The full spectrum of SLC22 OCT1 mutations illuminates the bridge between drug transporter biophysics and pharmacogenomics.

Mutations in transporters can impact an individual's response to drugs and cause many diseases. Few variants in transporters have been evaluated for their functional impact. Here, we combine saturation mutagenesis and multi-phenotypic screening to dissect the impact of 11,213 missense single-amino-acid deletions, and synonymous variants across the 554 residues of OCT1, a key liver xenobiotic transporter. By quantifying in parallel expression and substrate uptake, we find that most variants exert their primary effect on protein abundance, a phenotype not commonly measured alongside function. Using our mutagenesis results combined with structure prediction and molecular dynamic simulations, we develop accurate structure-function models of the entire transport cycle, providing biophysical characterization of all known and possible human OCT1 polymorphisms. This work provides a complete functional map of OCT1 variants along with a framework for integrating functional genomics, biophysical modeling, and human genetics to predict variant effects on disease and drug efficacy.

Deep mutational scanning of EccD3 reveals the molecular basis of its essentiality in the mycobacterium ESX secretion system.

Tuberculosis remains the deadliest infectious disease in the world and requires novel therapeutic targets. The ESX-3 secretion system, which is essential for iron and zinc homeostasis and thus M. tuberculosis survival, is a promising target. In this study, we perform a deep mutational scan on the ESX-3 core protein EccD3 in the model organism M. smegmatis. We systematically investigated the functional roles of 145 residues across the soluble ubiquitin-like domain, the conformationally distinct flexible linker, and selected transmembrane helices of EccD3. Our data combined with structural comparisons to ESX-5 complexes support a model where EccD3 stabilizes the complex, with the hinge motif within the linker being particularly sensitive to disruption. Our study is the first deep mutational scan in mycobacteria, which could help guide drug development toward novel treatment of tuberculosis. This study underscores the importance of context-specific mutational analyses for discovering essential protein interactions within mycobacterial systems.

Molecular basis of proton-sensing by G protein-coupled receptors.

Three proton-sensing G protein-coupled receptors (GPCRs), GPR4, GPR65, and GPR68, respond to changes in extracellular pH to regulate diverse physiology and are implicated in a wide range of diseases. A central challenge in determining how protons activate these receptors is identifying the set of residues that bind protons. Here, we determine structures of each receptor to understand the spatial arrangement of putative proton sensing residues in the active state. With a newly developed deep mutational scanning approach, we determined the functional importance of every residue in proton activation for GPR68 by generating ~9,500 mutants and measuring effects on signaling and surface expression. This unbiased screen revealed that, unlike other proton-sensitive cell surface channels and receptors, no single site is critical for proton recognition in GPR68. Instead, a network of titratable residues extend from the extracellular surface to the transmembrane region and converge on canonical class A GPCR activation motifs to activate proton-sensing GPCRs. More broadly, our approach integrating structure and unbiased functional interrogation defines a new framework for understanding the rich complexity of GPCR signaling.

DIMPLE: deep insertion, deletion, and missense mutation libraries for exploring protein variation in evolution, disease, and biology.

Insertions and deletions (indels) enable evolution and cause disease. Due to technical challenges, indels are left out of most mutational scans, limiting our understanding of them in disease, biology, and evolution. We develop a low cost and bias method, DIMPLE, for systematically generating deletions, insertions, and missense mutations in genes, which we test on a range of targets, including Kir2.1. We use DIMPLE to study how indels impact potassium channel structure, disease, and evolution. We find deletions are most disruptive overall, beta sheets are most sensitive to indels, and flexible loops are sensitive to deletions yet tolerate insertions.

The full spectrum of OCT1 (SLC22A1) mutations bridges transporter biophysics to drug pharmacogenomics.

Membrane transporters play a fundamental role in the tissue distribution of endogenous compounds and xenobiotics and are major determinants of efficacy and side effects profiles. Polymorphisms within these drug transporters result in inter-individual variation in drug response, with some patients not responding to the recommended dosage of drug whereas others experience catastrophic side effects. For example, variants within the major hepatic Human organic cation transporter OCT1 (SLC22A1) can change endogenous organic cations and many prescription drug levels. To understand how variants mechanistically impact drug uptake, we systematically study how all known and possible single missense and single amino acid deletion variants impact expression and substrate uptake of OCT1. We find that human variants primarily disrupt function via folding rather than substrate uptake. Our study revealed that the major determinants of folding reside in the first 300 amino acids, including the first 6 transmembrane domains and the extracellular domain (ECD) with a stabilizing and highly conserved stabilizing helical motif making key interactions between the ECD and transmembrane domains. Using the functional data combined with computational approaches, we determine and validate a structure-function model of OCT1s conformational ensemble without experimental structures. Using this model and molecular dynamic simulations of key mutants, we determine biophysical mechanisms for how specific human variants alter transport phenotypes. We identify differences in frequencies of reduced function alleles across populations with East Asians vs European populations having the lowest and highest frequency of reduced function variants, respectively. Mining human population databases reveals that reduced function alleles of OCT1 identified in this study associate significantly with high LDL cholesterol levels. Our general approach broadly applied could transform the landscape of precision medicine by producing a mechanistic basis for understanding the effects of human mutations on disease and drug response.

