molecular signatures
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Multi-protein assemblies are complex molecular systems that perform highly sophisticated biochemical functions in an orchestrated manner. They are subject to changes that are governed by the evolution of individual components. We performed a comparative analysis of the ancient and functionally conserved spliceosomal SF3b complex, to recognize molecular signatures that contribute to sequence divergence and functional specializations. For this, we recognized homologous sequences of individual SF3b proteins distributed across 10 supergroups of eukaryotes and identified all seven protein components of the complex in 578 eukaryotic species. Using sequence and structural analysis, we establish that proteins occurring on the surface of the SF3b complex harbor more sequence variation than the proteins that lie in the core. Further, we show through protein interface conservation patterns that the extent of conservation varies considerably between interacting partners. When we analyze phylogenetic distributions of individual components of the complex, we find that protein partners that are known to form independent subcomplexes are observed to share similar profiles, reaffirming the link between differential conservation of interface regions and their inter-dependence. When we extend our analysis to individual protein components of the complex, we find taxa-specific variability in molecular signatures of the proteins. These trends are discussed in the context of proline-rich motifs of SF3b4, functional and drug binding sites of SF3b1. Further, we report key protein-protein interactions between SF3b1 and SF3b6 whose presence is observed to be lineage-specific across eukaryotes. Together, our studies show the association of protein location within the complex and subcomplex formation patterns with the sequence conservation of SF3b proteins. In addition, our study underscores evolutionarily flexible elements that appear to confer adaptive features in individual components of the multi-protein SF3b complexes and may contribute to its functional adaptability.

This original article focuses on integrated metabolomics and transcriptomics analysis to understand the pathogenesis of hypersensitivity pneumonitis (HP).

Motivation: High-throughput technologies play a more and more significant role in discovering prognostic molecular signatures and identifying novel drug targets. It is common to apply Machine Learning (ML) methods to classify high-dimensional gene expression data and to determine a subset of features (genes) that is important for decisions of a ML model. One feature subset of important genes corresponds to one dataset and it is essential to sustain the stability of feature sets across different datasets with the same clinical endpoint since the selected genes are candidates for prognostic biomarkers. The stability of feature selection can be improved by including information of molecular networks into ML methods. Gene expression data can be assigned to the vertices of a molecular network's graph and then classified by a Graph Convolutional Neural Network (GCNN). GCNN is a contemporary deep learning approach that can be applied to graph-structured data. Layer-wise Relevance Propagation (LRP) is a technique to explain decisions of deep learning methods. In our recent work we developed Graph Layer-wise Relevance Propagation (GLRP) --- a method that adapts LRP to a graph convolution and explains patient-specific decisions of GCNN. GLRP delivers individual molecular signatures as patient-specific subnetworks that are parts of a molecular network representing background knowledge about biological mechanisms. GLRP gives a possibility to deliver the subset of features corresponding to a dataset as well, so that the stability of feature selection performed by GLRP can be measured and compared to that of other methods. Results: Utilizing two large breast cancer datasets, we analysed properties of feature sets selected by GLRP (GCNN+LRP) such as stability and permutation importance. We have implemented a graph convolutional layer of GCNN as a Keras layer so that the SHAP (SHapley Additive exPlanation) explanation method could be also applied to a Keras version of a GCNN model. We compare the stability of feature selection performed by GCNN+LRP to the stability of GCNN+SHAP and to other ML based feature selection methods. We conclude, that GCNN+LRP shows the highest stability among other feature selection methods including GCNN+SHAP. It was established that the permutation importance of features among GLRP subnetworks is lower than among GCNN+SHAP subnetworks, but in the context of the utilized molecular network, a GLRP subnetwork of an individual patient is on average substantially more connected (and interpretable) than a GCNN+SHAP subnetwork, which consists mainly of single vertices.