Lung Tissue Multi-Layer Network Analysis Uncovers the Molecular Heterogeneity of COPD.

BACKGROUND: Chronic Obstructive Pulmonary Disease (COPD) is a heterogeneous condition. We hypothesized that the unbiased integration of different COPD lung omics using a novel multi-layer approach may unravel mechanisms associated with clinical characteristics. METHODS: We profiled mRNA, miRNA and methylome in lung tissue samples from 135 former smokers with COPD. For each omic (layer) we built a patient network based on molecular similarity. The three networks were used to build a multi-layer network, and optimization of multiplex-modularity was employed to identify patient communities across the three distinct layers. Uncovered communities were related to clinical features. RESULTS: We identified five patient communities in the multi-layer network which were molecularly distinct and related to clinical characteristics, such as FEV1 and blood eosinophils. Two communities (C#3 and C#4) had both similarly low FEV1 values and emphysema, but were molecularly different: C#3, but not C#4, presented B and T cell signatures and a downregulation of secretory (SCGB1A1/SCGB3A1) and ciliated cells. A machine learning model was set up to discriminate C#3 and C#4 in our cohort, and to validate them in an independent cohort. Finally, using spatial transcriptomics we characterized the small airway differences between C#3 and C#4, identifying an upregulation of T/B cell homing chemokines, and bacterial response genes in C#3. CONCLUSIONS: A novel multi-layer network analysis is able to identify clinically relevant COPD patient communities. Patients with similarly low FEV1 and emphysema can have molecularly distinct small airways and immune response patterns, indicating that different endotypes can lead to similar clinical presentation.

Novel genotype-phenotype correlations, differential cerebellar allele-specific methylation, and a common origin of the (ATTTC)n insertion in spinocerebellar ataxia type 37.

Spinocerebellar ataxia subtype 37 (SCA37) is a rare disease originally identified in ataxia patients from the Iberian Peninsula with a pure cerebellar syndrome. SCA37 patients carry a pathogenic intronic (ATTTC)n repeat insertion flanked by two polymorphic (ATTTT)n repeats in the Disabled-1 (DAB1) gene leading to cerebellar dysregulation. Herein, we determine the precise configuration of the pathogenic 5'(ATTTT)n-(ATTTC)n-3'(ATTTT)n SCA37 alleles by CRISPR-Cas9 and long-read nanopore sequencing, reveal their epigenomic signatures in SCA37 lymphocytes, fibroblasts, and cerebellar samples, and establish new molecular and clinical correlations. The 5'(ATTTT)n-(ATTTC)n-3'(ATTTT)n pathogenic allele configurations revealed repeat instability and differential methylation signatures. Disease age of onset negatively correlated with the (ATTTC)n, and positively correlated with the 3'(ATTTT)n. Geographic origin and gender significantly correlated with age of onset. Furthermore, significant predictive regression models were obtained by machine learning for age of onset and disease evolution by considering gender, the (ATTTC)n, the 3'(ATTTT)n, and seven CpG positions differentially methylated in SCA37 cerebellum. A common 964-kb genomic region spanning the (ATTTC)n insertion was identified in all SCA37 patients analysed from Portugal and Spain, evidencing a common origin of the SCA37 mutation in the Iberian Peninsula originating 859 years ago (95% CI 647-1378). In conclusion, we demonstrate an accurate determination of the size and configuration of the regulatory 5'(ATTTT)n-(ATTTC)n-3'(ATTTT)n repeat tract, avoiding PCR bias amplification using CRISPR/Cas9-enrichment and nanopore long-read sequencing, resulting relevant for accurate genetic diagnosis of SCA37. Moreover, we determine novel significant genotype-phenotype correlations in SCA37 and identify differential cerebellar allele-specific methylation signatures that may underlie DAB1 pathogenic dysregulation.

Rare disease research workflow using multilayer networks elucidates the molecular determinants of severity in Congenital Myasthenic Syndromes.

Exploring the molecular basis of disease severity in rare disease scenarios is a challenging task provided the limitations on data availability. Causative genes have been described for Congenital Myasthenic Syndromes (CMS), a group of diverse minority neuromuscular junction (NMJ) disorders; yet a molecular explanation for the phenotypic severity differences remains unclear. Here, we present a workflow to explore the functional relationships between CMS causal genes and altered genes from each patient, based on multilayer network community detection analysis of complementary biomedical information provided by relevant data sources, namely protein-protein interactions, pathways and metabolomics. Our results show that CMS severity can be ascribed to the personalized impairment of extracellular matrix components and postsynaptic modulators of acetylcholine receptor (AChR) clustering. This work showcases how coupling multilayer network analysis with personalized -omics information provides molecular explanations to the varying severity of rare diseases; paving the way for sorting out similar cases in other rare diseases.