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bioRxiv2017

Computational investigation of biological and technical variability in high throughput phenotyping and cell line identification

High-throughput cell profiling experiments are characterizing cell phenotype under a broad variety of microenvironmental and therapeutic conditions. However, biological and technical variability are contributing to wide ranges of reported parameter values, even for standard cell lines grown in identical conditions. In this paper, we develop a mathematical model of cell proliferation assays that account for biological and technical variability and limitations of the experimental platforms, including (1) cell confluency effects, (2) biological variability and technical errors in pipetting, (3) biological variability in proliferation characteristics, (4) technical variability and uncertainty in measurement timing, (5) cell counting errors, and (6) the impact of limited temporal sampling. We use this model to create synthetic datasets with growth rates and measurement times typical of cancer cell cultures, and investigate the impact of the initial cell seeding density and the common practice of fitting exponential growth curves to three cell count measurements. We find that the combined sources of variability mask the sub-exponential growth characteristics of the synthetic datasets, and that researchers profiling the same cell lines under different seeding characteristics can find significant (p < 0.05) differences in the measured growth rates. Even seeding the cells at 1% of the confluent limit can cause significant (p < 0.05) differences in the measured growth rate from the ground truth. We explored the effect of reducing errors in each part of the virtual experimental system, and found the best improvements from reducing timing errors, reducing cell counting errors, or reducing the interval between measurements (to reduce the inaccuracy of the exponential growth assumption when fitting curves). Reducing biological variability and pipetting errors had the least impact, because any improvements are still masked by cell counting errors. We close with a discussion of recommended practices for high-throughput cell phenotyping and cell line identification systems.

cell biology↗

bioRxiv2017

Optical Coherence Tomography Reveals Mechanobiologically Stable Self-Organizing Di-Fork Architecture of Mice Cutaneous Scars

Scientific studies report crucial impacts of biomechanical effectors to modulate wound healing either by scarring or regeneration. Further, the biological decision to predominantly favor the former is still cryptic. Real-time visualization of biomechanical manifestations in situ in scarring is hence necessary. Endorsed by nanostructural testing, synthetic phantom analysis, and computational simulations, we found strong mechanobiological correlates for Swept Source Optical Coherence Tomography (SS-OCT) speckles in mice cutaneous repair (full-thickness) up to 10 months. The theoretical basis of the optomechanics to provide insights into scar form-factor and evolution is proposed. Optomechanical changes have been considered as the resultant of intrinsic (e.g. fiber elastic modulus) and gross tissue mechanics (extracellular matrix (ECM)) in maturing scars. Non-invasive optomechanics supported with microscopic findings reveal scars cross-sectional self-organizing di-fork architecture. Dual-compartment heterogeneity of di-fork exhibits stress-evading features with a dichotomy in inhabitant cellular stress-fiber distributions. This differential interactivity of scar with adjoining tissues reflects its architectural intelligence to compensate tissue loss (hypodermis/muscle) by assembling into a di-fork. Gradual establishment of baseline shifted lasting mechanobiological steady-state, later in scarring, expose scar as an alternate stable state within the skin. Significance StatementWound repair in mammals, predominantly culminates into function compromising scar that is occasionally fatal in vital organs. How the biological system often adopts scarring over a restorative regeneration is yet a conundrum. Wound and ambient mechanics play a pivotal role in deciding the healing fate. SS-OCT is hence demonstrated here as a non-invasive window to such mechanical manifestations during skin wound healing. This exposed gradual emergence of temporally maintained and stress-resilient di-fork architecture of the scar with differential neighborhood interfaces. Accommodation of such an alternate self-organizing steady-state of scar sheds light on its sustenance and paradoxical selection.

biophysics↗

bioRxiv2013

Inferring tree causal models of cancer progression with probability raising

Existing techniques to reconstruct tree models of progression for accumulative processes, such as cancer, seek to estimate causation by combining correlation and a frequentist notion of temporal priority. In this paper, we define a novel theoretical framework called CAPRESE (CAncer PRogression Extraction with Single Edges) to reconstruct such models based on the notion of probabilistic causation defined by Suppes. We consider a general reconstruction setting complicated by the presence of noise in the data due to biological variation, as well as experimental or measurement errors. To improve tolerance to noise we define and use a shrinkage-like estimator. We prove the correctness of our algorithm by showing asymptotic convergence to the correct tree under mild constraints on the level of noise. Moreover, on synthetic data, we show that our approach outperforms the state-of-the-art, that it is efficient even with a relatively small number of samples and that its performance quickly converges to its asymptote as the number of samples increases. For real cancer datasets obtained with different technologies, we highlight biologically significant differences in the progressions inferred with respect to other competing techniques and we also show how to validate conjectured biological relations with progression models.

