Removing bias against membrane proteins in interaction networks

Mapping the Human Cell Surface Interactome: A Key to Decode Cell-to-Cell Communication

Proteins on the surfaces of cells serve as physical connection points to bridge one cell with another, enabling direct communication between cells and cohesive structure. As biomedical research makes the leap from characterizing individual cells toward understanding the multicellular organization of the human body, the binding interactions between molecules on the surfaces of cells are foundational both for computational models and for clinical efforts to exploit these influential receptor pathways. To achieve this grander vision, we must assemble the full interactome of ways surface proteins can link together. This review investigates how close we are to knowing the human cell surface protein interactome. We summarize the current state of databases and systematic technologies to assemble surface protein interactomes, while highlighting substantial gaps that remain. We aim for this to serve as a road map for eventually building a more robust picture of the human cell surface protein interactome.

Recording negative results of protein-protein interaction assays: an easy way to deal with the biases and errors of interactomic data sets

In recent years, it has become increasingly common to use assays that can test whether two proteins interact, such as yeast two-hybrid and tandem affinity purification followed by mass spectrometry. Such techniques, particularly when applied at a large scale, suffer from high rates of false positives and false negatives. In addition, interactomic data sets are subjected to a number of biases, which limits considerably their usefulness to address biological questions. Interactomic databases only keep track of the positive results of protein interaction assays (those indicating that the tested proteins interact). Despite their importance, negative results (those indicating that the tested proteins do not interact) are not recorded in interactomic databases. Indeed, current interactomic databases do not support negative results. Here, I argue that systematically recording not only positive but also negative results of protein interaction assays would help scientists identify errors and deal with biases, thus enormously increasing the value of interactomic data sets. The challenges of implementing this change, along with potential solutions, are discussed.

Searching the overlap between network modules with specific betweeness (S2B) and its application to cross-disease analysis

Discovering disease-associated genes (DG) is strategic for understanding pathological mechanisms. DGs form modules in protein interaction networks and diseases with common phenotypes share more DGs or have more closely interacting DGs. This prompted the development of Specific Betweenness (S2B) to find genes associated with two related diseases. S2B prioritizes genes frequently and specifically present in shortest paths linking two disease modules. Top S2B scores identified genes in the overlap of artificial network modules more than 80% of the times, even with incomplete or noisy knowledge. Applied to Amyotrophic Lateral Sclerosis and Spinal Muscular Atrophy, S2B candidates were enriched in biological processes previously associated with motor neuron degeneration. Some S2B candidates closely interacted in network cliques, suggesting common molecular mechanisms for the two diseases. S2B is a valuable tool for DG prediction, bringing new insights into pathological mechanisms. More generally, S2B can be applied to infer the overlap between other types of network modules, such as functional modules or context-specific subnetworks. An R package implementing S2B is publicly available at https://github.com/frpinto/S2B .

Geometric characterisation of disease modules

There is an increasing accumulation of evidence supporting the existence of a hyperbolic geometry underlying the network representation of complex systems. In particular, it has been shown that the latent geometry of the human protein network (hPIN) captures biologically relevant information, leading to a meaningful visual representation of protein-protein interactions and translating challenging systems biology problems into measuring distances between proteins. Moreover, proteins can efficiently communicate with each other, without global knowledge of the hPIN structure, via a greedy routing (GR) process in which hyperbolic distances guide biological signals from source to target proteins. It is thanks to this effective information routing throughout the hPIN that the cell operates, communicates with other cells and reacts to environmental changes. As a result, the malfunction of one or a few members of this intricate system can disturb its dynamics and derive in disease phenotypes. In fact, it is known that the proteins associated with a single disease agglomerate non-randomly in the same region of the hPIN, forming one or several connected components known as the disease module (DM). Here, we present a geometric characterisation of DMs. First, we found that DM positions on the two-dimensional hyperbolic plane reflect their fragmentation and functional heterogeneity, rendering an informative picture of the cellular processes that the disease is affecting. Second, we used a distance-based dissimilarity measure to cluster DMs with shared clinical features. Finally, we took advantage of the GR strategy to study how defective proteins affect the transduction of signals throughout the hPIN.

