Response and acclimation of cyanobacteria to acidification: A comprehensive review.

Cyanobacteria, as vital components of aquatic ecosystems, face increasing challenges due to acidification driven by various anthropogenic and natural factors. Understanding how cyanobacteria adapt and respond to acidification is crucial for predicting their ecological dynamics and potential impacts on ecosystem health. This comprehensive review synthesizes current knowledge on the acclimation mechanisms and responses of cyanobacteria to acidification stress. Detailly, ecological roles of cyanobacteria were firstly briefly concluded, followed by the effects of acidification on aquatic ecosystems and cyanobacteria. Then the review focuses on the physiological, biochemical, and molecular strategies employed by cyanobacteria to cope with acidification stress, highlighting key adaptive mechanisms and their ecological implications. Finally, a summary of strategies to enhance acid resistance in cyanobacteria and future directions was discussed. Utilizing omics data and machine learning technology to build a cyanobacterial acid regulatory network allows for predicting the impact of acidification on cyanobacteria and inferring its broader effects on ecosystems. Additionally, acquiring acid-tolerant chassis cells of cyanobacteria through innovative techniques facilitates the advancement of environmentally friendly production of acidic chemicals. By synthesizing empirical evidence and theoretical frameworks, this review aims to elucidate the complex interplay between cyanobacteria and acidification stressors, providing insights for future research directions and ecosystem management strategies.

Self-balancing Incremental Broad Learning System with privacy protection.

Incremental learning algorithms have been developed as an efficient solution for fast remodeling in Broad Learning Systems (BLS) without a retraining process. Even though the structure and performance of broad learning are gradually showing superiority, private data leakage in broad learning systems is still a problem that needs to be solved. Recently, Multiparty Secure Broad Learning System (MSBLS) is proposed to allow two clients to participate training. However, privacy-preserving broad learning across multiple clients has received limited attention. In this paper, we propose a Self-Balancing Incremental Broad Learning System (SIBLS) with privacy protection by considering the effect of different data sample sizes from clients, which allows multiple clients to be involved in the incremental learning. Specifically, we design a client selection strategy to select two clients in each round by reducing the gap in the number of data samples in the incremental updating process. To ensure the security under the participation of multiple clients, we introduce a mediator in the data encryption and feature mapping process. Three classical datasets are used to validate the effectiveness of our proposed SIBLS, including MNIST, Fashion and NORB datasets. Experimental results show that our proposed SIBLS can have comparable performance with MSBLS while achieving better performance than federated learning in terms of accuracy and running time.

Engineered Cyanobacteria-Based Living Materials for Bioremediation of Heavy Metals Both In Vitro and In Vivo.

The pollution caused by heavy metals (HMs) represents a global concern due to their serious environmental threat. Photosynthetic cyanobacteria have a natural niche and the ability to remediate HMs such as cadmium. However, their practical application is hindered by a low tolerance to HMs and issues related to recycling. In response to these challenges, this study focuses on the development and evaluation of engineered cyanobacteria-based living materials for HMs bioremediation. Genes encoding phytochelatins (PCSs) and metallothioneins (MTs) were introduced into the model cyanobacterium Synechocystis sp. PCC 6803, creating PM/6803. The strain exhibited improved tolerance to multiple HMs and effectively removed a combination of Cd2+, Zn2+, and Cu2+. Using Cd2+ as a representative, PM/6803 achieved a bioremediation rate of approximately 21 µg of Cd2+/OD750 under the given test conditions. To facilitate its controllable application, PM/6803 was encapsulated using sodium alginate-based hydrogels (PM/6803@SA) to create "living materials" with different shapes. This system was feasible, biocompatible, and effective for removing Cd2+ under simulated conditions of zebrafish and mice models. Briefly, in vitro application of PM/6803@SA efficiently rescued zebrafish from polluted water containing Cd2+, while in vivo use of PM/6803@SA significantly decreased the Cd2+ content in mice bodies and restored their active behavior. The study offers feasible strategies for HMs bioremediation using the interesting biomaterials of engineered cyanobacteria both in vitro and in vivo.

