Biological interacting units identified in human protein networks reveal tissue-functional diversification and its impact on disease

PathwayMind: An innovative tool and database for pathway perturbation analysis, uncovering critical pathways for drugs and target protein sets

Pathway enrichment analysis is a valuable tool for researchers aiming to understand the mechanisms underlying any specific drug or disease associated gene lists derived from biological assays or large-scale genome (omics) experiments. By employing this method, researchers can pinpoint biological pathways that exhibit a higher level of enrichment for a given set of genes than would be anticipated by random chance. These biological pathways play a crucial role in understanding the disease pathophysiology, therapeutic strategy, polypharmacological effects, synergistic mechanisms, and target engagement. However many of the available tools do not fulfill this requirement as most of the available tools consider a flat hierarchy of protein involvement in the pathway and do not consider topological information or importance of molecules in the pathway. Here we propose a novel method to enrich the molecular pathways and prioritize them based on their importance and crucial role in the biological function using the graph and evidence-based approach and customized datasets called PathwayMind. It includes 2648 pathways, 4539 biological events, 2465847 protein-protein interactions and 124717 gene-to-pathway relationships and the role of 3510 unique initial genes in 11,992 molecular pathways. The current manuscript comprises three major steps: The first step is about the data extraction and datasets creation for pathway enrichment, and the second steps comprises pathway perturbation analysis to identify most perturbed biological pathways and the third steps includes validation of this approach along with standalone tools and visualization algorithms which disclose the molecular involvement and improve the interpretability of the results. The end-to-end pathway analysis can be performed in a few minutes to provide complete insights of your target or drug of interest. The current PathwayMind tool and its datasets could be very useful for the molecular scientists and system biologists who are interested to understand the therapeutic effects of their drugs or understanding the involvement of biological pathways for specific gene sets which does not require any prior bioinformatics training.

Analysis of asymptomatic Drosophila models for ALS and SMA reveals convergent impact on functional protein complexes linked to neuro-muscular degeneration

BACKGROUND

Spinal Muscular Atrophy (SMA) and Amyotrophic Lateral Sclerosis (ALS) share phenotypic and molecular commonalities, including the fact that they can be caused by mutations in ubiquitous proteins involved in RNA metabolism, namely SMN, TDP-43 and FUS. Although this suggests the existence of common disease mechanisms, there is currently no model to explain the resulting motor neuron dysfunction. In this work we generated a parallel set of Drosophila models for adult-onset RNAi and tagged neuronal expression of the fly orthologues of the three human proteins, named Smn, TBPH and Caz, respectively. We profiled nuclear and cytoplasmic bound mRNAs using a RIP-seq approach and characterized the transcriptome of the RNAi models by RNA-seq. To unravel the mechanisms underlying the common functional impact of these proteins on neuronal cells, we devised a computational approach based on the construction of a tissue-specific library of protein functional modules, selected by an overall impact score measuring the estimated extent of perturbation caused by each gene knockdown.

RESULTS

Transcriptome analysis revealed that the three proteins do not bind to the same RNA molecules and that only a limited set of functionally unrelated transcripts is commonly affected by their knock-down. However, through our integrative approach we were able to identify a concerted effect on protein functional modules, albeit acting through distinct targets. Most strikingly, functional annotation revealed that these modules are involved in critical cellular pathways for motor neurons, including neuromuscular junction function. Furthermore, selected modules were found to be significantly enriched in orthologues of human neuronal disease genes.

CONCLUSIONS

The results presented here show that SMA and ALS disease-associated genes linked to RNA metabolism functionally converge on neuronal protein complexes, providing a new hypothesis to explain the common motor neuron phenotype. The functional modules identified represent promising biomarkers and therapeutic targets, namely given their alteration in asymptomatic settings.