Isolation and Separation of Epidermal and Mesophyll Protoplasts from Rye Primary Leaves — Tissue-Specific Characteristics of Secondary Phenolic Product Accumulation

1986 ◽  
Vol 41 (1-2) ◽  
pp. 22-27 ◽  
Author(s):  
Margot Schulz ◽  
Gottfried Weissenböck
Keyword(s):  
Phenolic Compounds ◽  
Large Scale ◽  
Tissue Specificity ◽  
Light Period ◽  
Primary Leaf ◽  
Secondary Product ◽  
Tissue Specific ◽  
Mesophyll Protoplasts ◽  
Flavonoid Compounds ◽  
Product Accumulation

Abstract We have develop ed a technique for the large-scale isolation of epidermal and mesophyll proto­plasts, as well as the vascular strands, of rye primary leaf blades. Separation of the two types of protoplasts has been successful only from leaves harvested at the end of a 13-h light period, when chloroplasts were enriched in starch. The occurrence of different flavonoid compounds, and amounts, in epidermal and mesophyll protoplasts can be used as criteria for protoplast purity and viability since C-glucosylflavone O-glycosides are characteristic of epidermal protoplasts whereas flavone O-glucuronides and anthocyanins are typical of mesophyll protoplasts. Several non-flavonoid phenolic compounds are found only in the epidermal protoplast. These patterns of secondary product accumulation reflect the high tissue specificity of the rye leaf.

scholarly journals Tissue specificity of in vitro drug sensitivity

2017 ◽  
Vol 25 (2) ◽  
pp. 158-166 ◽  
Author(s):  
Fupan Yao ◽  
Seyed Ali Madani Tonekaboni ◽  
Zhaleh Safikhani ◽  
Petr Smirnov ◽  
Nehme El-Hachem ◽  
...  
Keyword(s):  
Drug Screening ◽  
Large Scale ◽  
Tissue Specificity ◽  
Drug Sensitivity ◽  
Cancer Drug ◽  
General Level ◽  
Response Curves ◽  
Tissue Specific ◽  
Clinical Indications

Abstract Objectives We sought to investigate the tissue specificity of drug sensitivities in large-scale pharmacological studies and compare these associations to those found in drug clinical indications. Materials and Methods We leveraged the curated cell line response data from PharmacoGx and applied an enrichment algorithm on drug sensitivity values’ area under the drug dose-response curves (AUCs) with and without adjustment for general level of drug sensitivity. Results We observed tissue specificity in 63% of tested drugs, with 8% of total interactions deemed significant (false discovery rate <0.05). By restricting the drug-tissue interactions to those with AUC > 0.2, we found that in 52% of interactions, the tissue was predictive of drug sensitivity (concordance index > 0.65). When compared with clinical indications, the observed overlap was weak (Matthew correlation coefficient, MCC = 0.0003, P > .10). Discussion While drugs exhibit significant tissue specificity in vitro, there is little overlap with clinical indications. This can be attributed to factors such as underlying biological differences between in vitro models and patient tumors, or the inability of tissue-specific drugs to bring additional benefits beyond gold standard treatments during clinical trials. Conclusion Our meta-analysis of pan-cancer drug screening datasets indicates that most tested drugs exhibit tissue-specific sensitivities in a large panel of cancer cell lines. However, the observed preclinical results do not translate to the clinical setting. Our results suggest that additional research into showing parallels between preclinical and clinical data is required to increase the translational potential of in vitro drug screening.

scholarly journals funMotifs: Tissue-specific transcription factor motifs

2019 ◽  
Author(s):  
Husen M. Umer ◽  
Karolina Smolinska-Garbulowska ◽  
Nour-al-dain Marzouka ◽  
Zeeshan Khaliq ◽  
Claes Wadelius ◽  
...  
Keyword(s):  
Large Scale ◽  
Tissue Specificity ◽  
Functional Characterization ◽  
Regulatory Elements ◽  
Web Based ◽  
Tissue Specific ◽  
Functional Variants ◽  
Genome Wide ◽  
Reporter Assays

ABSTRACTTranscription factors (TF) regulate gene expression by binding to specific sequences known as motifs. A bottleneck in our knowledge of gene regulation is the lack of functional characterization of TF motifs, which is mainly due to the large number of predicted TF motifs, and tissue specificity of TF binding. We built a framework to identify tissue-specific functional motifs (funMotifs) across the genome based on thousands of annotation tracks obtained from large-scale genomics projects including ENCODE, RoadMap Epigenomics and FANTOM. The annotations were weighted using a logistic regression model trained on regulatory elements obtained from massively parallel reporter assays. Overall, genome-wide predicted motifs of 519 TFs were characterized across fifteen tissue types. funMotifs summarizes the weighted annotations into a functional activity score for each of the predicted motifs. funMotifs enabled us to measure tissue specificity of different TFs and to identify candidate functional variants in TF motifs from the 1000 genomes project, the GTEx project, the GWAS catalogue, and in 2,515 cancer samples from the Pan-cancer analysis of whole genome sequences (PCAWG) cohort. To enable researchers annotate genomic variants or regions of interest, we have implemented a command-line pipeline and a web-based interface that can publicly be accessed on: http://bioinf.icm.uu.se/funmotifs.

