The systemic complexity of a monogenic disease: the molecular network of spinal muscular atrophy.

Monogenic diseases are well-suited paradigms for the causal analysis of disease-driving molecular patterns. Spinal Muscular Atrophy (SMA) is one such monogenic model caused by mutation or deletion of the Survival of motor neuron 1 (SMN1) gene. Although several functions of the SMN protein have been studied, single functions and pathways alone do not allow to identify critical disease-driving molecules. Here, we analyzed the systemic characteristics of SMA employing proteomics, phosphoproteomics, translatomics and interactomics from two mouse models with different disease-severities and genetics. This systems approach revealed sub-networks and proteins characterizing commonalities and differences of both models. To link the identified molecular networks with the disease-causing SMN protein, we combined SMN-interactome data with both proteomes creating a comprehensive representation of SMA. By this approach, disease hubs and bottlenecks between SMN and downstream pathways could be identified. Linking a disease-causing molecule with widespread molecular dysregulations via multiomics is a concept for analyses of monogenic diseases.

What could be the function of the spinal muscular atrophy-causing protein SMN in macrophages?

Spinal Muscular Atrophy (SMA), a neurodegenerative disorder, extends its impact beyond the nervous system. The central protein implicated in SMA, Survival Motor Neuron (SMN) protein, is ubiquitously expressed and functions in fundamental processes such as alternative splicing, translation, cytoskeletal dynamics and signaling. These processes are relevant for all cellular systems, including cells of the immune system such as macrophages. Macrophages are capable of modulating their splicing, cytoskeleton and expression profile in order to fulfil their role in tissue homeostasis and defense. However, less is known about impairment or dysfunction of macrophages lacking SMN and the subsequent impact on the immune system of SMA patients. We aimed to review the potential overlaps between SMN functions and macrophage mechanisms highlighting the need for future research, as well as the current state of research addressing the role of macrophages in SMA.

From data to discovery: AI-guided analysis of disease-relevant molecules in spinal muscular atrophy (SMA).

Spinal Muscular Atrophy is caused by partial loss of survival of motoneuron (SMN) protein expression. The numerous interaction partners and mechanisms influenced by SMN loss result in a complex disease. Current treatments restore SMN protein levels to a certain extent, but do not cure all symptoms. The prolonged survival of patients creates an increasing need for a better understanding of SMA. Although many SMN-protein interactions, dysregulated pathways, and organ phenotypes are known, the connections among them remain largely unexplored. Monogenic diseases are ideal examples for the exploration of cause-and-effect relationships to create a network describing the disease-context. Machine learning tools can utilize such knowledge to analyze similarities between disease-relevant molecules and molecules not described in the disease so far. We used an artificial intelligence-based algorithm to predict new genes of interest. The transcriptional regulation of 8 out of 13 molecules selected from the predicted set were successfully validated in an SMA mouse model. This bioinformatic approach, using the given experimental knowledge for relevance predictions, enhances efficient targeted research in SMA and potentially in other disease settings.

bootGSEA: a bootstrap and rank aggregation pipeline for multi-study and multi-omics enrichment analyses.

Introduction: Gene set enrichment analysis (GSEA) subsequent to differential expression analysis is a standard step in transcriptomics and proteomics data analysis. Although many tools for this step are available, the results are often difficult to reproduce because set annotations can change in the databases, that is, new features can be added or existing features can be removed. Finally, such changes in set compositions can have an impact on biological interpretation. Methods: We present bootGSEA, a novel computational pipeline, to study the robustness of GSEA. By repeating GSEA based on bootstrap samples, the variability and robustness of results can be studied. In our pipeline, not all genes or proteins are involved in the different bootstrap replicates of the analyses. Finally, we aggregate the ranks from the bootstrap replicates to obtain a score per gene set that shows whether it gains or loses evidence compared to the ranking of the standard GSEA. Rank aggregation is also used to combine GSEA results from different omics levels or from multiple independent studies at the same omics level. Results: By applying our approach to six independent cancer transcriptomics datasets, we showed that bootstrap GSEA can aid in the selection of more robust enriched gene sets. Additionally, we applied our approach to paired transcriptomics and proteomics data obtained from a mouse model of spinal muscular atrophy (SMA), a neurodegenerative and neurodevelopmental disease associated with multi-system involvement. After obtaining a robust ranking at both omics levels, both ranking lists were combined to aggregate the findings from the transcriptomics and proteomics results. Furthermore, we constructed the new R-package "bootGSEA," which implements the proposed methods and provides graphical views of the findings. Bootstrap-based GSEA was able in the example datasets to identify gene or protein sets that were less robust when the set composition changed during bootstrap analysis. Discussion: The rank aggregation step was useful for combining bootstrap results and making them comparable to the original findings on the single-omics level or for combining findings from multiple different omics levels.

The role of extracellular vesicle fusion with target cells in triggering systemic inflammation.

