AI-Assisted Rational Design and Activity Prediction of Biological Elements for Optimizing Transcription-Factor-Based Biosensors.

The rational design, activity prediction, and adaptive application of biological elements (bio-elements) are crucial research fields in synthetic biology. Currently, a major challenge in the field is efficiently designing desired bio-elements and accurately predicting their activity using vast datasets. The advancement of artificial intelligence (AI) technology has enabled machine learning and deep learning algorithms to excel in uncovering patterns in bio-element data and predicting their performance. This review explores the application of AI algorithms in the rational design of bio-elements, activity prediction, and the regulation of transcription-factor-based biosensor response performance using AI-designed elements. We discuss the advantages, adaptability, and biological challenges addressed by the AI algorithms in various applications, highlighting their powerful potential in analyzing biological data. Furthermore, we propose innovative solutions to the challenges faced by AI algorithms in the field and suggest future research directions. By consolidating current research and demonstrating the practical applications and future potential of AI in synthetic biology, this review provides valuable insights for advancing both academic research and practical applications in biotechnology.

Enhancing glucaric acid production from myo-inositol in Escherichia coli by eliminating cell-to-cell variation.

Glucaric acid (GA) is a value-added chemical and can be used to manufacture food additives, anticancer drugs, and polymers. The non-genetic cell-to-cell variations in GA biosynthesis are naturally inherent, indicating the presence of both high- and low-performance cells in culture. Low-performance cells can lead to nutrient waste and inefficient production. Furthermore, myo-inositol oxygenase (MIOX) is a key rate-limiting enzyme with the problem of low stability and activity in GA production. Therefore, eliminating cell-to-cell variations and increasing MIOX stability can select high-performance cells and improve GA production. In this study, an in vivo GA bioselector was constructed based on GA biosensor and tetracycline efflux pump protein TetA to continuously select GA-efficient production strains. Additionally, the upper limit of the GA biosensor was improved to 40 g/L based on ribosome-binding site optimization, achieving efficient enrichment of GA high-performance cells. A small ubiquitin-like modifier (SUMO) enhanced MIOX stability and activity. Overall, we used the GA bioselector and SUMO-MIOX fusion in fed-batch GA production and achieved a 5.52-g/L titer in Escherichia coli, which was 17-fold higher than that of the original strain.IMPORTANCEGlucaric acid is a non-toxic valuable product that was mainly synthesized by chemical methods. Due to the problems of non-selectivity, inefficiency, and environmental pollution, GA biosynthesis has attracted significant attention. The non-genetic cell-to-cell variations and MIOX stability were both critical factors for GA production. In addition, the high detection limit of the GA biosensor was a key condition for performing high-throughput screening of GA-efficient production strains. To increase GA titer, this work eliminated the cell-to-cell variations by GA bioselector constructed based on GA biosensor and TetA, and improved the stability and activity of MIOX in the GA biosynthetic pathway through fusing the SUMO to MIOX. Finally, these approaches improved the GA production by 17-fold to 5.52 g/L at 65 h. This study represents a significant step toward the industrial application of GA biosynthetic pathways in E. coli.

Transcription factor-based biosensor: A molecular-guided approach for advanced biofuel synthesis.

As a sustainable and renewable alternative to petroleum fuels, advanced biofuels shoulder the responsibility of energy saving, emission reduction and environmental protection. Traditional engineering of cell factories for production of advanced biofuels lacks efficient high-throughput screening tools and regulating systems, impeding the improvement of cellular productivity and yield. Transcription factor-based biosensors have been widely applied to monitor and regulate microbial cell factory products due to the advantages of fast detection and in-situ screening. This review updates the design and application of transcription factor-based biosensors tailored for advanced biofuels and related intermediates. The construction and genetic parts selection principle of biosensors are discussed. Strategies to enhance the performance of biosensor, including regulating promoter strength and RBS strength, optimizing plasmid copy number, implementing genetic amplifier, and modulating the structure of transcription factor, have also been summarized. We further review the application of biosensors in high-throughput screening of new metabolic engineering targets, evolution engineering, confirmation of protein function, and dynamic regulation of metabolic flux for higher production of advanced biofuels. At last, we discuss the current limitations and future trends of transcription factor-based biosensors.

