Bioaccumulation of therapeutic drugs by human gut bacteria.

Bacteria in the gut can modulate the availability and efficacy of therapeutic drugs. However, the systematic mapping of the interactions between drugs and bacteria has only started recently1 and the main underlying mechanism proposed is the chemical transformation of drugs by microorganisms (biotransformation). Here we investigated the depletion of 15 structurally diverse drugs by 25 representative strains of gut bacteria. This revealed 70 bacteria-drug interactions, 29 of which had not to our knowledge been reported before. Over half of the new interactions can be ascribed to bioaccumulation; that is, bacteria storing the drug intracellularly without chemically modifying it, and in most cases without the growth of the bacteria being affected. As a case in point, we studied the molecular basis of bioaccumulation of the widely used antidepressant duloxetine by using click chemistry, thermal proteome profiling and metabolomics. We find that duloxetine binds to several metabolic enzymes and changes the metabolite secretion of the respective bacteria. When tested in a defined microbial community of accumulators and non-accumulators, duloxetine markedly altered the composition of the community through metabolic cross-feeding. We further validated our findings in an animal model, showing that bioaccumulating bacteria attenuate the behavioural response of Caenorhabditis elegans to duloxetine. Together, our results show that bioaccumulation by gut bacteria may be a common mechanism that alters drug availability and bacterial metabolism, with implications for microbiota composition, pharmacokinetics, side effects and drug responses, probably in an individual manner.

Highly parallelized laboratory evolution of wine yeasts for enhanced metabolic phenotypes.

Adaptive Laboratory Evolution (ALE) of microorganisms can improve the efficiency of sustainable industrial processes important to the global economy. However, stochasticity and genetic background effects often lead to suboptimal outcomes during laboratory evolution. Here we report an ALE platform to circumvent these shortcomings through parallelized clonal evolution at an unprecedented scale. Using this platform, we evolved 104 yeast populations in parallel from many strains for eight desired wine fermentation-related traits. Expansions of both ALE replicates and lineage numbers broadened the evolutionary search spectrum leading to improved wine yeasts unencumbered by unwanted side effects. At the genomic level, evolutionary gains in metabolic characteristics often coincided with distinct chromosome amplifications and the emergence of side-effect syndromes that were characteristic of each selection niche. Several high-performing ALE strains exhibited desired wine fermentation kinetics when tested in larger liquid cultures, supporting their suitability for application. More broadly, our high-throughput ALE platform opens opportunities for rapid optimization of microbes which otherwise could take many years to accomplish.

Predictive evolution of metabolic phenotypes using model-designed environments.

Adaptive evolution under controlled laboratory conditions has been highly effective in selecting organisms with beneficial phenotypes such as stress tolerance. The evolution route is particularly attractive when the organisms are either difficult to engineer or the genetic basis of the phenotype is complex. However, many desired traits, like metabolite secretion, have been inaccessible to adaptive selection due to their trade-off with cell growth. Here, we utilize genome-scale metabolic models to design nutrient environments for selecting lineages with enhanced metabolite secretion. To overcome the growth-secretion trade-off, we identify environments wherein growth becomes correlated with a secondary trait termed tacking trait. The latter is selected to be coupled with the desired trait in the application environment where the trait manifestation is required. Thus, adaptive evolution in the model-designed selection environment and subsequent return to the application environment is predicted to enhance the desired trait. We experimentally validate this strategy by evolving Saccharomyces cerevisiae for increased secretion of aroma compounds, and confirm the predicted flux-rerouting using genomic, transcriptomic, and proteomic analyses. Overall, model-designed selection environments open new opportunities for predictive evolution.

Adaptive laboratory evolution of microbial co-cultures for improved metabolite secretion.

Adaptive laboratory evolution has proven highly effective for obtaining microorganisms with enhanced capabilities. Yet, this method is inherently restricted to the traits that are positively linked to cell fitness, such as nutrient utilization. Here, we introduce coevolution of obligatory mutualistic communities for improving secretion of fitness-costly metabolites through natural selection. In this strategy, metabolic cross-feeding connects secretion of the target metabolite, despite its cost to the secretor, to the survival and proliferation of the entire community. We thus co-evolved wild-type lactic acid bacteria and engineered auxotrophic Saccharomyces cerevisiae in a synthetic growth medium leading to bacterial isolates with enhanced secretion of two B-group vitamins, viz., riboflavin and folate. The increased production was specific to the targeted vitamin, and evident also in milk, a more complex nutrient environment that naturally contains vitamins. Genomic, proteomic and metabolomic analyses of the evolved lactic acid bacteria, in combination with flux balance analysis, showed altered metabolic regulation towards increased supply of the vitamin precursors. Together, our findings demonstrate how microbial metabolism adapts to mutualistic lifestyle through enhanced metabolite exchange.

Model-guided development of an evolutionarily stable yeast chassis.

