Disease genes and intracellular protein networks

S. Bortoluzzi; C. Romualdi; A. Bisognin; G. A. Danieli

doi:10.1152/physiolgenomics.00095.2003

Disease genes and intracellular protein networks

Physiological Genomics ◽

10.1152/physiolgenomics.00095.2003 ◽

2003 ◽

Vol 15 (3) ◽

pp. 223-227 ◽

Cited By ~ 14

Author(s):

S. Bortoluzzi ◽

C. Romualdi ◽

A. Bisognin ◽

G. A. Danieli

Keyword(s):

Protein Network ◽

Cdna Libraries ◽

Protein Networks ◽

Disease Genes ◽

Intracellular Protein ◽

Scale Free ◽

Specific Distribution ◽

Human Genes ◽

Genome Level ◽

Important Nodes

By a computational approach we reconstructed genomic transcriptional profiles of 19 different adult human tissues, based on information on activity of 27,924 genes obtained from unbiased UniGene cDNA libraries. In each considered tissue, a small number of genes resulted highly expressed or “tissue specific.” Distribution of gene expression levels in a tissue appears to follow a power law, thus suggesting a correspondence between transcriptional profile and “scale-free” topology of protein networks. The expression of 737 genes involved in Mendelian diseases was analyzed, compared with a large reference set of known human genes. Disease genes resulted significantly more expressed than expected. The possible correspondence of their products to important nodes of intracellular protein network is suggested. Auto-organization of the protein network, its stability in time in the differentiated state, and relationships with the degree of genetic variability at genome level are discussed.

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Accurate and Scalable Techniques for the Complex/Pathway Membership Problem in Protein Networks

Advances in Bioinformatics ◽

10.1155/2009/787128 ◽

2009 ◽

Vol 2009 ◽

pp. 1-9 ◽

Cited By ~ 1

Author(s):

Orhan Çamoğlu ◽

Tolga Can ◽

Ambuj K. Singh

Keyword(s):

Monte Carlo Simulation ◽

Monte Carlo ◽

Hierarchical Clustering ◽

Network Flow ◽

High Efficiency ◽

Protein Network ◽

Protein Networks ◽

Membership Problem ◽

Physical Interactions

A protein network shows physical interactions as well as functional associations. An important usage of such networks is to discover unknown members of partially known complexes and pathways. A number of methods exist for such analyses, and they can be divided into two main categories based on their treatment of highly connected proteins. In this paper, we show that methods that are not affected by the degree (number of linkages) of a protein give more accurate predictions for certain complexes and pathways. We propose a network flow-based technique to compute the association probability of a pair of proteins. We extend the proposed technique using hierarchical clustering in order to scale well with the size of proteome. We also show that top-k queries are not suitable for a large number of cases, and threshold queries are more meaningful in these cases. Network flow technique with clustering is able to optimize meaningful threshold queries and answer them with high efficiency compared to a similar method that uses Monte Carlo simulation.

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Effect of manipulation of milk composition and curd-forming conditions on the formation, structure and properties of milk curd

Journal of Dairy Research ◽

10.1017/s0022029900025449 ◽

1987 ◽

Vol 54 (2) ◽

pp. 303-313 ◽

Cited By ~ 23

Author(s):

Margaret L. Green

Keyword(s):

Environmental Conditions ◽

Milk Composition ◽

Protein Network ◽

Protein Networks ◽

Structure And Properties ◽

Coagulation Time ◽

Cationic Materials ◽

Concentrated Milk ◽

Ca Depletion ◽

Better Than

SummaryMilks whose compositions had been altered by deliberate manipulation or by contact with various environmental conditions relevant to cheesemaking were treated with rennet in such a way as normally to give a constant coagulation time. Rates of curd formation and whey loss, curd structure and fat retention were determined. Ca depletion in concentrated milks reduced firming and syneresis rates and gave curds with finer protein networks. Increased temperature of curd formation increased the curd-firming rate and curd coarseness, but decreased the syneresis rate at 30 °C. Prior treatment of concentrated milk with rennet in the cold gave a much finer protein network which retained fat better than curd formed normally. Despite increasing firming and syneresis rates, acidified milk gave a slightly finer curd with a better fat retention than normal. Addition of cationic materials stimulated aggregation and the curd retained fat better than normal, although the curd structure was unaffected. The results indicate that the processes of firming and syneresis have related mechanisms, and that the curd structure is not simply dependent on curd-forming conditions, but on the number of aggregating particles and the forces between them.

