Inferring ligand-receptor cellular networks from bulk and spatial transcriptomic datasets with BulkSignalR.

The study of cellular networks mediated by ligand-receptor interactions has attracted much attention recently owing to single-cell omics. However, rich collections of bulk data accompanied with clinical information exists and continue to be generated with no equivalent in single-cell so far. In parallel, spatial transcriptomic (ST) analyses represent a revolutionary tool in biology. A large number of ST projects rely on multicellular resolution, for instance the Visium™ platform, where several cells are analyzed at each location, thus producing localized bulk data. Here, we describe BulkSignalR, a R package to infer ligand-receptor networks from bulk data. BulkSignalR integrates ligand-receptor interactions with downstream pathways to estimate statistical significance. A range of visualization methods complement the statistics, including functions dedicated to spatial data. We demonstrate BulkSignalR relevance using different datasets, including new Visium liver metastasis ST data, with experimental validation of protein colocalization. A comparison with other ST packages shows the significantly higher quality of BulkSignalR inferences. BulkSignalR can be applied to any species thanks to its built-in generic ortholog mapping functionality.

A cell-to-patient machine learning transfer approach uncovers novel basal-like breast cancer prognostic markers amongst alternative splice variants.

BACKGROUND: Breast cancer is amongst the 10 first causes of death in women worldwide. Around 20% of patients are misdiagnosed leading to early metastasis, resistance to treatment and relapse. Many clinical and gene expression profiles have been successfully used to classify breast tumours into 5 major types with different prognosis and sensitivity to specific treatments. Unfortunately, these profiles have failed to subclassify breast tumours into more subtypes to improve diagnostics and survival rate. Alternative splicing is emerging as a new source of highly specific biomarkers to classify tumours in different grades. Taking advantage of extensive public transcriptomics datasets in breast cancer cell lines (CCLE) and breast cancer tumours (TCGA), we have addressed the capacity of alternative splice variants to subclassify highly aggressive breast cancers. RESULTS: Transcriptomics analysis of alternative splicing events between luminal, basal A and basal B breast cancer cell lines identified a unique splicing signature for a subtype of tumours, the basal B, whose classification is not in use in the clinic yet. Basal B cell lines, in contrast with luminal and basal A, are highly metastatic and express epithelial-to-mesenchymal (EMT) markers, which are hallmarks of cell invasion and resistance to drugs. By developing a semi-supervised machine learning approach, we transferred the molecular knowledge gained from these cell lines into patients to subclassify basal-like triple negative tumours into basal A- and basal B-like categories. Changes in splicing of 25 alternative exons, intimately related to EMT and cell invasion such as ENAH, CD44 and CTNND1, were sufficient to identify the basal-like patients with the worst prognosis. Moreover, patients expressing this basal B-specific splicing signature also expressed newly identified biomarkers of metastasis-initiating cells, like CD36, supporting a more invasive phenotype for this basal B-like breast cancer subtype. CONCLUSIONS: Using a novel machine learning approach, we have identified an EMT-related splicing signature capable of subclassifying the most aggressive type of breast cancer, which are basal-like triple negative tumours. This proof-of-concept demonstrates that the biological knowledge acquired from cell lines can be transferred to patients data for further clinical investigation. More studies, particularly in 3D culture and organoids, will increase the accuracy of this transfer of knowledge, which will open new perspectives into the development of novel therapeutic strategies and the further identification of specific biomarkers for drug resistance and cancer relapse.

iMOKA: k-mer based software to analyze large collections of sequencing data.

iMOKA (interactive multi-objective k-mer analysis) is a software that enables comprehensive analysis of sequencing data from large cohorts to generate robust classification models or explore specific genetic elements associated with disease etiology. iMOKA uses a fast and accurate feature reduction step that combines a Naïve Bayes classifier augmented by an adaptive entropy filter and a graph-based filter to rapidly reduce the search space. By using a flexible file format and distributed indexing, iMOKA can easily integrate data from multiple experiments and also reduces disk space requirements and identifies changes in transcript levels and single nucleotide variants. iMOKA is available at https://github.com/RitchieLabIGH/iMOKA and Zenodo https://doi.org/10.5281/zenodo.4008947 .

GECKO is a genetic algorithm to classify and explore high throughput sequencing data.

Comparative analysis of high throughput sequencing data between multiple conditions often involves mapping of sequencing reads to a reference and downstream bioinformatics analyses. Both of these steps may introduce heavy bias and potential data loss. This is especially true in studies where patient transcriptomes or genomes may vary from their references, such as in cancer. Here we describe a novel approach and associated software that makes use of advances in genetic algorithms and feature selection to comprehensively explore massive volumes of sequencing data to classify and discover new sequences of interest without a mapping step and without intensive use of specialized bioinformatics pipelines. We demonstrate that our approach called GECKO for GEnetic Classification using k-mer Optimization is effective at classifying and extracting meaningful sequences from multiple types of sequencing approaches including mRNA, microRNA, and DNA methylome data.

Mini-Exome Coupled to Read-Depth Based Copy Number Variation Analysis in Patients with Inherited Ataxias.