Identifying LasR Quorum Sensors with Improved Signal Specificity by Mapping the Sequence-Function Landscape.

Programmable intercellular signaling using components of naturally occurring quorum sensing can allow for coordinated functions to be engineered in microbial consortia. LuxR-type transcriptional regulators are widely used for this purpose and are activated by homoserine lactone (HSL) signals. However, they often suffer from imperfect molecular discrimination of structurally similar HSLs, causing misregulation within engineered consortia containing multiple HSL signals. Here, we studied one such example, the regulator LasR from Pseudomonas aeruginosa. We elucidated its sequence-function relationship for ligand specificity using targeted protein engineering and multiplexed high-throughput biosensor screening. A pooled combinatorial saturation mutagenesis library (9,486 LasR DNA sequences) was created by mutating six residues in LasR's ß5 sheet with single, double, or triple amino acid substitutions. Sort-seq assays were performed in parallel using cognate and noncognate HSLs to quantify each corresponding sensor's response to each HSL signal, which identified hundreds of highly specific variants. Sensor variants identified were individually assayed and exhibited up to 60.6-fold (p = 0.0013) improved relative activation by the cognate signal compared to the wildtype. Interestingly, we uncovered prevalent mutational epistasis and previously unidentified residues contributing to signal specificity. The resulting sensors with negligible signal crosstalk could be broadly applied to engineer bacteria consortia.

Expanded or Risk Factor-Based Annual Screening for Hepatitis C Virus (HCV) Among Persons With HIV: Which Is the Best Approach?

Introduction. Universal one-time screening for hepatitis C virus (HCV) is recommended for all adults. For persons with HIV (PWH), guidelines recommend HCV screening at entry into care and annually in men who have unprotected sex with other men (MSM) and persons who inject drugs (PWID). Public health experts recommend expanded annual screening in all PWH given concerns for undiagnosed new HCV diagnoses when risk factors are not assessed. Electronic medical record (EMR) with clinical decision support using a Best Practice Advisory (BPA) tool can aid HCV risk factor assessment. We conducted a prospective study among three HIV clinics to compare the two screening approaches. Methods. Two clinics implemented the EMR-triggered risk factor-based screening; one clinic used the expanded screening approach. We evaluated BPA uptake and compared HCV testing and positivity rates from August 12, 2019 to March 12, 2020. Results. In the risk factor-based screening clinics, of 1,343 PWH, 239 tests were performed with 139 attributed to the BPA (testing rate 10%). At the expanded screening site, among 434 patients, 237 HCV tests were performed (testing rate 55%). The risk factor-based screening sites were less likely to test for HCV (odds ratio [OR] = 0.0884, p < .01) and identify positive cases (OR = 0.55, p = .025). Conclusions. An EMR-based clinical-decision support tool was successfully implemented for HCV risk factor-based screening resulting in a lower HCV annual screening rate compared with an expanded approach. Although in this group of HIV clinics with limited longitudinal follow-up, no previously undiagnosed HCV cases were detected, additional work is needed to guide the design of the best approach.

Strategies to reinvigorate exhausted CD8+ T cells in tumor microenvironment.

CD8+ T cell exhaustion is a stable dysfunctional state driven by chronic antigen stimulation in the tumor microenvironment (TME). Differentiation of exhausted CD8+ T cells (CD8+ TEXs) is accompanied by extensive transcriptional, epigenetic and metabolic reprogramming. CD8+ TEXs are mainly characterized by impaired proliferative and cytotoxic capacity as well as the increased expression of multiple co-inhibitory receptors. Preclinical tumor studies and clinical cohorts have demonstrated that T cell exhaustion is firmly associated with poor clinical outcomes in a variety of cancers. More importantly, CD8+ TEXs are regarded as the main responder to immune checkpoint blockade (ICB). However, to date, a large number of cancer patients have failed to achieve durable responses after ICB. Therefore, improving CD8+ TEXs may be a breakthrough point to reverse the current dilemma of cancer immunotherapy and eliminate cancers. Strategies to reinvigorate CD8+ TEXs in TME mainly include ICB, transcription factor-based therapy, epigenetic therapy, metabolism-based therapy and cytokine therapy, which target on different aspects of exhaustion progression. Each of them has its advantages and application scope. In this review, we mainly focus on the major advances of current strategies to reinvigorate CD8+ TEXs in TME. We summarize their efficacy and mechanisms, identify the promising monotherapy and combined therapy and propose suggestions to enhance the treatment efficacy to significantly boost anti-tumor immunity and achieve better clinical outcomes.