Position Matters: Network Centrality Considerably Impacts Rates of Protein Evolution in the Human Protein-Protein Interaction Network

The proteins of any organism evolve at disparate rates. A long list of factors affecting rates of protein evolution have been identified. However, the relative importance of each factor in determining rates of protein evolution remains unresolved. The prevailing view is that evolutionary rates are dominantly determined by gene expression, and that other factors such as network centrality have only a marginal effect, if any. However, this view is largely based on analyses in yeasts, and accurately measuring the importance of the determinants of rates of protein evolution is complicated by the fact that the different factors are often correlated with each other, and by the relatively poor quality of available functional genomics data sets. Here, we use correlation, partial correlation and principal component regression analyses to measure the contributions of several factors to the variability of the rates of evolution of human proteins. For this purpose, we analyzed the entire human protein–protein interaction data set and the human signal transduction network—a network data set of exceptionally high quality, obtained by manual curation, which is expected to be virtually free from false positives. In contrast with the prevailing view, we observe that network centrality (measured as the number of physical and nonphysical interactions, betweenness, and closeness) has a considerable impact on rates of protein evolution. Surprisingly, the impact of centrality on rates of protein evolution seems to be comparable, or even superior according to some analyses, to that of gene expression. Our observations seem to be independent of potentially confounding factors and from the limitations (biases and errors) of interactomic data sets.

Equine skeletal muscle adaptations to exercise and training: evidence of differential regulation of autophagosomal and mitochondrial components

BACKGROUND

A single bout of exercise induces changes in gene expression in skeletal muscle. Regular exercise results in an adaptive response involving changes in muscle architecture and biochemistry, and is an effective way to manage and prevent common human diseases such as obesity, cardiovascular disorders and type II diabetes. However, the biomolecular mechanisms underlying such responses still need to be fully elucidated. Here we performed a transcriptome-wide analysis of skeletal muscle tissue in a large cohort of untrained Thoroughbred horses (n = 51) before and after a bout of high-intensity exercise and again after an extended period of training. We hypothesized that regular high-intensity exercise training primes the transcriptome for the demands of high-intensity exercise.

RESULTS

An extensive set of genes was observed to be significantly differentially regulated in response to a single bout of high-intensity exercise in the untrained cohort (3241 genes) and following multiple bouts of high-intensity exercise training over a six-month period (3405 genes). Approximately one-third of these genes (1025) and several biological processes related to energy metabolism were common to both the exercise and training responses. We then developed a novel network-based computational analysis pipeline to test the hypothesis that these transcriptional changes also influence the contextual molecular interactome and its dynamics in response to exercise and training. The contextual network analysis identified several important hub genes, including the autophagosomal-related gene GABARAPL1, and dynamic functional modules, including those enriched for mitochondrial respiratory chain complexes I and V, that were differentially regulated and had their putative interactions 're-wired' in the exercise and/or training responses.

CONCLUSION

Here we have generated for the first time, a comprehensive set of genes that are differentially expressed in Thoroughbred skeletal muscle in response to both exercise and training. These data indicate that consecutive bouts of high-intensity exercise result in a priming of the skeletal muscle transcriptome for the demands of the next exercise bout. Furthermore, this may also lead to an extensive 're-wiring' of the molecular interactome in both exercise and training and include key genes and functional modules related to autophagy and the mitochondrion.

HIPPIE v2.0: enhancing meaningfulness and reliability of protein-protein interaction networks

The increasing number of experimentally detected interactions between proteins makes it difficult for researchers to extract the interactions relevant for specific biological processes or diseases. This makes it necessary to accompany the large-scale detection of protein–protein interactions (PPIs) with strategies and tools to generate meaningful PPI subnetworks. To this end, we generated the Human Integrated Protein–Protein Interaction rEference or HIPPIE (http://cbdm.uni-mainz.de/hippie/). HIPPIE is a one-stop resource for the generation and interpretation of PPI networks relevant to a specific research question. We provide means to generate highly reliable, context-specific PPI networks and to make sense out of them. We just released the second major update of HIPPIE, implementing various new features. HIPPIE grew substantially over the last years and now contains more than 270 000 confidence scored and annotated PPIs. We integrated different types of experimental information for the confidence scoring and the construction of context-specific networks. We implemented basic graph algorithms that highlight important proteins and interactions. HIPPIE's graphical interface implements several ways for wet lab and computational scientists alike to access the PPI data.

Evolutionary Influenced Interaction Pattern as Indicator for the Investigation of Natural Variants Causing Nephrogenic Diabetes Insipidus

The importance of short membrane sequence motifs has been shown in many works and emphasizes the related sequence motif analysis. Together with specific transmembrane helix-helix interactions, the analysis of interacting sequence parts is helpful for understanding the process during membrane protein folding and in retaining the three-dimensional fold. Here we present a simple high-throughput analysis method for deriving mutational information of interacting sequence parts. Applied on aquaporin water channel proteins, our approach supports the analysis of mutational variants within different interacting subsequences and finally the investigation of natural variants which cause diseases like, for example, nephrogenic diabetes insipidus. In this work we demonstrate a simple method for massive membrane protein data analysis. As shown, the presented in silico analyses provide information about interacting sequence parts which are constrained by protein evolution. We present a simple graphical visualization medium for the representation of evolutionary influenced interaction pattern pairs (EIPPs) adapted to mutagen investigations of aquaporin-2, a protein whose mutants are involved in the rare endocrine disorder known as nephrogenic diabetes insipidus, and membrane proteins in general. Furthermore, we present a new method to derive new evolutionary variations within EIPPs which can be used for further mutagen laboratory investigations.