Bioinformatics↗

bioRxiv2018

Interaction of modified oligonucleotides with nuclear proteins, formation of novel nuclear structures and sequence-independent effects on RNA processing

Oligonucleotides and nucleic acid analogues that alter gene expression are showing therapeutic promise for selected human diseases. The modification of synthetic nucleic acids to protect against nuclease degradation and to influence drug function is common practice, however, such modifications may also confer unexpected physicochemical and biological properties. Here we report backbone-specific effects of modified oligonucleotides on subnuclear organelles, altered distribution of nuclear proteins, the appearance of novel structured nuclear inclusions, and modification of RNA processing in cultured cells transfected with antisense oligonucleotides on a phosphorothioate backbone. Phosphodiester and phosphorodiamidate morpholino oligomers elicited no such consequences. Disruption of subnuclear structures and proteins elicit severe phenotypic disturbances, revealed by transcriptomic analysis of fibroblasts exhibiting such disruption. These data suggest that the toxic effects and adverse events reported after clinical evaluation of phosphorothioate nucleic acid drugs may be mediated, at least in part, by non-specific interaction of nuclear components with the phosphorothioate backbone.

molecular biology↗

bioRxiv2016

Evolutionary stability of microbial communities with antibiotic degrading species

A major puzzle in ecology is how antibiotic resistant, sensitive and producer species coexist in close proximity. Recently, we showed that antibiotic degrading species dramatically alter community dynamics: replacing intrinsic resistance with resistance through degradation generates stable communities resilient to spatial mixing, large species abundance perturbations, changes in interaction strengths, and differences in species growth rates. In addition to ecological stability, it is interesting to consider evolutionary stability of these communities to the appearance of cheater species that either cease production or degradation of antibiotics. Our investigation of evolutionary stability of cyclical 3-species communities revealed that these communities are robust to cheaters that stop degrading antibiotics. Our simulations also showed that cheaters that stop producing antibiotics do not take over the community, yet they can transiently invade and cause community collapse. In the analytical approximation we initially investigated, production cheaters with a small fitness advantage can invade the community simply because the benefit of inhibiting competitors is shared among all cells. Here, we consider evolutionary stability to cheaters in our complete model, where spatial mixing is introduced only after a short range colonization step. In this regime, an antibiotic producer cell directly benefits from killing nearby competitor species as it has a greater chance of colonizing the newly voided spaces created by the action of its antibiotic. Simulating response to cheater invasions for the cyclical three species community and for random 4-species ecologically stable topologies, we find that these communities can be fully resilient to both degradation and production cheaters. The strength of selection against cheaters varies with the area of the zone of inhibition around producers and is maximized for weak inhibition, where there is less overlap between the killing zones of neighboring cells. These results may aid the construction of complex synthetic communities that are both ecological and evolutionary stable.

Evolutionary Biology↗

bioRxiv2017

Dynamical predictors of an imminent phenotypic switch in bacteria

Single cells can stochastically switch across thresholds imposed by regulatory networks. Such thresholds can act as a tipping point, drastically changing global phenotypic states. In ecology and economics, imminent transitions across such tipping points can be predicted using dynamical early warning indicators. A typical example is \"flickering\" of a fast variable, predicting a longer-lasting switch from a low to a high state or vice versa. Considering the different timescales between metabolite and protein fluctuations in bacteria, we hypothesized that metabolic early warning indicators predict imminent transitions across a network threshold caused by enzyme saturation. We used stochastic simulations to determine if flickering predicts phenotypic transitions, accounting for a variety of molecular physiological parameters, including enzyme affinity, burstiness of enzyme gene expression, homeostatic feedback, and rates of metabolic precursor influx. In most cases, we found that metabolic flickering rates are robustly peaked near the enzyme saturation threshold. The degree of fluctuation was amplified by product inhibition of the enzyme. We conclude that sensitivity to flickering in fast variables may be a possible natural or synthetic strategy to prepare physiological states for an imminent transition.

systems biology↗

bioRxiv2014

An analytical approach to bistable biological circuit discrimination using real algebraic geometry

