Dissect Gene Expression Research Articles

Dissecting gene expression networks in the developing hippocampus through the lens of NEIL3 depletion

Gene regulation in the hippocampus is fundamental for its development, synaptic plasticity, memory formation, and adaptability. Comparisons of gene expression among different developmental stages, distinct cell types, and specific experimental conditions have identified differentially expressed genes contributing to the organization and functionality of hippocampal circuits. The NEIL3 DNA glycosylase, one of the DNA repair enzymes, plays an important role in hippocampal maturation and neuron functionality by shaping transcription. While differential gene expression (DGE) analysis has identified key genes involved, broader gene expression patterns crucial for high-order hippocampal functions remain uncharted. By utilizing the weighted gene co-expression network analysis (WGCNA), we mapped gene expression networks in immature (p8-neonatal) and mature (3 m-adult) hippocampal circuits in wild-type and NEIL3-deficient mice. Our study unveiled intricate gene network structures underlying hippocampal maturation, delineated modules of co-expressed genes, and pinpointed highly interconnected hub genes specific to the maturity of hippocampal subregions. We investigated variations within distinct gene network modules following NEIL3 depletion, uncovering NEIL3-targeted hub genes that influence module connectivity and specificity. By integrating WGCNA with DGE, we delve deeper into the NEIL3-dependent molecular intricacies of hippocampal maturation. This study provides a comprehensive systems-level analysis for assessing the potential correlation between gene connectivity and functional connectivity within the hippocampal network, thus shaping hippocampal function throughout development.

Alteration of chromosome structure impacts gene expressions implicated in pancreatic ductal adenocarcinoma cells

BackgroundPancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy with a five-year survival rate of approximately 10%. Genetic mutations are pivotal drivers in PDAC pathogenesis, but recent investigations also revealed the involvement of non-genetic alterations in the disease development. In this study, we undertook a multi-omics approach, encompassing ATAC-seq, RNA-seq, ChIP-seq, and Hi-C methodologies, to dissect gene expression alterations arising from changes in chromosome accessibility and chromatin three-dimensional interactions in PDAC.ResultsOur findings indicate that chromosomal structural alterations can lead to abnormal expressions on key genes during PDAC development. Notably, overexpression of oncogenes FGFR2, FOXA2, CYP2R1, and CPOX can be attributed to the augmentation of promoter accessibility, coupled with long-range interactions with distal elements. Additionally, our findings indicate that chromosomal structural alterations caused by genomic instability can lead to abnormal expressions in PDACs. As an example, by analyzing chromosomal changes, we identified a putative oncogenic gene, LPAR1, which shows upregulated expression in both PDAC cell lines and clinical samples. The overexpression is correlated with alterations in LPAR1-associated 3D genome structure and chromatin state. We further demonstrated that high LPAR1 activity is required for enhanced PDAC cell migration in vitro.ConclusionsCollectively, our findings reveal that the chromosomal conformational alterations, in addition to the well-known genetic mutations, are critical for PDAC tumorigenesis.

Widening the landscape of transcriptional regulation of green algal photoprotection

Availability of light and CO2, substrates of microalgae photosynthesis, is frequently far from optimal. Microalgae activate photoprotection under strong light, to prevent oxidative damage, and the CO2 Concentrating Mechanism (CCM) under low CO2, to raise intracellular CO2 levels. The two processes are interconnected; yet, the underlying transcriptional regulators remain largely unknown. Employing a large transcriptomic data compendium of Chlamydomonas reinhardtii’s responses to different light and carbon supply, we reconstruct a consensus genome-scale gene regulatory network from complementary inference approaches and use it to elucidate transcriptional regulators of photoprotection. We show that the CCM regulator LCR1 also controls photoprotection, and that QER7, a Squamosa Binding Protein, suppresses photoprotection- and CCM-gene expression under the control of the blue light photoreceptor Phototropin. By demonstrating the existence of regulatory hubs that channel light- and CO2-mediated signals into a common response, our study provides an accessible resource to dissect gene expression regulation in this microalga.

