Facile interrogation of high-order epistasis between distal sites using next-generation sequencing

Deep mutational scanning (DMS) combines next-generation sequencing and protein engineering to construct sequence-function landscapes and rapidly identify fitness optima. Practical use of these landscapes requires identification of all mutations in each protein variant due to the potential effects of epistasis, the interdependence between residues resulting in non-additive phenotypes. This phenomenon plays an important role in protein evolution and is often a necessary step along the path towards protein fitness optima. However, current methods to assign distal mutations to their corresponding gene are work-intensive, costly, and introduce potential sources of error. To overcome these limitations, we introduce a method compatible with DMS that matches distal mutations to their corresponding gene without additional experimental steps. Using this approach to screen ~2,000,000 unique protein variants, we engineer a human G protein-coupled receptor with a 15-fold improvement in ligand binding affinity and observe prevalent epistasis between distal residues within the ligand binding pocket. Compared to variants containing only proximal substitutions, those harboring missense mutations in distal sites demonstrate significantly greater functional activity in our screen. This method can be applied immediately to all experiments using Illumina next-generation sequencing and provides a facile approach to illuminate complex mechanisms underlying key protein functions. Please click Additional Files below to see the full abstract

Strategies to engineer G protein-coupled receptor ligand binding properties

G protein-coupled receptors (GPCRs) comprise a family of integral membrane proteins that mediate eukaryotic cells\u27 responses to a wide array of extracellular signals. As a result of their ligand specificity, sensitivity, and capacity for signal transduction, GPCRs have great potential as biosensors for a wide range of molecules (e.g. toxins, value-added products, biomarkers for disease). Despite their inherent advantages, many GPCRs are difficult to express functionally and in high numbers within heterologous hosts such as yeast. Thus, there is a need to engineer functionally expressed GPCRs to bind ligands of interest with high affinities and specificities. Towards this end, we have optimized a high-throughput screening methodology to engineer variants of the human adenosine A2A receptor (hA2AR) with improved binding affinity towards a target ligand. Specifically, a fluorescent ligand binding assay was used in concert with fluorescence-activated cell sorting (FACS) to isolate yeast cells expressing desirable hA2AR mutants. After four rounds of sorting, we observed convergence of mutated residues towards a consensus sequence and a 3.5-fold increase in cellular mean fluorescence intensity upon incubation with fluorescent ligand. Additionally, we demonstrate the importance of vector choice and concomitant mitotic stability in influencing hA2AR yield and cellular homogeneity in yeast. The use of a mitotically stable integrating vector results in increased GPCR yield compared to non-integrating (i.e. centromeric and episomal) vectors. Yields of hA2AR are improved further by increasing vector integration frequency, where gene copy number is shown to have a greater effect on protein yield at lower relative copy numbers. Further, the growth of cells in raffinose-containing media prior to gene induction is shown to improve cellular homogeneity of yeast expressing hA2AR under the control of an inducible galactose promoter. In all, these results are envisioned to benefit both GPCR expression and engineering in yeast. The use of this platform to further evolve and isolate improved hA2AR variants is expected to generate mutants with even greater binding affinity and specificity towards ligands of interest. Please click Additional Files below to see the full abstract

Functional production of transporters from biomass-degrading anaerobic fungi for metabolic engineering

Membrane-embedded transporters and receptors are increasingly becoming targets for the metabolic engineering community that aims to enhance the performance and stability of microbial production strains. Anaerobic gut fungi inhabit the digestive tract of herbivores such as cows and sheep, and excel at degrading raw plant biomass into fermentable sugars. Recently, a transcriptomic analysis of three strains of gut fungi suggested that they display a plethora of carbohydrate binding proteins on their surface, including G-protein coupled receptors with a novel architecture; and possess a multitude of small-solute transporters that are of chief biotechnological interest: transporters for sugars, amino acids, lipids, drugs, and metals. Here, we introduced genes encoding gut fungal fluoride transporters into Saccharomyces cerevisiae, and show that with codon optimization, the yeast produce large quantities of functional and correctly membrane-localized transporters capable of bolstering solvent tolerance. We are currently expanding our approach to putative drug- and sugar-transporters and receptors sourced from the anaerobic fungi. These results in part explain the physiology of these understudied fungi, and highlight the critical role that their membrane proteins play towards their existence in competitive, extreme environments. Notably, the work expands on the toolbox of receptor and transporter proteins that can be used to enhance the performance and stability of model microbial cell factory strains. Please click Additional Files below to see the full abstract

