scholarly journals Tissue-specific genes as an underutilized resource in drug discovery

2018 ◽  
Author(s):  
Maria Ryaboshapkina ◽  
Mårten Hammar
Keyword(s):  
Drug Targets ◽  
Tissue Specificity ◽  
Drug Research ◽  
Phase 1 ◽  
Evolutionary Conservation ◽  
Disease Genes ◽  
Mendelian Disease ◽  
Tissue Specific ◽  
Protein Interactome ◽  
The Relationship

ABSTRACTTissue-specific genes are believed to be good drug targets due to improved safety. Here we show that this intuitive notion is not reflected in phase 1 and 2 clinical trials, despite the historic success of tissue-specific targets and their 2.3-fold overrepresentation among targets of marketed non-oncology drugs. We compare properties of tissue-specific genes and drug targets. We show that tissue-specificity of the target may also be related to efficacy of the drug. The relationship may be indirect (enrichment in Mendelian disease genes) or direct (elevated ability to spread perturbations in human protein-protein interactome for tissue-specifically produced enzymes and secreted proteins). Reduced evolutionary conservation of tissue-specific genes may represent a bottleneck for drug projects, prompting development of novel models with smaller evolutionary gap to humans. We highlight numerous open opportunities to use tissue-specific genes in drug research and hope that the current study will facilitate discovery efforts.

scholarly journals A convex optimization approach for identification of human tissue-specific interactomes

2016 ◽  
Author(s):  
Shahin Mohammadi ◽  
Ananth Grama
Keyword(s):  
Human Tissue ◽  
Drug Targets ◽  
Protein Complexes ◽  
Disease Genes ◽  
Optimization Approach ◽  
Cell Type Specificity ◽  
Tissue Specific ◽  
Parkinson’S Diseases ◽  
Coordinated Expression ◽  
Validation Tests

AbstractMotivation:Analysis of organism-specific interactomes has yielded novel insights into cellular function and coordination, understanding of pathology, and identification of markers and drug targets. Genes, however, can exhibit varying levels of cell-type specificity in their expression, and their coordinated expression manifests in tissue-specific function and pathology. Tissue-specific/ selective interaction mechanisms have significant applications in drug discovery, as they are more likely to reveal drug targets. Furthermore, tissue-specific transcription factors (tsTFs) are significantly implicated in human disease, including cancers. Finally, disease genes and protein complexes have the tendency to be differentially expressed in tissues in which defects cause pathology. These observations motivate the construction of refined tissue-specific interactomes from organism-specific interactomes.Results:We present a novel technique for constructing human tissue-specific interactomes. Using a variety of validation tests (ESEA, GO Enrichment, Disease-Gene Subnetwork Compactness), we show that our proposed approach significantly outperforms state of the art techniques. Finally, using case studies of Alzheimer’s and Parkinson’s diseases, we show that tissue-specific interactomes derived from our study can be used to construct pathways implicated in pathology and demonstrate the use of these pathways in identifying novel targets.Availability:http://www.cs.purdue.edu/homes/mohammas/projects/ActPro.html

scholarly journals A benchmark of gene expression tissue-specificity metrics

2015 ◽  
Author(s):  
Nadezda Kryuchkova ◽  
Marc Robinson-Rechavi
Keyword(s):  
Gene Expression ◽  
Molecular Evolution ◽  
Expression Pattern ◽  
Tissue Specificity ◽  
Evolutionary Rate ◽  
Evolutionary Conservation ◽  
Rna Seq ◽  
Functional Feature ◽  
Tissue Specific

One of the major properties of genes is their expression pattern. Notably, genes are often classified as tissue-specific or housekeeping. This property is of interest to molecular evolution as an explanatory factor of, e.g., evolutionary rate, as well as a functional feature which may in itself evolve. While many different methods of measuring tissue specificity have been proposed and used for such studies, there has been no comparison or benchmarking of these methods to our knowledge, and little justification of their use. In this study we compare nine measures of tissue-specificity. Most methods were established for ESTs and microarrays, and several were later adapted to RNA-seq. We analyze their capacity to distinguish gene categories, their robustness to the choice and number of tissues used, and their capture of evolutionary conservation signal.

scholarly journals A comparative study of disease genes and drug targets in the human protein interactome

