Rhodopsin-based light-harvesting system for sustainable synthetic biology.

Rhodopsins, a diverse class of light-sensitive proteins found in various life domains, have attracted considerable interest for their potential applications in sustainable synthetic biology. These proteins exhibit remarkable photochemical properties, undergoing conformational changes upon light absorption that drive a variety of biological processes. Exploiting rhodopsin's natural properties could pave the way for creating sustainable and energy-efficient technologies. Rhodopsin-based light-harvesting systems offer innovative solutions to a few key challenges in sustainable engineering, from bioproduction to renewable energy conversion. In this opinion article, we explore the recent advancements and future possibilities of employing rhodopsins for sustainable engineering, underscoring the transformative potential of these biomolecules.

Fast simulation for interacting four-level Rydberg atoms: electromagnetically induced transparency and Autler-Townes splitting.

Quantum sensing using Rydberg atoms is an emerging technology for precise measurement of electric fields. However, most existing computational methods are all based on a single-particle model and neglect Rydberg-Rydberg interaction between atoms. In this study, we introduce the interaction term into the conventional four-level optical Bloch equations. By incorporating fast iterations and solving for the steady-state solution efficiently, we avoid the computation of a massive 4N × 4N dimensional matrix. Additionally, we apply the Doppler frequency shift to each atom used in the calculation, eliminating the requirement for an additional Doppler iteration. These schemes allow for the calculation of the interaction between 7000 atoms around one minute. Based on the many-body model, we investigate the Rydberg-Rydberg interaction of Rydberg atoms under different atomic densities. Furthermore, we compare our results with the literature data of a three-level system and the experimental results of our own four-level system. The results demonstrate the validity of our model, with an effective error of 4.59% compared to the experimental data. Finally, we discover that the many-body model better predicts the linear range for measuring electric fields than the single-particle model, making it highly applicable in precise electric field measurements.

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.

Wavelength division multiplexing based on the coupling effect of helical edge states in two-dimensional dielectric photonic crystals.

Photonic topological insulators with topologically protected edge states featuring one-way, robustness and backscattering-immunity possess extraordinary abilities to steer and manipulate light. In this work, we construct a topological heterostructure (TH) consisting of a domain of nontrivial pseudospin-type topological photonic crystals (PCs) sandwiched between two domains of trivial PCs based on two-dimensional all-dielectric core-shell PCs in triangle lattice. We consider three THs with different number of layers in the middle nontrivial domain (i.e., one-layer, two-layer, three-layer) and demonstrate that the projected band diagrams of the three THs host interesting topological waveguide states (TWSs) with properties of one-way, large-area, broad-bandwidth and robustness due to coupling effect of the helical edge states associated with the two domain-wall interfaces. Moreover, taking advantage of the tunable bandgap between the TWSs by the layer number of the middle domain due to the coupling effect, a topological Y-splitter with functionality of wavelength division multiplexing is explicitly demonstrated exploiting the unique feature of the dispersion curves of TWSs in the three THs. Our work not only offers a new method to realize pseudospin-polarized large-area TWSs with tunable mode-width, but also could provide new opportunities for practical applications in on-chip multifunctional (i.e., wavelength division multiplexing) photonic devices with topological protection and information processing with pseudospin-dependent transport.

High-Q metasurface signal isolator for 1.5T surface coil magnetic resonance imaging on the go.

The combination of surface coils and metamaterials remarkably enhance magnetic resonance imaging (MRI) performance for significant local staging flexibility. However, due to the coupling in between, impeded signal-to-noise ratio (SNR) and low-contrast resolution, further hamper the future growth in clinical MRI. In this paper, we propose a high-Q metasurface decoupling isolator fueled by topological LC loops for 1.5T surface coil MRI system, increasing the magnetic field up to fivefold at 63.8 MHz. We have employed a polarization conversion mechanism to effectively eliminate the coupling between the MRI metamaterial and the radio frequency (RF) surface transmitter-receiver coils. Furthermore, a high-Q metasurface isolator was achieved by taking advantage of bound states in the continuum (BIC) for extremely high-resolution MRI and spectroscopy. An equivalent physical model of the miniaturized metasurface design was put forward through LC circuit analysis. This study opens up a promising route for the easy-to-use and portable surface coil MRI scanners.

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.

Calibrating Bayesian Decoders of Neural Spiking Activity.

Accurately decoding external variables from observations of neural activity is a major challenge in systems neuroscience. Bayesian decoders, which provide probabilistic estimates, are some of the most widely used. Here we show how, in many common settings, the probabilistic predictions made by traditional Bayesian decoders are overconfident. That is, the estimates for the decoded stimulus or movement variables are more certain than they should be. We then show how Bayesian decoding with latent variables, taking account of low-dimensional shared variability in the observations, can improve calibration, although additional correction for overconfidence is still needed. Using data from males, we examine (1) decoding the direction of grating stimuli from spike recordings in the primary visual cortex in monkeys, (2) decoding movement direction from recordings in the primary motor cortex in monkeys, (3) decoding natural images from multiregion recordings in mice, and (4) decoding position from hippocampal recordings in rats. For each setting, we characterize the overconfidence, and we describe a possible method to correct miscalibration post hoc. Properly calibrated Bayesian decoders may alter theoretical results on probabilistic population coding and lead to brain-machine interfaces that more accurately reflect confidence levels when identifying external variables.

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.

Integrative single-cell characterization of a frugivorous and an insectivorous bat kidney and pancreas.