Studies of Verapamil Binding to Human Serum Albumin By High-Performance Affinity Chromatography

The binding of verapamil to the protein human serum albumin (HSA) was examined by using high performance affinity chromatography. Many previous reports have investigated the binding of verapamil with HSA, but the exact strength and nature of this interaction (e.g., the number and location of binding sites) is still unclear. In this study, frontal analysis indicated that at least one major binding site was present for R- and S-verapamil on HSA, with estimated association equilibrium constants on the order of 104 M−1 and a 1.4-fold difference in these values for the verapamil enantiomers at pH 7.4 and 37°C. The presence of a second, weaker group of binding sites on HSA was also suggested by these results. Competitive binding studies using zonal elution were carried out between verapamil and various probe compounds that have known interactions with several major and minor sites on HSA. R/S-Verapamil was found to have direct competition with S-warfarin, indicating that verapamil was binding to Sudlow site I (i.e., the warfarin-azapropazone site of HSA). The average association equilibrium constant for R- and S-verapamil at this site was 1.4 (±0.1) × 104 M−1. Verapamil did not have any notable binding to Sudlow site II of HSA but did appear to have some weak allosteric interactions with L-tryptophan, a probe for this site. An allosteric interaction between verapamil and tamoxifen (a probe for the tamoxifen site) was also noted, which was consistent with the binding of verapamil at Sudlow site I. No interaction was seen between verapamil and digitoxin, a probe for the digitoxin site of HSA. These results gave good agreement with previous observations made in the literature and help provide a more detailed description of how verapamil is transported in blood and of how it may interact with other drugs in the body

Studies of Verapamil Binding to Human Serum Albumin By High-Performance Affinity Chromatography

The binding of verapamil to the protein human serum albumin (HSA) was examined by using high performance affinity chromatography. Many previous reports have investigated the binding of verapamil with HSA, but the exact strength and nature of this interaction (e.g., the number and location of binding sites) is still unclear. In this study, frontal analysis indicated that at least one major binding site was present for R- and S-verapamil on HSA, with estimated association equilibrium constants on the order of 104 M−1 and a 1.4-fold difference in these values for the verapamil enantiomers at pH 7.4 and 37°C. The presence of a second, weaker group of binding sites on HSA was also suggested by these results. Competitive binding studies using zonal elution were carried out between verapamil and various probe compounds that have known interactions with several major and minor sites on HSA. R/S-Verapamil was found to have direct competition with S-warfarin, indicating that verapamil was binding to Sudlow site I (i.e., the warfarin-azapropazone site of HSA). The average association equilibrium constant for R- and S-verapamil at this site was 1.4 (±0.1) × 104 M−1. Verapamil did not have any notable binding to Sudlow site II of HSA but did appear to have some weak allosteric interactions with L-tryptophan, a probe for this site. An allosteric interaction between verapamil and tamoxifen (a probe for the tamoxifen site) was also noted, which was consistent with the binding of verapamil at Sudlow site I. No interaction was seen between verapamil and digitoxin, a probe for the digitoxin site of HSA. These results gave good agreement with previous observations made in the literature and help provide a more detailed description of how verapamil is transported in blood and of how it may interact with other drugs in the body

A Pilot Study Comparing HPV-Positive and HPV-Negative Head and Neck Squamous Cell Carcinomas by Whole Exome Sequencing.

Background. Next-generation sequencing of cancers has identified important therapeutic targets and biomarkers. The goal of this pilot study was to compare the genetic changes in a human papillomavirus- (HPV-)positive and an HPV-negative head and neck tumor. Methods. DNA was extracted from the blood and primary tumor of a patient with an HPV-positive tonsillar cancer and those of a patient with an HPV-negative oral tongue tumor. Exome enrichment was performed using the Agilent SureSelect All Exon Kit, followed by sequencing on the ABI SOLiD platform. Results. Exome sequencing revealed slightly more mutations in the HPV-negative tumor (73) in contrast to the HPV-positive tumor (58). Multiple mutations were noted in zinc finger genes (ZNF3, 10, 229, 470, 543, 616, 664, 638, 716, and 799) and mucin genes (MUC4, 6, 12, and 16). Mutations were noted in MUC12 in both tumors. Conclusions. HPV-positive HNSCC is distinct from HPV-negative disease in terms of evidence of viral infection, p16 status, and frequency of mutations. Next-generation sequencing has the potential to identify novel therapeutic targets and biomarkers in HNSCC