Biomolecular circuits with two distinct and stable steady states have been identified as essential components in a wide range of biological networks, with a variety of mechanisms and topologies giving rise to their important bistable property. Understanding the differences between circuit implementations is an important question, particularly for the synthetic biologist faced with determining which bistable circuit design out of many is best for their specific application. In this work we explore the applicability of Sturms theorem--a tool from 19th-century real algebraic geometry--to comparing \"functionally equivalent\" bistable circuits without the need for numerical simulation. We first consider two genetic toggle variants and two different positive feedback circuits, and show how specific topological properties present in each type of circuit can serve to increase the size of the regions of parameter space in which they function as switches. We then demonstrate that a single competitive monomeric activator added to a purely-monomeric (and otherwise monostable) mutual repressor circuit is sufficient for bistability. Finally, we compare our approach with the Routh-Hurwitz method and derive consistent, yet more powerful, parametric conditions. The predictive power and ease of use of Sturms theorem demonstrated in this work suggests that algebraic geometric techniques may be underutilized in biomolecular circuit analysis.

Systems Biology↗

bioRxiv2017

Synthesis of a eukaryotic chromosome reveals a role for N6-methyladenine in nucleosome organization

Biochemical studies of chromatin have typically used either artificial DNA templates with unnaturally high affinity for histones, or small genomic DNA fragments deprived of their cognate physical environment. It has thus been difficult to dissect chromatin structure and function within fully native DNA substrates. Here, we circumvent these limitations by exploiting the minimalist genome of the eukaryote Oxytricha trifallax, whose notably small ~3kb chromosomes mainly encode single genes. Guided by high-resolution epigenomic maps of nucleosome organization, transcription, and DNA N6-methyladenine (m6dA) locations, we reconstruct full-length Oxytricha chromosomes in vitro and use these synthetic facsimiles to dissect the influence of m6dA and histone post-translational modifications on nucleosome organization. We show that m6dA directly disfavors nucleosomes in a quantitative manner, leading to local decreases in nucleosome occupancy that are synergistic with histone acetylation. The effect of m6dA can be partially reversed by the action of an ATP-dependent chromatin remodeler. Furthermore, erasing m6dA marks from Oxytricha chromosomes leads to proportional increases in nucleosome occupancy across the genome. This work showcases Oxytricha chromosomes as powerful yet practical models for studying eukaryotic chromatin and transcription in the context of biologically relevant DNA substrates.\n\nHighlightsO_LIDe novo synthesis of complete, epigenetically defined Oxytricha chromosomes\nC_LIO_LIEpigenomic profiles of chromatin organization in Oxytrichas miniature chromosomes\nC_LIO_LIm6dA directly disfavors nucleosome occupancy in natural and synthetic chromosomes\nC_LIO_LIHistone acetylation and chromatin remodelers temper the impact of m6dA on chromatin\nC_LI

biochemistry↗

bioRxiv2017

Which are major players, canonical or non-canonical strigolactones?

Strigolactones (SLs) can be classified into two structurally distinct groups: canonical and non-canonical SLs. Canonical SLs contain the ABCD ring system, and non-canonical SLs lack the A, B, or C ring but have the enol ether-D ring moiety which is essential for biological activities. The simplest non-canonical SL is the SL biosynthetic intermediate carlactone (CL). In plants, CL and its oxidized metabolites such as carlactonoic acid and methyl carlactonoate, are present in root and shoot tissues. In some plant species including black oat (Avena strigosa), sunflower (Helianthus annuus), and maize (Zea mays), non-canonical SLs are major germination stimulants in the root exudates. Various plant species such as tomato (Solanum lycopersicum) release carlactonoic acid, and poplar (Populus spp.) was found to exude methyl carlactonoate into the rhizosphere. These results suggest that both canonical and non-canonical SLs are active as host recognition signals in the rhizosphere. In contrast, limited distribution of canonical SLs in the plant kingdom and structure- and stereo-specific transportation of canonical SLs from roots to shoots suggest that plant hormones inhibiting shoot branching are not canonical SLs but are rather non-canonical SLs.\n\nSupplemental files: Synthesis of 7-hydroxy-5-deoxystrigol stereoisomers, spectroscopic data of synthetic compounds and the natural stimulant in dokudami root exudates.\n\nScheme S1. Synthesis of racemic mixture of 7- and 7{beta}-hydroxy-5-deoxystrigol.\n\nFig. S1. 1H NMR spectrum of natural stimulant in dokudami root exudates.\n\nFig. S2. LC-MS/MS chromatograms of synthetic standards and natural stimulant.\n\nFig. S3. LC-MS/MS chromatograms of synthetic 7{beta}-hydroxy-5-deoxystrigol and natural stimulant.\n\nFig. S4. LC-MS/MS chromatograms of synthetic 7{beta}-hydroxy-5-deoxystrigol and natural stimulant.\n\nHighlightThe chemistry of canonical and non-canonical strigolactones and their distribution in the plant kingdom are summarized in relation to their biological activities in the rhizosphere and in plants.

