Associations between HIV and human pathways revealed by protein-protein interactions and correlated gene expression profiles

Background

AIDS is one of the most devastating diseases in human history. Decades of studies have revealed host factors required for HIV infection, indicating that HIV exploits host processes for its own purposes. HIV infection leads to AIDS as well as various comorbidities. The associations between HIV and human pathways and diseases may reveal non-obvious relationships between HIV and non-HIV-defining diseases.

Principal Findings

Human biological pathways were evaluated and statistically compared against the presence of HIV host factor related genes. All of the obtained scores comparing HIV targeted genes and biological pathways were ranked. Different rank results based on overlapping genes, recovered virus-host interactions, co-expressed genes, and common interactions in human protein-protein interaction networks were obtained. Correlations between rankings suggested that these measures yielded diverse rankings. Rank combination of these ranks led to a final ranking of HIV-associated pathways, which revealed that HIV is associated with immune cell-related pathways and several cancer-related pathways. The proposed method is also applicable to the evaluation of associations between other pathogens and human pathways and diseases.

Conclusions

Our results suggest that HIV infection shares common molecular mechanisms with certain signaling pathways and cancers. Interference in apoptosis pathways and the long-term suppression of immune system functions by HIV infection might contribute to tumorigenesis. Relationships between HIV infection and human pathways of disease may aid in the identification of common drug targets for viral infections and other diseases.     

Combining gene expression profiles and protein-protein interaction data to infer gene functions

The ever-increasing flow of gene expression profiles and protein-protein interactions has catalyzed many computational approaches for inference of gene functions. Despite all the efforts, there is still room for improvement, for the information enriched in each biological data source has not been exploited to its fullness. A composite method is proposed for classifying unannotated genes based on expression data and protein-protein interaction (PPI) data, which extracts information from both data sources in novel ways. With the noise nature of expression data taken into consideration, importance is attached to the consensus expression patterns of gene classes instead of the actual expression profiles of individual genes, thus characterizing the composite method with enhanced robustness against microarray data variation. With regard to the PPI network, the traditional clear-cut binary attitude towards inter- and intra-functional interactions is abandoned, whereas a more objective perspective into the PPI network structure is formed through incorporating the varied function-function interaction probabilities into the algorithm. The composite method was implemented in two numerical experiments, where its improvement over single-data-source based methods was observed and the superiority of the novel data handling operations was discussed.     

Relationships between polydnavirus genomes and viral gene expression

Polydnavirus genomes and viral gene functions are atypical for viruses. Polydnaviruses are the only group of viruses with segmented DNA genomes and have an unusual obligate mutualistic association with parasitic Hymenoptera, in which the virus is required for survival of the wasp host and vice versa. The virus replicates asymptomatically in the wasp host but severely disrupts lepidopteran host physiology in the absence of viral DNA replication. It is not surprising then that viral gene expression is divergent in its two insect hosts and that differences in viral gene expression are linked to these divergent functions. Some viral genes are expressed only in the wasp host while other viral genes are expressed only in the lepidopteran host and are presumed to be involved in the disruption of host physiological systems. Our laboratory has described the expression and regulation of a family of viral genes implicated in suppressing the lepidopteran immune system, the cys-motif genes. In conjunction with these studies we have described the physical organization of additional viral gene segments. We have cloned, mapped and begun the sequence analysis of selected viral DNA segments. We have noted that some viral DNA segments are nested and that nested viral DNA segments encode the abundantly expressed, secreted cys-motif genes. Conversely, other viral segments are not nested, encode less abundantly expressed genes and may be targeted intra-cellularly. These results suggest that nesting of segments in polydnavirus genomes may be linked to the levels of gene expression. By extension, the unique, segmented organization of polydnavirus genomes may be associated, in part, with the requirement for divergent levels of viral gene expression in lepidopteran hosts in the absence of viral DNA replication.     

