Biology subjects

Salis, H.

Publications and source records attributed to Salis, H..

3 recordsLinked to original sources

bioRxiv2025

Quantifying the impact of genetic mutations on enhancer dynamics

Transcriptional regulation is mediated by enhancers, yet how genetic perturbations alter enhancer activity and gene expression remains poorly understood. We developed UDI-UMI-STARR-seq, which integrates dual indexes and unique molecular identifiers, and combined it with RNA-seq to profile the effects of perturbations on enhancer activity and target gene expression. We applied this approach to a library of 253,632 fragments representing 46,142 cell type-specific candidate enhancers and assessed the impact of CRISPR/Cas9-mediated deletion of six transcription factors (or TFs; ATF2, CTCF, FOXA1, LEF1, TCF7L2, and SCRT1) with diverse regulatory roles. Across knockout lines, we identified responsive enhancers that were either repressed or induced, often through motifs such as the p53 family of TFs. Enhancer-gene mapping revealed TF-specific programs, including repression of Wnt/p53 targets with ATF2 or LEF1 loss, downregulation of the FIRRE locus with CTCF loss, and compensatory upregulation of RNA polymerase II regulators following FOXA1 depletion. A deep learning model trained on enhancer sequences recapitulated core principles of enhancer grammar, including cooperative motif syntax and the influence of flanking sequence context. Applying this framework to the neurodevelopmental disorder-associated 16p12.1 deletion identified responsive enhancers linked to genes involved in axon guidance, synaptic plasticity, and translational control, providing a scalable readout of enhancer dynamics generalizable to genetic mutations.

genomics↗

bioRxiv2019

Controlling Heterogeneity and Increasing Titer from Riboswitch-Regulated Bacillus subtilis Spores for Time-Delayed Protein Expression Applications

Sporulated cells have potential as time-delayed expression chassis of proteins for applications such as on-demand biologics production, whole cell biosensors, or oral vaccines. However, the desired attributes of high expression rates and low product variances are difficult to maintain from germinated spores. In this work we study the effect of an integrating vs. theta replicating plasmid in a wild-type Bacillus subtilis and two PolY mutants. The cells were engineered to produce a fluorescent reporter protein (RFP) under the control of a riboswitch activated by theophylline. This allowed for greater sensitivity to point mutations. The fluorescence and cell growth curves were fit with a custom kinetic model and a peak kinetic rate (LKPmax) was extracted for each clonal population (n = 30 for all cell, vector, and growth combinations). Plasmid based expression yields higher (8.7x) expression rates due to an increased copy number of the expression cassette (10x over integrated). The variance of LKPmax values increased 2.07x after sporulation for the wild type strain. This increase in variance from sporulation is very similar to what is observed with UV exposure. This effect can be partially mitigated by the use of PolY knockouts observed in suspended cell growths and adherent biofilms.

microbiology↗

bioRxiv2017

An automated model test system for systematic development and improvement of gene expression models

Gene expression models greatly accelerate the engineering of synthetic metabolic pathways and genetic circuits by predicting sequence-function relationships and reducing trial-and-error experimentation. However, developing models with more accurate predictions is a significant challenge, even though they are essential to engineering complex genetic systems. Here we present a model test system that combines advanced statistics, machine learning, and a database of 9862 characterized genetic systems to automatically quantify model accuracies, accept or reject mechanistic hypotheses, and identify areas for model improvement. We also introduce Model Capacity, a new information theoretic metric that enables correct model comparisons across datasets. We demonstrate the model test system by comparing six models of translation initiation rate, evaluating 100 mechanistic hypotheses, and uncovering new sequence determinants that control protein expression levels. We applied these results to develop a biophysical model of translation initiation rate with significant improvements in accuracy. Automated model test systems will dramatically accelerate the development of gene expression models, and thereby transition synthetic biology into a mature engineering discipline.

synthetic biology↗