Semi-rational design of nitroarene dioxygenase for catalytic ability toward 2,4-dichloronitrobenzene.

Rieske non-heme dioxygenase family enzymes play an important role in the aerobic biodegradation of nitroaromatic pollutants, but no active dioxygenases are available in nature for initial reactions in the degradation of many refractory pollutants like 2,4-dichloronitrobenzene (24DCNB). Here, we report the engineering of hotspots in 2,3-dichloronitrobenzene dioxygenase from Diaphorobacter sp. strain JS3051, achieved through molecular dynamic simulation analysis and site-directed mutagenesis, with the aim of enhancing its catalytic activity toward 24DCNB. The computationally predicted activity scores were largely consistent with the detected activities in wet experiments. Among them, the two most beneficial mutations (E204M and M248I) were obtained, and the combined mutant reached up to a 62-fold increase in activity toward 24DCNB, generating a single product, 3,5-dichlorocatechol, which is a naturally occurring compound. In silico analysis confirmed that residue 204 affected the substrate preference for meta-substituted nitroarenes, while residue 248 may influence substrate preference by interaction with residue 295. Overall, this study provides a framework for manipulating nitroarene dioxygenases using computational methods to address various nitroarene contamination problems.IMPORTANCEAs a result of human activities, various nitroaromatic pollutants continue to enter the biosphere with poor degradability, and dioxygenation is an important kickoff step to remove toxic nitro-groups and convert them into degradable products. The biodegradation of many nitroarenes has been reported over the decades; however, many others still lack corresponding enzymes to initiate their degradation. Although rieske non-heme dioxygenase family enzymes play extraordinarily important roles in the aerobic biodegradation of various nitroaromatic pollutants, prediction of their substrate specificity is difficult. This work greatly improved the catalytic activity of dioxygenase against 2,4-dichloronitrobenzene by computer-aided semi-rational design, paving a new way for the evolution strategy of nitroarene dioxygenase. This study highlights the potential for using enzyme structure-function information with computational pre-screening methods to rapidly tailor the catalytic functions of enzymes toward poorly biodegradable contaminants.

S-nitrosocysteamine-functionalised porous graphene oxide nanosheets as nitric oxide delivery vehicles for cardiovascular applications.

Nitric oxide (NO) is a key signalling molecule released by vascular endothelial cells that is essential for vascular health. Low NO bioactivity is associated with cardiovascular diseases, such as hypertension, atherosclerosis, and heart failure and NO donors are a mainstay of drug treatment. However, many NO donors are associated with the development of tolerance and adverse effects, so new formulations for controlled and targeted release of NO would be advantageous. Herein, we describe the design and characterisation of a novel NO delivery system via the reaction of acidified sodium nitrite with thiol groups that had been introduced by cysteamine conjugation to porous graphene oxide nanosheets, thereby generating S-nitrosated nanosheets. An NO electrode, ozone-based chemiluminescence and electron paramagnetic resonance spectroscopy were used to measure NO released from various graphene formulations, which was sustained at >5 × 10-10 mol cm-2 min-1 for at least 3 h, compared with healthy endothelium (cf. 0.5-4 × 10-10 mol cm-2 min-1). Single cell Raman micro-spectroscopy showed that vascular endothelial and smooth muscle cells (SMCs) took up graphene nanostructures, with intracellular NO release detected via a fluorescent NO-specific probe. Functionalised graphene had a dose-dependent effect to promote proliferation in endothelial cells and to inhibit growth in SMCs, which was associated with cGMP release indicating intracellular activation of canonical NO signalling. Chemiluminescence detected negligible production of toxic N-nitrosamines. Our findings demonstrate the utility of porous graphene oxide as a NO delivery vehicle to release physiologically relevant amounts of NO in vitro, thereby highlighting the potential of these formulations as a strategy for the treatment of cardiovascular diseases.

A metabolic map and artificial intelligence-aided identification of nasopharyngeal carcinoma via a single-cell Raman platform.