Refined phylogenetic profiles method for predicting protein-protein interactions

MOTIVATION: The increasing availability of complete genome sequences provides excellent opportunity for the further development of tools for functional studies in proteomics. Several experimental approaches and in silico algorithms have been developed to cluster proteins into networks of biological significance that may provide new biological insights, especially into understanding the functions of many uncharacterized proteins. Among these methods, the phylogenetic profiles method has been widely used to predict protein-protein interactions. It involves the selection of reference organisms and identification of homologous proteins. Up to now, no published report has systematically studied the effects of the reference genome selection and the identification of homologous proteins upon the accuracy of this method. RESULTS: In this study, we optimized the phylogenetic profiles method by integrating phylogenetic relationships among reference organisms and sequence homology information to improve prediction accuracy. Our results revealed that the selection of the reference organisms set and the criteria for homology identification significantly are two critical factors for the prediction accuracy of this method. Our refined phylogenetic profiles method shows greater performance and potentially provides more reliable functional linkages compared with previous methods.     

An atlas of protein-protein interactions across mouse tissues

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Coordination of gene expression between organellar and nuclear genomes

Following the acquisition of chloroplasts and mitochondria by eukaryotic cells during endosymbiotic evolution, most of the genes in these organelles were either lost or transferred to the nucleus. Encoding organelle-destined proteins in the nucleus allows for host control of the organelle. In return, organelles send signals to the nucleus to coordinate nuclear and organellar activities. In photosynthetic eukaryotes, additional interactions exist between mitochondria and chloroplasts. Here we review recent advances in elucidating the intracellular signalling pathways that coordinate gene expression between organelles and the nucleus, with a focus on photosynthetic plants.     

An improved method for scoring protein-protein interactions using semantic similarity within the gene ontology

Background  

Semantic similarity measures are useful to assess the physiological relevance of protein-protein interactions (PPIs). They quantify similarity between proteins based on their function using annotation systems like the Gene Ontology (GO). Proteins that interact in the cell are likely to be in similar locations or involved in similar biological processes compared to proteins that do not interact. Thus the more semantically similar the gene function annotations are among the interacting proteins, more likely the interaction is physiologically relevant. However, most semantic similarity measures used for PPI confidence assessment do not consider the unequal depth of term hierarchies in different classes of cellular location, molecular function, and biological process ontologies of GO and thus may over-or under-estimate similarity.     

Evolutionary rate depends on number of protein-protein interactions independently of gene expression level

Background

Whether or not a protein's number of physical interactions with other proteins plays a role in determining its rate of evolution has been a contentious issue. A recent analysis suggested that the observed correlation between number of interactions and evolutionary rate may be due to experimental biases in high-throughput protein interaction data sets.

Discussion

The number of interactions per protein, as measured by some protein interaction data sets, shows no correlation with evolutionary rate. Other data sets, however, do reveal a relationship. Furthermore, even when experimental biases of these data sets are taken into account, a real correlation between number of interactions and evolutionary rate appears to exist.

Summary

A strong and significant correlation between a protein's number of interactions and evolutionary rate is apparent for interaction data from some studies. The extremely low agreement between different protein interaction data sets indicates that interaction data are still of low coverage and/or quality. These limitations may explain why some data sets reveal no correlation with evolutionary rates.

     

Correlation between gene expression and GO semantic similarity

This research analyzes some aspects of the relationship between gene expression, gene function, and gene annotation. Many recent studies are implicitly based on the assumption that gene products that are biologically and functionally related would maintain this similarity both in their expression profiles as well as in their gene ontology (GO) annotation. We analyze how accurate this assumption proves to be using real publicly available data. We also aim to validate a measure of semantic similarity for GO annotation. We use the Pearson correlation coefficient and its absolute value as a measure of similarity between expression profiles of gene products. We explore a number of semantic similarity measures (Resnik, Jiang, and Lin) and compute the similarity between gene products annotated using the GO. Finally, we compute correlation coefficients to compare gene expression similarity against GO semantic similarity. Our results suggest that the Resnik similarity measure outperforms the others and seems better suited for use in gene ontology. We also deduce that there seems to be correlation between semantic similarity in the GO annotation and gene expression for the three GO ontologies. We show that this correlation is negligible up to a certain semantic similarity value; then, for higher similarity values, the relationship trend becomes almost linear. These results can be used to augment the knowledge provided by clustering algorithms and in the development of bioinformatic tools for finding and characterizing gene products.     