Spatial transcriptomic and single-nucleus analysis reveals heterogeneity in a gigantic single-celled syncytium.

In multicellular organisms, the specification, coordination, and compartmentalization of cell types enable the formation of complex body plans. However, some eukaryotic protists such as slime molds generate diverse and complex structures while remaining in a multinucleate syncytial state. It is unknown if different regions of these giant syncytial cells have distinct transcriptional responses to environmental encounters and if nuclei within the cell diversify into heterogeneous states. Here, we performed spatial transcriptome analysis of the slime mold Physarum polycephalum in the plasmodium state under different environmental conditions and used single-nucleus RNA-sequencing to dissect gene expression heterogeneity among nuclei. Our data identifies transcriptome regionality in the organism that associates with proliferation, syncytial substructures, and localized environmental conditions. Further, we find that nuclei are heterogenous in their transcriptional profile and may process local signals within the plasmodium to coordinate cell growth, metabolism, and reproduction. To understand how nuclei variation within the syncytium compares to heterogeneity in single-nucleus cells, we analyzed states in single Physarum amoebal cells. We observed amoebal cell states at different stages of mitosis and meiosis, and identified cytokinetic features that are specific to nuclei divisions within the syncytium. Notably, we do not find evidence for predefined transcriptomic states in the amoebae that are observed in the syncytium. Our data shows that a single-celled slime mold can control its gene expression in a region-specific manner while lacking cellular compartmentalization and suggests that nuclei are mobile processors facilitating local specialized functions. More broadly, slime molds offer the extraordinary opportunity to explore how organisms can evolve regulatory mechanisms to divide labor, specialize, balance competition with cooperation, and perform other foundational principles that govern the logic of life.

Identification of Nifurtimox and Chrysin as Anti-Influenza Virus Agents by Clinical Transcriptome Signature Reversion.

The rapid development in the field of transcriptomics provides remarkable biomedical insights for drug discovery. In this study, a transcriptome signature reversal approach was conducted to identify the agents against influenza A virus (IAV) infection through dissecting gene expression changes in response to disease or compounds’ perturbations. Two compounds, nifurtimox and chrysin, were identified by a modified Kolmogorov–Smirnov test statistic based on the transcriptional signatures from 81 IAV-infected patients and the gene expression profiles of 1309 compounds. Their activities were verified in vitro with half maximal effective concentrations (EC50s) from 9.1 to 19.1 μM against H1N1 or H3N2. It also suggested that the two compounds interfered with multiple sessions in IAV infection by reversing the expression of 28 IAV informative genes. Through network-based analysis of the 28 reversed IAV informative genes, a strong synergistic effect of the two compounds was revealed, which was confirmed in vitro. By using the transcriptome signature reversion (TSR) on clinical datasets, this study provides an efficient scheme for the discovery of drugs targeting multiple host factors regarding clinical signs and symptoms, which may also confer an opportunity for decelerating drug-resistant variant emergence.

HIF-Mediated and Non-HIF-Mediated Differential Gene Expressions in Sickle Cell Reticulocyte and Their Impact on Clinical Manifestations