Transcription Factor-Directed Dopaminergic Neuron Differentiation from Human Pluripotent Stem Cells.

The ability to differentiate pluripotent stem cells and to generate specific cell types is a long-standing goal of regenerative medicine. This can be accomplished by recreating the developmental trajectories using sequential activation of the corresponding signaling pathways, or more recently-by direct programming of cell identities using lineage-specific transcription factors. Notably, to be functional in cell replacement therapies, generation of complex cell types, such as specialized neuronal sub-types of the brain, requires precise induction of molecular profiles and regional specification of the cells. However, the induction of the correct cellular identity and marker gene expression can be hampered by technical challenges, one of which is the robust co-expression of multiple transcription factors that is often required for correct cell identity specification. Here, we describe in detail a method for co-expression of seven transcription factors required for efficient induction of dopaminergic neurons with midbrain characteristics from human embryonic and induced pluripotent stem cells.

Directed Differentiation of Human iPSCs into Microglia-Like Cells Using Defined Transcription Factors.

The generation of a homogeneous population of microglia from human induced pluripotent stem cells (hiPSCs) is crucial to modeling neurological disorders, as well as the carrying out of drug screening and toxicity testing. Here, we provide a stepwise protocol for the simple, robust, and efficient differentiation of hiPSCs into microglia-like cells (iMGs) by overexpression of SPI1 and CEBPA. This protocol details hiPSC culture, lentivirus production, lentivirus delivery, and, finally, the differentiation and validation of the iMG cells.

In vitro validation of chromogenic substrate assay for evaluation of surrogate FVIII-activity of emicizumab.

[Introduction] Emicizumab, a bispecific antibody mimicking activated factor VIII (FVIII), is increasingly used in prophylaxis against bleeding in hemophilia A. Human factor-based chromogenic substrate assay (hCSA) shows concentration-dependency between emicizumab and reported FVIII activity. However, the assay measurement settings have not been optimized for emicizumab, and the reported FVIII activity cannot be directly referred as surrogate FVIII activity. [Materials and Methods] For in vitro validation of hCSA-reported surrogate FVIII activity, we compared the equation curves for emicizumab concentration with surrogate FVIII activity using spiked plasma in the thrombin generation assay (TGA), hCSA, and clot waveform analysis (CWA). Then, we generated conversion equations for hCSA-reported surrogate FVIII value to that of TGA. We also assessed the additive effect of rFVIII onto 340 nM (i.e., 50 µg/mL) emicizumab using the same assays. [Results] With 1:20 diluted plasma, halving hCSA-reported surrogate FVIII activity can be approximated to that in TGA triggered by the extrinsic pathway reagent (27.3 IU/dL vs. 13.9 IU/dL) under therapeutic emicizumab concentration. Both in TGA and hCSA, the additive effect of added FVIII on therapeutic emicizumab concentration (340 nM) was maintained at low levels of FVIII but gradually decreased at higher levels. [Conclusions] Surrogate FVIII activity can be estimated simply by halving hCSA-reported FVIII value, and the additive effect of FVIII on emicizumab diminishes at high concentrations. Based on our in vitro study, a clinical study is currently being conducted to compare individual variation of surrogate FVIII activity in hCSA and TGA.

Directed Evolution of Transcription Factor-Based Biosensors for Altered Effector Specificity.

Transcription factor-based biosensors are important tools in Synthetic Biology for the sensing of industrially valuable molecules and clinically important metabolites, therefore presenting applications in the bioremediation, industrial biotechnology, and biomedical fields. The directed evolution of allosteric transcription factors (aTFs) with the aim of altering effector specificity has the potential for the development of new biosensors to detect natural and nonnatural molecules, expanding the scope of available aTF-based biosensors. In this chapter, we delineate a general method for the directed evolution of aTFs. The theory of library design is discussed, along with the detailed methodology for an improved transformation of combined libraries, and the experimental search space by counterselection using fluorescence-activated cell sorting (FACS) is presented.