Extracellular vesicles (EVs) play a crucial role in intercellular communication by transferring bioactive molecules from donor to recipient cells. As a result, EV fusion leads to the modulation of cellular functions and has an impact on both physiological and pathological processes in the recipient cell. This study explores the impact of EV fusion on cellular responses to inflammatory signaling. Our findings reveal that fusion renders non-responsive cells susceptible to inflammatory signaling, as evidenced by increased NF-κB activation and the release of inflammatory mediators. Syntaxin-binding protein 1 is essential for the merge and activation of intracellular signaling. Subsequent analysis show that EVs transfer their functionally active receptors to target cells, making them prone to an otherwise unresponsive state. EVs in complex with their agonist, require no further stimulation of the target cells to trigger mobilization of NF-κB. While receptor antagonists were unable to inhibit NF-κB activation, blocking of the fusion between EVs and their target cells with heparin mitigated inflammation in mice challenged with EVs.

Dysregulation of Tweak and Fn14 in skeletal muscle of spinal muscular atrophy mice.

BACKGROUND: Spinal muscular atrophy (SMA) is a childhood neuromuscular disorder caused by depletion of the survival motor neuron (SMN) protein. SMA is characterized by the selective death of spinal cord motor neurons, leading to progressive muscle wasting. Loss of skeletal muscle in SMA is a combination of denervation-induced muscle atrophy and intrinsic muscle pathologies. Elucidation of the pathways involved is essential to identify the key molecules that contribute to and sustain muscle pathology. The tumor necrosis factor-like weak inducer of apoptosis (TWEAK)/TNF receptor superfamily member fibroblast growth factor-inducible 14 (Fn14) pathway has been shown to play a critical role in the regulation of denervation-induced muscle atrophy as well as muscle proliferation, differentiation, and metabolism in adults. However, it is not clear whether this pathway would be important in highly dynamic and developing muscle. METHODS: We thus investigated the potential role of the TWEAK/Fn14 pathway in SMA muscle pathology, using the severe Taiwanese Smn-/-; SMN2 and the less severe Smn2B/- SMA mice, which undergo a progressive neuromuscular decline in the first three post-natal weeks. We also used experimental models of denervation and muscle injury in pre-weaned wild-type (WT) animals and siRNA-mediated knockdown in C2C12 muscle cells to conduct additional mechanistic investigations. RESULTS: Here, we report significantly dysregulated expression of Tweak, Fn14, and previously proposed downstream effectors during disease progression in skeletal muscle of the two SMA mouse models. In addition, siRNA-mediated Smn knockdown in C2C12 myoblasts suggests a genetic interaction between Smn and the TWEAK/Fn14 pathway. Further analyses of SMA, Tweak-/-, and Fn14-/- mice revealed dysregulated myopathy, myogenesis, and glucose metabolism pathways as a common skeletal muscle feature, providing further evidence in support of a relationship between the TWEAK/Fn14 pathway and Smn. Finally, administration of the TWEAK/Fn14 agonist Fc-TWEAK improved disease phenotypes in the two SMA mouse models. CONCLUSIONS: Our study provides mechanistic insights into potential molecular players that contribute to muscle pathology in SMA and into likely differential responses of the TWEAK/Fn14 pathway in developing muscle.

Impairment of the neurotrophic signaling hub B-Raf contributes to motoneuron degeneration in spinal muscular atrophy.

Spinal muscular atrophy (SMA) is a motoneuron disease caused by deletions of the Survival of Motoneuron 1 gene (SMN1) and low SMN protein levels. SMN restoration is the concept behind a number of recently approved drugs which result in impressive yet limited effects. Since SMN has already been enhanced in treated patients, complementary SMN-independent approaches are needed. Previously, a number of altered signaling pathways which regulate motoneuron degeneration have been identified as candidate targets. However, signaling pathways form networks, and their connectivity is still unknown in SMA. Here, we used presymptomatic SMA mice to elucidate the network of altered signaling in SMA. The SMA network is structured in two clusters with AKT and 14-3-3 ζ/δ in their centers. Both clusters are connected by B-Raf as a major signaling hub. The direct interaction of B-Raf with 14-3-3 ζ/δ is important for an efficient neurotrophic activation of the MEK/ERK pathway and crucial for motoneuron survival. Further analyses in SMA mice revealed that both proteins were down-regulated in motoneurons and the spinal cord with B-Raf being reduced at presymptomatic stages. Primary fibroblasts and iPSC-derived motoneurons from SMA patients both showed the same pattern of down-regulation. This mechanism is conserved across species since a Caenorhabditis elegans SMA model showed less expression of the B-Raf homolog lin-45 Accordingly, motoneuron survival was rescued by a cell autonomous lin-45 expression in a C. elegans SMA model resulting in improved motor functions. This rescue was effective even after the onset of motoneuron degeneration and mediated by the MEK/ERK pathway.