Cis/transgene optimization: systematic discovery of some key gene expression elements integrating bioinformatics and computational biology

2016 ◽  
Author(s):  
Saeid Kadkhodaei ◽  
Farahnaz S. Golestan Hashemi ◽  
Morvarid Akhavan Rezaei ◽  
Sahar Abbasiliasi ◽  
Tan Joo Shun ◽  
...  
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Computational Analysis ◽  
Production Systems ◽  
Marker Gene ◽  
Optimization Procedure ◽  
Expression Vectors ◽  
Secretion Systems ◽  
Specific Expression ◽  
Gene Optimization

In recombinant protein production, quantity and quality are the major challenges particularly for large scale and high-throughput production systems. The present study mainly focused on computational analysis and in silico systematic discovery of some key functional gene expression elements in microalgae Dunaliella salina as a case study which there is no or poor information in this regard. Among the key factors, we took a shot at matrix attachment regions (MARs), translation initiation sites (TIS), signal peptide (SP) sequences, gene optimization and transformation system. Computational analysis of MARs sequences provided enough information about the structure of these sequences and led us to design an artificial MAR sequence considering the essential motifs and underlain rules. As the consensus TIS, we revealed that A-3, G-6C-5C-4 and G+1C+2G+3 arrange the specific context in this microalgae which help in locating the ribosome at the correct reading frame. Bioinformatics studies unveiled the sequence of MASTRAPLLALLALLCAGSARA with the highest signal score as the specific SP for secretion systems. A multi-criteria optimization procedure was performed to redesign the coding sequence of the BAR selectable marker gene. The optimized version of the gene mainly covered the host codon preference, the less structured mRNA and exposure of TIS. As the intragenic factors, we selected an efficient promoter, a 5ˈ-UTR and an intron from the closely related species (Chlamydomonas Sp.) to construct the specific expression vectors. The expression cassettes containing optimized genetic elements could be delivered into the microalgae cells and conferred the resistance to the transformants for at least 90 generations. The findings indicated that the MARs flanking the expression cassette along with the optimized expression elements particularly codon adaptation could potentially improve transformation efficiency and stability. The findings can be efficiently deployed as an empirical model for systematic discovery of the key expression elements and optimization of the cis/transgenes.

scholarly journals The combined detection of Amphiregulin, Cyclin A1 and DDX20/Gemin3 expression predicts aggressive forms of oral squamous cell carcinoma

2021 ◽  
Author(s):  
Ekaterina Bourova-Flin ◽  
Samira Derakhshan ◽  
Afsaneh Goudarzi ◽  
Tao Wang ◽  
Anne-Laure Vitte ◽  
...  
Keyword(s):  
Gene Expression ◽  
Squamous Cell ◽  
Large Scale ◽  
Biomarker Discovery ◽  
Gene Expression Signature ◽  
Predictive Biomarkers ◽  
Specific Gene ◽  
Specific Expression ◽  
Intrinsic Nature ◽  
Independent Cohort

Abstract Background Large-scale genetic and epigenetic deregulations enable cancer cells to ectopically activate tissue-specific expression programmes. A specifically designed strategy was applied to oral squamous cell carcinomas (OSCC) in order to detect ectopic gene activations and develop a prognostic stratification test. Methods A dedicated original prognosis biomarker discovery approach was implemented using genome-wide transcriptomic data of OSCC, including training and validation cohorts. Abnormal expressions of silent genes were systematically detected, correlated with survival probabilities and evaluated as predictive biomarkers. The resulting stratification test was confirmed in an independent cohort using immunohistochemistry. Results A specific gene expression signature, including a combination of three genes, AREG, CCNA1 and DDX20, was found associated with high-risk OSCC in univariate and multivariate analyses. It was translated into an immunohistochemistry-based test, which successfully stratified patients of our own independent cohort. Discussion The exploration of the whole gene expression profile characterising aggressive OSCC tumours highlights their enhanced proliferative and poorly differentiated intrinsic nature. Experimental targeting of CCNA1 in OSCC cells is associated with a shift of transcriptomic signature towards the less aggressive form of OSCC, suggesting that CCNA1 could be a good target for therapeutic approaches.