2015 ◽  
Vol 16 (Suppl 5) ◽  
pp. S1 ◽  
Author(s):  
Jingchun Sun ◽  
Kevin Zhu ◽  
W Zheng ◽  
Hua Xu
Keyword(s):  
Comparative Study ◽  
Drug Targets ◽  
Human Protein ◽  
Disease Genes ◽  
Protein Interactome

scholarly journals MendelVar: gene prioritization at GWAS loci using phenotypic enrichment of Mendelian disease genes

2020 ◽  
Author(s):  
Maria K Sobczyk ◽  
Tom R Gaunt ◽  
Lavinia Paternoster
Keyword(s):  
Complex Traits ◽  
Drug Targets ◽  
Gene Annotation ◽  
Molecular Evidence ◽  
Disease Genes ◽  
Mendelian Disease ◽  
Phenotype Ontology ◽  
Transfer Of Knowledge ◽  
Genetic Associations ◽  
Human Phenotype

AbstractGene prioritisation at GWAS loci necessities careful assembly and examination of different types of molecular evidence to arrive at a set of plausible candidates. In many human traits, common small-effect mutations may subtly dysregulate the function of the very same genes which are impacted by rare, large-effect mutations causing Mendelian disease of similar phenotype. However, information on gene-Mendelian disease associations, rare pathogenic mutations driving the disease, and the disease phenotype ontology is dispersed across many data sources and does not integrate easily with enrichment analysis.MendelVar is a new webserver facilitating transfer of knowledge from Mendelian disease research into interpretation of genetic associations from GWAS of complex traits. MendelVar allows querying of pre-defined or LD-determined genomic intervals against a comprehensive integrated database to find overlap with genes linked to Mendelian disease. Next, MendelVar looks for enrichment of any Human Phenotype Ontology, Disease Ontology and other ontology/pathway terms associated with identified Mendelian genes. In addition, MendelVar provides a list of all overlapping pathogenic and likely pathogenic variants for Mendelian disease sourced from ClinVar.Inclusion of information obtained from MendelVar in post-GWAS gene annotation pipelines can strengthen the case for causal importance of some genes. Moreover, as genes with Mendelian disease evidence may make for more successful drug targets, this may be particularly useful in drug discovery pipelines. Taking GWAS summary statistics for male-pattern baldness, intelligence and atopic dermatitis, we demonstrate the use of MendelVar in prioritizing candidate genes at these loci which are linked to relevant enriched ontology terms. MendelVar is freely available at https://mendelvar.mrcieu.ac.uk/

A Survey on Computational Methods for Essential Proteins and Genes Prediction

2019 ◽  
Vol 14 (3) ◽  
pp. 211-225 ◽  
Author(s):  
Ming Fang ◽  
Xiujuan Lei ◽  
Ling Guo
Keyword(s):  
Computational Methods ◽  
Drug Targets ◽  
Disease Genes ◽  
Essential Proteins ◽  
Experimental Identification ◽  
Cellular Life ◽  
Different Types ◽  
The Stability ◽  
Human Disease Genes ◽  
Biology Research

Background: Essential proteins play important roles in the survival or reproduction of an organism and support the stability of the system. Essential proteins are the minimum set of proteins absolutely required to maintain a living cell. The identification of essential proteins is a very important topic not only for a better comprehension of the minimal requirements for cellular life, but also for a more efficient discovery of the human disease genes and drug targets. Traditionally, as the experimental identification of essential proteins is complex, it usually requires great time and expense. With the cumulation of high-throughput experimental data, many computational methods that make useful complements to experimental methods have been proposed to identify essential proteins. In addition, the ability to rapidly and precisely identify essential proteins is of great significance for discovering disease genes and drug design, and has great potential for applications in basic and synthetic biology research. Objective: The aim of this paper is to provide a review on the identification of essential proteins and genes focusing on the current developments of different types of computational methods, point out some progress and limitations of existing methods, and the challenges and directions for further research are discussed.

Molecular biology of enzyme adaptations in higher eukaryotes

Genome ◽  
1989 ◽  
Vol 31 (1) ◽  
pp. 272-283 ◽  
Author(s):  
Donal A. Hickey ◽  
Bernhard F. Benkel ◽  
Charalambos Magoulas
Keyword(s):  
Molecular Biology ◽  
Tissue Specificity ◽  
Enzyme System ◽  
Direct Interaction ◽  
Evolutionary Genetics ◽  
Short Term ◽  
Eukaryotic Genes ◽  
Higher Eukaryotes ◽  
The Relationship