Reticulocytosis in sickle cell disease (SCD) is driven by tissue hypoxia from hemolytic anemia and vascular occlusion. Gene expression changes caused by hypoxia and other factors during reticulocytosis may impact SCD outcomes. We detected 1226 differentially expressed genes in SCD reticulocyte transcriptome compared to normal Black controls. To assess the role of hypoxia-mediating HIFs from other regulation of changes of the SCD reticulocyte transcriptome, we compared differential expression in SCD to that in Chuvash erythrocytosis (CE), a disorder characterized by constitutive upregulation of HIFs in normoxia. Of the SCD differentially expressed genes, 28% were shared between CE and SCD and thus classified as HIF-mediated. The HIF-mediated changes were generally in genes promoting erythroid maturation. We found that genes encoding the response to endoplasmic reticulum stress generally lacked HIF mediation.We then investigated the clinical correlation of erythroid gene expression for the 1226 differentially expressed genes detected in SCD reticulocytes, using clinical measures and gene expression data previously profiled in peripheral blood mononuclear cells (PBMCs) of 157 SCD patients at the University of Illinois at Chicago (UIC). Normal PBMCs contain only a small number of erythroid progenitors, but in SCD or CE PBMCs the erythroid transcriptome is enriched due to elevated circulating erythroid progenitors from heightened erythropoiesis (PMID: 32399971). We applied deconvolution analysis to assess the clinical correlation of erythroid gene expression, using a 16-gene expression signature of erythroid progenitors previously identified in SCD PBMCs. Deconvolution analysis uses the proportion of cell/tissue or specific marker genes (here the erythroid specific 16-gene signature) to dissect gene expression variation in biological samples with cell/tissue type heterogeneity. We correlated, in the 157 UIC patients, erythroid gene expression with i) degree of anemia as indicated by hemoglobin concentration, ii) vaso-occlusive severe pain episodes per year, and iii) degree of hemolysis measured by a hemolysis index. The analysis identified 231 genes associated with at least one of the complications. Increased expression of 40 erythroid specific genes, including 15 HIF-mediated genes, was associated with all three complications. These 40 genes are all upregulated in SCD reticulocytes and correlated with low hemoglobin concentration, frequent severe pain episodes, and high hemolysis index, suggesting that these manifestations may share a relationship to stress erythropoiesis-driven transcriptional activity.Expression quantitative trait loci (eQTL) contain genetic polymorphisms that associate with gene expression level, which can be viewed as a natural experiment to investigate the causal relations between gene expression change and phenotypic outcomes. To assess the causal effect of erythroid gene expression, we tested association between erythroid eQTL and the clinical manifestations in 906 SCD patients from the Walk-PHaSST and PUSH cohorts. We first mapped erythroid eQTL in the 157 UIC patients, who were previously genotyped by array, applying deconvolution algorithm on the same PBMC data for the 1226 differential genes in SCD reticulocytes, and detected 54 distinct eQTL for 30 genes at 5% false discovery rate. After adjusting for multiple comparisons, we found that the C allele of rs16911905, located in the β-globin cluster and associated with increased erythroid expression of HBD (encodes δ-globin of hemoglobin A 2), significantly correlated with lower hemoglobin concentration (β=-0.064, 95% CI -0.092 - -0.036, P=6.7×10 -6). The C allele was also associated with higher hemolytic rate (P=0.031), less frequent pain episodes (P=0.045), and increased erythroid expression of HBB here encoding sickle β-globin (P=5.1x10 -5). The association of the C allele with lower hemoglobin concentration was then validated in 242 patients from the UIC cohort (β=-0.071, 95% CI -0.13 - -0.011, P=0.023), as was the trend of association with higher hemolytic rate (P=0.0031) and less pain episodes (P=0.034).Our findings reveal HIF- and non-HIF-mediated genes in SCD stress erythropoiesis, and identify novel clinical associations for a HBD eQTL. Our study highlights the correlation of altered erythroid gene expression with SCD hemolytic and vaso-occlusive manifestations. DisclosuresSaraf: Global Blood Therapeutics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding; Pfizer: Research Funding. Gordeuk: Modus Therapeutics: Consultancy; Novartis: Research Funding; Incyte: Research Funding; Emmaus: Consultancy, Research Funding; Global Blood Therapeutics: Consultancy, Research Funding; CSL Behring: Consultancy.

Differential resting-state patterns across networks are spatially associated with Comt and Trmt2a gene expression patterns in a mouse model of 22q11.2 deletion

