Spatial covariance analysis reveals the residue-by-residue thermodynamic contribution of variation to the CFTR fold

Proteostasis Landscapes of Selective versus Poorly Responsive CFTR Variants Reveals Structural Vulnerabilities to Correction

Cystic Fibrosis (CF) is a lethal genetic disorder caused by variants in CF transmembrane conductance regulator (CFTR). Many disease variants are treatable with corrector compounds, which enhance the folding and trafficking of CFTR. However, correctors fail to elicit a response for every CFTR variant. Approximately 3% of persons with CF harbor poorly responsive CFTR variants. Here, we reveal that a group of poorly responsive variants overlap with selectively responsive variants in a critical domain interface (nucleotide-binding domain 1/intracellular loop 4 - NBD1/ICL4). Affinity purification mass spectrometry proteomics was used to profile the protein homeostasis (proteostasis) changes of CFTR variants during corrector treatment to assess modulated interactions with protein folding and maturation pathways. Responsive variant interactions converged on similar proteostasis pathways during correction. In contrast, poorly responsive variants subtly diverged, revealing a partial restoration of protein quality control surveillance and a capacity to correct some mutations. Computational structural modeling showed that corrector VX-445 failed to confer enough NBD1 stability to poorly responsive variants. NBD1 secondary stabilizing mutations rescued poorly responsive variants, revealing structural vulnerabilities in NBD1 required for treating poor responders. Our study provides a framework for discerning the underlying protein quality control and structural defects of CFTR variants not reached with existing drugs. These insights can help expand therapeutics to all susceptible CFTR variants to enhance personalized medicine efforts.

Tracing genetic diversity captures the molecular basis of misfolding disease

Genetic variation in human populations can result in the misfolding and aggregation of proteins, giving rise to systemic and neurodegenerative diseases that require management by proteostasis. Here, we define the role of GRP94, the endoplasmic reticulum Hsp90 chaperone paralog, in managing alpha-1-antitrypsin deficiency on a residue-by-residue basis using Gaussian process regression-based machine learning to profile the spatial covariance relationships that dictate protein folding arising from sequence variants in the population. Covariance analysis suggests a role for the ATPase activity of GRP94 in controlling the N- to C-terminal cooperative folding of alpha-1-antitrypsin responsible for the correction of liver aggregation and lung-disease phenotypes of alpha-1-antitrypsin deficiency. Gaussian process-based spatial covariance profiling provides a standard model built on covariant principles to evaluate the role of proteostasis components in guiding information flow from genome to proteome in response to genetic variation, potentially allowing us to intervene in the onset and progression of complex multi-system human diseases.

Benchmarking AlphaMissense pathogenicity predictions against cystic fibrosis variants

Variants in the cystic fibrosis transmembrane conductance regulator gene (CFTR) result in cystic fibrosis-a lethal autosomal recessive disorder. Missense variants that alter a single amino acid in the CFTR protein are among the most common cystic fibrosis variants, yet tools for accurately predicting molecular consequences of missense variants have been limited to date. AlphaMissense (AM) is a new technology that predicts the pathogenicity of missense variants based on dual learned protein structure and evolutionary features. Here, we evaluated the ability of AM to predict the pathogenicity of CFTR missense variants. AM predicted a high pathogenicity for CFTR residues overall, resulting in a high false positive rate and fair classification performance on CF variants from the CFTR2.org database. AM pathogenicity score correlated modestly with pathogenicity metrics from persons with CF including sweat chloride level, pancreatic insufficiency rate, and Pseudomonas aeruginosa infection rate. Correlation was also modest with CFTR trafficking and folding competency in vitro. By contrast, the AM score correlated well with CFTR channel function in vitro-demonstrating the dual structure and evolutionary training approach learns important functional information despite lacking such data during training. Different performance across metrics indicated AM may determine if polymorphisms in CFTR are recessive CF variants yet cannot differentiate mechanistic effects or the nature of pathophysiology. Finally, AM predictions offered limited utility to inform on the pharmacological response of CF variants i.e., theratype. Development of new approaches to differentiate the biochemical and pharmacological properties of CFTR variants is therefore still needed to refine the targeting of emerging precision CF therapeutics.

