Solid-Phase-Supported Chemoenzymatic Synthesis and Analysis of Chondroitin Sulfate Proteoglycan Glycopeptides.

Proteoglycans (PGs), consisting of glycosaminoglycans (GAGs) linked with the core protein through a tetrasaccharide linkage region, play roles in many important biological events. The chemical synthesis of PG glycopeptides is extremely challenging. In this work, the enzymes required for synthesis of chondroitin sulfate (CS) PG (CSPG) have been expressed and the suitable sequence of enzymatic reactions has been established. To expedite CSPG synthesis, the peptide acceptor was immobilized on solid phase and the glycan units were directly installed enzymatically onto the peptide. Subsequent enzymatic chain elongation and sulfation led to the successful synthesis of CSPG glycopeptides. The CS dodecasaccharide glycopeptide was the longest homogeneous CS glycopeptide synthesized to date. The enzymatic synthesis was much more efficient than the chemical synthesis of the corresponding CS glycopeptides, which could reduce the total number of synthetic steps by 80%. The structures of the CS glycopeptides were confirmed by mass spectrometry analysis and NMR studies. In addition, the interactions between the CS glycopeptides and cathepsin G were studied. The sulfation of glycan chain was found to be important for binding with cathepsin G. This efficient chemoenzymatic strategy opens new avenues to investigate the structures and functions of PGs.

A comprehensive assessment of selective amino acid 15N-labeling in human embryonic kidney 293 cells for NMR spectroscopy.

A large proportion of human proteins contain post-translational modifications that cannot be synthesized by prokaryotes. Thus, mammalian expression systems are often employed to characterize structure/function relationships using NMR spectroscopy. Here we define the selective isotope labeling of secreted, post-translationally modified proteins using human embryonic kidney (HEK)293 cells. We determined that alpha-[15N]- atoms from 10 amino acids experience minimal metabolic scrambling (C, F, H, K, M, N, R, T, W, Y). Two more interconvert to each other (G, S). Six others experience significant scrambling (A, D, E, I, L, V). We also demonstrate that tuning culture conditions suppressed V and I scrambling. These results define expectations for 15N-labeling in HEK293 cells.

A mechanism for hereditary angioedema caused by a methionine-379-to-lysine substitution in kininogens.

ABSTRACT: Hereditary angioedema (HAE) is associated with episodic kinin-induced swelling of the skin and mucosal membranes. Most patients with HAE have low plasma C1-inhibitor activity, leading to increased generation of the protease plasma kallikrein (PKa) and excessive release of the nanopeptide bradykinin from high-molecular-weight kininogen (HK). However, disease-causing mutations in at least 10% of patients with HAE appear to involve genes for proteins other than C1-inhibitor. A point mutation in the Kng1 gene encoding HK and low-molecular weight kininogen (LK) was identified recently in a family with HAE. The mutation changes a methionine (Met379) to lysine (Lys379) in both proteins. Met379 is adjacent to the Lys380-Arg381 cleavage site at the N-terminus of the bradykinin peptide. Recombinant wild-type (Met379) and variant (Lys379) versions of HK and LK were expressed in HEK293 cells. PKa-catalyzed kinin release from HK and LK was not affected by the Lys379 substitutions. However, kinin release from HK-Lys379 and LK-Lys379 catalyzed by the fibrinolytic protease plasmin was substantially greater than from wild-type HK-Met379 and LK-Met379. Increased kinin release was evident when fibrinolysis was induced in plasma containing HK-Lys379 or LK-Lys379 compared with plasma containing wild-type HK or LK. Mass spectrometry revealed that the kinin released from wild-type and variant kininogens by PKa is bradykinin. Plasmin also released bradykinin from wild-type kininogens but cleaved HK-Lys379 and LK-Lys379 after Lys379 rather than Lys380, releasing the decapeptide Lys-bradykinin (kallidin). The Met379Lys substitutions make HK and LK better plasmin substrates, reinforcing the relationship between fibrinolysis and kinin generation.

Uniform [13C,15N]-labeled and glycosylated IgG1 Fc expressed in Saccharomyces cerevisiae.