BACKGROUND: Nasopharyngeal carcinoma (NPC) is a complex cancer influenced by various factors. This study explores the use of single-cell Raman spectroscopy as a potential diagnostic tool for investigating biomolecular changes associated with NPC carcinogenesis. METHODS: Seven NPC cell lines, one immortalised nasopharyngeal epithelial cell line, six nasopharyngeal mucosa tissues and seven NPC tissue samples were analysed by performing confocal Raman spectroscopic measurements and imaging. The single-cell Raman spectral dataset was used to quantify relevant biomolecules and build machine learning classification models. Metabolomic profiles were investigated using ultra-performance liquid chromatography-tandem mass spectrometer (UPLC-MS/MS). RESULTS: By generating a metabolic map of seven NPC cell lines, we identified an interplay of altered metabolic processes involving nucleic acids, amino acids, lipids and sugars. The results from spatially resolved Raman maps and UPLC-MS/MS metabolomics were consistent, revealing an increase of unsaturated fatty acids in cancer cells, particularly in highly metastatic 5-8F and poorly differentiated CNE2 cells. The classification model achieved a nearly perfect classification when identifying NPC and non-NPC cells with an ROC-AUC of 0.99 and a value of 0.97 when identifying 13 tissue samples. CONCLUSION: This study unveils a complex interplay of metabolic network and highlights the potential roles of unsaturated fatty acids in NPC progression and metastasis. This renders further research to provide deeper insights into NPC pathogenesis, identify new metabolic targets and improve the efficacy of targeted therapies in NPC. Artificial intelligence-aided analysis of single-cell Raman spectra has achieved high accuracies in the classification of both cancer cells and patient tissues, paving the way for a simple, less invasive and accurate diagnostic test.

Rapid detection of six Oceanobacillus species in Daqu starter using single-cell Raman spectroscopy combined with machine learning.

Many traditional fermented foods and beverages industries around the world request the addition of multi-species starter cultures. However, the microbial community in starter cultures is subject to fluctuations due to their exposure to an open environment during fermentation. A rapid detection approach to identify the microbial composition of starter culture is essential to ensure the quality of the final products. Here, we applied single-cell Raman spectroscopy (SCRS) combined with machine learning to monitor Oceanobacillus species in Daqu starter, which plays crucial roles in the process of Chinese baijiu. First, a total of six Oceanobacillus species (O. caeni, O. kimchii, O. iheyensis, O. sojae, O. oncorhynchi subsp. Oncorhynchi and O. profundus) were detected in 44 Daqu samples by amplicon sequencing and isolated by pure culture. Then, we created a reference database of these Oceanobacillus strains which correlated their taxonomic data and single-cell Raman spectra (SCRS). Based on the SCRS dataset, five machine-learning algorithms were used to classify Oceanobacillus strains, among which support vector machine (SVM) showed the highest rate of accuracy. For validation of SVM-based model, we employed a synthetic microbial community composed of varying proportions of Oceanobacillus species and demonstrated a remarkable accuracy, with a mean error was less than 1% between the predicted result and the expected value. The relative abundance of six different Oceanobacillus species during Daqu fermentation was predicted within 60 min using this method, and the reliability of the method was proved by correlating the Raman spectrum with the amplicon sequencing profiles by partial least squares regression. Our study provides a rapid, non-destructive and label-free approach for rapid identification of Oceanobacillus species in Daqu starter culture, contributing to real-time monitoring of fermentation process and ensuring high-quality products.

Engineering artificial photosynthesis based on rhodopsin for CO2 fixation.

Microbial rhodopsin, a significant contributor to sustaining life through light harvesting, holds untapped potential for carbon fixation. Here, we construct an artificial photosynthesis system which combines the proton-pumping ability of rhodopsin with an extracellular electron uptake mechanism, establishing a pathway to drive photoelectrosynthetic CO2 fixation by Ralstonia eutropha (also known as Cupriavidus necator) H16, a facultatively chemolithoautotrophic soil bacterium. R. eutropha is engineered to heterologously express an extracellular electron transfer pathway of Shewanella oneidensis MR-1 and Gloeobacter rhodopsin (GR). Employing GR and the outer-membrane conduit MtrCAB from S. oneidensis, extracellular electrons and GR-driven proton motive force are integrated into R. eutropha's native electron transport chain (ETC). Inspired by natural photosynthesis, the photoelectrochemical system splits water to supply electrons to R. eutropha via the Mtr outer-membrane route. The light-activated proton pump - GR, supported by canthaxanthin as an antenna, powers ATP synthesis and reverses the ETC to regenerate NADH/NADPH, facilitating R. eutropha's biomass synthesis from CO2. Overexpression of a carbonic anhydrase further enhances CO2 fixation. This artificial photosynthesis system has the potential to advance the development of efficient photosynthesis, redefining our understanding of the ecological role of microbial rhodopsins in nature.

High-throughput single-cell sorting by stimulated Raman-activated cell ejection.