scholarly journals AdaTiSS: A Novel Data-Adaptive Robust Method for Quantifying Tissue Specificity Scores

2019 ◽  
Author(s):  
Meng Wang ◽  
Lihua Jiang ◽  
Michael P. Snyder
Keyword(s):  
Large Scale ◽  
Tissue Specificity ◽  
Population Distribution ◽  
Specific Gene ◽  
Robust Method ◽  
Gene Expressions ◽  
Regression Problem ◽  
Tissue Specific ◽  
Tissue Specific Gene ◽  
Data Adaptive

AbstractMotivationAccurately detecting tissue specificity (TS) in genes helps researchers understand tissue functions at the molecular level, and further identify disease mechanisms and discover tissue-specific therapeutic targets. The Genotype-Tissue Expression (GTEx) project (Consortium, 2015), and the Human Protein Atlas (HPA) project (Uhlén, et al., 2015) are two publicly available data resources, providing large-scale gene expressions across multiple tissue types. Multiple tissue comparisons, technical background noise and unknown variation factors make it challenging to accurately identify tissue specific gene expressions. Several methods worked on measuring the overall TS in gene expressions and classifying genes into tissue-enrichment categories. There still lacks a robust method to provide quantitative TS scores for each tissue.MethodsWe recognized that the key to quantify tissue specific gene expressions is to properly define a concept of expression population. We considered that inside the population, the sample expressions from various tissues are more or less balanced, and the outlier expressions outside the population may indicate tissue specificity. We then formulated the question to robustly estimate the population distribution. In a linear regression problem, we developed a novel data-adaptive robust estimation based on density-power-weight under unknown outlier distribution and non-vanishing outlier proportion (Wang, et al., 2019). In the question of quantifying TS, we focused on the Gaussian-population mixture model. We took into account gene heterogeneities and applied the robust data-adaptive procedure to estimate the population. With the robustly estimated population parameters, we constructed the AdaTiSS algorithm to obtain data-adaptive quantitative TS scores.ResultsOur TS scores from the AdaTiSS algorithm achieve the goal that the TS scores are comparable across tissues and also across genes, which standardize gene expressions in terms of TS. Compared to the categorical TS method such as the HPA criterion, our method provides more information on the population fitting, and shows advantages in quantitatively analyzing tissue specific functions, making the biology functional analysis more precise. We also discuss some limitations and possible future [email protected]

Incorporation of [14C]Phenylalanine and [14C] Cinnamic Acid into Leaf Pieces and Mesophyll Protoplasts from Oat Primary Leaves for Studies on Flavonoid Metabolism at the Tissue and Cell Level

1981 ◽  
Vol 36 (3-4) ◽  
pp. 222-233 ◽  
Author(s):  
Margareta Proksch
Keyword(s):  
Cinnamic Acid ◽  
Pulse Labelling ◽  
Acid Solutions ◽  
Cell Level ◽  
Abaxial Epidermis ◽  
Mesophyll Cells ◽  
Tissue Specific ◽  
Mesophyll Protoplasts ◽  
Main Site ◽  
End Products

When the abaxial epidermis was peeled from 5 to 6 day old oat primary leaves, and 3 cm segments were floated on radioactive phenylalanine or cinnamic acid solutions, more than 90 per cent of the radioactivity was incorporated within 3 to 7 h depending on the developmental stage of the leaf. C-glycosylflavones were labelled within 15 min and radioactivity in these compounds increased for several hours. Pulse labelling and pulse chase experiments with either phenylalanine or cinnamic acid, unequivocally demonstrate that oat flavones are stable end products of metabolism. However, this procedure does not distinguish between sequential biosynthesis of various flavones and their interconversion. Cinnamic acid was more efficiently (ca. 20 x) converted into oat leaf flavones than was phenylalanine, when the precursor was fed to leaf pieces, and flavones recovered from mesophyll protoplasts. Different labelling patterns were obtained with whole leaf segments and protoplasts which apparently reflect differences in tissue specific flavone biosynthesis of mesophyll and epidermis. Isolated mesophyll protoplasts incubated with [14C]cinnamic acid synthesize 14C-labelled flavones characteristic of the mesophyll, as well as several unidentified phenylpropanoid derivatives not found in the intact tissue. Data suggest that photosynthetically active mesophyll cells are a main site of tissue specific flavone biosynthesis

Cooperativity of sequence elements mediates tissue specificity of the rat insulin II gene