Engineering Fatty Acid Biosynthesis in Microalgae: Recent Progress and Perspectives.

Microalgal lipids hold significant potential for the production of biodiesel and dietary supplements. To enhance their cost-effectiveness and commercial competitiveness, it is imperative to improve microalgal lipid productivity. Metabolic engineering that targets the key enzymes of the fatty acid synthesis pathway, along with transcription factor engineering, are effective strategies for improving lipid productivity in microalgae. This review provides a summary of the advancements made in the past 5 years in engineering the fatty acid biosynthetic pathway in eukaryotic microalgae. Furthermore, this review offers insights into transcriptional regulatory mechanisms and transcription factor engineering aimed at enhancing lipid production in eukaryotic microalgae. Finally, the review discusses the challenges and future perspectives associated with utilizing microalgae for the efficient production of lipids.

Predictors associated with successful weaning of veno-venous extracorporeal membrane oxygenation and mortality in adult patients with severe acute lung failure: Protocol of a pooled data analysis of cohort studies.

BACKGROUND: Severe acute lung failure (ALF) often necessitates veno-venous extracorporeal membrane oxygenation (VV-ECMO), where identifying predictors of weaning success and mortality remains crucial yet challenging. The study aims to identify predictors of weaning success and mortality in adults undergoing VV-ECMO for severe ALF, a gap in current clinical knowledge. METHODS AND ANALYSIS: PubMed, EMBASE, and the Cochrane Central Register of Controlled Trials will be searched for cohort studies examining the predictive factors of successful weaning and mortality in adult patients on VV-ECMO due to severe ALF. Risk of bias assessment will be conducted using the Newcastle-Ottawa scale for each included study. The primary outcomes will be successful weaning from VV-ECMO and all-cause mortality. Between-study heterogeneity will be evaluated using the I2 statistic. Sensitivity, subgroup, and meta-regression analyses will be performed to ascertain potential sources of heterogeneity and assess the robustness of our results. We will use the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) tool to recommend the level of evidence. DISCUSSION: This study seeks to provide clinically significant insights into predictors for weaning and mortality during VV-ECMO treatment for ALF, aiming to support clinical decisions and potentially influence health policy, thereby improving patient outcomes. ETHICS AND DISSEMINATION: Given the absence of direct engagement with human subjects or access to personal medical records, ethical approval for this study is deemed unnecessary. The study findings will be shared at a scientific conference either at the global or national level. Alternatively, the results will be presented for publication in a rigorously peer-reviewed journal regarding critical care medicine.

Engineering a solar formic acid/pentose (SFAP) pathway in Escherichia coli for lactic acid production.

Microbial CO2 fixation into lactic acid (LA) is an important approach for low-carbon biomanufacturing. Engineering microbes to utilize CO2 and sugar as co-substrates can create efficient pathways through input of moderate reducing power to drive CO2 fixation into product. However, to achieve complete conservation of organic carbon, how to engineer the CO2-fixing modules compatible with native central metabolism and merge the processes for improving bioproduction of LA is a big challenge. In this study, we designed and constructed a solar formic acid/pentose (SFAP) pathway in Escherichia coli, which enabled CO2 fixation merging into sugar catabolism to produce LA. In the SFAP pathway, adequate reducing equivalents from formate oxidation drive glucose metabolism shifting from glycolysis to the pentose phosphate pathway. The Rubisco-based CO2 fixation and sequential reduction of C3 intermediates are conducted to produce LA stoichiometrically. CO2 fixation theoretically can bring a 20% increase of LA production compared with sole glucose feedstock. This SFAP pathway in the integration of photoelectrochemical cell and an engineered Escherichia coli opens an efficient way for fixing CO2 into value-added bioproducts.

Evaluating the Demand for Nucleic Acid Testing in Different Scenarios of COVID-19 Transmission: A Simulation Study.