scholarly journals A Novel Method Facilitating the Simple and Low-Cost Preparation of Human Osteochondral Slice Explants for Large-Scale Native Tissue Analysis

2021 ◽  
Vol 22 (12) ◽  
pp. 6394
Author(s):  
Jacob Spinnen ◽  
Lennard K. Shopperly ◽  
Carsten Rendenbach ◽  
Anja A. Kühl ◽  
Ufuk Sentürk ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Viability ◽  
Large Scale ◽  
Marker Gene ◽  
Cell Swelling ◽  
Tissue Cell ◽  
Joint Research ◽  
Sample Number ◽  
Tnf Α ◽  
Novel Method

For in vitro modeling of human joints, osteochondral explants represent an acceptable compromise between conventional cell culture and animal models. However, the scarcity of native human joint tissue poses a challenge for experiments requiring high numbers of samples and makes the method rather unsuitable for toxicity analyses and dosing studies. To scale their application, we developed a novel method that allows the preparation of up to 100 explant cultures from a single human sample with a simple setup. Explants were cultured for 21 days, stimulated with TNF-α or TGF-β3, and analyzed for cell viability, gene expression and histological changes. Tissue cell viability remained stable at >90% for three weeks. Proteoglycan levels and gene expression of COL2A1, ACAN and COMP were maintained for 14 days before decreasing. TNF-α and TGF-β3 caused dose-dependent changes in cartilage marker gene expression as early as 7 days. Histologically, cultures under TNF-α stimulation showed a 32% reduction in proteoglycans, detachment of collagen fibers and cell swelling after 7 days. In conclusion, thin osteochondral slice cultures behaved analogously to conventional punch explants despite cell stress exerted during fabrication. In pharmacological testing, both the shorter diffusion distance and the lack of need for serum in the culture suggest a positive effect on sensitivity. The ease of fabrication and the scalability of the sample number make this manufacturing method a promising platform for large-scale preclinical testing in joint research.

scholarly journals A curated transcriptome dataset collection to investigate the functional programming of human hematopoietic cells in early life

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 414 ◽  
Author(s):  
Mahbuba Rahman ◽  
Sabri Boughorbel ◽  
Scott Presnell ◽  
Charlie Quinn ◽  
Chiara Cugno ◽  
...  
Keyword(s):  
Gene Expression ◽  
Early Life ◽  
Functional Programming ◽  
Web Application ◽  
Large Scale ◽  
Single Gene ◽  
Hematopoietic Cells ◽  
Biomedical Knowledge ◽  
Specific Expression ◽  
Age Related

Compendia of large-scale datasets made available in public repositories provide an opportunity to identify and fill gaps in biomedical knowledge. But first, these data need to be made readily accessible to research investigators for interpretation. Here we make available a collection of transcriptome datasets to investigate the functional programming of human hematopoietic cells in early life. Thirty two datasets were retrieved from the NCBI Gene Expression Omnibus (GEO) and loaded in a custom web application called the Gene Expression Browser (GXB), which was designed for interactive query and visualization of integrated large-scale data. Quality control checks were performed. Multiple sample groupings and gene rank lists were created allowing users to reveal age-related differences in transcriptome profiles, changes in the gene expression of neonatal hematopoietic cells to a variety of immune stimulators and modulators, as well as during cell differentiation. Available demographic, clinical, and cell phenotypic information can be overlaid with the gene expression data and used to sort samples. Web links to customized graphical views can be generated and subsequently inserted in manuscripts to report novel findings. GXB also enables browsing of a single gene across projects, thereby providing new perspectives on age- and developmental stage-specific expression of a given gene across the human hematopoietic system. This dataset collection is available at: http://developmentalimmunology.gxbsidra.org/dm3/geneBrowser/list.

