Robust transcriptional indicators of immune cell death revealed by spatiotemporal transcriptome analyses.

Recognition of a pathogen by the plant immune system often triggers a form of regulated cell death traditionally known as the hypersensitive response (HR). This type of cell death occurs precisely at the site of pathogen recognition, and it is restricted to a few cells. Extensive research has shed light on how plant immune receptors are mechanistically activated. However, two central key questions remain largely unresolved: how does cell death zonation take place, and what are the mechanisms that underpin this phenomenon? Consequently, bona fide transcriptional indicators of HR are lacking, which prevents deeper insight into its mechanisms before cell death becomes macroscopic and precludes early or live observation. In this study, to identify the transcriptional indicators of HR we used the paradigmatic Arabidopsis thaliana-Pseudomonas syringae pathosystem and performed a spatiotemporally resolved gene expression analysis that compared infected cells that will undergo HR upon pathogen recognition with bystander cells that will stay alive and activate immunity. Our data revealed unique and time-dependent differences in the repertoire of differentially expressed genes, expression profiles, and biological processes derived from tissue undergoing HR and that of its surroundings. Furthermore, we generated a pipeline based on concatenated pairwise comparisons between time, zone, and treatment that enabled us to define 13 robust transcriptional HR markers. Among these genes, the promoter of an uncharacterized AAA-ATPase was used to obtain a fluorescent reporter transgenic line that displays a strong spatiotemporally resolved signal specifically in cells that will later undergo pathogen-triggered cell death. This valuable set of genes can be used to define cells that are destined to die upon infection with HR-triggering bacteria, opening new avenues for specific and/or high-throughput techniques to study HR processes at a single-cell level.

Prediction of combination therapies based on topological modeling of the immune signaling network in multiple sclerosis.

BACKGROUND: Multiple sclerosis (MS) is a major health problem, leading to a significant disability and patient suffering. Although chronic activation of the immune system is a hallmark of the disease, its pathogenesis is poorly understood, while current treatments only ameliorate the disease and may produce severe side effects. METHODS: Here, we applied a network-based modeling approach based on phosphoproteomic data to uncover the differential activation in signaling wiring between healthy donors, untreated patients, and those under different treatments. Based in the patient-specific networks, we aimed to create a new approach to identify drug combinations that revert signaling to a healthy-like state. We performed ex vivo multiplexed phosphoproteomic assays upon perturbations with multiple drugs and ligands in primary immune cells from 169 subjects (MS patients, n=129 and matched healthy controls, n=40). Patients were either untreated or treated with fingolimod, natalizumab, interferon-ß, glatiramer acetate, or the experimental therapy epigallocatechin gallate (EGCG). We generated for each donor a dynamic logic model by fitting a bespoke literature-derived network of MS-related pathways to the perturbation data. Last, we developed an approach based on network topology to identify deregulated interactions whose activity could be reverted to a "healthy-like" status by combination therapy. The experimental autoimmune encephalomyelitis (EAE) mouse model of MS was used to validate the prediction of combination therapies. RESULTS: Analysis of the models uncovered features of healthy-, disease-, and drug-specific signaling networks. We predicted several combinations with approved MS drugs that could revert signaling to a healthy-like state. Specifically, TGF-ß activated kinase 1 (TAK1) kinase, involved in Transforming growth factor ß-1 proprotein (TGF-ß), Toll-like receptor, B cell receptor, and response to inflammation pathways, was found to be highly deregulated and co-druggable with all MS drugs studied. One of these predicted combinations, fingolimod with a TAK1 inhibitor, was validated in an animal model of MS. CONCLUSIONS: Our approach based on donor-specific signaling networks enables prediction of targets for combination therapy for MS and other complex diseases.

MAPK pathway and B cells overactivation in multiple sclerosis revealed by phosphoproteomics and genomic analysis.

Dysregulation of signaling pathways in multiple sclerosis (MS) can be analyzed by phosphoproteomics in peripheral blood mononuclear cells (PBMCs). We performed in vitro kinetic assays on PBMCs in 195 MS patients and 60 matched controls and quantified the phosphorylation of 17 kinases using xMAP assays. Phosphoprotein levels were tested for association with genetic susceptibility by typing 112 single-nucleotide polymorphisms (SNPs) associated with MS susceptibility. We found increased phosphorylation of MP2K1 in MS patients relative to the controls. Moreover, we identified one SNP located in the PHDGH gene and another on IRF8 gene that were associated with MP2K1 phosphorylation levels, providing a first clue on how this MS risk gene may act. The analyses in patients treated with disease-modifying drugs identified the phosphorylation of each receptor's downstream kinases. Finally, using flow cytometry, we detected in MS patients increased STAT1, STAT3, TF65, and HSPB1 phosphorylation in CD19+ cells. These findings indicate the activation of cell survival and proliferation (MAPK), and proinflammatory (STAT) pathways in the immune cells of MS patients, primarily in B cells. The changes in the activation of these kinases suggest that these pathways may represent therapeutic targets for modulation by kinase inhibitors.