plant biology↗

bioRxiv2013

Pathways to social evolution: reciprocity, relatedness, and synergy

Many organisms live in populations structured by space and by class, exhibit plastic responses to their social partners, and are subject to non-additive ecological and fitness effects. Social evolution theory has long recognized that all of these factors can lead to different selection pressures but has only recently attempted to synthesize how these factors interact. Using models for both discrete and continuous phenotypes, we show that analyzing these factors in a consistent framework reveals that they interact with one another in ways previously overlooked. Specifically, behavioral responses (reciprocity), genetic relatedness, and synergy interact in non-trivial ways that cannot be easily captured by simple summary indices of assortment. We demonstrate the importance of these interactions by showing how they have been neglected in previous synthetic models of social behavior both within and between species. These interactions also affect the level of behavioral responses that can evolve in the long run; proximate biological mechanisms are evolutionarily stable when they generate enough responsiveness relative to the level of responsiveness that exactly balances the ecological costs and benefits. Given the richness of social behavior across taxa, these interactions should be a boon for empirical research as they are likely crucial for describing the complex relationship linking ecology, demography, and social behavior.

Evolutionary Biology↗

bioRxiv2018

Emergent oscillations in dense adaptive cell populations

Collective oscillations of cells in a population appear under diverse biological contexts. Here, we establish a set of common principles by categorising the response of individual cells against a time-varying signal. A positive intracellular signal relay of sufficient gain from participating cells is required to sustain the oscillations, together with phase matching. The two conditions yield quantitative predictions for the onset cell density and frequency in terms of measured single-cell and signal response functions. Through mathematical constructions, we show that cells that adapt to a constant stimulus fulfil the phase requirement by developing a leading phase in an active frequency window that enables cell-to-signal energy flow. Analysis of dynamical quorum sensing in several cellular systems with increasing biological complexity reaffirms the pivotal role of adaptation in powering oscillations in an otherwise dissipative cell-to-cell communication channel. The physical conditions identified also apply to synthetic oscillatory systems.

biophysics↗

bioRxiv2018

Scalable Nonlinear Programming Framework for Parameter Estimation in Dynamic Biological System Models

We present a nonlinear programming (NLP) framework for the scalable solution of parameter estimation problems that arise in dynamic modeling of biological systems. Such problems are computationally challenging because they often involve highly nonlinear and stif differential equations as well as many experimental data sets and parameters. The proposed framework uses cutting-edge modeling and solution tools which are computationally efficient, robust, and easy-to-use. Specifically, our framework uses a time discretization approach that: i) avoids repetitive simulations of the dynamic model, ii) enables fully algebraic model implementations and computation of derivatives, and iii) enables the use of computationally efficient nonlinear interior point solvers that exploit sparse and structured linear algebra techniques. We demonstrate these capabilities by solving estimation problems for synthetic human gut microbiome community models. We show that an instance with 156 parameters, 144 differential equations, and 1,704 experimental data points can be solved in less than 3 minutes using our proposed framework (while an off-the-shelf simulation-based solution framework requires over 7 hours). We also create large instances to show that the proposed framework is scalable and can solve problems with up to 2,352 parameters, 2,304 differential equations, and 20,352 data points in less than 15 minutes. Competing methods reported in the computational biology literature cannot address problems of this level of complexity. The proposed framework is flexible, can be broadly applied to dynamic models of biological systems, and enables the implementation of sophisticated estimation techniques to quantify parameter uncertainty, to diagnose observability/uniqueness issues, to perform model selection, and to handle outliers.\n\nAuthor summaryConstructing and validating dynamic models of biological systems spanning biomolecular networks to ecological systems is a challenging problem. Here we present a scalable computational framework to rapidly infer parameters in complex dynamic models of biological systems from large-scale experimental data. The framework was applied to infer parameters of a synthetic microbial community model from large-scale time series data. We also demonstrate that this framework can be used to analyze parameter uncertainty, to diagnose whether the experimental data are sufficient to uniquely determine the parameters, to determine the model that best describes the data, and to infer parameters in the face of data outliers.