1990 ◽  
Vol 10 (4) ◽  
pp. 1784-1788
Author(s):  
Y P Hwung ◽  
Y Z Gu ◽  
M J Tsai
Keyword(s):  
Beta Cell ◽  
Tissue Specificity ◽  
Promoter Element ◽  
Heterologous Promoter ◽  
Specific Expression ◽  
Tissue Specific ◽  
Cis Acting ◽  
Insulin Promoter ◽  
Sequence Elements ◽  
Flanking Region

The 5'-flanking region of the rat insulin II gene (-448 to +50) is sufficient for tissue-specific expression. To further determine the tissue-specific cis-acting element(s), important sequences defined by linker-scanning mutagenesis were placed upstream of a heterologous promoter and transfected into insulin-producing and -nonproducing cells. Rat insulin promoter element 3 (RIPE3), which spans from -125 to -86, was shown to confer beta-cell-specific expression in either orientation. However, two subregions of RIPE3, RIPE3a and RIPE3b (defined by linker-scanning mutations), displayed only marginal activities. These results suggest that the two subregions cooperate to confer tissue specificity, presumably via their cognate binding factors.

scholarly journals Large Scale Gene Expression Meta-Analysis Reveals Tissue-Specific, Sex-Biased Gene Expression in Humans

2016 ◽  
Vol 7 ◽  
Author(s):  
Benjamin T. Mayne ◽  
Tina Bianco-Miotto ◽  
Sam Buckberry ◽  
James Breen ◽  
Vicki Clifton ◽  
...  
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Meta Analysis ◽  
Tissue Specific

scholarly journals Analyzing cancer gene expression data through the lens of normal tissue-specificity

2021 ◽  
Author(s):  
H. Robert Frost
Keyword(s):  
Gene Expression ◽  
Normal Tissue ◽  
Tissue Specificity ◽  
Cancer Genomics ◽  
Expression Profiles ◽  
Genetic Alterations ◽  
Cancer Type ◽  
Tissue Specific ◽  
Normal Tissues ◽  
Cancer Types

AbstractThe genetic alterations that underlie cancer development are highly tissue-specific with the majority of driving alterations occurring in only a few cancer types and with alterations common to multiple cancer types often showing a tissue-specific functional impact. This tissue-specificity means that the biology of normal tissues carries important information regarding the pathophysiology of the associated cancers, information that can be leveraged to improve the power and accuracy of cancer genomic analyses. Research exploring the use of normal tissue data for the analysis of cancer genomics has primarily focused on the paired analysis of tumor and adjacent normal samples. Efforts to leverage the general characteristics of normal tissue for cancer analysis has received less attention with most investigations focusing on understanding the tissue-specific factors that lead to individual genomic alterations or dysregulated pathways within a single cancer type. To address this gap and support scenarios where adjacent normal tissue samples are not available, we explored the genome-wide association between the transcriptomes of 21 solid human cancers and their associated normal tissues as profiled in healthy individuals. While the average gene expression profiles of normal and cancerous tissue may appear distinct, with normal tissues more similar to other normal tissues than to the associated cancer types, when transformed into relative expression values, i.e., the ratio of expression in one tissue or cancer relative to the mean in other tissues or cancers, the close association between gene activity in normal tissues and related cancers is revealed. As we demonstrate through an analysis of tumor data from The Cancer Genome Atlas and normal tissue data from the Human Protein Atlas, this association between tissue-specific and cancer-specific expression values can be leveraged to improve the prognostic modeling of cancer, the comparative analysis of different cancer types, and the analysis of cancer and normal tissue pairs.

scholarly journals Determinants of rat albumin promoter tissue specificity analyzed by an improved transient expression system.

1987 ◽  
Vol 7 (7) ◽  
pp. 2425-2434 ◽  
Author(s):  
J M Heard ◽  
P Herbomel ◽  
M O Ott ◽  
A Mottura-Rollier ◽  
M Weiss ◽  
...  
Keyword(s):  
Transient Expression ◽  
Tissue Specificity ◽  
Transcriptional Start Site ◽  
Expression System ◽  
Hepatoma Cells ◽  
Sequence Motif ◽  
Specific Expression ◽  
Tissue Specific ◽  
Albumin Gene ◽  
Ccaat Box

The 150-base-pairs region located upstream of the transcriptional start site of the rat albumin gene contains all of the critical sequences necessary for this gene's tissue-specific expression in rat hepatoma cells. In transient expression assays using an improved CAT system or direct mRNA analysis we were able to detect a faithful transcription from the albumin promoter in albumin-negative dedifferentiated H5 hepatoma cells which was 250-fold weaker than in differentiated H4II hepatoma cells producing albumin. This strong tissue specificity could be completely overcome through the cis action of a non-tissue-specific enhancer. Two upstream regions from nucleotides -151 to -119 and from -118 to -94, were required for efficient transcription in H4II cells. Each region contained a sequence motif highly conserved among different species. The effect of the -151/-119 region was strictly tissue specific, while the -118/-94 region was also involved in the low level of transcription observed in H5 cells. Finally, sequences between the CCAAT box and the TATA box also contributed to the overall tissue specificity of rat albumin gene transcription.

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