Single-cell sorting is essential to explore cellular heterogeneity in biology and medicine. Recently developed Raman-activated cell sorting (RACS) circumvents the limitations of fluorescence-activated cell sorting, such as the cytotoxicity of labels. However, the sorting throughputs of all forms of RACS are limited by the intrinsically small cross-section of spontaneous Raman scattering. Here, we report a stimulated Raman-activated cell ejection (S-RACE) platform that enables high-throughput single-cell sorting based on high-resolution multi-channel stimulated Raman chemical imaging, in situ image decomposition, and laser-induced cell ejection. The performance of this platform was illustrated by sorting a mixture of 1 µm polymer beads, where 95% yield, 98% purity, and 14 events per second throughput were achieved. Notably, our platform allows live cell ejection, allowing for the growth of single colonies of bacteria and fungi after sorting. To further illustrate the chemical selectivity, lipid-rich Rhodotorula glutinis cells were successfully sorted from a mixture with Saccharomyces cerevisiae, confirmed by downstream quantitative PCR. Furthermore, by integrating a closed-loop feedback control circuit into the system, we realized real-time single-cell imaging and sorting, and applied this method to precisely eject regions of interest from a rat brain tissue section. The reported S-RACE platform opens exciting opportunities for a wide range of single-cell applications in biology and medicine.

Developing a Blood Cell-Based Diagnostic Test for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Using Peripheral Blood Mononuclear Cells.

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is characterized by debilitating fatigue that profoundly impacts patients' lives. Diagnosis of ME/CFS remains challenging, with most patients relying on self-report, questionnaires, and subjective measures to receive a diagnosis, and many never receiving a clear diagnosis at all. In this study, a single-cell Raman platform and artificial intelligence are utilized to analyze blood cells from 98 human subjects, including 61 ME/CFS patients of varying disease severity and 37 healthy and disease controls. These results demonstrate that Raman profiles of blood cells can distinguish between healthy individuals, disease controls, and ME/CFS patients with high accuracy (91%), and can further differentiate between mild, moderate, and severe ME/CFS patients (84%). Additionally, specific Raman peaks that correlate with ME/CFS phenotypes and have the potential to provide insights into biological changes and support the development of new therapeutics are identified. This study presents a promising approach for aiding in the diagnosis and management of ME/CFS and can be extended to other unexplained chronic diseases such as long COVID and post-treatment Lyme disease syndrome, which share many of the same symptoms as ME/CFS.

Application of magnetic-nanoparticle functionalized whole-cell biosensor array for bioavailability and ecotoxicity estimation at urban contaminated sites.

The bioavailability and ecotoxicity of pollutants are important for urban ecological systems and human health, particularly at contaminated urban sites. Therefore, whole-cell bioreporters are used in many studies to assess the risks of priority chemicals; however, their application is restricted by low throughput for specific compounds and complicated operations for field tests. In this study, an assembly technology for manufacturing Acinetobacter-based biosensor arrays using magnetic nanoparticle functionalization was developed to solve this problem. The bioreporter cells maintained high viability, sensitivity, and specificity in sensing 28 priority chemicals, seven heavy metals, and seven inorganic compounds in a high-throughput manner, and their performance remained acceptable for at least 20 d. We also tested the performance by assessing 22 real environmental soil samples from urban areas in China, and our results showed positive correlations between the biosensor estimation and chemical analysis. Our findings prove the feasibility of the magnetic nanoparticle-functionalized biosensor array to recognize the types and toxicities of multiple contaminants for online environmental monitoring at contaminated sites.

Deep insight into oriented propionate production from food waste: Microbiological interpretation and design practice.

Using mixed microbial cultures (MMCs) for oriented volatile fatty acids (VFAs) refining in an open environment is a typical challenge due to the microbial diversiform and the process complexity. Especially for carbohydrate-rich waste (such as food waste), butyrate-type fermentation is usually dominant in a single-stage MMCs anaerobic process, while the production of odd-carbon VFAs (such as propionate) is difficult although it plays a significant role in chemicals industries. In this study, firstly, we gave a new perspective on the rationality of the oriented propionate production using MMCs with lactate as feedstock by conducting in-depth microbial informatics and reaction analysis. Secondly, we verified the feasibility of the "food waste-lactate-propionate" route to reverse the original butyrate-type fermentation situation and explore mechanisms for maintaining stability. In the first stage, a defined lactate fermentation microbiome was used to produce lactate-containing broth (80% of total chemical oxygen demand) at pH=4. In the second stage, an undomesticated undefined anaerobic microbiome was used to drive propionate production (45.26% ± 2.23% of total VFAs) under optimized conditions (C/N = 100:1-200:1 and pH=5.0). The low pH environment in the first stage enhanced the lactic acid bacteria to resist the invasion of non-functional flanking bacteria, making the community stable. In the second stage, the system maintained the propionate-type fermentation due to the absence of the ecological niche of the invasive lactic acid bacteria; The selection of propionate-producing specialists was a necessary but not sufficient condition for propionate-type fermentation. At last, this study proposed an enhanced engineering strategy framework for understanding elaborate MMCs fermentation.