scholarly journals Assessing the Gene Regulatory Landscape in 1,188 Human Tumors

2017 ◽  
Author(s):  
C Calabrese ◽  
K Lehmann ◽  
L Urban ◽  
F Liu ◽  
S Erkek ◽  
...  
Keyword(s):  
Gene Expression ◽  
Genetic Variation ◽  
Large Scale ◽  
Human Cancer ◽  
Expression Patterns ◽  
Regulatory Elements ◽  
Whole Genome ◽  
Somatic Variation ◽  
Specific Expression ◽  
Large Scale Assessment

AbstractCancer is characterised by somatic genetic variation, but the effect of the majority of non-coding somatic variants and the interface with the germline genome are still unknown. We analysed the whole genome and RNA-Seq data from 1,188 human cancer patients as provided by the Pan-cancer Analysis of Whole Genomes (PCAWG) project to map cis expression quantitative trait loci of somatic and germline variation and to uncover the causes of allele-specific expression patterns in human cancers. The availability of the first large-scale dataset with both whole genome and gene expression data enabled us to uncover the effects of the non-coding variation on cancer. In addition to confirming known regulatory effects, we identified novel associations between somatic variation and expression dysregulation, in particular in distal regulatory elements. Finally, we uncovered links between somatic mutational signatures and gene expression changes, including TERT and LMO2, and we explained the inherited risk factors in APOBEC-related mutational processes. This work represents the first large-scale assessment of the effects of both germline and somatic genetic variation on gene expression in cancer and creates a valuable resource cataloguing these effects.

A suite of novel promoters and terminators for plant biotechnology

2003 ◽  
Vol 30 (4) ◽  
pp. 443 ◽  
Author(s):  
Petra H. D. Schünmann ◽  
Danny J. Llewellyn ◽  
Brian Surin ◽  
Petra Boevink ◽  
Robert C. De Feyter ◽  
...  
Keyword(s):  
Solanum Tuberosum L ◽  
Marker Gene ◽  
Plant Biotechnology ◽  
Subterranean Clover ◽  
Expression Vectors ◽  
Crop Species ◽  
Specific Expression ◽  
Circular Dna ◽  
Subterranean Clover Stunt Virus ◽  
Gene Assay

The gene regulation signals from subterranean clover stunt virus (SCSV) were investigated for their expression in dicot plants. The SCSV genome has at least eight circular DNA molecules. Each circular DNA component contains a promoter element, a single open reading frame and a terminator. The promoters from seven of the segments were examined for their strength and tissue specificity in transgenic tobacco (Nicotiana tabacum L.), potato (Solanum tuberosum L.) and cotton (Gossypium hirsutum L.) using a GUS reporter gene assay system. While the promoters of many of the segments were poorly expressed, promoters derived from segments 4 and 7 were shown to direct high levels of expression in various plant tissues and organs. The segment 1 promoter directs predominantly callus-specific expression and, when used to control a selectable marker gene, facilitated the transformation of all three species (tobacco, potato and cotton). From the results, a suite of plant expression vectors (pPLEX) derived from the SCSV genome were constructed and used here to produce herbicide- and insect-resistant cotton, demonstrating their utility in the expression of foreign genes in dicot crop species and their potential for use in agricultural biotechnology.

scholarly journals Large-Scale Differential Gene Expression Transcriptomic Analysis Identifies a Metabolic Signature Shared by All Cancer Cells

Biomolecules ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 701 ◽  
Author(s):  
Areej Abu Rmaileh ◽  
Balakrishnan Solaimuthu ◽  
Mayur Tanna ◽  
Anees Khatib ◽  
Michal Ben Yosef ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differential Gene Expression ◽  
Large Scale ◽  
Essential Genes ◽  
Transformed Cells ◽  
Specific Expression ◽  
Normal Tissues ◽  
Metabolic Signature ◽  
Wide Range ◽  
Differential Gene