INTRODUCTION: The 2019 novel coronavirus (COVID-19) has been recognized as the most severe human infectious disease pandemic in the past century. To enhance our ability to control potential infectious diseases in the future, this study simulated the influence of nucleic acid testing on the transmission of COVID-19 across varied scenarios. Additionally, it assessed the demand for nucleic acid testing under different circumstances, aiming to furnish a decision-making foundation for the implementation of nucleic acid screening measures, the provision of emergency materials, and the allocation of human resources. METHODS: Considering the transmission dynamics of COVID-19 and the preventive measures implemented by countries, we explored three distinct levels of epidemic intensity: community transmission, outbreak, and sporadic cases. Integrating the theory of scenario analysis, we formulated six hypothetical epidemic scenarios, each corresponding to possible occurrences during different phases of the pandemic. We developed an improved SEIR model, validated its accuracy using real-world data, and conducted a comprehensive analysis and prediction of COVID-19 infections under these six scenarios. Simultaneously, we assessed the testing resource requirements associated with each scenario. RESULTS: We compared the predicted number of infections simulated by the modified SEIR model with the actual reported cases in Israel to validate the model. The root mean square error (RMSE) was 350.09, and the R-squared (R2) was 0.99, indicating a well-fitted model. Scenario 4 demonstrated the most effective prevention and control outcomes. Strengthening non-pharmaceutical interventions and increasing nucleic acid testing frequency, even under low testing capacity, resulted in a delayed epidemic peak by 78 days. The proportion of undetected cases decreased from 77.83% to 31.21%, and the overall testing demand significantly decreased, meeting maximum demand even with low testing capacity. The initiation of testing influenced case detection probability. Under high testing capacity, increasing testing frequency elevated the detection rate from 36.40% to 77.83%. Nucleic acid screening proved effective in reducing the demand for testing resources under diverse epidemic prevention and control strategies. While effective interventions and nucleic acid screening measures substantially diminished the demand for testing-related resources, varying degrees of insufficient testing capacity may still persist. CONCLUSIONS: The nucleic acid detection strategy proves effective in promptly identifying and isolating infected individuals, thereby mitigating the infection peak and extending the time to peak. In situations with constrained testing capacity, implementing more stringent measures can notably decrease the number of infections and alleviate resource demands. The improved SEIR model demonstrates proficiency in predicting both reported and unreported cases, offering valuable insights for future infection risk assessments. Rapid evaluations of testing requirements across diverse scenarios can aptly address resource limitations in specific regions, offering substantial evidence for the formulation of future infectious disease testing strategies.

Evaluating the predictive value of initial lactate/albumin ratios in determining prognosis of sepsis patients.

Sepsis remains a significant clinical challenge owing to its complex pathophysiology and variable prognosis. The early identification of patients at a higher risk of poor outcomes can be crucial for improving treatment strategies. This study aimed to evaluate the predictive value of early serum lactate and albumin levels and the lactate/albumin (L/A) ratio for 28-day prognosis in patients with sepsis. Patients diagnosed with sepsis between January 2021 and December 2022 were evaluated using a retrospective cohort methodology. Inclusion followed the International Consensus on sepsis and septic shock (Sepsis-3) guidelines and patients were selected based on well-defined criteria. Variables such as lactate, albumin, and the L/A ratio were documented within the first 24 hours of admission. Statistical analyses were performed using various tools, including the nonparametric Mann-Whitney U test and receiver operating characteristic curves. A total of 301 patients were divided into the survival (n = 167) and death (n = 134) groups. Notable differences were detected in the incidence of pulmonary infection, shock, lactate, albumin, and the L/A ratio. The L/A ratio was identified as a key predictor with an area under the curve of 0.868, an optimal cutoff value of >0.17, a sensitivity of 56.21%, and a specificity of 94.18%. Significant disparities in mortality rates and survival times were observed for the lactate, albumin, and L/A levels. This study underscores the predictive value of early serum lactate and albumin levels and the L/A ratio for 28-day prognosis in patients with sepsis, with the L/A ratio showing a superior predictive capability. These findings highlight the importance of L/A ratio as a robust and precise marker for evaluating the future clinical course of patients with sepsis, potentially aiding early detection and management.