Artificial intelligence-aided rapid and accurate identification of clinical fungal infections by single-cell Raman spectroscopy.

Integrating artificial intelligence and new diagnostic platforms into routine clinical microbiology laboratory procedures has grown increasingly intriguing, holding promises of reducing turnaround time and cost and maximizing efficiency. At least one billion people are suffering from fungal infections, leading to over 1.6 million mortality every year. Despite the increasing demand for fungal diagnosis, current approaches suffer from manual bias, long cultivation time (from days to months), and low sensitivity (only 50% produce positive fungal cultures). Delayed and inaccurate treatments consequently lead to higher hospital costs, mobility and mortality rates. Here, we developed single-cell Raman spectroscopy and artificial intelligence to achieve rapid identification of infectious fungi. The classification between fungi and bacteria infections was initially achieved with 100% sensitivity and specificity using single-cell Raman spectra (SCRS). Then, we constructed a Raman dataset from clinical fungal isolates obtained from 94 patients, consisting of 115,129 SCRS. By training a classification model with an optimized clinical feedback loop, just 5 cells per patient (acquisition time 2 s per cell) made the most accurate classification. This protocol has achieved 100% accuracies for fungal identification at the species level. This protocol was transformed to assessing clinical samples of urinary tract infection, obtaining the correct diagnosis from raw sample-to-result within 1 h.

Metabolic Reprogramming in Colon Cancer Cells Persistently Infected with Newcastle Disease Virus.

Newcastle disease virus (NDV) is an oncolytic agent against various types of mammalian cancers. As with all cancer therapies, the development of cancer resistance, both innate and acquired, is becoming a challenge. In this study, we investigated persistently NDV-infected Caco-2 colon cancer cells, designated as virus-resistant (VR) Caco-2 cells, which were then able to resist NDV-mediated oncolysis. We applied single-cell Raman spectroscopy, combined with deuterium isotope probing (Raman-DIP) techniques, to investigate the metabolic adaptations and dynamics in VR Caco-2 cells. A linear discriminant analysis (LDA) model demonstrated excellent performance in differentiating VR Caco-2 from Caco-2 cells at single-cell level. By comparing the metabolic profiles in a time-resolved manner, the de novo synthesis of proteins and lipids was found upregulated, along with decreased DNA synthesis in VR Caco-2. The results suggest that VR Caco-2 cells might reprogram their metabolism and divert energy from proliferation to protein synthesis and lipidic modulation. The ability to identify and characterise single resistant cells among a population of cancer cells would help develop a deeper understanding of the resistance mechanisms and better tactics for developing effective cancer treatment.

Growth-coupled enzyme engineering through manipulation of redox cofactor regeneration.

Enzymes need to be efficient, robust, and highly specific for their effective use in commercial bioproduction. These properties can be introduced using various enzyme engineering techniques, with random mutagenesis and directed evolution (DE) often being chosen when there is a lack of structural information -or mechanistic understanding- of the enzyme. The screening or selection step of DE is the limiting part of this process, since it must ideally be (ultra)-high throughput, specifically target the catalytic activity of the enzyme and have an accurately quantifiable metric for said activity. Growth-coupling selection strategies involve coupling a desired enzyme activity to cellular metabolism and therefore growth, where growth (rate) becomes the output metric. Redox cofactors (NAD+/NADH and NADP+/NADPH) have recently been identified as promising target molecules for growth coupling, owing to their essentiality for cellular metabolism and ubiquitous nature. Redox cofactor oxidation or reduction can be disrupted through metabolic engineering and the use of specific culturing conditions, rendering the cell inviable unless a 'rescue' reaction complements the imposed metabolic deficiency. Using this principle, enzyme variants displaying improved cofactor oxidation or reduction rates can be selected for through an increased growth rate of the cell. In recent years, several E. coli strains have been developed that are deficient in the oxidation or reduction of NAD+/NADH and NADP+/NADPH pairs, and of non-canonical redox cofactor pairs NMN+/NMNH and NCD+/NCDH, which provides researchers with a versatile toolbox of enzyme engineering platforms. A range of redox cofactor dependent enzymes have since been engineered using a variety of these strains, demonstrating the power of using this growth-coupling technique for enzyme engineering. This review aims to summarize the metabolic engineering involved in creating strains auxotrophic for the reduced or oxidized state of redox cofactors, and the resulting successes in using them for enzyme engineering. Perspectives on the unique features and potential future applications of this technique are also presented.