Protease Activities Triggered by Ralstonia solanacearum Infection in Susceptible and Tolerant Tomato Lines.

Activity-based protein profiling (ABPP) is a powerful proteomic technique to display protein activities in a proteome. It is based on the use of small molecular probes that react with the active site of proteins in an activity-dependent manner. We used ABPP to dissect the protein activity changes that occur in the intercellular spaces of tolerant (Hawaii 7996) and susceptible (Marmande) tomato plants in response to R. solanacearum, the causing agent of bacterial wilt, one of the most destructive bacterial diseases in plants. The intercellular space -or apoplast- is the first battlefield where the plant faces R. solanacearum Here, we explore the possibility that the limited R. solanacearum colonization reported in the apoplast of tolerant tomato is partly determined by its active proteome. Our work reveals specific activation of papain-like cysteine proteases (PLCPs) and serine hydrolases (SHs) in the leaf apoplast of the tolerant tomato Hawaii 7996 on R. solanacearum infection. The P69 family members P69C and P69F, and an unannotated lipase (Solyc02g077110.2.1), were found to be post-translationally activated. In addition, protein network analysis showed that deeper changes in network topology take place in the susceptible tomato variety, suggesting that the tolerant cultivar might be more prepared to face R. solanacearum in its basal state. Altogether this work identifies significant changes in the activity of 4 PLCPs and 27 SHs in the tomato leaf apoplast in response to R. solanacearum, most of which are yet to be characterized. Our findings denote the importance of novel proteomic approaches such as ABPP to provide new insights on old and elusive questions regarding the molecular basis of resistance to R. solanacearum.

An artificial miRNA system reveals that relative contribution of translational inhibition to miRNA-mediated regulation depends on environmental and developmental factors in Arabidopsis thaliana.

Development and fitness of any organism rely on properly controlled gene expression. This is especially true for plants, as their development is determined by both internal and external cues. MicroRNAs (miRNAs) are embedded in the genetic cascades that integrate and translate those cues into developmental programs. miRNAs negatively regulate their target genes mainly post-transcriptionally through two co-existing mechanisms; mRNA cleavage and translational inhibition. Despite our increasing knowledge about the genetic and biochemical processes involved in those concurrent mechanisms, little is known about their relative contributions to the overall miRNA-mediated regulation. Here we show that co-existence of cleavage and translational inhibition is dependent on growth temperature and developmental stage. We found that efficiency of an artificial miRNA-mediated (amiRNA) gene silencing declines with age during vegetative development in a temperature-dependent manner. That decline is mainly due to a reduction on the contribution from translational inhibition. Both, temperature and developmental stage were also found to affect mature amiRNA accumulation and the expression patterns of the core players involved in miRNA biogenesis and action. Therefore, that suggests that each miRNA family specifically regulates their respective targets, while temperature and growth might influence the performance of miRNA-dependent regulation in a more general way.

Efficient randomization of biological networks while preserving functional characterization of individual nodes.

BACKGROUND: Networks are popular and powerful tools to describe and model biological processes. Many computational methods have been developed to infer biological networks from literature, high-throughput experiments, and combinations of both. Additionally, a wide range of tools has been developed to map experimental data onto reference biological networks, in order to extract meaningful modules. Many of these methods assess results' significance against null distributions of randomized networks. However, these standard unconstrained randomizations do not preserve the functional characterization of the nodes in the reference networks (i.e. their degrees and connection signs), hence including potential biases in the assessment. RESULTS: Building on our previous work about rewiring bipartite networks, we propose a method for rewiring any type of unweighted networks. In particular we formally demonstrate that the problem of rewiring a signed and directed network preserving its functional connectivity (F-rewiring) reduces to the problem of rewiring two induced bipartite networks. Additionally, we reformulate the lower bound to the iterations' number of the switching-algorithm to make it suitable for the F-rewiring of networks of any size. Finally, we present BiRewire3, an open-source Bioconductor package enabling the F-rewiring of any type of unweighted network. We illustrate its application to a case study about the identification of modules from gene expression data mapped on protein interaction networks, and a second one focused on building logic models from more complex signed-directed reference signaling networks and phosphoproteomic data. CONCLUSIONS: BiRewire3 it is freely available at https://www.bioconductor.org/packages/BiRewire/ , and it should have a broad application as it allows an efficient and analytically derived statistical assessment of results from any network biology tool.

Signaling networks in MS: a systems-based approach to developing new pharmacological therapies.