Despite the prevalence and importance of glycoproteins in human biology, methods for isotope labeling suffer significant limitations. Common prokaryotic platforms do not produce mammalian post-translation modifications that are essential to the function of many human glycoproteins, including immunoglobulin G1 (IgG1). Mammalian expression systems require complex media and thus introduce significant costs to achieve uniform labeling. Expression with Pichia is available, though expertise and equipment requirements surpass E. coli culture. We developed a system utilizing Saccharomyces cerevisiae, [13C]-glucose, and [15N]-ammonium chloride with complexity comparable to E. coli. Here we report two vectors for expressing the crystallizable fragment (Fc) of IgG1 for secretion into the culture medium, utilizing the ADH2 or DDI2 promoters. We also report a strategy to optimize the expression yield using orthogonal Taguchi arrays. Lastly, we developed two different media formulations, a standard medium which provides 86-92% 15N and 30% 13C incorporation into the polypeptide, or a rich medium which provides 98% 15N and 95% 13C incorporation as determined by mass spectrometry. This advance represents an expression and optimization strategy accessible to experimenters with the capability to grow and produce proteins for NMR-based experiments using E. coli.

Gene regulation network inference using k-nearest neighbor-based mutual information estimation: revisiting an old DREAM.

BACKGROUND: A cell exhibits a variety of responses to internal and external cues. These responses are possible, in part, due to the presence of an elaborate gene regulatory network (GRN) in every single cell. In the past 20 years, many groups worked on reconstructing the topological structure of GRNs from large-scale gene expression data using a variety of inference algorithms. Insights gained about participating players in GRNs may ultimately lead to therapeutic benefits. Mutual information (MI) is a widely used metric within this inference/reconstruction pipeline as it can detect any correlation (linear and non-linear) between any number of variables (n-dimensions). However, the use of MI with continuous data (for example, normalized fluorescence intensity measurement of gene expression levels) is sensitive to data size, correlation strength and underlying distributions, and often requires laborious and, at times, ad hoc optimization. RESULTS: In this work, we first show that estimating MI of a bi- and tri-variate Gaussian distribution using k-nearest neighbor (kNN) MI estimation results in significant error reduction as compared to commonly used methods based on fixed binning. Second, we demonstrate that implementing the MI-based kNN Kraskov-Stoögbauer-Grassberger (KSG) algorithm leads to a significant improvement in GRN reconstruction for popular inference algorithms, such as Context Likelihood of Relatedness (CLR). Finally, through extensive in-silico benchmarking we show that a new inference algorithm CMIA (Conditional Mutual Information Augmentation), inspired by CLR, in combination with the KSG-MI estimator, outperforms commonly used methods. CONCLUSIONS: Using three canonical datasets containing 15 synthetic networks, the newly developed method for GRN reconstruction-which combines CMIA, and the KSG-MI estimator-achieves an improvement of 20-35% in precision-recall measures over the current gold standard in the field. This new method will enable researchers to discover new gene interactions or better choose gene candidates for experimental validations.

A spatially resolved stochastic model reveals the role of supercoiling in transcription regulation.

In Escherichia coli, translocation of RNA polymerase (RNAP) during transcription introduces supercoiling to DNA, which influences the initiation and elongation behaviors of RNAP. To quantify the role of supercoiling in transcription regulation, we developed a spatially resolved supercoiling model of transcription. The integrated model describes how RNAP activity feeds back with the local DNA supercoiling and how this mechanochemical feedback controls transcription, subject to topoisomerase activities and stochastic topological domain formation. This model establishes that transcription-induced supercoiling mediates the cooperation of co-transcribing RNAP molecules in highly expressed genes, and this cooperation is achieved under moderate supercoiling diffusion and high topoisomerase unbinding rates. It predicts that a topological domain could serve as a transcription regulator, generating substantial transcriptional noise. It also shows the relative orientation of two closely arranged genes plays an important role in regulating their transcription. The model provides a quantitative platform for investigating how genome organization impacts transcription.

Metabolic15N labeling of the N-glycosylated immunoglobulin G1 Fc with an engineered Saccharomyces cerevisiae strain.