CFTR Folding: From Structure and Proteostasis to Cystic Fibrosis Personalized Medicine

Cystic fibrosis (CF) is a lethal genetic disease caused by mutations in the chloride ion channel cystic fibrosis transmembrane conductance regulator (CFTR). Class-II mutants of CFTR lack intermolecular interactions important for CFTR structural stability and lead to misfolding. Misfolded CFTR is detected by a diverse suite of proteostasis factors that preferentially bind and route mutant CFTR toward premature degradation, resulting in reduced plasma membrane CFTR levels and impaired chloride ion conductance associated with CF. CF treatment has been vastly improved over the past decade by the availability of small molecules called correctors. Correctors directly bind CFTR, stabilize its structure by conferring thermodynamically favorable interactions that compensate for mutations, and thereby lead to downstream folding fidelity. However, each of over 100 Class-II CF causing mutations causes unique structural defects and shows a unique response to drug treatment, described as theratype. Understanding CFTR structural defects, the proteostasis factors evaluating those defects, and the stabilizing effects of CFTR correctors will illuminate a path toward personalized medicine for CF. Here, we review recent advances in our understanding of CFTR folding, focusing on structure, corrector binding sites, the mechanisms of proteostasis factors that evaluate CFTR, and the implications for CF personalized medicine.

Understanding the host-pathogen evolutionary balance through Gaussian process modeling of SARS-CoV-2

We have developed a machine learning (ML) approach using Gaussian process (GP)-based spatial covariance (SCV) to track the impact of spatial-temporal mutational events driving host-pathogen balance in biology. We show how SCV can be applied to understanding the response of evolving covariant relationships linking the variant pattern of virus spread to pathology for the entire SARS-CoV-2 genome on a daily basis. We show that GP-based SCV relationships in conjunction with genome-wide co-occurrence analysis provides an early warning anomaly detection (EWAD) system for the emergence of variants of concern (VOCs). EWAD can anticipate changes in the pattern of performance of spread and pathology weeks in advance, identifying signatures destined to become VOCs. GP-based analyses of variation across entire viral genomes can be used to monitor micro and macro features responsible for host-pathogen balance. The versatility of GP-based SCV defines starting point for understanding nature's evolutionary path to complexity through natural selection.

Recent advances in proteomic-based diagnostics of cystic fibrosis

INTRODUCTION

Cystic fibrosis (CF) is a genetic disease characterized by thick and sticky mucus accumulation, which may harm numerous internal organs. Various variables such as gene modifiers, environmental factors, age of diagnosis, and CF transmembrane conductance regulator (CFTR) gene mutations influence phenotypic disease diversity. Biomarkers that are based on genomic information may not accurately represent the underlying mechanism of the disease as well as its lethal complications. Therefore, recent advancements in mass spectrometry (MS)-based proteomics may provide deep insights into CF mechanisms and cellular functions by examining alterations in the protein expression patterns from various samples of individuals with CF.

AREAS COVERED

We present current developments in MS-based proteomics, its application, and findings in CF. In addition, the future roles of proteomics in finding diagnostic and prognostic novel biomarkers.

EXPERT OPINION

Despite significant advances in MS-based proteomics, extensive research in a large cohort for identifying and validating diagnostic, prognostic, predictive, and therapeutic biomarkers for CF disease is highly needed.

Structure basis of CFTR folding, function and pharmacology

The root cause of cystic fibrosis (CF), the most common life-shortening genetic disease in the Caucasian population, is the loss of function of the CFTR protein, which serves as a phosphorylation-activated, ATP-gated anion channel in numerous epithelia-lining tissues. In the past decade, high-throughput drug screening has made a significant stride in developing highly effective CFTR modulators for the treatment of CF. Meanwhile, structural-biology studies have succeeded in solving the high-resolution three-dimensional (3D) structure of CFTR in different conformations. Here, we provide a brief overview of some striking features of CFTR folding, function and pharmacology, in light of its specific structural features within the ABC-transporter superfamily. A particular focus is given to CFTR's first nucleotide-binding domain (NBD1), because folding of NBD1 constitutes a bottleneck in the CFTR protein biogenesis pathway, and ATP binding to this domain plays a unique role in the functional stability of CFTR. Unraveling the molecular basis of CFTR folding, function, and pharmacology would inspire the development of next-generation mutation-specific CFTR modulators.