bioinformatics↗

bioRxiv2017

Immune receptors with exogenous domain fusions form evolutionary hotspots in grass genomes

BackgroundThe plant immune system is innate, encoded in the germline. Using it efficiently, plants are capable of recognizing a diverse range of rapidly evolving pathogens. A recently described phenomenon shows that plant immune receptors are able to recognize pathogen effectors through the acquisition of exogenous protein domains from other plant genes.\n\nResultsWe showed that plant immune receptors with integrated domains are distributed unevenly across their phylogeny in grasses. Using phylogenetic analysis, we uncovered a major integration clade, whose members underwent repeated independent integration events producing diverse fusions. This clade is ancestral in grasses with members often found on syntenic chromosomes. Analyses of these fusion events revealed that homologous receptors can be fused to diverse domains. Furthermore, we discovered a 43 amino acids long motif that was associated with this dominant integration clade and was located immediately upstream of the fusion site. Sequence analysis revealed that DNA transposition and/or ectopic recombination are the most likely mechanisms of NLR-ID formation.\n\nConclusionsThe identification of this subclass of plant immune receptors that is naturally adapted to new domain integration will inform biotechnological approaches for generating synthetic receptors with novel pathogen baits.

evolutionary biology↗

bioRxiv2018

A Comprehensive Tool Set for Inducible, Cell Type-Specific Gene Expression in Arabidopsis

Understanding the context-specific role of gene function is a key objective of modern biology. To this end, we generated a resource for inducible cell-type specific trans-activation based on the well-established combination of the chimeric GR-LhG4 transcription factor and the synthetic pOp promoter. Harnessing the flexibility of the GreenGate cloning system, we produced a comprehensive set of GR-LhG4 driver lines targeting most tissues in the Arabidopsis shoot and root with a strong focus on the indeterminate meristems. We show that, when combined with effectors under control of the pOp promoter, tight temporal and spatial control of gene expression is achieved. In particular, inducible expression in F1 plants obtained from crosses of driver and effector lines allows rapid assessment of the cell type-specific impact of an effector with high temporal resolution. Thus, our comprehensive and flexible toolbox is suited to overcome the limitations of ubiquitous genetic approaches, the outputs of which are often difficult to interpret due to widespread existence of compensatory mechanisms and the integration of diverging effects in different cell types.\n\nOne sentence summary: A set of lines enabling spatio-temporal control of gene expression in Arabidopsis.

plant biology↗

bioRxiv2016

Combining inferred regulatory and reconstructed metabolic networks enhances phenotype prediction in yeast

Gene regulatory and metabolic network models have been used successfully in many organisms, but inherent differences between them make networks difficult to integrate. Probabilistic Regulation Of Metabolism (PROM) provides a partial solution, but it does not incorporate network inference and underperforms in eukaryotes. We present an Integrated Deduced REgulation And Metabolism (IDREAM) method that combines statistically inferred Environment and Gene Regulatory Influence Network (EGRIN) models with the PROM framework to create enhanced metabolic-regulatory network models. We used IDREAM to predict phenotypes and genetic interactions between transcription factors and genes encoding metabolic activities in the eukaryote, Saccharomyces cerevisiae. IDREAM models contain many fewer interactions than PROM and yet produce significantly more accurate growth predictions. IDREAM consistently outperformed PROM using any of three popular yeast metabolic models and across three experimental growth conditions. Importantly, IDREAMs enhanced accuracy makes it possible to identify subtle synthetic growth defects. With experimental validation, these novel genetic interactions involving the pyruvate dehydrogenase complex suggested a new role for fatty acid-responsive factor Oaf1 in regulating acetyl-CoA production in glucose grown cells.\n\nAuthor SummaryThe integration of gene regulatory and metabolic network models is an important goal in computational biology, in order to develop methods that can identify the underlying mechanistic links in biological networks and advance metabolic engineering techniques. In this paper, we develop a framework called Integrated Deduced REgulation And Metabolism (IDREAM) that can improve our ability to predict phenotypes of microorganisms, and particularly it can address the challenges in evaluating phenotypic consequence of perturbing transcriptional regulation of metabolism in a eukaryotic cell. We compare the predictive performance of an IDREAM S. cerevisiae model with a PROM model using a TRN available from the YEASTRACT database. IDREAM outperforms PROM using any of three popular yeast metabolic models and across three experimental growth conditions, making it possible to identify subtle synthetic growth defects, and a new role for Oaf1 in the regulation of acetyl-CoA biosynthesis.

