It is always exciting when a new study opens up a whole new way of thinking about a scientific problem. This is especially true if the problem is a health condition that is both poorly understood and incurable. Systemic lupus erythematosus (SLE) is an autoimmune disease in which the immune system attacks healthy tissue – including the skin, joints and many internal organs – by mistake. Moreover, the underlying causes of the disease are not fully understood, so available treatments only tackle its symptoms. Now, in eLife, Patrick Gaffney, Edward Wakeland and colleagues – who include Prithvi Raj and Ekta Rai as joint first authors – report an extensive sequencing study of over 1,000 people with SLE that provides many new insights into the genetics of lupus (Raj et al., 2016).
The researchers – who are based at the University of Texas Southwestern Medical Center, the Oklahoma Medical Research Foundation and other centers in the United States, Belgium and India – focused on 16 regions of the human genome that are known to influence a person’s risk of SLE. Their findings highlight the complexity of the changes in the genome that can predispose someone to develop SLE. Amongst the treasure-trove of data is one gold nugget that stands apart: the discovery that the expression level of the so-called Human Leukocyte Antigen (HLA) class II genes contributes to risk of the disease. This is an entirely new twist on an old story.
The HLA genes are found in a long stretch of DNA on chromosome 6 that contains more than 140 protein-coding genes, many of which are important for immune responses (MHC Sequencing Consortium, 1999; de Bakker et al., 2006). The class I genes encode proteins that are found on the outer membrane of all cells, whereas the class II genes encode proteins found only on specialized immune cells called antigen-presenting cells. In both cases, these proteins bind to small fragments of other proteins and then display them to the immune system. Class I proteins display fragments from within our own cells and protect us from cancer, viral infections and certain bacteria that can live within our cells. Class II proteins, on the other hand, display fragments from foreign invaders and are important for instructing the immune system to mount neutralizing responses to parasites and bacteria that live outside of cells.
The HLA class I and II genes are amongst the most variable DNA sequences in the human genome. There are more than 8,000 different variants, or alleles, of class I genes and more than 2,500 class II alleles (Robinson et al., 2015). These alleles encode slightly different proteins, each with the potential to bind to, and display, different protein fragments.
Hundreds of studies have previously linked class I and II variants with the risk of specific diseases and, without exception, these studies have emphasized the sequence diversity of class I and II genes as the important factor (Welter et al., 2014. For example, individuals who carry the class I allele known as B27 are strongly predisposed to developing a type of arthritis called ankylosing spondylitis, whereas people without the B27 allele appear to be protected against the disease (Caffrey and James, 1973).
The new findings show that the HLA class II alleles conferring risk for SLE encode proteins that are found in greater numbers on the surface of antigen-presenting cells than those encoded by alleles that do not increase the risk (Raj et al., 2016). These data suggest, for the first time, that the abundance of class II proteins on antigen-presenting cells could strongly influence risk of certain diseases.
It remains to be determined how differences in the abundance of class II molecules translate to increased disease risk. Higher levels of class II molecules on antigen-presenting cells could increase the chances that circulating immune cells bind to these molecules, and lead to more efficient immune and autoimmune responses. Another possibility is that the extra class II proteins on antigen-presenting cells could adversely affect a developmental process that would normally eliminate those immune cells that have the potential to cause autoimmune disorders.
Further studies now need to extend the analysis of Raj, Rai et al. to the other major class II alleles that are associated with disease in humans. It will also be important to see if this paradigm extends to the class I alleles. These studies will improve our understanding of the earliest events in autoimmunity and hopefully lead to new treatment options for autoimmune disorders.
© 2016, Wuster et al.
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Genetic effects on complex traits may depend on context, such as age, sex, environmental exposures, or social settings. However, it remains often unclear if the extent of context dependency, or gene-by-environment interaction (GxE), merits more involved models than the additive model typically used to analyze data from genome-wide association studies (GWAS). Here, we suggest considering the utility of GxE models in GWAS as a trade-off between bias and variance parameters. In particular, we derive a decision rule for choosing between competing models for the estimation of allelic effects. The rule weighs the increased estimation noise when context is considered against the potential bias when context dependency is ignored. In the empirical example of GxSex in human physiology, the increased noise of context-specific estimation often outweighs the bias reduction, rendering GxE models less useful when variants are considered independently. However, for complex traits, we argue that the joint consideration of context dependency across many variants mitigates both noise and bias. As a result, polygenic GxE models can improve both estimation and trait prediction. Finally, we exemplify (using GxDiet effects on longevity in fruit flies) how analyses based on independently ascertained ‘top hits’ alone can be misleading, and that considering polygenic patterns of GxE can improve interpretation.
Deep Mutational Scanning (DMS) is an emerging method to systematically test the functional consequences of thousands of sequence changes to a protein target in a single experiment. Because of its utility in interpreting both human variant effects and protein structure-function relationships, it holds substantial promise to improve drug discovery and clinical development. However, applications in this domain require improved experimental and analytical methods. To address this need, we report novel DMS methods to precisely and quantitatively interrogate disease-relevant mechanisms, protein-ligand interactions, and assess predicted response to drug treatment. Using these methods, we performed a DMS of the melanocortin-4 receptor (MC4R), a G-protein-coupled receptor (GPCR) implicated in obesity and an active target of drug development efforts. We assessed the effects of >6600 single amino acid substitutions on MC4R’s function across 18 distinct experimental conditions, resulting in >20 million unique measurements. From this, we identified variants that have unique effects on MC4R-mediated Gαs- and Gαq-signaling pathways, which could be used to design drugs that selectively bias MC4R’s activity. We also identified pathogenic variants that are likely amenable to a corrector therapy. Finally, we functionally characterized structural relationships that distinguish the binding of peptide versus small molecule ligands, which could guide compound optimization. Collectively, these results demonstrate that DMS is a powerful method to empower drug discovery and development.