High throughput investigation of vanillin toxicity to yeast using ambr15 microbioreactors and flow cytometry

Vanillin (4-hydroxy-3-methoxybenzaldehyde) is the main flavoring component of the vanilla bean and synthetic vanilla flavoring agent and biotechnological production in metabolically engineered yeast has been attempted. However, vanillin is also one of the most toxic of the aromatic aldehydes to yeast, which became known both through the metabolic engineering attempts and in its effects on yeast during cellulosic ethanol fermentation. Cellulosic sugars are derived from lignocellulosic biomass following pretreatment; this process generates a range of yeast toxicants and inhibitors of which vanillin is among the most potent. Several reports utilizing transcriptomics and gene knockouts have been undertaken in yeast to determine the mechanism(s) of toxicity of vanillin and these have concluded protein production effects or increased radical oxygen species (ROS) and among others. Here we investigated the mechanism and potency of vanillin toxicity to Saccharomyces cerevisiae yeast using a parallel microbioreactor system and by assessing cell physiology impacts via flow cytometry. An initial screen of vanillin (0.5-10 mM) toward S. cerevisiae BY4171 indicated potent inhibition of yeast growth at concentrations \u3e2 mM. Control yeast strains (empty vector) or those expressing modified Turquoise Fluorescence Protein (mTFP), were used thereafter. Yeast innocula (OD600 ~0.4) were cultured in the Sartorius/TAP Biosystems ambr15 microbioreactor system, 11 ml final volume at 30°C for up to 99 h in the presence of vanillin at concentrations of 2, 4, 6, 8 and 10 mM in batch mode. Microbioreactor dissolved oxygen (DO) and stirring rates were maintained by the control system and automated sampling of the reactors was conducted at specified time intervals. Biomass production (OD600nm) and supernatant vanillin and vanillyl alcohol concentrations were determined. Single cell physiology indicators such as: membrane integrity, ROS generation and mitochondrial membrane potential were measured with the aid of specific fluorescent dyes and TFP levels were measured using a Beckman Coulter Cytoflex flow cytometer. Please click Additional Files below to see the full abstract

Metabolic engineering of yeast for increased production of cyclopropane fatty acids

Biological production of chemicals and fuels using whole cells is an important and growing segment of manufacturing and among the various forms, microorganisms are the most successfully utilized. In particular, yeasts such as Saccharomyces cerevisiae are both widely used production organisms and metabolic models for oleaginous yeasts. Fatty acid-containing lipids are one example of moderate value, highly versatile chemicals produced by yeasts that are used in a broad range of industries for lubrication, cosmetics, fuels and polymers. Production levels of standard fatty acids by yeasts has increased enormously over the past 10 years through the application of metabolic pathway engineering, flux analysis, computational approaches and to a lesser extent, bioprocessing improvements. Combined, these advances have brought yeast-based fatty acid production close to commercial reality. Functionalized fatty acids such as those containing hydroxyl or cyclopropyl groups are more valuable as chemical feedstocks and are an attractive target for yeast production as commercial supply is limited. Cyclopropane fatty acids, possessing a strained 3-membered ring and having a saturated chain, are especially attractive as they have application in cosmetics and specialty lubrication. However, cyclopropyl fatty acids present greater challenges for metabolic engineering as they are not produced naturally by yeast. Please click Additional Files below to see the full abstract

Surface Integral Analogy Approaches to Computing Noise Generated by a 3D High-Lift Wing Configuration

Three surface integral approaches of the acoustic analogies are studied to predict the noise from a three-dimensional, high-lift wing configuration. The approaches refer to the Kirchhoff method, the Ffowcs Williams and Hawkings method of the permeable integral surface and the Curle method. The first two approaches are used to compute the noise generated by the core flow region where the energetic structures exist. The last approach is adopted to predict the noise specifically from the pressure perturbation on the wall. A new way to construct the integral surface that encloses the core region is proposed for the first two methods. Considering the local properties of the flow around the complex objective – the actual wing with high-lift devices – the integral surface based on the vorticity is constructed to follow the flow structures. The noise from the core flow region is based on the dependent integral quantities, which are indicated by the Kirchhoff formulation and by the FWH formulation. The role of each wall component on noise contribution is analyzed using the Curle method. The results of the three methods are then compared

