA.

Network analysis of transcriptomic diversity amongst resident tissue macrophages and dendritic cells in the mouse mononuclear phagocyte system.

The mononuclear phagocyte system (MPS) is a family of cells including progenitors, circulating blood monocytes, resident tissue macrophages, and dendritic cells (DCs) present in every tissue in the body. To test the relationships between markers and transcriptomic diversity in the MPS, we collected from National Center for Biotechnology Information Gene Expression Omnibus (NCBI-GEO) a total of 466 quality RNA sequencing (RNA-seq) data sets generated from mouse MPS cells isolated from bone marrow, blood, and multiple tissues. The primary data were randomly downsized to a depth of 10 million reads and requantified. The resulting data set was clustered using the network analysis tool BioLayout. A sample-to-sample matrix revealed that MPS populations could be separated based upon tissue of origin. Cells identified as classical DC subsets, cDC1s and cDC2s, and lacking Fcgr1 (encoding the protein CD64) were contained within the MPS cluster, no more distinct than other MPS cells. A gene-to-gene correlation matrix identified large generic coexpression clusters associated with MPS maturation and innate immune function. Smaller coexpression gene clusters, including the transcription factors that drive them, showed higher expression within defined isolated cells, including monocytes, macrophages, and DCs isolated from specific tissues. They include a cluster containing Lyve1 that implies a function in endothelial cell (EC) homeostasis, a cluster of transcripts enriched in intestinal macrophages, and a generic lymphoid tissue cDC cluster associated with Ccr7. However, transcripts encoding Adgre1, Itgax, Itgam, Clec9a, Cd163, Mertk, Mrc1, Retnla, and H2-a/e (encoding class II major histocompatibility complex [MHC] proteins) and many other proposed macrophage subset and DC lineage markers each had idiosyncratic expression profiles. Coexpression of immediate early genes (for example, Egr1, Fos, Dusp1) and inflammatory cytokines and chemokines (tumour necrosis factor [Tnf], Il1b, Ccl3/4) indicated that all tissue disaggregation and separation protocols activate MPS cells. Tissue-specific expression clusters indicated that all cell isolation procedures also co-purify other unrelated cell types that may interact with MPS cells in vivo. Comparative analysis of RNA-seq and single-cell RNA-seq (scRNA-seq) data from the same lung cell populations indicated that MPS heterogeneity implied by global cluster analysis may be even greater at a single-cell level. This analysis highlights the power of large data sets to identify the diversity of MPS cellular phenotypes and the limited predictive value of surface markers to define lineages, functions, or subpopulations.

Abstract 4410: Automatic detection of consensus gene clusters across multiple single-cell datasets

Abstract Identifying co-expressed gene clusters can provide evidence for cellular activities. Thus, co-expression clustering will be a routine step in single-cell RNA-seq data analysis. We show that commonly used clustering methods for bulk-seq data are not suitable for single-cell data, and produce results that substantially disagree in the biological expectations of co-expressed genes. Herein, we present scLM, a tailored method for single-cell data, to extract co-expressed gene clusters matching the biological expectations and outperform widely used methods. Additionally, scLM can simultaneously cluster multiple single-cell datasets, i.e. consensus clustering, enabling users to leverage the large quantity of public data for novel comparative analysis. ScLM takes raw count data as input and preserves biological variations without being influenced by batch effects from multiple datasets. Results from both simulation data and real experimental data demonstrate that scLM outperforms the existing methods with considerably improved accuracy. To illustrate the biological insights of scLM, we apply it to our in-house and public experimental scRNA-Seq datasets. ScLM identifies novel functional gene modules and refines cell states, which facilitates mechanism discovery and understanding of complex biosystems such as cancers. Citation Format: Qianqian Song, Jing Su, Lance D. Miller, Wei Zhang. Automatic detection of consensus gene clusters across multiple single-cell datasets [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 4410.

Transcriptome and Co-Expression Network Analyses Identify the Molecular Signatures Underlying Drought Resistance in Yellowhorn

Drought is a key factor that limits plant growth and yield. Yellowhorn is an important and vigorously promoted oil tree in China. It can survive under certain drought conditions, but a lack of water severely restricts its growth and results in yield losses in arid and semi-arid areas. Therefore, it is important to identify the key pathways and genes to understand the mechanisms of its drought resistance. In this study, we evaluated drought resistance in four types of yellowhorn, and obtained 2669 and 2451 differentially expressed genes (DEGs) via the transcriptome analysis of the comparison of water-saving/water-consuming and fast-growing/slow-growing yellowhorn, respectively, under long-term drought conditions. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of DEGs showed the key biological processes and metabolic pathways involved in drought resistance, which demonstrated that there are both the same and different biological processes involved in regulating water use efficiency (WUE) and growth in response to drought stress. Furthermore, the network analysis indicated hub genes (especially seven protein kinases) and potential co-expressed gene clusters in a greenyellow module associated with WUE and a blue module associated with growth. These identified hub genes and key biological processes can significantly enhance our knowledge about the molecular mechanisms of drought resistance in yellowhorn.

Gene Expression Network Analysis of Precursor Lesions in Familial Pancreatic Cancer.

Purpose: High-grade pancreatic intraepithelial neoplasia (PanIN) are aggressive premalignant lesions, associated with risk of progression to pancreatic ductal adenocarcinoma (PDAC). A depiction of co-dysregulated gene activity in high-grade familial pancreatic cancer (FPC)-related PanIN lesions may characterize the molecular events during the progression from familial PanIN to PDAC.Materials and Methods: We performed weighted gene coexpression network analysis (WGCNA) to identify clusters of coexpressed genes associated with FPC-related PanIN lesions in 13 samples with PanIN-2/3 from FPC predisposed individuals, 6 samples with PDAC from sporadic pancreatic cancer (SPC) patients, and 4 samples of normal donor pancreatic tissue.Results: WGCNA identified seven differentially expressed gene (DEG) modules and two commonly expressed gene (CEG) modules with significant enrichment for Gene Ontology (GO) terms in FPC and SPC, including three upregulated (p < 5e-05) and four downregulated (p < 6e-04) gene modules in FPC compared to SPC. Among the DEG modules, the upregulated modules include 14 significant genes (p < 1e-06): ALOX12-AS1, BCL2L11, EHD4, C4B, BTN3A3, NDUFA11, RBM4B, MYOC, ZBTB47, TTTY15, NAPRT, LOC102606465, LOC100505711, and PTK2. The downregulated modules include 170 genes (p < 1e-06), among them 13 highly significant genes (p < 1e-10): COL10A1, SAMD9, PLPP4, COMP, POSTN, IGHV4–31, THBS2, MMP9, FNDC1, HOPX, TMEM200A, INHBA, and SULF1. The DEG modules are enriched for GO terms related to mitochondrial structure and adenosine triphosphate metabolic processes, extracellular structure and binding properties, humoral and complement mediated immune response, ligand-gated ion channel activity, and transmembrane receptor activity. Among the CEG modules, IL22RA1, DPEP1, and BCAT1 were found as highly connective hub genes associated with both FPC and SPC.Conclusion: FPC-related PanIN lesions exhibit a common molecular basis with SPC as shown by gene network activities and commonly expressed high-connectivity hub genes. The differential molecular pathology of FPC and SPC involves multiple coexpressed gene clusters enriched for GO terms including extracellular activities and mitochondrion function.