Cancer-dependent metabolic rewiring is often manifested by selective expression of enzymes essential for the transformed cells’ viability. However, the metabolic variations between normal and transformed cells are not fully characterized, and therefore, a systematic analysis will result in the identification of unknown cellular mechanisms crucial for tumorigenesis. Here, we applied differential gene expression transcriptome analysis to examine the changes in metabolic gene profiles between a wide range of normal tissues and cancer samples. We found that, in contrast to normal tissues which exhibit a tissue-specific expression profile, cancer samples are more homogenous despite their diverse origins. This similarity is due to a “proliferation metabolic signature” (PMS), composed of 158 genes (87 upregulated and 71 downregulated gene sets), where 143 are common to all proliferative cells but 15 are cancer specific. Intriguingly, the PMS gene set is enriched for genes encoding rate-limiting enzymes, and its upregulated set with genes associated with poor patient outcome and essential genes. Among these essential genes is ribulose-5-phosphate-3-epimerase (RPE), which encodes a pentose phosphate pathway enzyme and whose role in cancer is still unclear. Collectively, we identified a set of metabolic genes that can serve as novel cancer biomarkers and potential targets for drug development.

scholarly journals Local genetic effects on gene expression across 44 human tissues

2016 ◽  
Author(s):  
François Aguet ◽  
Andrew A. Brown ◽  
Stephane E. Castel ◽  
Joe R. Davis ◽  
Pejman Mohammadi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Complex Traits ◽  
Large Scale ◽  
Genetic Diseases ◽  
Tissue Expression ◽  
Specific Expression ◽  
Tissue Specific ◽  
Regulatory Variation ◽  
Functional Variants ◽  
Trait Locus

AbstractExpression quantitative trait locus (eQTL) mapping provides a powerful means to identify functional variants influencing gene expression and disease pathogenesis. We report the identification of cis-eQTLs from 7,051 post-mortem samples representing 44 tissues and 449 individuals as part of the Genotype-Tissue Expression (GTEx) project. We find a cis-eQTL for 88% of all annotated protein-coding genes, with one-third having multiple independent effects. We identify numerous tissue-specific cis-eQTLs, highlighting the unique functional impact of regulatory variation in diverse tissues. By integrating large-scale functional genomics data and state-of-the-art fine-mapping algorithms, we identify multiple features predictive of tissue-specific and shared regulatory effects. We improve estimates of cis-eQTL sharing and effect sizes using allele specific expression across tissues. Finally, we demonstrate the utility of this large compendium of cis-eQTLs for understanding the tissue-specific etiology of complex traits, including coronary artery disease. The GTEx project provides an exceptional resource that has improved our understanding of gene regulation across tissues and the role of regulatory variation in human genetic diseases.

scholarly journals Transcriptome plasticity underlying plant root colonization and insect invasion by Pseudomonas protegens

2020 ◽  
Vol 14 (11) ◽  
pp. 2766-2782 ◽  
Author(s):  
Pilar Vesga ◽  
Pascale Flury ◽  
Jordan Vacheron ◽  
Christoph Keel ◽  
Daniel Croll ◽  
...  
Keyword(s):  
Large Scale ◽  
Root Colonization ◽  
Galleria Mellonella ◽  
Plant Root ◽  
Secretion Systems ◽  
Specific Expression ◽  
Wheat Roots ◽  
Evolutionary Origins ◽  
Specific Factors ◽  
High Degree