First-principle metabolic modelling holds potential for designing microbial chassis that are resilient against phenotype reversal due to adaptive mutations. Yet, the theory of model-based chassis design has rarely been put to rigorous experimental test. Here, we report the development of Saccharomyces cerevisiae chassis strains for dicarboxylic acid production using genome-scale metabolic modelling. The chassis strains, albeit geared for higher flux towards succinate, fumarate and malate, do not appreciably secrete these metabolites. As predicted by the model, introducing product-specific TCA cycle disruptions resulted in the secretion of the corresponding acid. Adaptive laboratory evolution further improved production of succinate and fumarate, demonstrating the evolutionary robustness of the engineered cells. In the case of malate, multi-omics analysis revealed a flux bypass at peroxisomal malate dehydrogenase that was missing in the yeast metabolic model. In all three cases, flux balance analysis integrating transcriptomics, proteomics and metabolomics data confirmed the flux re-routing predicted by the model. Taken together, our modelling and experimental results have implications for the computer-aided design of microbial cell factories.

Automatic Ergonomic Risk Assessment Using a Variational Deep Network Architecture.

Ergonomic risk assessment is vital for identifying work-related human postures that can be detrimental to the health of a worker. Traditionally, ergonomic risks are reported by human experts through time-consuming and error-prone procedures; however, automatic algorithmic methods have recently started to emerge. To further facilitate the automatic ergonomic risk assessment, this paper proposes a novel variational deep learning architecture to estimate the ergonomic risk of any work-related task by utilizing the Rapid Entire Body Assessment (REBA) framework. The proposed method relies on the processing of RGB images and the extraction of 3D skeletal information that is then fed to a novel deep network for accurate and robust estimation of REBA scores for both individual body parts and the entire body. Through a variational approach, the proposed method processes the skeletal information to construct a descriptive skeletal latent space that can accurately model human postures. Moreover, the proposed method distills knowledge from ground truth ergonomic risk scores and leverages it to further enhance the discrimination ability of the skeletal latent space, leading to improved accuracy. Experiments on two well-known datasets (i.e., University of Washington Indoor Object Manipulation (UW-IOM) and Technische Universität München (TUM) Kitchen) validate the ability of the proposed method to achieve accurate results, overcoming current state-of-the-art methods.

Artificial Intelligence Technologies for Sign Language.

AI technologies can play an important role in breaking down the communication barriers of deaf or hearing-impaired people with other communities, contributing significantly to their social inclusion. Recent advances in both sensing technologies and AI algorithms have paved the way for the development of various applications aiming at fulfilling the needs of deaf and hearing-impaired communities. To this end, this survey aims to provide a comprehensive review of state-of-the-art methods in sign language capturing, recognition, translation and representation, pinpointing their advantages and limitations. In addition, the survey presents a number of applications, while it discusses the main challenges in the field of sign language technologies. Future research direction are also proposed in order to assist prospective researchers towards further advancing the field.

Uric acid as an independent predictor of coronary artery disease in essential hypertension: Data from an 8-year-follow-up study.

The role of serum uric acid (SUA) in cardiovascular risk prediction remains to be further determined. We assessed the predictive value of SUA for the incidence of coronary artery disease (CAD) in 2287 essential hypertensive patients who were followed up for a mean period of 8 years. The distribution of SUA levels at baseline was split by the median (5.2 mg/dL) and subjects were classified into those with high and low values. Hypertensives who developed CAD (n = 57) compared to those without CAD at follow-up (n = 2230) had at baseline higher SUA. In multivariate Cox regression model, among established confounders, high SUA (hazard ratio = 1.216, P = .016) turned out to be independent predictor of CAD. In essential hypertensive patients SUA independently predicts CAD.

AI nutrition recommendation using a deep generative model and ChatGPT.

In recent years, major advances in artificial intelligence (AI) have led to the development of powerful AI systems for use in the field of nutrition in order to enhance personalized dietary recommendations and improve overall health and well-being. However, the lack of guidelines from nutritional experts has raised questions on the accuracy and trustworthiness of the nutritional advice provided by such AI systems. This paper aims to address this issue by introducing a novel AI-based nutrition recommendation method that leverages the speed and explainability of a deep generative network and the use of novel sophisticated loss functions to align the network with established nutritional guidelines. The use of a variational autoencoder to robustly model the anthropometric measurements and medical condition of users in a descriptive latent space, as well as the use of an optimizer to adjust meal quantities based on users' energy requirements enable the proposed method to generate highly accurate, nutritious and personalized weekly meal plans. Coupled with the ability of ChatGPT to provide an unparalleled pool of meals from various cuisines, the proposed method can achieve increased meal variety, accuracy and generalization capabilities. Extensive experiments on 3000 virtual user profiles and 84000 daily meal plans, as well as 1000 real profiles and 7000 daily meal plans, demonstrate the exceptional accuracy of the proposed diet recommendation method in generating weekly meal plans that are appropriate for the users in terms of energy intake and nutritional requirements, as well as the easiness with which it can be integrated into future diet recommendation systems.