Construction of Xylose-Utilizing Cyanobacterial Chassis for Bioproduction Under Photomixotrophic Conditions.

Xylose is a major component of lignocellulose and the second most abundant sugar present in nature after glucose; it, therefore, has been considered to be a promising renewable resource for the production of biofuels and chemicals. However, no natural cyanobacterial strain is known capable of utilizing xylose. Here, we take the fast-growing cyanobacteria Synechococcus elongatus UTEX 2973 as an example to develop the synthetic biology-based methodology of constructing a new xylose-utilizing cyanobacterial chassis with increased acetyl-CoA for bioproduction.

Molecular Mechanism of SOX18 in Lipopolysaccharide-Induced Injury of Human Umbilical Vein Endothelial Cells.

Endothelial dysfunction is associated with the progression of sepsis. This study sought to probe the molecular route of sex-determining region on the Y chromosome-box transcription factor 18 (SOX18) in sepsis-associated endothelial injury. Human umbilical vein endothelial cells (HUVECs) were treated with lipopolysaccharide (LPS) to establish the sepsis cell model. Cell viability, lactate dehydrogenase (LDH) release, oxidative stress (reactive oxygen species/malondialdehyde/superoxide dismutase), and inflammation (interleukin-1ß/tumor necrosis factor-α/interleukin-6) were evaluated by cell counting kit-8 assay and relevant assay kits. The expression levels of SOX18, microRNA (miR)-204-5p, and cadherin-2 (CDH2) in cells were determined by real-time quantitative polymerase chain reaction and Western blot assay. The interaction of SOX18, miR-204-5p, and CDH2 was analyzed by chromatin immunoprecipitation and dual-luciferase assay. LPS induced HUVECs injury and downregulation of SOX18. SOX18 overexpression increased cell viability, while decreased LDH activity, oxidative stress, and inflammation. SOX18 bound to the miR-204-5p promoter to promote miR-204-5p expression, and further repressed CDH2 expression. miR-204-5p knockdown and CDH2 overexpression abrogated the protective role of SOX18 in HUVECs injury. Overall, SOX18 alleviated LPS-induced injury of HUVECs by promoting miR-204-5p and repressing CDH2, suggesting it as a potential target for sepsis treatment.

Optimal resource allocation model for COVID-19: a systematic review and meta-analysis.

BACKGROUND: A lack of health resources is a common problem after the outbreak of infectious diseases, and resource optimization is an important means to solve the lack of prevention and control capacity caused by resource constraints. This study systematically evaluated the similarities and differences in the application of coronavirus disease (COVID-19) resource allocation models and analyzed the effects of different optimal resource allocations on epidemic control. METHODS: A systematic literature search was conducted of CNKI, WanFang, VIP, CBD, PubMed, Web of Science, Scopus and Embase for articles published from January 1, 2019, through November 23, 2023. Two reviewers independently evaluated the quality of the included studies, extracted and cross-checked the data. Moreover, publication bias and sensitivity analysis were evaluated. RESULTS: A total of 22 articles were included for systematic review; in the application of optimal allocation models, 59.09% of the studies used propagation dynamics models to simulate the allocation of various resources, and some scholars also used mathematical optimization functions (36.36%) and machine learning algorithms (31.82%) to solve the problem of resource allocation; the results of the systematic review show that differential equation modeling was more considered when testing resources optimization, the optimization function or machine learning algorithm were mostly used to optimize the bed resources; the meta-analysis results showed that the epidemic trend was obviously effectively controlled through the optimal allocation of resources, and the average control efficiency was 0.38(95%CI 0.25-0.51); Subgroup analysis revealed that the average control efficiency from high to low was health specialists 0.48(95%CI 0.37-0.59), vaccines 0.47(95%CI 0.11-0.82), testing 0.38(95%CI 0.19-0.57), personal protective equipment (PPE) 0.38(95%CI 0.06-0.70), beds 0.34(95%CI 0.14-0.53), medicines and equipment for treatment 0.32(95%CI 0.12-0.51); Funnel plots and Egger's test showed no publication bias, and sensitivity analysis suggested robust results. CONCLUSION: When the data are insufficient and the simulation time is short, the researchers mostly use the constructor for research; When the data are relatively sufficient and the simulation time is long, researchers choose differential equations or machine learning algorithms for research. In addition, our study showed that control efficiency is an important indicator to evaluate the effectiveness of epidemic prevention and control. Through the optimization of medical staff and vaccine allocation, greater prevention and control effects can be achieved.