Copy number variations (CNV) involving multiple genes are ideal models to study polygenic neuropsychiatric disorders. Since 22q11.2 deletion is regarded as the most important single genetic risk factor for developing schizophrenia, characterizing the effects of this CNV on neural networks offers a unique avenue towards delineating polygenic interactions conferring risk for the disorder. We used a Df(h22q11)/+ mouse model of human 22q11.2 deletion to dissect gene expression patterns that would spatially overlap with differential resting-state functional connectivity (FC) patterns in this model (N = 12 Df(h22q11)/+ mice, N = 10 littermate controls). To confirm the translational relevance of our findings, we analyzed tissue samples from schizophrenia patients and healthy controls using machine learning to explore whether identified genes were co-expressed in humans. Additionally, we employed the STRING protein-protein interaction database to identify potential interactions between genes spatially associated with hypo- or hyper-FC. We found significant associations between differential resting-state connectivity and spatial gene expression patterns for both hypo- and hyper-FC. Two genes, Comt and Trmt2a, were consistently over-expressed across all networks. An analysis of human datasets pointed to a disrupted co-expression of these two genes in the brain in schizophrenia patients, but not in healthy controls. Our findings suggest that COMT and TRMT2A form a core genetic component implicated in differential resting-state connectivity patterns in the 22q11.2 deletion. A disruption of their co-expression in schizophrenia patients points out a prospective cause for the aberrance of brain networks communication in 22q11.2 deletion syndrome on a molecular level.

Towards spatio-temporally resolved developmental cardiac gene regulatory networks in zebrafish.

Heart formation in the zebrafish involves a rapid, complex series of morphogenetic events in three-dimensional space that spans cardiac lineage specification through to chamber formation and maturation. This process is tightly orchestrated by a cardiac gene regulatory network (GRN), which ensures the precise spatio-temporal deployment of genes critical for heart formation. Alterations of the timing or spatial localisation of gene expression can have a significant impact in cardiac ontogeny and may lead to heart malformations. Hence, a better understanding of the cellular and molecular basis of congenital heart disease relies on understanding the behaviour of cardiac GRNs with precise spatiotemporal resolution. Here, we review the recent technical advances that have expanded our capacity to interrogate the cardiac GRN in zebrafish. In particular, we focus on studies utilising high-throughput technologies to systematically dissect gene expression patterns, both temporally and spatially during heart development.

Identification of Two Subgroups of FOLFOX Resistance Patterns and Prediction of FOLFOX Response in Colorectal Cancer Patients

To dissect gene expression subgroups of FOLFOX resistance colorectal cancer(CRC) and predict FOLFOX response, gene expression data of 83 stage IV CRC tumor samples (FOLFOX responder n = 42, non-responder n = 41) are used to develop a novel iterative supervised learning method IML. IML identified two mutually exclusive subgroups of CRC patients that rely on different DNA damage repair proteins and resist FOLFOX. IML was validated in two validation sets (HR = 2.6, p Value = 0.02; HR = 2.36, p value = 0.02). A subgroup of mesenchymal subtype patients benefit from FOLFOX. Different subgroups of FOLFOX nonresponders may need to be treated differently.

Isolated C. elegans germ nuclei exhibit distinct genomic profiles of histone modification and gene expression

BackgroundThe wide variety of specialized permissive and repressive mechanisms by which germ cells regulate developmental gene expression are not well understood genome-wide. Isolation of germ cells with high integrity and purity from living animals is necessary to address these open questions, but no straightforward methods are currently available.ResultsHere we present an experimental paradigm that permits the isolation of nuclei from C. elegans germ cells at quantities sufficient for genomic analyses. We demonstrate that these nuclei represent a very pure population and are suitable for both transcriptome analysis (RNA-seq) and chromatin immunoprecipitation (ChIP-seq) of histone modifications. From these data, we find unexpected germline- and soma-specific patterns of gene regulation.ConclusionsThis new capacity removes a major barrier in the field to dissect gene expression mechanisms in the germ line of C. elegans. Consequent discoveries using this technology will be relevant to conserved regulatory mechanisms across species.

Transcriptomics analysis of propiconazole-treated Cochliobolus sativus reveals new putative azole targets in the plant pathogen.

