Functional human tissues engineered from patient-specific induced pluripotent stem cells (hiPSCs) hold great promise for investigating the progression, mechanisms, and treatment of musculoskeletal diseases in a controlled and systematic manner. For example, bioengineered models of innervated human skeletal muscle could be used to identify novel therapeutic targets and treatments for patients with complex central and peripheral nervous system disorders. There is a need to develop standardized and objective quantitative methods for engineering and using these complex tissues, in order increase their robustness, reproducibility, and predictiveness across users. Here we describe a standardized method for engineering an isogenic, patient specific human neuromuscular junction (NMJ) that allows for automated quantification of NMJ function to diagnose disease using a small sample of blood serum and evaluate new therapeutic modalities. By combining tissue engineering, optogenetics, microfabrication, optoelectronics and video processing, we created a novel platform for the precise investigation of the development and degeneration of human NMJ. We demonstrate the utility of this platform for the detection and diagnosis of myasthenia gravis, an antibody-mediated autoimmune disease that disrupts the NMJ function.

Testing for variation in BRCA1 and BRCA2 (commonly referred to as BRCA1/2), has emerged as a standard clinical practice and is helping countless women better understand and manage their heritable risk of breast and ovarian cancer. Yet the increased rate of BRCA1/2 testing has led to an increasing number of Variants of Uncertain Significance (VUS), and the rate of VUS discovery currently outpaces the rate of clinical variant interpretation. Computational prediction is a key component of the variant interpretation pipeline. In the CAGI5 ENIGMA Challenge, six prediction teams submitted predictions on 326 newly-interpreted variants from the ENIGMA Consortium. By evaluating these predictions against the new interpretations, we have gained a number of insights on the state of the art of variant prediction and specific steps to further advance this state of the art.

Background

The Critical Assessment of Genome Interpretation (CAGI) aims to advance the state-of-the-art for computational prediction of genetic variant impact, particularly where relevant to disease. The five complete editions of the CAGI community experiment comprised 50 challenges, in which participants made blind predictions of phenotypes from genetic data, and these were evaluated by independent assessors.

Results

Performance was particularly strong for clinical pathogenic variants, including some difficult-to-diagnose cases, and extends to interpretation of cancer-related variants. Missense variant interpretation methods were able to estimate biochemical effects with increasing accuracy. Assessment of methods for regulatory variants and complex trait disease risk was less definitive and indicates performance potentially suitable for auxiliary use in the clinic.

Conclusions

Results show that while current methods are imperfect, they have major utility for research and clinical applications. Emerging methods and increasingly large, robust datasets for training and assessment promise further progress ahead.