Oscillation of type IV pili regulated by the circadian clock in cyanobacterium Synechococcus elongatus PCC7942.

Type IV pili (TFP) are known to be functionally related to cell motilities and natural transformation in many bacteria. However, the molecular and ecological functions of the TFP have rarely been reported for photosynthetic cyanobacteria. Here, by labeling pili in model cyanobacterium Synechococcus elongatus PCC 7942 (Syn7942), we have quantitatively characterized the TFP and its driven twitching motility in situ at the single-cell level. We found an oscillating pattern of TFP in accordance with the light and dark periods during light-dark cycles, which is correlated positively to the oscillating pattern of the natural transformation efficiency. We further showed that the internal circadian clock plays an important role in regulating the oscillating pattern of TFP, which is also supported by evidences at the molecular level by tracking the expression of 16 TFP-related genes. This study adds a detailed picture toward the gap between TFP and its relations to circadian regulations in Syn7942.

Biochar loaded with ferrihydrite and Bacillus pseudomycoides enhances remediation of co-existed Cd(II) and As(III) in solution.

Bioremediation is one of the effective ways for heavy metal remediation. Iron-modified biochar (F@BC) loaded with Bacillus pseudomycoides (BF@BC) was synthesized to remove the coexistence of cadmium (Cd) and arsenic (As) in solutions. The results showed that B. pseudomycoides significantly increased the removal rate of Cd(II) by enhancing the specific surface area and Si-containing functional groups of biochar (BC). The surface of F@BC was enriched with Fe-containing functional groups, significantly improving As(III) adsorption. The combination of ferrihydrite and strains on BF@BC enhanced the removal of Cd(II) and As(III). It also promoted the oxidation of As(III) by producing an abundance of hydroxyl radicals (·OH). The maximum saturated adsorption capacity of BF@BC for Cd(II) and As(III) increased by 52.47% and 2.99 folds compared with BC, respectively. This study suggests that biochar loaded with Fe and bacteria could be sustainable for the remediation of the coexistence of Cd(II) and As(III) in solutions.

Identification of acidic stress-responsive genes and acid tolerance engineering in Synechococcus elongatus PCC 7942.

Cyanobacteria are excellent autotrophic photosynthetic chassis employed in synthetic biology, and previous studies have suggested that they have alkaline tolerance but low acid tolerance, significantly limiting their productivity as photosynthetic chassis and necessitating investigations into the acid stress resistance mechanism. In this study, differentially expressed genes were obtained by RNA sequencing-based comparative transcriptomic analysis under long-term acidic stress conditions and acidic shock treatment, in the model cyanobacterium Synechococcus elongatus PCC 7942. A pathway enrichment analysis revealed the upregulated and downregulated pathways during long-term acidic and shock stress treatment. The subsequent single gene knockout and phenotype analysis showed that under acidic stress conditions, the strains with chlL, chlN, pex, synpcc7942_2038, synpcc7942_1890, or synpcc7942_2547 knocked out grew worse than the wild type, suggesting their involvement in acid tolerance. This finding was further confirmed by introducing the corresponding genes back into the knockout mutant individually. Moreover, individual overexpression of the chlL and chlN genes in the wild type successfully improved the tolerance of S. elongatus PCC 7942 to acidic stress. This work successfully identified six genes involved in acidic stress responses, and overexpressing chIL or chIN individually successfully improved acid tolerance in S. elongatus PCC 7942, providing valuable information to better understand the acid resistance mechanism in S. elongatus PCC 7942 and novel insights into the robustness and tolerance engineering of cyanobacterial chassis. KEY POINTS: • DEGs were identified by RNA-seq based transcriptomics analysis in response to acidic stress in S. elongatus PCC 7942. • Six genes were identified to be involved in acid tolerance in S. elongatus PCC 7942. • Overexpression of chIL or chIN individually successfully improved the acid tolerance of S. elongatus PCC 7942.