The pathogenesis of multiple sclerosis (MS) involves alterations to multiple pathways and processes, which represent a significant challenge for developing more-effective therapies. Systems biology approaches that study pathway dysregulation should offer benefits by integrating molecular networks and dynamic models with current biological knowledge for understanding disease heterogeneity and response to therapy. In MS, abnormalities have been identified in several cytokine-signaling pathways, as well as those of other immune receptors. Among the downstream molecules implicated are Jak/Stat, NF-Kb, ERK1/3, p38 or Jun/Fos. Together, these data suggest that MS is likely to be associated with abnormalities in apoptosis/cell death, microglia activation, blood-brain barrier functioning, immune responses, cytokine production, and/or oxidative stress, although which pathways contribute to the cascade of damage and can be modulated remains an open question. While current MS drugs target some of these pathways, others remain untouched. Here, we propose a pragmatic systems analysis approach that involves the large-scale extraction of processes and pathways relevant to MS. These data serve as a scaffold on which computational modeling can be performed to identify disease subgroups based on the contribution of different processes. Such an analysis, targeting these relevant MS-signaling pathways, offers the opportunity to accelerate the development of novel individual or combination therapies.

Phosphoproteomic analyses reveal novel cross-modulation mechanisms between two signaling pathways in yeast.

Cells respond to environmental stimuli via specialized signaling pathways. Concurrent stimuli trigger multiple pathways that integrate information, predominantly via protein phosphorylation. Budding yeast responds to NaCl and pheromone via two mitogen-activated protein kinase cascades, the high osmolarity, and the mating pathways, respectively. To investigate signal integration between these pathways, we quantified the time-resolved phosphorylation site dynamics after pathway co-stimulation. Using shotgun mass spectrometry, we quantified 2,536 phosphopeptides across 36 conditions. Our data indicate that NaCl and pheromone affect phosphorylation events within both pathways, which thus affect each other at more levels than anticipated, allowing for information exchange and signal integration. We observed a pheromone-induced down-regulation of Hog1 phosphorylation due to Gpd1, Ste20, Ptp2, Pbs2, and Ptc1. Distinct Ste20 and Pbs2 phosphosites responded differently to the two stimuli, suggesting these proteins as key mediators of the information exchange. A set of logic models was then used to assess the role of measured phosphopeptides in the crosstalk. Our results show that the integration of the response to different stimuli requires complex interconnections between signaling pathways.

Data-derived modeling characterizes plasticity of MAPK signaling in melanoma.

The majority of melanomas have been shown to harbor somatic mutations in the RAS-RAF-MEK-MAPK and PI3K-AKT pathways, which play a major role in regulation of proliferation and survival. The prevalence of these mutations makes these kinase signal transduction pathways an attractive target for cancer therapy. However, tumors have generally shown adaptive resistance to treatment. This adaptation is achieved in melanoma through its ability to undergo neovascularization, migration and rearrangement of signaling pathways. To understand the dynamic, nonlinear behavior of signaling pathways in cancer, several computational modeling approaches have been suggested. Most of those models require that the pathway topology remains constant over the entire observation period. However, changes in topology might underlie adaptive behavior to drug treatment. To study signaling rearrangements, here we present a new approach based on Fuzzy Logic (FL) that predicts changes in network architecture over time. This adaptive modeling approach was used to investigate pathway dynamics in a newly acquired experimental dataset describing total and phosphorylated protein signaling over four days in A375 melanoma cell line exposed to different kinase inhibitors. First, a generalized strategy was established to implement a parameter-reduced FL model encoding non-linear activity of a signaling network in response to perturbation. Next, a literature-based topology was generated and parameters of the FL model were derived from the full experimental dataset. Subsequently, the temporal evolution of model performance was evaluated by leaving time-defined data points out of training. Emerging discrepancies between model predictions and experimental data at specific time points allowed the characterization of potential network rearrangement. We demonstrate that this adaptive FL modeling approach helps to enhance our mechanistic understanding of the molecular plasticity of melanoma.

Multi-parametric analysis and modeling of relationships between mitochondrial morphology and apoptosis.

Mitochondria exist as a network of interconnected organelles undergoing constant fission and fusion. Current approaches to study mitochondrial morphology are limited by low data sampling coupled with manual identification and classification of complex morphological phenotypes. Here we propose an integrated mechanistic and data-driven modeling approach to analyze heterogeneous, quantified datasets and infer relations between mitochondrial morphology and apoptotic events. We initially performed high-content, multi-parametric measurements of mitochondrial morphological, apoptotic, and energetic states by high-resolution imaging of human breast carcinoma MCF-7 cells. Subsequently, decision tree-based analysis was used to automatically classify networked, fragmented, and swollen mitochondrial subpopulations, at the single-cell level and within cell populations. Our results revealed subtle but significant differences in morphology class distributions in response to various apoptotic stimuli. Furthermore, key mitochondrial functional parameters including mitochondrial membrane potential and Bax activation, were measured under matched conditions. Data-driven fuzzy logic modeling was used to explore the non-linear relationships between mitochondrial morphology and apoptotic signaling, combining morphological and functional data as a single model. Modeling results are in accordance with previous studies, where Bax regulates mitochondrial fragmentation, and mitochondrial morphology influences mitochondrial membrane potential. In summary, we established and validated a platform for mitochondrial morphological and functional analysis that can be readily extended with additional datasets. We further discuss the benefits of a flexible systematic approach for elucidating specific and general relationships between mitochondrial morphology and apoptosis.