Rescue of Rare CFTR Trafficking Mutants Highlights a Structural Location-Dependent Pattern for Correction

Cystic Fibrosis (CF) is a genetic disease caused by mutations in the gene encoding the Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) channel. Currently, more than 2100 variants have been identified in the gene, with a large number being very rare. The approval of modulators that act on mutant CFTR protein, correcting its molecular defect and thus alleviating the burden of the disease, revolutionized the field of CF. However, these drugs do not apply to all patients with CF, especially those with rare mutations-for which there is a lack of knowledge on the molecular mechanisms of the disease and the response to modulators. In this work, we evaluated the impact of several rare putative class II mutations on the expression, processing, and response of CFTR to modulators. Novel cell models consisting of bronchial epithelial cell lines expressing CFTR with 14 rare variants were created. The variants studied are localized at Transmembrane Domain 1 (TMD1) or very close to the signature motif of Nucleotide Binding Domain 1 (NBD1). Our data show that all mutations analyzed significantly decrease CFTR processing and while TMD1 mutations respond to modulators, those localized in NBD1 do not. Molecular modeling calculations confirm that the mutations in NBD1 induce greater destabilization of CFTR structure than those in TMD1. Furthermore, the structural proximity of TMD1 mutants to the reported binding site of CFTR modulators such as VX-809 and VX-661, make them more efficient in stabilizing the CFTR mutants analyzed. Overall, our data suggest a pattern for mutation location and impact in response to modulators that correlates with the global effect of the mutations on CFTR structure.

Capturing the conversion of the pathogenic alpha-1-antitrypsin fold by ATF6 enhanced proteostasis

Genetic variation in alpha-1 antitrypsin (AAT) causes AAT deficiency (AATD) through liver aggregation-associated gain-of-toxic pathology and/or insufficient AAT activity in the lung manifesting as chronic obstructive pulmonary disease (COPD). Here, we utilize 71 AATD-associated variants as input through Gaussian process (GP)-based machine learning to study the correction of AAT folding and function at a residue-by-residue level by pharmacological activation of the ATF6 arm of the unfolded protein response (UPR). We show that ATF6 activators increase AAT neutrophil elastase (NE) inhibitory activity, while reducing polymer accumulation for the majority of AATD variants, including the prominent Z variant. GP-based profiling of the residue-by-residue response to ATF6 activators captures an unexpected role of the "gate" area in managing AAT-specific activity. Our work establishes a new spatial covariant (SCV) understanding of the convertible state of the protein fold in response to genetic perturbation and active environmental management by proteostasis enhancement for precision medicine.

Triangulating variation in the population to define mechanisms for precision management of genetic disease

To understand mechanistically how the protein fold is shaped by therapeutics to inform precision management of disease, we developed variation-capture (VarC) mapping. VarC triangulates sparse sequence variation information found in the population using Gaussian process regression (GPR)-based machine learning to define the combined pairwise-residue interactions contributing to dynamic protein function in the individual in response to therapeutics. Using VarC mapping, we now reveal the pairwise-residue covariant relationships across the entire protein fold of cystic fibrosis (CF) transmembrane conductance regulator (CFTR) to define the molecular mechanisms of clinically approved CF chemical modulators. We discover an energetically destabilized covariant core containing a di-acidic YKDAD endoplasmic reticulum (ER) exit code that is only weakly corrected by current therapeutics. Our results illustrate that VarC provides a generalizable tool to triangulate information from genetic variation in the population to mechanistically discover therapeutic strategies that guide precision management of the individual.