Fast-growing cyanobacterial chassis for synthetic biology application.

Carbon neutrality by 2050 has become one of the most urgent challenges the world faces today. To address the issue, it is necessary to develop and promote new technologies related with CO2 recycling. Cyanobacteria are the only prokaryotes performing oxygenic photosynthesis, capable of fixing CO2 into biomass under sunlight and serving as one of the most important primary producers on earth. Notably, recent progress on synthetic biology has led to utilizing model cyanobacteria such as Synechocystis sp. PCC 6803 and Synechococcus elongatus PCC 7942 as chassis for "light-driven autotrophic cell factories" to produce several dozens of biofuels and various fine chemicals directly from CO2. However, due to the slow growth rate and low biomass accumulation in the current chassis, the productivity for most products is still lower than the threshold necessary for large-scale commercial application, raising the importance of developing high-efficiency cyanobacterial chassis with fast growth and/or higher biomass accumulation capabilities. In this article, we critically reviewed recent progresses on identification, systems biology analysis, and engineering of fast-growing cyanobacterial chassis. Specifically, fast-growing cyanobacteria identified in recent years, such as S. elongatus UTEX 2973, S. elongatus PCC 11801, S. elongatus PCC 11802 and Synechococcus sp. PCC 11901 was comparatively analyzed. In addition, the progresses on their recent application in converting CO2 into chemicals, and genetic toolboxes developed for these new cyanobacterial chassis were discussed. Finally, the article provides insights into future challenges and perspectives on the synthetic biology application of cyanobacterial chassis.

A network meta-analysis of risk factors of infection among close contacts of COVID-19.

Objective: We aimed to use network meta-analysis to compare the impact of infection risk factors of close contacts with COVID-19, identify the most influential factors and rank their subgroups. It can provide a theoretical basis for the rapid and accurate tracking and management of close contacts. Methods: We searched nine databases from December 1, 2019 to August 2, 2023, which only took Chinese and English studies into consideration. Odd ratios (ORs) were calculated from traditional meta-estimated secondary attack rates (SARs) for different risk factors, and risk ranking of these risk factors was calculated by the surface under the cumulative ranking curve (SUCRA). Results: 25 studies with 152647 participants identified. Among all risk factors, the SUCRA of type of contact was 69.6 % and ranked first. Among six types of contact, compared with transportation contact, medical contact, social contact and other, daily contact increased risk of infection by 12.11 (OR: 12.11, 95 % confidence interval (CI): 6.51-22.55), 7.76 (OR: 7.76, 95 % CI: 4.09-14.73), 4.65 (OR: 4.65, 95 % CI: 2.66-8.51) and 8.23 OR: 8.23, 95 % CI: 4.23-16.01) times, respectively. Overall, SUCRA ranks from highest to lowest as daily contact (94.7 %), contact with pollution subjects (78.4 %), social contact (60.8 %), medical contact (31.8 %), other (27.9 %), transportation contact (6.4 %). Conclusion: The type of contact had the greatest impact on COVID-19 close contacts infection among the risk factors we included. Daily contact carried the greatest risk of infection among six types of contact, followed by contact with pollution subjects, social contact, other, medical contact and transportation contact. The results can provide scientific basis for rapid assess the risk of infection among close contacts based on fewer risk factors and pay attention to high-risk close contacts during management, thereby reducing tracking and management costs.

The nonlinear relationship between air quality and housing prices by machine learning.

