Machine learning linked evolutionary biosensor array for highly sensitive and specific molecular identification

Novel Signal Peptides and Episomal Plasmid System for Enhanced Protein Secretion in Engineered Bacteroides Species

The genus Bacteroides, a predominant group in the human gut microbiome, presents significant potential for microbiome engineering and the development of live biotherapeutics aimed at treating gut diseases. Despite its promising capabilities, tools for effectively engineering Bacteroides species have been limited. In our study, we have made a breakthrough by identifying novel signal peptides in Bacteroides thetaiotaomicron and Akkermansia muciniphila. These peptides facilitate efficient protein transport across cellular membranes in Bacteroides, a critical step for therapeutic applications. Additionally, we have developed an advanced episomal plasmid system. This system demonstrates superior protein secretion capabilities compared to traditional chromosomal integration plasmids, making it a vital tool for enhancing the delivery of therapeutic proteins in Bacteroides species. Initially, the stability of this episomal plasmid posed a challenge; however, we have overcome this by incorporating an essential gene-based selection system. This novel strategy not only ensures plasmid stability but also aligns with the growing need for antibiotic-free selection methods in clinical settings. Our work, therefore, not only provides a more robust secretion system for Bacteroides but also sets a new standard for the development of live biotherapeutics.

Advancements in SARS-CoV-2 Testing: Enhancing Accessibility through Machine Learning-Enhanced Biosensors

The COVID-19 pandemic highlighted the importance of widespread testing for SARS-CoV-2, leading to the development of various new testing methods. However, traditional invasive sampling methods can be uncomfortable and even painful, creating barriers to testing accessibility. In this article, we explore how machine learning-enhanced biosensors can enable non-invasive sampling for SARS-CoV-2 testing, revolutionizing the way we detect and monitor the virus. By detecting and measuring specific biomarkers in body fluids or other samples, these biosensors can provide accurate and accessible testing options that do not require invasive procedures. We provide examples of how these biosensors can be used for non-invasive SARS-CoV-2 testing, such as saliva-based testing. We also discuss the potential impact of non-invasive testing on accessibility and accuracy of testing. Finally, we discuss potential limitations or biases associated with the machine learning algorithms used to improve the biosensors and explore future directions in the field of machine learning-enhanced biosensors for SARS-CoV-2 testing, considering their potential impact on global healthcare and disease control.

Enzymatic Electrochemical/Fluorescent Nanobiosensor for Detection of Small Chemicals

The detection of small molecules has attracted enormous interest in various fields, including the chemical, biological, and healthcare fields. In order to achieve such detection with high accuracy, up to now, various types of biosensors have been developed. Among those biosensors, enzymatic biosensors have shown excellent sensing performances via their highly specific enzymatic reactions with small chemical molecules. As techniques used to implement the sensing function of such enzymatic biosensors, electrochemical and fluorescence techniques have been mostly used for the detection of small molecules because of their advantages. In addition, through the incorporation of nanotechnologies, the detection property of each technique-based enzymatic nanobiosensors can be improved to measure harmful or important small molecules accurately. This review provides interdisciplinary information related to developing enzymatic nanobiosensors for small molecule detection, such as widely used enzymes, target small molecules, and electrochemical/fluorescence techniques. We expect that this review will provide a broad perspective and well-organized roadmap to develop novel electrochemical and fluorescent enzymatic nanobiosensors.

Engineering Modular and Highly Sensitive Cell-Based Biosensors for Aromatic Contaminant Monitoring and High-Throughput Enzyme Screening

Although a variety of whole-cell-based biosensors have been developed for different applications in recent years, most cannot meet practical requirements due to insufficient sensing performance. Here, we constructed two sets of modular genetic circuits by serial and parallel modes capable of significantly amplifying the input/output signal in Escherichia coli. The biosensors are engineered using σ⁵⁴-dependent phenol-responsive regulator DmpR as a sensor and enhanced green fluorescent protein as a reporter. Cells harboring serial and parallel genetic circuits displayed nearly 9- and 16-fold higher sensitivity than the general circuit. The genetic circuits enabled rapid detection of six phenolic contaminants in 12 h and showed the low limit of detection of 2.5 and 2.2 ppb for benzopyrene (BaP) and tetracycline (Tet), with a broad detection range of 0.01-1 and 0.005-5 μM, respectively. Furthermore, the positive rate was as high as 73% when the biosensor was applied to screen intracellular enzymes with ester-hydrolysis activity from soil metagenomic libraries using phenyl acetate as a phenolic substrate. Several novel enzymes were isolated, identified, and biochemically characterized, including serine peptidases and alkaline phosphatase family protein/metalloenzyme. Consequently, this study provides a new signal amplification method for cell-based biosensors that can be widely applied to environmental contaminant assessment and screening of intracellular enzymes.