Cochliobolus sativus (anamorph: Bipolaris sorokiniana) is a filamentous fungus from the class Dothideomycetes. It is a pathogen of cereals including wheat and barley, and causes foliar spot blotch, root rot, black point on grains, head blight, leaf blight, and seedling blight diseases. Annual yields of these economically important cereals are severely reduced due to this pathogen attack. Evolution of fungicide resistant pathogen strains, availability of a limited number of potent antifungal compounds, and their efficacy are the acute issues in field management of phytopathogenic fungi. Propiconazole is a widely used azole fungicide to control the disease in fields. The known targets of azoles are the demethylase enzymes involved in ergosterol biosynthesis. Nonetheless, azoles have multiple modes of action, some of which have not been explored yet. Identifying the off-target effects of fungicides by dissecting gene expression profiles in response to them can provide insights into their modes of action and possible mechanisms of fungicide resistance. Moreover it can also reveal additional targets for development of new fungicides. Hence, we analyzed the global gene expression profile of C. sativus on exposure to sub-lethal doses of propiconazole in a time series. The gene expression patterns were confirmed using quantitative reverse transcriptase PCR (qRT-PCR). This study revealed overexpression of target genes from the sterol biosynthesis pathway supporting the reported mode of resistance against azoles. In addition, some new potential targets have also been identified, which could be explored to develop new fungicides and plant protection strategies.

Coherent Feedforward Regulation of Gene Expression by Caulobacter σT and GsrN during Hyperosmotic Stress.

GsrN is a conserved small RNA that is under transcriptional control of the general stress sigma factor, σT, and that functions as a posttranscriptional regulator of Caulobacter crescentus survival under multiple stress conditions. We have defined features of GsrN structure that determine survival under hyperosmotic stress, and we have applied transcriptomic and proteomic methods to identify regulatory targets of GsrN under hyperosmotic conditions. The 5' end of GsrN, which includes a conserved cytosine-rich stem-loop structure, is necessary for cell survival after osmotic upshock. GsrN both activates and represses gene expression in this stress condition. Expression of an uncharacterized open reading frame predicted to encode a glycine zipper protein, osrP, is strongly activated by GsrN. Our data support a model in which GsrN physically interacts with osrP mRNA through its 5' C-rich stem-loop to enhance OsrP protein expression. We conclude that sigT, gsrN, and osrP form a coherent feedforward loop in which σT activates gsrN and osrP transcription during stress and GsrN activates OsrP protein expression at the posttranscriptional level. This study delineates transcriptional and posttranscriptional layers of Caulobacter gene expression control during hyperosmotic stress, uncovers a new regulatory target of GsrN, and defines a coherent feedforward motif in the Caulobacter general stress response (GSR) regulatory network.IMPORTANCE Bacteria inhabit diverse niches and must adapt their physiology to constant environmental fluctuations. A major response to environmental perturbation is to change gene expression. Caulobacter and other alphaproteobacteria initiate a complex gene expression program known as the general stress response (GSR) under conditions including oxidative stress, osmotic stress, and nutrient limitation. The GSR enables cell survival in these environments. Understanding how bacteria survive stress requires that we dissect gene expression responses, such as the GSR, at the molecular level. This study is significant, as it defines transcriptional and posttranscriptional layers of gene expression regulation in response to hyperosmotic stress. We further provide evidence that a coherent feedforward motif influences the system properties of the Caulobacter GSR pathway.

Along the Central Dogma-Controlling Gene Expression with Small Molecules.

The central dogma of molecular biology, that DNA is transcribed into RNA and RNA translated into protein, was coined in the early days of modern biology. Back in the 1950s and 1960s, bacterial genetics first opened the way toward understanding life as the genetically encoded interaction of macromolecules. As molecular biology progressed and our knowledge of gene control deepened, it became increasingly clear that expression relied on many more levels of regulation. In the process of dissecting mechanisms of gene expression, specific small-molecule inhibitors played an important role and became valuable tools of investigation. Small molecules offer significant advantages over genetic tools, as they allow inhibiting a process at any desired time point, whereas mutating or altering the gene of an important regulator would likely result in a dead organism. With the advent of modern sequencing technology, it has become possible to monitor global cellular effects of small-molecule treatment and thereby overcome the limitations of classical biochemistry, which usually looks at a biological system in isolation. This review focuses on several molecules, especially natural products, that have played an important role in dissecting gene expression and have opened up new fields of investigation as well as clinical venues for disease treatment.

