Biology subjects

Salis, H. M.

Publications and source records attributed to Salis, H. M..

10 recordsLinked to original sources

bioRxiv2026

A sequence-to-function model to predict T7 transcription rates and redesign T7 expression systems with lowered production of immunogenic RNA byproducts

T7 RNA polymerase is widely used to produce RNA using a canonical T7 promoter; however, it will also bind to low-affinity sites to generate cryptic transcription and produce RNA byproducts, which reduce full-length mRNA purity and yield. When manufacturing therapeutic RNAs for clinical applications, RNA byproducts must be removed using costly downstream purification and can cause adverse immunogenicity. To predict T7 transcription rates and reduce cryptic transcription, we designed 11588 T7 promoters and measured their mRNA levels, spanning a 6300-fold range within in vitro transcription reactions. We developed the T7 Promoter Calculator, a sequence-to-function machine learning model that predicts the T7 transcription rate on arbitrary DNA sequence across a 500-fold range with high accuracy (R2 = 0.80), accounting for both core and flanking motif sequences. We combined the model with generative design to remove low-affinity T7 sites from a therapeutic T7 expression system, resulting in a 2-fold increase in full-length mRNA purity. The automated design of T7 expression systems to remove undesired RNA byproducts increases mRNA purity and lowers downstream separation costs, while reducing adverse immunogenicity.

synthetic biology↗

bioRxiv2026

A Low-Cost, High-Throughput Design-Build-Test Pipeline for Engineering Genetic Systems: Stress Testing with Complex Structural Proteins

Genetic systems engineering is constrained by high DNA synthesis costs, assembly inefficiencies, and challenges in expressing complex proteins. To address these limitations, we developed a highly parallel, low-cost pipeline for the design, assembly, and functional screening of genetic systems, which we stress-tested on highly repetitive structural proteins, including spider silk, biocements, reflectins, and talins. The integrated pipeline combines computational genetic systems design, low-cost many-plasmid DNA assembly from oligopools, automated many-to-many mapping using nanopore sequencing data, and a label-free biosensor to measure single-cell protein expression levels. We applied this pipeline to build 240 plasmids, achieving an 88% success rate (up to 2000 bp) using standard clonal isolation and 58% assembly efficiency (up to 5600 bp) without selective DNA purification, while lowering material costs by up to 24-fold. We applied the biosensor to identify genetic factors that create distinct cellular subpopulations with varying protein expression levels. Overall, the integrated pipeline will dramatically lower the cost of high-throughput synthetic biology, while demonstrating how designing genetic systems to improve build efficiency ("design for build") and directly incorporating biosensors into genetic systems ("design for test") will greatly accelerate design-build-test workflows.

synthetic biology↗

bioRxiv2024

An Autonomous Microbial Sensor Enables Long-term Detection of TNT Explosive in Natural Soil

Microbes can be engineered to detect target chemicals, but when they operate in real-world environments, it remains unclear how competition with natural microbes affect their performance over long time periods. We engineered sensors and memory-storing genetic circuits inside Bacillus subtilis to sense and respond to the TNT explosive, using predictive models for rational design. We characterized their ability to detect TNT in a natural soil system, measuring single-cell and population-level behavior over a 28-day period. The autonomous microbial sensor activated its response by 14-fold when exposed to low TNT concentrations and maintained stable activation for over 21 days, exhibiting exponential decay dynamics at the population-level with a half-life of about 5 days. Our results show that engineered soil bacteria can carry out long-term detection of an important chemical in natural soil with competitive growth dynamics serving as additional biocontainment.

synthetic biology↗

bioRxiv2021

Automated Model-Predictive Design of Synthetic Promoters to Control Transcriptional Profiles in Bacteria

Transcription rates are regulated by the interactions between RNA polymerase, sigma factor, and promoter DNA sequences in bacteria. However, it remains unclear how non-canonical sequence motifs collectively control transcription rates. Here, we combined massively parallel assays, biophysics, and machine learning to develop a 346-parameter model that predicts site-specific transcription initiation rates for any {sigma}70 promoter sequence, validated across 17396 bacterial promoters with diverse sequences. We applied the model to predict genetic context effects, design {sigma}70 promoters with desired transcription rates, and identify undesired promoters inside engineered genetic systems. The model provides a biophysical basis for understanding gene regulation in natural genetic systems and precise transcriptional control for engineering synthetic genetic systems. One-Sentence SummaryA 346-parameter model predicted DNAs interactions with RNA polymerase initiation complex, enabling accurate transcription rate predictions and automated promoter design in bacterial genetic systems.