Risk factor-based screening compared to universal screening for gestational diabetes mellitus in marginalized Burman and Karen populations on the Thailand-Myanmar border: An observational cohort.

Background: Gestational diabetes mellitus (GDM) contributes to maternal and neonatal morbidity. As data from marginalized populations remains scarce, this study compares risk-factor-based to universal GDM screening in a low resource setting. Methods: This is a secondary analysis of data from a prospective preterm birth cohort. Pregnant women were enrolled in the first trimester and completed a 75g oral glucose tolerance test (OGTT) at 24-32 weeks' gestation. To define GDM cases, Hyperglycaemia and Adverse Pregnancy Outcomes (HAPO trial) criteria were used. All GDM positive cases were treated. Sensitivity and specificity of risk-factor-based selection for screening (criteria: age ≥30y, obesity (Body mass index (BMI) ≥27.5kg/m 2), previous GDM, 1 st degree relative with diabetes, previous macrosomia (≥4kg), previous stillbirth, or symphysis-fundal height ≥90th percentile) was compared to universal screening using the OGTT as the gold standard. Adverse maternal and neonatal outcomes were compared by GDM status. Results: GDM prevalence was 13.4% (50/374) (95% CI: 10.3-17.2). Three quarters of women had at least one risk factor (n=271 women), with 37/50 OGTT positive cases correctly identified: sensitivity 74.0% (59.7-85.4) and specificity 27.8% (3.0-33.0). Burman women (self-identified) accounted for 29.1% of the cohort population, but 38.0% of GDM cases. Percentiles for birthweight (p=0.004), head circumference (p=0.002), and weight-length ratio (p=0.030) were higher in newborns of GDM positive compared with non-GDM mothers. 21.7% (75/346) of newborns in the cohort were small-for-gestational age (≤10 th percentile). In Burman women, overweight/obese BMI was associated with a significantly increased adjusted odds ratio 5.03 (95% CI: 1.43-17.64) for GDM compared with normal weight, whereas in Karen women, the trend in association was similar but not significant (OR 2.36; 95% CI 0.95-5.89). Conclusions: Risk-factor-based screening missed one in four GDM positive women. Considering the benefits of early detection of GDM and the limited additional cost of universal screening, a two-step screening program was implemented.

Biosensor-based isolation of amino acid-producing Vibrio natriegens strains.

The marine bacterium Vibrio natriegens has recently been demonstrated to be a promising new host for molecular biology and next generation bioprocesses. V. natriegens is a Gram-negative, non-pathogenic slight-halophilic bacterium, with a high nutrient versatility and a reported doubling time of under 10 min. However, V. natriegens is not an established model organism yet, and further research is required to promote its transformation into a microbial workhorse. In this work, the potential of V. natriegens as an amino acid producer was investigated. First, the transcription factor-based biosensor LysG, from Corynebacterium glutamicum, was adapted for expression in V. natriegens to facilitate the detection of positively charged amino acids. A set of different biosensor variants were constructed and characterized, using the expression of a fluorescent protein as sensor output. After random mutagenesis, one of the LysG-based sensors was used to screen for amino acid producer strains. Here, fluorescence-activated cell sorting enabled the selective sorting of highly fluorescent cells, i.e. potential producer cells. Using this approach, individual L-lysine, L-arginine and L-histidine producers could be obtained producing up to 1 mM of the effector amino acid, extracellularly. Genome sequencing of the producer strains provided insight into the amino acid production metabolism of V. natriegens. This work demonstrates the successful expression and application of transcription factor-based biosensors in V. natriegens and provides insight into the underlying physiology, forming a solid basis for further development of this promising microbe.

Biosensor-based growth-coupling and spatial separation as an evolution strategy to improve small molecule production of Corynebacterium glutamicum.