The predominant protein expression host for NMR spectroscopy is Escherichia coli, however, it does not synthesize appropriate post-translation modifications required for mammalian protein function and is not ideal for expressing naturally secreted proteins that occupy an oxidative environment. Mammalian expression platforms can address these limitations; however, these are not amenable to cost-effective uniform 15 N labeling resulting from highly complex growth media requirements. Yeast expression platforms combine the simplicity of bacterial expression with the capabilities of mammalian platforms, however yeasts require optimization prior to isotope labeling. Yeast expression will benefit from methods to boost protein expression levels and developing labeling conditions to facilitate growth and high isotope incorporation within the target protein. In this work, we describe a novel platform based on the yeast Saccharomyces cerevisiae that simultaneously expresses the Kar2p chaperone and protein disulfide isomerase in the ER to facilitate the expression of secreted proteins. Furthermore, we developed a growth medium for uniform 15 N labeling. We recovered 2.2 mg/L of uniformly 15 N-labeled human immunoglobulin (Ig)G1 Fc domain with 90.6% 15 N labeling. NMR spectroscopy revealed a high degree of similarity between the yeast and mammalian-expressed IgG1 Fc domains. Furthermore, we were able to map the binding interaction between IgG1 Fc and the Z domain through chemical shift perturbations. This platform represents a novel cost-effective strategy for 15 N-labeled immunoglobulin fragments.

The antibody-binding Fc gamma receptor IIIa / CD16a is N-glycosylated with high occupancy at all five sites.

The antibody-binding Fc γ receptors (FcγRs) trigger life-saving immune responses and many therapeutic monoclonal antibodies require FcγR engagement for full effect. One proven strategy to improve the efficacy of antibody therapies is to increase receptor binding affinity, in particular binding to FcγRIIIa/CD16a. Currently, affinities are measured using recombinantly-expressed soluble extracellular FcγR domains and CD16a-mediated antibody-dependent immune responses are characterized using cultured cells. It is notable that CD16a is highly processed with multiple N-glycosylation sites, and preventing individual N-glycan modifications affects affinity. Furthermore, multiple groups have demonstrated that CD16a N-glycan composition is variable and composition impacts antibody binding affinity. The level of N-glycosylation at each site is not known though computational prediction indicates low to moderate potential at each site based on primary sequence (40-70%). Here we quantify occupancy of the extracellular domains using complementary mass spectrometry-based methods. All five sites of the tighter-binding CD16a V158 allotype showed 65-100% N-glycan occupancy in proteomics-based experiments. These observations were confirmed using intact protein mass spectrometry that demonstrated the predominant species corresponded to CD16a V158 with five N-glycans, with a smaller contribution from CD16a with four N-glycans. Occupancy was likewise high for the membrane-bound receptor at all detected N-glycosylation sites using CD16a purified from cultured human natural killer cells. Occupancy of the N162 site, critical for antibody binding, appeared independent of N169 occupancy based on analysis of the T171A mutant protein. The weaker-binding CD16a F158 allotype showed higher occupancy of >93% at each site.

Reconstructing an epigenetic landscape using a genetic pulling approach.

Cells use genetic switches to shift between alternate stable gene expression states, e.g., to adapt to new environments or to follow a developmental pathway. Conceptually, these stable phenotypes can be considered as attractive states on an epigenetic landscape with phenotypic changes being transitions between states. Measuring these transitions is challenging because they are both very rare in the absence of appropriate signals and very fast. As such, it has proved difficult to experimentally map the epigenetic landscapes that are widely believed to underly developmental networks. Here, we introduce a nonequilibrium perturbation method to help reconstruct a regulatory network's epigenetic landscape. We derive the mathematical theory needed and then use the method on simulated data to reconstruct the landscapes. Our results show that with a relatively small number of perturbation experiments it is possible to recover an accurate representation of the true epigenetic landscape. We propose that our theory provides a general method by which epigenetic landscapes can be studied. Finally, our theory suggests that the total perturbation impulse required to induce a switch between metastable states is a fundamental quantity in developmental dynamics.

A pairwise distance distribution correction (DDC) algorithm to eliminate blinking-caused artifacts in SMLM.