synthetic biology↗

bioRxiv2021

Tuning Cell-free Composition Controls the Time-delay, Dynamics, and Productivity of TX-TL Expression

The composition of TX-TL cell-free expression systems are adjusted by adding macromolecular crowding agents and salts. However, the effects of these cosolutes on the dynamics of individual gene expression processes have not been systematically quantified. Here, we carry out kinetic mRNA and protein level measurements on libraries of genetic constructs using the common cosolutes PEG-8000, Ficoll-400, and magnesium glutamate. By combining these measurements with biophysical modeling, we show that cosolutes have differing effects on transcription initiation, translation initiation, and translation elongation rates with trade-offs between time-delays, expression tunability, and maximum expression productivity. We also confirm that biophysical models can predict translation initiation rates in TX-TL using E. coli lysate. We discuss how cosolute composition can be tuned to maximize performance across different cell-free applications, including biosensing, diagnostics, and biomanufacturing.

synthetic biology↗

bioRxiv2021

Selenium-dependent metabolic reprogramming during inflammation and resolution

Trace element selenium (Se) is incorporated as the 21st amino acid, selenocysteine (Sec), into selenoproteins through tRNA[Ser]Sec. Selenoproteins act as gatekeepers of redox homeostasis and modulate immune function to effect anti-inflammation and resolution. However, mechanistic underpinnings involving metabolic reprogramming during inflammation and resolution remain poorly understood. Bacterial endotoxin lipopolysaccharide (LPS) activation of murine bone marrow-derived macrophages (BMDMs) cultured in the presence or absence of Se (as selenite) was used to examine temporal changes in the proteome and metabolome by multiplexed tandem mass tag-quantitative proteomics, metabolomics, and machine-learning approaches. Kinetic deltagram and clustering analysis indicated addition of Se led to extensive reprogramming of cellular metabolism upon stimulation with LPS enhancing PPP, TCA cycle, and OXPHOS, to aid in the phenotypic transition towards alternatively activated macrophages, synonymous with resolution of inflammation. Remodeling of metabolic pathways and consequent metabolic adaptation towards pro-resolving phenotypes began with Se treatment at 0 h and became most prominent around 8 h post LPS stimulation that included succinate dehydrogenase complex (Sdh), pyruvate kinase (Pkm), and sedoheptulosekinase (Shpk). Se-dependent modulation of these pathways predisposed BMDMs to preferentially increase OXPHOS to efficiently regulate inflammation and its timely resolution. Use of macrophages lacking selenoproteins, indicated that all three metabolic nodes were sensitive to selenoproteome expression. Furthermore, inhibition of Sdh with dimethylmalonate affected the pro-resolving effects of Se by increasing the resolution interval in a murine peritonitis model. In summary, our studies provide novel insights into the role of cellular Se via metabolic reprograming to facilitate anti-inflammation and proresolution.

immunology↗

bioRxiv2020

Systematic Quantification of Sequence and Structural Determinants Controlling mRNA stability in Bacterial Operons

mRNA degradation is a central process that affects all gene expression levels, and yet the determinants that control mRNA decay rates remain poorly characterized. Here, we applied a synthetic biology, learn-by-design approach to elucidate the sequence and structural determinants that control mRNA stability in bacterial operons. We designed, constructed, and characterized 82 operons, systematically varying RNAse binding site characteristics, translation initiation rates, and transcriptional terminator efficiencies in the 5 UTR, intergenic, and 3 UTR regions, and measuring their mRNA levels using RT-qPCR assays. We show that introducing long single-stranded RNA into 5 UTRs reduced mRNA levels by up to 9.4-fold and that lowering translation rates reduced mRNA levels by up to 11.8-fold. We also found that RNAse binding sites in intergenic regions had much lower effects on mRNA levels. Surprisingly, changing transcriptional termination efficiency or introducing long single-stranded RNA into 3 UTRs had no effect on upstream mRNA levels. From these measurements, we developed and validated biophysical models of ribosome protection and RNAse activity with excellent quantitative correspondence. We also formulated design rules to rationally control a mRNAs stability, facilitating the automated design of engineered genetic systems with desired functionalities.

synthetic biology↗

bioRxiv2020

The Synthesis Success Calculator: Predicting the Rapid Synthesis of DNA Fragments with Machine Learning

