Small-molecule biosensors for high-throughput metabolic engineering

Allosteric transcription factors (aTFs) have proven widely applicable for biotechnology and synthetic biology as ligand-specific biosensors enabling real-time monitoring, selection and regulation of cellular metabolism. However, both the biosensor specificity and the correlation between ligand concentration and biosensor output signal, also known as the transfer function, often needs to be optimized before meeting application needs. In this presentation we outline a versatile and high-throughput method to evolve and functionalize prokaryotic aTF ligand specificity and transfer functions in a eukaryote chassis, namely baker’s yeast Saccharomyces cerevisiae. From a single round of directed evolution of the aTF ligand-binding domain coupled with various toggled selection regimes, we robustly select aTF variants evolved for change in ligand specificity, increased dynamic output range, shifts in operational range, and a complete inversion of function from activation to repression. Importantly, by targeting only the ligand-binding domain, the evolved biosensors display DNA-binding affinities similar to parental aTFs and are functional when ported back into a non-native prokaryote chassis. The developed platform technology thus leverages aTF evolvability for the development of new biosensors with user-defined small-molecule specificities and transfer functions. Finally, the presentation will highlight examples on biosensor applications for high-throughput metabolic engineering. Please click Additional Files below to see the full abstract

Plex: Towards Reliability using Pretrained Large Model Extensions

A recent trend in artificial intelligence is the use of pretrained models for language and vision tasks, which have achieved extraordinary performance but also puzzling failures. Probing these models' abilities in diverse ways is therefore critical to the field. In this paper, we explore the reliability of models, where we define a reliable model as one that not only achieves strong predictive performance but also performs well consistently over many decision-making tasks involving uncertainty (e.g., selective prediction, open set recognition), robust generalization (e.g., accuracy and proper scoring rules such as log-likelihood on in- and out-of-distribution datasets), and adaptation (e.g., active learning, few-shot uncertainty). We devise 10 types of tasks over 40 datasets in order to evaluate different aspects of reliability on both vision and language domains. To improve reliability, we developed ViT-Plex and T5-Plex, pretrained large model extensions for vision and language modalities, respectively. Plex greatly improves the state-of-the-art across reliability tasks, and simplifies the traditional protocol as it improves the out-of-the-box performance and does not require designing scores or tuning the model for each task. We demonstrate scaling effects over model sizes up to 1B parameters and pretraining dataset sizes up to 4B examples. We also demonstrate Plex's capabilities on challenging tasks including zero-shot open set recognition, active learning, and uncertainty in conversational language understanding.Comment: Code available at https://goo.gle/plex-cod

Genome-Wide Gene Expression Analysis in Response to Organophosphorus Pesticide Chlorpyrifos and Diazinon in C. elegans

Organophosphorus pesticides (OPs) were originally designed to affect the nervous system by inhibiting the enzyme acetylcholinesterase, an important regulator of the neurotransmitter acetylcholine. Over the past years evidence is mounting that these compounds affect many other processes. Little is known, however, about gene expression responses against OPs in the nematode Caenorhabditis elegans. This is surprising because C. elegans is extensively used as a model species in toxicity studies. To address this question we performed a microarray study in C. elegans which was exposed for 72 hrs to two widely used Ops, chlorpyrifos and diazinon, and a low dose mixture of these two compounds. Our analysis revealed transcriptional responses related to detoxification, stress, innate immunity, and transport and metabolism of lipids in all treatments. We found that for both compounds as well as in the mixture, these processes were regulated by different gene transcripts. Our results illustrate intense, and unexpected crosstalk between gene pathways in response to chlorpyrifos and diazinon in C. elegans