Investigating genetic drivers of juvenile dermatomyositis pathogenesis using bioinformatics methods

IgM nephropathy in a patient with dermatomyositis following COVID-19 vaccination: A case report

BACKGROUND

Dermatomyositis (DM) is a systemic autoimmune disease characterized by distinct skin lesions and a clinically heterogeneous constellation of systemic manifestations. This disease poses a challenge to clinicians because of its rarity, diverse clinical presentations, and variable organ involvement, resulting from an autoimmune attack on affected organs, which could be triggered by environmental factors in genetically susceptible individuals. Renal involvement is rare, with immunoglobulin M (IgM) nephropathy yet to be reported in patients with DM.

CASE PRESENTATION

A 38-year-old man was admitted to Shariati Hospital, affiliated with Tehran University of Medical Sciences, with proximal weakness of the upper and lower extremities that had developed in the preceding month after receiving the Sinopharm COVID-19 vaccine. The patient was diagnosed with DM based on the heliotrope rash, Gottron's papules, progressive proximal muscle weakness, and paraclinical findings. IgM nephropathy developed subsequently, diagnosed by light and immunofluorescence microscopy.

CONCLUSION

We describe the first case of IgM nephropathy in a DM patient following COVID-19 vaccination. This phenomenon requires further investigation into the possible crosslinks between the pathogenesis of IgM nephropathy with DM and the COVID-19 vaccine. Diagnosing renal complications in DM patients promptly and accurately can help to achieve the best outcomes.

Kernel-based hierarchical structural component models for pathway analysis

MOTIVATION

Pathway analyses have led to more insight into the underlying biological functions related to the phenotype of interest in various types of omics data. Pathway-based statistical approaches have been actively developed, but most of them do not consider correlations among pathways. Because it is well known that there are quite a few biomarkers that overlap between pathways, these approaches may provide misleading results. In addition, most pathway-based approaches tend to assume that biomarkers within a pathway have linear associations with the phenotype of interest, even though the relationships are more complex.

RESULTS

To model complex effects including nonlinear effects, we propose a new approach, Hierarchical structural CoMponent analysis using Kernel (HisCoM-Kernel). The proposed method models nonlinear associations between biomarkers and phenotype by extending the kernel machine regression and analyzes entire pathways simultaneously by using the biomarker-pathway hierarchical structure. HisCoM-Kernel is a flexible model that can be applied to various omics data. It was successfully applied to three omics datasets generated by different technologies. Our simulation studies showed that HisCoM-Kernel provided higher statistical power than other existing pathway-based methods in all datasets. The application of HisCoM-Kernel to three types of omics dataset showed its superior performance compared to existing methods in identifying more biologically meaningful pathways, including those reported in previous studies.

AVAILABILITY AND IMPLEMENTATION

Freely available at http://statgen.snu.ac.kr/software/HisCom-Kernel/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.