Evolutionary engineering is a powerful method to improve the performance of microbial cell factories, but can typically not be applied to enhance the production of chemicals due to the lack of an appropriate selection regime. We report here on a new strategy based on transcription factor-based biosensors, which directly couple production to growth. The growth of Corynebacterium glutamicum was coupled to the intracellular concentration of branched-chain amino acids, by integrating a synthetic circuit based on the Lrp biosensor upstream of two growth-regulating genes, pfkA and hisD. Modelling and experimental data highlight spatial separation as key strategy to limit the selection of 'cheater' strains that escaped the evolutionary pressure. This approach facilitated the isolation of strains featuring specific causal mutations enhancing amino acid production. We envision that this strategy can be applied with the plethora of known biosensors in various microbes, unlocking evolution as a feasible strategy to improve production of chemicals.

Terahertz time-domain spectroscopy for powder compact porosity and pore shape measurements: An error analysis of the anisotropic bruggeman model.

Terahertz time-domain spectroscopy (THz-TDS) is a novel technique which has been applied for pore structure analysis and porosity measurements. For this, mainly the anisotropic Bruggeman (AB-EMA) model is applied to correlate the effective refractive index (n eff) of a tablet and the porosity as well as to evaluate the pore shape based on the depolarisation factor L. This paper investigates possible error sources of the AB-EMA for THz-TDS based tablet analysis. The effect of absorption and tablet anisotropy - changes of pore shape with porosity and density distribution - have been investigated. The results suggest that high tablet absorption has a negligible effect on the accuracy of the AB-EMA. In regards of tablet anisotropy the accuracy of the porosity determination is not impaired significantly. However, density distribution and variations in the pore shape with porosity resulted in an unreliable extraction of the tablet pore shape. As an extension of the AB-EMA a new concept was introduced to convert the model into bounds for L. This new approach was found useful to investigate tablet pore shape but also the applicability of the AB-EMA for an unknown set of data.

[Progress in transcription factor-based metabolite biosensors].

Transcription factor-based biosensors (TFBs) play an essential role in metabolic engineering and synthetic biology. TFBs sense the metabolite concentration signals and convert them into specific signal output. They hold high sensitivity, strong specificity, brief analysis speed, and are widely used in response to target metabolites. Here we reviewe the principles of TFBs, the application examples, and challenges faced in recent years in microbial cells, including detecting target metabolite concentrations, high-throughput screening, adaptive laboratory evolutionary selection, and dynamic control. Simultaneously, to overcome the challenges in the application, we also focus on reviewing the performance tuning strategies of TFBs, mainly including traditional and computer-aided tuning strategies. We also discuss the opportunities and challenges that TFBs may face in practical applications, and propose the future research trend.

Automated Rational Strain Construction Based on High-Throughput Conjugation.

Molecular cloning is the core of synthetic biology, as it comprises the assembly of DNA and its expression in target hosts. At present, however, cloning is most often a manual, time-consuming, and repetitive process that highly benefits from automation. The automation of a complete rational cloning procedure, i.e., from DNA creation to expression in the target host, involves the integration of different operations and machines. Examples of such workflows are sparse, especially when the design is rational (i.e., the DNA sequence design is fixed and not based on randomized libraries) and the target host is less genetically tractable (e.g., not sensitive to heat-shock transformation). In this study, an automated workflow for the rational construction of plasmids and their subsequent conjugative transfer into the biotechnological platform organism Corynebacterium glutamicum is presented. The whole workflow is accompanied by a custom-made software tool. As an application example, a rationally designed library of transcription factor-biosensors based on the regulator Lrp was constructed and characterized. A sensor with an improved dynamic range was obtained, and insights from the screening provided evidence for a dual regulator function of C. glutamicum Lrp.

Progress in transcription factor-based metabolite biosensors / 生物工程学报

Transcription factor-based biosensors (TFBs) play an essential role in metabolic engineering and synthetic biology. TFBs sense the metabolite concentration signals and convert them into specific signal output. They hold high sensitivity, strong specificity, brief analysis speed, and are widely used in response to target metabolites. Here we reviewe the principles of TFBs, the application examples, and challenges faced in recent years in microbial cells, including detecting target metabolite concentrations, high-throughput screening, adaptive laboratory evolutionary selection, and dynamic control. Simultaneously, to overcome the challenges in the application, we also focus on reviewing the performance tuning strategies of TFBs, mainly including traditional and computer-aided tuning strategies. We also discuss the opportunities and challenges that TFBs may face in practical applications, and propose the future research trend.