Short- and long-range cis interactions between integrated HPV genomes and cellular chromatin dysregulate host gene expression in early cervical carcinogenesis

Development of cervical cancer is directly associated with integration of human papillomavirus (HPV) genomes into host chromosomes and subsequent modulation of HPV oncogene expression, which correlates with multi-layered epigenetic changes at the integrated HPV genomes. However, the process of integration itself and dysregulation of host gene expression at sites of integration in our model of HPV16 integrant clone natural selection has remained enigmatic. We now show, using a state-of-the-art ‘HPV integrated site capture’ (HISC) technique, that integration likely occurs through microhomology-mediated repair (MHMR) mechanisms via either a direct process, resulting in host sequence deletion (in our case, partially homozygously) or via a ‘looping’ mechanism by which flanking host regions become amplified. Furthermore, using our ‘HPV16-specific Region Capture Hi-C’ technique, we have determined that chromatin interactions between the integrated virus genome and host chromosomes, both at short- (<500 kbp) and long-range (>500 kbp), appear to drive local host gene dysregulation through the disruption of host:host interactions within (but not exceeding) host structures known as topologically associating domains (TADs). This mechanism of HPV-induced host gene expression modulation indicates that integration of virus genomes near to or within a ‘cancer-causing gene’ is not essential to influence their expression and that these modifications to genome interactions could have a major role in selection of HPV integrants at the early stage of cervical neoplastic progression.     

Context-specific infinite mixtures for clustering gene expression profiles across diverse microarray dataset

MOTIVATION: Identifying groups of co-regulated genes by monitoring their expression over various experimental conditions is complicated by the fact that such co-regulation is condition-specific. Ignoring the context-specific nature of co-regulation significantly reduces the ability of clustering procedures to detect co-expressed genes due to additional 'noise' introduced by non-informative measurements. RESULTS: We have developed a novel Bayesian hierarchical model and corresponding computational algorithms for clustering gene expression profiles across diverse experimental conditions and studies that accounts for context-specificity of gene expression patterns. The model is based on the Bayesian infinite mixtures framework and does not require a priori specification of the number of clusters. We demonstrate that explicit modeling of context-specificity results in increased accuracy of the cluster analysis by examining the specificity and sensitivity of clusters in microarray data. We also demonstrate that probabilities of co-expression derived from the posterior distribution of clusterings are valid estimates of statistical significance of created clusters. AVAILABILITY: The open-source package gimm is available at http://eh3.uc.edu/gimm.     

A semi-parametric statistical model for integrating gene expression profiles across different platforms

Determining differentially expressed genes (DEGs) between biological samples is the key to understand how genotype gives rise to phenotype. RNA-seq and microarray are two main technologies for profiling gene expression levels. However, considerable discrepancy has been found between DEGs detected using the two technologies. Integration data across these two platforms has the potential to improve the power and reliability of DEG detection. We propose a rank-based semi-parametric model to determine DEGs using information across different sources and apply it to the integration of RNA-seq and microarray data. By incorporating both the significance of differential expression and the consistency across platforms, our method effectively detects DEGs with moderate but consistent signals. We demonstrate the effectiveness of our method using simulation studies, MAQC/SEQC data and a synthetic microRNA dataset. Our integration method is not only robust to noise and heterogeneity in the data, but also adaptive to the structure of data. In our simulations and real data studies, our approach shows a higher discriminate power and identifies more biologically relevant DEGs than eBayes, DEseq and some commonly used meta-analysis methods.     