Abstract Pseudomonas protegens shows a high degree of lifestyle plasticity since it can establish both plant-beneficial and insect-pathogenic interactions. While P. protegens protects plants against soilborne pathogens, it can also invade insects when orally ingested leading to the death of susceptible pest insects. The mechanism whereby pseudomonads effectively switch between lifestyles, plant-beneficial or insecticidal, and the specific factors enabling plant or insect colonization are poorly understood. We generated a large-scale transcriptomics dataset of the model P. protegens strain CHA0 which includes data from the colonization of wheat roots, the gut of Plutella xylostella after oral uptake and the Galleria mellonella hemolymph after injection. We identified extensive plasticity in transcriptomic profiles depending on the environment and specific factors associated to different hosts or different stages of insect infection. Specifically, motor-activity and Reb toxin-related genes were highly expressed on wheat roots but showed low expression within insects, while certain antimicrobial compounds (pyoluteorin), exoenzymes (a chitinase and a polyphosphate kinase), and a transposase exhibited insect-specific expression. We further identified two-partner secretion systems as novel factors contributing to pest insect invasion. Finally, we use genus-wide comparative genomics to retrace the evolutionary origins of cross-kingdom colonization.

scholarly journals ARBic: An All-Round Biclustering Algorithm for Analyzing Gene Expression Data

2021 ◽  
Author(s):  
Xiangyu Liu ◽  
Zhengchang Su ◽  
Guojun Li
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Large Scale ◽  
Expression Patterns ◽  
Data Matrix ◽  
Expression Data ◽  
Specific Expression ◽  
Longest Path ◽  
Background Data ◽  
Effectiveness And Efficiency

Abstract Background: Identifying significant biclusters of genes with specific expression patterns is an effective approach to reveal functionally correlated genes in gene expression data. However, existing algorithms are limited to finding either broad or narrow biclusters but both due to failure of balancing between effectiveness and efficiency. Methods: We developed a new algorithm ARBic which can accurately identify any meaningful biclusters of shape no matter broad or narrow in a large scale gene expression data matrix, even when the values in the biclusters to be identified have the same distribution as that the background data has. ARBic is developed by integrating column-based and row-based strategies into biclustering procedure. The column-based strategy borrowed from ReBic, a recently published biclustering tool, prefers to narrow bicluters. The row-based strategy newly designed in this article by repeatedly finding a longest path in a specific directed graph prefers to broader ones. Result and Conclusion: When tested and compared to other seven salient biclustering algorithms on simulated datasets, ARBic achieved recovery, relevance and f1-scores 29% higher than the second best algorithm. Furthermore, ARBic substantially outperforms all of them on real datasets and robusts to noises, shapes of biclusters and types of datasets.Code: https://github.com/holyzews/ARBicData: https://doi.org/10.5281/zenodo.5121018

scholarly journals Titrating gene expression with series of systematically compromised CRISPR guide RNAs

2019 ◽  
Author(s):  
Marco Jost ◽  
Daniel A. Santos ◽  
Reuben A. Saunders ◽  
Max A. Horlbeck ◽  
John S. Hawkins ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Biology ◽  
Large Scale ◽  
Control Gene ◽  
Specific Expression ◽  
Expression Phenotype ◽  
Control Gene Expression ◽  
Human Genes ◽  
A Genome ◽  
Multiple Cell

AbstractBiological phenotypes arise from the degrees to which genes are expressed, but the lack of tools to precisely control gene expression limits our ability to evaluate the underlying expression-phenotype relationships. Here, we describe a readily implementable approach to titrate expression of human genes using series of systematically compromised sgRNAs and CRISPR interference. We empirically characterize the activities of compromised sgRNAs using large-scale measurements across multiple cell models and derive the rules governing sgRNA activity using deep learning, enabling construction of a compact sgRNA library to titrate expression of ∼2,400 genes involved in central cell biology and a genome-wide in silico library. Staging cells along a continuum of gene expression levels combined with rich single-cell RNA-seq readout reveals gene-specific expression-phenotype relationships with expression level-specific responses. Our work provides a general tool to control gene expression, with applications ranging from tuning biochemical pathways to identifying suppressors for diseases of dysregulated gene expression.

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