Protein-protein interaction databases

Years of meticulous curation of scientific literature and increasingly reliable computational predictions have resulted in creation of vast databases of protein interaction data. Over the years, these repositories have become a basic framework in which experiments are analyzed and new directions of research are explored. Here we present an overview of the most widely used protein-protein interaction databases and the methods they employ to gather, combine, and predict interactions. We also point out the trade-off between comprehensiveness and accuracy and the main pitfall scientists have to be aware before adopting protein interaction databases in any single-gene or genome-wide analysis.

A comprehensive strategy to identify stoichiometric membrane protein interactomes

There are numerous experimental approaches to identify the interaction networks of soluble proteins, but strategies for the identification of membrane protein interactomes remain limited. We discuss in detail the logic of an experimental design that led us to identify the interactome of a membrane protein of complex membrane topology, the calcium activated chloride channel Anoctamin 1/Tmem16a (Ano1). We used covalent chemical stabilizers of protein-protein interactions combined with magnetic bead immuno-affinity chromatography, quantitative SILAC mass-spectrometry and in silico network construction. This strategy led us to define a putative Ano1 interactome from which we selected key components for functional testing. We propose a combination of procedures to narrow down candidate proteins interacting with a membrane protein of interest for further functional studies.

Neurodevelopmental disorders: mechanisms and boundary definitions from genomes, interactomes and proteomes

Neurodevelopmental disorders such as intellectual disability, autism spectrum disorder and schizophrenia lack precise boundaries in their clinical definitions, epidemiology, genetics and protein-protein interactomes. This calls into question the appropriateness of current categorical disease concepts. Recently, there has been a rising tide to reformulate neurodevelopmental nosological entities from biology upward. To facilitate this developing trend, we propose that identification of unique proteomic signatures that can be strongly associated with patient's risk alleles and proteome-interactome-guided exploration of patient genomes could define biological mechanisms necessary to reformulate disorder definitions.

Beyond LDL-C lowering: distinct molecular sphingolipids are good indicators of proprotein convertase subtilisin/kexin type 9 (PCSK9) deficiency

OBJECTIVES

Inhibition of proprotein convertase subtilisin/kexin type 9 (PCSK9) has been proposed to be a potential new therapeutic target for treatment of hypercholesterolaemia. However, little is known about the effects of PCSK9 inhibition on the lipidome.

METHODS

We performed molecular lipidomic analyses of plasma samples obtained from PCSK9-deficient mice, and serum of human carriers of a loss-of-function variant in the PCSK9 gene (R46L).

RESULTS

In both mouse and man, PCSK9 deficiency caused a decrease in several cholesteryl esters (CE) and short fatty acid chain containing sphingolipid species such as CE 16:0, glucosyl/galactosylceramide (Glc/GalCer) d18:1/16:0, and lactosylceramide (LacCer) d18:1/16:0. In mice, the changes in lipid concentrations were most prominent when animals were given regular chow diet. In man, a number of molecular lipid species was shown to decrease significantly even when LDL-cholesterol was non-significantly reduced by 10% only. Western diet attenuated the lipid lowering potency of PCSK9 deficiency in mice.

CONCLUSIONS

Plasma molecular lipid species may be utilized for characterizing novel compounds inhibiting PCSK9 and as sensitive efficacy markers of the PCSK9 inhibition.

Disentangling function from topology to infer the network properties of disease genes

BACKGROUND

The topological features of disease genes within interaction networks are the subject of intense study, as they shed light on common mechanisms of pathology and are useful for uncovering additional disease genes. Computational analyses typically try to uncover whether disease genes exhibit distinct network features, as compared to all genes.

RESULTS

We demonstrate that the functional composition of disease gene sets is an important confounding factor in these types of analyses. We consider five disease sets and show that while they indeed have distinct topological features, they are also enriched in functions that a priori exhibit distinct network properties. To address this, we develop a computational framework to assess the network properties of disease genes based on a sampling algorithm that generates control gene sets that are functionally similar to the disease set. Using our function-constrained sampling approach, we demonstrate that for most of the topological properties studied, disease genes are more similar to sets of genes with similar functional make-up than they are to randomly selected genes; this suggests that these observed differences in topological properties reflect not only the distinguishing network features of disease genes but also their functional composition. Nevertheless, we also highlight many cases where disease genes have distinct topological properties even when accounting for function.

CONCLUSIONS

Our approach is an important first step in extracting the residual topological differences in disease genes when accounting for function, and leads to new insights into the network properties of disease genes.