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Yin and Yang of disease genes and death genes between reciprocally scale-free biological networks

Nucleic Acids Research ◽

10.1093/nar/gkt683 ◽

2013 ◽

Vol 41 (20) ◽

pp. 9209-9217 ◽

Cited By ~ 25

Author(s):

Hyun Wook Han ◽

Jung Hun Ohn ◽

Jisook Moon ◽

Ju Han Kim

Keyword(s):

Biological Networks ◽

Disease Genes ◽

Scale Free ◽

Yin And Yang ◽

Death Genes

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Genetic variation in mRNA coding sequences of highly conserved genes

Physiological Genomics ◽

10.1152/physiolgenomics.2001.5.3.113 ◽

2001 ◽

Vol 5 (3) ◽

pp. 113-118 ◽

Cited By ~ 9

Author(s):

ANNELOOR L. M. A. TEN ASBROEK ◽

JEFFREY OLSEN ◽

DAVID HOUSMAN ◽

FRANK BAAS ◽

VINCE STANTON

Keyword(s):

Disease Genes ◽

Protein Coding ◽

Coding Sequences ◽

Human Genes ◽

Evolutionarily Conserved ◽

Conserved Genes ◽

The Difference ◽

Minimum Estimate ◽

Human Disease Genes ◽

Evolutionary Comparisons

The frequency and distribution of genetic polymorphism in the human genome is a question of major importance. We have studied this in highly conserved genes, which encode crucial functions such as DNA replication, mRNA transcription, and translation. Evolutionary comparisons suggest that these genes are under particularly strong selective pressure, and their frequency of nucleotide sequence polymorphism would be expected to represent a minimum estimate for sequence variation throughout the genome. We have analyzed the complete coding sequence and the 3′-untranslated region (3′-UTR) of 22 human genes, most of which have homologs in all cellular organisms and all of which are at least 25% amino acid identical to homologs in yeast. Comparisons with similar studies of less conserved human disease genes indicate that 1) evolutionarily conserved genes are, on average, less polymorphic than disease related genes; 2) the difference in polymorphism levels is attributable almost entirely to reduced levels of variation in protein coding sequences, whereas noncoding sequences have similar levels of polymorphism; and 3) the character of polymorphism, in terms of the spectrum and frequency of mutational changes, is similar.

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An Approach to Investigate Intracellular Protein Network Responses

Chemical Research in Toxicology ◽

10.1021/tx400247g ◽

2014 ◽

Vol 27 (1) ◽

pp. 17-26 ◽

Cited By ~ 8

Author(s):

Holly N. Currie ◽

Julie A. Vrana ◽

Alice A. Han ◽

Giovanni Scardoni ◽

Nate Boggs ◽

...

Keyword(s):

Protein Network ◽

Intracellular Protein

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Analysis of human genes with protein–protein interaction network for detecting disease genes

Physica A Statistical Mechanics and its Applications ◽

10.1016/j.physa.2013.12.046 ◽

2014 ◽

Vol 398 ◽

pp. 217-228 ◽

Cited By ~ 11

Author(s):

Shun-yao Wu ◽

Feng-jing Shao ◽

Ren-cheng Sun ◽

Yi Sui ◽

Ying Wang ◽

...

Keyword(s):

Protein Interaction ◽

Protein Interaction Network ◽

Interaction Network ◽

Disease Genes ◽

Protein Protein Interaction ◽

Human Genes ◽

Protein Protein Interaction Network

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BioNetwork Bench: Database and Software for Storage, Query, and Analysis of Gene and Protein Networks

Bioinformatics and Biology Insights ◽

10.4137/bbi.s9728 ◽

2012 ◽

Vol 6 ◽

pp. BBI.S9728

Author(s):

Oksana Kohutyuk ◽

Fadi Towfic ◽

M. Heather West Greenlee ◽

Vasant Honavar

Keyword(s):

Gene Expression ◽

Open Source ◽

High Throughput ◽

Network Models ◽

Protein Network ◽

Protein Networks ◽

Public And Private ◽

Multiple Networks ◽

Private Datasets ◽

User Friendly

Gene and protein networks offer a powerful approach for integration of the disparate yet complimentary types of data that result from high-throughput analyses. Although many tools and databases are currently available for accessing such data, they are left unutilized by bench scientists as they generally lack features for effective analysis and integration of both public and private datasets and do not offer an intuitive interface for use by scientists with limited computational expertise. We describe BioNetwork Bench, an open source, user-friendly suite of database and software tools for constructing, querying, and analyzing gene and protein network models. It enables biologists to analyze public as well as private gene expression; interactively query gene expression datasets; integrate data from multiple networks; store and selectively share the data and results. Finally, we describe an application of BioNetwork Bench to the assembly and iterative expansion of a gene network that controls the differentiation of retinal progenitor cells into rod photoreceptors. The tool is available from http://bionetworkbench.sourceforge.net/ Background The emergence of high-throughput technologies has allowed many biological investigators to collect a great deal of information about the behavior of genes and gene products over time or during a particular disease state. Gene and protein networks offer a powerful approach for integration of the disparate yet complimentary types of data that result from such high-throughput analyses. There are a growing number of public databases, as well as tools for visualization and analysis of networks. However, such databases and tools have yet to be widely utilized by bench scientists, as they generally lack features for effective analysis and integration of both public and private datasets and do not offer an intuitive interface for use by biological scientists with limited computational expertise. Results We describe BioNetwork Bench, an open source, user-friendly suite of database and software tools for constructing, querying, and analyzing gene and protein network models. BioNetwork Bench currently supports a broad class of gene and protein network models (eg, weighted and un-weighted, undirected graphs, multi-graphs). It enables biologists to analyze public as well as private gene expression, macromolecular interaction and annotation data; interactively query gene expression datasets; integrate data from multiple networks; query multiple networks for interactions of interest; store and selectively share the data as well as results of analyses. BioNetwork Bench is implemented as a plug-in for, and hence is fully interoperable with, Cytoscape, a popular open-source software suite for visualizing macromolecular interaction networks. Finally, we describe an application of BioNetwork Bench to the problem of assembly and iterative expansion of a gene network that controls the differentiation of retinal progenitor cells into rod photoreceptors. Conclusions BioNetwork Bench provides a suite of open source software for construction, querying, and selective sharing of gene and protein networks. Although initially aimed at a community of biologists interested in retinal development, the tool can be adapted easily to work with other biological systems simply by populating the associated database with the relevant datasets.