Mapping protein-protein interactions between MutL and MutH by cross-linking

Strand discrimination in Escherichia coli DNA mismatch repair requires the activation of the endonuclease MutH by MutL. There is evidence that MutH binds to the N-terminal domain of MutL in an ATP-dependent manner; however, the interaction sites and the molecular mechanism of MutH activation have not yet been determined. We used a combination of site-directed mutagenesis and site-specific cross-linking to identify protein interaction sites between the proteins MutH and MutL. Unique cysteine residues were introduced in cysteine-free variants of MutH and MutL. The introduced cysteines were modified with the cross-linking reagent 4-maleimidobenzophenone. Photoactivation resulted in cross-links verified by mass spectrometry of some of the single cysteine variants to their respective Cys-free partner proteins. Moreover, we mapped the site of interaction by cross-linking different combinations of single cysteine MutH and MutL variants with thiol-specific homobifunctional cross-linkers of varying length. These results were used to model the MutH.MutL complex and to explain the ATP dependence of this interaction.     

Evolutionary rate depends on number of protein-protein interactions independently of gene expression level: Response

A response to Fraser HB, Hirsh AE: Evolutionary rate depends on number of protein-protein interactions independently of gene expression level. BMC Evol Biol 2004, 4: 13     

Analysis of a water mediated protein-protein interactions within RNase T1

Buried and well-ordered solvent molecules are an integral part of each folded protein. For a few individual water molecules, the exchange kinetics with solvent have been described in great detail. So far, little is known about the energetics of this exchange process. Here, we present an experimental approach to investigate water-mediated intramolecular protein-protein interactions by use of double mutant cycles. As a first example, we analyzed the interdependence of the contribution of two side chains (Asn9 and Thr93) to the conformational stability of RNase T1. In the folded state, both side chains are involved in the "solvation of the same water molecule WAT1. The coupling of the contributions of Asn9 and Thr93 to the conformational stability of RNase T1 was measured by urea unfolding and differential scanning calorimetry. The structural integrity of each mutant was analyzed by X-ray crystallography. We find that the effects of the Asn9Ala and the Thr93Ala mutations on the conformational stability are additive in the corresponding double mutant. We conclude that the free energy of the WAT1 mediated intramolecular protein-protein interaction in the folded state is very similar to solvent mediated protein-protein interaction in the unfolded state.     

Mapping of protein-protein interactions within the DNA-dependent protein kinase complex.

In mammalian cells, the Ku and DNA-dependent protein kinase catalytic subunit (DNA-PKcs) proteins are required for the correct and efficient repair of DNA double-strand breaks. Ku comprises two tightly-associated subunits of approximately 69 and approximately 83 kDa, which are termed Ku70 and Ku80 (or Ku86), respectively. Previously, a number of regions of both Ku subunits have been demonstrated to be involved in their interaction, but the molecular mechanism of this interaction remains unknown. We have identified a region in Ku70 (amino acid residues 449-578) and a region in Ku80 (residues 439-592) that participate in Ku subunit interaction. Sequence analysis reveals that these interaction regions share sequence homology and suggests that the Ku subunits are structurally related. On binding to a DNA double-strand break, Ku is able to interact with DNA-PKcs, but how this interaction is mediated has not been defined. We show that the extreme C-terminus of Ku80, specifically the final 12 amino acid residues, mediates a highly specific interaction with DNA-PKcs. Strikingly, these residues appear to be conserved only in Ku80 sequences from vertebrate organisms. These data suggest that Ku has evolved to become part of the DNA-PK holo-enzyme by acquisition of a protein-protein interaction motif at the C-terminus of Ku80.     

Standardizing global gene expression analysis between laboratories and across platforms

To facilitate collaborative research efforts between multi-investigator teams using DNA microarrays, we identified sources of error and data variability between laboratories and across microarray platforms, and methods to accommodate this variability. RNA expression data were generated in seven laboratories, which compared two standard RNA samples using 12 microarray platforms. At least two standard microarray types (one spotted, one commercial) were used by all laboratories. Reproducibility for most platforms within any laboratory was typically good, but reproducibility between platforms and across laboratories was generally poor. Reproducibility between laboratories increased markedly when standardized protocols were implemented for RNA labeling, hybridization, microarray processing, data acquisition and data normalization. Reproducibility was highest when analysis was based on biological themes defined by enriched Gene Ontology (GO) categories. These findings indicate that microarray results can be comparable across multiple laboratories, especially when a common platform and set of procedures are used.