Artificial intelligence aids in development of nanomedicines for cancer management

Over the last decade, the nanomedicine has experienced unprecedented development in diagnosis and management of diseases. A number of nanomedicines have been approved in clinical use, which has demonstrated the potential value of clinical transition of nanotechnology-modified medicines from bench to bedside. The application of artificial intelligence (AI) in development of nanotechnology-based products could transform the healthcare sector by realizing acquisition and analysis of large datasets, and tailoring precision nanomedicines for cancer management. AI-enabled nanotechnology could improve the accuracy of molecular profiling and early diagnosis of patients, and optimize the design pipeline of nanomedicines by tuning the properties of nanomedicines, achieving effective drug synergy, and decreasing the nanotoxicity, thereby, enhancing the targetability, personalized dosing and treatment potency of nanomedicines. Herein, the advances in AI-enabled nanomedicines in cancer management are elaborated and their application in diagnosis, monitoring and therapy as well in precision medicine development is discussed.

A Framework for Biosensors Assisted by Multiphoton Effects and Machine Learning

The ability to interpret information through automatic sensors is one of the most important pillars of modern technology. In particular, the potential of biosensors has been used to evaluate biological information of living organisms, and to detect danger or predict urgent situations in a battlefield, as in the invasion of SARS-CoV-2 in this era. This work is devoted to describing a panoramic overview of optical biosensors that can be improved by the assistance of nonlinear optics and machine learning methods. Optical biosensors have demonstrated their effectiveness in detecting a diverse range of viruses. Specifically, the SARS-CoV-2 virus has generated disturbance all over the world, and biosensors have emerged as a key for providing an analysis based on physical and chemical phenomena. In this perspective, we highlight how multiphoton interactions can be responsible for an enhancement in sensibility exhibited by biosensors. The nonlinear optical effects open up a series of options to expand the applications of optical biosensors. Nonlinearities together with computer tools are suitable for the identification of complex low-dimensional agents. Machine learning methods can approximate functions to reveal patterns in the detection of dynamic objects in the human body and determine viruses, harmful entities, or strange kinetics in cells.

E.P.A.S.S: Electroanalytical Pillbox Assessment Sensor System, A Case Study Using Metformin Hydrochloride

Adulteration of medications is an emerging and significant threat to human health and well-being, even though adulterants are still often not considered seriously in clinical or forensic toxicology. Screening of drug adulterations is a major challenge and concern for regulatory authorities worldwide. Metformin hydrochloride, an important drug to treat diabetes, is found to be adulterated worldwide and a major reason to worry about the health and safety procedure. We have demonstrated a first-of-a-kind electrochemical biomedical device utilizing exfoliated graphene oxide (GO)─Nafion-modified customized gold screen-printed electrodes (spiral electrochemical notification-coupled electrode, SENCE), driven by electrochemical adsorptive stripping voltammetry, to identify the trace level adulteration in metformin. The GO-Nafion-SPE interface has been characterized by powder X-ray diffraction, X-ray photoelectron spectroscopy, scanning electron microscopy, energy-dispersive X-ray analysis, and Fourier transform infrared. Custom-made screen-printed SENCEs have been functionalized with GO nanoparticles (transducer) to obtain a fingerprint signal response of metformin using differential pulse voltammetry. A linear calibrated dose response has been obtained with n = 3 repetitions with a low limit of detection of 10 ppm for metformin. We have used the sensing response as a function of adulteration, and it is extensively supported by rigorous statistical analysis along with the help of the machine learning tool. This is a first-of-its-kind IoT-enabled electrochemical sensor and analysis platform that can detect drug adulteration as a low, medium, and high output.

Advances in engineering and optimization of transcription factor-based biosensors for plug-and-play small molecule detection

Transcription factor (TF)-based biosensors have been applied in biotechnology for a variety of functions, including protein engineering, dynamic control, environmental detection, and point-of-care diagnostics. Such biosensors are promising analytical tools due to their wide range of detectable ligands and modular nature. However, designing biosensors tailored for applications of interest with the desired performance parameters, including ligand specificity, remains challenging. Biosensors often require significant engineering and tuning to meet desired specificity, sensitivity, dynamic range, and operating range parameters. Another limitation is the orthogonality of biosensors across hosts, given the role of the cellular context. Here, we describe recent advances and examples in the engineering and optimization of TF-based biosensors for plug-and-play small molecule detection. We highlight novel developments in TF discovery and biosensor design, TF specificity engineering, and biosensor tuning, with emphasis on emerging computational methods.