Single-molecule localization microscopy (SMLM) relies on the blinking behavior of a fluorophore, which is the stochastic switching between fluorescent and dark states. Blinking creates multiple localizations belonging to the same fluorophore, confounding quantitative analyses and interpretations. Here we present a method, termed distance distribution correction (DDC), to eliminate blinking-caused repeat localizations without any additional calibrations. The approach relies on obtaining the true pairwise distance distribution of different fluorophores naturally from the imaging sequence by using distances between localizations separated by a time much longer than the average fluorescence survival time. We show that, using the true pairwise distribution, we can define and maximize the likelihood, obtaining a set of localizations void of blinking artifacts. DDC results in drastic improvements in obtaining the closest estimate of the true spatial organization and number of fluorescent emitters in a wide range of applications, enabling accurate reconstruction and quantification of SMLM images.

Modeling binary and graded cone cell fate patterning in the mouse retina.

Nervous systems are incredibly diverse, with myriad neuronal subtypes defined by gene expression. How binary and graded fate characteristics are patterned across tissues is poorly understood. Expression of opsin photopigments in the cone photoreceptors of the mouse retina provides an excellent model to address this question. Individual cones express S-opsin only, M-opsin only, or both S-opsin and M-opsin. These cell populations are patterned along the dorsal-ventral axis, with greater M-opsin expression in the dorsal region and greater S-opsin expression in the ventral region. Thyroid hormone signaling plays a critical role in activating M-opsin and repressing S-opsin. Here, we developed an image analysis approach to identify individual cone cells and evaluate their opsin expression from immunofluorescence imaging tiles spanning roughly 6 mm along the D-V axis of the mouse retina. From analyzing the opsin expression of ~250,000 cells, we found that cones make a binary decision between S-opsin only and co-expression competent fates. Co-expression competent cells express graded levels of S- and M-opsins, depending nonlinearly on their position in the dorsal-ventral axis. M- and S-opsin expression display differential, inverse patterns. Using these single-cell data, we developed a quantitative, probabilistic model of cone cell decisions in the retinal tissue based on thyroid hormone signaling activity. The model recovers the probability distribution for cone fate patterning in the mouse retina and describes a minimal set of interactions that are necessary to reproduce the observed cell fates. Our study provides a paradigm describing how differential responses to regulatory inputs generate complex patterns of binary and graded cell fates.

Automatic error control during forward flux sampling of rare events in master equation models.

Enhanced sampling methods, such as forward flux sampling (FFS), have great capacity for accelerating stochastic simulations of nonequilibrium biochemical systems involving rare events. However, the description of the tradeoffs between simulation efficiency and error in FFS remains incomplete. We present a novel and mathematically rigorous analysis of the errors in FFS that, for the first time, covers the contribution of every phase of the simulation. We derive a closed form expression for the optimally efficient count of samples to take in each FFS phase in terms of a fixed constraint on sampling error. We introduce a new method, forward flux pilot sampling (FFPilot), that is designed to take full advantage of our optimizing equation without prior information or assumptions about the phase weights and costs along the transition path. In simulations of both single and multidimensional gene regulatory networks, FFPilot is able to completely control sampling error. We then discuss how memory effects can introduce additional error when relaxation along the transition path is slow. This extra error can be traced to correlations between the FFS phases and can be controlled by monitoring the covariance between them. Finally, we show that, in sets of simulations with matched error, FFPilot is on the order of tens-to-hundreds of times faster than direct sampling and noticeably more efficient than previous FFS methods.

Neural network control of focal position during time-lapse microscopy of cells.

Live-cell microscopy is quickly becoming an indispensable technique for studying the dynamics of cellular processes. Maintaining the specimen in focus during image acquisition is crucial for high-throughput applications, especially for long experiments or when a large sample is being continuously scanned. Automated focus control methods are often expensive, imperfect, or ill-adapted to a specific application and are a bottleneck for widespread adoption of high-throughput, live-cell imaging. Here, we demonstrate a neural network approach for automatically maintaining focus during bright-field microscopy. Z-stacks of yeast cells growing in a microfluidic device were collected and used to train a convolutional neural network to classify images according to their z-position. We studied the effect on prediction accuracy of the various hyperparameters of the neural network, including downsampling, batch size, and z-bin resolution. The network was able to predict the z-position of an image with ±1 µm accuracy, outperforming human annotators. Finally, we used our neural network to control microscope focus in real-time during a 24 hour growth experiment. The method robustly maintained the correct focal position compensating for 40 µm of focal drift and was insensitive to changes in the field of view. About ~100 annotated z-stacks were required to train the network making our method quite practical for custom autofocus applications.