Next-generation sequencing (NGS) has an established diagnostic value for inherited ataxia. However, the need of a rigorous process of analysis and validation remains challenging. Moreover, copy number variations (CNV) or dynamic expansions of repeated sequence are classically considered not adequately detected by exome sequencing technique. We applied a strategy of mini-exome coupled to read-depth based CNV analysis to a series of 33 patients with probable inherited ataxia and onset <50 years. The mini-exome consisted of the capture of 4,813 genes having associated clinical phenotypes. Pathogenic variants were found in 42% and variants of uncertain significance in 24% of the patients. These results are comparable to those from whole exome sequencing and better than previous targeted NGS studies. CNV and dynamic expansions of repeated CAG sequence were identified in three patients. We identified both atypical presentation of known ataxia genes (ATM, NPC1) and mutations in genes very rarely associated with ataxia (ERCC4, HSD17B4). We show that mini-exome bioinformatics data analysis allows the identification of CNV and dynamic expansions of repeated sequence. Our study confirms the diagnostic value of the proposed genetic analysis strategy. We also provide an algorithm for the multidisciplinary process of analysis, interpretation, and validation of NGS data.

Endothelial, epithelial, and fibroblast cells exhibit specific splicing programs independently of their tissue of origin.

Alternative splicing is the main mechanism of increasing the proteome diversity coded by a limited number of genes. It is well established that different tissues or organs express different splicing variants. However, organs are composed of common major cell types, including fibroblasts, epithelial, and endothelial cells. By analyzing large-scale data sets generated by The ENCODE Project Consortium and after extensive RT-PCR validation, we demonstrate that each of the three major cell types expresses a specific splicing program independently of its organ origin. Furthermore, by analyzing splicing factor expression across samples, publicly available splicing factor binding site data sets (CLIP-seq), and exon array data sets after splicing factor depletion, we identified several splicing factors, including ESRP1 and 2, MBNL1, NOVA1, PTBP1, and RBFOX2, that contribute to establishing these cell type-specific splicing programs. All of the analyzed data sets are freely available in a user-friendly web interface named FasterDB, which describes all known splicing variants of human and mouse genes and their splicing patterns across several dozens of normal and cancer cells as well as across tissues. Information regarding splicing factors that potentially contribute to individual exon regulation is also provided via a dedicated CLIP-seq and exon array data visualization interface. To the best of our knowledge, FasterDB is the first database integrating such a variety of large-scale data sets to enable functional genomics analyses at exon-level resolution.

Splicing switch of an epigenetic regulator by RNA helicases promotes tumor-cell invasiveness.

Both epigenetic and splicing regulation contribute to tumor progression, but the potential links between these two levels of gene-expression regulation in pathogenesis are not well understood. Here, we report that the mouse and human RNA helicases Ddx17 and Ddx5 contribute to tumor-cell invasiveness by regulating alternative splicing of several DNA- and chromatin-binding factors, including the macroH2A1 histone. We show that macroH2A1 splicing isoforms differentially regulate the transcription of a set of genes involved in redox metabolism. In particular, the SOD3 gene that encodes the extracellular superoxide dismutase and plays a part in cell migration is regulated in an opposite manner by macroH2A1 splicing isoforms. These findings reveal a new regulatory pathway in which splicing factors control the expression of histone variant isoforms that in turn drive a transcription program to switch tumor cells to an invasive phenotype.

Misregulation of miR-1 processing is associated with heart defects in myotonic dystrophy.

Myotonic dystrophy is an RNA gain-of-function disease caused by expanded CUG or CCUG repeats, which sequester the RNA binding protein MBNL1. Here we describe a newly discovered function for MBNL1 as a regulator of pre-miR-1 biogenesis and find that miR-1 processing is altered in heart samples from people with myotonic dystrophy. MBNL1 binds to a UGC motif located within the loop of pre-miR-1 and competes for the binding of LIN28, which promotes pre-miR-1 uridylation by ZCCHC11 (TUT4) and blocks Dicer processing. As a consequence of miR-1 loss, expression of GJA1 (connexin 43) and CACNA1C (Cav1.2), which are targets of miR-1, is increased in both DM1- and DM2-affected hearts. CACNA1C and GJA1 encode the main calcium- and gap-junction channels in heart, respectively, and we propose that their misregulation may contribute to the cardiac dysfunctions observed in affected persons.

Real-time imaging of cotranscriptional splicing reveals a kinetic model that reduces noise: implications for alternative splicing regulation.

Splicing is a key process that expands the coding capacity of genomes. Its kinetics remain poorly characterized, and the distribution of splicing time caused by the stochasticity of single splicing events is expected to affect regulation efficiency. We conducted a small-scale survey on 40 introns in human cells and observed that most were spliced cotranscriptionally. Consequently, we constructed a reporter system that splices cotranscriptionally and can be monitored in live cells and in real time through the use of MS2-GFP. All small nuclear ribonucleoproteins (snRNPs) are loaded on nascent pre-mRNAs, and spliceostatin A inhibits splicing but not snRNP recruitment. Intron removal occurs in minutes and is best described by a model where several successive steps are rate limiting. Each pre-mRNA molecule is predicted to require a similar time to splice, reducing kinetic noise and improving the regulation of alternative splicing. This model is relevant to other kinetically controlled processes acting on few molecules.