Anti-HCV prevalence and risk factor-based screening for hepatitis C in pregnant women and their partners in Sweden.

BACKGROUND: The hepatitis C virus (HCV) prevalence in Sweden is estimated to be <0.5%, but unclear in pregnant women. The dominating route of transmission is drug use (DU), blood transfusions constituted a risk before 1992. The aim was to examine the anti-HCV prevalence and risk factors for HCV among pregnant women and their partners to evaluate screening strategies. METHODS: Pregnant women and partners in Örebro County and in southern Stockholm were offered HCV-screening when visiting an antenatal clinic in 2013-2016, and completed a questionnaire concerning the country of birth, knowledge of HCV-status and HCV risk factors. RESULTS: In Örebro 2,827 pregnant women and 707 partners, and in Stockholm 1,281 pregnant women and 320 partners participated. Anti-HCV was positive in 34 (0.7%) (25 pregnant women) and the associated risk factors were DU (n = 27), partner with HCV (n = 24) and not born in Sweden (n = 8). HCV RNA was positive in 23 (0.4%), 4 previously unknown and 10 who had been lost to follow-up. The most effective risk factor-based screening model for pregnant women included DU, blood transfusions, born in high prevalence country, partner with HCV, resulting in 538 (13%) pregnant women tested with 96% sensitivity, 87% specificity. CONCLUSIONS: In this study of expecting parents in two Swedish regions, the anti-HCV prevalence was 0.7% and 0.4% were viraemic, of which about 60% were previously unknown or lost to follow-up. Awaiting more studies, including cost-benefit analysis evaluating universal screening, we recommend this improved risk factor-based screening model to identify HCV-infected individuals who need follow-up and therapy.

Sensitive and Rapid Phenotyping of Microbes With Soluble Methane Monooxygenase Using a Droplet-Based Assay.

Methanotrophs with soluble methane monooxygenase (sMMO) show high potential for various ecological and biotechnological applications. Here, we developed a high throughput method to identify sMMO-producing microbes by integrating droplet microfluidics and a genetic circuit-based biosensor system. sMMO-producers and sensor cells were encapsulated in monodispersed droplets with benzene as the substrate and incubated for 5 h. The sensor cells were analyzed as the reporter for phenol-sensitive transcription activation of fluorescence. Various combinations of methanotrophs and biosensor cells were investigated to optimize the performance of our droplet-integrated transcriptional factor biosensor system. As a result, the conditions to ensure sMMO activity to convert the starting material, benzene, into phenol, were determined. The biosensor signals were sensitive and quantitative under optimal conditions, showing that phenol is metabolically stable within both cell species and accumulates in picoliter-sized droplets, and the biosensor cells are healthy enough to respond quantitatively to the phenol produced. These results show that our system would be useful for rapid evaluation of phenotypes of methanotrophs showing sMMO activity, while minimizing the necessity of time-consuming cultivation and enzyme preparation, which are required for conventional analysis of sMMO activity.

Development and optimization of N-acetylneuraminic acid biosensors in Bacillus subtilis.

Transcriptional factor (TF)-based metabolite-responsive biosensors are important tools for screening engineered enzymes with desired properties and for the dynamic regulation of biosynthetic pathways. However, TF-based biosensor construction is often constrained by undesired effects of TF-binding site sequence insertion on gene expression and unpredictable optimal TF expression levels. In the present study, a stepwise TF-based biosensor construction approach was developed using an N-acetylneuraminic acid (NeuAc) biosensor for Bacillus subtilis, as a case study. Specifically, 12 promoters with various strengths were selected as the first promoter library. Next, binding site sequences for the NanR were inserted into various positions of the selected promoter sequences to develop the second promoter library, resulting in 6 engineered promoters containing TF-binding site sequences (NanO), without major effects on promoter strength. NanR expression cassettes with different expression levels were further integrated to construct the biosensor library, yielding 9 NeuAc biosensors with efficient repression in the absence of NeuAc. Finally, biosensor activation was characterized by testing fold changes in expression levels of the green fluorescent protein reporter in the presence of NeuAc in vivo, which revealed 61-fold activation when NeuAc was present. The NeuAc biosensor developed in this study can be used for screening engineered enzymes for enhanced NeuAc biosynthesis in B. subtilis.