The synthesis and assembly of long DNA fragments has greatly accelerated synthetic biology and biotechnology research. However, long turnaround times or synthesis failures create unpredictable bottlenecks in the design-build-test-learn cycle. We developed a machine learning model, called the Synthesis Success Calculator, to predict whether a long DNA fragment can be readily synthesized with a short turnaround time. The model also identifies the sequence determinants associated with the synthesis outcome. We trained a random forest classifier using biophysical features and a compiled dataset of 1076 DNA fragment sequences to achieve high predictive performance (F1 score of 0.928 on 251 unseen sequences). Feature importance analysis revealed that repetitive DNA sequences were the most important contributor to synthesis failures. We then applied the Synthesis Success Calculator across large sequence datasets and found that 84.9% of the Escherichia coli MG1655 genome, but only 34.4% of sampled plasmids in NCBI, could be readily synthesized. Overall, the Synthesis Success Calculator can be applied on its own to prevent synthesis failures or embedded within optimization algorithms to design large genetic systems that can be rapidly synthesized and assembled.Competing Interest StatementThe authors have declared no competing interest.View Full Text

synthetic biology↗

bioRxiv2017

Automated Parameterization of Predictive Kinetic Metabolic Models from Sparse Datasets for Efficient Optimization of Many-Enzyme Heterologous Pathways

Engineered heterologous metabolic pathways can convert low-cost feedstock into high-value products, though it remains a significant challenge to reliably and efficiently maximize end-product biosynthesis, particularly when many enzymes must be co-expressed together. When current approaches are applied to many-enzyme pathways, the construction and characterization process is highly iterative and laborious, while generating high-dimensional datasets that remain difficult to analyze for forward engineering efforts. To overcome these challenges, we developed a new algorithm that determines the highly non-linear and high-dimensional relationship between a pathways enzyme expression levels and its end-product productivity from common characterization of a small number of heterologous pathway variants. We combined kinetic metabolic modeling, elementary mode analysis, model reduction, de-dimensionalization, and genetic algorithm optimization into an automated procedure that parameterizes accurate kinetic metabolic models from sparsely characterized pathway variant libraries with varied enzyme expression levels. The resulting Pathway Maps are used to determine rate-limiting steps, predict optimal expression levels, identify allosteric interactions, rank-order enzyme kinetics, and prioritize protein engineering efforts. We demonstrate the Pathway Map Calculator algorithm on two experimental datasets, a 3-enzyme carotenoid biosynthesis pathway and a 9-enzyme limonene biosynthesis pathway, as well as a series of in silico pathway examples to rigorously demonstrate the algorithms accuracy, linear scaling, and high tolerance to measurement noise. By greatly reducing experimental efforts and providing quantitative forward engineering predictions, the Pathway Map Calculator has the potential to dramatically accelerate the engineering of many-enzyme heterologous metabolic pathways.\n\nHighlightsO_LIWe developed an automated algorithm that uses a small number of characterized pathway variants to determine the pathways expression-productivity relationship.\nC_LIO_LIThe Pathway Map Calculator is accurate, scales linearly on many-enzyme pathways, distinguishes allosteric interactions, and tolerates substantial measurement noise.\nC_LIO_LIPathway Maps are used to predict optimal enzyme expression levels, identify rate-limiting steps, and prioritize protein engineering efforts\nC_LI

synthetic biology↗

bioRxiv2016

Precise Quantification of Translation Inhibition by mRNA Structures that Overlap with the Ribosomal Footprint in N-terminal Coding Sequences

A mRNAs translation rate is controlled by several sequence determinants, including the presence of RNA structures within the N-terminal regions of its coding sequences. However, the physical rules that govern when such mRNA structures will inhibit translation remain unclear. Here, we introduced systematically designed RNA hairpins into the N-terminal coding region of a reporter protein with steadily increasing distances from the start codon, followed by characterization of their mRNA and expression levels in E. coli. We found that the mRNAs; translation rates were repressed, by up to 1410-fold, when mRNA structures overlapped with the ribosomes footprint. In contrast, when the mRNA structure was located outside the ribosomes footprint, translation was repressed by less than 2-fold. By combining our measurements with biophysical modeling, we determined that the ribosomal footprint extends 13 nucleotides into the N-terminal coding region and, when a mRNA structure overlaps or partially overlaps with the ribosomal footprint, the free energy to unfold only the overlapping structure controlled the extent of translation repression. Overall, our results provide precise quantification of the rules governing translation initiation at N-terminal coding regions, improving the predictive design of post-transcriptional regulatory elements that regulate translation rate.

genetics↗