CellExpress: a comprehensive microarray-based cancer cell line and clinical sample gene expression analysis online system.

With the advancement of high-throughput technologies, gene expression profiles in cell lines and clinical samples are widely available in the public domain for research. However, a challenge arises when trying to perform a systematic and comprehensive analysis across independent datasets. To address this issue, we developed a web-based system, CellExpress, for analyzing the gene expression levels in more than 4000 cancer cell lines and clinical samples obtained from public datasets and user-submitted data. First, a normalization algorithm can be utilized to reduce the systematic biases across independent datasets. Next, a similarity assessment of gene expression profiles can be achieved through a dynamic dot plot, along with a distance matrix obtained from principal component analysis. Subsequently, differentially expressed genes can be visualized using hierarchical clustering. Several statistical tests and analytical algorithms are implemented in the system for dissecting gene expression changes based on the groupings defined by users. Lastly, users are able to upload their own microarray and/or next-generation sequencing data to perform a comparison of their gene expression patterns, which can help classify user data, such as stem cells, into different tissue types. In conclusion, CellExpress is a user-friendly tool that provides a comprehensive analysis of gene expression levels in both cell lines and clinical samples. The website is freely available at http://cellexpress.cgm.ntu.edu.tw/. Source code is available at https://github.com/LeeYiFang/Carkinos under the MIT License. Database URL: http://cellexpress.cgm.ntu.edu.tw/

Network analyses elucidate the role of SMYD3 in esophageal squamous cell carcinoma.

SMYD3 is a member of the SET and myeloid‐Nervy‐DEAF‐1 (MYND) domain‐containing protein family of methyltransferases, which are known to play critical roles in carcinogenesis. Expression of SMYD3 is elevated in various cancers, including esophageal squamous cell carcinoma (ESCC), and is correlated with the survival time of patients with ESCC. Here, we dissect gene expression data, from a previously described KYSE150 ESCC cell line in which SMYD3 had been knocked down, by integration with the protein–protein interaction (PPI) network, to find the new potential biological roles of SMYD3 and subsequent target genes. By construction of a specific PPI network, differentially expressed genes (DEGs), following SMYD3 knockdown, were identified as interacting with thousands of neighboring proteins. Enrichment analyses from the DAVID Functional Annotation Chart found significant Gene Ontology (GO) terms associated with transcription activities, which were closely related to SMYD3 function. For example, YAP1 and GATA3 might be a target gene for SMYD3 to regulate transcription. Enrichment annotation of the total DEG PPI network by GO ‘Biological Process’ generated a connected functional map and found 532 significant terms, including known and potential biological roles of SMYD3 protein, such as expression regulation, signal transduction, cell cycle, cell metastasis, and invasion. Subcellular localization analyses found that DEGs and their interacting proteins were distributed in multiple layers, which might reflect the intricate biological processes at the spatial level. Our analysis of the PPI network has provided important clues for future detection of the biological roles and mechanisms, as well as the target genes of SMYD3.

Dissecting Gene Expression Changes Accompanying a Ploidy-Based Phenotypic Switch.

Aneuploidy, a state in which the chromosome number deviates from a multiple of the haploid count, significantly impacts human health. The phenotypic consequences of aneuploidy are believed to arise from gene expression changes associated with the altered copy number of genes on the aneuploid chromosomes. To dissect the mechanisms underlying altered gene expression in aneuploids, we used RNA-seq to measure transcript abundance in colonies of the haploid Saccharomyces cerevisiae strain F45 and two aneuploid derivatives harboring disomies of chromosomes XV and XVI. F45 colonies display complex “fluffy” morphologies, while the disomic colonies are smooth, resembling laboratory strains. Our two disomes displayed similar transcriptional profiles, a phenomenon not driven by their shared smooth colony morphology nor simply by their karyotype. Surprisingly, the environmental stress response (ESR) was induced in F45, relative to the two disomes. We also identified genes whose expression reflected a nonlinear interaction between the copy number of a transcriptional regulatory gene on chromosome XVI, DIG1, and the copy number of other chromosome XVI genes. DIG1 and the remaining chromosome XVI genes also demonstrated distinct contributions to the effect of the chromosome XVI disome on ESR gene expression. Expression changes in aneuploids appear to reflect a mixture of effects shared between different aneuploidies and effects unique to perturbing the copy number of particular chromosomes, including nonlinear copy number interactions between genes. The balance between these two phenomena is likely to be genotype- and environment-specific.