Characterization Of Drug Interactions With Serum Proteins by Using High-Performance Affinity Chromatography

The binding of drugs with serum proteins can affect the activity, distribution, rate of excretion, and toxicity of pharmaceutical agents in the body. One tool that can be used to quickly analyze and characterize these interactions is high-performance affinity chromatography (HPAC). This review shows how HPAC can be used to study drug-protein binding and describes the various applications of this approach when examining drug interactions with serum proteins. Methods for determining binding constants, characterizing binding sites, examining drug-drug interactions, and studying drug-protein dissociation rates will be discussed. Applications that illustrate the use of HPAC with serum binding agents such as human serum albumin, α1-acid glycoprotein, and lipoproteins will be presented. Recent developments will also be examined, such as new methods for immobilizing serum proteins in HPAC columns, the utilization of HPAC as a tool in personalized medicine, and HPAC methods for the high-throughput screening and characterization of drug-protein binding

Characterization Of Drug Interactions With Serum Proteins by Using High-Performance Affinity Chromatography

The binding of drugs with serum proteins can affect the activity, distribution, rate of excretion, and toxicity of pharmaceutical agents in the body. One tool that can be used to quickly analyze and characterize these interactions is high-performance affinity chromatography (HPAC). This review shows how HPAC can be used to study drug-protein binding and describes the various applications of this approach when examining drug interactions with serum proteins. Methods for determining binding constants, characterizing binding sites, examining drug-drug interactions, and studying drug-protein dissociation rates will be discussed. Applications that illustrate the use of HPAC with serum binding agents such as human serum albumin, α1-acid glycoprotein, and lipoproteins will be presented. Recent developments will also be examined, such as new methods for immobilizing serum proteins in HPAC columns, the utilization of HPAC as a tool in personalized medicine, and HPAC methods for the high-throughput screening and characterization of drug-protein binding

LONGO: An R package for interactive gene length dependent analysis for neuronal identity

Motivation: Reprogramming somatic cells into neurons holds great promise to model neuronal development and disease. The efficiency and success rate of neuronal reprogramming, however, may vary between different conversion platforms and cell types, thereby necessitating an unbiased, systematic approach to estimate neuronal identity of converted cells. Recent studies have demonstrated that long genes (\u3e100 kb from transcription start to end) are highly enriched in neurons, which provides an opportunity to identify neurons based on the expression of these long genes. Results: We have developed a versatile R package, LONGO, to analyze gene expression based on gene length. We propose a systematic analysis of long gene expression (LGE) with a metric termed the long gene quotient (LQ) that quantifies LGE in RNA-seq or microarray data to validate neuronal identity at the single-cell and population levels. This unique feature of neurons provides an opportunity to utilize measurements of LGE in transcriptome data to quickly and easily distinguish neurons from non-neuronal cells. By combining this conceptual advancement and statistical tool in a user-friendly and interactive software package, we intend to encourage and simplify further investigation into LGE, particularly as it applies to validating and improving neuronal differentiation and reprogramming methodologies. Availability and implementation: LONGO is freely available for download at https://github.com/biohpc/longo. Supplementary information: Supplementary data are available at Bioinformatics online

A case report and genetic characterization of a massive acinic cell carcinoma of the parotid with delayed distant metastases.

We describe the presentation, management, and clinical outcome of a massive acinic cell carcinoma of the parotid gland. The primary tumor and blood underwent exome sequencing which revealed deletions in CDKN2A as well as PPP1R13B, which induces p53. A damaging nonsynonymous mutation was noted in EP300, a histone acetylase which plays a role in cellular proliferation. This study provides the first insights into the genetic underpinnings of this cancer. Future large-scale efforts will be necessary to define the mutational landscape of salivary gland malignancies to identify therapeutic targets and biomarkers of treatment failure