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Computational determination of gene age and characterization of evolutionary dynamics in human

Briefings in Bioinformatics ◽

10.1093/bib/bby074 ◽

2018 ◽

Vol 20 (6) ◽

pp. 2141-2149 ◽

Cited By ~ 1

Author(s):

Hongyan Yin ◽

Mengwei Li ◽

Lin Xia ◽

Chaozu He ◽

Zhang Zhang

Keyword(s):

Evolutionary Dynamics ◽

Biological Diversity ◽

Monogenic Disease ◽

Disease Genes ◽

Mendelian Disease ◽

Evolutionary Time ◽

New Genes ◽

Human Genes ◽

Gene Age

Abstract Genes originate at different evolutionary time scales and possess different ages, accordingly presenting diverse functional characteristics and reflecting distinct adaptive evolutionary innovations. In the past decades, progresses have been made in gene age identification by a variety of methods that are principally based on comparative genomics. Here we summarize methods for computational determination of gene age and evaluate the effectiveness of different computational methods for age identification. Our results show that improved age determination can be achieved by combining homolog clustering with phylogeny inference, which enables more accurate age identification in human genes. Accordingly, we characterize evolutionary dynamics of human genes based on an extremely long evolutionary time scale spanning ~4,000 million years from archaea/bacteria to human, revealing that young genes are clustered on certain chromosomes and that Mendelian disease genes (including monogenic disease and polygenic disease genes) and cancer genes exhibit divergent evolutionary origins. Taken together, deciphering genes’ ages as well as their evolutionary dynamics is of fundamental significance in unveiling the underlying mechanisms during evolution and better understanding how young or new genes become indispensable integrants coupled with novel phenotypes and biological diversity.

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Genomic annotation of 15,809 ESTs identified from pooled early gestation human eyes

Physiological Genomics ◽

10.1152/physiolgenomics.00121.2005 ◽

2006 ◽

Vol 25 (1) ◽

pp. 9-15 ◽

Cited By ~ 3

Author(s):

K. W. Choy ◽

C. C. Wang ◽

A. Ogura ◽

T. K. Lau ◽

M. S. Rogers ◽

...

Keyword(s):

Cdna Library ◽

Genomic Analysis ◽

Retinal Disease ◽

Cdna Libraries ◽

Disease Genes ◽

Protein Database ◽

Evolutionary Mechanisms ◽

Early Gestation ◽

Human Eye ◽

Functional Genomic Analysis

To complement cDNA libraries from the human eye at early gestation and to discover candidate genes associated with early ocular development, we used freshly dissected human eyeballs from week 9–14 of gestation to construct the early human fetal eye cDNA library. A total of 15,809 clones were isolated and sequenced from the unamplified and unnormalized library. We screened 11,246 good-quality ESTs, leading to the identification of 5,534 nonredundant clusters. Among them, 4,010 (72%) genes matched in the human protein database (Ensembl). The remaining 28% (1,524) corresponded to potentially novel or previously unidentified ESTs. We used BLASTX to compare our EST data with eight organisms and found common expression of a high portion of genes: Caenorhabditis briggsae (26%), Caenorhabditis elegans (27%), Anopheles gambiae (37%), Drosophila melanogaster (32%), Danio rerio (42%), Fugu rubripes (49%), Rattus norvegicusvalitus (52%), and Mus musculus (59%). Nevertheless, 48% (2,680 of 5,534) of the genes expressed in the early developing eye were not shared with current NEIBank human eye cDNA data. In addition, eight known retinal disease genes existed in our ESTs. Among them, six ( COL11A1, BBS5, PDE6B, OAT, VMD2, and PGK1) were conserved among the genomes of other organisms, indicating that our annotated EST set provides not only a valuable resource for gene discovery and functional genomic analysis but also for phylogenetic analysis. Our foremost early gestation human eye cDNA library could provide detailed comparisons across species to identify physiological functions of genes and to elucidate evolutionary mechanisms.

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