Fentanyl Assay Derived from Intermolecular Interaction-Enabled Small Molecule Recognition (iMSR) with Differential Impedance Analysis for Point-of-Care Testing

Rapid and effective differentiation and quantification of a small molecule drug, such as fentanyl, in bodily fluids are major challenges for diagnosis and personal medication. However, the current toxicology methods used to measure drug concentration and metabolites require laboratory-based testing, which is not an efficient or cost-effective way to treat patients in a timely manner. Here, we show an assay for monitoring fentanyl levels by combining the intermolecular interaction-enabled small molecule recognition (iMSR) with differential impedance analysis of conjugated polymers. The differential interactions with the designed anchor interface were transduced through the perturbance of the electric status of the flexible conducting polymer. This assay showed excellent fentanyl selectivity against common interferences, as well as in variable body fluids through either testing strips or skin patches. Directly using the patient blood, the sensor provided 1%-5% of the average deviation compared to the "gold" standard method LC-MS results in the medically relevant fentanyl range of 20-90 nM. The superior sensing properties, in conjunction with mechanical flexibility and compatibility, enabled point-of-care detection and provided a promising avenue for applications beyond the scope of biomarker detection.

Enriching intracellular macrolides in Escherichia coli improved the sensitivity of bioluminescent sensing systems

A repressor protein MphR and an enhanced green fluorescent protein (eGFP) were used to construct a bioluminescent sensing system for macrolide analysis in Escherichia coli host cells. We deleted TolC, an efflux pump for macrolides in E. coli, to promote the intracellular accumulation of macrolides. The binding constant (K_1/2) of the sensing system constructed in an E. coli strain was decreased up to 33-fold with deleted TolC, and its sensitivity to the macrolides erythromycin, azithromycin, roxithromycin, and pikromycin was increased. The limit of detection of the bioluminescent sensing system for serum azithromycin was 4.1 nM. The ability to detect serum azithromycin concentrations was confirmed by analyzing photographs using ImageJ software. We also developed a novel sensing system for the immune suppressor FK506, another macrolide that is frequently prescribed. Deleting TolC also significantly improved the sensitivity of this sensing system. Bioluminescent sensing systems constructed in TolC mutants were sensitive to various macrolides, indicating their potential for clinical application with hand-held devices.

Programming living sensors for environment, health and biomanufacturing

Synthetic biology offers new tools and capabilities of engineering cells with desired functions for example as new biosensing platforms leveraging engineered microbes. In the last two decades, bacterial cells have been programmed to sense and respond to various input cues for versatile purposes including environmental monitoring, disease diagnosis and adaptive biomanufacturing. Despite demonstrated proof-of-concept success in the laboratory, the real-world applications of microbial sensors have been restricted due to certain technical and societal limitations. Yet, most limitations can be addressed by new technological developments in synthetic biology such as circuit design, biocontainment and machine learning. Here, we summarize the latest advances in synthetic biology and discuss how they could accelerate the development, enhance the performance and address the present limitations of microbial sensors to facilitate their use in the field. We view that programmable living sensors are promising sensing platforms to achieve sustainable, affordable and easy-to-use on-site detection in diverse settings.

Machine Learning-Reinforced Noninvasive Biosensors for Healthcare

The emergence and development of noninvasive biosensors largely facilitate the collection of physiological signals and the processing of health-related data. The utilization of appropriate machine learning algorithms improves the accuracy and efficiency of biosensors. Machine learning-reinforced biosensors are started to use in clinical practice, health monitoring, and food safety, bringing a digital revolution in healthcare. Herein, the recent advances in machine learning-reinforced noninvasive biosensors applied in healthcare are summarized. First, different types of noninvasive biosensors and physiological signals collected are categorized and summarized. Then machine learning algorithms adopted in subsequent data processing are introduced and their practical applications in biosensors are reviewed. Finally, the challenges faced by machine learning-reinforced biosensors are raised, including data privacy and adaptive learning capability, and their prospects in real-time monitoring, out-of-clinic diagnosis, and onsite food safety detection are proposed.

Genetically Encoded Biosensor-Based Screening for Directed Bacteriophage T4 Lysozyme Evolution

Lysozyme is widely used as a model protein in studies of structure–function relationships. Recently, lysozyme has gained attention for use in accelerating the degradation of secondary sludge, which mainly consists of bacteria. However, a high-throughput screening system for lysozyme engineering has not been reported. Here, we present a lysozyme screening system using a genetically encoded biosensor. We first cloned bacteriophage T4 lysozyme (T4L) into a plasmid under control of the araBAD promoter. The plasmid was expressed in Escherichia coli with no toxic effects on growth. Next, we observed that increased soluble T4L expression decreased the fluorescence produced by the genetic enzyme screening system. To investigate T4L evolution based on this finding, we generated a T4L random mutation library, which was screened using the genetic enzyme screening system. Finally, we identified two T4L variants showing 1.4-fold enhanced lytic activity compared to native T4L. To our knowledge, this is the first report describing the use of a genetically encoded biosensor to investigate bacteriophage T4L evolution. Our approach can be used to investigate the evolution of other lysozymes, which will expand the applications of lysozyme.