Natural variation in stochastic photoreceptor specification and color preference in Drosophila.

Each individual perceives the world in a unique way, but little is known about the genetic basis of variation in sensory perception. In the fly eye, the random mosaic of color-detecting R7 photoreceptor subtypes is determined by stochastic on/off expression of the transcription factor Spineless (Ss). In a genome-wide association study, we identified a naturally occurring insertion in a regulatory DNA element in ss that lowers the ratio of SsON to SsOFF cells. This change in photoreceptor fates shifts the innate color preference of flies from green to blue. The genetic variant increases the binding affinity for Klumpfuss (Klu), a zinc finger transcriptional repressor that regulates ss expression. Klu is expressed at intermediate levels to determine the normal ratio of SsON to SsOFF cells. Thus, binding site affinity and transcription factor levels are finely tuned to regulate stochastic expression, setting the ratio of alternative fates and ultimately determining color preference.

Biospark: scalable analysis of large numerical datasets from biological simulations and experiments using Hadoop and Spark.

Data-parallel programming techniques can dramatically decrease the time needed to analyze large datasets. While these methods have provided significant improvements for sequencing-based analyses, other areas of biological informatics have not yet adopted them. Here, we introduce Biospark, a new framework for performing data-parallel analysis on large numerical datasets. Biospark builds upon the open source Hadoop and Spark projects, bringing domain-specific features for biology. AVAILABILITY AND IMPLEMENTATION: Source code is licensed under the Apache 2.0 open source license and is available at the project website: https://www.assembla.com/spaces/roberts-lab-public/wiki/Biospark CONTACT: eroberts@jhu.eduSupplementary information: Supplementary data are available at Bioinformatics online.

The Chd1 chromatin remodeler can sense both entry and exit sides of the nucleosome.

Chromatin remodelers are essential for establishing and maintaining the placement of nucleosomes along genomic DNA. Yet how chromatin remodelers recognize and respond to distinct chromatin environments surrounding nucleosomes is poorly understood. Here, we use Lac repressor as a tool to probe how a DNA-bound factor influences action of the Chd1 remodeler. We show that Chd1 preferentially shifts nucleosomes away from Lac repressor, demonstrating that a DNA-bound factor defines a barrier for nucleosome positioning. Rather than an absolute block in sliding, the barrier effect was achieved by altered rates of nucleosome sliding that biased redistribution of nucleosomes away from the bound Lac repressor site. Remarkably, in addition to slower sliding toward the LacO site, the presence of Lac repressor also stimulated sliding in the opposite direction. These experiments therefore demonstrate that Chd1 responds to the presence of a bound protein on both entry and exit sides of the nucleosome. This sensitivity to both sides of the nucleosome allows for a faster and sharper response than would be possible by responding to only the entry side, and we speculate that dual entry/exit sensitivity is also important for regularly spaced nucleosome arrays generated by Chd1 and the related ISWI remodelers.

Gradient sensing by a bistable regulatory motif enhances signal amplification but decreases accuracy in individual cells.

Many vital eukaryotic cellular functions require the cell to respond to a directional gradient of a signaling molecule. The first two steps in any eukaryotic chemotactic/chemotropic pathway are gradient detection and cell polarization. Like many processes, such chemotactic and chemotropic decisions are made using a relatively small number of molecules and are thus susceptible to internal and external fluctuations during signal transduction. Large cell-to-cell variations in the magnitude and direction of a response are therefore possible and do, in fact, occur in natural systems. In this work we use three-dimensional probabilistic modeling of a simple gradient sensing pathway to study the capacity for individual cells to accurately determine the direction of a gradient, despite fluctuations. We include a stochastic external gradient in our simulations using a novel gradient boundary condition modeling a point emitter a short distance away. We compare and contrast three different variants of the pathway, one monostable and two bistable. The simulation data show that an architecture combining bistability with spatial positive feedback permits the cell to both accurately detect and internally amplify an external gradient. We observe strong polarization in all individual cells, but in a distribution of directions centered on the gradient. Polarization accuracy in our study was strongly dependent upon a spatial positive feedback term that allows the pathway to trade accuracy for polarization strength. Finally, we show that additional feedback links providing information about the gradient to multiple levels in the pathway can help the cell to refine initial inaccuracy in the polarization direction.

