Biology subjects

Alper, H. S.

Publications and source records attributed to Alper, H. S..

9 recordsLinked to original sources

bioRxiv2024

A Linear Mixed Effects Model for Evaluating Synthetic Gene Circuits

A significant advancement in synthetic biology is the development of synthetic gene circuits with predictive Boolean logic. However, there is no universally accepted or applied statistical test to analyze the performance of these circuits. Many basic statistical tests fail to capture the predicted logic (OR, AND, etc.) and most studies neglect statistical analysis entirely. As synthetic gene circuits shift toward advanced applications, primarily in computing, biosensing, and human health, it is critical to standardize the statistical methods used to evaluate gate success. Here, we propose the application of a linear mixed effects model to analyze and quantify genetic Boolean logic gate performance. First, we analyzed 144 currently published Boolean logic gates for trends and used unsupervised machine learning (k-means clustering) to validate the statistical model. Next, we utilized the model to generate estimates for the fixed effect of the ON state, {beta}, as a general descriptor of the Boolean nature of a circuit and used Monte Carlo simulations to recommend sample sizes for evaluating gate performance. Finally, we examined {beta} as a holistic metric for circuit performance using a series of nested repressor OR gates with intentionally degraded performance. We observed a linear correlation between {beta} and the predicted translation rate, highlighting the use of {beta} for the forward design of new Boolean gates. In summary, we utilized a linear mixed effects model to describe synthetic gene circuits and determined that the fixed effect, {beta}, is an appropriate descriptor of gate behavior that can be used to statistically evaluate performance. Significance statementThere is no standard method for statistically evaluating the success of biological Boolean logic gates and common statistical tests are not used or incorrectly applied. We propose the use of a linear mixed effects model to estimate the value for {beta}, a parameter which describes the difference between ON and OFF gate states. A null hypothesis of {beta} = 0 can then be tested to determine if there is a statistically significant difference between the two states.

synthetic biology↗

bioRxiv2024

Genome-scale prediction of gene ontology from mass fingerprints reveals new metabolic gene functions

Mass-based fingerprinting can characterize unknown strains, however expansion of these methods to predict specific gene functions is lacking. Therefore, rapid mass fingerprinting was developed to functionally profile a comprehensive yeast knockout library. Matrix assisted laser desorption ionization (MALDI)-time of flight (TOF) mass fingerprints of 3,238 Saccharomyces cerevisiae knockouts were digitized for correlation with gene ontology (GO) annotations. Random forests and support vector machine (SVM) algorithms precisely assigned GO accessions with AUC scores all above 0.83. SVM was the best predictor with average true positive and true negative rates of 0.975 and 0.991, respectively. The SVM model suggested new functions for 28 uncharacterized yeast genes. Metabolomics analysis of two knockouts (YDR215C and YLR122C) of uncharacterized genes predicted to be involved in methylation-related metabolism, showed altered intracellular contents of methionine-related metabolites. Increased S-adenosylmethionine in YDR215C highlights potential for enhancement of methylation pathways. These results demonstrate that MALDI-TOF fingerprints can be rapidly digitized, resulting in datasets that enable prediction of microbial genotypes and even the function of specific genes. This fingerprinting method can inform optimal bioproduction chassis selection.

systems biology↗

bioRxiv2024

Single-Cell Phenotyping of Extracellular Electron Transfer via Microdroplet Encapsulation

Electroactive organisms contribute to metal cycling, pollutant removal, and other redox-driven environmental processes. Studying this phenomenon in high-throughput is challenging since extracellular reduction cannot easily be traced back to its cell of origin within a mixed population. Here, we describe the development of a microdroplet emulsion system to enrich EET-capable organisms. We validated our system using the model electroactive organism S. oneidensis and describe the tooling of a benchtop microfluidic system for oxygen-limited processes. We demonstrated enrichment of EET-capable phenotypes from a mixed wild-type and EET-knockout population. As a proof-of-concept application, bacteria were collected from iron sedimentation from Town Lake (Austin, TX) and subjected to microdroplet enrichment. We observed an increase in EET-capable organisms in the sorted population that was distinct when compared to a population enriched in a bulk culture more closely akin to traditional techniques for discovering EET-capable bacteria. Finally, two bacterial species, C. sakazakii and V. fessus not previously shown to be electroactive, were further cultured and characterized for their ability to reduce channel conductance in an organic electrochemical transistor (OECT) and to reduce soluble Fe(III). We characterized two bacterial species not previously shown to exhibit electrogenic behavior. Our results demonstrate the utility of a microdroplet emulsions for identifying putative EET-capable bacteria and how this technology can be leveraged in tandem with existing methods.

bioengineering↗

bioRxiv2021

Mapping enzyme catalysis with metabolomic biosensing

Enzymes are represented across a vast space of protein sequences and structural forms and have activities that far exceed the best chemical catalysts; however, engineering them to have novel or enhanced activity is limited by technologies for sensing product formation. Here, we describe a general and scalable approach for characterizing enzyme activity that uses the metabolism of the host cell as a biosensor by which to infer product formation. Since different products consume different molecules in their synthesis, they perturb host metabolism in unique ways that can be measured by mass spectrometry. This provides a general way by which to sense product formation, to discover unexpected products and map the effects of mutagenesis.

