Mycobacterium tuberculosis-specific phagosome proteome and underlying signaling pathways

The phagosome is very important to host immunity and tissue homeostasis maintenance. The destiny of the phagosome is closely associated with the outcome of the pathogen within. Most pathogens are successfully delivered to the lysosome and destroyed via the fusion of the phagosome with the lysosome. Mycobacterium tuberculosis has evolved multiple tactics to deflect the normal fusion process, such as delaying the phagosome maturation and acidification, thereby evading the immune recognition and subsequent elimination. Identification of the specific constituents of M. tuberculosis phagosome and the underlying signaling pathways are pivotal to define the key molecular features of this process and better targets to control this recalcitrant pathogen. Proteomic profiling is a comprehensive approach to define the protein inventory. In this review, currently available mycobacteria-containing phagosome proteome data were compiled. Ten putative evolutionarily conserved phagosome proteins were summarized. Unique proteins of the M. tuberculosis-containing phagosome proteome were compiled via comparison with other phagosomes, especially the inert latex bead phagosome. Signaling events associated with these unique proteins, such as Rab GTPase and PI3P, were also found and discussed. The data will facilitate better characterization of the M. tuberculosis specific phagosome constituents and involved signaling, and host-derived targets for better tuberculosis control.     

Domain altering SNPs in the human proteome and their impact on signaling pathways

Single nucleotide polymorphisms (SNPs) constitute an important mode of genetic variations observed in the human genome. A small fraction of SNPs, about four thousand out of the ten million, has been associated with genetic disorders and complex diseases. The present study focuses on SNPs that fall on protein domains, 3D structures that facilitate connectivity of proteins in cell signaling and metabolic pathways. We scanned the human proteome using the PROSITE web tool and identified proteins with SNP containing domains. We showed that SNPs that fall on protein domains are highly statistically enriched among SNPs linked to hereditary disorders and complex diseases. Proteins whose domains are dramatically altered by the presence of an SNP are even more likely to be present among proteins linked to hereditary disorders. Proteins with domain-altering SNPs comprise highly connected nodes in cellular pathways such as the focal adhesion, the axon guidance pathway and the autoimmune disease pathways. Statistical enrichment of domain/motif signatures in interacting protein pairs indicates extensive loss of connectivity of cell signaling pathways due to domain-altering SNPs, potentially leading to hereditary disorders.     

Modeling of cell signaling pathways in macrophages by semantic networks

Background  

Substantial amounts of data on cell signaling, metabolic, gene regulatory and other biological pathways have been accumulated in literature and electronic databases. Conventionally, this information is stored in the form of pathway diagrams and can be characterized as highly "compartmental" (i.e. individual pathways are not connected into more general networks). Current approaches for representing pathways are limited in their capacity to model molecular interactions in their spatial and temporal context. Moreover, the critical knowledge of cause-effect relationships among signaling events is not reflected by most conventional approaches for manipulating pathways.     

Extending signaling pathways with protein-interaction networks. Application to apoptosis

Cells exploit signaling pathways during responses to environmental changes, and these processes are often modulated during disease. Particularly, relevant human pathologies such as cancer or viral infections require downregulating apoptosis signaling pathways to progress. As a result, the identification of proteins responsible for these changes is essential for the diagnostics and development of therapeutics. Transferring functional annotation within protein interaction networks has proven useful to identify such proteins, although this is not a trivial task. Here, we used different scoring methods to transfer annotation from 53 well-studied members of the human apoptosis pathways (as known by 2005) to their protein interactors. All scoring methods produced significant predictions (compared to a random negative model), but its number was too large to be useful. Thus, we made a final prediction using specific combinations of scoring methods and compared it to the proteins related to apoptosis signaling pathways during the last 5 years. We propose 273 candidate proteins that may be relevant in apoptosis signaling pathways. Although some of them have known functions consistent with their proposed apoptotsis involvement, the majority have not been annotated yet, leaving room for further experimental studies. We provide our predictions at http://sbi.imim.es/web/Apoptosis.php.     

Core signaling pathways of survival/death in autophagy-related cancer networks

Autophagy (macroautophagy), an evolutionarily conserved lysosomal degradation process, is implicated in a wide variety of pathological processes including cancer. Autophagy plays the Janus role in regulating several survival or death signaling pathways that may decide the fate of cancer cell. Accumulating evidence has revealed the core molecular machinery of autophagy in tumor initiation and progression; however, the intricate relationships between autophagy and cancer are still in its infancy. In this review, we summarize several key survival/death pathways such as mTOR subnetwork, Beclin 1 interactome, and p53 signaling that may play the crucial roles for the regulation of the autophagy-related cancer networks. Therefore, a better understanding of the relationships between autophagy and cancer may ultimately allow cancer biologists and clinicians to harness core autophagic pathways for the discovery of potential novel drug targets.     

Large scale statistical inference of signaling pathways from RNAi and microarray data

Background  

The advent of RNA interference techniques enables the selective silencing of biologically interesting genes in an efficient way. In combination with DNA microarray technology this enables researchers to gain insights into signaling pathways by observing downstream effects of individual knock-downs on gene expression. These secondary effects can be used to computationally reverse engineer features of the upstream signaling pathway.     