A biophysical model of supercoiling dependent transcription predicts a structural aspect to gene regulation.

BACKGROUND: Transcription in Escherichia coli generates positive supercoiling in the DNA, which is relieved by the enzymatic activity of gyrase. Recently published experimental evidence suggests that transcription initiation and elongation are inhibited by the buildup of positive supercoiling. It has therefore been proposed that intermittent binding of gyrase plays a role in transcriptional bursting. Considering that transcription is one of the most fundamental cellular processes, it is desirable to be able to account for the buildup and release of positive supercoiling in models of transcription. RESULTS: Here we present a detailed biophysical model of gene expression that incorporates the effects of supercoiling due to transcription. By directly linking the amount of positive supercoiling to the rate of transcription, the model predicts that highly transcribed genes' mRNA distributions should substantially deviate from Poisson distributions, with enhanced density at low mRNA copy numbers. Additionally, the model predicts a high degree of correlation between expression levels of genes inside the same supercoiling domain. CONCLUSIONS: Our model, incorporating the supercoiling state of the gene, makes specific predictions that differ from previous models of gene expression. Genes in the same supercoiling domain influence the expression level of neighboring genes. Such structurally dependent regulation predicts correlations between genes in the same supercoiling domain. The topology of the chromosome therefore creates a higher level of gene regulation, which has broad implications for understanding the evolution and organization of bacterial genomes.

Dynamics of simple gene-network motifs subject to extrinsic fluctuations.

Cellular processes do not follow deterministic rules; even in identical environments genetically identical cells can make random choices leading to different phenotypes. This randomness originates from fluctuations present in the biomolecular interaction networks. Most previous work has been focused on the intrinsic noise (IN) of these networks. Yet, especially for high-copy-number biomolecules, extrinsic or environmental noise (EN) has been experimentally shown to dominate the variation. Here, we develop an analytical formalism that allows for calculation of the effect of EN on gene-expression motifs. We introduce a method for modeling bounded EN as an auxiliary species in the master equation. The method is fully generic and is not limited to systems with small EN magnitudes. We focus our study on motifs that can be viewed as the building blocks of genetic switches: a nonregulated gene, a self-inhibiting gene, and a self-promoting gene. The role of the EN properties (magnitude, correlation time, and distribution) on the statistics of interest are systematically investigated, and the effect of fluctuations in different reaction rates is compared. Due to its analytical nature, our formalism can be used to quantify the effect of EN on the dynamics of biochemical networks and can also be used to improve the interpretation of data from single-cell gene-expression experiments.

Simulation of reaction diffusion processes over biologically relevant size and time scales using multi-GPU workstations.

Simulation of in vivo cellular processes with the reaction-diffusion master equation (RDME) is a computationally expensive task. Our previous software enabled simulation of inhomogeneous biochemical systems for small bacteria over long time scales using the MPD-RDME method on a single GPU. Simulations of larger eukaryotic systems exceed the on-board memory capacity of individual GPUs, and long time simulations of modest-sized cells such as yeast are impractical on a single GPU. We present a new multi-GPU parallel implementation of the MPD-RDME method based on a spatial decomposition approach that supports dynamic load balancing for workstations containing GPUs of varying performance and memory capacity. We take advantage of high-performance features of CUDA for peer-to-peer GPU memory transfers and evaluate the performance of our algorithms on state-of-the-art GPU devices. We present parallel e ciency and performance results for simulations using multiple GPUs as system size, particle counts, and number of reactions grow. We also demonstrate multi-GPU performance in simulations of the Min protein system in E. coli. Moreover, our multi-GPU decomposition and load balancing approach can be generalized to other lattice-based problems.