bioengineering↗

bioRxiv2021

Deep learning redesign of PETase for practical PET degrading applications

Plastic waste poses an ecological challenge1. While current plastic waste management largely relies on unsustainable, energy-intensive, or even hazardous physicochemical and mechanical processes, enzymatic degradation offers a green and sustainable route for plastic waste recycling2. Poly(ethylene terephthalate) (PET) has been extensively used in packaging and for the manufacture of fabrics and single-used containers, accounting for 12% of global solid waste3. The practical application of PET hydrolases has been hampered by their lack of robustness and the requirement for high processing temperatures. Here, we use a structure-based, deep learning algorithm to engineer an extremely robust and highly active PET hydrolase. Our best resulting mutant (FAST-PETase: Functional, Active, Stable, and Tolerant PETase) exhibits superior PET-hydrolytic activity relative to both wild-type and engineered alternatives, (including a leaf-branch compost cutinase and its mutant4) and possesses enhanced thermostability and pH tolerance. We demonstrate that whole, untreated, post-consumer PET from 51 different plastic products can all be completely degraded by FAST-PETase within one week, and in as little as 24 hours at 50 {degrees}C. Finally, we demonstrate two paths for closed-loop PET recycling and valorization. First, we re-synthesize virgin PET from the monomers recovered after enzymatic depolymerization. Second, we enable in situ microbially-enabled valorization using a Pseudomonas strain together with FAST-PETase to degrade PET and utilize the evolved monomers as a carbon source for growth and polyhydroxyalkanoate production. Collectively, our results demonstrate the substantial improvements enabled by deep learning and a viable route for enzymatic plastic recycling at the industrial scale.

bioengineering↗

bioRxiv2021

Evolving a generalist biosensor for bicyclic monoterpenes

Prokaryotic transcription factors can be repurposed as analytical and synthetic tools for precise chemical measurement and regulation. Monoterpenes encompass a broad chemical family that are commercially valuable as flavors, cosmetics, and fragrances, but have proven difficult to measure, especially in cells. Herein, we develop genetically-encoded, generalist monoterpene biosensors by using directed evolution to expand the effector specificity of the camphor-responsive TetR-family regulator CamR from Pseudomonas putida. Using a novel negative selection coupled with a high-throughput positive screen (Seamless Enrichment of Ligand-Inducible Sensors, SELIS), we evolve CamR biosensors that can recognize four distinct monoterpenes: borneol, fenchol, eucalyptol, and camphene. Different evolutionary trajectories surprisingly yielded common mutations, emphasizing the utility of CamR as a platform for creating generalist biosensors. Systematic promoter optimization driving the reporter increased the systems signal-to-noise ratio to 150-fold. These sensors can serve as a starting point for the high-throughput screening and dynamic regulation of bicyclic monoterpene production strains.

synthetic biology↗

bioRxiv2021

Using structurally fungible biosensors to evolve improved alkaloid biosyntheses

A key bottleneck in the microbial production of therapeutic plant metabolites is identifying enzymes that can greatly improve yield. The facile identification of genetically encoded biosensors can overcome this limitation and become part of a general method for engineering scaled production. We have developed a unique combined screening and selection approach that quickly refines the affinities and specificities of generalist transcription factors, and using RamR as a starting point we evolve highly specific (>100-fold preference) and sensitive (EC50 <30 M) biosensors for the alkaloids tetrahydropapaverine, papaverine, glaucine, rotundine, and noscapine. High resolution structures reveal multiple evolutionary avenues for the fungible effector binding site, and the creation of new pockets for different chemical moieties. These sensors further enabled the evolution of a streamlined pathway for tetrahydropapaverine, an immediate precursor to four modern pharmaceuticals, collapsing multiple methylation steps into a single evolved enzyme. Our methods for evolving biosensors now enable the rapid engineering of pathways for therapeutic alkaloids.

synthetic biology↗

bioRxiv2020

Development of a growth coupled dynamic regulation network balancing malonyl-CoA node to enhance (2S)-naringenin synthesis in E. coli

Generally, high- and low-performance nongenetic variants and young and aged cells co-existed in culture at all growth phases. In this regard, individually and dynamically regulating the metabolic flux of single cells based on their cellular state is highly useful for improving the performance of populations. However, balancing the trade-offs between biomass formation and compound over-production requires sophisticated dynamic regulation networks (DRNs) which can be challenging. Here, we developed a growth coupled NCOMB (Naringenin-Coumaric acid-Malonyl-CoA-Balanced) DRN with systematic optimization of (2S)-naringenin and p-coumaric acid-responsive regulation pathways for real-time control of intracellular supply of malonyl-CoA. In doing so, the acyl carrier protein was used as a new critical node for fine-tuning malonyl-CoA consumption instead of fatty acid synthase. Following directed evolution of the NCOMB DRN, we obtained a strain with cumulative 8.4-fold improvement in (2S)-naringenin production with balanced cell growth, demonstrating the high efficiency of this system for improving pathway production.

synthetic biology↗

bioRxiv2018

Validating genome-wide CRISPR-Cas9 function in the non-conventional yeast Yarrowia lipolytica

Genome-wide mutational screens are central to understanding the genetic underpinnings of evolved and engineered phenotypes. The widespread adoption of CRISPR-Cas9 genome editing has enabled such screens in many organisms, but identifying functional sgRNAs still remains a challenge. To address this limitation, we developed a methodology to quantify the cutting efficiency of each sgRNA in a genome-scale library in the biotechnologically important yeast Yarrowia lipolytica. Screening in the presence and absence of native DNA repair enabled high-throughput quantification of sgRNA function leading to the identification of high efficiency sgRNAs that cover 94% of genes. Library validation enhanced the classification of essential genes by identifying inactive guides that create false negatives and mask the effects of successful disruptions. Quantification of guide effectiveness also creates a dataset from which functional determinants of CRISPR-Cas9 can be identified. Finally, application of the library identified mutations that led to high lipid accumulation and eliminated pseudohyphal morphology.

synthetic biology↗