Plug-and-Play Knowledge Injection for Pre-trained Language Models

Injecting external knowledge can improve the performance of pre-trained language models (PLMs) on various downstream NLP tasks. However, massive retraining is required to deploy new knowledge injection methods or knowledge bases for downstream tasks. In this work, we are the first to study how to improve the flexibility and efficiency of knowledge injection by reusing existing downstream models. To this end, we explore a new paradigm plug-and-play knowledge injection, where knowledge bases are injected into frozen existing downstream models by a knowledge plugin. Correspondingly, we propose a plug-and-play injection method map-tuning, which trains a mapping of knowledge embeddings to enrich model inputs with mapped embeddings while keeping model parameters frozen. Experimental results on three knowledge-driven NLP tasks show that existing injection methods are not suitable for the new paradigm, while map-tuning effectively improves the performance of downstream models. Moreover, we show that a frozen downstream model can be well adapted to different domains with different mapping networks of domain knowledge. Our code and models are available at https://github.com/THUNLP/Knowledge-Plugin.Comment: ACL 202

Improved Weighted Covariance Based Detector for Spectrum Sensing in Rayleigh Fading Channel

In this letter, we propose an improved weighted covariance based detector (IWCD) for spatially correlated time-varying Rayleigh fading channel. The proposed method uses adaptive weights that are tailored to the dynamic nature of the channels. These weights can be chosen manually to meet practical requirements or derived theoretically by optimizing some performance index, such that the IWCD outperforms traditional weighted covariance-based detectors (WCDs), which rely heavily on data-aided weights determined by the sample covariance matrix (SCM). Performance merits in terms of the probabilities of false alarm and detection are analyzed in the low signal-to-noise-ratio (SNR) regime. Besides, the optimal weights are derived via maximizing the modified deflection coefficient (MDC). A reasonable estimator of the optimal weights is also constructed armed with the available samples at hand. Theoretical analyses and experimental results demonstrate the superiority of our proposed method over existing works in various scenarios

Detection of neural connections with ex vivo MRI using a ferritin-encoding trans-synaptic virus

The elucidation of neural networks is essential to understanding the mechanisms of brain functions and brain disorders. Neurotropic virus-based trans-synaptic tracing tools have become an effective method for dissecting the structure and analyzing the function of neural-circuitry. However, these tracing systems rely on fluorescent signals, making it hard to visualize the panorama of the labeled networks in mammalian brain in vivo. One MRI method, Diffusion Tensor Imaging (DTI), is capable of imaging the networks of the whole brain in live animals but without information of anatomical connections through synapses. In this report, a chimeric gene coding for ferritin and enhanced green fluorescent protein (EGFP) was integrated into Vesicular stomatitis virus (VSV), a neurotropic virus that is able to spread anterogradely in synaptically connected networks. After the animal was injected with the recombinant VSV (rVSV), rVSV-Ferritin-EGFP, into the somatosensory cortex (SC) for four days, the labeled neural-network was visualized in the postmortem whole brain with a T2-weighted MRI sequence. The modified virus transmitted from SC to synaptically connected downstream regions. The results demonstrate that rVSV-Ferritin-EGFP could be used as a bimodal imaging vector for detecting synaptically connected neural-network with both ex vivo MRI and fluorescent imaging. The strategy in the current study has the potential to longitudinally monitor the global structure of a given neural-network in living animals

A molecular toolkit of cross-feeding strains for engineering synthetic yeast communities

Engineered microbial consortia often have enhanced system performance and robustness compared with single-strain biomanufacturing production platforms. However, few tools are available for generating co-cultures of the model and key industrial host Saccharomyces cerevisiae. Here we engineer auxotrophic and overexpression yeast strains that can be used to create co-cultures through exchange of essential metabolites. Using these strains as modules, we engineered two- and three-member consortia using different cross-feeding architectures. Through a combination of ensemble modelling and experimentation, we explored how cellular (for example, metabolite production strength) and environmental (for example, initial population ratio, population density and extracellular supplementation) factors govern population dynamics in these systems. We tested the use of the toolkit in a division of labour biomanufacturing case study and show that it enables enhanced and tuneable antioxidant resveratrol production. We expect this toolkit to become a useful resource for a variety of applications in synthetic ecology and biomanufacturing