Leveraging single-cell Raman spectroscopy and single-cell sorting for the detection and identification of yeast infections.

Invasive fungal infection serves as a great threat to human health. Discrimination between fungal and bacterial infections at the earliest stage is vital for effective clinic practice; however, traditional culture-dependent microscopic diagnosis of fungal infection usually requires several days, meanwhile, culture-independent immunological and molecular methods are limited by the detectable type of pathogens and the issues with high false-positive rates. In this study, we proposed a novel culture-independent phenotyping method based on single-cell Raman spectroscopy for the rapid discrimination between fungal and bacterial infections. Three Raman biomarkers, including cytochrome c, peptidoglycan, and nucleic acid, were identified through hierarchical clustering analysis of Raman spectra across 12 types of most common yeast and bacterial pathogens. Compared to those of bacterial pathogens, the single cells of yeast pathogens demonstrated significantly stronger Raman peaks for cytochrome c, but weaker signals for peptidoglycan and nucleic acid. A two-step protocol combining the three biomarkers was established and able to differentiate fungal infections from bacterial infections with an overall accuracy of 94.9%. Our approach was also used to detect ten raw urinary tract infection samples. Successful identification of fungi was achieved within half an hour after sample obtainment. We further demonstrated the accurate fungal species taxonomy achieved with Raman-assisted cell ejection. Our findings demonstrate that Raman-based fungal identification is a novel, facile, reliable, and with a breadth of coverage approach, that has a great potential to be adopted in routine clinical practice to reduce the turn-around time of invasive fungal disease (IFD) diagnostics.

Engineering a Rhodopsin-Based Photo-Electrosynthetic System in Bacteria for CO2 Fixation.

A key goal of synthetic biology is to engineer organisms that can use solar energy to convert CO2 to biomass, chemicals, and fuels. We engineered a light-dependent electron transfer chain by integrating rhodopsin and an electron donor to form a closed redox loop, which drives rhodopsin-dependent CO2 fixation. A light-driven proton pump comprising Gloeobacter rhodopsin (GR) and its cofactor retinal have been assembled in Ralstonia eutropha (Cupriavidus necator) H16. In the presence of light, this strain fixed inorganic carbon (or bicarbonate) leading to 20% growth enhancement, when formate was used as an electron donor. We found that an electrode from a solar panel can replace organic compounds to serve as the electron donor, mediated by the electron shuttle molecule riboflavin. In this new autotrophic and photo-electrosynthetic system, GR is augmented by an external photocell for reductive CO2 fixation. We demonstrated that this hybrid photo-electrosynthetic pathway can drive the engineered R. eutropha strain to grow using CO2 as the sole carbon source. In this system, a bioreactor with only two inputs, light and CO2, enables the R. eutropha strain to perform a rhodopsin-dependent autotrophic growth. Light energy alone, supplied by a solar panel, can drive the conversion of CO2 into biomass with a maximum electron transfer efficiency of 20%.

A quantitative RT-qLAMP for the detection of SARS-CoV-2 and human gene in clinical application.

Reverse transcription (RT) - loop-mediated isothermal amplification (LAMP) assay is a rapid and one-step method to detect SARS-CoV-2 in the pandemic. Quantitative estimation of the viral load of SARS-CoV-2 in patient samples could help physicians make decisions on clinical treatment and patient management. Here, we propose to use a quantitative LAMP (qLAMP) method to evaluate the viral load of SARS-CoV-2 in samples. We used threshold time (TT) values of qLAMP, the isothermal incubation time required for the fluorescent or colorimetric signal to reach the threshold, to indicate the viral load of clinical samples. Similar to the cycle threshold (Ct ) values in conventional qPCR, TT values of qLAMP show a linear relationship to the copy numbers of SARS-CoV-2. The higher the viral loadings, the lower qLAMP TT values are. The RT-qLAMP assay was demonstrated to quantify the viral loads of synthesized full-length RNA, inactivated viral particles (BBIBP-CorV), and clinical samples within 15 min by fluorescent reading and 25 min by colorimetric reading. The RT-qLAMP has been applied to detect Alpha, Beta, Kappa, Delta, and Omicron variants of SARS-CoV-2, as well as the human beta-actin gene, and their TT values showed the linear patterns. The RT-qLAMP assays were evaluated by 64 clinical samples (25 positives and 39 negatives) for the assessment of viral loads, and it was also used to quantify the human beta-actin gene, which was used as a control and an indicator of sampling quality in clinical swab samples. The result of RT-qLAMP was in good agreement with the result of RT-qPCR. The RT-qLAMP assay detected all clinical samples, including those with Ct  = 35, within 10 min using fluorescent reading.