Effects of underfeeding and oral vancomycin on gut microbiome and nutrient absorption in humans.

Direct evidence in humans for the impact of the microbiome on nutrient absorption is lacking. We conducted an extended inpatient study using two interventions that we hypothesized would alter the gut microbiome and nutrient absorption. In each, stool calorie loss, a direct proxy of nutrient absorption, was measured. The first phase was a randomized cross-over dietary intervention in which all participants underwent in random order 3 d of over- and underfeeding. The second was a randomized, double-blind, placebo-controlled pharmacologic intervention using oral vancomycin or matching placebo (NCT02037295). Twenty-seven volunteers (17 men and 10 women, age 35.1 ± 7.3, BMI 32.3 ± 8.0), who were healthy other than having impaired glucose tolerance and obesity, were enrolled and 25 completed the entire trial. The primary endpoints were the effects of dietary and pharmacological intervention on stool calorie loss. We hypothesized that stool calories expressed as percentage of caloric intake would increase with underfeeding compared with overfeeding and increase during oral vancomycin treatment. Both primary endpoints were met. Greater stool calorie loss was observed during underfeeding relative to overfeeding and during vancomycin treatment compared with placebo. Key secondary endpoints were to evaluate the changes in gut microbial community structure as evidenced by amplicon sequencing and metagenomics. We observed only a modest perturbation of gut microbial community structure with under- versus overfeeding but a more widespread change in community structure with reduced diversity with oral vancomycin. Increase in Akkermansia muciniphila was common to both interventions that resulted in greater stool calorie loss. These results indicate that nutrient absorption is sensitive to environmental perturbations and support the translational relevance of preclinical models demonstrating a possible causal role for the gut microbiome in dietary energy harvest.

The structure of the endogenous ESX-3 secretion system.

The ESX (or Type VII) secretion systems are protein export systems in mycobacteria and many Gram-positive bacteria that mediate a broad range of functions including virulence, conjugation, and metabolic regulation. These systems translocate folded dimers of WXG100-superfamily protein substrates across the cytoplasmic membrane. We report the cryo-electron microscopy structure of an ESX-3 system, purified using an epitope tag inserted with recombineering into the chromosome of the model organism Mycobacterium smegmatis. The structure reveals a stacked architecture that extends above and below the inner membrane of the bacterium. The ESX-3 protomer complex is assembled from a single copy of the EccB3, EccC3, and EccE3 and two copies of the EccD3 protein. In the structure, the protomers form a stable dimer that is consistent with assembly into a larger oligomer. The ESX-3 structure provides a framework for further study of these important bacterial transporters.

A Machine Learning Approach for Using the Postmortem Skin Microbiome to Estimate the Postmortem Interval.

Research on the human microbiome, the microbiota that live in, on, and around the human person, has revolutionized our understanding of the complex interactions between microbial life and human health and disease. The microbiome may also provide a valuable tool in forensic death investigations by helping to reveal the postmortem interval (PMI) of a decedent that is discovered after an unknown amount of time since death. Current methods of estimating PMI for cadavers discovered in uncontrolled, unstudied environments have substantial limitations, some of which may be overcome through the use of microbial indicators. In this project, we sampled the microbiomes of decomposing human cadavers, focusing on the skin microbiota found in the nasal and ear canals. We then developed several models of statistical regression to establish an algorithm for predicting the PMI of microbial samples. We found that the complete data set, rather than a curated list of indicator species, was preferred for training the regressor. We further found that genus and family, rather than species, are the most informative taxonomic levels. Finally, we developed a k-nearest- neighbor regressor, tuned with the entire data set from all nasal and ear samples, that predicts the PMI of unknown samples with an average error of ±55 accumulated degree days (ADD). This study outlines a machine learning approach for the use of necrobiome data in the prediction of the PMI and thereby provides a successful proof-of- concept that skin microbiota is a promising tool in forensic death investigations.