Efficient algorithms for detecting signaling pathways in protein interaction networks.

The interpretation of large-scale protein network data depends on our ability to identify significant substructures in the data, a computationally intensive task. Here we adapt and extend efficient techniques for finding paths and trees in graphs to the problem of identifying pathways in protein interaction networks. We present linear-time algorithms for finding paths and trees in networks under several biologically motivated constraints. We apply our methodology to search for protein pathways in the yeast protein-protein interaction network. We demonstrate that our algorithm is capable of reconstructing known signaling pathways and identifying functionally enriched paths and trees in an unsupervised manner. The algorithm is very efficient, computing optimal paths of length 8 within minutes and paths of length 10 in about three hours.     

Effector-triggered immunity signaling: from gene-for-gene pathways to protein-protein interaction networks

In its simplicity and testability, Flor's gene-for-gene hypothesis has been a powerful driver in plant immunity research for decades. Once the molecular underpinnings of gene-for-gene resistance had come into sharper focus, there was a reassessment of Flor's hypothesis and a name change to effector-triggered immunity. As implied by the name change and exemplified by pioneering studies, plant immunity is increasingly described in terms of protein rather than genetic interactions. This progress leads to a reinterpretation of old concepts of pathogen recognition and resistance signaling and, of course, opens up new questions. Here, we provide a brief historical overview of resistance gene function and how a new focus on protein interactions can lead to a deeper understanding of the logic of plant innate immunity signaling.     

Mass spectrometry analysis of the structural proteome

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Predicting protein-protein interactions on a proteome scale by matching evolutionary and structural similarities at interfaces using PRISM

Prediction of protein-protein interactions at the structural level on the proteome scale is important because it allows prediction of protein function, helps drug discovery and takes steps toward genome-wide structural systems biology. We provide a protocol (termed PRISM, protein interactions by structural matching) for large-scale prediction of protein-protein interactions and assembly of protein complex structures. The method consists of two components: rigid-body structural comparisons of target proteins to known template protein-protein interfaces and flexible refinement using a docking energy function. The PRISM rationale follows our observation that globally different protein structures can interact via similar architectural motifs. PRISM predicts binding residues by using structural similarity and evolutionary conservation of putative binding residue 'hot spots'. Ultimately, PRISM could help to construct cellular pathways and functional, proteome-scale annotation. PRISM is implemented in Python and runs in a UNIX environment. The program accepts Protein Data Bank-formatted protein structures and is available at http://prism.ccbb.ku.edu.tr/prism_protocol/.     

A methodology for the structural and functional analysis of signaling and regulatory networks

Background  

Structural analysis of cellular interaction networks contributes to a deeper understanding of network-wide interdependencies, causal relationships, and basic functional capabilities. While the structural analysis of metabolic networks is a well-established field, similar methodologies have been scarcely developed and applied to signaling and regulatory networks.     

Dynamic probabilistic threshold networks to infer signaling pathways from time-course perturbation data

Background

Network inference deals with the reconstruction of molecular networks from experimental data. Given N molecular species, the challenge is to find the underlying network. Due to data limitations, this typically is an ill-posed problem, and requires the integration of prior biological knowledge or strong regularization. We here focus on the situation when time-resolved measurements of a system’s response after systematic perturbations are available.

Results

We present a novel method to infer signaling networks from time-course perturbation data. We utilize dynamic Bayesian networks with probabilistic Boolean threshold functions to describe protein activation. The model posterior distribution is analyzed using evolutionary MCMC sampling and subsequent clustering, resulting in probability distributions over alternative networks. We evaluate our method on simulated data, and study its performance with respect to data set size and levels of noise. We then use our method to study EGF-mediated signaling in the ERBB pathway.

Conclusions

Dynamic Probabilistic Threshold Networks is a new method to infer signaling networks from time-series perturbation data. It exploits the dynamic response of a system after external perturbation for network reconstruction. On simulated data, we show that the approach outperforms current state of the art methods. On the ERBB data, our approach recovers a significant fraction of the known interactions, and predicts novel mechanisms in the ERBB pathway.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-250) contains supplementary material, which is available to authorized users.     

Genome-level longitudinal expression of signaling pathways and gene networks in pediatric septic shock

We have conducted longitudinal studies focused on the expression profiles of signaling pathways and gene networks in children with septic shock. Genome-level expression profiles were generated from whole blood-derived RNA of children with septic shock (n=30) corresponding to day one and day three of septic shock, respectively. Based on sequential statistical and expression filters, day one and day three of septic shock were characterized by differential regulation of 2,142 and 2,504 gene probes, respectively, relative to controls (n=15). Venn analysis demonstrated 239 unique genes in the day one dataset, 598 unique genes in the day three dataset, and 1,906 genes common to both datasets. Functional analyses demonstrated time-dependent, differential regulation of genes involved in multiple signaling pathways and gene networks primarily related to immunity and inflammation. Notably, multiple and distinct gene networks involving T cell- and MHC antigen-related biology were persistently downregulated on both day one and day three. Further analyses demonstrated large scale, persistent downregulation of genes corresponding to functional annotations related to zinc homeostasis. These data represent the largest reported cohort of patients with septic shock subjected to longitudinal genome-level expression profiling. The data further advance our genome-level understanding of pediatric septic shock and support novel hypotheses.     