Abstract 1841: Dissecting gene expression programs that define tumor aggression and patient outcome in pancreatic cancer

Abstract By the year 2020, pancreatic cancer (PDAC) is projected to be the second leading cause of cancer deaths in the United States. Current systemic therapies offer limited survival advantages in subgroups of patients, suggesting underlying biological differences in this deadly cancer. Our lab has recently identified keratin-17 (K17) gene expression as a prognostic and predictive biomarker in PDAC. Furthermore, we found that K17 acts as an oncoprotein by promoting degradation of tumor suppressor p27KIP1, while knockdown of K17 limits tumor size and sensitizes cancer cells to chemotherapy agents. Together, these findings suggest that targeting K17 expression in PDACs may increase patient survival and response to treatment. The factors that lead to increased K17 gene expression in only a subset of PDAC cases, however, are unknown. K17 is expressed in embryonic ectoderm and aberrantly induced in keratinocytes from psoriasis patients through paracrine signaling from the microenvironment. To determine how K17 gene expression is upregulated in PDAC, we tested the effects of two major contributing factors in PDAC pathogenesis: driver mutations and inflammatory mediators present in tumor microenvironment. Using in vitro and in vivo loss- and gain-of function approaches, we modulated oncogene expression in and performed acute cytokine treatments on cells to test for changes in K17 gene expression. K17 expression was evaluated by qRT-PCR and western blot. We found that forced expression of oncogenes like KRASG12D, Q61K or TP53R248W significantly increase K17 expression, yet these cell-autonomous factors are not sufficient to explain K17 gene expression in PDACs. In addition, we found that pro-inflammatory cytokines, typically released by helper T cells, induce a strong increase in K17 gene expression. Together these findings suggest that K17 gene expression across otherwise clinically identical PDAC cases may be explained by early transformation events and maintained by positive feedback loops associated with patient specific adaptive immune response. Ongoing studies are seeking to test the combined effects of genetic and paracrine events in K17 expression, and understand the signaling pathways and transcription factors downstream of these inducing factors. Ultimately, insight into the mechanisms that mediate K17 overexpression in PDACs could help guide future research to develop K17 as a potential therapeutic target. Citation Format: Danielle J. Fassler, Luisa F. Escobar-Hoyos, Kenneth R. Shroyer. Dissecting gene expression programs that define tumor aggression and patient outcome in pancreatic cancer. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 1841.

Immune cell subsets and their gene expression profiles from human PBMC isolated by Vacutainer Cell Preparation Tube (CPT™) and standard density gradient.