Using a dataset encompassing 228 cities in China spanning from 2005 to 2019, this study explores the nonlinear relationship between air quality and housing prices and devises a strategy that incorporates the instrumental variable and machine learning to address the endogeneity issue. Both traditional models and machine learning models find air pollution affects housing prices in a diminishing manner. The negative impact of air pollution on housing prices decreases when the degree of air pollution intensifies. Such a characteristic is more pronounced in Eastern China and cities with fewer land resource constraints and larger populations. Mechanism analysis also reveals that air pollution could affect residents' perceived air quality and the industrial structure, further contributing to the nonlinear relationship between air quality and housing prices. The further SHapley Additive exPlanations (SHAP) evaluates the importance of air quality in determining housing prices and finds that air quality's contribution outweighs educational and medical resources. The contribution of air quality also shows a distinct regional disparity and has become increasingly important in recent years. The findings refine the benefit assessment accuracy related to air quality improvement.

An immune cell atlas reveals the dynamics of human macrophage specification during prenatal development.

Macrophages are heterogeneous and play critical roles in development and disease, but their diversity, function, and specification remain inadequately understood during human development. We generated a single-cell RNA sequencing map of the dynamics of human macrophage specification from PCW 4-26 across 19 tissues. We identified a microglia-like population and a proangiogenic population in 15 macrophage subtypes. Microglia-like cells, molecularly and morphologically similar to microglia in the CNS, are present in the fetal epidermis, testicle, and heart. They are the major immune population in the early epidermis, exhibit a polarized distribution along the dorsal-lateral-ventral axis, and interact with neural crest cells, modulating their differentiation along the melanocyte lineage. Through spatial and differentiation trajectory analysis, we also showed that proangiogenic macrophages are perivascular across fetal organs and likely yolk-sac-derived as microglia. Our study provides a comprehensive map of the heterogeneity and developmental dynamics of human macrophages and unravels their diverse functions during development.

[Advances in using adaptive laboratory evolution technology for engineering of photosynthetic cyanobacteria].

Cyanobacteria are the only prokaryotes capable of oxygenic photosynthesis, which have potential to serve as "autotrophic cell factories". However, the synthesis of biofuels and chemicals using cyanobacteria as chassis are suffered from poor stress tolerance and low yield, resulting in low economic feasibility for industrial production. Thus, it's urgent to construct new cyanobacterial chassis by means of synthetic biology. In recent years, adaptive laboratory evolution (ALE) has made great achievements in chassis engineering, including optimizing growth rate, increasing tolerance, enhancing substrate utilization and increasing product yield. ALE has also made some progress in improving the tolerance of cyanobacteria to high light intensity, heavy metal ions, high concentrations of salt and organic solvents. However, the engineering efficiency of ALE strategy in cyanobacteria is generally low, and the molecular mechanisms underpinning the tolerance to various stresses have not been fully elucidated. To this end, this review summarizes the ALE-associated technical strategies and their applications in cyanobacteria chassis engineering, following by discussing how to construct larger ALE mutation library, increase mutation frequency of strains and shorten evolution time. Moreover, exploration of the construction principles and strategies for constructing multi-stress tolerant cyanobacteria, and efficient analysis the mutant libraries of evolved strains as well as construction of strains with high yield and strong robustness are discussed, with the aim to facilitate the engineering of cyanobacteria chassis and the application of engineered cyanobacteria in the future.

Overexpression of S-R enhances the accumulation of biomass, fatty acids, and ß-carotene in Schizochytrium.

Strategies for enhancing biomass accumulation and increasing the production of fatty acids and ß-carotene in Schizochytrium are hindered by the lack of suitable targets. In this study, S-R, a RING (really interesting new gene) finger domain-containing protein, was identified in Schizochytrium, with homologs found in the family Thraustochytriaceae. Transgenic strains overexpressing S-R showed a minor improvement in cell growth but a significant increase in total fatty acids content by 1.29- to 1.36-fold. Almost all individual saturated fatty acids exhibited significant increases, with the greatest increase observed in the C14:0 content, by 1.52- to 1.78-fold. Additionally, the ß-carotene content of S-R strains was significantly upregulated. Overexpression of s-r conferred hypersaline tolerance in Schizochytrium, with a significant increase in dry cell weight, total fatty acids and ß-carotene, likely due to the upregulation of glycerol and proline. This study provides a feasible strategy to engineer Thraustochytriaceae for efficient biomass and biochemical production.