De-orphaning the structural proteome through reciprocal comparison of evolutionarily important structural features

Function prediction frequently relies on comparing genes or gene products to search for relevant similarities. Because the number of protein structures with unknown function is mushrooming, however, we asked here whether such comparisons could be improved by focusing narrowly on the key functional features of protein structures, as defined by the Evolutionary Trace (ET). Therefore a series of algorithms was built to (a) extract local motifs (3D templates) from protein structures based on ET ranking of residue importance; (b) to assess their geometric and evolutionary similarity to other structures; and (c) to transfer enzyme annotation whenever a plurality was reached across matches. Whereas a prototype had only been 80% accurate and was not scalable, here a speedy new matching algorithm enabled large-scale searches for reciprocal matches and thus raised annotation specificity to 100% in both positive and negative controls of 49 enzymes and 50 non-enzymes, respectively-in one case even identifying an annotation error-while maintaining sensitivity ( approximately 60%). Critically, this Evolutionary Trace Annotation (ETA) pipeline requires no prior knowledge of functional mechanisms. It could thus be applied in a large-scale retrospective study of 1218 structural genomics enzymes and reached 92% accuracy. Likewise, it was applied to all 2935 unannotated structural genomics proteins and predicted enzymatic functions in 320 cases: 258 on first pass and 62 more on second pass. Controls and initial analyses suggest that these predictions are reliable. Thus the large-scale evolutionary integration of sequence-structure-function data, here through reciprocal identification of local, functionally important structural features, may contribute significantly to de-orphaning the structural proteome.     

Mapping molar shapes on signaling pathways

A major challenge in evolutionary developmental biology is to understand how genetic mutations underlie phenotypic changes. In principle, selective pressures on the phenotype screen the gene pool of the population. Teeth are an excellent model for understanding evolutionary changes in the genotype-phenotype relationship since they exist throughout vertebrates. Genetically modified mice (mutants) with abnormalities in teeth have been used to explore tooth development. The relationship between signaling pathways and molar shape, however, remains elusive due to the high intrinsic complexity of tooth crowns. This hampers our understanding of the extent to which developmental factors explored in mutants explain developmental and phenotypic variation in natural species that represent the consequence of natural selection. Here we combine a novel morphometric method with two kinds of data mining techniques to extract data sets from the three-dimensional surface models of lower first molars: i) machine learning to maximize classification accuracy of 22 mutants, and ii) phylogenetic signal for 31 Murinae species. Major shape variation among mutants is explained by the number of cusps and cusp distribution on a tooth crown. The distribution of mutant mice in morphospace suggests a nonlinear relationship between the signaling pathways and molar shape variation. Comparative analysis of mutants and wild murines reveals that mutant variation overlaps naturally occurring diversity, including more ancestral and derived morphologies. However, taxa with transverse lophs are not fully covered by mutant variation, suggesting experimentally unexplored developmental factors in the evolutionary radiation of Murines.     

How do chemokines navigate neutrophils to the target site: Dissecting the structural mechanisms and signaling pathways

Chemokines play crucial roles in combating microbial infection and initiating tissue repair by recruiting neutrophils in a timely and coordinated manner. In humans, no less than seven chemokines (CXCL1, CXCL2, CXCL3, CXCL5, CXCL6, CXCL7, and CXCL8) and two receptors (CXCR1 and CXCR2) mediate neutrophil functions but in a context dependent manner. Neutrophil-activating chemokines reversibly exist as monomers and dimers, and their receptor binding triggers conformational changes that are coupled to G-protein and β-arrestin signaling pathways. G-protein signaling activates a variety of effectors including Ca²⁺ channels and phospholipase C. β-arrestin serves as a multifunctional adaptor and is coupled to several signaling hubs including MAP kinase and tyrosine kinase pathways. Both G-protein and β-arrestin signaling pathways play important non-overlapping roles in neutrophil trafficking and activation. Functional studies have established many similarities but distinct differences for a given chemokine and between chemokines at the level of monomer vs. dimer, CXCR1 vs. CXCR2 activation, and G-protein vs. β-arrestin pathways. We propose that two forms of the ligand binding two receptors and activating two signaling pathways enables fine-tuned neutrophil function compared to a single form, a single receptor, or a single pathway. We summarize the current knowledge on the molecular mechanisms by which chemokine monomers/dimers activate CXCR1/CXCR2 and how these interactions trigger G-protein/β-arrestin-coupled signaling pathways. We also discuss current challenges and knowledge gaps, and likely advances in the near future that will lead to a better understanding of the relationship between the chemokine-CXCR1/CXCR2-G-protein/β-arrestin axis and neutrophil function.