Molecular genetics of IBM

As noted previously, IBM shares clinical and pathological similarities with other late-onset rimmed-vacuolar distal myopathies, e.g., distal myopathy caused by mutations in DNAJB6, GNE myopathy (GNE), MSP1 (VCP), MSP2 (HNRNPA2B1), MSP3 (HNRNPA1), and MSP4 (SQSTM1 + TIA1). However, the latter follow Mendelian inheritance (except MSP4) and often have a positive family history and material available for studies. In IBM, due to the late age of onset and slowly progressive symptoms, often the parents are not available to study the vertical transmission of possibly pathogenic genetic variants. A couple of studies also observed rare variants in VCP and SQSTM1 in a few IBM patients (Weihl et al., 2015; Gang et al., 2016). However, these findings have not been replicated, and variant pathogenicity has not been explored further.

A hypothesis of a possible multi-factorial influence on pathomechanisms in IBM (Machado et al., 2014) is postulated, supported by negative DNA sequencing results, further indicating the need to explore a possible non-Mendelian inheritance in IBM.

2.4.1 Interpretation of rare variants in IBM

Since a few studies have observed rare variants in candidate genes such as VCP and SQSTM1 (Weihl et al., 2015; Gang et al., 2016), patients diagnosed with IBM should undergo sequencing of already known genes and candidate genes, especially those known to cause the overlapping rimmed-vacuolar phenotypes. On the one hand, identifying a previously known pathogenic variant in these genes in a probable-IBM patient can help re-evaluate the diagnosis. On the other hand,

observations of rare/unique variants in these genes can help understand their incidence and burden with a good sample size.

A recent study also suggested enrichment of rare variants in FYCO1 and its association with IBM pathology (Güttsches et al., 2017). However, rare variants in FYCO1 are commonly seen in the population (unpublished data) and are seen routinely in sequencing analyses of neuromuscular patients. Careful statistical re-analysis of genetic data and a gene burden test on related genes can help understand the burden of rare variants in IBM patients.

2.4.2 Association studies in IBM

Previous studies of SNP genotyping and HLA typing have shown strong genetic linkage to the 8.1 ancestral haplotype region on chromosome 6 (Garlepp et al., 1994; Koffman et al., 1998; Kok et al., 1999; Lampe et al., 2003; Badrising et al., 2004; Price et al., 2004; O'Hanlon et al., 2005; Mastaglia et al., 2006; Scott et al., 2006; Needham et al., 2008; Mastaglia et al., 2009; Rojana-udomsart et al., 2011; Rojana-udomsart et al., 2012b; Rojana-udomsart et al., 2013; Rothwell et al., 2017;

Rothwell et al., 2019). These studies primarily identified association with HLA-DRB1 alleles, namely, *03:01, *01:01, and *13:01. Additionally, polymorphisms within the MHC region in the genes BTNL2 and NOTCH4 have also been included in susceptibility associations (Scott et al., 2011; Scott et al., 2012). Contrastingly, due to the rarity of the disease, these studies have remained underpowered to find higher resolution associations or other significant non-HLA associations. Due to this reason, traditional genotyping-based association studies at exome-wide (EWAS) or genome-wide (GWAS) level have not yet been performed in IBM.

2.4.3 Gene expression studies in IBM

Previous studies have shown distinct gene expression profiles in IIMs. Using microarray-based methods, Greenberg and colleagues showed the molecular profile of different inflammatory myopathies (Greenberg et al., 2002). They showed heavy overexpression of cytokines and immunoglobulins in IBM. They also noticed increased expression of genes related to actin cytoskeleton that could provide interesting new information regarding muscle pathogenesis in IIMs. However, the small study size may partly limit the interpretation of gene expression differences correctly. Greenberg and colleagues later improved their findings by studying microarray data from 411 muscle samples, including 40 IBM muscles (Greenberg et al., 2019). They showed an IBM-specific signature of highly differentiated CD8+ T-cell effector memory and terminally differentiated effector cells or TEMRA. The authors also found a correlation of KLRG1 expression with T cell cytotoxicity and suggested that KLRG1 could be a promising therapeutic target for IBM patients.

Using an RNA-seq based method on a small cohort, Amici and colleagues suggested that perturbations of calcium regulations could be significant in IBM muscles (Amici et al., 2017). The authors supplemented their findings by comparing protein to transcript ratio in IBM vs. normal muscles. Pinal-Fernandez and colleagues studied differential signature of interferons in IIMs using RNA-seq. They identified that genes associated with type 1 interferon (IFN1) pathway are expressed low in IBM compared to other IIMs. In contrast, genes expressed in the type 2 interferon (IFN2) pathway are high in IBM and DM (Fernandez et al., 2019). Later, Pinal-Fernandez and colleagues differentiated between these patterns using a specialized machine learning approach to suggest a unique gene expression profile in IBM (Pinal-Fernandez et al., 2020).

While the above studies looked for changes in coding genes, a few studies also analyzed the non-coding genes in IIMs.

Hamman and colleagues compared different IIMs and showed a differential expression profile of lncRNAs such as H19, lncMyoD, and MALAT1 (Hamann et al., 2017). However, due to the small cohort size, the interpretation of these results could be challenging. Eisenberg and colleagues studied the miRNA profile of different primary muscle disorders using microarray-based methods (Eisenberg et al., 2007). They showed that miRNAs associated with MAPK signaling, T cell receptor signaling, and actin cytoskeleton are differentially expressed in the IBM transcriptome.