Multicellular eukaryotes have evolved complex homeostatic mechanisms that buffer the majority of their cells from direct interaction with the external environment. Thus, in these organisms long-term adaptations are generally achieved by modulating the developmental profile and tissue specificity of gene expression. Nevertheless, a subset of eukaryotic genes are still involved in direct responses to environmental fluctuations. It is the adaptative responses in the expression of these genes that buffers many other genes from direct environmental effects. Both microevolutionary and macroevolutionary patterns of change in the structure and regulation of such genes are illustrated by the sequences encoding α-amylases. The molecular biology and evolution of α-amylases in Drosophila and other higher eukaryotes are presented. The amylase system illustrates the effects of both long-term and short-term natural selection, acting on both the structural and regulatory components of a gene–enzyme system. This system offers an opportunity for linking evolutionary genetics to molecular biology, and it allows us to explore the relationship between short-term microevolutionary changes and long-term adaptations.Key words: gene regulation, molecular evolution, eukaryotes, Drosophila, amylase.

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 On the scope and limitations of baker's yeast as a model organism for studying human tissue-specific pathways

2014 ◽  
Author(s):  
Shahin Mohammadi ◽  
Baharak Saberidokht ◽  
Shankar Subramaniam ◽  
Ananth Grama
Keyword(s):  
Drug Targets ◽  
Model Organism ◽  
Functional Space ◽  
Computational Method ◽  
Therapeutic Interventions ◽  
Human Tissues ◽  
Human Interactome ◽  
Tissue Specific ◽  
Complex Disorders ◽  
Human Genes

Budding yeast, S. cerevisiae, has been used extensively as a model organism for studying cellular processes in evolutionarily distant species, including humans. However, different human tissues, while inheriting a similar genetic code, exhibit distinct anatomical and physiological properties. Specific biochemical processes and associated biomolecules that differentiate various tissues are not completely understood, neither is the extent to which a unicellular organism, such as yeast, can be used to model these processes within each tissue. We propose a novel computational and statistical framework to systematically quantify the suitability of yeast as a model organism for different human tissues. We develop a computational method for dissecting the human interactome into tissue-specific cellular networks. Using these networks, we simultaneously partition the functional space of human genes, and their corresponding pathways, based on their conservation both across species and among different tissues. We study these sub-spaces in detail, and relate them to the overall similarity of each tissue with yeast. Many complex disorders are driven by a coupling of housekeeping (universally expressed in all tissues) and tissue-selective (expressed only in specific tissues) dysregulated pathways. We show that human-specific subsets of tissue-selective genes are significantly associated with the onset and development of a number of pathologies. Consequently, they provide excellent candidates as drug targets for therapeutic interventions. We also present a novel tool that can be used to assess the suitability of the yeast model for studying tissue-specific physiology and pathophysiology in humans.

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 The relationship between position and expression of genes on the kangaroo X chromosome suggests a tissue-specific spread of inactivation from a single control site

1988 ◽  
Vol 51 (2) ◽  
pp. 103-109 ◽  
Author(s):  
Jennifer A. Marshall Graves ◽  
Garey W. Dawson
Keyword(s):  
Alternative Hypothesis ◽  
Organizer Region ◽  
Nucleolus Organizer Region ◽  
Control Site ◽  
Specific Pattern ◽  
Nucleolus Organizer ◽  
Tissue Specific ◽  
Expression Of Genes ◽  
Control Locus ◽  
The Relationship

SummaryIn marsupials, X chromosome inactivation is paternal and incomplete. The tissue-specific pattern of inactivation of X-linked loci (G6PD, PGK, GLA) has been attributed to a piecemeal inactivation of different regions of the X. We here propose an alternative hypothesis, in which inactivation of the marsupial X is a chromosome-wide event, but is differentially regulated in different tissues. This hypothesis was suggested by the relationship between the positions and activity of genes on the kangaroo paternal X. In the absence of an HPRT polymorphism, we have used somatic cell hybridization to assess the activity of the paternal HPRT allele in lymphocytes and fibroblasts. The absence of the paternal X, and of the paternal forms of G6PD or PGK, from 33 cell hybrids made by fusing HPRT-deficient rodent cells with lymphocytes or fibroblasts of heterozygous females, suggests that the HPRT gene on the paternal X is inactive in both tissues and therefore not selectable. Since HPRT is located medially on the Xq near GLA, which shares the same characteristics of activity, we suggest that the locus-specific and tissue-specific patterns of activity result from a differential spread of inactivation from a single control locus, located near HPRT and GLA, outwards in both directions to G6PD and PGK. The nucleolus organizer region on the short arm does not seem to be part of the inactivated unit.

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