A large-scale analysis of tissue-specific pathology and gene expression of human disease genes and complexes.

Heritable diseases are caused by germ-line mutations that, despite tissuewide presence, often lead to tissue-specific pathology. Here, we make a systematic analysis of the link between tissue-specific gene expression and pathological manifestations in many human diseases and cancers. Diseases were systematically mapped to tissues they affect from disease-relevant literature in PubMed to create a disease-tissue covariation matrix of high-confidence associations of >1,000 diseases to 73 tissues. By retrieving >2,000 known disease genes, and generating 1,500 disease-associated protein complexes, we analyzed the differential expression of a gene or complex involved in a particular disease in the tissues affected by the disease, compared with nonaffected tissues. When this analysis is scaled to all diseases in our dataset, there is a significant tendency for disease genes and complexes to be overexpressed in the normal tissues where defects cause pathology. In contrast, cancer genes and complexes were not overexpressed in the tissues from which the tumors emanate. We specifically identified a complex involved in XY sex reversal that is testis-specific and down-regulated in ovaries. We also identified complexes in Parkinson disease, cardiomyopathies, and muscular dystrophy syndromes that are similarly tissue specific. Our method represents a conceptual scaffold for organism-spanning analyses and reveals an extensive list of tissue-specific draft molecular pathways, both known and unexpected, that might be disrupted in disease.

Integrative analysis for finding genes and networks involved in diabetes and other complex diseases.

We have developed an integrative analysis method combining genetic interactions, identified using type 1 diabetes genome scan data, and a high-confidence human protein interaction network. Resulting networks were ranked by the significance of the enrichment of proteins from interacting regions. We identified a number of new protein network modules and novel candidate genes/proteins for type 1 diabetes. We propose this type of integrative analysis as a general method for the elucidation of genes and networks involved in diabetes and other complex diseases.

A human phenome-interactome network of protein complexes implicated in genetic disorders.

We performed a systematic, large-scale analysis of human protein complexes comprising gene products implicated in many different categories of human disease to create a phenome-interactome network. This was done by integrating quality-controlled interactions of human proteins with a validated, computationally derived phenotype similarity score, permitting identification of previously unknown complexes likely to be associated with disease. Using a phenomic ranking of protein complexes linked to human disease, we developed a Bayesian predictor that in 298 of 669 linkage intervals correctly ranks the known disease-causing protein as the top candidate, and in 870 intervals with no identified disease-causing gene, provides novel candidates implicated in disorders such as retinitis pigmentosa, epithelial ovarian cancer, inflammatory bowel disease, amyotrophic lateral sclerosis, Alzheimer disease, type 2 diabetes and coronary heart disease. Our publicly available draft of protein complexes associated with pathology comprises 506 complexes, which reveal functional relationships between disease-promoting genes that will inform future experimentation.

A wiring of the human nucleolus.

Recent proteomic efforts have created an extensive inventory of the human nucleolar proteome. However, approximately 30% of the identified proteins lack functional annotation. We present an approach of assigning function to uncharacterized nucleolar proteins by data integration coupled to a machine-learning method. By assembling protein complexes, we present a first draft of the human ribosome biogenesis pathway encompassing 74 proteins and hereby assign function to 49 previously uncharacterized proteins. Moreover, the functional diversity of the nucleolus is underlined by the identification of a number of protein complexes with functions beyond ribosome biogenesis. Finally, we were able to obtain experimental evidence of nucleolar localization of 11 proteins, which were predicted by our platform to be associates of nucleolar complexes. We believe other biological organelles or systems could be "wired" in a similar fashion, integrating different types of data with high-throughput proteomics, followed by a detailed biological analysis and experimental validation.

The louse-borne human pathogen Bartonella quintana is a genomic derivative of the zoonotic agent Bartonella henselae.

We present the complete genomes of two human pathogens, Bartonella quintana (1,581,384 bp) and Bartonella henselae (1,931,047 bp). The two pathogens maintain several similarities in being transmitted by insect vectors, using mammalian reservoirs, infecting similar cell types (endothelial cells and erythrocytes) and causing vasculoproliferative changes in immunocompromised hosts. A primary difference between the two pathogens is their reservoir ecology. Whereas B. quintana is a specialist, using only the human as a reservoir, B. henselae is more promiscuous and is frequently isolated from both cats and humans. Genome comparison elucidated a high degree of overall similarity with major differences being B. henselae specific genomic islands coding for filamentous hemagglutinin, and evidence of extensive genome reduction in B. quintana, reminiscent of that found in Rickettsia prowazekii. Both genomes are reduced versions of chromosome I from the highly related pathogen Brucella melitensis. Flanked by two rRNA operons is a segment with similarity to genes located on chromosome II of B. melitensis, suggesting that it was acquired by integration of megareplicon DNA in a common ancestor of the two Bartonella species. Comparisons of the vector-host ecology of these organisms suggest that the utilization of host-restricted vectors is associated with accelerated rates of genome degradation and may explain why human pathogens transmitted by specialist vectors are outnumbered by zoonotic agents, which use vectors of broad host ranges.

Computational inference of scenarios for alpha-proteobacterial genome evolution.

The alpha-proteobacteria, from which mitochondria are thought to have originated, display a 10-fold genome size variation and provide an excellent model system for studies of genome size evolution in bacteria. Here, we use computational approaches to infer ancestral gene sets and to quantify the flux of genes along the branches of the alpha-proteobacterial species tree. Our study reveals massive gene expansions at branches diversifying plant-associated bacteria and extreme losses at branches separating intracellular bacteria of animals and humans. Alterations in gene numbers have mostly affected functional categories associated with regulation, transport, and small-molecule metabolism, many of which are encoded by paralogous gene families located on auxiliary chromosomes. The results suggest that the alpha-proteobacterial ancestor contained 3,000-5,000 genes and was a free-living, aerobic, and motile bacterium with pili and surface proteins for host cell and environmental interactions. Approximately one third of the ancestral gene set has no homologs among the eukaryotes. More than 40% of the genes without eukaryotic counterparts encode proteins that are conserved among the alpha-proteobacteria but for which no function has yet been identified. These genes that never made it into the eukaryotes but are widely distributed in bacteria may represent bacterial drug targets and should be prime candidates for future functional characterization.

Mitochondrial gene history and mRNA localization: is there a correlation?

Phylogenetic studies of the yeast mitochondrial proteome have shown a complex evolutionary scenario, in which proteins of bacterial origin form complexes with proteins of eukaryotic origin. Exciting new results from whole-genome microarray studies of subcellular mRNA localizations have shown that mRNAs that are of putative bacterial origin are mainly translated on polysomes that are associated with the mitochondrion, whereas those of eukaryotic origin are generally translated on free cytosolic polysomes. Understanding these newly discovered relationships promises insights into old questions about organelle origins and mRNA localization in the eukaryotic cell.