BackgroundHigh quality genetic material is an essential pre-requisite when analyzing gene expression using microarray technology. Peripheral blood mononuclear cells (PBMC) are frequently used for genomic analyses, but several factors can affect the integrity of nucleic acids prior to their extraction, including the methods of PBMC collection and isolation. Due to the lack of the relevant data published, we compared the Ficoll-Paque density gradient centrifugation and BD Vacutainer cell preparation tube (CPT) protocols to determine if either method offered a distinct advantage in preparation of PBMC-derived immune cell subsets for their use in gene expression analysis. We evaluated the yield and purity of immune cell subpopulations isolated from PBMC derived by both methods, the quantity and quality of extracted nucleic acids, and compared gene expression in PBMC and individual immune cell types from Ficoll and CPT isolation protocols using Affymetrix microarrays.ResultsThe mean yield and viability of fresh PBMC acquired by the CPT method (1.16 × 106 cells/ml, 93.3 %) were compatible to those obtained with Ficoll (1.34 × 106 cells/ml, 97.2 %). No differences in the mean purity, recovery, and viability of CD19+ (B cells), CD8+ (cytotoxic T cells), CD4+ (helper T cell) and CD14+ (monocytes) positively selected from CPT- or Ficoll-isolated PBMC were found. Similar quantities of high quality RNA and DNA were extracted from PBMC and immune cells obtained by both methods. Finally, the PBMC isolation methods tested did not impact subsequent recovery and purity of individual immune cell subsets and, importantly, their gene expression profiles.ConclusionsOur findings demonstrate that the CPT and Ficoll PBMC isolation protocols do not differ in their ability to purify high quality immune cell subpopulations. Since there was no difference in the gene expression profiles between immune cells obtained by these two methods, the Ficoll isolation can be substituted by the CPT protocol without conceding phenotypic changes of immune cells and compromising the gene expression studies. Given that the CPT protocol is less elaborate, minimizes cells’ handling and processing time, this method offers a significant operating advantage, especially in large-scale clinical studies aiming at dissecting gene expression in PBMC and PBMC-derived immune cell subpopulations.Electronic supplementary materialThe online version of this article (doi:10.1186/s12865-015-0113-0) contains supplementary material, which is available to authorized users.

烟草基因瞬时表达体系的建立与优化研究

本研究以GUS基因和RIN基因作为报告基因，利用农杆菌(Agrobacterium tumefaciens) GV3101对烟草叶片注射侵染，探究了农杆菌侵染浓度对本氏烟草(Nicotiana benthamiana)叶片瞬时表达效果的影响，建立并优化了烟草中的瞬时表达体系。结果表明，β-葡萄糖苷酶(GUS β-glucuronidase)基因与RIN基因在OD600 = 1.0的侵染浓度时表达效果最佳。农杆菌介导的烟草叶片瞬时表达方法简单高效，结果准确可靠，从种子播种到收获蛋白只需25天左右。该体系的建立与优化为基因表达和蛋白功能性研究等方面提供了一定支持。 To establish and optimize a transient expression system in Nicotiana benthamiana, the method was developed with the β-glucuronidase (GUS) and Ripening Inhibitor (RIN) as marker genes. Using the agrobacterium-mediated transformation method, GV3101 was used for the effects of different bacteria concentration on the efficiency of protein transient expression in Nicotiana benthamiana. Observed by the results, a higher transient expression level of GUS & RIN gene could be obtained as OD600 value of A. tumefaciens for intiltration 1.0. The entire process only took 25 days from sowing seed to protein analysis. Therefore, this method is simple and rapid. It has a potential application in dissecting gene expression and function in Brassica napus.

Dissecting gene expression at the blood-brain barrier.

The availability of genome-wide expression data for the blood-brain barrier is an invaluable resource that has recently enabled the discovery of several genes and pathways involved in the development and maintenance of the blood-brain barrier, particularly in rodent models. The broad distribution of published data sets represents a viable starting point for the molecular dissection of the blood-brain barrier and will further direct the discovery of novel mechanisms of blood-brain barrier formation and function. Technical advances in purifying brain endothelial cells, the key cell that forms the critical barrier, have allowed for greater specificity in gene expression comparisons with other central nervous system cell types, and more systematic characterizations of the molecular composition of the blood-brain barrier. Nevertheless, our understanding of how the blood-brain barrier changes during aging and disease is underrepresented. Blood-brain barrier data sets from a wider range of experimental paradigms and species, including invertebrates and primates, would be invaluable for investigating the function and evolution of the blood-brain barrier. Newer technologies in gene expression profiling, such as RNA-sequencing, now allow for finer resolution of transcriptomic changes, including isoform specificity and RNA-editing. As our field continues to utilize more advanced expression profiling in its ongoing efforts to elucidate the blood-brain barrier, including in disease and drug delivery, we will continue to see rapid advances in our understanding of the molecular mediators of barrier biology. We predict that the recently published data sets, combined with forthcoming genomic and proteomic blood-brain barrier data sets, will continue to fuel the molecular genetic revolution of blood-brain barrier biology.