Frugivory evolved multiple times in mammals, including bats. However, the cellular and molecular components driving it remain largely unknown. Here, we use integrative single-cell sequencing (scRNA-seq and scATAC-seq) on insectivorous (Eptesicus fuscus; big brown bat) and frugivorous (Artibeus jamaicensis; Jamaican fruit bat) bat kidneys and pancreases and identify key cell population, gene expression and regulatory differences associated with the Jamaican fruit bat that also relate to human disease, particularly diabetes. We find a decrease in loop of Henle and an increase in collecting duct cells, and differentially active genes and regulatory elements involved in fluid and electrolyte balance in the Jamaican fruit bat kidney. The Jamaican fruit bat pancreas shows an increase in endocrine and a decrease in exocrine cells, and differences in genes and regulatory elements involved in insulin regulation. We also find that these frugivorous bats share several molecular characteristics with human diabetes. Combined, our work provides insights from a frugivorous mammal that could be leveraged for therapeutic purposes.

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.

Effect of environmental factors on expression of staphylococcal enterotoxin genes.

Staphylococcal enterotoxins (SEs) secreted by Staphylococcus aureus (S. aureus) can cause foodborne disease, nausea, vomiting and diarrhea, and even death. Regulation of SE expression is related to accessory gene regulators (Agr). It is important to reveal which environmental factors influence regulation of SE expression to prevent SE food poisoning outbreak. Hence, natural environmental factors which may have an impact on SE expression were selected, such as temperature, food types, strains, and competing strains. Seven strains of S. aureus carrying different SE genes were collected from the Chinese Academy of Inspection and Quarantine (CAIQ) strain bank for study. Strains were cultured with different conditions. Temperature was 8 °C, 22 °C, and 30 °C. Food type was milk powder and nutrient broth. Competing strains were Vibrio parahaemolyticus (V. parahaemolyticus), Escherichia coli (E. coli), and Bacillus cereus (B. cereus). The expression culture solution was pretreated by centrifugation, then determined by using SDS-PAGE, and distinguished SEs apart from each other by HPLC-ESI-TOF. There are 168 samples collected from SE expression culture; the result of SDS-PAGE suggests 23 samples were positive for SEs, and the other 145 samples were negative for SEs. The result of HPLC-ESI-TOF suggests that SEs with similar molecular weight can be distinguished in terms of m/z. The most important factor contributing to regulate expression of SEs was estimated by logistic regressive analysis. The result shows that McFadden R2 is 0.213; p value is 0.000 (p < 0.05); this result illustrates that the model is valid and meaningful. Strains, food types, temperature, and competing strands can explain the 21% change in SE expression. Temperature (z = 3.029, p = 0.002 < 0.01), strains (z = - 3.132, p = 0.002 < 0.01), and food types (z = - 2.415, p = 0.016 < 0.05) have significant impact on SE expression, and the competing strains (z = 1.230, p = 0.219 > 0.05) have no impact on the SE expression. More important impact on SE expression was estimated by OR value; the result shows that strength of temperature influencing on SE expression is bigger than strains and food types in terms of values of OR, temperature (OR = 2.862), strains (OR = 0.641), and food types (OR = 0.561); consequently, temperature is a key factor for stimulating SE expression and had high expression at 30 °C. Therefore, food easily contaminated with S. aureus should be monitored intensively at early and late summer, when proper temperature for expressing SEs may result in S. aureus food poisoning prevalence.

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.

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.

Construction and validation of risk model of EMT-related prognostic genes for kidney renal clear cell carcinoma.

BACKGROUND: Kidney renal clear cell carcinoma (KIRC) is a prevalent type of urological malignancy. The present study aimed to predict biomarkers for KIRC. METHODS: We collected transcriptomic and clinical information for KIRC from The Cancer Genome Atlas and GSE22541 cohorts. RESULTS: Unsupervised clustering of 35 epithelial-mesenchymal transformation (EMT)-related differentially expressed gene profiles divided samples into two clusters with distinct immune characteristics. Six genes (IL20RB, DDC, ANKRD36BP2, F2RL1, TEK, and AMN) were found to construct a prognostic risk model using multivariate Cox regression analysis. Kaplan-Meier analysis suggested the better prognosis of the KIRC patients in the low-risk group than that in the high-risk group. Immune infiltration analyses was conducted using xCell and single-sample gene set enrichment analysis, indicating that the risk score was associated with the immune microenvironment of the KIRC. Prognostic marker gene-targeted medications with high drug sensitivity were predicted in KIRC patients. CONCLUSIONS: In summary, the present study identified IL20RB, DDC, ANKRD36BP2, F2RL1, TEK, and AMN as prognostic biomarkers, providing insight into immunotherapy and gene-targeted drugs of KIRC.

Measurement sensitivity analysis and on-orbit calibration of systematic errors for a narrow field-of-view camera.

Narrow field-of-view (FOV) cameras enable long-range observations and have been often used in deep space exploration missions. To solve the problem of systematic error calibration for a narrow FOV camera, the sensitivity of the camera systematic errors to the angle between the stars is analyzed theoretically, based on a measurement system for observing the angle between stars. In addition, the systematic errors for a narrow FOV camera are classified into "Non-attitude Errors" and "Attitude Errors". Furthermore, the on-orbit calibration methods for the two types of errors are researched. Simulations show that the proposed method is more effective in the on-orbit calibration of systematic errors for a narrow FOV camera than the traditional calibration methods.