Diagnosis of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome With Partial Least Squares Discriminant Analysis: Relevance of Blood Extracellular Vesicles.

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS), a chronic disease characterized by long-lasting persistent debilitating widespread fatigue and post-exertional malaise, remains diagnosed by clinical criteria. Our group and others have identified differentially expressed miRNA profiles in the blood of patients. However, their diagnostic power individually or in combinations seems limited. A Partial Least Squares-Discriminant Analysis (PLS-DA) model initially based on 817 variables: two demographic, 34 blood analytic, 136 PBMC miRNAs, 639 Extracellular Vesicle (EV) miRNAs, and six EV features, selected an optimal number of five components, and a subset of 32 regressors showing statistically significant discriminant power. The presence of four EV-features (size and z-values of EVs prepared with or without proteinase K treatment) among the 32 regressors, suggested that blood vesicles carry relevant disease information. To further explore the features of ME/CFS EVs, we subjected them to Raman micro-spectroscopic analysis, identifying carotenoid peaks as ME/CFS fingerprints, possibly due to erythrocyte deficiencies. Although PLS-DA analysis showed limited capacity of Raman fingerprints for diagnosis (AUC = 0.7067), Raman data served to refine the number of PBMC miRNAs from our previous model still ensuring a perfect classification of subjects (AUC=1). Further investigations to evaluate model performance in extended cohorts of patients, to identify the precise ME/CFS EV components detected by Raman and to reveal their functional significance in the disease are warranted.

Reprogramming Synthetic Cells for Targeted Cancer Therapy.

Advances in synthetic biology enable the reprogramming of bacteria as smart agents to specifically target tumors and locally release anticancer drugs in a highly controlled manner. However, the bench-to-bedside translation of engineered bacteria is often impeded by genetic instability and the potential risk of uncontrollable replication of engineered bacteria inside the patient. SimCells (simple cells) are chromosome-free bacteria controlled by designed gene circuits, which can bypass the interference of the native gene network in bacteria and eliminate the risk of bacterial uncontrolled growth. Here, we describe the reprogramming of SimCells and mini-SimCells to serve as "safe and live drugs" for targeted cancer therapy. We engineer SimCells to display nanobodies on the surface for the binding of carcinoembryonic antigen (CEA), which is an important biomarker found commonly in colorectal cancer cells. We show that SimCells and mini-SimCells with surface display of anti-CEA nanobody can specifically bind CEA-expressing Caco2 cancer cells in vitro while leaving the non-CEA-expressing SW80 cancer cells untouched. These cancer-targeting SimCells and mini-SimCells induced cancer cell death in vitro by compromising the plasma membrane of cancer cells. The cancer-killing effect can be further enhanced by an aspirin/salicylate inducible gene circuit that converts salicylate into catechol, a potent anticancer. This work highlights the potential of SimCells and mini-SimCells for targeted cancer therapy and lays the foundation for the application of synthetic biology to medicine.

Clinical Perspective of Antimicrobial Resistance in Bacteria.

Antimicrobial resistance (AMR) has become a global clinical problem in recent years. With the discovery of antibiotics, infections were not a deadly problem for clinicians as they used to be. However, worldwide AMR comes with the overuse/misuse of antibiotics and the spread of resistance is deteriorated by a multitude of mobile genetic elements and relevant resistant genes. This review provides an overview of the current situation, mechanism, epidemiology, detection methods and clinical treatment for antimicrobial resistant genes in clinical important bacteria including methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant Enterococcus (VRE), penicillin-resistant Streptococcus pneumoniae (PRSP), extended-spectrum ß-lactamase-producing Enterobacteriaceae, acquired AmpC ß-lactamase-producing Enterobacteriaceae, carbapenemase-producing Enterobacteriaceae (CPE), multidrug-resistant (MDR) Acinetobacter baumannii and Pseudomonas aeruginosa.