systems biology↗

bioRxiv2018

Dynamic Distribution Decomposition for Single-Cell Snapshot Time Series Identifies Subpopulations and Trajectories during iPSC Differentiation

Recent high-dimensional single-cell technologies such as mass cytometry are enabling time series experiments to monitor the temporal evolution of cell state distributions and to identify dynamically important cell states, such as fate decision states in differentiation. However, these technologies are destructive, and require analysis approaches that temporally map between cell state distributions across time points. Current approaches to approximate the single-cell time series as a dynamical system suffer from too restrictive assumptions about the type of kinetics, or link together pairs of sequential measurements in a discontinuous fashion.\n\nWe propose Dynamic Distribution Decomposition (DDD), an operator approximation approach to infer a continuous distribution map between time points. On the basis of single-cell snapshot time series data, DDD approximates the continuous time Perron-Frobenius operator by means of a finite set of basis functions. This procedure can be interpreted as a continuous time Markov chain over a continuum of states. By only assuming a memoryless Markov (autonomous) process, the types of dynamics represented are more general than those represented by other common models, e.g., chemical reaction networks, stochastic differential equations. Additionally, the continuity assumption ensures that the same dynamical system maps between all time points, not arbitrarily changing at each time point. We demonstrate the ability of DDD to reconstruct dynamically important cell states and their transitions both on synthetic data, as well as on mass cytometry time series of iPSC reprogramming of a fibroblast system. We use DDD to find previously identified subpopulations of cells and to visualize differentiation trajectories.\n\nDynamic Distribution Decomposition allows interpreting high-dimensional snapshot time series data as a low-dimensional Markov process, thereby enabling an interpretable dynamics analysis for a variety of biological processes by means of identifying their dynamically important cell states.\n\nAuthor summaryHigh-dimensional single-cell snapshot measurements are now increasingly utilized to study dynamic processes. Such measurements enable us to evaluate cell population distributions and their evolution over time. However, it is not trivial to map these distribution across time and to identify dynamically important cell states, i.e. bottleneck regions of state space exhibiting a high degree of change. We present Dynamic Distribution Decomposition (DDD) achieving this task by encoding single-cell measurements as linear combination of basis function distributions and evolving these as a linear system. We demonstrate reconstruction of dynamically important states for synthetic data of a bifurcated diffusion process and mass cytometry data for iPSC reprogramming.

systems biology↗

bioRxiv2015

Estimating cell cycle model parameters using systems identification

A current challenge in data-driven mathematical modeling of cancer is identifying biologically-relevant parameters of mathematical models from sparse and often noisy experimental data of mixed types. We describe a cell cycle model and outline how to use the Optimization Toolbox in Matlab to estimate its timescale parameters, given flow cytometry and cell viability (synthetic) data, and illustrate the technique with simulated data. This technique can be similarly applied to a variety of cell cycle models, particularly as more laboratories begin to use high-content, quantitative cell screening and imaging platforms. An advanced version of this work (CellPD: cell line phenotype digitizer) will be released as open source in early 2016 at MultiCellDS.org.

Cancer Biology↗

bioRxiv2014

Comprehensive mutational scanning of a kinase in vivo reveals substrate-dependent fitness landscapes

Deep mutational scanning has emerged as a promising tool for mapping sequence-activity relationships in proteins1-4, RNA5 and DNA6-8. In this approach, diverse variants of a sequence of interest are first ranked according to their activities in a relevant pooled assay, and this ranking is then used to infer the shape of the fitness landscape around the wild-type sequence. Little is currently know, however, about the degree to which such fitness landscapes are dependent on the specific assay conditions from which they are inferred. To explore this issue, we performed deep mutational scanning of APH(3)II, a Tn5 transposon-derived kinase that confers resistance to aminoglycoside antibiotics9, in E. coli under selection with each of six structurally diverse antibiotics at a range of inhibitory concentrations. We found that the resulting fitness landscapes showed significant dependence on both antibiotic structure and concentration. This shows that the notion of essential amino acid residues is context-dependent, but also that this dependence can be exploited to guide protein engineering. Specifically, we found that differential analysis of fitness landscapes allowed us to generate synthetic APH(3)II variants with orthogonal substrate specificities.

Molecular Biology↗